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+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "... CVE - Bench , we collect 40 Common Vulnerabilities and ... We apply CVE - Bench to evaluate various LLM agents under both zero-day and one-day settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "... CVE - Bench , we collect 40 Common Vulnerabilities and ... We apply CVE - Bench to evaluate various LLM agents under both zero-day and one-day settings."}
+{"idx": 1, "title": "CVE-2021-44228 - Apache Log4j2 Remote Code Execution", "date": "", "ddg_snippet": "... you will find a curated list of external links that provide in-depth information, practical solutions, and valuable tools related to CVE -2021-44228 .", "subpage_snippet": "", "source": "cvefeed.io", "link": "https://cvefeed.io/vuln/detail/CVE-2021-44228", "content": "... you will find a curated list of external links that provide in-depth information, practical solutions, and valuable tools related to CVE -2021-44228 ."}
+{"idx": 2, "title": "x-cmd blog (daily) | [240528] Git Vulnerability CVE-2024-32002", "date": "", "ddg_snippet": "24] Kafka 4 .0.0 Release: Farewell to Zo... ... 23] x-cmd released v0. 5 . 4 : Simplified D... ... 30] Ruby 3 . 4 .0 Released | Co-op Transla...", "subpage_snippet": "", "source": "www.x-cmd.com", "link": "https://www.x-cmd.com/blog/240528/", "content": "24] Kafka 4 .0.0 Release: Farewell to Zo... ... 23] x-cmd released v0. 5 . 4 : Simplified D... ... 30] Ruby 3 . 4 .0 Released | Co-op Transla..."}
+{"idx": 3, "title": "2023", "date": "", "ddg_snippet": "... $2 Per Hour: Exclusive | Time · 2023-01-18: Risky Biz News: Google Search and ... $5 monthly subscription · 2023-01-24: Microsoft will stop selling Windows ...", "subpage_snippet": "", "source": "www.samsclass.info", "link": "https://www.samsclass.info/old_news_2023.html", "content": "... $2 Per Hour: Exclusive | Time · 2023-01-18: Risky Biz News: Google Search and ... $5 monthly subscription · 2023-01-24: Microsoft will stop selling Windows ..."}
+{"idx": 4, "title": "Orion: Fuzzing Workflow Automation", "date": "", "ddg_snippet": "Across our benchmark suite, Orion reduces human effort by 46–204 × \\times depending on the workflow stage, and we demonstrate its effectiveness ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15195v1", "content": "Across our benchmark suite, Orion reduces human effort by 46–204 × \\times depending on the workflow stage, and we demonstrate its effectiveness ..."}
+{"idx": 5, "title": "How Does Time Horizon Vary Across Domains? - METR", "date": "", "ddg_snippet": "We estimated the time horizon of frontier models released since 2019 on a benchmark combining three sets of software and research tasks ranging from ...", "subpage_snippet": "", "source": "evals.alignment.org", "link": "https://evals.alignment.org/blog/2025-07-14-how-does-time-horizon-vary-across-domains/", "content": "We estimated the time horizon of frontier models released since 2019 on a benchmark combining three sets of software and research tasks ranging from ..."}
+{"idx": 6, "title": "pkgsrc-WIP-changes by thread", "date": "", "ddg_snippet": "Thread index Last updated: Sat Jun 28 12:01:21 2025 Timezone is UTC ... TODO: + chapel-1.30.0. ... Update to 1.20.1.", "subpage_snippet": "", "source": "mail-index.NetBSD.org", "link": "http://mail-index.NetBSD.org/pkgsrc-wip-changes/2023/03/thread2.html", "content": "Thread index Last updated: Sat Jun 28 12:01:21 2025 Timezone is UTC ... TODO: + chapel-1.30.0. ... Update to 1.20.1."}
+{"idx": 7, "title": "Tags | Palette", "date": "", "ddg_snippet": "... agent mode 2 ... open-policy- agent 2 ... terraform 5", "subpage_snippet": "", "source": "docs.spectrocloud.com", "link": "https://docs.spectrocloud.com/tags/", "content": "... agent mode 2 ... open-policy- agent 2 ... terraform 5"}
+{"idx": 8, "title": "Discourse Version 2.3 - Releases - Discourse Meta", "date": "", "ddg_snippet": "post annotations by staff, including automatic friendly nudge post annotation visible next to first posts and returning posts of long absent users ...", "subpage_snippet": "", "source": "meta.discourse.org", "link": "https://meta.discourse.org/t/discourse-version-2-3/96690", "content": "post annotations by staff, including automatic friendly nudge post annotation visible next to first posts and returning posts of long absent users ..."}
+{"idx": 9, "title": "x-cmd blog (daily) | [240913] How to Check Hard Disk Health on", "date": "", "ddg_snippet": "31] IntelliJ IDEA 2025.1. 4 Re-released:... ... 24] Kafka 4 .0.0 Release: Farewell to Zo... ... 23] x-cmd released v0. 5 . 4 : Simplified D...", "subpage_snippet": "", "source": "www.x-cmd.com", "link": "https://www.x-cmd.com/blog/240913/", "content": "31] IntelliJ IDEA 2025.1. 4 Re-released:... ... 24] Kafka 4 .0.0 Release: Farewell to Zo... ... 23] x-cmd released v0. 5 . 4 : Simplified D..."}
diff --git a/data/sampled_jsons/'Algebraic_Combinatorics_Dataset_Repository'_S18_characters_dataset_Appendix_B.1_training_examples_-.jsonl b/data/sampled_jsons/'Algebraic_Combinatorics_Dataset_Repository'_S18_characters_dataset_Appendix_B.1_training_examples_-.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85e62119d14e419ccd204c57778409175eeedbb1
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+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "To fill the gap we present the Algebraic Combinatorics Dataset Repository (ACD Repo)1, a collection of 9 datasets consisting of many examples along with an associated question (s).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "To fill the gap we present the Algebraic Combinatorics Dataset Repository (ACD Repo)1, a collection of 9 datasets consisting of many examples along with an associated question (s)."}
+{"idx": 1, "title": "PDF 18.212: AlgebraicCombinatorics - Stanford University", "date": "", "ddg_snippet": "As the title suggests, this is a class on combinatorics . Combinatorics is the area of mathematics that studies discrete objects, like graphs, permutations, and various diagrams, looking at objects that we can count or list. These days, there are two main flavors: Stanley-style and Erdős-style. Stanley-style (also enumerative, algebraic , or geometric) combinatorics deals with counting objects ...", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~lindrew/18.212.pdf", "content": "As the title suggests, this is a class on combinatorics . Combinatorics is the area of mathematics that studies discrete objects, like graphs, permutations, and various diagrams, looking at objects that we can count or list. These days, there are two main flavors: Stanley-style and Erdős-style. Stanley-style (also enumerative, algebraic , or geometric) combinatorics deals with counting objects ..."}
+{"idx": 2, "title": "Clustering-Datasets/01. UCI/letter.arff at master - GitHub", "date": "", "ddg_snippet": "This repository contains the collection of UCI (real-life) datasets and Synthetic (artificial) datasets (with cluster labels and MATLAB files) ready to use with clustering algorithms. - milaan9/Clu...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/milaan9/Clustering-Datasets/blob/master/01.+UCI/letter.arff", "content": "This repository contains the collection of UCI (real-life) datasets and Synthetic (artificial) datasets (with cluster labels and MATLAB files) ready to use with clustering algorithms. - milaan9/Clu..."}
+{"idx": 3, "title": "PDF ALGEBRAIC COMBINATORICS - Mersenne", "date": "", "ddg_snippet": "There are two examples of extra special p-groups of order p3: the Heisenberg group Heis(p) given by upper-triangular 3 × 3 matrices over the field Fp with 1's on the diagonal, and the semidirect product Cp2 ⋊ Cp where Cp acts non-trivially on Cp2.", "subpage_snippet": "", "source": "alco.centre-mersenne.org", "link": "https://alco.centre-mersenne.org/item/10.5802/alco.270.pdf", "content": "There are two examples of extra special p-groups of order p3: the Heisenberg group Heis(p) given by upper-triangular 3 × 3 matrices over the field Fp with 1's on the diagonal, and the semidirect product Cp2 ⋊ Cp where Cp acts non-trivially on Cp2."}
+{"idx": 4, "title": "Computational Complexity in Algebraic Combinatorics", "date": "", "ddg_snippet": "Abstract. Algebraic combinatorics originated in algebra and representation theory, studying their discrete objects and integral quantities via combinatorial methods which have since developed inde-pendent and self-contained lives and brought us some beautiful formulas and combinatorial interpre-tations. The flagship hook-length formula counts the number of Standard Young Tableaux, which also ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10586646", "content": "Abstract. Algebraic combinatorics originated in algebra and representation theory, studying their discrete objects and integral quantities via combinatorial methods which have since developed inde-pendent and self-contained lives and brought us some beautiful formulas and combinatorial interpre-tations. The flagship hook-length formula counts the number of Standard Young Tableaux, which also ..."}
+{"idx": 5, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "In this paper we introduce the Algebraic Combinatorics Dataset Repository , a collection of research-level mathemat-ics datasets structured for machine learning and designed to accelerate mathematical discovery.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366v1", "content": "In this paper we introduce the Algebraic Combinatorics Dataset Repository , a collection of research-level mathemat-ics datasets structured for machine learning and designed to accelerate mathematical discovery."}
+{"idx": 6, "title": "PDF An Introduction to Algebraic Combinatorics - LMU", "date": "", "ddg_snippet": "Abstract. This is an introduction to algebraic combinatorics , writ- ten for a quarter-long graduate course. It starts with a rigorous in- troduction to formal power series with some combinatorial applica- tions, then discusses integer partitions (proving Jacobi's triple prod- uct identity), permutations (Lehmer codes, cycles) and subtractive methods (alternating sums, cancellations and ...", "subpage_snippet": "", "source": "www.cip.ifi.lmu.de", "link": "https://www.cip.ifi.lmu.de/~grinberg/t/21s/lecs.pdf", "content": "Abstract. This is an introduction to algebraic combinatorics , writ- ten for a quarter-long graduate course. It starts with a rigorous in- troduction to formal power series with some combinatorial applica- tions, then discusses integer partitions (proving Jacobi's triple prod- uct identity), permutations (Lehmer codes, cycles) and subtractive methods (alternating sums, cancellations and ..."}
+{"idx": 7, "title": "PDF A Appendix - papers.nips.cc", "date": "", "ddg_snippet": "How the dataset can be read 100 The dataset can be most conveniently read using Croissant to obtain the dataset records in a standard-101 ized fashion. Users can download the dataset and use the provided Croissant metadata file to load the 102 dataset records. Detailed instructions are provided within the data directory of our public GitHub 103 repository 104 Alternatively, users can manually ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/af9199ef7d5ff267451a667170124c04-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "How the dataset can be read 100 The dataset can be most conveniently read using Croissant to obtain the dataset records in a standard-101 ized fashion. Users can download the dataset and use the provided Croissant metadata file to load the 102 dataset records. Detailed instructions are provided within the data directory of our public GitHub 103 repository 104 Alternatively, users can manually ..."}
+{"idx": 8, "title": "PDF Topics in Algebraic Combinatorics", "date": "", "ddg_snippet": "The adjacency matrix of the graph G is the p ×p matrix A = A(G), over the field of complex numbers, whose (i, j)-entry aij is equal to the number of edges incident to vi and vj. Thus A is a real symmetric matrix (and hence has real eigenvalues) whose trace is the number of loops in G. For instance, if G is the graph", "subpage_snippet": "", "source": "math.mit.edu", "link": "https://math.mit.edu/~rstan/algcomb/algcomb.pdf", "content": "The adjacency matrix of the graph G is the p ×p matrix A = A(G), over the field of complex numbers, whose (i, j)-entry aij is equal to the number of edges incident to vi and vj. Thus A is a real symmetric matrix (and hence has real eigenvalues) whose trace is the number of loops in G. For instance, if G is the graph"}
+{"idx": 9, "title": "PDF 18 - Massachusetts Institute of Technology", "date": "", "ddg_snippet": "These are my lecture notes from 18.211, Combinatorial Analysis, at the Massachusetts Institute of Technology, taught this semester (Fall 2018) by Professor Yufei Zhao1.", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/rmwu/static/files/notes/18-211-notes.pdf", "content": "These are my lecture notes from 18.211, Combinatorial Analysis, at the Massachusetts Institute of Technology, taught this semester (Fall 2018) by Professor Yufei Zhao1."}
diff --git a/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_Machine_Unlearning_Definition_4.2.jsonl b/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_Machine_Unlearning_Definition_4.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d979d0b0a2a4b08eb2203cb3954794c75a759a6c
--- /dev/null
+++ b/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_Machine_Unlearning_Definition_4.2.jsonl
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+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ..."}
+{"idx": 1, "title": "Leveraging Per-Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ..."}
+{"idx": 2, "title": "PDF Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A ...", "date": "", "ddg_snippet": "Abstract Machine unlearning focuses on eficiently removing spe-cific data from trained models, addressing privacy and compli-ance concerns with reasonable costs. Although exact unlearn-ing ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing inter-est in inexact unlearning methods.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/usenixsecurity25-naderloui.pdf", "content": "Abstract Machine unlearning focuses on eficiently removing spe-cific data from trained models, addressing privacy and compli-ance concerns with reasonable costs. Although exact unlearn-ing ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing inter-est in inexact unlearning methods."}
+{"idx": 3, "title": "PDF Forget to Flourish: Leveraging Machine-Unlearning on Pretrained ...", "date": "", "ddg_snippet": "At its core, our approach leverages machine unlearning Cao and Yang (2015); Guo et al. (2019) to poison the pre-trained LLM. The original objective of unlearning is to make the model forget specific data points that it has seen during training so that it produces a high loss for those data points, and it becomes dificult to reconstruct those samples Gu et al. (2024). Motivated by data ...", "subpage_snippet": "", "source": "www.merl.com", "link": "https://www.merl.com/publications/docs/TR2024-168.pdf", "content": "At its core, our approach leverages machine unlearning Cao and Yang (2015); Guo et al. (2019) to poison the pre-trained LLM. The original objective of unlearning is to make the model forget specific data points that it has seen during training so that it produces a high loss for those data points, and it becomes dificult to reconstruct those samples Gu et al. (2024). Motivated by data ..."}
+{"idx": 4, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per-instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (R ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "Abstract We present a principled, per-instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (R ..."}
+{"idx": 5, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Leveraging-Per-Instance-Privacy-for-Machine-Sepahvand-Thudi/dca9861c26bd83a7b1c30fb4255810afdd1e4aa3", "content": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}
+{"idx": 6, "title": "Machine Unlearning: A Comprehensive Survey - arXiv.org", "date": "", "ddg_snippet": "This survey aims to systematically classify a wide range of machine unlearning studies, discussing their differences, connections, and open problems. We categorize current unlearning methods into four key areas: centralized unlearning , federated unlearning , unlearning verification, and privacy and security issues in unlearning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07406", "content": "This survey aims to systematically classify a wide range of machine unlearning studies, discussing their differences, connections, and open problems. We categorize current unlearning methods into four key areas: centralized unlearning , federated unlearning , unlearning verification, and privacy and security issues in unlearning ."}
+{"idx": 7, "title": "A survey on machine unlearning: Techniques and new emerged privacy ...", "date": "", "ddg_snippet": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions , implementation methods, and real-world applications.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2214212625000481", "content": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions , implementation methods, and real-world applications."}
+{"idx": 8, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite"}
+{"idx": 9, "title": "A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks", "date": "", "ddg_snippet": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions , implementation methods, and real-world applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.06186", "content": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions , implementation methods, and real-world applications."}
diff --git a/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Section_6.2_Figure_1.jsonl b/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Section_6.2_Figure_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..acf6468d89183eb33528fd21c4734ba55f10b374
--- /dev/null
+++ b/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Section_6.2_Figure_1.jsonl
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+{"idx": 0, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Figure 7. SGD unlearning . Unlearning time vs. privacy score for ResNet-18 on SVHN (left) and ViT-small on CIFAR-10 (right).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "Figure 7. SGD unlearning . Unlearning time vs. privacy score for ResNet-18 on SVHN (left) and ViT-small on CIFAR-10 (right)."}
+{"idx": 1, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Armed with per - instance privacy losses, we revisit Chien et al. ’s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin unlearning ” a.k.a “noisy fine-tuning”), based on training without the forget set.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "Armed with per - instance privacy losses, we revisit Chien et al. ’s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin unlearning ” a.k.a “noisy fine-tuning”), based on training without the forget set."}
+{"idx": 2, "title": "Leveraging Per -Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per - instance guarantees using Rényi divergence.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per - instance guarantees using Rényi divergence."}
+{"idx": 3, "title": "CS PhD, UofT - Cited by 775 - Machine Learning - Computer Security", "date": "", "ddg_snippet": "Leveraging Per - Instance Privacy for Machine Unlearning .ICML 2025 Workshop on Machine Unlearning for Generative AI, 0. The system can't perform the operation now.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=bTEybH0AAAAJ&hl=en", "content": "Leveraging Per - Instance Privacy for Machine Unlearning .ICML 2025 Workshop on Machine Unlearning for Generative AI, 0. The system can't perform the operation now."}
+{"idx": 4, "title": "Frontiers in Machine Learning: Synthesizing May... - DEV Community", "date": "", "ddg_snippet": "Per - Instance Machine Unlearning Enables Targeted Data Removal Advances in machine unlearning provide tools for efficient and fair removal of specific data influences from trained models.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/khanali21/frontiers-in-machine-learning-synthesizing-may-2025-arxiv-cslg-advances-in-efficiency-18o5", "content": "Per - Instance Machine Unlearning Enables Targeted Data Removal Advances in machine unlearning provide tools for efficient and fair removal of specific data influences from trained models."}
+{"idx": 5, "title": "Nicolas PAPERNOT | Professor (Assistant) | Doctor of Philosophy", "date": "", "ddg_snippet": "Leveraging Per - Instance Privacy for Machine Unlearning . Machine unlearning aims to remove points from the training dataset of a machine learning model after training; for example when a user requests their data to be deleted.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Nicolas-Papernot", "content": "Leveraging Per - Instance Privacy for Machine Unlearning . Machine unlearning aims to remove points from the training dataset of a machine learning model after training; for example when a user requests their data to be deleted."}
+{"idx": 6, "title": "Address: bc 1 qrnu 9 j0w9grqty0p08ut8wg8vpdwhw0admsua8f", "date": "", "ddg_snippet": "bc 1 qrnu 9 j0w9grqty0p08ut8wg8vpdwhw0admsua8f. Confirmed balance. Privacy Policy.", "subpage_snippet": "", "source": "mempool.space", "link": "https://mempool.space/address/bc1qrnu9j0w9grqty0p08ut8wg8vpdwhw0admsua8f", "content": "bc 1 qrnu 9 j0w9grqty0p08ut8wg8vpdwhw0admsua8f. Confirmed balance. Privacy Policy."}
+{"idx": 7, "title": "CleverHans Lab - Anvith", "date": "", "ddg_snippet": "Leveraging Per - Instance Privacy for Machine Unlearning Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite. BibTeX.", "subpage_snippet": "", "source": "cleverhans.io", "link": "https://cleverhans.io/members/anvith.html", "content": "Leveraging Per - Instance Privacy for Machine Unlearning Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite. BibTeX."}
+{"idx": 8, "title": "ГДЗ по Математике 3 класс тематический контроль Голубь...", "date": "", "ddg_snippet": "Периметр квадрата находится по формуле: Р= a * 4 . Например периметр квадрата со стороной 3 см находится: Р=3*4 Р=12 см.", "subpage_snippet": "", "source": "otvechalka.su", "link": "https://otvechalka.su/matematika/3-klass/tematicheskij-kontrol-golub/stranicza-66/", "content": "Периметр квадрата находится по формуле: Р= a * 4 . Например периметр квадрата со стороной 3 см находится: Р=3*4 Р=12 см."}
+{"idx": 9, "title": "Номер 4, страница 9 - гдз по английскому языку 6 класс (spotlight)...", "date": "", "ddg_snippet": "Английский язык (english), 6 класс Рабочая тетрадь (workbook), авторы: Ваулина Юлия Евгеньевна (Vaulina Julia), Дули Дженни (Dooley Jenny), Подоляко Ольга Евгеньевна (Podolyako Olga), Эванс Вирджиния (Evans Virginia), издательство Просвещение, Москва...", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/6-klass/english/vaulina-spotlight-rabochaja-tetrad/01-5-4", "content": "Английский язык (english), 6 класс Рабочая тетрадь (workbook), авторы: Ваулина Юлия Евгеньевна (Vaulina Julia), Дули Дженни (Dooley Jenny), Подоляко Ольга Евгеньевна (Podolyako Olga), Эванс Вирджиния (Evans Virginia), издательство Просвещение, Москва..."}
diff --git a/data/sampled_jsons/0ObGn4e1IS_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding.jsonl b/data/sampled_jsons/0ObGn4e1IS_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding.jsonl
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index 0000000000000000000000000000000000000000..ec1df00b582dc7073451dde9741b31745f86df8b
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+{"idx": 0, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... Gumiho - a amd Collection - Hugging Face Gumiho: A Hybrid Architecture to Prioritize Early Tokens in... Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Mar 13, 2025 · Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ... Jun 12, 2025 · Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Paper • 2503.10135 •Published Mar 13 Upvote - Share collection View history Collection guide Browse collections May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ... 🌟 Introducing Gumiho : A Hybrid Architecture for Enhanced Speculative Decoding 🌟 In the realm of Large Language Models (LLMs), speculative decoding (SPD) serves as a pivotal technique for ... Mar 13, 2025 · Abstract and Figures Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Abstract Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multi-ple heads to predict a sequence of future tokens , where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens , enabling eficient multi- token ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10135", "content": "Mar 13, 2025 · Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ... Jun 12, 2025 · Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Paper • 2503.10135 •Published Mar 13 Upvote - Share collection View history Collection guide Browse collections May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ... 🌟 Introducing Gumiho : A Hybrid Architecture for Enhanced Speculative Decoding 🌟 In the realm of Large Language Models (LLMs), speculative decoding (SPD) serves as a pivotal technique for ... Mar 13, 2025 · Abstract and Figures Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Abstract Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multi-ple heads to predict a sequence of future tokens , where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens , enabling eficient multi- token ..."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ..."}
+{"idx": 2, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0ObGn4e1IS", "content": "May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ..."}
+{"idx": 3, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "🌟 Introducing Gumiho : A Hybrid Architecture for Enhanced Speculative Decoding 🌟 In the realm of Large Language Models (LLMs), speculative decoding (SPD) serves as a pivotal technique for ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/abdullah-kasri_gumiho-a-hybrid-architecture-to-prioritize-activity-7306519551876161536-HoDU", "content": "🌟 Introducing Gumiho : A Hybrid Architecture for Enhanced Speculative Decoding 🌟 In the realm of Large Language Models (LLMs), speculative decoding (SPD) serves as a pivotal technique for ..."}
+{"idx": 4, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Mar 13, 2025 · Abstract and Figures Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389821466_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding", "content": "Mar 13, 2025 · Abstract and Figures Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM)."}
+{"idx": 5, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Abstract Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multi-ple heads to predict a sequence of future tokens , where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens , enabling eficient multi- token ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135v1", "content": "Abstract Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multi-ple heads to predict a sequence of future tokens , where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens , enabling eficient multi- token ..."}
+{"idx": 6, "title": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10135v1", "content": "Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM)."}
+{"idx": 7, "title": "Paper page - Gumiho : A Hybrid Architecture to Prioritize Early ...", "date": "", "ddg_snippet": "Gumiho , a hybrid model combining serial and parallel heads with varying architectures , improves speculative decoding for large language models by prioritizing accuracy for early tokens . AI-generated summary.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.10135", "content": "Gumiho , a hybrid model combining serial and parallel heads with varying architectures , improves speculative decoding for large language models by prioritizing accuracy for early tokens . AI-generated summary."}
+{"idx": 8, "title": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens ... | alphaXiv", "date": "", "ddg_snippet": "View recent discussion. Abstract: Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.10135v1", "content": "View recent discussion. Abstract: Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads."}
+{"idx": 9, "title": "A Token is Worth over 1,000 Tokens : Efficient Knowledge Distillation...", "date": "", "ddg_snippet": "[1] Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding . Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/a-token-is-worth-over-1-000-tokens-efficient-knowledge-distillation-through-low-rank-clone/1131559626442014785-108592", "content": "[1] Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding . Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM)."}
diff --git a/data/sampled_jsons/0yzOEMbShU_Beyond_Self-Repellent_Kernels_History-Driven_Target_Efficient_Nonlinear_MCMC_General_Grap.jsonl b/data/sampled_jsons/0yzOEMbShU_Beyond_Self-Repellent_Kernels_History-Driven_Target_Efficient_Nonlinear_MCMC_General_Grap.jsonl
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index 0000000000000000000000000000000000000000..10e243dee1df45b52d85fc14ad744748f641e6db
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+{"idx": 0, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs , for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "content": "We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs , for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ..."}
+{"idx": 1, "title": "Our paper on efficient MCMC on graphs accepted at ICML 2025", "date": "", "ddg_snippet": "Excited to announce that our paper, \" Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Non-linear MCMC on General Graphs ,\" has been chosen for an 𝗢𝗿𝗮𝗹 ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/jie-hu-ncsu_icml2025-mcmc-activity-7337284173516210176-FGlQ", "content": "Excited to announce that our paper, \" Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Non-linear MCMC on General Graphs ,\" has been chosen for an 𝗢𝗿𝗮𝗹 ..."}
+{"idx": 2, "title": "PDF Self-Repellent Random Walks on General Graphs - IJCAI", "date": "", "ddg_snippet": "Self-repellent random walks on general graphs - achieving minimal sampling variance via nonlinear markov chains. In International Conference on Machine Learning.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0929.pdf", "content": "Self-repellent random walks on general graphs - achieving minimal sampling variance via nonlinear markov chains. In International Conference on Machine Learning."}
+{"idx": 3, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "We propose a history-driven target (HDT) frame- work in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on dis- crete state spaces, such as general undirected graphs , for efficient sampling from target distri- bution µ. With broad applications in network sci- ence and distributed optimization, recent innova- tions like the self-repellent random walk (SRRW) achieve near ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0yzOEMbShU", "content": "We propose a history-driven target (HDT) frame- work in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on dis- crete state spaces, such as general undirected graphs , for efficient sampling from target distri- bution µ. With broad applications in network sci- ence and distributed optimization, recent innova- tions like the self-repellent random walk (SRRW) achieve near ..."}
+{"idx": 4, "title": "Self-Repellent Random Walks on General Graphs - Achieving Minimal ...", "date": "", "ddg_snippet": "We consider random walks on discrete state spaces, such as general undirected graphs , where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/doshi23a.html", "content": "We consider random walks on discrete state spaces, such as general undirected graphs , where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random ..."}
+{"idx": 5, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "We propose a history-driven target (HDT)framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs , for efficient sampling from target distribution 𝝁𝝁{\\bm{\\mu}}bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "We propose a history-driven target (HDT)framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs , for efficient sampling from target distribution 𝝁𝝁{\\bm{\\mu}}bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ..."}
+{"idx": 6, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "Overview Introduces novel approach for efficient graph sampling using history-driven MCMC Improves upon self -repelling random walks by considering full sampling history Develops theoretical framework for convergence and mixing properties Demonstrates superior performance on both synthetic and real-world graphs Provides practical implementation guidelines for general graph structures Plain ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-self-repellent-kernels-history-driven-target", "content": "Overview Introduces novel approach for efficient graph sampling using history-driven MCMC Improves upon self -repelling random walks by considering full sampling history Develops theoretical framework for convergence and mixing properties Demonstrates superior performance on both synthetic and real-world graphs Provides practical implementation guidelines for general graph structures Plain ..."}
+{"idx": 7, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs , for efficient sampling from target distribution $\\\\boldsymbol{\\\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Beyond-Self-Repellent-Kernels:-History-Driven-MCMC-Hu-Ma/59bcb50ca3406f2761472b5934c7b8ba1131ff81", "content": "We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs , for efficient sampling from target distribution $\\\\boldsymbol{\\\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ..."}
+{"idx": 8, "title": "Self-Repellent Random Walks on General Graphs - Achieving Minimal ...", "date": "", "ddg_snippet": "We consider random walks on discrete state spaces, such as general undirected graphs , where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0929", "content": "We consider random walks on discrete state spaces, such as general undirected graphs , where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures."}
+{"idx": 9, "title": "Track: Oral 5C Probablistic Models", "date": "", "ddg_snippet": "We propose a * history-driven target (HDT)* framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs , for efficient sampling from target distribution $\\\\boldsymbol {\\\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW ...", "subpage_snippet": "", "source": "dev.icml.cc", "link": "https://dev.icml.cc/virtual/2025/session/46916", "content": "We propose a * history-driven target (HDT)* framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs , for efficient sampling from target distribution $\\\\boldsymbol {\\\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW ..."}
diff --git a/data/sampled_jsons/0yzOEMbShU_Beyond_Self-Repellent_Kernels_Section_4.5_LRU_cache_Equation_15.jsonl b/data/sampled_jsons/0yzOEMbShU_Beyond_Self-Repellent_Kernels_Section_4.5_LRU_cache_Equation_15.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b8148f19bc5b9d8d3597dd4675df4e29658d2989
--- /dev/null
+++ b/data/sampled_jsons/0yzOEMbShU_Beyond_Self-Repellent_Kernels_Section_4.5_LRU_cache_Equation_15.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "A recent breakthrough is the self-repellent random walk (SRRW) over general graphs (Doshi et al., 2023), a nonlinear Markov chain that modifies a time-reversible Markov chain with µ-invariant transition kernel P, as shown by Box ① in Figure 1(a).1Unlike non-backtracking random walks relying solely on the most recent history (Alon et al ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0yzOEMbShU", "content": "A recent breakthrough is the self-repellent random walk (SRRW) over general graphs (Doshi et al., 2023), a nonlinear Markov chain that modifies a time-reversible Markov chain with µ-invariant transition kernel P, as shown by Box ① in Figure 1(a).1Unlike non-backtracking random walks relying solely on the most recent history (Alon et al ..."}
+{"idx": 1, "title": "[2505.18300v2] Beyond Self-Repellent Kernels: History-Driven ...", "date": "", "ddg_snippet": "May 23, 2025 · View a PDF of the paper titled Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs, by Jie Hu and 2 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300v2", "content": "May 23, 2025 · View a PDF of the paper titled Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs, by Jie Hu and 2 other authors"}
+{"idx": 2, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies."}
+{"idx": 3, "title": "Modeling Cache Performance Beyond LRU LRU-2 vs 2-LRU: An Analytical Study - cs A4.pdf - ResearchGate Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of Beyond Self-Repellent Kernels: History-Driven Target Towards...", "date": "", "ddg_snippet": "We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec . 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4 ), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu a virtual cache before the LRU cache (2- LRU ) provides a huge benefit for small caches in IRM and non-IRM environments. Having an accurate model to calculate the miss rates of replacement algorithms is crucial for performance analysis of large-scale interconnected caches. The kernel section is different from a pullback attractor, although some rela-tions between kernel section and attracting set can be established in Section 2. In particular, both kernel section ... What is a cache model in LRU? r = (In LRU , the oldest age distribution uses 1.) The complete cache model is given by the age (Eq. 1), hit (Eq. 4 ), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. Why does LRU use thrash-resistance? LRU uses recency alone, leading to its pathologies (e.g., thrashing). Most high-performance policies combine recency with other techniques. Protection: When the working set does not fit in the cache, some policies protect a portion of the working set against eviction to avoid thrashing . This is equivalent to thrash-resistance [23,42]. May 1, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands.", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/sanchez/papers/2016.model.hpca.pdf", "content": "We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec . 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4 ), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu a virtual cache before the LRU cache (2- LRU ) provides a huge benefit for small caches in IRM and non-IRM environments. Having an accurate model to calculate the miss rates of replacement algorithms is crucial for performance analysis of large-scale interconnected caches. The kernel section is different from a pullback attractor, although some rela-tions between kernel section and attracting set can be established in Section 2. In particular, both kernel section ... What is a cache model in LRU? r = (In LRU , the oldest age distribution uses 1.) The complete cache model is given by the age (Eq. 1), hit (Eq. 4 ), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. Why does LRU use thrash-resistance? LRU uses recency alone, leading to its pathologies (e.g., thrashing). Most high-performance policies combine recency with other techniques. Protection: When the working set does not fit in the cache, some policies protect a portion of the working set against eviction to avoid thrashing . This is equivalent to thrash-resistance [23,42]. May 1, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands."}
+{"idx": 4, "title": "LRU-2 vs 2-LRU: An Analytical Study - cs", "date": "", "ddg_snippet": "a virtual cache before the LRU cache (2- LRU ) provides a huge benefit for small caches in IRM and non-IRM environments. Having an accurate model to calculate the miss rates of replacement algorithms is crucial for performance analysis of large-scale interconnected caches.", "subpage_snippet": "", "source": "www.cs.usask.ca", "link": "https://www.cs.usask.ca/faculty/makaroff/papers/LRU2.pdf", "content": "a virtual cache before the LRU cache (2- LRU ) provides a huge benefit for small caches in IRM and non-IRM environments. Having an accurate model to calculate the miss rates of replacement algorithms is crucial for performance analysis of large-scale interconnected caches."}
+{"idx": 5, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards...", "date": "", "ddg_snippet": "May 1, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0yzOEMbShU", "content": "May 1, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands."}
+{"idx": 6, "title": "Beyond Self - Repellent Kernels : History-Driven Target Towards...", "date": "", "ddg_snippet": "4 . 5 Least Recently Used ( LRU ) Cache Scheme.. This self - repellent scheme shows remarkable success, achieving near-zero variance for large. α\\alphaitalic_α. at a rate of.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "4 . 5 Least Recently Used ( LRU ) Cache Scheme.. This self - repellent scheme shows remarkable success, achieving near-zero variance for large. α\\alphaitalic_α. at a rate of."}
+{"idx": 7, "title": "Как предотвратить повторное вычисление функции с lru _ cache", "date": "", "ddg_snippet": "Как работает Least Recently Used ( LRU ) алгоритм. Параметры функции lru _ cache .ftl. cached _property def sumdata( self )", "subpage_snippet": "", "source": "python-school.ru", "link": "https://python-school.ru/blog/python/lru_cache/", "content": "Как работает Least Recently Used ( LRU ) алгоритм. Параметры функции lru _ cache .ftl. cached _property def sumdata( self )"}
+{"idx": 8, "title": "lru - cache - npm", "date": "", "ddg_snippet": "Start using lru - cache in your project by running `npm i lru - cache `.However, note that using some of the features will necessarily impact performance, by causing the cache to have to do more work. See the \"Performance\" section below. Installation. npm install lru - cache --save.", "subpage_snippet": "", "source": "www.npmjs.com", "link": "https://www.npmjs.com/package/lru-cache", "content": "Start using lru - cache in your project by running `npm i lru - cache `.However, note that using some of the features will necessarily impact performance, by causing the cache to have to do more work. See the \"Performance\" section below. Installation. npm install lru - cache --save."}
+{"idx": 9, "title": "A4.pdf - ResearchGate", "date": "", "ddg_snippet": "The kernel section is different from a pullback attractor, although some rela-tions between kernel section and attracting set can be established in Section 2. In particular, both kernel section ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Renhai-Wang/publication/335512223_Asymptotic_autonomy_of_kernel_sections_for_Newton-Boussinesq_equations_on_unbounded_zonary_domains/links/5d75dda04585151ee4a8c319/Asymptotic-autonomy-of-kernel-sections-for-Newton-Boussinesq-equations-on-unbounded-zonary-domains.pdf", "content": "The kernel section is different from a pullback attractor, although some rela-tions between kernel section and attracting set can be established in Section 2. In particular, both kernel section ..."}
diff --git a/data/sampled_jsons/10l1pGeOcK_SAFE_Finding_Sparse_and_Flat_Minima_Table_7_VGG-19_CIFAR-10_SAFE_accuracy_90%_95%_98%_spa_year_2024.jsonl b/data/sampled_jsons/10l1pGeOcK_SAFE_Finding_Sparse_and_Flat_Minima_Table_7_VGG-19_CIFAR-10_SAFE_accuracy_90%_95%_98%_spa_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Figure 2: Validation accuracy (mean±std) of VGG-19 and ResNet-20/32 models on CIFAR - 10 /100 pruned across different sparsity levels and methods. SAFE consistently achieves superior performance across a broad range of sparsity levels.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=10l1pGeOcK&name=pdf", "content": "Figure 2: Validation accuracy (mean±std) of VGG-19 and ResNet-20/32 models on CIFAR - 10 /100 pruned across different sparsity levels and methods. SAFE consistently achieves superior performance across a broad range of sparsity levels."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity ..."}
+{"idx": 2, "title": "Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32.", "date": "", "ddg_snippet": "Download scientific diagram | Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32. from publication: SAFE : Finding Sparse and Flat Minima to Improve Pruning | Sparsifying neural ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Best-performing-penalty-parameter-of-SAFE-for-VGG-19-and-ResNet-20-32_tbl3_392531034", "content": "Download scientific diagram | Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32. from publication: SAFE : Finding Sparse and Flat Minima to Improve Pruning | Sparsifying neural ..."}
+{"idx": 3, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time.Specifically, we formulate pruning as a sparsity ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LOG-postech/safe-torch", "content": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time.Specifically, we formulate pruning as a sparsity ..."}
+{"idx": 4, "title": "PDF Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Penalizes x iterate to move slightly closer to z during flatness-inducing minimization. This gradually moves x towards sparsity during flatness induction without sudden changes, yielding a sparse and flat minima .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46658.pdf", "content": "Penalizes x iterate to move slightly closer to z during flatness-inducing minimization. This gradually moves x towards sparsity during flatness induction without sudden changes, yielding a sparse and flat minima ."}
+{"idx": 5, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "We train ResNet-20 on CIFAR-10 to {70 %, 80 %, 90 %, 95 %} sparsity with Safe using linear, cosine schedules, and constant λ, and observe how this affects the final accuracy of the sparse network in Table 12.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v2", "content": "We train ResNet-20 on CIFAR-10 to {70 %, 80 %, 90 %, 95 %} sparsity with Safe using linear, cosine schedules, and constant λ, and observe how this affects the final accuracy of the sparse network in Table 12."}
+{"idx": 6, "title": "[이남훈 교수] SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee. \" SAFE : Finding Sparse and Flat Minima to Improve Pruning\", International Conference on Machine Learning (ICML), 2025. [성과와 관련된 이미지] 이전글[이남훈 교수] SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation", "subpage_snippet": "", "source": "cse.postech.ac.kr", "link": "https://cse.postech.ac.kr/csepostech/research/latest-research.do?mode=view&articleNo=24246&title=[이남훈+교수]+SAFE:+Finding+Sparse+and+Flat+Minima+to+Improve+Pruning", "content": "Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee. \" SAFE : Finding Sparse and Flat Minima to Improve Pruning\", International Conference on Machine Learning (ICML), 2025. [성과와 관련된 이미지] 이전글[이남훈 교수] SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation"}
+{"idx": 7, "title": "Low validation accuracy VGG-19 CIFAR-10 CNN - Reddit", "date": "", "ddg_snippet": "Hey Guys, I am trying to train a VGG-19 CNN on CIFAR-10 dataset using data augmentation and batch normalization. The code can be found VGG-19 CNN. I took two approaches to training the model: Using early stopping: loss = 2.2816 and accuracy = 47.1700% Without early stopping: loss = 3.3211 and accuracy = 56.6800% The loss and accuracy are on validation data. Also, when the model is trained ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/deeplearning/comments/hxk3g8/low_validation_accuracy_vgg19_cifar10_cnn/", "content": "Hey Guys, I am trying to train a VGG-19 CNN on CIFAR-10 dataset using data augmentation and batch normalization. The code can be found VGG-19 CNN. I took two approaches to training the model: Using early stopping: loss = 2.2816 and accuracy = 47.1700% Without early stopping: loss = 3.3211 and accuracy = 56.6800% The loss and accuracy are on validation data. Also, when the model is trained ..."}
+{"idx": 8, "title": "GitHub - xijia-tao/Implementation-of-VGGs-on-CIFAR-10: The repository ...", "date": "", "ddg_snippet": "The repository contains a detailed analysis on implementing VGG19 and (plain-layered) VGG34 on the CIFAR-10 dataset with code, and an explanation on the distinctive difference between them. It serves the purpose of storing my HW2 for the ICRA training, HKU, 2020. - xijia-tao/Implementation-of- VGGs -on- CIFAR - 10", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xijia-tao/Implementation-of-VGGs-on-CIFAR-10", "content": "The repository contains a detailed analysis on implementing VGG19 and (plain-layered) VGG34 on the CIFAR-10 dataset with code, and an explanation on the distinctive difference between them. It serves the purpose of storing my HW2 for the ICRA training, HKU, 2020. - xijia-tao/Implementation-of- VGGs -on- CIFAR - 10"}
+{"idx": 9, "title": "publications | Dongyeop Lee - GitHub Pages", "date": "", "ddg_snippet": "2025 SAFE : Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust ...", "subpage_snippet": "", "source": "edong6768.github.io", "link": "https://edong6768.github.io/publications/", "content": "2025 SAFE : Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust ..."}
diff --git a/data/sampled_jsons/2.5_Score_Based_Denoising_noise_augmentation_score_function_posterior_mean.jsonl b/data/sampled_jsons/2.5_Score_Based_Denoising_noise_augmentation_score_function_posterior_mean.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4afbe787af86116b6b47845e57879fab1431b2bf
--- /dev/null
+++ b/data/sampled_jsons/2.5_Score_Based_Denoising_noise_augmentation_score_function_posterior_mean.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diffusion-Based Posterior Sampling Methods", "date": "", "ddg_snippet": "4 Sept 2025 — Explore diffusion- based posterior sampling methods that blend score models with soft data consistency to robustly solve noisy , ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/diffusion-based-posterior-sampling-methods", "content": "4 Sept 2025 — Explore diffusion- based posterior sampling methods that blend score models with soft data consistency to robustly solve noisy , ..."}
+{"idx": 1, "title": "DiffAug: A Diffuse-and-Denoise Augmentation for Training ...", "date": "", "ddg_snippet": "9 Dec 2024 — We introduce DiffAug, a simple and efficient diffusion- based augmentation technique to train image classifiers for the crucial yet challenging goal of improved ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95014", "content": "9 Dec 2024 — We introduce DiffAug, a simple and efficient diffusion- based augmentation technique to train image classifiers for the crucial yet challenging goal of improved ..."}
+{"idx": 2, "title": "Adaptive and Iterative Point Cloud Denoising with Score‐ ...", "date": "", "ddg_snippet": "21 Apr 2025 — In this paper, we propose an adaptive and iterative point cloud denoising method based on the score - based diffusion model.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1111/cgf.70149?af=R", "content": "21 Apr 2025 — In this paper, we propose an adaptive and iterative point cloud denoising method based on the score - based diffusion model."}
+{"idx": 3, "title": "Score-based Diffusion Models in Function Space", "date": "", "ddg_snippet": "by JH Lim · 2025 · Cited by 61 — We develop a mathematically rigorous framework for denoising score matching with function -valued data called DDO by formulating and extending all necessary ...", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume26/23-1472/23-1472.pdf", "content": "by JH Lim · 2025 · Cited by 61 — We develop a mathematically rigorous framework for denoising score matching with function -valued data called DDO by formulating and extending all necessary ..."}
+{"idx": 4, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "6 Jun 2025 — We present a post-training score - based denoising technique that allows one to remove the noise in the generated samples. Report issue for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "6 Jun 2025 — We present a post-training score - based denoising technique that allows one to remove the noise in the generated samples. Report issue for ..."}
+{"idx": 5, "title": "Don't Play Favorites: Minority Guidance for Diffusion Models", "date": "", "ddg_snippet": "Our metric, which we call minority score , is based on Tweedie's formula (Robbins, 1992) that yields the posterior mean of a clean sample given a noise -corrupted ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2301.12334v2", "content": "Our metric, which we call minority score , is based on Tweedie's formula (Robbins, 1992) that yields the posterior mean of a clean sample given a noise -corrupted ..."}
+{"idx": 6, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "2.5 . Score Based Denoising . Training with noise augmentation introduces an additional challenge: models trained on the noisy distribution q(y) nat- urally ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/659866a5e8e65a343fe4954105e3fd09410fb122.pdf", "content": "2.5 . Score Based Denoising . Training with noise augmentation introduces an additional challenge: models trained on the noisy distribution q(y) nat- urally ..."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "4 days ago — We show how to sample from resulting posteriors by using this probability function for variational inference. Our results , including experiments ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=surrogate+posterior+variance", "content": "4 days ago — We show how to sample from resulting posteriors by using this probability function for variational inference. Our results , including experiments ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Specifically, we show that image denoising problems without clean images can be addressed by finding the mode of the posterior distribution and that the ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=sequential+denoising", "content": "Specifically, we show that image denoising problems without clean images can be addressed by finding the mode of the posterior distribution and that the ..."}
+{"idx": 9, "title": "Deep generative models for Bayesian inference on high- ...", "date": "", "ddg_snippet": "by TSW Stevens · 2025 · Cited by 1 — To achieve posterior sampling with pre-trained DMs, one can substitute the score function in equation (2.4) with the factorization of equation ( 2.5 ) leading to ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12177526/", "content": "by TSW Stevens · 2025 · Cited by 1 — To achieve posterior sampling with pre-trained DMs, one can substitute the score function in equation (2.4) with the factorization of equation ( 2.5 ) leading to ..."}
diff --git a/data/sampled_jsons/2208.01565.jsonl b/data/sampled_jsons/2208.01565.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a667ac95c4a2541d19472a61cc1a1645c64da843
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+{"idx": 0, "title": "[2208.01565] Approximate Bayesian Neural Operators ... arXiv:2208.01565v1 [cs.LG] 2 Aug 2022 dblp: Approximate Bayesian Neural Operators: Uncertainty ... Emilia Magnani - dblp Emilia Magnani - Google Scholar Neural Operator induced Gaussian Process framework for ... Position: Epistemic Artificial Intelligence is Essential", "date": "", "ddg_snippet": "Aug 2, 2022 · Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically ... Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ... Bibliographic details on Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022) University of Tübingen - Cited by 41 - Probabilistic Numerics - Machine Learning Nov 1, 2024 · The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared… The success of artificial intelligence (AI), especially deep learning (LeCun et al., 2015), is indisputable. AI systems now perform many tasks at or above human levels, with generative models advancing into creativity (Ramesh et al., 2022), large language models (LLMs) (Brown et al., 2020) excelling in language manipulation, and significant advancements in multimodal AI (Radford et al., 2021 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2208.01565", "content": "Aug 2, 2022 · Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically ... Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ... Bibliographic details on Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022) University of Tübingen - Cited by 41 - Probabilistic Numerics - Machine Learning Nov 1, 2024 · The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared… The success of artificial intelligence (AI), especially deep learning (LeCun et al., 2015), is indisputable. AI systems now perform many tasks at or above human levels, with generative models advancing into creativity (Ramesh et al., 2022), large language models (LLMs) (Brown et al., 2020) excelling in language manipulation, and significant advancements in multimodal AI (Radford et al., 2021 ..."}
+{"idx": 1, "title": "Сколько будет 1565% от 2208? - Калькулятор №1", "date": "", "ddg_snippet": "Например, он поможет узнать сколько будет 1565% от 2208? Введите проценты (например ' 1565'), число (например ' 2208') и нажмите кнопку 'Рассчитать'.", "subpage_snippet": "", "source": "calculator1.ru", "link": "https://calculator1.ru/percent-of-number/1565/2208/", "content": "Например, он поможет узнать сколько будет 1565% от 2208? Введите проценты (например ' 1565'), число (например ' 2208') и нажмите кнопку 'Рассчитать'."}
+{"idx": 2, "title": "НОД и НОК чисел 1565 и 2208 - подробное решение", "date": "", "ddg_snippet": "Наименьшее общее кратное (НОК) целых чисел 1565 и 2208 — это наименьшее натуральное число, которое делится на 1565 и на 2208 без остатка.", "subpage_snippet": "", "source": "nod-nok.ru", "link": "https://nod-nok.ru/primery/nod-i-nok-chisel-1565-2208/", "content": "Наименьшее общее кратное (НОК) целых чисел 1565 и 2208 — это наименьшее натуральное число, которое делится на 1565 и на 2208 без остатка."}
+{"idx": 3, "title": "arXiv:2208.01565v1 [cs.LG] 2 Aug 2022", "date": "", "ddg_snippet": "Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.01565.pdf", "content": "Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ..."}
+{"idx": 4, "title": "Emilia Magnani - Google Scholar", "date": "", "ddg_snippet": "E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig. arXiv preprint arXiv: 2208 . 01565 , 2022.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=_zPcNdEAAAAJ&hl=en", "content": "E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig. arXiv preprint arXiv: 2208 . 01565 , 2022."}
+{"idx": 5, "title": "Emilia Magnani - dblp", "date": "", "ddg_snippet": "Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022)", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/206/6101", "content": "Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022)"}
+{"idx": 6, "title": "Approximate Bayesian Neural Operators: Uncertainty Quantication for", "date": "", "ddg_snippet": "arXiv: 2208 . 01565 v1 [cs.LG] 2 Aug 2022. Emilia Magnani1 Nicholas Krämer1 Runa Eschenhagen1 Lorenzo Rosasco3,4 Philipp Hennig1,2. 1University of Tübingen, Germany...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.01565", "content": "arXiv: 2208 . 01565 v1 [cs.LG] 2 Aug 2022. Emilia Magnani1 Nicholas Krämer1 Runa Eschenhagen1 Lorenzo Rosasco3,4 Philipp Hennig1,2. 1University of Tübingen, Germany..."}
+{"idx": 7, "title": "Approximate Bayesian Neural Operators: Uncertainty Quantification for...", "date": "", "ddg_snippet": "Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. August 2022. DOI:10.48550/arXiv. 2208 . 01565 . Authors: Emilia Magnani.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/362430096_Approximate_Bayesian_Neural_Operators_Uncertainty_Quantification_for_Parametric_PDEs", "content": "Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. August 2022. DOI:10.48550/arXiv. 2208 . 01565 . Authors: Emilia Magnani."}
+{"idx": 8, "title": "Размеры Ягуар И-Пэйс и вес. Какие габариты Jaguar I-Pace?", "date": "", "ddg_snippet": "Масса, кг. 90 kWh AWD S. 4682 x 1895 x 1565. 2208.", "subpage_snippet": "", "source": "www.drom.ru", "link": "https://www.drom.ru/catalog/jaguar/i-pace/specs/dimensions/", "content": "Масса, кг. 90 kWh AWD S. 4682 x 1895 x 1565. 2208."}
+{"idx": 9, "title": "Результат вычитания: 1565 минус -643 | Универсальные онлайн...", "date": "", "ddg_snippet": "Результат вычитания: 2208.", "subpage_snippet": "", "source": "ai-calc.ru", "link": "https://ai-calc.ru/matematicheskie/minus/1565+-643/", "content": "Результат вычитания: 2208."}
diff --git a/data/sampled_jsons/23zxLtvder_SPD_Sync-Point_Drop_Efficient_Tensor_Parallelism_Large_Language_Models_Figure_7c.jsonl b/data/sampled_jsons/23zxLtvder_SPD_Sync-Point_Drop_Efficient_Tensor_Parallelism_Large_Language_Models_Figure_7c.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..790a2a223ab9310542c1e60a7101080421e7c43a
--- /dev/null
+++ b/data/sampled_jsons/23zxLtvder_SPD_Sync-Point_Drop_Efficient_Tensor_Parallelism_Large_Language_Models_Figure_7c.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "SPD : Sync - Point Drop for Efficient Tensor Parallelism of Large ...", "date": "", "ddg_snippet": "Tensor parallelism provides an effective way to increase server large language model (LLM) inference efficiency despite adding an additional communication cost.", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/sync-point-drop", "content": "Tensor parallelism provides an effective way to increase server large language model (LLM) inference efficiency despite adding an additional communication cost."}
+{"idx": 2, "title": "SPD : Sync - Point Drop for efficient tensor parallelism of Large ...", "date": "", "ddg_snippet": "Overview SPD ( Sync - Point Drop ) reduces communication overhead in tensor parallelism for LLMsIdentifies and selectively drops unnecessary synchronization operationsTraining and running large language models is a bit like trying to cook a massive meal in a...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/spd-sync-point-drop-efficient-tensor-parallelism", "content": "Overview SPD ( Sync - Point Drop ) reduces communication overhead in tensor parallelism for LLMsIdentifies and selectively drops unnecessary synchronization operationsTraining and running large language models is a bit like trying to cook a massive meal in a..."}
+{"idx": 3, "title": "Figure 1. Tensor parallelism applied on transformer decoder block (in...", "date": "", "ddg_snippet": "SPD : Sync - Point Drop for efficient tensor parallelism of Large Language Models .To enable SPD directly on decoder block, we first introduce a block design for SPD that minimizes negative effects resulting from reduced communication (see Figure 3). ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Tensor-parallelism-applied-on-transformer-decoder-block-in-2-GPUs-distributed-inference_fig1_389510233", "content": "SPD : Sync - Point Drop for efficient tensor parallelism of Large Language Models .To enable SPD directly on decoder block, we first introduce a block design for SPD that minimizes negative effects resulting from reduced communication (see Figure 3). ..."}
+{"idx": 4, "title": "Sync - Point Drop for Efficient Tensor Parallelism - Kifinity", "date": "", "ddg_snippet": "The rapid expansion of large language models (LLMs) necessitates efficient distributed inference across multiple computing units. Communication overheads from techniques like Tensor Parallelism hinder scalability and low latency.", "subpage_snippet": "", "source": "www.kifinity.com", "link": "https://www.kifinity.com/post/sync-point-drop-machinelearning-apple-e5f9168f", "content": "The rapid expansion of large language models (LLMs) necessitates efficient distributed inference across multiple computing units. Communication overheads from techniques like Tensor Parallelism hinder scalability and low latency."}
+{"idx": 5, "title": "Articles by Hyun-Bin Kim | Synthical", "date": "", "ddg_snippet": "SPD : Sync - Point Drop for Efficient Tensor Parallelism of Large Language Models .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/9ace9273-e17c-4820-9272-302eab60023e/articles", "content": "SPD : Sync - Point Drop for Efficient Tensor Parallelism of Large Language Models ."}
+{"idx": 6, "title": "GitHub - Toseic/LLM-inference-arxiv-daily: Automatically Update...", "date": "", "ddg_snippet": "SPD : Sync - Point Drop for efficient tensor parallelism of Large Language Models . Han-Byul Kim et.al.KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Toseic/LLM-inference-arxiv-daily", "content": "SPD : Sync - Point Drop for efficient tensor parallelism of Large Language Models . Han-Byul Kim et.al.KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse."}
+{"idx": 7, "title": "Ускорение инференса LLM через тензорный... — AI на vc.ru", "date": "", "ddg_snippet": "Применение модельного параллелизма ( model parallelism ) подразумевает наличие более 1 GPU в сервере или хотя бы по 1 GPU на двух серверах.Настройки в vLLM. Выберите tensor _ parallel _size, кратный hidden_size и num_heads модели.", "subpage_snippet": "", "source": "vc.ru", "link": "https://vc.ru/ai/2052555-uskorenie-inferensa-llm-s-pomoshchyu-tenzornogo-parallelizma", "content": "Применение модельного параллелизма ( model parallelism ) подразумевает наличие более 1 GPU в сервере или хотя бы по 1 GPU на двух серверах.Настройки в vLLM. Выберите tensor _ parallel _size, кратный hidden_size и num_heads модели."}
+{"idx": 8, "title": "Reducing LLM Inference Costs While Preserving Performance", "date": "", "ddg_snippet": "Model Parallelism : To serve very large models (tens to hundreds of billions of parameters), model parallelism is necessary – splitting the model across multiple GPUs or nodes.", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/reducing-llm-inference-costs-while", "content": "Model Parallelism : To serve very large models (tens to hundreds of billions of parameters), model parallelism is necessary – splitting the model across multiple GPUs or nodes."}
+{"idx": 9, "title": "Ключевые понятия LLM / Хабр", "date": "", "ddg_snippet": "Современные языковые модели ( large language models ) стали ключевым элементом в развитии искусственного интеллекта и обработки естественного языка. Модели, основанные на глубоком обучении и архитектуре трансформеров, способны генерировать текст, отвечать на...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/bothub/articles/928752/", "content": "Современные языковые модели ( large language models ) стали ключевым элементом в развитии искусственного интеллекта и обработки естественного языка. Модели, основанные на глубоком обучении и архитектуре трансформеров, способны генерировать текст, отвечать на..."}
diff --git a/data/sampled_jsons/2405.17618_equation_7_L_ra2c_sample-wise_reverse_A2C.jsonl b/data/sampled_jsons/2405.17618_equation_7_L_ra2c_sample-wise_reverse_A2C.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..205c439a451931812a27922d40bc91ed7bc217c2
--- /dev/null
+++ b/data/sampled_jsons/2405.17618_equation_7_L_ra2c_sample-wise_reverse_A2C.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Advantage Actor Critic (A2C) - Hugging Face", "date": "", "ddg_snippet": "Advantage Actor Critic ( A2C ) using Robotics Simulations with PyBullet 🤖 Now that you've studied the theory behind Advantage Actor Critic ( A2C ), you're ready to train your A2C agent using Stable-Baselines3 in robotic environments.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/deep-rl-a2c", "content": "Advantage Actor Critic ( A2C ) using Robotics Simulations with PyBullet 🤖 Now that you've studied the theory behind Advantage Actor Critic ( A2C ), you're ready to train your A2C agent using Stable-Baselines3 in robotic environments."}
+{"idx": 1, "title": "Advantage Actor-Critic (A2C) algorithm in Reinforcement ... - Medium", "date": "", "ddg_snippet": "Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science-in-your-pocket/advantage-actor-critic-a2c-algorithm-in-reinforcement-learning-with-codes-and-examples-using-e810273c0c9e", "content": "Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym"}
+{"idx": 2, "title": "Advantage Actor Critic Tutorial: minA2C - Towards Data Science", "date": "", "ddg_snippet": "In the field of Reinforcement Learning, the Advantage Actor Critic ( A2C ) algorithm combines two types of Reinforcement Learning algorithms (Policy Based and Value Based) together.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/advantage-actor-critic-tutorial-mina2c-7a3249962fc8/", "content": "In the field of Reinforcement Learning, the Advantage Actor Critic ( A2C ) algorithm combines two types of Reinforcement Learning algorithms (Policy Based and Value Based) together."}
+{"idx": 3, "title": "GitHub - Jitu0110/RLMujoco: SAC, PPO, A2C implementation on Mujoco ...", "date": "", "ddg_snippet": "SAC, PPO, A2C implementation on Mujoco environments : Humanoid-v4, Ant-v4, Cheetah-v4 . Includes reward manipulation. - Jitu0110/RLMujoco", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Jitu0110/RLMujoco", "content": "SAC, PPO, A2C implementation on Mujoco environments : Humanoid-v4, Ant-v4, Cheetah-v4 . Includes reward manipulation. - Jitu0110/RLMujoco"}
+{"idx": 4, "title": "Actor-Critic Methods, Advantage Actor-Critic (A2C) and Generalized ...", "date": "", "ddg_snippet": "Actor-Critic Methods, Advantage Actor-Critic ( A2C ) and Generalized Advantage Estimation (GAE) 18 minute read", "subpage_snippet": "", "source": "avandekleut.github.io", "link": "https://avandekleut.github.io/a2c/", "content": "Actor-Critic Methods, Advantage Actor-Critic ( A2C ) and Generalized Advantage Estimation (GAE) 18 minute read"}
+{"idx": 5, "title": "6.3 A2C Algorithm | Reinforcement Learning - The Actor-Critic Algorithm ...", "date": "", "ddg_snippet": "A complete look at the Actor-Critic ( A2C ) algorithm, used in deep reinforcement learning, which enables a learned reinforcing signal to be more informative for a policy than the rewards available from an environment.", "subpage_snippet": "", "source": "www.informit.com", "link": "https://www.informit.com/articles/article.aspx?p=2995356&seqNum=3", "content": "A complete look at the Actor-Critic ( A2C ) algorithm, used in deep reinforcement learning, which enables a learned reinforcing signal to be more informative for a policy than the rewards available from an environment."}
+{"idx": 6, "title": "Advantage Actor Critic (A2C) implementation - Medium", "date": "", "ddg_snippet": "Advantage Actor Critic ( A2C ) implementation Internet is full of very good resources to learn about reinforcement learning algorithms, and of course advantage actor critic is not an exception.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/deeplearningmadeeasy/advantage-actor-critic-a2c-implementation-944e98616b", "content": "Advantage Actor Critic ( A2C ) implementation Internet is full of very good resources to learn about reinforcement learning algorithms, and of course advantage actor critic is not an exception."}
+{"idx": 7, "title": "PDF Sample-wise Label Confidence Incorporation for Learning with Noisy Labels", "date": "", "ddg_snippet": "To alleviate this challenge, we propose a sample-wise label confidence in-corporation into our method. This incorporation leads to re-duced penalties for inaccurate predictions of noisy samples with lower confidence levels.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Ahn_Sample-wise_Label_Confidence_Incorporation_for_Learning_with_Noisy_Labels_ICCV_2023_paper.pdf", "content": "To alleviate this challenge, we propose a sample-wise label confidence in-corporation into our method. This incorporation leads to re-duced penalties for inaccurate predictions of noisy samples with lower confidence levels."}
+{"idx": 8, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss. We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss. We demonstrate performance improvements across various tasks and scales."}
+{"idx": 9, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "from p. We propose that the reverse RL losses for A2C and PPO also incorporate reverse information to address noisy factors in the RL training p ocedure. The RCE loss ( Equation 5) defines log 0 = Z where Z < 0 is some constant", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618", "content": "from p. We propose that the reverse RL losses for A2C and PPO also incorporate reverse information to address noisy factors in the RL training p ocedure. The RCE loss ( Equation 5) defines log 0 = Z where Z < 0 is some constant"}
diff --git a/data/sampled_jsons/2410.09536_Section_5.3_random_segment_length_authors_explanation.jsonl b/data/sampled_jsons/2410.09536_Section_5.3_random_segment_length_authors_explanation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..efa4add89ff1b33a9144b5e5447edb3573495e25
--- /dev/null
+++ b/data/sampled_jsons/2410.09536_Section_5.3_random_segment_length_authors_explanation.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "transformer-based off-policy episodic reinforcement learning", "date": "", "ddg_snippet": "by G Li · 2024 · Cited by 7 — In our experiments, we identified the segment length L as a key hyperparameter. The best results were achieved by randomly sampling L at each ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "by G Li · 2024 · Cited by 7 — In our experiments, we identified the segment length L as a key hyperparameter. The best results were achieved by randomly sampling L at each ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 2, "title": "Reinforcement Learning with Action Chunking", "date": "", "ddg_snippet": "We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/fe95604beeb8813dfe73e2988998dacf80355101.pdf", "content": "We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks."}
diff --git a/data/sampled_jsons/2502.00136_dataset_empirical_studies.jsonl b/data/sampled_jsons/2502.00136_dataset_empirical_studies.jsonl
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index 0000000000000000000000000000000000000000..b9fa95d11377d866a5671f4c5bd22d1544cf5664
--- /dev/null
+++ b/data/sampled_jsons/2502.00136_dataset_empirical_studies.jsonl
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+{"idx": 0, "title": "List of empires - Wikipedia", "date": "", "ddg_snippet": "\"ahmadnagar and the sur empire , 1537 to 1553- a study of contemporary documents\". Proceedings of the Indian History Congress. 44: 176–188.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/List_of_empires", "content": "\"ahmadnagar and the sur empire , 1537 to 1553- a study of contemporary documents\". Proceedings of the Indian History Congress. 44: 176–188."}
+{"idx": 1, "title": "[ 2502 . 00136 ] A Checks-and-Balances Framework for Context-Aware...", "date": "", "ddg_snippet": "Computer Science > Computation and Language. arXiv: 2502 . 00136 (cs). [Submitted on 31 Jan 2025 (v1), last revised 26 May 2025 (this version, v2)]. Title:A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment.(or arXiv: 2502 . 00136 v2 [cs.CL] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "Computer Science > Computation and Language. arXiv: 2502 . 00136 (cs). [Submitted on 31 Jan 2025 (v1), last revised 26 May 2025 (this version, v2)]. Title:A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment.(or arXiv: 2502 . 00136 v2 [cs.CL] for this version)."}
+{"idx": 2, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets?search=Ab+test", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 3, "title": "UCI Machine Learning Repository | Discover datasets around the world!", "date": "", "ddg_snippet": "This data set is designed for testing indexing schemes in time series databases. It is a much larger dataset than has been used in any published study (That we are currently aware of). It contains one million data points.", "subpage_snippet": "", "source": "archive.ics.uci.edu", "link": "https://archive.ics.uci.edu/dataset/136/pseudo+periodic+synthetic+time+series", "content": "This data set is designed for testing indexing schemes in time series databases. It is a much larger dataset than has been used in any published study (That we are currently aware of). It contains one million data points."}
+{"idx": 4, "title": "Department of Computer Science - Datasets - Aalborg...", "date": "", "ddg_snippet": "Datasets . 1 - 25 out of 136 results. Start date (descending).Additional file 3 of The Danish-American Research Exchange (DARE): a cross-sectional study of a binational research education program.", "subpage_snippet": "", "source": "vbn.aau.dk", "link": "https://vbn.aau.dk/en/organisations/institut-for-datalogi/datasets/", "content": "Datasets . 1 - 25 out of 136 results. Start date (descending).Additional file 3 of The Danish-American Research Exchange (DARE): a cross-sectional study of a binational research education program."}
+{"idx": 5, "title": "Сотни бронемашин в год, в перспективе тысячи... | Дзен", "date": "", "ddg_snippet": "Дорогой Друг, пожалуйста, поставь лайк, оставь комментарий и подпишись - нас почти 71 000. Спасибо каждому! 2502.", "subpage_snippet": "", "source": "dzen.ru", "link": "https://dzen.ru/a/ZFiZUAkh2WS60Zio", "content": "Дорогой Друг, пожалуйста, поставь лайк, оставь комментарий и подпишись - нас почти 71 000. Спасибо каждому! 2502."}
+{"idx": 6, "title": "DIY акустика. Теория и практика построения... - Форум onliner.by", "date": "", "ddg_snippet": "Автобарахолка. Отзывы об авто 2502.", "subpage_snippet": "", "source": "forum.onliner.by", "link": "https://forum.onliner.by/viewtopic.php?t=15946879&start=5100", "content": "Автобарахолка. Отзывы об авто 2502."}
+{"idx": 7, "title": "Топ игроков в режиме Миссии - 38 неделя 2025 год | crossout-info", "date": "", "ddg_snippet": "The_Nobody_. Каратель 75% 4 10891 4540. 2502. CBR-420.", "subpage_snippet": "", "source": "crossout-info.com", "link": "https://crossout-info.com/season/pvp/2025-38", "content": "The_Nobody_. Каратель 75% 4 10891 4540. 2502. CBR-420."}
+{"idx": 8, "title": "Новости Владивостока и Приморского края - МК во Владивостоке", "date": "", "ddg_snippet": "2502. Елена Соколова. За год «шашлычный набор» заметно подорожал.", "subpage_snippet": "", "source": "vlad.mk.ru", "link": "https://vlad.mk.ru/", "content": "2502. Елена Соколова. За год «шашлычный набор» заметно подорожал."}
+{"idx": 9, "title": "Терафлекс цена в Озерске от 564 руб., купить Терафлекс...", "date": "", "ddg_snippet": "В наличии. Цена: 2502.136. 2 352₽. Купить.", "subpage_snippet": "", "source": "apteka.ru", "link": "https://apteka.ru/ozersk/preparation/terafleks/", "content": "В наличии. Цена: 2502.136. 2 352₽. Купить."}
diff --git a/data/sampled_jsons/2502.10875_Table_1_statistics_ML-1M_Amazon_Beauty_Amazon_Toys_Games_density_train.jsonl b/data/sampled_jsons/2502.10875_Table_1_statistics_ML-1M_Amazon_Beauty_Amazon_Toys_Games_density_train.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..505fb319f178d65ae6bafae53e55907eeab23ded
--- /dev/null
+++ b/data/sampled_jsons/2502.10875_Table_1_statistics_ML-1M_Amazon_Beauty_Amazon_Toys_Games_density_train.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Amazon.com: Beauty & Personal Care", "date": "", "ddg_snippet": "Explore Beauty and Personal Care products on Amazon . Shop makeup, skin care, hair care, nail polish, beauty appliances, men's grooming & more, from best-selling brands like Olay, Neutrogena, Dove, L'Oreal Paris, and more.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/Beauty-Makeup-Skin-Hair-Products/b?node=3760911", "content": "Explore Beauty and Personal Care products on Amazon . Shop makeup, skin care, hair care, nail polish, beauty appliances, men's grooming & more, from best-selling brands like Olay, Neutrogena, Dove, L'Oreal Paris, and more."}
+{"idx": 1, "title": "Amazon beauty - statistics & facts | Statista", "date": "", "ddg_snippet": "Amazon has an advantageous market position over other beauty retailers' websites, as it offers competitive prices and provides a one-stop shopping experience with cross-merchandising.", "subpage_snippet": "", "source": "www.statista.com", "link": "https://www.statista.com/topics/11222/amazon-beauty/", "content": "Amazon has an advantageous market position over other beauty retailers' websites, as it offers competitive prices and provides a one-stop shopping experience with cross-merchandising."}
+{"idx": 2, "title": "Beauty Brands On Amazon Statistics 2025 - Free Yourself", "date": "", "ddg_snippet": "Here are the top 10 beauty brands on Amazon in 2025, based on sales performance, consumer reviews, and market influence: 1 . CeraVe CeraVe leads Amazon's beauty category with a 9.6% market share and over 1.8 million reviews, underscoring its dominance in dermatologist-recommended skincare. 2. Hero Cosmetics", "subpage_snippet": "", "source": "freeyourself.com", "link": "https://freeyourself.com/blogs/news/beauty-brands-on-amazon-statistics", "content": "Here are the top 10 beauty brands on Amazon in 2025, based on sales performance, consumer reviews, and market influence: 1 . CeraVe CeraVe leads Amazon's beauty category with a 9.6% market share and over 1.8 million reviews, underscoring its dominance in dermatologist-recommended skincare. 2. Hero Cosmetics"}
+{"idx": 3, "title": "Amazon Q2 2025: Top 25 Beauty and Personal Care Products", "date": "", "ddg_snippet": "The Top 25 list for Q2 2025 reads like a well-stocked beauty pantry—familiar favorites, proven formulas, and a few strategic surprises that hint at what's next.", "subpage_snippet": "", "source": "beautymatter.com", "link": "https://beautymatter.com/articles/amazon-q2-2025-top-25-beauty-and-personal-care-products", "content": "The Top 25 list for Q2 2025 reads like a well-stocked beauty pantry—familiar favorites, proven formulas, and a few strategic surprises that hint at what's next."}
+{"idx": 4, "title": "Amazon.com: Beauty", "date": "", "ddg_snippet": "Pamper your skin with a selection of luxurious beauty products that deliver visible results, from improved hydration to a more youthful appearance.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/beauty/s?k=beauty", "content": "Pamper your skin with a selection of luxurious beauty products that deliver visible results, from improved hydration to a more youthful appearance."}
+{"idx": 5, "title": "Amazon.com. Spend less. Smile more.", "date": "", "ddg_snippet": "Free shipping on millions of items. Get the best of Shopping and Entertainment with Prime. Enjoy low prices and great deals on the largest selection of everyday essentials and other products, including fashion, home, beauty , electronics, Alexa Devices, sporting goods, toys , automotive, pets, baby, books, video games , musical instruments, office supplies, and more.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/", "content": "Free shipping on millions of items. Get the best of Shopping and Entertainment with Prime. Enjoy low prices and great deals on the largest selection of everyday essentials and other products, including fashion, home, beauty , electronics, Alexa Devices, sporting goods, toys , automotive, pets, baby, books, video games , musical instruments, office supplies, and more."}
+{"idx": 6, "title": "kaiboon0216/Amazon-beauty-products-analysis - GitHub", "date": "", "ddg_snippet": "Amazon - beauty -products-analysis Using data science process to gain business insights from the Amazon beauty datasets and determine the factors that contribute to the high sales of a beauty product Our anaylsis are focused on answering the following questions:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kaiboon0216/Amazon-beauty-products-analysis", "content": "Amazon - beauty -products-analysis Using data science process to gain business insights from the Amazon beauty datasets and determine the factors that contribute to the high sales of a beauty product Our anaylsis are focused on answering the following questions:"}
+{"idx": 7, "title": "Amazon.com: Glamazon Beauty", "date": "", "ddg_snippet": "Glamazon Beauty Cosmetics for all skin types To move between items, use your keyboard's up or down arrows.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/stores/page/1FE60412-06FB-4A68-822C-DA83D4150194", "content": "Glamazon Beauty Cosmetics for all skin types To move between items, use your keyboard's up or down arrows."}
+{"idx": 8, "title": "Daily arXiv Papers - 2025-09-18", "date": "", "ddg_snippet": "We train Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to balance Arabic specialization with base-model strengths. On Arabic-centric benchmarks, Hala achieves state-of-the-art results within both the \"nano\" ($\\leq$2B) and \"small\" (7-9B) categories, outperforming their bases.", "subpage_snippet": "", "source": "blog.yueqianlin.com", "link": "https://blog.yueqianlin.com/daily-publication/250918/", "content": "We train Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to balance Arabic specialization with base-model strengths. On Arabic-centric benchmarks, Hala achieves state-of-the-art results within both the \"nano\" ($\\leq$2B) and \"small\" (7-9B) categories, outperforming their bases."}
+{"idx": 9, "title": "Where are the robotic bricklayers? | Hacker News", "date": "", "ddg_snippet": "In the context of automation, the \"robotic revolution\" has already happened between the 4 industrial revolutions - basically, robots exist en masse already, we just don't call them robots. The majority of manufacturing and processing is already hyper optimized to only needing a few overseers to make sure the machines are running smoothly.", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=28054606", "content": "In the context of automation, the \"robotic revolution\" has already happened between the 4 industrial revolutions - basically, robots exist en masse already, we just don't call them robots. The majority of manufacturing and processing is already hyper optimized to only needing a few overseers to make sure the machines are running smoothly."}
diff --git a/data/sampled_jsons/2QdsjiNXgj_On_a_Connection_Between_Imitation_Learning_and_RLHF_Section_5_density_ratio_DIL_Bregman_d.jsonl b/data/sampled_jsons/2QdsjiNXgj_On_a_Connection_Between_Imitation_Learning_and_RLHF_Section_5_density_ratio_DIL_Bregman_d.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a9550263773018df6ca98b8ffba9d09c7a8b3873
--- /dev/null
+++ b/data/sampled_jsons/2QdsjiNXgj_On_a_Connection_Between_Imitation_Learning_and_RLHF_Section_5_density_ratio_DIL_Bregman_d.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning from human feedback - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v . t. e. In machine learning , reinforcement learning from human feedback is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback", "content": "Machine learningand data mining. v . t. e. In machine learning , reinforcement learning from human feedback is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences..."}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2QdsjiNXgj", "content": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 2, "title": "ON A CONNECTION BETWEEN IMITATION LEARNING ...", "date": "", "ddg_snippet": "by T Xiao · Cited by 14 — This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2QdsjiNXgj", "content": "by T Xiao · Cited by 14 — This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical ..."}
+{"idx": 3, "title": "On a Connection Between Imitation Learning and RLHF | alphaXiv", "date": "", "ddg_snippet": "Density Ratio Estimation: Instead of learning a reward function, DIL estimates the density ratio between the chosen responses and a reference distribution using Bregman divergence . Objective Function: The DIL objective can be expressed as", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.05079v1", "content": "Density Ratio Estimation: Instead of learning a reward function, DIL estimates the density ratio between the chosen responses and a reference distribution using Bregman divergence . Objective Function: The DIL objective can be expressed as"}
+{"idx": 4, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 5, "title": "[Literature Review] On a Connection Between Imitation Learning ...", "date": "", "ddg_snippet": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-a-connection-between-imitation-learning-and-rlhf", "content": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning ."}
+{"idx": 6, "title": "Density Ratio Matching under the Bregman Divergence : A Unified...", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF . DIL provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/215704952_Density_Ratio_Matching_under_the_Bregman_Divergence_A_Unified_Framework_of_Density_Ratio_Estimation", "content": "On a Connection Between Imitation Learning and RLHF . DIL provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants."}
+{"idx": 7, "title": "How to Leverage Demonstration Data in Alignment for Large Language", "date": "", "ddg_snippet": "Then, we make use of the connection between imitation learning and density ratio estimation that can be solved with simple classification in an entirely offline fashion.BC is typically cast as KL divergence minimization between the learning policy and expert policy as", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.744.pdf", "content": "Then, we make use of the connection between imitation learning and density ratio estimation that can be solved with simple classification in an entirely offline fashion.BC is typically cast as KL divergence minimization between the learning policy and expert policy as"}
+{"idx": 8, "title": "RLHF on Google Cloud | Google Cloud Blog", "date": "", "ddg_snippet": "What is RLHF ? RLHF tuning consists of two phases: reward modeling and reinforcement learning . 1. Reward modeling For reward modeling, data is collected in the form of comparisons. First off, we feed the same prompt into one or more LLMs to create multiple responses.", "subpage_snippet": "", "source": "cloud.google.com", "link": "https://cloud.google.com/blog/products/ai-machine-learning/rlhf-on-google-cloud", "content": "What is RLHF ? RLHF tuning consists of two phases: reward modeling and reinforcement learning . 1. Reward modeling For reward modeling, data is collected in the form of comparisons. First off, we feed the same prompt into one or more LLMs to create multiple responses."}
+{"idx": 9, "title": "Imitation Learning — Stable Baselines3 2.7.1a3 documentation", "date": "", "ddg_snippet": "You can install imitation with pip install imitation . The imitation documentation has more details on how to use the library, including a quick start guide for the impatient.", "subpage_snippet": "", "source": "stable-baselines3.readthedocs.io", "link": "https://stable-baselines3.readthedocs.io/en/master/guide/imitation.html", "content": "You can install imitation with pip install imitation . The imitation documentation has more details on how to use the library, including a quick start guide for the impatient."}
diff --git a/data/sampled_jsons/2uheUFcFsM_Normalizing_Flows_are_Capable_Generative_Models_Section_2.5_Score_Based_Denoising.jsonl b/data/sampled_jsons/2uheUFcFsM_Normalizing_Flows_are_Capable_Generative_Models_Section_2.5_Score_Based_Denoising.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a0501590179594ce70f1001260a5e8c46376d4e9
--- /dev/null
+++ b/data/sampled_jsons/2uheUFcFsM_Normalizing_Flows_are_Capable_Generative_Models_Section_2.5_Score_Based_Denoising.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Normalizing Flows are Capable Generative Models - OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 1, "title": "(PDF) Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386577118_Normalizing_Flows_are_Capable_Generative_Models", "content": "Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ..."}
+{"idx": 2, "title": "[2412.06329] Normalizing Flows are Capable Generative Models Images Normalizing Flow Models - GitHub Pages Normalizing Flows are Capable Generative Models Normalizing Flows are Capable Generative Models - OpenReview Normalizing Flows Explained - emergentmind.com (PDF) Normalizing Flows are Capable Generative Models Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly ... View all We continue our study over another type of likelihood based generative models . As before, we assume we are given access to a dataset D of n-dimensional datapoints x. So far we have learned two types of likelihood based generative models : 1. Autoregressive Models : pθ(x)=∏i=1Npθ(xi|x See full list on deepgenerativemodels.github.io In normalizing flows , we wish to map simple distributions (easy to sample and evaluate densities) to complex ones (learned via data). The change of variables formula describe how to evaluate densities of a random variable that is a deterministic transformation from another variable. Change of Variables: Z and X be random variables which are related... See full list on deepgenerativemodels.github.io Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core. May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ... Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ... We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.06329", "content": "Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly ... View all We continue our study over another type of likelihood based generative models . As before, we assume we are given access to a dataset D of n-dimensional datapoints x. So far we have learned two types of likelihood based generative models : 1. Autoregressive Models : pθ(x)=∏i=1Npθ(xi|x See full list on deepgenerativemodels.github.io In normalizing flows , we wish to map simple distributions (easy to sample and evaluate densities) to complex ones (learned via data). The change of variables formula describe how to evaluate densities of a random variable that is a deterministic transformation from another variable. Change of Variables: Z and X be random variables which are related... See full list on deepgenerativemodels.github.io Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core. May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ... Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ... We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively."}
+{"idx": 3, "title": "Normalizing Flow Models - GitHub Pages Normalizing Flows are Capable Generative Models Normalizing Flows are Capable Generative Models - OpenReview Normalizing Flows Explained - emergentmind.com (PDF) Normalizing Flows are Capable Generative Models Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "We continue our study over another type of likelihood based generative models . As before, we assume we are given access to a dataset D of n-dimensional datapoints x. So far we have learned two types of likelihood based generative models : 1. Autoregressive Models : pθ(x)=∏i=1Npθ(xi|x See full list on deepgenerativemodels.github.io In normalizing flows , we wish to map simple distributions (easy to sample and evaluate densities) to complex ones (learned via data). The change of variables formula describe how to evaluate densities of a random variable that is a deterministic transformation from another variable. Change of Variables: Z and X be random variables which are related... See full list on deepgenerativemodels.github.io Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core. May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ... Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ... We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively.", "subpage_snippet": "", "source": "deepgenerativemodels.github.io", "link": "https://deepgenerativemodels.github.io/notes/flow/", "content": "We continue our study over another type of likelihood based generative models . As before, we assume we are given access to a dataset D of n-dimensional datapoints x. So far we have learned two types of likelihood based generative models : 1. Autoregressive Models : pθ(x)=∏i=1Npθ(xi|x See full list on deepgenerativemodels.github.io In normalizing flows , we wish to map simple distributions (easy to sample and evaluate densities) to complex ones (learned via data). The change of variables formula describe how to evaluate densities of a random variable that is a deterministic transformation from another variable. Change of Variables: Z and X be random variables which are related... See full list on deepgenerativemodels.github.io Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core. May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ... Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ... We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively."}
+{"idx": 4, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core.", "subpage_snippet": "", "source": "aiwithmike.substack.com", "link": "https://aiwithmike.substack.com/p/normalizing-flows-are-capable-generative", "content": "Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core."}
+{"idx": 5, "title": "Normalizing Flows Explained - emergentmind.com", "date": "", "ddg_snippet": "Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/normalizing-flows", "content": "Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ..."}
+{"idx": 6, "title": "Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively."}
+{"idx": 7, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs.Second, we identify a post-training score based denoising technique that allows one to remove the noise portion of the generated samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v2", "content": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs.Second, we identify a post-training score based denoising technique that allows one to remove the noise portion of the generated samples."}
+{"idx": 8, "title": "Normalizing Flows are Capable Generative Models | alphaXiv", "date": "", "ddg_snippet": "Abstract: Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2412.06329v1", "content": "Abstract: Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 9, "title": "Normalizing Flows (NFs)", "date": "", "ddg_snippet": "Normalizing flows are likelihood- based generative models that convert simple distributions into complex data densities using sequences of invertible, differentiable transformations.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/normalizing-flows-nfs", "content": "Normalizing flows are likelihood- based generative models that convert simple distributions into complex data densities using sequences of invertible, differentiable transformations."}
diff --git a/data/sampled_jsons/3079_games_sitegithub.comFLAIROxah2ac2_OR_sitedocs.ah2ac2.com.jsonl b/data/sampled_jsons/3079_games_sitegithub.comFLAIROxah2ac2_OR_sitedocs.ah2ac2.com.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..859b76dd1c6d127fd4f0c45240041a39a433345d
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+++ b/data/sampled_jsons/3079_games_sitegithub.comFLAIROxah2ac2_OR_sitedocs.ah2ac2.com.jsonl
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+{"idx": 0, "title": "Free Games to Play Now - Free Online Games 2025 Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "The Best Free Online Games Reviewed. Find the Best Game Here & Play Now. We've Reviewed the Best Browser Games. Compare and Register Now.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=top5onlinegames-sg.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=vhXBU2Tpv4sEyKLxBWHDy3C7UfjBbGs22e1suhadHjuAbfSkxI_v_CUcnJ9Fgujwaj5hsPpOkI7bNGOqYxKd3J4vsozNhY0Bm0syNdMde_DQWpjvNaYkIYo-ouAXlUXD.8mcT65qnQMkI97-ZloFcVg&rut=ed036a80484010b355a21a02324a5773a39d506ea7f6df9b091ebeaf7610f161&u3=https://www.bing.com/aclick?ld=e8bHn4vNNqEcU1DScv6hZKADVUCUzSP1DC71cIaM6PPgaSjYAm3__QASr1r4von_pM0o33LKFNYwYKYxzvy83VmOScRR08SIHjtGzastQMPtpd2ubAc0nGUUe-ss0yFg15lNcRnRbBjdHJl5LTEQW2d0SaLASu8CWaEXe0TduVIV5s3sRMyLkgkbPLhTJfW1OuCBAgQA&u=aHR0cHMlM2ElMmYlMmZ3d3cudG9wNW9ubGluZWdhbWVzLXNnLmNvbSUyZiUzZnRtcGx0JTNkMS4zJTI2a2V5d29yZCUzZGdhbWVzJTI2Y21wZ2lkJTNkNjA0MTk1NjEyJTI2YWRncnBpZCUzZDEzNDE0MDY2MTY5MDE0MDIlMjZrd2lkJTNka3dkLTgzODM4OTg4NjMzNjIzJTNhbG9jLTE2NCUyNmdlb2lkJTNkMTY0JTI2bXQlM2RwJTI2bnclM2RzJTI2ZGUlM2RjJTI2YWRpZCUzZDgzODM4MTgwNzk0NDcxJTI2bXNjbGtpZCUzZDc3ODNhYTc4MjdiNjE0OWJmYTI1YzJhYTNhZThiYWE1JTI2Y21wZ25hbWUlM2RHZW5lcmFsJTI1MjBOZXclMjUyMFN0cnVjdHVyZV9hbGxfYWxsX2FsbCUyNmFkZ3JwbmFtZSUzZGdhbWVzJTI2YWRhY2MlM2RGMTEwOTlZRw&rlid=7783aa7827b6149bfa25c2aa3ae8baa5&vqd=4-265520474365168234381812084953229358325&iurl={1}IG=8CC3EA84A970460B89D42F6F14EA1582&CID=1FF7BB145C276EE806EDAD645D4C6F0C&ID=DevEx,5037.1", "content": "The Best Free Online Games Reviewed. Find the Best Game Here & Play Now. We've Reviewed the Best Browser Games. Compare and Register Now."}
+{"idx": 1, "title": "Games Play Games - Play Online Games Now For Free Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "izzygames.com We have a huge collection in all kind of genres in Online Games and Download Games. Free Games on IzzYgames.com Play Online now or Download the Indie Games", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=izzygames.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=6wVmZ1YnMb9xyi48pb5G5EgRGfj4aZK0YpyahT-9EeJgGSONGsn9RvRBKKUZcE1vk2K5xlbdf2vL7FU2yQQrfZkxJbhHq4GzuMhcMGdkk4qeEblghfNf8LY0zxOfnX4r.kPZzE6eYOrajuu22wMVUgw&rut=be0a558ad7eb61df0b9ace60705ccd64668accfb491932172ab0684ef965d8de&u3=https://www.bing.com/aclick?ld=e8ZK_dZw3R38TnDIsihFt4KzVUCUx6rNTelQnzV5tpQRlchKXu059CAIPmi3nPTr0dqmwTofslebbHe_8u0fyyCXPc5B8qUDNb4av9yF0phmazWcWzQcuDssmEGDqZUvg91R8-y7Eup2lMm1I8G5q3FIye5o4bog072hjjdUu7-UEWstGUNpYXRGH4ZhbRXxRIKzC0qA&u=aHR0cCUzYSUyZiUyZnd3dy5penp5Z2FtZXMuY29tJTJmJTNmbXNjbGtpZCUzZDVlNjkwZjdhZDE2NDEyMzhmZWU3NjdhOGNjM2FiOTY1JTI2dXRtX3NvdXJjZSUzZGJpbmclMjZ1dG1fbWVkaXVtJTNkY3BjJTI2dXRtX2NhbXBhaWduJTNkR29vZ2xlJTI1MjBTZWFyY2glMjUyMFRleHQtTGlua3MlMjZ1dG1fdGVybSUzZGdhbWVzJTI1MjBwbGF5JTI1MjBnYW1lcyUyNnV0bV9jb250ZW50JTNkSVpHJTI1MjAtJTI1MjBGcmVlJTI1MjBPbmxpbmUlMjUyMEdhbWVz&rlid=5e690f7ad1641238fee767a8cc3ab965&vqd=4-103805040900328288908480999290784911953&iurl={1}IG=8CC3EA84A970460B89D42F6F14EA1582&CID=1FF7BB145C276EE806EDAD645D4C6F0C&ID=DevEx,5043.1+https://duckduckgo.com/y.js?ad_domain=izzygames.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=6wVmZ1YnMb9xyi48pb5G5EgRGfj4aZK0YpyahT-9EeJgGSONGsn9RvRBKKUZcE1vk2K5xlbdf2vL7FU2yQQrfZkxJbhHq4GzuMhcMGdkk4qeEblghfNf8LY0zxOfnX4r.kPZzE6eYOrajuu22wMVUgw&rut=be0a558ad7eb61df0b9ace60705ccd64668accfb491932172ab0684ef965d8de&u3=https://www.bing.com/aclick?ld=e8ZK_dZw3R38TnDIsihFt4KzVUCUx6rNTelQnzV5tpQRlchKXu059CAIPmi3nPTr0dqmwTofslebbHe_8u0fyyCXPc5B8qUDNb4av9yF0phmazWcWzQcuDssmEGDqZUvg91R8-y7Eup2lMm1I8G5q3FIye5o4bog072hjjdUu7-UEWstGUNpYXRGH4ZhbRXxRIKzC0qA&u=aHR0cCUzYSUyZiUyZnd3dy5penp5Z2FtZXMuY29tJTJmJTNmbXNjbGtpZCUzZDVlNjkwZjdhZDE2NDEyMzhmZWU3NjdhOGNjM2FiOTY1JTI2dXRtX3NvdXJjZSUzZGJpbmclMjZ1dG1fbWVkaXVtJTNkY3BjJTI2dXRtX2NhbXBhaWduJTNkR29vZ2xlJTI1MjBTZWFyY2glMjUyMFRleHQtTGlua3MlMjZ1dG1fdGVybSUzZGdhbWVzJTI1MjBwbGF5JTI1MjBnYW1lcyUyNnV0bV9jb250ZW50JTNkSVpHJTI1MjAtJTI1MjBGcmVlJTI1MjBPbmxpbmUlMjUyMEdhbWVz&rlid=5e690f7ad1641238fee767a8cc3ab965&vqd=4-103805040900328288908480999290784911953&iurl={1}IG=8CC3EA84A970460B89D42F6F14EA1582&CID=1FF7BB145C276EE806EDAD645D4C6F0C&ID=DevEx,5043.1", "content": "izzygames.com We have a huge collection in all kind of genres in Online Games and Download Games. Free Games on IzzYgames.com Play Online now or Download the Indie Games"}
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diff --git a/data/sampled_jsons/32717_ImagineFSL_Self-Supervised_Pretraining_Table_1_Flowers_16-shot.jsonl b/data/sampled_jsons/32717_ImagineFSL_Self-Supervised_Pretraining_Table_1_Flowers_16-shot.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3d5e8a82a8616aa7888757885d6e57b1eb66cae7
--- /dev/null
+++ b/data/sampled_jsons/32717_ImagineFSL_Self-Supervised_Pretraining_Table_1_Flowers_16-shot.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "Transformers typically are first pretrained by self - supervised learning on a large generic dataset, followed by supervised fine-tuning on a small task-specific dataset.Tasks for pretraining and fine-tuning commonly include", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "Transformers typically are first pretrained by self - supervised learning on a large generic dataset, followed by supervised fine-tuning on a small task-specific dataset.Tasks for pretraining and fine-tuning commonly include"}
+{"idx": 1, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "We introduce a novel CLIP adaptation methodology called ImagineFSL , involving pretraining on the imagined base set followed by fine-tuning on downstream few- shot tasks. We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32717", "content": "We introduce a novel CLIP adaptation methodology called ImagineFSL , involving pretraining on the imagined base set followed by fine-tuning on downstream few- shot tasks. We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
+{"idx": 2, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance. Based on on this finding, we propose an improved self-supervised method tailored for few- shot scenarios, enhancing the transferability of repre-sentations from synthetic to real image domains.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance. Based on on this finding, we propose an improved self-supervised method tailored for few- shot scenarios, enhancing the transferability of repre-sentations from synthetic to real image domains."}
+{"idx": 3, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023 ... - GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ..."}
+{"idx": 4, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ..."}
+{"idx": 5, "title": "[2407.18125] Self-supervised pre-training with diffusion ...", "date": "", "ddg_snippet": "Jul 25, 2024 · To our knowledge, this work represents the first application of diffusion models for self-supervised learning in landmark detection, which may offer a valuable pre-training approach in few- shot regimes, for mitigating data scarcity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.18125", "content": "Jul 25, 2024 · To our knowledge, this work represents the first application of diffusion models for self-supervised learning in landmark detection, which may offer a valuable pre-training approach in few- shot regimes, for mitigating data scarcity."}
+{"idx": 6, "title": "Few-shot intent detection with self-supervised pretraining ...", "date": "", "ddg_snippet": "Nov 1 , 2024 · Our method is closely related to few- shot learning, few- shot intent detection tasks, prototypical networks, and self-supervised multi-task pretraining . In this section, we review each of these related efforts in turn.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320324003923", "content": "Nov 1 , 2024 · Our method is closely related to few- shot learning, few- shot intent detection tasks, prototypical networks, and self-supervised multi-task pretraining . In this section, we review each of these related efforts in turn."}
+{"idx": 7, "title": "[2408.13385] MICM: Rethinking Unsupervised Pretraining for...", "date": "", "ddg_snippet": "Unsupervised Few- Shot Learning (U- FSL ) seeks to bridge this divide by reducing reliance on annotated datasets during initial training phases.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.13385", "content": "Unsupervised Few- Shot Learning (U- FSL ) seeks to bridge this divide by reducing reliance on annotated datasets during initial training phases."}
+{"idx": 8, "title": "Stable Diffusion XL — Nunchaku 1 .0. 1 documentation", "date": "", "ddg_snippet": "torch.bfloat 16 , variant=\"fp 16 \" 12 ).to(\"cuda\") 13 prompt = \"A cinematic shot of a baby racoon wearing an intricate italian priest robe.\"", "subpage_snippet": "", "source": "nunchaku.tech", "link": "https://nunchaku.tech/docs/nunchaku/usage/sdxl.html", "content": "torch.bfloat 16 , variant=\"fp 16 \" 12 ).to(\"cuda\") 13 prompt = \"A cinematic shot of a baby racoon wearing an intricate italian priest robe.\""}
+{"idx": 9, "title": "[PDF] Self - Supervised Visual Feature Learning... | Semantic Scholar", "date": "", "ddg_snippet": "With the self - supervised pre - trained 3DRotNet from large datasets, the recognition accuracy is boosted up by 20.4% on UCF101 and 16 .7% on HMDB51 respectively, compared to the models trained from scratch.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Self-Supervised-Visual-Feature-Learning-With-Deep-A-Jing-Tian/4c94ee7df6bc2bfcac76703be4f059a79010f7e5", "content": "With the self - supervised pre - trained 3DRotNet from large datasets, the recognition accuracy is boosted up by 20.4% on UCF101 and 16 .7% on HMDB51 respectively, compared to the models trained from scratch."}
diff --git a/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_202.jsonl b/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_202.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..86fb2a8764448062d8cc73abd125b372892a46c6
--- /dev/null
+++ b/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_202.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stochastic - Wikipedia", "date": "", "ddg_snippet": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic", "content": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve..."}
+{"idx": 1, "title": "CVPR Poster Deterministic - to - Stochastic Diverse Latent Feature ...", "date": "", "ddg_snippet": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33113", "content": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task."}
+{"idx": 2, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task."}
+{"idx": 3, "title": "10 Papers Accepted at CVPR 2025", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Hua Yu, Weiming Liu, Gui Xu, Yaqing Hou, Yew-Soon Ong, Qiang Zhang.", "subpage_snippet": "", "source": "www.a-star.edu.sg", "link": "https://www.a-star.edu.sg/cfar/news/news/features/10-papers-accepted-at-cvpr-2025", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Hua Yu, Weiming Liu, Gui Xu, Yaqing Hou, Yew-Soon Ong, Qiang Zhang."}
+{"idx": 4, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "Abstract: Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "Abstract: Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task."}
+{"idx": 5, "title": "Contribute to 52CV/ CVPR - 2025 -Papers development by creating an...", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .LAL: Enhancing 3D Human Motion Prediction with Latency -aware Auxiliary Learning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/52CV/CVPR-2025-Papers", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .LAL: Enhancing 3D Human Motion Prediction with Latency -aware Auxiliary Learning."}
+{"idx": 6, "title": "CVPR 2025 Accepted Papers-CSDN博客", "date": "", "ddg_snippet": "for Training-free Open-vocabulary Attribute Detection Marco Garosi · Alessandro Conti · Gaowen Liu · Elisa Ricci · Massimiliano Mancini Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Hua Yu · Weiming Liu · Gui Xu · Yaqing Hou...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/u013963578/article/details/146183100", "content": "for Training-free Open-vocabulary Attribute Detection Marco Garosi · Alessandro Conti · Gaowen Liu · Elisa Ricci · Massimiliano Mancini Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Hua Yu · Weiming Liu · Gui Xu · Yaqing Hou..."}
+{"idx": 7, "title": "UNIF: United Neural Implicit Functions for Clothed Human ...", "date": "", "ddg_snippet": "Computer Vision and Pattern Recognition . [4] Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/unif-united-neural-implicit-functions-for-clothed-human-reconstruction-and-animation/867766725599297675-108597", "content": "Computer Vision and Pattern Recognition . [4] Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis ."}
+{"idx": 8, "title": "Meet Seed at CVPR 2025 : 12 Papers Accepted and 2 Talks", "date": "", "ddg_snippet": "The IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ) 2025 was held from June 11 to 15 in Nashville, Tennessee, USA.", "subpage_snippet": "", "source": "research.doubao.com", "link": "https://research.doubao.com/en/blog/meet-seed-at-cvpr-2025-12-papers-accepted-and-2-talks", "content": "The IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ) 2025 was held from June 11 to 15 in Nashville, Tennessee, USA."}
+{"idx": 9, "title": "Awesome Human Motion | Awesome- Human - Motion", "date": "", "ddg_snippet": "( CVPR 2025 ) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al.", "subpage_snippet": "", "source": "foruck.github.io", "link": "https://foruck.github.io/Awesome-Human-Motion/", "content": "( CVPR 2025 ) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al."}
diff --git a/data/sampled_jsons/33266_UVGS-_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_'Branched_mapping_layers.jsonl b/data/sampled_jsons/33266_UVGS-_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_'Branched_mapping_layers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cfa5684c35b44a4ffad95e33966125b0c2004528
--- /dev/null
+++ b/data/sampled_jsons/33266_UVGS-_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_'Branched_mapping_layers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) UVGS : Reimagining Unstructured 3 D Gaussian Splatting ...", "date": "", "ddg_snippet": "multiple layers of UV maps , where each UVGS pixel holds. the top-K opacity values of 3DGS points. Branched mapping layers : The rationale behind using . branched mapping layers in both forward and reverse map -. ping networks is to prevent the incompatibility issues aris", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "multiple layers of UV maps , where each UVGS pixel holds. the top-K opacity values of 3DGS points. Branched mapping layers : The rationale behind using . branched mapping layers in both forward and reverse map -. ping networks is to prevent the incompatibility issues aris"}
+{"idx": 1, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... UVGS: Reimagining Unstructured 3D Gaussian Splatting UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... Graphics Programming weekly - Issue 378 - February 9th, 2025 ... UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... CVPR 2025 Open Access Repository UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting (PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using U… (PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using U… (PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using U… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Feb 3, 2025 · 3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. 3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. 3D Gaussian Splatting ( 3DGS ) has demonstrated supe-rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. Feb 9, 2025 · UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping The paper introduces turning 3D Gaussian Splatting from unstructured into structured structure using spherical UV mapping Feb 3, 2025 · 3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and... 3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. What is 3D Gaussian splatting? 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. Who wrote uvgs reimagining unstructured 3D Gaussian splatting using UV mapping? author = { Rai, Aashish and Wang , Dilin and Jain, Mihir and Sarafianos, Nikolaos and Chen, Kefan and Sridhar, Srinath and Prakash, Aayush}, title = {UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, Can a single Gaussian be represented by a spherical mapping point? Thus, a single Gaussian can be represented by 3.1 . Spherical Mapping points using neural networks. T o address this issue, we in- ance issue for faster and better feature extraction. W e pro- points and well structured. We prefer spherical mapping as tions for objects that extend far in the Z-direction. canonical space. Which opacity value can be represented by a single Gaussian? an opacity value oi∈R. In practice, the color is represented RGB values. Thus, a single Gaussian can be represented by 3.1 . Spherical Mapping points using neural networks. T o address this issue, we in- ance issue for faster and better feature extraction. W e pro- points and well structured. We prefer spherical mapping as What is the difference between get3d and gaussiancube? While Get3D and GaussianCube achieve higher resolution , they suffer from 3D inconsistency, numerous artifacts, and lack richness in 3D detail. In contrast, our method generates high-quality, high-resolution objects that are 3D consistent with sharp, well-defined edges. Can uvgs be treated as a typical RGB image? The compressed UVGS can be treated as typical RGB images . Remarkably, we discover that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. Our novel representation makes it effortless to leverage foundational 2D models, such as diffusion models, to directly model 3DGS. Feb 3, 2025 · However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "Feb 3, 2025 · 3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. 3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. 3D Gaussian Splatting ( 3DGS ) has demonstrated supe-rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. Feb 9, 2025 · UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping The paper introduces turning 3D Gaussian Splatting from unstructured into structured structure using spherical UV mapping Feb 3, 2025 · 3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and... 3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. What is 3D Gaussian splatting? 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. Who wrote uvgs reimagining unstructured 3D Gaussian splatting using UV mapping? author = { Rai, Aashish and Wang , Dilin and Jain, Mihir and Sarafianos, Nikolaos and Chen, Kefan and Sridhar, Srinath and Prakash, Aayush}, title = {UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, Can a single Gaussian be represented by a spherical mapping point? Thus, a single Gaussian can be represented by 3.1 . Spherical Mapping points using neural networks. T o address this issue, we in- ance issue for faster and better feature extraction. W e pro- points and well structured. We prefer spherical mapping as tions for objects that extend far in the Z-direction. canonical space. Which opacity value can be represented by a single Gaussian? an opacity value oi∈R. In practice, the color is represented RGB values. Thus, a single Gaussian can be represented by 3.1 . Spherical Mapping points using neural networks. T o address this issue, we in- ance issue for faster and better feature extraction. W e pro- points and well structured. We prefer spherical mapping as What is the difference between get3d and gaussiancube? While Get3D and GaussianCube achieve higher resolution , they suffer from 3D inconsistency, numerous artifacts, and lack richness in 3D detail. In contrast, our method generates high-quality, high-resolution objects that are 3D consistent with sharp, well-defined edges. Can uvgs be treated as a typical RGB image? The compressed UVGS can be treated as typical RGB images . Remarkably, we discover that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. Our novel representation makes it effortless to leverage foundational 2D models, such as diffusion models, to directly model 3DGS. Feb 3, 2025 · However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ."}
+{"idx": 2, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting", "date": "", "ddg_snippet": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges.", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/", "content": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges."}
+{"idx": 3, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "3D Gaussian Splatting ( 3DGS ) has demonstrated supe-rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "3D Gaussian Splatting ( 3DGS ) has demonstrated supe-rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges."}
+{"idx": 4, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Feb 3, 2025 · However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/UVGS:-Reimagining-Unstructured-3D-Gaussian-using-UV-Rai-Wang/10f0d88160981f85fd6c270e65496ec5948a98d2", "content": "Feb 3, 2025 · However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ."}
+{"idx": 5, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the incompatibility issues arising due the the different value distribution of 3DGS attributes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v2", "content": "Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the incompatibility issues arising due the the different value distribution of 3DGS attributes."}
+{"idx": 6, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "OverviewIntroduces UV mapping techniques to enhance 3 D Gaussian Splatting Introduces spherical and cylindrical UV mapping for different object types", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/uvgs-reimagining-unstructured-3d-gaussian-splatting-using", "content": "OverviewIntroduces UV mapping techniques to enhance 3 D Gaussian Splatting Introduces spherical and cylindrical UV mapping for different object types"}
+{"idx": 7, "title": "Free 3 D Gaussian Splatting Tool | Polycam", "date": "", "ddg_snippet": "Gaussian splatting is a rasterization technique used for 3 D reconstruction and rendering. In essence, it is a method to create photorealistic scenes from a sampling of images.", "subpage_snippet": "", "source": "poly.cam", "link": "https://poly.cam/tools/gaussian-splatting", "content": "Gaussian splatting is a rasterization technique used for 3 D reconstruction and rendering. In essence, it is a method to create photorealistic scenes from a sampling of images."}
+{"idx": 8, "title": "UVGS Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=1uMuXsJScvY", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 9, "title": "Graphics Programming weekly - Issue... | Jendrik Illner - 3 D Programmer", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .The paper presents how the mapping allows existing models to better operate on the image formats. Shows improvements in compression and quality.", "subpage_snippet": "", "source": "www.jendrikillner.com", "link": "https://www.jendrikillner.com/post/graphics-programming-weekly-issue-378/", "content": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .The paper presents how the mapping allows existing models to better operate on the image formats. Shows improvements in compression and quality."}
diff --git a/data/sampled_jsons/33381_Video_ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval.jsonl b/data/sampled_jsons/33381_Video_ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e52538bcb5d2819e96ec0b4ef128a20f039bfdc0
--- /dev/null
+++ b/data/sampled_jsons/33381_Video_ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ..."}
+{"idx": 1, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expan-sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong indi-vidual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "www.celsodemelo.net", "link": "http://www.celsodemelo.net/static/publications/Video_ColBERT_CVPR_2025_DistA.pdf", "content": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expan-sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong indi-vidual, yet compatible, representations for encoding video content."}
+{"idx": 2, "title": "GitHub - yogesh-iitj/Video-ColBERT", "date": "", "ddg_snippet": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to-video retrieval . Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yogesh-iitj/Video-ColBERT", "content": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to-video retrieval . Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ..."}
+{"idx": 3, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094542", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial ..."}
+{"idx": 4, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT offers an advanced approach to text-to-video retrieval by refining interaction strategies and achieving competitive performance, opening new pathways for multimodal retrieval research.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2503.19009", "content": "Video-ColBERT offers an advanced approach to text-to-video retrieval by refining interaction strategies and achieving competitive performance, opening new pathways for multimodal retrieval research."}
+{"idx": 5, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009", "content": "Abstract In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained ..."}
+{"idx": 6, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to-video re-trieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video re-trieval , our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "Abstract In this work, we tackle the problem of text-to-video re-trieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video re-trieval , our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos ."}
+{"idx": 7, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos , and finds that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Video-ColBERT:-Contextualized-Late-Interaction-for-Reddy-Martin/bff2f91c763830a2d14dbbbeca150e92ede02323", "content": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos , and finds that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in ..."}
+{"idx": 8, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.html", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos ."}
+{"idx": 9, "title": "Alexander Martin", "date": "", "ddg_snippet": "Inspired by the success of late interaction techniques in text -document, text -image, and text - video re - trieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assess- ment between queries and videos .", "subpage_snippet": "", "source": "alexmartin1722.github.io", "link": "https://alexmartin1722.github.io/", "content": "Inspired by the success of late interaction techniques in text -document, text -image, and text - video re - trieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assess- ment between queries and videos ."}
diff --git a/data/sampled_jsons/33381_Video_ColBERT_Table_3_MMSF_MMSV_R@1_scores_year_2023.jsonl b/data/sampled_jsons/33381_Video_ColBERT_Table_3_MMSF_MMSV_R@1_scores_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b715f4a85099c082307d93a43036dfe429d3fae7
--- /dev/null
+++ b/data/sampled_jsons/33381_Video_ColBERT_Table_3_MMSF_MMSV_R@1_scores_year_2023.jsonl
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+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video -ColBERT is a text-to-video retrieval method using fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33381", "content": "Video -ColBERT is a text-to-video retrieval method using fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual ..."}
+{"idx": 1, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "VIDEO-COLBERT (depicted in Fig. 2) has 3 main aspects: (i) fine-grained spa-tial and temporal interaction, performing MMS on both in-dependent frames and their contextualized representations, (ii) query and visual expansion tokens which allow for addi-tional information to be encoded for abstract queries and for additional high-level temporal ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "VIDEO-COLBERT (depicted in Fig. 2) has 3 main aspects: (i) fine-grained spa-tial and temporal interaction, performing MMS on both in-dependent frames and their contextualized representations, (ii) query and visual expansion tokens which allow for addi-tional information to be encoded for abstract queries and for additional high-level temporal ..."}
+{"idx": 2, "title": "GitHub - stanford-futuredata/ColBERT: ColBERT: state-of-the-art neural ...", "date": "", "ddg_snippet": "Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage. As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings (shown above in blue). Then at search time, it embeds every query into another matrix (shown in green) and efficiently finds ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/stanford-futuredata/ColBERT", "content": "Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage. As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings (shown above in blue). Then at search time, it embeds every query into another matrix (shown in green) and efficiently finds ..."}
+{"idx": 3, "title": "ColBERT: A complete guide - Medium", "date": "", "ddg_snippet": "Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@varun030403/colbert-a-complete-guide-1552468335ae", "content": "Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators."}
+{"idx": 4, "title": "Exciting Research Alert: Video-ColBERT - A Breakthrough in Text-to ...", "date": "", "ddg_snippet": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim (MMS) operations on both ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/singhsidhukuldeep_exciting-research-alert-video-colbert-activity-7336927383549071360-eAP7", "content": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim (MMS) operations on both ..."}
+{"idx": 5, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content."}
+{"idx": 6, "title": "minimal_colbert_usage_example.ipynb - Colab", "date": "", "ddg_snippet": "This notebook gives you a minimal usage example of downloading our ColBERT checkpoint to encode passages and queries to create a (term-x-term dot-product & max-pool & sum) score of their relevance.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/sebastian-hofstaetter/neural-ranking-kd/blob/master/minimal_colbert_usage_example.ipynb", "content": "This notebook gives you a minimal usage example of downloading our ColBERT checkpoint to encode passages and queries to create a (term-x-term dot-product & max-pool & sum) score of their relevance."}
+{"idx": 7, "title": "The Late Show with Stephen Colbert - YouTube", "date": "", "ddg_snippet": "Welcome to the official YouTube channel for \"The Late Show with Stephen Colbert \"! Weeknights at 11:35pm/10:35c. Welcome to the official YouTube channel for \"The Late Show with Stephen Colbert \"!", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/c/ColbertLateShow/videos", "content": "Welcome to the official YouTube channel for \"The Late Show with Stephen Colbert \"! Weeknights at 11:35pm/10:35c. Welcome to the official YouTube channel for \"The Late Show with Stephen Colbert \"!"}
+{"idx": 8, "title": "Stephen Colbert: all things Colbert - Reddit", "date": "", "ddg_snippet": "r/stephencolbert: Subreddit devoted to news, photos, memes, and gifs about the man, the myth, the legend, Stephen Colbert .", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/stephencolbert/", "content": "r/stephencolbert: Subreddit devoted to news, photos, memes, and gifs about the man, the myth, the legend, Stephen Colbert ."}
+{"idx": 9, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video ...", "date": "", "ddg_snippet": "We report num- bers pertaining to both offline index creation and query-time ranking. During video indexing, we see that MMS FVis no more expensive than MMS V, as they involve identical for- ward passes through the video encoder. At query-time, de- spite MMS FVinvolving more dot products, latency is vir- tually the same as the single-level ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Reddy_Video-ColBERT_Contextualized_Late_CVPR_2025_supplemental.pdf", "content": "We report num- bers pertaining to both offline index creation and query-time ranking. During video indexing, we see that MMS FVis no more expensive than MMS V, as they involve identical for- ward passes through the video encoder. At query-time, de- spite MMS FVinvolving more dot products, latency is vir- tually the same as the single-level ..."}
diff --git a/data/sampled_jsons/33775_Instant_Gaussian_Stream_Equation_6_interpolated_motion_feature.jsonl b/data/sampled_jsons/33775_Instant_Gaussian_Stream_Equation_6_interpolated_motion_feature.jsonl
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index 0000000000000000000000000000000000000000..a17d14cef37ff4dbe3ef5b55389816bca12f2ead
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+++ b/data/sampled_jsons/33775_Instant_Gaussian_Stream_Equation_6_interpolated_motion_feature.jsonl
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+{"idx": 1, "title": "GitHub - yjb 6 /IGS: [CVPR25 Highlight] Instant Gaussian Stream : Fast...", "date": "", "ddg_snippet": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting."}
+{"idx": 2, "title": "A particle executes a simple harmonic motion of time period T. Find th", "date": "", "ddg_snippet": "Step 1: Understand the Motion In simple harmonic motion , the displacement x of the particle from the mean position can be described by the equation : x(t)=Asin(ωt) where: - A is the amplitude, - ω is the angular frequency, given by ω=2πT (with T being the time period).", "subpage_snippet": "", "source": "www.doubtnut.com", "link": "https://www.doubtnut.com/qna/9527437", "content": "Step 1: Understand the Motion In simple harmonic motion , the displacement x of the particle from the mean position can be described by the equation : x(t)=Asin(ωt) where: - A is the amplitude, - ω is the angular frequency, given by ω=2πT (with T being the time period)."}
+{"idx": 3, "title": "Bayes theorem, the geometry of changing beliefs - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=HZGCoVF3YvM", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features ."}
+{"idx": 4, "title": "Full text of \"1 Fundamentals Of Finite Element Analysis David V. Hutton&quo...", "date": "", "ddg_snippet": "The form of Equation 6 .53 suggests that expression of the interpolation functions in terms of the nodal coordinates is algebraically complex. Fortunately, the complexity can be reduced by a more judicious choice of coordinates.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/stream/1FundamentalsOfFiniteElementAnalysisDavidV.Hutton/1_Fundamentals+of+Finite+Element+Analysis+-+David+V.+Hutton_djvu.txt", "content": "The form of Equation 6 .53 suggests that expression of the interpolation functions in terms of the nodal coordinates is algebraically complex. Fortunately, the complexity can be reduced by a more judicious choice of coordinates."}
+{"idx": 5, "title": "8 Best Noise Reduction Plugins 2025 (And Freebies)", "date": "", "ddg_snippet": "The “Suggest” feature is a clever touch; it analyzes the audio and gives you a solid starting point. I don’t always keep the exact settings it proposes, but it saves me time and gets me in the ballpark quickly. For fast turnaround jobs, this feature has been gold.", "subpage_snippet": "", "source": "integraudio.com", "link": "https://integraudio.com/8-noise-reduction-plugins/", "content": "The “Suggest” feature is a clever touch; it analyzes the audio and gives you a solid starting point. I don’t always keep the exact settings it proposes, but it saves me time and gets me in the ballpark quickly. For fast turnaround jobs, this feature has been gold."}
+{"idx": 6, "title": "Free Image Background Remover | Adobe Express", "date": "", "ddg_snippet": "See what people are saying about Adobe Express. I enjoy using the remove background feature in Adobe Express during my product launches! In just a few steps, I’m able to quickly remove the original background and add a fun one with my product.\"", "subpage_snippet": "", "source": "www.adobe.com", "link": "https://www.adobe.com/express/feature/image/remove-background", "content": "See what people are saying about Adobe Express. I enjoy using the remove background feature in Adobe Express during my product launches! In just a few steps, I’m able to quickly remove the original background and add a fun one with my product.\""}
+{"idx": 7, "title": "Online Frame Rate Converter Personal – AI Upscales Video to 60fps...", "date": "", "ddg_snippet": "Lower FPS to have smaller files.Natural-looking interpolated motion using AI.", "subpage_snippet": "", "source": "pixelfox.ai", "link": "https://pixelfox.ai/blog/online-frame-rate-converter-personal-ai-upscales-video-to-60fps-120fps-and-144fps-with-pixelfox", "content": "Lower FPS to have smaller files.Natural-looking interpolated motion using AI."}
+{"idx": 8, "title": "Blur Image Backgrounds for Free with AI Ease", "date": "", "ddg_snippet": "Instantly blur background of your photo using AI Ease online free tool. Upload, and seamlessly download your image with blurred background in seconds.Blur Effects. Motion .", "subpage_snippet": "", "source": "www.aiease.ai", "link": "https://www.aiease.ai/app/blur-image-background", "content": "Instantly blur background of your photo using AI Ease online free tool. Upload, and seamlessly download your image with blurred background in seconds.Blur Effects. Motion ."}
+{"idx": 9, "title": "iPhone Black Screen During Call: 7 Ways to Stop it", "date": "", "ddg_snippet": "reduce motion feature Another potential solution is to disable the Reduce Motion feature . Here is how to do it: 1) Open the Settings app on your iPhone.", "subpage_snippet": "", "source": "mspoweruser.com", "link": "https://mspoweruser.com/iphone-black-screen-during-call/", "content": "reduce motion feature Another potential solution is to disable the Reduce Motion feature . Here is how to do it: 1) Open the Settings app on your iPhone."}
diff --git a/data/sampled_jsons/33775_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_G.jsonl b/data/sampled_jsons/33775_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_G.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..78a4e76480e8e58d6eebd64a0190b128af70a995
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+++ b/data/sampled_jsons/33775_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_G.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Triplane meets gaussian splatting : Fast and generalizable single-view 3d reconstruction with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10324–10335, 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "Triplane meets gaussian splatting : Fast and generalizable single-view 3d reconstruction with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10324–10335, 2024."}
+{"idx": 1, "title": "CVPR Poster Instant Gaussian Stream : Fast and Generalizable ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33775", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points..."}
+{"idx": 2, "title": "(PDF) Instant Gaussian Stream : Fast and Generalizable Streaming ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space..."}
+{"idx": 3, "title": "GitHub - yjb6/IGS: [CVPR25 Highlight] Instant Gaussian Stream : Fast ...", "date": "", "ddg_snippet": "title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting } Streaming Reconstruction . Data Preparation. Step 1: Prepare Inputs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting } Streaming Reconstruction . Data Preparation. Step 1: Prepare Inputs."}
+{"idx": 4, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Yan_ Instant _ Gaussian _ Stream _ Fast _ and _ Generalizable _ Streaming _ of _ Dynamic _ Scene @CVPR2025@CVF.In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene@CVPR2025@CVF", "content": "Yan_ Instant _ Gaussian _ Stream _ Fast _ and _ Generalizable _ Streaming _ of _ Dynamic _ Scene @CVPR2025@CVF.In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 5, "title": "Instant Gaussian Stream : Fast and Generalizable ... | alphaXiv", "date": "", "ddg_snippet": "Instant Gaussian Stream (IGS) is a framework designed for fast and generalizable streaming reconstruction of dynamic scenes for Free-Viewpoint Video. It achieves per-frame reconstruction times of 2-3 seconds while maintaining high rendering quality by combining a motion...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.16979", "content": "Instant Gaussian Stream (IGS) is a framework designed for fast and generalizable streaming reconstruction of dynamic scenes for Free-Viewpoint Video. It achieves per-frame reconstruction times of 2-3 seconds while maintaining high rendering quality by combining a motion..."}
+{"idx": 6, "title": "Papers by Rui Peng with links to code and results.", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting .In this paper, we propose SCGaussian, a Structure Consistent Gaussian Splatting method using matching priors to learn 3D consistent scene structure.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Rui+Peng", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting .In this paper, we propose SCGaussian, a Structure Consistent Gaussian Splatting method using matching priors to learn 3D consistent scene structure."}
+{"idx": 7, "title": "3D Gaussian Splatting - Explained! - YouTube", "date": "", "ddg_snippet": "Gaussian Splatting is taking the world of 3D graphics by storm. Learn how this revolutionary technique can render photoreal scenes in real-time for cutting-e...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=sQcrZHvrEnU", "content": "Gaussian Splatting is taking the world of 3D graphics by storm. Learn how this revolutionary technique can render photoreal scenes in real-time for cutting-e..."}
+{"idx": 8, "title": "Free 3D Gaussian Splatting Tool | Polycam", "date": "", "ddg_snippet": "Gaussian splatting can effectively render shiny, reflective objects as well as long and thin details . Splatting also excels at capturing large, expansive spaces without sacrificing smaller details . With Polycam, you can enjoy additional features including", "subpage_snippet": "", "source": "poly.cam", "link": "https://poly.cam/tools/gaussian-splatting", "content": "Gaussian splatting can effectively render shiny, reflective objects as well as long and thin details . Splatting also excels at capturing large, expansive spaces without sacrificing smaller details . With Polycam, you can enjoy additional features including"}
+{"idx": 9, "title": "MVSGS: Gaussian splatting radiation field enhancement using...", "date": "", "ddg_snippet": "MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo.For more complex scene representations, appropriate high disparity Gaussian scaling is necessary because it opens up the possibility of more efficient and detailed scene representation.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40747-024-01691-x", "content": "MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo.For more complex scene representations, appropriate high disparity Gaussian scaling is necessary because it opens up the possibility of more efficient and detailed scene representation."}
diff --git a/data/sampled_jsons/33775_Instant_Gaussian_Stream_Table_2_Meeting_Room_storage_MB_values.jsonl b/data/sampled_jsons/33775_Instant_Gaussian_Stream_Table_2_Meeting_Room_storage_MB_values.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f1033158d3ec1836df288e9a91f1826d3e8bd212
--- /dev/null
+++ b/data/sampled_jsons/33775_Instant_Gaussian_Stream_Table_2_Meeting_Room_storage_MB_values.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "per, we propose Instant Gaussian Stream (IGS), a fast and view images is a valuable area of research, with appliFor the Meeting Room dataset, we train the Gaus - sians of the first frame using 15,000 iterations, compressing the number of Gaussians at 7,000 iterations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "per, we propose Instant Gaussian Stream (IGS), a fast and view images is a valuable area of research, with appliFor the Meeting Room dataset, we train the Gaus - sians of the first frame using 15,000 iterations, compressing the number of Gaussians at 7,000 iterations."}
+{"idx": 1, "title": "7 Ways To Make A Small Living Room Feel Larger Instantly !", "date": "", "ddg_snippet": "If you have zero room for a table add a narrow console table behind the sofa to display decor on, add a decorative box for your media remotes and store a few books! Our living room .", "subpage_snippet": "", "source": "www.settingforfour.com", "link": "https://www.settingforfour.com/make-small-living-room-feel-larger/", "content": "If you have zero room for a table add a narrow console table behind the sofa to display decor on, add a decorative box for your media remotes and store a few books! Our living room ."}
+{"idx": 2, "title": "Cultist Circle Calculator | Optimize Your EFT Sacrifices", "date": "", "ddg_snippet": "Find the cheapest way to hit your target base value —fast. Auto‑select suggests optimal 1–5 item combos from live prices. Minimize cost while meeting 350k/400k thresholds. Full list of items in Tools > Base Value Table . Pin items you own; exclude categories or specific items.", "subpage_snippet": "", "source": "cultistcircle.com", "link": "https://cultistcircle.com/", "content": "Find the cheapest way to hit your target base value —fast. Auto‑select suggests optimal 1–5 item combos from live prices. Minimize cost while meeting 350k/400k thresholds. Full list of items in Tools > Base Value Table . Pin items you own; exclude categories or specific items."}
+{"idx": 3, "title": "Правила использования at, in, on в английском языке - Skypro", "date": "", "ddg_snippet": "В деловых письмах: «The meeting is scheduled at 10:00 AM on Tuesday, in the conference room .»В академических текстах: «The experiment conducted in 2024 showed significant results.»", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/media/eng-pravila-ispolzovaniya-at-in-on-v-anglijskom-yazyke/", "content": "В деловых письмах: «The meeting is scheduled at 10:00 AM on Tuesday, in the conference room .»В академических текстах: «The experiment conducted in 2024 showed significant results.»"}
+{"idx": 4, "title": "Twin Flame: What It Is + 11 Signs You've Found Yours | mindbodygreen", "date": "", "ddg_snippet": "Before the two of you meet , you will have an awareness that your other half is out there for you. There is deep longing in this phase, Spinelli notes, and there will be inner work during this phase to prepare you for meeting your twin flame.", "subpage_snippet": "", "source": "www.mindbodygreen.com", "link": "https://www.mindbodygreen.com/articles/twin-flames-signs-meaning-and-stages", "content": "Before the two of you meet , you will have an awareness that your other half is out there for you. There is deep longing in this phase, Spinelli notes, and there will be inner work during this phase to prepare you for meeting your twin flame."}
+{"idx": 5, "title": "AI Video Translator with Voice Cloning & Lip Sync | Free Trial", "date": "", "ddg_snippet": "MP3 to Text Instantly convert MP3 files to text using AI transcription, available in 125+ languages. Streamers can connect their OBS & vMix accounts to the web captioner and start translating live video content to provide translated captions to their audiences. Localize Content with AI. 06.", "subpage_snippet": "", "source": "maestra.ai", "link": "https://maestra.ai/tools/video-translator", "content": "MP3 to Text Instantly convert MP3 files to text using AI transcription, available in 125+ languages. Streamers can connect their OBS & vMix accounts to the web captioner and start translating live video content to provide translated captions to their audiences. Localize Content with AI. 06."}
+{"idx": 6, "title": "ikea.com/gb/en/cat/pax-system-19086", "date": "", "ddg_snippet": "PAX system Personal storage where you can choose exactly what you want.", "subpage_snippet": "", "source": "www.ikea.com", "link": "https://www.ikea.com/gb/en/cat/pax-system-19086/", "content": "PAX system Personal storage where you can choose exactly what you want."}
+{"idx": 7, "title": "How to Create a Class Rank Calculator in Excel - Step... | MyExcelOnline", "date": "", "ddg_snippet": "Excel’s RANK.EQ and RANK.AVG functions assign ranks to values in a dataset. You can combine ranking with IF or FILTER to handle specific scenarios like ties or grade thresholds.", "subpage_snippet": "", "source": "www.myexcelonline.com", "link": "https://www.myexcelonline.com/blog/class-rank-calculator-in-excel/", "content": "Excel’s RANK.EQ and RANK.AVG functions assign ranks to values in a dataset. You can combine ranking with IF or FILTER to handle specific scenarios like ties or grade thresholds."}
+{"idx": 8, "title": "Download Microsoft Teams Desktop and Mobile Apps | Microsoft Teams", "date": "", "ddg_snippet": "Unlimited group meetings for up to 30 hours and 300 participants. 10 GB of cloud storage per user. Real-time collaboration with file sharing, tasks, and polling. Teams.Chat, call, meet . 1 TB of cloud storage per user. 10+ additional apps (including Microsoft Bookings, Planner, and Forms).", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/microsoft-teams/download-app", "content": "Unlimited group meetings for up to 30 hours and 300 participants. 10 GB of cloud storage per user. Real-time collaboration with file sharing, tasks, and polling. Teams.Chat, call, meet . 1 TB of cloud storage per user. 10+ additional apps (including Microsoft Bookings, Planner, and Forms)."}
+{"idx": 9, "title": "Convert MB to GB", "date": "", "ddg_snippet": "Instant free online tool for megabyte to gigabyte conversion or vice versa. The megabyte [ MB ] to gigabyte [GB] conversion table and conversion steps are also listed.", "subpage_snippet": "", "source": "www.unitconverters.net", "link": "https://www.unitconverters.net/data-storage/mb-to-gb.htm", "content": "Instant free online tool for megabyte to gigabyte conversion or vice versa. The megabyte [ MB ] to gigabyte [GB] conversion table and conversion steps are also listed."}
diff --git a/data/sampled_jsons/34016_EntityErasure_Table_4_classifier-free_guidance_Text-CFG_Image-CFG_sundries_year_2023.jsonl b/data/sampled_jsons/34016_EntityErasure_Table_4_classifier-free_guidance_Text-CFG_Image-CFG_sundries_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..95e1d1de72fb27125a971af8f2b9564e1975b77d
--- /dev/null
+++ b/data/sampled_jsons/34016_EntityErasure_Table_4_classifier-free_guidance_Text-CFG_Image-CFG_sundries_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "As shown in Table 4 , when the weight of Image-CFG is set to 1, effectively turning off Image-CFG , both MSN and MARS increase significantly, which signals an increase in sundries generation.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "As shown in Table 4 , when the weight of Image-CFG is set to 1, effectively turning off Image-CFG , both MSN and MARS increase significantly, which signals an increase in sundries generation."}
+{"idx": 1, "title": "Classifier-free Guidance with Adaptive Scaling - GitHub", "date": "", "ddg_snippet": "Classifier-free Guidance with Adaptive Scaling Classifier-free guidance ( CFG ) is an essential mechanism in contemporary text -driven diffusion models. In practice, in controlling the impact of guidance we can see the trade-off between the quality of the generated images and correspondence to the prompt.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/gmum/beta-CFG", "content": "Classifier-free Guidance with Adaptive Scaling Classifier-free guidance ( CFG ) is an essential mechanism in contemporary text -driven diffusion models. In practice, in controlling the impact of guidance we can see the trade-off between the quality of the generated images and correspondence to the prompt."}
+{"idx": 2, "title": "CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models", "date": "", "ddg_snippet": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality outputs, frequently result in issues like mode collapse. Contrary to the widespread belief that these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.08070", "content": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality outputs, frequently result in issues like mode collapse. Contrary to the widespread belief that these ..."}
+{"idx": 3, "title": "GitHub Pages - CFG++", "date": "", "ddg_snippet": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG's reliance on high guidance scales presents notable drawbacks.", "subpage_snippet": "", "source": "cfgpp-diffusion.github.io", "link": "https://cfgpp-diffusion.github.io/", "content": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG's reliance on high guidance scales presents notable drawbacks."}
+{"idx": 4, "title": "CVPR Poster EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries . To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34016", "content": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries . To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference ..."}
+{"idx": 5, "title": "[2502.10574] Classifier-free Guidance with Adaptive Scaling", "date": "", "ddg_snippet": "Classifier-free guidance ( CFG ) is an essential mechanism in contemporary text -driven diffusion models. In practice, in controlling the impact of guidance we can see the trade-off between the quality of the generated images and correspondence to the prompt. When we use strong guidance , generated images fit the conditioned text perfectly but at the cost of their quality. Dually, we can use small ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10574", "content": "Classifier-free guidance ( CFG ) is an essential mechanism in contemporary text -driven diffusion models. In practice, in controlling the impact of guidance we can see the trade-off between the quality of the generated images and correspondence to the prompt. When we use strong guidance , generated images fit the conditioned text perfectly but at the cost of their quality. Dually, we can use small ..."}
+{"idx": 6, "title": "[Iclr2025] Cfg++ : Manifold-constrained Classifier Free Guidance for ...", "date": "", "ddg_snippet": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG requires high guidance scales, which has notable drawbacks: Mode collapse and saturation Poor invertibility Unnatural, curved PF-ODE trajectory We propose a simple fix to this seemingly inherent limitation and propose CFG++ 🚀, which corrects the off-manifold ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CFGpp-diffusion/CFGpp", "content": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG requires high guidance scales, which has notable drawbacks: Mode collapse and saturation Poor invertibility Unnatural, curved PF-ODE trajectory We propose a simple fix to this seemingly inherent limitation and propose CFG++ 🚀, which corrects the off-manifold ..."}
+{"idx": 7, "title": "[2306.17806] Stay on topic with Classifier-Free Guidance", "date": "", "ddg_snippet": "Classifier-Free Guidance ( CFG ) has recently emerged in text -to- image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q\\\\&A ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.17806", "content": "Classifier-Free Guidance ( CFG ) has recently emerged in text -to- image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q\\\\&A ..."}
+{"idx": 8, "title": "Classifier-free guidance resolution weighting - GitHub", "date": "", "ddg_snippet": "In section 3.4, the ControlNet paper talks about CFG -RW, quoting: In challenging cases, e.g., when no prompts are given, adding it to both ϵuc and ϵc will completely remove CFG guidance (Figure 5b); using only ϵc will make the guidance very strong (Figure 5c). Our solution is to first add the conditioning image to ϵ_c and then multiply a weight wi to each connection between Stable ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lllyasviel/ControlNet/discussions/560", "content": "In section 3.4, the ControlNet paper talks about CFG -RW, quoting: In challenging cases, e.g., when no prompts are given, adding it to both ϵuc and ϵc will completely remove CFG guidance (Figure 5b); using only ϵc will make the guidance very strong (Figure 5c). Our solution is to first add the conditioning image to ϵ_c and then multiply a weight wi to each connection between Stable ..."}
+{"idx": 9, "title": "CFG++: Manifold-constrained Classifier Free Guidance for Diffusion ...", "date": "", "ddg_snippet": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality outputs, frequently result in issues like mode collapse. Contrary to the widespread belief that these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=E77uvbOTtp", "content": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality outputs, frequently result in issues like mode collapse. Contrary to the widespread belief that these ..."}
diff --git a/data/sampled_jsons/35068_XLRS-Bench_Table_2_Qwen2-VL_Avg_score_Chinese_English_year_2023-2024.jsonl b/data/sampled_jsons/35068_XLRS-Bench_Table_2_Qwen2-VL_Avg_score_Chinese_English_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a7cc1e577736b8abc3eeda8cd6a2eb09b52930ce
--- /dev/null
+++ b/data/sampled_jsons/35068_XLRS-Bench_Table_2_Qwen2-VL_Avg_score_Chinese_English_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Qwen Qwen2.5-Math: The world's leading open-sourced mathematical LLMs | Qwen", "date": "", "ddg_snippet": "September 18, 2024 - While CoT plays a vital role in ... computation, symbolic manipulation, and algorithmic manipulation. Qwen2.5-Math-1.5B/7B/72B-Instruct achieve 79.7, 85.3, and 87.8 respectively on the MATH benchmark using TIR....", "subpage_snippet": "", "source": "qwenlm.github.io", "link": "https://qwenlm.github.io/blog/qwen2.5-math/", "content": "September 18, 2024 - While CoT plays a vital role in ... computation, symbolic manipulation, and algorithmic manipulation. Qwen2.5-Math-1.5B/7B/72B-Instruct achieve 79.7, 85.3, and 87.8 respectively on the MATH benchmark using TIR...."}
+{"idx": 1, "title": "GitHub GitHub - QwenLM/Qwen2.5-VL: Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.", "date": "", "ddg_snippet": "Qwen2 . 5 - VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud. - QwenLM/ Qwen2 . 5 - VL", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/QwenLM/Qwen2.5-VL", "content": "Qwen2 . 5 - VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud. - QwenLM/ Qwen2 . 5 - VL"}
+{"idx": 2, "title": "Qwen Qwen2.5: A Party of Foundation Models! | Qwen", "date": "", "ddg_snippet": "September 18, 2024 - GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In the past three months since Qwen2 ’s release, numerous developers have built new models on the Qwen2 language models, providing us with valuable feedback. During this period, we have focused on creating smarter and more knowledgeable ...", "subpage_snippet": "", "source": "qwenlm.github.io", "link": "https://qwenlm.github.io/blog/qwen2.5/", "content": "September 18, 2024 - GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In the past three months since Qwen2 ’s release, numerous developers have built new models on the Qwen2 language models, providing us with valuable feedback. During this period, we have focused on creating smarter and more knowledgeable ..."}
+{"idx": 3, "title": "Qwen Qwen2.5 VL! Qwen2.5 VL! Qwen2.5 VL! | Qwen", "date": "", "ddg_snippet": "January 26, 2025 - QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We release Qwen2 . 5 - VL , the new flagship vision-language model of Qwen and also a significant leap from the previous Qwen2 - VL . To try the latest model, feel free to visit Qwen Chat and choose Qwen2 . 5 - VL -72B-Instruct.", "subpage_snippet": "", "source": "qwenlm.github.io", "link": "https://qwenlm.github.io/blog/qwen2.5-vl/", "content": "January 26, 2025 - QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We release Qwen2 . 5 - VL , the new flagship vision-language model of Qwen and also a significant leap from the previous Qwen2 - VL . To try the latest model, feel free to visit Qwen Chat and choose Qwen2 . 5 - VL -72B-Instruct."}
+{"idx": 4, "title": "arXiv Qwen2 Technical Report", "date": "", "ddg_snippet": "July 15, 2024 - The flagship model, Qwen2-72B, showcases remarkable performance: 84.2 on MMLU, 37.9 on GPQA, 64.6 on HumanEval, 89.5 on GSM8K, and 82.4 on BBH as a base language model. The instruction-tuned variant, Qwen2-72B-Instruct, attains 9.1 on MT-Bench, 48.1 on Arena-Hard, and 35.7 on LiveCodeBench.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10671v1", "content": "July 15, 2024 - The flagship model, Qwen2-72B, showcases remarkable performance: 84.2 on MMLU, 37.9 on GPQA, 64.6 on HumanEval, 89.5 on GSM8K, and 82.4 on BBH as a base language model. The instruction-tuned variant, Qwen2-72B-Instruct, attains 9.1 on MT-Bench, 48.1 on Arena-Hard, and 35.7 on LiveCodeBench."}
+{"idx": 5, "title": "arXiv [2502.13923] Qwen2.5-VL Technical Report", "date": "", "ddg_snippet": "February 19, 2025 - We introduce Qwen2 . 5 - VL , the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative functionalities. Qwen2 . 5 - VL achieves a major leap forward in understanding and interacting with the world through enhanced ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.13923", "content": "February 19, 2025 - We introduce Qwen2 . 5 - VL , the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative functionalities. Qwen2 . 5 - VL achieves a major leap forward in understanding and interacting with the world through enhanced ..."}
+{"idx": 6, "title": "Hugging Face Alibaba-NLP/gme-Qwen2-VL-7B-Instruct · Release the UMRB", "date": "", "ddg_snippet": "Great work!!!! @ zyznull it would be better is you can release the UMR benchmark, thanks.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct/discussions/2", "content": "Great work!!!! @ zyznull it would be better is you can release the UMR benchmark, thanks."}
+{"idx": 7, "title": "OpenRouter Qwen2.5 VL 3B Instruct - API, Providers, Stats", "date": "", "ddg_snippet": "Qwen2 . 5 VL 3B is a multimodal LLM from the Qwen Team with the following key enhancements: · SoTA understanding of images of various resolution & ratio: Qwen2 . 5 - VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc", "subpage_snippet": "", "source": "openrouter.ai", "link": "https://openrouter.ai/qwen/qwen2.5-vl-3b-instruct:free", "content": "Qwen2 . 5 VL 3B is a multimodal LLM from the Qwen Team with the following key enhancements: · SoTA understanding of images of various resolution & ratio: Qwen2 . 5 - VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc"}
+{"idx": 8, "title": "Ollama qwen2.5", "date": "", "ddg_snippet": "Qwen2 .5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support.", "subpage_snippet": "", "source": "ollama.com", "link": "https://ollama.com/library/qwen2.5", "content": "Qwen2 .5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support."}
+{"idx": 9, "title": "ResearchGate (PDF) QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation", "date": "", "ddg_snippet": "May 8, 2025 - PDF | The rapid advancement of Chinese large language models (LLMs) underscores the need for domain-specific evaluations to ensure reliable... | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/391575253_QualBench_Benchmarking_Chinese_LLMs_with_Localized_Professional_Qualifications_for_Vertical_Domain_Evaluation", "content": "May 8, 2025 - PDF | The rapid advancement of Chinese large language models (LLMs) underscores the need for domain-specific evaluations to ensure reliable... | Find, read and cite all the research you need on ResearchGate"}
diff --git a/data/sampled_jsons/3Z827FtMNe_Great_Models_Think_Alike_and_this_Undermines_AI_Oversight.jsonl b/data/sampled_jsons/3Z827FtMNe_Great_Models_Think_Alike_and_this_Undermines_AI_Oversight.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf38cdbdde9b9dbbc10ba80c6ebbc1f48d0e8262
--- /dev/null
+++ b/data/sampled_jsons/3Z827FtMNe_Great_Models_Think_Alike_and_this_Undermines_AI_Oversight.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as ''AI Oversight'' . We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04313", "content": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as ''AI Oversight'' . We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on ..."}
+{"idx": 1, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \" AI Oversight \". We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement (CAPA): a metric for LM similarity based on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=3Z827FtMNe", "content": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \" AI Oversight \". We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement (CAPA): a metric for LM similarity based on ..."}
+{"idx": 2, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures.", "subpage_snippet": "", "source": "model-similarity.github.io", "link": "https://model-similarity.github.io/", "content": "As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures."}
+{"idx": 3, "title": "The AI oversight trap: When smarter models make the same mistakes", "date": "", "ddg_snippet": "A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight .", "subpage_snippet": "", "source": "www.devdiscourse.com", "link": "https://www.devdiscourse.com/article/technology/3256561-the-ai-oversight-trap-when-smarter-models-make-the-same-mistakes", "content": "A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight ."}
+{"idx": 4, "title": "Great Models Think Alike and this Undermines AI Oversight | Cool Papers ...", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing *Chance Adjusted Probabilistic Agreement (CAPA)*--a metric for LM similarity based on overlap in model mistakes. Using CAPA, we first show that *LLM-as-a-judge* scores favor models similar to the judge, generalizing recent self-preference results.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/3Z827FtMNe@OpenReview", "content": "We study how model similarity affects both aspects of AI oversight by proposing *Chance Adjusted Probabilistic Agreement (CAPA)*--a metric for LM similarity based on overlap in model mistakes. Using CAPA, we first show that *LLM-as-a-judge* scores favor models similar to the judge, generalizing recent self-preference results."}
+{"idx": 5, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "The above results show the benefits of diverse models for AI oversight - less similarity between models reduces bias in LLM-as-a-judge, and also leads to greater gains when training on LM annotations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v1", "content": "The above results show the benefits of diverse models for AI oversight - less similarity between models reduces bias in LLM-as-a-judge, and also leads to greater gains when training on LM annotations."}
+{"idx": 6, "title": "ICML Poster Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA) --a metric for LM similarity based on overlap in model mistakes. Using CAPA, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46528", "content": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA) --a metric for LM similarity based on overlap in model mistakes. Using CAPA, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results."}
+{"idx": 7, "title": "Great Models Think Alike and This Undermines AI Oversight", "date": "", "ddg_snippet": "The document discusses the challenges of AI oversight as language model capabilities advance, highlighting the importance of model similarity in evaluating and supervising these models . It introduces a new metric, Chance Adjusted Probabilistic Agreement (CAPA), to assess model similarity based on the overlap in model mistakes, revealing concerning trends of increasing correlation in model ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/826032662/Great-Models-Think-Alike-and-this-Undermines-AI-Oversight", "content": "The document discusses the challenges of AI oversight as language model capabilities advance, highlighting the importance of model similarity in evaluating and supervising these models . It introduces a new metric, Chance Adjusted Probabilistic Agreement (CAPA), to assess model similarity based on the overlap in model mistakes, revealing concerning trends of increasing correlation in model ..."}
+{"idx": 8, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "TL;DR: We propose a measure for model similarity, finding increasingly correlated failures as model capabilities improve, and show the negative effects of similarity on AI oversight paradigms like LLM-as-a-Judge and Weak-to-Strong Generalization.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3Z827FtMNe", "content": "TL;DR: We propose a measure for model similarity, finding increasingly correlated failures as model capabilities improve, and show the negative effects of similarity on AI oversight paradigms like LLM-as-a-Judge and Weak-to-Strong Generalization."}
+{"idx": 9, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Great Models Think Alike and this Undermines AI Oversight Shashwat Goel · Joschka Strüber · Ilze Auzina · Karuna Chandra · Ponnurangam Kumaraguru · Douwe Kiela · Ameya Prabhu · Matthias Bethge · Jonas Geiping Keywords: [ LLM as a Judge ] [ Model Differences ] [ Model Similarity ] [ AI Oversight ] [ Weak to Strong Generalization ]", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/10000468", "content": "Great Models Think Alike and this Undermines AI Oversight Shashwat Goel · Joschka Strüber · Ilze Auzina · Karuna Chandra · Ponnurangam Kumaraguru · Douwe Kiela · Ameya Prabhu · Matthias Bethge · Jonas Geiping Keywords: [ LLM as a Judge ] [ Model Differences ] [ Model Similarity ] [ AI Oversight ] [ Weak to Strong Generalization ]"}
diff --git a/data/sampled_jsons/46n3izUNiv_Section_G_IP-Adapter_CLIP_encoding_VAE_latent_space.jsonl b/data/sampled_jsons/46n3izUNiv_Section_G_IP-Adapter_CLIP_encoding_VAE_latent_space.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d93901cef6cddea5089013cedd17852fd5d1831
--- /dev/null
+++ b/data/sampled_jsons/46n3izUNiv_Section_G_IP-Adapter_CLIP_encoding_VAE_latent_space.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ip - adapter plus is not working with full face - Githubissues", "date": "", "ddg_snippet": "Hi. I am trying to use ip - adapter with openvino, two model is working well ( ip - adapter _sd15.bin, ip - adapter _sd15_light.bin) but, I wish to use \" ip - adapter -full-face_sd15.bin\", it is not working well some modification of example is avaiable to convert that model...", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/openvinotoolkit/openvino_notebooks/2400", "content": "Hi. I am trying to use ip - adapter with openvino, two model is working well ( ip - adapter _sd15.bin, ip - adapter _sd15_light.bin) but, I wish to use \" ip - adapter -full-face_sd15.bin\", it is not working well some modification of example is avaiable to convert that model..."}
+{"idx": 1, "title": "ComfyUI: Beginner to Advance Nodes Guide | Stable Diffusion Tutorials", "date": "", "ddg_snippet": "5. CheckPointLoader( VAE ): VAE (Variational Auto Encoder ) connect to the VAE Decode box.Steps: It refers to the inference steps means the number of steps the diffusion mechanism needs to generate an intermediate latent image sample processed in latent space .", "subpage_snippet": "", "source": "www.stablediffusiontutorials.com", "link": "https://www.stablediffusiontutorials.com/2024/04/comfyui-tutorial.html", "content": "5. CheckPointLoader( VAE ): VAE (Variational Auto Encoder ) connect to the VAE Decode box.Steps: It refers to the inference steps means the number of steps the diffusion mechanism needs to generate an intermediate latent image sample processed in latent space ."}
+{"idx": 2, "title": "How to Troubleshoot and Solve ComfyUI Model Issues - ComfyUI", "date": "", "ddg_snippet": "SD3 models use 16-channel latent space with triple text encoder conditioning ( CLIP -L + OpenCLIP bigG + T5-XXL).", "subpage_snippet": "", "source": "docs.comfy.org", "link": "https://docs.comfy.org/troubleshooting/model-issues", "content": "SD3 models use 16-channel latent space with triple text encoder conditioning ( CLIP -L + OpenCLIP bigG + T5-XXL)."}
+{"idx": 3, "title": "Model Database | Krita AI Handbook", "date": "", "ddg_snippet": "ControlNet, IP - Adapter and Clip Vision provide the various control layers. Upscaler models add more options to the upscale workspace. Checkpoint lists the recommended diffusion models used by the plugin’s default styles.", "subpage_snippet": "", "source": "docs.interstice.cloud", "link": "https://docs.interstice.cloud/models/", "content": "ControlNet, IP - Adapter and Clip Vision provide the various control layers. Upscaler models add more options to the upscale workspace. Checkpoint lists the recommended diffusion models used by the plugin’s default styles."}
+{"idx": 4, "title": "stabilityai/stable-diffusion-3.5-large · Hugging Face", "date": "", "ddg_snippet": "Text Encoders ： CLIPs : OpenCLIP-ViT/ G , CLIP -ViT/L, context length 77 tokens. T5: T5-xxl, context length 77/256 tokens at different stages of training. Training Data and StrategyModel tree for stabilityai/stable-diffusion-3.5-large. Adapters . 349 models.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large", "content": "Text Encoders ： CLIPs : OpenCLIP-ViT/ G , CLIP -ViT/L, context length 77 tokens. T5: T5-xxl, context length 77/256 tokens at different stages of training. Training Data and StrategyModel tree for stabilityai/stable-diffusion-3.5-large. Adapters . 349 models."}
+{"idx": 5, "title": "WAN 2.2 ComfyUI Workflow: Runs on Just 8GB VRAM", "date": "", "ddg_snippet": "CLIP . VAE . Text encoders . Uses FP8 precision for lower memory usage. It’s kind of like a “best of WAN” setup, but with some version-specific quirks", "subpage_snippet": "", "source": "aistudynow.com", "link": "https://aistudynow.com/wan-2-2-comfyui-workflow-runs-on-just-8gb-vram/", "content": "CLIP . VAE . Text encoders . Uses FP8 precision for lower memory usage. It’s kind of like a “best of WAN” setup, but with some version-specific quirks"}
+{"idx": 6, "title": "Getting Started with ComfyUI", "date": "", "ddg_snippet": "CLIP Text Encode ( Prompt ): Typically every workflow contains two of these nodes– one for the positive prompt and one for the negative prompt. The negative prompt guides the model from unwanted concepts ( e. g ., “ugly”, “deformed” ).", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/introduction-to-comfyui-for-stable-diffusion/", "content": "CLIP Text Encode ( Prompt ): Typically every workflow contains two of these nodes– one for the positive prompt and one for the negative prompt. The negative prompt guides the model from unwanted concepts ( e. g ., “ugly”, “deformed” )."}
+{"idx": 7, "title": "Stable Diffusion. Курс молодого бойца / Хабр", "date": "", "ddg_snippet": "IP Adapter . Особенно хочется отметить \"магическую\" модель IPAdapter, которая использует исходное изображение для генерации подсказки уровня промпта.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/784104/", "content": "IP Adapter . Особенно хочется отметить \"магическую\" модель IPAdapter, которая использует исходное изображение для генерации подсказки уровня промпта."}
+{"idx": 8, "title": "Обучите свою собственную модель FLUX LoRA с поддержкой LOW...", "date": "", "ddg_snippet": "Скачайте VAE (Variational Auto Encoder ) и сохраните его в папке fluxgym/models/ vae : ae.sft. Скачайте модель Flux Dev из репозитория Cocktailpeanut Huggingface и поместите ее в папку fluxgym/models/unet: flux1-dev.sft. Шаг 2: Запуск fluxgym.", "subpage_snippet": "", "source": "d00m4ace.com", "link": "https://d00m4ace.com/posts/obuchite-svoju-sobstvennuju-model-flux-lora-s-podderzhkoj-low-vram-12gbslash16gbslash20gb-na-windows-10/", "content": "Скачайте VAE (Variational Auto Encoder ) и сохраните его в папке fluxgym/models/ vae : ae.sft. Скачайте модель Flux Dev из репозитория Cocktailpeanut Huggingface и поместите ее в папку fluxgym/models/unet: flux1-dev.sft. Шаг 2: Запуск fluxgym."}
+{"idx": 9, "title": "How to run Wan 2.1 Video on ComfyUI - Stable Diffusion Art", "date": "", "ddg_snippet": "Download the Wan VAE model wan_2.1_ vae .safetensors and put it in ComfyUI > models > vae .", "subpage_snippet": "", "source": "stable-diffusion-art.com", "link": "https://stable-diffusion-art.com/wan-2-1/", "content": "Download the Wan VAE model wan_2.1_ vae .safetensors and put it in ComfyUI > models > vae ."}
diff --git a/data/sampled_jsons/46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms_full_paper.jsonl b/data/sampled_jsons/46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms_full_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..777093ca0186b48d386e37c8213f71ab324d911e
--- /dev/null
+++ b/data/sampled_jsons/46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms_full_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "In this paper , we introduce a new framework to enable members of the collective to learn strategies h efficiently and to infer the parameters that determine their success on the platform .out directly using the term g(x). 16. Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=46yLEXtav4", "content": "In this paper , we introduce a new framework to enable members of the collective to learn strategies h efficiently and to infer the parameters that determine their success on the platform .out directly using the term g(x). 16. Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 2, "title": "(PDF) Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848421_Statistical_Collusion_by_Collectives_on_Learning_Platforms", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 3, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Statistical-Collusion-by-Collectives-on-Learning-Platforms-deaeb81c-5365-4cec-9af2-e4d286dfd04a", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 4, "title": "ICML 2025 Statistical Collusion by Collectives on Learning ...", "date": "", "ddg_snippet": "Papers . Workshops. Community.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47263", "content": "Papers . Workshops. Community."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms - Paper ...", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may formand seek ways to influence these platforms to align with their own interests.This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/608798/statistical-collusion-by-collectives-on-learning-platforms", "content": "As platforms increasingly rely on learning algorithms, collectives may formand seek ways to influence these platforms to align with their own interests.This can be achieved by coordinated submission of altered data."}
+{"idx": 6, "title": "[Literature Review] Statistical Collusion by Collectives on Learning ...", "date": "", "ddg_snippet": "The paper titled \" Statistical Collusion by Collectives on Learning Platforms \" by Etienne Gauthier, Francis Bach, and Michael I. Jordan presents a framework for analyzing how collectives can influence machine learning platforms by coordinating data submission strategies.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "The paper titled \" Statistical Collusion by Collectives on Learning Platforms \" by Etienne Gauthier, Francis Bach, and Michael I. Jordan presents a framework for analyzing how collectives can influence machine learning platforms by coordinating data submission strategies."}
+{"idx": 7, "title": "GauthierE/ statistical - collusion : Statistical Collusion by Collectives ...", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879.utils.py # main code used across all notebooks. Description of Experiments. This repository considers four key scenarios from the paper : Signal planting with feature-label strategy.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879.utils.py # main code used across all notebooks. Description of Experiments. This repository considers four key scenarios from the paper : Signal planting with feature-label strategy."}
+{"idx": 8, "title": "ICML 2025 Review Controversies Spark Academic Debate | CSPaper", "date": "", "ddg_snippet": "Of his four submissions, the paper with the lowest average score (2.75) was accepted as a poster, while the three papers with higher scores (3.0) were rejected. Statistical Collusion by Collectives on Learning Platforms Examines collective manipulation in ML platforms .", "subpage_snippet": "", "source": "cspaper.org", "link": "https://cspaper.org/topic/62/icml-2025-review-controversies-spark-academic-debate", "content": "Of his four submissions, the paper with the lowest average score (2.75) was accepted as a poster, while the three papers with higher scores (3.0) were rejected. Statistical Collusion by Collectives on Learning Platforms Examines collective manipulation in ML platforms ."}
+{"idx": 9, "title": "Publications", "date": "", "ddg_snippet": "Statistical collusion by collectives on learning platforms .Workshop on Statistical Learning Techniques for Solving Systems Problems, Whistler, BC, 2007.", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jordan/publications.html", "content": "Statistical collusion by collectives on learning platforms .Workshop on Statistical Learning Techniques for Solving Systems Problems, Whistler, BC, 2007."}
diff --git a/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_year_2024.jsonl b/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2838f3681a540d24d7badf96d67ea2ed03f5ee5b
--- /dev/null
+++ b/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.18603", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation ..."}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language Models | Cool Papers ...", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/4AmFA0qNQ2@OpenReview", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation ..."}
+{"idx": 2, "title": "GitHub - google-deepmind/librispeech-long: LibriSpeech-Long is a ...", "date": "", "ddg_snippet": "About LibriSpeech-Long is a benchmark dataset for long- form speech generation and processing. Released as part of \"Long- Form Speech Generation with Spoken Language Models \" (arXiv 2024).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/google-deepmind/librispeech-long", "content": "About LibriSpeech-Long is a benchmark dataset for long- form speech generation and processing. Released as part of \"Long- Form Speech Generation with Spoken Language Models \" (arXiv 2024)."}
+{"idx": 3, "title": "Long-Form Speech Generation with Spoken Language Models | AI Research ...", "date": "", "ddg_snippet": "The research also doesn't fully address multilingual capabilities. Conclusion This breakthrough represents a significant step toward natural, long- form speech generation . The combination of language modeling and audio processing techniques opens new possibilities for applications in audiobooks, virtual assistants, and educational content.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/long-form-speech-generation-spoken-language-models", "content": "The research also doesn't fully address multilingual capabilities. Conclusion This breakthrough represents a significant step toward natural, long- form speech generation . The combination of language modeling and audio processing techniques opens new possibilities for applications in audiobooks, virtual assistants, and educational content."}
+{"idx": 4, "title": "PDF Researcher develops 'SpeechSSM,' opening up possibilities for a 24-hour ...", "date": "", "ddg_snippet": "Recently, spoken language models (SLMs) have been highlighted as next- generation technology that surpasses the limitations of text-based language models by learning human speech without text to understand and generate linguistic and non-linguistic information.", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-07-speechssm-possibilities-hour-ai-voice.pdf", "content": "Recently, spoken language models (SLMs) have been highlighted as next- generation technology that surpasses the limitations of text-based language models by learning human speech without text to understand and generate linguistic and non-linguistic information."}
+{"idx": 5, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Abstract We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken lan-guage models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of co-herence, architectural issues with long-sequence training or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.18603", "content": "Abstract We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken lan-guage models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of co-herence, architectural issues with long-sequence training or ..."}
+{"idx": 6, "title": "Long-Form Speech Generation with Spoken Language Models ...", "date": "", "ddg_snippet": "This research presents SpeechSSM, a new type of speech language model that can generate long, coherent spoken audio (like a 16-minute story) without using text. Current models struggle with long-f...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46499/paper", "content": "This research presents SpeechSSM, a new type of speech language model that can generate long, coherent spoken audio (like a 16-minute story) without using text. Current models struggle with long-f..."}
+{"idx": 7, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Our work makes initial progress on naturalistic, audio-native, long- form speech generation : We introduce SpeechSSM, the first spoken language model for long- form speech . Our 2B and 9B models : produces speech textlessly and in constant memory, for unbounded real-time generation ; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ...", "subpage_snippet": "", "source": "google.github.io", "link": "https://google.github.io/tacotron/publications/speechssm/", "content": "Our work makes initial progress on naturalistic, audio-native, long- form speech generation : We introduce SpeechSSM, the first spoken language model for long- form speech . Our 2B and 9B models : produces speech textlessly and in constant memory, for unbounded real-time generation ; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ..."}
+{"idx": 8, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "SpeechSSM is derived, the first speech language model family to learn from and sample long- form spoken audio in a single decoding session without text intermediates and leverage recent advances in linear-time sequence modeling to greatly surpass current Transformer spoken LMs in coherence and efficiency on multi-minute generations while still matching them at the utterance level. We consider ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Long-Form-Speech-Generation-with-Spoken-Language-Park-Salazar/b70f44b066d7adcf89cdd0870a81e3266afdcb6d", "content": "SpeechSSM is derived, the first speech language model family to learn from and sample long- form spoken audio in a single decoding session without text intermediates and leverage recent advances in linear-time sequence modeling to greatly surpass current Transformer spoken LMs in coherence and efficiency on multi-minute generations while still matching them at the utterance level. We consider ..."}
+{"idx": 9, "title": "Breaking New Ground in Voice Technology - scisimple.com", "date": "", "ddg_snippet": "With these considerations we propose SpeechSSM, the first speech language model to learn from and sample long- form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates, based on recent advances in linear-time sequence modeling.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-01-26-breaking-new-ground-in-voice-technology--akg2dxo", "content": "With these considerations we propose SpeechSSM, the first speech language model to learn from and sample long- form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates, based on recent advances in linear-time sequence modeling."}
diff --git a/data/sampled_jsons/4HQaMUYWAT_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_theoretical_f.jsonl b/data/sampled_jsons/4HQaMUYWAT_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_theoretical_f.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e1c3712237b2cc3b30e7c4a48f8922802e10554a
--- /dev/null
+++ b/data/sampled_jsons/4HQaMUYWAT_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_theoretical_f.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04375", "content": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ..."}
+{"idx": 1, "title": "An Analysis for Reasoning Bias of Language Models with Small...", "date": "", "ddg_snippet": "Additionally, experiments on real-world language tasks corroborate our theoretical insights. This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4HQaMUYWAT", "content": "Additionally, experiments on real-world language tasks corroborate our theoretical insights. This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models ."}
+{"idx": 2, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "We provide a theoretical framework from the perspective of model training dynamics to explain these phenomena. Additionally, experiments on real-world language tasks corroborate our theoretical ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848015_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization", "content": "We provide a theoretical framework from the perspective of model training dynamics to explain these phenomena. Additionally, experiments on real-world language tasks corroborate our theoretical ..."}
+{"idx": 3, "title": "Furyton/awesome-language-model-analysis - GitHub", "date": "", "ddg_snippet": "This paper list focuses on the theoretical and empirical analysis of language models , especially large language models (LLMs). The papers in this list investigate the learning behavior, generalization ability, and other properties of language models through theoretical analysis , empirical analysis , or a combination of both.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Furyton/awesome-language-model-analysis", "content": "This paper list focuses on the theoretical and empirical analysis of language models , especially large language models (LLMs). The papers in this list investigate the learning behavior, generalization ability, and other properties of language models through theoretical analysis , empirical analysis , or a combination of both."}
+{"idx": 4, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "Further analysis of initial training dynamics suggests that specific model components, particularly the embedding space and self-attention mechanisms , play piv-otal roles in shaping these learning biases. We provide a theoretical framework from the perspec-tive of model training dynamics to explain these phenomena.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375v2", "content": "Further analysis of initial training dynamics suggests that specific model components, particularly the embedding space and self-attention mechanisms , play piv-otal roles in shaping these learning biases. We provide a theoretical framework from the perspec-tive of model training dynamics to explain these phenomena."}
+{"idx": 5, "title": "An Analysis for Reasoning Bias of Language Models With Small Initialization", "date": "", "ddg_snippet": "Additionally, experiments on real-world language tasks corroborate our theoretical insights. This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375v1", "content": "Additionally, experiments on real-world language tasks corroborate our theoretical insights. This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models ."}
+{"idx": 6, "title": "An analysis for reasoning bias of language models with small initialization", "date": "", "ddg_snippet": "Further analysis of initial training dynamics suggests that specific model components, particularly the embedding space and self-attention mechanisms , play pivotal roles in shaping these learning biases. We provide a theoretical framework from the perspective of model training dynamics to explain these phenomena.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04375v1", "content": "Further analysis of initial training dynamics suggests that specific model components, particularly the embedding space and self-attention mechanisms , play pivotal roles in shaping these learning biases. We provide a theoretical framework from the perspective of model training dynamics to explain these phenomena."}
+{"idx": 7, "title": "Unpacking the bias of large language models - MIT News", "date": "", "ddg_snippet": "MIT researchers discovered the underlying cause of position bias , a phenomenon that causes large language models to overemphasize the beginning or end of a document or conversation, while neglecting the middle. They built a theoretical framework that can be used to diagnose and correct position bias in future model designs, leading to more accurate, reliable AI agents.", "subpage_snippet": "", "source": "news.mit.edu", "link": "https://news.mit.edu/2025/unpacking-large-language-model-bias-0617", "content": "MIT researchers discovered the underlying cause of position bias , a phenomenon that causes large language models to overemphasize the beginning or end of a document or conversation, while neglecting the middle. They built a theoretical framework that can be used to diagnose and correct position bias in future model designs, leading to more accurate, reliable AI agents."}
+{"idx": 8, "title": "Zhongwang Zhang (张众望) - Homepage", "date": "", "ddg_snippet": "An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang†, Zhi-Qin John Xu† This paper reveals how initialization scales shape transformer-based models' task preferences: smaller scales induce reasoning bias through structured embeddings, while larger scales promote memorization. We attribute this to differential label-driven embedding ...", "subpage_snippet": "", "source": "sjtuzzw.github.io", "link": "https://sjtuzzw.github.io/", "content": "An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang†, Zhi-Qin John Xu† This paper reveals how initialization scales shape transformer-based models' task preferences: smaller scales induce reasoning bias through structured embeddings, while larger scales promote memorization. We attribute this to differential label-driven embedding ..."}
+{"idx": 9, "title": "PDF Applied Math Ph.D. Seminar - amphds.yingzhouli.com", "date": "", "ddg_snippet": "Abstract: Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating excep-tional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas ...", "subpage_snippet": "", "source": "amphds.yingzhouli.com", "link": "https://amphds.yingzhouli.com/download_file/2025Spring/20250529.pdf", "content": "Abstract: Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating excep-tional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas ..."}
diff --git a/data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_ensemble_LUNO-LA_Flip_OOD_year_2024.jsonl b/data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_ensemble_LUNO-LA_Flip_OOD_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..192de4ce5ec462b2db068250aafbe3a5bf50acac
--- /dev/null
+++ b/data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_ensemble_LUNO-LA_Flip_OOD_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian...", "date": "", "ddg_snippet": "3 LUNO : Linearized Predictive Uncertainty in Neural Operators . 3.1 Function-Valued Gaussian Processes and Probabilistic Currying.D.6.3 Evaluation of a single trajectory. Linearization Turns Neural Operators into Function-Valued Gaussian Processes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "3 LUNO : Linearized Predictive Uncertainty in Neural Operators . 3.1 Function-Valued Gaussian Processes and Probabilistic Currying.D.6.3 Evaluation of a single trajectory. Linearization Turns Neural Operators into Function-Valued Gaussian Processes."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into... | OpenReview", "date": "", "ddg_snippet": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differential equations (PDEs) or propagators of time-dependent PDEs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differential equations (PDEs) or propagators of time-dependent PDEs."}
+{"idx": 2, "title": "GitHub - MethodsOfMachineLearning/ luno : LUNO : Linearized...", "date": "", "ddg_snippet": "luno - Linearized Uncertainty for Neural Operators . This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes\" by Magnani et al.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno", "content": "luno - Linearized Uncertainty for Neural Operators . This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes\" by Magnani et al."}
+{"idx": 3, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian...", "date": "", "ddg_snippet": "Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2406.05072", "content": "Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data."}
+{"idx": 4, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian...", "date": "", "ddg_snippet": "Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data.We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/linearization-turns-neural-operators-into-function-valued", "content": "Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data.We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes."}
+{"idx": 5, "title": "(PDF) Linearization Turns Neural Operators into Function-Valued...", "date": "", "ddg_snippet": "Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes", "content": "Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data."}
+{"idx": 6, "title": "Neural PDE operator learning — The Dan MacKinlay stable of...", "date": "", "ddg_snippet": "2024. “ Linearization Turns Neural Operators into Function-Valued Gaussian Processes.” Mahesh, Collins, Bonev, et al. 2024. “Huge Ensembles Part I: Design of Ensemble Weather Forecasts Using Spherical Fourier Neural Operators .”", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/ml_pde_operator", "content": "2024. “ Linearization Turns Neural Operators into Function-Valued Gaussian Processes.” Mahesh, Collins, Bonev, et al. 2024. “Huge Ensembles Part I: Design of Ensemble Weather Forecasts Using Spherical Fourier Neural Operators .”"}
+{"idx": 7, "title": "Publications", "date": "", "ddg_snippet": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes.", "subpage_snippet": "", "source": "2bys.github.io", "link": "https://2bys.github.io/publications/", "content": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes."}
+{"idx": 8, "title": "dblp: List of computer science publications by Emilia Magnani", "date": "", "ddg_snippet": "Emilia Magnani, Ernesto De Vito, Philipp Hennig, Lorenzo Rosasco: Learning convolution operators on compact Abelian groups.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/206/6101.html", "content": "Emilia Magnani, Ernesto De Vito, Philipp Hennig, Lorenzo Rosasco: Learning convolution operators on compact Abelian groups."}
+{"idx": 9, "title": "Virginia Aglietti - Senior Research Scientist @ Google... | LinkedIn", "date": "", "ddg_snippet": "Excited to be at ICML presenting our work: \" Linearization turns neural operators into function-valued Gaussian processes\"! If you’re around, come…", "subpage_snippet": "", "source": "uk.linkedin.com", "link": "https://uk.linkedin.com/in/virginia-aglietti-a80321a4", "content": "Excited to be at ICML presenting our work: \" Linearization turns neural operators into function-valued Gaussian processes\"! If you’re around, come…"}
diff --git a/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_limitations.jsonl b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..15ef12763174b6a5550290739115c1c7c39a4bfd
--- /dev/null
+++ b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 1, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 3, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 4, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts ."}
+{"idx": 5, "title": "infolab.stanford.edu", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ..."}
+{"idx": 6, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Three-Branch-Checks-and-Balances-Frameworkfor-of-Chang/5918a91419cf95db8599b086590facf63f124702/figure/4", "content": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 7, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v3", "content": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ..."}
+{"idx": 8, "title": "Ethical Guardrails for AI: A Checks-and-Balances Approach", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124", "subpage_snippet": "", "source": "www.zerna.io", "link": "https://www.zerna.io/page/security/presentation_set/security-llm-research/presentation/security-ethical-alignment-fairness/slide/security-paper-2502_00136", "content": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124"}
+{"idx": 9, "title": "PDF A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461.pdf", "content": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues"}
diff --git a/data/sampled_jsons/4uOEiitySn_Limitations_and_Future_Work_section.jsonl b/data/sampled_jsons/4uOEiitySn_Limitations_and_Future_Work_section.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f7cdac3c8104a3c2cdc91ffb98b094d5daf8bd0
--- /dev/null
+++ b/data/sampled_jsons/4uOEiitySn_Limitations_and_Future_Work_section.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "4.7.7. Known Limitations and Future Work — OpenFAST...", "date": "", "ddg_snippet": "The following list contains known current limitations in the code: Tight coupling is not yet supported. Only nontapered two-node Euler-Bernoulli (E-B) or Timoshenko (T) element formulations are available.(In the future , a generic cross section will be allowed.)", "subpage_snippet": "", "source": "openfast.readthedocs.io", "link": "https://openfast.readthedocs.io/en/v3.0.0/source/user/subdyn/future_work.html", "content": "The following list contains known current limitations in the code: Tight coupling is not yet supported. Only nontapered two-node Euler-Bernoulli (E-B) or Timoshenko (T) element formulations are available.(In the future , a generic cross section will be allowed.)"}
+{"idx": 1, "title": "Coefficients? 3. conclusions, limitations and future work", "date": "", "ddg_snippet": "The data collected to answer research questions 5 and 6 was analysed using linear regression.Surprisingly, most of the students did not complain about working schedules, week-end work or divided daily working hours nor about overtime work .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/figures/2137663/table-14-coefficients-conclusions-limitations-and-future", "content": "The data collected to answer research questions 5 and 6 was analysed using linear regression.Surprisingly, most of the students did not complain about working schedules, week-end work or divided daily working hours nor about overtime work ."}
+{"idx": 2, "title": "Migrating Dokoon from Django REST Framework to GraphQL...", "date": "", "ddg_snippet": "Frontend Inefficiencies: Excessive fetch calls in index.js and login.js to aggregate data from multiple endpoints, coupled with error-handling verbosity. 2. GraphQL Adoption: Architectural Rationale. The migration to GraphQL (via graphene-django) addressed these limitations through", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/migrating-dokoon-from-django-rest-framework-graphql-guide-darbandi-yrkte", "content": "Frontend Inefficiencies: Excessive fetch calls in index.js and login.js to aggregate data from multiple endpoints, coupled with error-handling verbosity. 2. GraphQL Adoption: Architectural Rationale. The migration to GraphQL (via graphene-django) addressed these limitations through"}
+{"idx": 3, "title": "Limitations and Future Work | SpringerLink", "date": "", "ddg_snippet": "Data collection through expert interviews is relatively time-consuming and represents a key challenge. Within the limited time of a dissertation, only a limited number of interviews can be conducted in a reasonable amount of time.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-658-43905-7_7", "content": "Data collection through expert interviews is relatively time-consuming and represents a key challenge. Within the limited time of a dissertation, only a limited number of interviews can be conducted in a reasonable amount of time."}
+{"idx": 4, "title": "7.4 Limitations and Future Work", "date": "", "ddg_snippet": "Глава: 7.4 Limitations and Future Work . ВУЗ: НИЯУ МИФИ.Crowdsourcing tasks should include a carefully designed explanation of how negative terms work, ideally with example images that demonstrate the effect of negative terms. 7.3.6Provide dedicated user interfaces.", "subpage_snippet": "", "source": "studfile.net", "link": "https://studfile.net/preview/21440528/page:8/", "content": "Глава: 7.4 Limitations and Future Work . ВУЗ: НИЯУ МИФИ.Crowdsourcing tasks should include a carefully designed explanation of how negative terms work, ideally with example images that demonstrate the effect of negative terms. 7.3.6Provide dedicated user interfaces."}
+{"idx": 5, "title": "Limitations and Future Work in Dark Pattern Mitigation | HackerNoon", "date": "", "ddg_snippet": "The article acknowledges limitations in the study, including taxonomic diversity challenges and biases in participant recruitment. It highlights the need to expand the end-user-empowerment approach to mobile platforms, improve geographic diversity, and conduct larger-scale...", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/limitations-and-future-work", "content": "The article acknowledges limitations in the study, including taxonomic diversity challenges and biases in participant recruitment. It highlights the need to expand the end-user-empowerment approach to mobile platforms, improve geographic diversity, and conduct larger-scale..."}
+{"idx": 6, "title": "Limitations and Future Work | gasgiant/FFT-Ocean | DeepWiki", "date": "", "ddg_snippet": "This document outlines the current limitations of the FFT-Ocean implementation and identifies potential areas for future development. As a prototype system, FFT-Ocean has several constraints that affe.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/gasgiant/FFT-Ocean/5-limitations-and-future-work", "content": "This document outlines the current limitations of the FFT-Ocean implementation and identifies potential areas for future development. As a prototype system, FFT-Ocean has several constraints that affe."}
+{"idx": 7, "title": "Limitations and Future Work - Understanding NUMA Effects on...", "date": "", "ddg_snippet": "There are some limitations to this work . Amortized batch freeing can cause problems if data structures have enormous nodes that really should be freed as soon as possible. For example, Brown et al. [4] introduced a data structure called a concurrent interpolation search tree...", "subpage_snippet": "", "source": "1library.co", "link": "https://1library.co/ca/article/limitations-and-future-work.7977130", "content": "There are some limitations to this work . Amortized batch freeing can cause problems if data structures have enormous nodes that really should be freed as soon as possible. For example, Brown et al. [4] introduced a data structure called a concurrent interpolation search tree..."}
+{"idx": 8, "title": "How to Write an Effective Conclusion for a Research Article-CSDN博客", "date": "", "ddg_snippet": "4. Limitations and Future Work . Description and Function.Description and Function: This section combines the limitations of the study with recommendations for future research.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/u013441358/article/details/142728208", "content": "4. Limitations and Future Work . Description and Function.Description and Function: This section combines the limitations of the study with recommendations for future research."}
+{"idx": 9, "title": "I've put these 10 prompts together: hopefully it will help... | Medium", "date": "", "ddg_snippet": "Read the introduction and conclusion sections of the paper. Provide a brief summary (3-4 sentences) of the context, objectives, and overarching conclusions drawn by the authors. 3. List and summarize important references[List of limitations and future work suggestions].", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ttahovsky/ive-put-these-10-prompts-together-hopefully-it-will-help-someone-d2a701162345", "content": "Read the introduction and conclusion sections of the paper. Provide a brief summary (3-4 sentences) of the context, objectives, and overarching conclusions drawn by the authors. 3. List and summarize important references[List of limitations and future work suggestions]."}
diff --git a/data/sampled_jsons/4ufjBV6S4I_RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_3.1_retriever_p.jsonl b/data/sampled_jsons/4ufjBV6S4I_RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_3.1_retriever_p.jsonl
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--- /dev/null
+++ b/data/sampled_jsons/4ufjBV6S4I_RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_3.1_retriever_p.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Retrieval-augmented generation - Wikipedia", "date": "", "ddg_snippet": "Retrieval-augmented generation is a technique that enables large language models to retrieve and incorporate new information . With RAG , LLMs do not respond to user queries until they refer to a specified set of documents.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Retrieval-augmented_generation", "content": "Retrieval-augmented generation is a technique that enables large language models to retrieve and incorporate new information . With RAG , LLMs do not respond to user queries until they refer to a specified set of documents."}
+{"idx": 1, "title": "Генерация, дополненная поиском — Википедия", "date": "", "ddg_snippet": "RAG расширяет базу знаний LLM до неограниченных размеров, дает быстрый доступ к специализированным доменам знаний или к внутренней базе знаний организации без необходимости переобучения модели.", "subpage_snippet": "", "source": "ru.wikipedia.org", "link": "https://ru.wikipedia.org/wiki/Генерация,_дополненная_поиском", "content": "RAG расширяет базу знаний LLM до неограниченных размеров, дает быстрый доступ к специализированным доменам знаний или к внутренней базе знаний организации без необходимости переобучения модели."}
+{"idx": 2, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "In this work, we introduce RAGGED , a framework for system-atically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets. Our analysis reveals that reader ro-bustness to noise is the key determinant of RAG stability and scalability.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4ufjBV6S4I", "content": "In this work, we introduce RAGGED , a framework for system-atically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets. Our analysis reveals that reader ro-bustness to noise is the key determinant of RAG stability and scalability."}
+{"idx": 3, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG ... RAGGED: Towards Informed Design of Scalable and Stable RAG ... RAGGED: Towards Informed Design of RAGGED: Towards Informed Design of Retrieval Augmented ... [2403.09040] RAGGED: Towards Informed Design of Retrieval ... RAGGED: Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "Mar 14, 2024 · Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader ... In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Which retriever to use? How many documents? Which reader to use? Why RAG Matters: Access to up-to-date knowledge, improved accuracy for complex tasks, cost-effective knowledge integration Challenges: Noisy data, retriever -reader mismatch, diverse task requirements. Solution: The RAGGED framework, a systematic tool for optimizing RAG configurations. Retrieval-augmented generation ( RAG ) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ... To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems . On a set of representative DBQA tasks, we study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures. This work introduces RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets, and reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09040", "content": "Mar 14, 2024 · Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader ... In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Which retriever to use? How many documents? Which reader to use? Why RAG Matters: Access to up-to-date knowledge, improved accuracy for complex tasks, cost-effective knowledge integration Challenges: Noisy data, retriever -reader mismatch, diverse task requirements. Solution: The RAGGED framework, a systematic tool for optimizing RAG configurations. Retrieval-augmented generation ( RAG ) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ... To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems . On a set of representative DBQA tasks, we study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures. This work introduces RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets, and reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly ..."}
+{"idx": 4, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/neulab/ragged", "content": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability."}
+{"idx": 5, "title": "RAGGED: Towards Informed Design of", "date": "", "ddg_snippet": "Which retriever to use? How many documents? Which reader to use? Why RAG Matters: Access to up-to-date knowledge, improved accuracy for complex tasks, cost-effective knowledge integration Challenges: Noisy data, retriever -reader mismatch, diverse task requirements. Solution: The RAGGED framework, a systematic tool for optimizing RAG configurations.", "subpage_snippet": "", "source": "adaptive-foundation-models.org", "link": "https://adaptive-foundation-models.org/posters/RAGGED_AFM_2024_Poster.pdf", "content": "Which retriever to use? How many documents? Which reader to use? Why RAG Matters: Access to up-to-date knowledge, improved accuracy for complex tasks, cost-effective knowledge integration Challenges: Noisy data, retriever -reader mismatch, diverse task requirements. Solution: The RAGGED framework, a systematic tool for optimizing RAG configurations."}
+{"idx": 6, "title": "RAGGED: Towards Informed Design of Retrieval Augmented ...", "date": "", "ddg_snippet": "Retrieval-augmented generation ( RAG ) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KDXj60FpJr", "content": "Retrieval-augmented generation ( RAG ) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ..."}
+{"idx": 7, "title": "[2403.09040] RAGGED: Towards Informed Design of Retrieval ...", "date": "", "ddg_snippet": "To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems . On a set of representative DBQA tasks, we study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2403.09040", "content": "To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems . On a set of representative DBQA tasks, we study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures."}
+{"idx": 8, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "This work introduces RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets, and reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/RAGGED:-Towards-Informed-Design-of-Scalable-and-RAG-Hsia-Shaikh/ec0082509c157b493fefdefa50dfe0712c5e481d", "content": "This work introduces RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets, and reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly ..."}
+{"idx": 9, "title": "RAGGED : Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4ufjBV6S4I", "content": "Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets."}
diff --git a/data/sampled_jsons/51x0dfsD8A_Hierarchical_Overlapping_Clustering_Algorithm_1_2-OC_costtemp_function.jsonl b/data/sampled_jsons/51x0dfsD8A_Hierarchical_Overlapping_Clustering_Algorithm_1_2-OC_costtemp_function.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..26899b8701e6f4f5c9db4eacc1313a615be476fe
--- /dev/null
+++ b/data/sampled_jsons/51x0dfsD8A_Hierarchical_Overlapping_Clustering_Algorithm_1_2-OC_costtemp_function.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cluster analysis - Wikipedia", "date": "", "ddg_snippet": "Overlapping clustering (also: alternative clustering , multi-view clustering ): objects may belong to more than one cluster ; usually involving hard clusters . Hierarchical clustering : objects that belong to a child cluster also belong to the parent cluster .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cluster_analysis", "content": "Overlapping clustering (also: alternative clustering , multi-view clustering ): objects may belong to more than one cluster ; usually involving hard clusters . Hierarchical clustering : objects that belong to a child cluster also belong to the parent cluster ."}
+{"idx": 1, "title": "8 Clustering Algorithms in Machine Learning that All Data Scientists...", "date": "", "ddg_snippet": "This is a hierarchical clustering algorithm , but the downside is that it doesn't scale well when working with large data sets. It works by iterating over all of the data points and shifts them towards the mode. The mode in this context is the high density area of data points in a region.", "subpage_snippet": "", "source": "www.freecodecamp.org", "link": "https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/", "content": "This is a hierarchical clustering algorithm , but the downside is that it doesn't scale well when working with large data sets. It works by iterating over all of the data points and shifts them towards the mode. The mode in this context is the high density area of data points in a region."}
+{"idx": 2, "title": "Clustering in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "1 . Hard Clustering : In hard clustering , each data point strictly belongs to exactly one cluster , no overlap is allowed. This approach assigns a clear membership, making it easier to interpret and use for definitive segmentation tasks.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/clustering-in-machine-learning/", "content": "1 . Hard Clustering : In hard clustering , each data point strictly belongs to exactly one cluster , no overlap is allowed. This approach assigns a clear membership, making it easier to interpret and use for definitive segmentation tasks."}
+{"idx": 3, "title": "R: Plots of an Agglomerative Hierarchical Clustering", "date": "", "ddg_snippet": "The banner displays the hierarchy of clusters , and is equivalent to a tree. See Rousseeuw (1986) or chapter 5 of Kaufman and Rousseeuw (1990).", "subpage_snippet": "", "source": "stat.ethz.ch", "link": "https://stat.ethz.ch/R-manual/R-devel/library/cluster/html/plot.agnes.html", "content": "The banner displays the hierarchy of clusters , and is equivalent to a tree. See Rousseeuw (1986) or chapter 5 of Kaufman and Rousseeuw (1990)."}
+{"idx": 4, "title": "Fernando Gama, Santiago Segarra, and Alejandro Ribeiro", "date": "", "ddg_snippet": "IV Hierarchical overlapping clustering algorithm . Algorithm 1 Hierarchical overlapping clustering algorithm O. Input: J: no. of perturbations, N : network, H: hierarchical clustering method, perturbation(·). Output: KX : nested collection of coverings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1611.01393", "content": "IV Hierarchical overlapping clustering algorithm . Algorithm 1 Hierarchical overlapping clustering algorithm O. Input: J: no. of perturbations, N : network, H: hierarchical clustering method, perturbation(·). Output: KX : nested collection of coverings."}
+{"idx": 5, "title": "Кластеризация в ML: от теоретических основ популярных... / Хабр", "date": "", "ddg_snippet": "Статья «Modern hierarchical , agglomerative clustering algorithms », Daniel Müllner.Статья «On Spectral Clustering : Analysis and an algorithm », Andrew Y. Ng, Michael I . Jordan, Yair Weiss. Документация: описание SpectralClustering, SpectralClustering (алгоритм).", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/798331/", "content": "Статья «Modern hierarchical , agglomerative clustering algorithms », Daniel Müllner.Статья «On Spectral Clustering : Analysis and an algorithm », Andrew Y. Ng, Michael I . Jordan, Yair Weiss. Документация: описание SpectralClustering, SpectralClustering (алгоритм)."}
+{"idx": 6, "title": "StandardScaler — scikit-learn 1 .7. 2 documentation", "date": "", "ddg_snippet": "Demo of HDBSCAN clustering algorithm .A demo of K-Means clustering on the handwritten digits data. Comparing different hierarchical linkage methods on toy datasets.", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html", "content": "Demo of HDBSCAN clustering algorithm .A demo of K-Means clustering on the handwritten digits data. Comparing different hierarchical linkage methods on toy datasets."}
+{"idx": 7, "title": "GeoGebra Classic", "date": "", "ddg_snippet": "Floor Function . Ceil Function . [ ... ″ ϕ. ς.", "subpage_snippet": "", "source": "autgeo.online", "link": "https://autgeo.online/", "content": "Floor Function . Ceil Function . [ ... ″ ϕ. ς."}
+{"idx": 8, "title": "Introducing Scikit-Learn | Python Data Science Handbook", "date": "", "ddg_snippet": "Limited object hierarchy : Only algorithms are represented by Python classes; datasets are represented in standard formats (NumPy arrays, Pandas DataFrames, SciPy sparse matrices) and parameter names use standard Python strings.", "subpage_snippet": "", "source": "jakevdp.github.io", "link": "https://jakevdp.github.io/PythonDataScienceHandbook/05.02-introducing-scikit-learn.html", "content": "Limited object hierarchy : Only algorithms are represented by Python classes; datasets are represented in standard formats (NumPy arrays, Pandas DataFrames, SciPy sparse matrices) and parameter names use standard Python strings."}
+{"idx": 9, "title": "(Решено) Упр.148 ГДЗ Макарычев 7 класс по алгебре", "date": "", "ddg_snippet": "Решение # 1 . Изображение Решите уравнение:а) 2 x + 9 = 13 - x; б) 14- y= 19-11y; в) 0,5а + 11 = 4- 3а; г) l, 2 n + 1 = 1 - n; д) 1 ,7 - 0,3m = 2 + 1 ,7m; е) 0,8x + 14 = 2 - 1 ,6x; ж) 15...", "subpage_snippet": "", "source": "reshak.ru", "link": "https://reshak.ru/otvet/makar7.php?otvet1=new/148", "content": "Решение # 1 . Изображение Решите уравнение:а) 2 x + 9 = 13 - x; б) 14- y= 19-11y; в) 0,5а + 11 = 4- 3а; г) l, 2 n + 1 = 1 - n; д) 1 ,7 - 0,3m = 2 + 1 ,7m; е) 0,8x + 14 = 2 - 1 ,6x; ж) 15..."}
diff --git a/data/sampled_jsons/51x0dfsD8A_time_complexity_section_page_7.jsonl b/data/sampled_jsons/51x0dfsD8A_time_complexity_section_page_7.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5a5a00778d9d631c5899ba314e8f4233fae8e452
--- /dev/null
+++ b/data/sampled_jsons/51x0dfsD8A_time_complexity_section_page_7.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Understanding Time Complexity with Simple Examples", "date": "", "ddg_snippet": "Is the Time Complexity of an Algorithm/Code the same as the Running/Execution Time of Code? The Time Complexity of an algorithm/code is not equal to the actual time required to execute a particular code, but the number of times a statement executes. We can prove this by using the time command. For example: Write code in C/C++ or any other language to find the maximum between N numbers, where N ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/understanding-time-complexity-simple-examples/", "content": "Is the Time Complexity of an Algorithm/Code the same as the Running/Execution Time of Code? The Time Complexity of an algorithm/code is not equal to the actual time required to execute a particular code, but the number of times a statement executes. We can prove this by using the time command. For example: Write code in C/C++ or any other language to find the maximum between N numbers, where N ..."}
+{"idx": 1, "title": "Big O Cheat Sheet - Time Complexity Chart - freeCodeCamp.org", "date": "", "ddg_snippet": "In this guide, you have learned what time complexity is all about, how performance is determined using the Big O notation, and the various time complexities that exists with examples.", "subpage_snippet": "", "source": "www.freecodecamp.org", "link": "https://www.freecodecamp.org/news/big-o-cheat-sheet-time-complexity-chart/", "content": "In this guide, you have learned what time complexity is all about, how performance is determined using the Big O notation, and the various time complexities that exists with examples."}
+{"idx": 2, "title": "Chapter 7: Time Complexity Flashcards | Quizlet", "date": "", "ddg_snippet": "Study with Quizlet and memorize flashcards containing terms like time complexity class, multi-tape time complexity , nondeterministic time complexity and more.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/286207298/chapter-7-time-complexity-flash-cards/", "content": "Study with Quizlet and memorize flashcards containing terms like time complexity class, multi-tape time complexity , nondeterministic time complexity and more."}
+{"idx": 3, "title": "Week 7 - Complexity Analysis | PDF | Time Complexity - Scribd", "date": "", "ddg_snippet": "The document provides a comprehensive overview of complexity analysis, focusing on time and space complexity , and how to measure the efficiency of programs using Big O notation. It includes examples of code with their respective time and space complexities, as well as built-in functions and their complexities. Additionally, it discusses the implications of exceeding time or memory limits in ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/869863876/Week-7-Complexity-Analysis", "content": "The document provides a comprehensive overview of complexity analysis, focusing on time and space complexity , and how to measure the efficiency of programs using Big O notation. It includes examples of code with their respective time and space complexities, as well as built-in functions and their complexities. Additionally, it discusses the implications of exceeding time or memory limits in ..."}
+{"idx": 4, "title": "PDF *6.7 TIME COMPLEXITY - discoveringcs.net", "date": "", "ddg_snippet": "(Exercise 6.7.1 more explicitly illustrates how a string comparison works.) Put another way, the best-case time complexity of a string comparison is constant, or O(1), because it does not depend on the input size, and the worst-case time complexity for a string comparison is linearly proportional to n, or O(n).", "subpage_snippet": "", "source": "www.discoveringcs.net", "link": "https://www.discoveringcs.net/optional_sections/6.7.pdf", "content": "(Exercise 6.7.1 more explicitly illustrates how a string comparison works.) Put another way, the best-case time complexity of a string comparison is constant, or O(1), because it does not depend on the input size, and the worst-case time complexity for a string comparison is linearly proportional to n, or O(n)."}
+{"idx": 5, "title": "PDF Time Complexity Analysis", "date": "", "ddg_snippet": "Exact Time Complexity Analysis Reminder: The RAM Model Each \"simple\" operation (+, -, =, if, call) takes 1 time step. Loops and subroutine calls are not simple operations. They depend upon the size of the data and the contents of a subroutine. Each memory access takes 1 step.", "subpage_snippet": "", "source": "emilydolson.github.io", "link": "https://emilydolson.github.io/msu_algorithms_spring_2025/slides/TimeComplexityAnalysis.pdf", "content": "Exact Time Complexity Analysis Reminder: The RAM Model Each \"simple\" operation (+, -, =, if, call) takes 1 time step. Loops and subroutine calls are not simple operations. They depend upon the size of the data and the contents of a subroutine. Each memory access takes 1 step."}
+{"idx": 6, "title": "Complexity Analysis Cheat Sheet: Time & Space Complexity", "date": "", "ddg_snippet": "Master algorithm complexity analysis with this comprehensive reference covering Big O notation, time /space complexity , and practical analysis techniques.", "subpage_snippet": "", "source": "clelandco.com", "link": "https://clelandco.com/cheat-sheets/algorithm-analysis/complexity-analysis", "content": "Master algorithm complexity analysis with this comprehensive reference covering Big O notation, time /space complexity , and practical analysis techniques."}
+{"idx": 7, "title": "Time and Space Complexity - GeeksforGeeks", "date": "", "ddg_snippet": "Time Complexity : The time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the input. Note that the time to run is a function of the length of the input and not the actual execution time of the machine on which the algorithm is running on.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/time-complexity-and-space-complexity/", "content": "Time Complexity : The time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the input. Note that the time to run is a function of the length of the input and not the actual execution time of the machine on which the algorithm is running on."}
+{"idx": 8, "title": "Time and Space Complexity in Data Structures Explained", "date": "", "ddg_snippet": "Understand time and space complexity in data structures. Learn how to optimize performance and enhance your coding efficiency with practical examples and insights.", "subpage_snippet": "", "source": "www.simplilearn.com", "link": "https://www.simplilearn.com/tutorials/data-structure-tutorial/time-and-space-complexity", "content": "Understand time and space complexity in data structures. Learn how to optimize performance and enhance your coding efficiency with practical examples and insights."}
+{"idx": 9, "title": "Explain Time Complexity and Space Complexity with Examples", "date": "", "ddg_snippet": "This guide simplifies time and space complexity , crucial concepts for optimizing algorithms. Learn how to measure performance using Big-O notation (O (1), O (n), O (log n)) with clear examples and real-life analogies. Master DSA and ace coding interviews by understanding how algorithms scale in speed and memory usage.", "subpage_snippet": "", "source": "www.c-sharpcorner.com", "link": "https://www.c-sharpcorner.com/article/explain-time-complexity-and-space-complexity-with-examples/", "content": "This guide simplifies time and space complexity , crucial concepts for optimizing algorithms. Learn how to measure performance using Big-O notation (O (1), O (n), O (log n)) with clear examples and real-life analogies. Master DSA and ace coding interviews by understanding how algorithms scale in speed and memory usage."}
diff --git a/data/sampled_jsons/6yBhoJn6qy_Causal_Modeling_of_Climate_Activism_on_Reddit_Figure_A.1_appendix_subreddits.jsonl b/data/sampled_jsons/6yBhoJn6qy_Causal_Modeling_of_Climate_Activism_on_Reddit_Figure_A.1_appendix_subreddits.jsonl
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--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "Previous social media studies on climate 96 action have analyzed the changes in climate debate after extreme 97 weather events [18, 34, 38, 41], political events [10, 22] or media 98 coverage [18]. However, these studies are either associational or fo- 99 cus on single causal pathways for the phenomena of interest.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "Previous social media studies on climate 96 action have analyzed the changes in climate debate after extreme 97 weather events [18, 34, 38, 41], political events [10, 22] or media 98 coverage [18]. However, these studies are either associational or fo- 99 cus on single causal pathways for the phenomena of interest."}
+{"idx": 1, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit - Researchr", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/LentiAMM25", "content": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]"}
+{"idx": 3, "title": "\"Causal Modeling of Climate Activism on Reddit.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2410-10562", "content": "Bibliographic details on Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 4, "title": "Causal Modeling of Climate Activism on Reddit | Article Information | J ...", "date": "", "ddg_snippet": "Article \"Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ...", "subpage_snippet": "", "source": "jglobal.jst.go.jp", "link": "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202402219833468228", "content": "Article \"Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ..."}
+{"idx": 5, "title": "(PDF) Analyzing Climate Change Discussions on Reddit", "date": "", "ddg_snippet": "Climate action is one of the United Nations Sustainable Development Goals. We contribute to this effort by analyzing climate change topics on the Reddit social curation platform, which contains ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366548402_Analyzing_climate_change_discussions_on_Reddit", "content": "Climate action is one of the United Nations Sustainable Development Goals. We contribute to this effort by analyzing climate change topics on the Reddit social curation platform, which contains ..."}
+{"idx": 6, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "We developed a rich and comprehensive causal model to study the interplay between different determinants of climate activism on Reddit . This work represents a first attempt to apply a multi-causal model to social media data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "We developed a rich and comprehensive causal model to study the interplay between different determinants of climate activism on Reddit . This work represents a first attempt to apply a multi-causal model to social media data."}
+{"idx": 7, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a quantitative, causal understanding of why people approach activism .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6yBhoJn6qy", "content": "Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a quantitative, causal understanding of why people approach activism ."}
+{"idx": 8, "title": "Causal Modeling of Climate Activism on Reddit - Corrado Monti", "date": "", "ddg_snippet": "Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales", "subpage_snippet": "", "source": "www.corradomonti.com", "link": "https://www.corradomonti.com/causal-modeling-of-climate-activism-on-reddit.html", "content": "Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales"}
+{"idx": 9, "title": "How to Build a Causal Inference Model to Explore Whether Global Warming ...", "date": "", "ddg_snippet": "A Proposed Causal Model The purpose of this article is to show that causal inference machine learning solutions can be used to build models to solve complex, large and meaningful real-world problems, hence I am going to put forward a proposal for the causal links that would be tested and improved by climate and energy experts in a real-world model.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/how-to-build-a-causal-inference-model-to-explore-whether-global-warming-is-caused-by-human-activity-2bcf1830e071/", "content": "A Proposed Causal Model The purpose of this article is to show that causal inference machine learning solutions can be used to build models to solve complex, large and meaningful real-world problems, hence I am going to put forward a proposal for the causal links that would be tested and improved by climate and energy experts in a real-world model."}
diff --git a/data/sampled_jsons/7ESHFpqjNO_Learning_Place_Cell_Representations_Equation_1_spatial_encoding_objective_function.jsonl b/data/sampled_jsons/7ESHFpqjNO_Learning_Place_Cell_Representations_Equation_1_spatial_encoding_objective_function.jsonl
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index 0000000000000000000000000000000000000000..0ec18efee69df197fe561e888ba7c03f65ce6d77
--- /dev/null
+++ b/data/sampled_jsons/7ESHFpqjNO_Learning_Place_Cell_Representations_Equation_1_spatial_encoding_objective_function.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Place Cell Representations and Context-Dependent ...", "date": "", "ddg_snippet": "How place cell responses emerge, and how these representations remap is not fully understood. In this work, we propose a similarity-based objective function that translates proximity in space, to proximity in representation . We show that a neural network trained to minimize the proposed objective learns place -like representations .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=7ESHFpqjNO", "content": "How place cell responses emerge, and how these representations remap is not fully understood. In this work, we propose a similarity-based objective function that translates proximity in space, to proximity in representation . We show that a neural network trained to minimize the proposed objective learns place -like representations ."}
+{"idx": 1, "title": "Learning Place Cell Representations and Context ...", "date": "", "ddg_snippet": "6 Nov 2024 — This paper introduces a novel objective function for modeling spatial representation akin to that of the hippocampus by forcing the spatially close positions ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7ESHFpqjNO&noteId=F5hjJWYocY", "content": "6 Nov 2024 — This paper introduces a novel objective function for modeling spatial representation akin to that of the hippocampus by forcing the spatially close positions ..."}
+{"idx": 2, "title": "Decoding the Cognitive map: Learning place cells and remapping", "date": "", "ddg_snippet": "May 27, 2025 · Our model provides a normative framework for learning spatial representations previously reserved for biological place cells , providing new insight into place cell field formation and remapping.", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/99302v2", "content": "May 27, 2025 · Our model provides a normative framework for learning spatial representations previously reserved for biological place cells , providing new insight into place cell field formation and remapping."}
+{"idx": 3, "title": "Mechanisms of experience-dependent place-cell referencing in ...", "date": "", "ddg_snippet": "Apr 1 , 2025 · Through multiday imaging and acute whole- cell recordings in behaving mice, Qian, Li and Magee provide insight into place field formation in general and specifically how the hippocampus adaptively ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41593-025-01930-5", "content": "Apr 1 , 2025 · Through multiday imaging and acute whole- cell recordings in behaving mice, Qian, Li and Magee provide insight into place field formation in general and specifically how the hippocampus adaptively ..."}
+{"idx": 4, "title": "Place Cell - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "A place cell is a type of excitatory pyramidal neuron in the hippocampus that fires at specific locations within an animal's local environment, forming a place field. These cells play a crucial role in encoding spatial memory and can reactivate during rest or sleep, representing distinct maps of different environments.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/neuroscience/place-cell", "content": "A place cell is a type of excitatory pyramidal neuron in the hippocampus that fires at specific locations within an animal's local environment, forming a place field. These cells play a crucial role in encoding spatial memory and can reactivate during rest or sleep, representing distinct maps of different environments."}
+{"idx": 5, "title": "Learning Conjunctive Representations - bioRxiv", "date": "", "ddg_snippet": "May 30, 2024 · Training a feedforward network to minimize the spatial objective function (2), results in the emergence of place -like representations in the output units. These representations are analogous to place cells observed in the Hippocampus [O’Keefe and Dostrovsky, 1971], as illustrated by the example ratemaps in Fig. 2a).", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2024.05.30.596595.full.pdf", "content": "May 30, 2024 · Training a feedforward network to minimize the spatial objective function (2), results in the emergence of place -like representations in the output units. These representations are analogous to place cells observed in the Hippocampus [O’Keefe and Dostrovsky, 1971], as illustrated by the example ratemaps in Fig. 2a)."}
+{"idx": 6, "title": "Visual Place Cell Encoding: A Computational Model for Spatial ...", "date": "", "ddg_snippet": "Apr 22, 2025 · These results suggest that structured visual input, even in the absence of motion cues or reward-driven learning , is sufficient to generate place - cell -like spatial representations and support biologically inspired cognitive mapping.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.15953", "content": "Apr 22, 2025 · These results suggest that structured visual input, even in the absence of motion cues or reward-driven learning , is sufficient to generate place - cell -like spatial representations and support biologically inspired cognitive mapping."}
+{"idx": 7, "title": "Coordinated learning of grid cell and place cell spatial and ...", "date": "", "ddg_snippet": "A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC3866446/", "content": "A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells ..."}
+{"idx": 8, "title": "Learning Place Cell Representations and Context-Dependent...", "date": "", "ddg_snippet": "Hippocampal place cells are known for their spatially selective firing patterns, which has led to the suggestion that they encode an animal's location. However, place cells also respond to contextual cues, such as smell.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/7ESHFpqjNO@OpenReview", "content": "Hippocampal place cells are known for their spatially selective firing patterns, which has led to the suggestion that they encode an animal's location. However, place cells also respond to contextual cues, such as smell."}
+{"idx": 9, "title": "Learning an Efficient Hippocampal Place Map from... | eNeuro", "date": "", "ddg_snippet": "Measuring the uniformity of place cell representation . For place cells that meets the criteria defined above, the field center (xc, yc) fitted by Equation 11 indicates the spatial location that the place cell responds to.", "subpage_snippet": "", "source": "www.eneuro.org", "link": "https://www.eneuro.org/content/8/4/ENEURO.0557-20.2021", "content": "Measuring the uniformity of place cell representation . For place cells that meets the criteria defined above, the field center (xc, yc) fitted by Equation 11 indicates the spatial location that the place cell responds to."}
diff --git a/data/sampled_jsons/7uqVfZW6Mo__Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features.jsonl b/data/sampled_jsons/7uqVfZW6Mo__Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..247f00b160106cc12a0fb7fd3f262780d9fac955
--- /dev/null
+++ b/data/sampled_jsons/7uqVfZW6Mo__Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations , selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.03558", "content": "Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations , selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field ..."}
+{"idx": 1, "title": "Darkbblue/generic-diffusion-feature - GitHub", "date": "", "ddg_snippet": "Diffusion feature is a quite popular way to utilize generative diffusion models for discrimination. It's very simple: just extract some internal activations from a diffusion model , and then use these 2D features to replace image inputs of any discriminative model . There have been quite many diffusion feature studies. But we notice that almost all of them experiment with Stable Diffusion v1.4 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Darkbblue/generic-diffusion-feature", "content": "Diffusion feature is a quite popular way to utilize generative diffusion models for discrimination. It's very simple: just extract some internal activations from a diffusion model , and then use these 2D features to replace image inputs of any discriminative model . There have been quite many diffusion feature studies. But we notice that almost all of them experiment with Stable Diffusion v1.4 ..."}
+{"idx": 2, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7uqVfZW6Mo", "content": "To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores."}
+{"idx": 3, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations , such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/633780c1344d0c95e4d2dd3431fe08d9-Abstract-Conference.html", "content": "However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations , such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked."}
+{"idx": 4, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Unlocking superior discriminative features from diffusion models , this research reveals key activation properties for effective feature selection, surpassing state-of-the-art methods.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/spotlight-others/7uqvfzw6mo/", "content": "Unlocking superior discriminative features from diffusion models , this research reveals key activation properties for effective feature selection, surpassing state-of-the-art methods."}
+{"idx": 5, "title": "PDF Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features Benyuan Meng, Qianqian Xu*, Zitai Wang, Xiaochun Cao, Qingming Huang*", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/96411.pdf", "content": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features Benyuan Meng, Qianqian Xu*, Zitai Wang, Xiaochun Cao, Qingming Huang*"}
+{"idx": 6, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations , selecting a small yet effective subset poses a fundamental problem. To this end, the ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2410.03558v3", "content": "View recent discussion. Abstract: Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations , selecting a small yet effective subset poses a fundamental problem. To this end, the ..."}
+{"idx": 7, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Through a series of experiments, the paper shows that not all diffusion model activations are equally useful as features for image classification. This suggests that there may be untapped potential in these internal representations that could be unlocked through further research.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/not-all-diffusion-model-activations-have-been", "content": "Through a series of experiments, the paper shows that not all diffusion model activations are equally useful as features for image classification. This suggests that there may be untapped potential in these internal representations that could be unlocked through further research."}
+{"idx": 8, "title": "Discffusion: Discriminative Diffusion Models as Few-shot Vision and ...", "date": "", "ddg_snippet": "Diffusion models , such as Stable Diffusion , have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trained diffusion models for discriminative tasks such as image-text matching ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.10722", "content": "Diffusion models , such as Stable Diffusion , have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trained diffusion models for discriminative tasks such as image-text matching ..."}
+{"idx": 9, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "This paper investigates the effective selection of diverse activations from diffusion models for discriminative tasks, revealing universal properties that enhance feature selection methods and demonstrate superior performance in semantic segmentation and correspondence compared to existing state-of-the-art approaches.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/64311?from=subpath-search", "content": "This paper investigates the effective selection of diverse activations from diffusion models for discriminative tasks, revealing universal properties that enhance feature selection methods and demonstrate superior performance in semantic segmentation and correspondence compared to existing state-of-the-art approaches."}
diff --git a/data/sampled_jsons/ACDRepo_Schubert_polynomials_accuracy_table_1_sitegithub.com.jsonl b/data/sampled_jsons/ACDRepo_Schubert_polynomials_accuracy_table_1_sitegithub.com.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fdc0b250ca4b81df80189709299aa91864550c2f
--- /dev/null
+++ b/data/sampled_jsons/ACDRepo_Schubert_polynomials_accuracy_table_1_sitegithub.com.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - AcraeaTerpsicore/Schubert-polynomials-and-patterns-in ...", "date": "", "ddg_snippet": "Overview This repository provides complete computational verification of results from the research paper \" Schubert polynomials and patterns in permutations\" (arXiv:2412.02932v1) by Peter L. Guo and Zhuowei Lin. Our implementations compute and verify all numerical values, tables , and conjectures presented in the paper using first-principles mathematical definitions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcraeaTerpsicore/Schubert-polynomials-and-patterns-in-permutations", "content": "Overview This repository provides complete computational verification of results from the research paper \" Schubert polynomials and patterns in permutations\" (arXiv:2412.02932v1) by Peter L. Guo and Zhuowei Lin. Our implementations compute and verify all numerical values, tables , and conjectures presented in the paper using first-principles mathematical definitions."}
+{"idx": 1, "title": "Schubert-polynomials-and-patterns-in-permutations/cu_du_table ... - GitHub", "date": "", "ddg_snippet": "Print [\" TABLE : Permutations in S_m for m ≤ 5 with nonzero values of c_u and d_u\"];", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcraeaTerpsicore/Schubert-polynomials-and-patterns-in-permutations/blob/main/cu_du_table_display.wl", "content": "Print [\" TABLE : Permutations in S_m for m ≤ 5 with nonzero values of c_u and d_u\"];"}
+{"idx": 2, "title": "GitHub - pnnl/ML4AlgComb: ML Benchmarks in Algebraic Combinatorics", "date": "", "ddg_snippet": "Schubert polynomial structure constants: Schubert polynomials are a family of polynomials indexed by permutations of S n . Developed to study the cohomology ring of the flag variety, they have deep connections to algebraic geometry, Lie theory, and representation theory.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "Schubert polynomial structure constants: Schubert polynomials are a family of polynomials indexed by permutations of S n . Developed to study the cohomology ring of the flag variety, they have deep connections to algebraic geometry, Lie theory, and representation theory."}
+{"idx": 3, "title": "Schubert-Polynomial-Package/SchubertPolynomials.m at master - GitHub", "date": "", "ddg_snippet": "A Mathematica package for studying the combinatorics of Schubert polynomials , permutations, pipe dreams, Loretnzian polynomials , and more. - avstdi/ Schubert - Polynomial -Package", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/avstdi/Schubert-Polynomial-Package/blob/master/SchubertPolynomials.m", "content": "A Mathematica package for studying the combinatorics of Schubert polynomials , permutations, pipe dreams, Loretnzian polynomials , and more. - avstdi/ Schubert - Polynomial -Package"}
+{"idx": 4, "title": "GitHub - matthematics/schubmult: Program for computing Littlewood ...", "date": "", "ddg_snippet": "schubmult_py is for multiplying ordinary Schubert polynomials . schubmult_yz is for multiplying double Schubert polynomials in different sets of coefficient variables (labeled y and z), and schubmult_double is for multiplying double Schubert polynomials in the same set of coefficient variables.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/matthematics/schubmult", "content": "schubmult_py is for multiplying ordinary Schubert polynomials . schubmult_yz is for multiplying double Schubert polynomials in different sets of coefficient variables (labeled y and z), and schubmult_double is for multiplying double Schubert polynomials in the same set of coefficient variables."}
+{"idx": 5, "title": "VivianePons/multipolynomial-bases - GitHub", "date": "", "ddg_snippet": "A Sage package to work on multipolynomials bases ( Schubert , Grothendieck, Key) - VivianePons/multipolynomial-bases", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/VivianePons/multipolynomial-bases", "content": "A Sage package to work on multipolynomials bases ( Schubert , Grothendieck, Key) - VivianePons/multipolynomial-bases"}
+{"idx": 6, "title": "A catalogue of polynomial approximations - GitHub", "date": "", "ddg_snippet": "These text files report the polynomial approximations on the Pareto front of a few computational efficiency metrics and of accuracy . The current version only considers single and double float polynomials , with degree at most 16, for a few transcendentals: sin, cos, atan, exp, log, log1px, and lg1px ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pkhuong/polynomial-approximation-catalogue", "content": "These text files report the polynomial approximations on the Pareto front of a few computational efficiency metrics and of accuracy . The current version only considers single and double float polynomials , with degree at most 16, for a few transcendentals: sin, cos, atan, exp, log, log1px, and lg1px ..."}
+{"idx": 7, "title": "SchubertPolynomials.jl/README.md at main - GitHub", "date": "", "ddg_snippet": "This package provides functions for computing Schubert polynomials via bumpless pipe dreams and drift polynomials . The former (\"BPDs\") are gadgets which enumerate terms in a Schubert polynomial , developed by Lam-Lee-Shimozono. The latter are generalizations of Schur polynomials that William Fulton and I have found useful. The package also uses \"standard\" ways of computing (divided difference ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pseudoeffective/SchubertPolynomials.jl/blob/main/README.md", "content": "This package provides functions for computing Schubert polynomials via bumpless pipe dreams and drift polynomials . The former (\"BPDs\") are gadgets which enumerate terms in a Schubert polynomial , developed by Lam-Lee-Shimozono. The latter are generalizations of Schur polynomials that William Fulton and I have found useful. The package also uses \"standard\" ways of computing (divided difference ..."}
+{"idx": 8, "title": "GitHub - GunterMueller/SYMMETRICA: SYMMETRICA- written in C by Axel ...", "date": "", "ddg_snippet": "SYMMETRICA- written in C by Axel Kohnert, is a collection of routines to compute with symmetric functions and Schubert polynomials , ordinary, modular, and projective representations of the symmetr...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GunterMueller/SYMMETRICA/", "content": "SYMMETRICA- written in C by Axel Kohnert, is a collection of routines to compute with symmetric functions and Schubert polynomials , ordinary, modular, and projective representations of the symmetr..."}
+{"idx": 9, "title": "GitHub - ryankeleti/pipedreams: Pipe dreaming", "date": "", "ddg_snippet": "Pipe dreams A little library for computing Schubert polynomials from pipe dreams.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ryankeleti/pipedreams", "content": "Pipe dreams A little library for computing Schubert polynomials from pipe dreams."}
diff --git a/data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Appendix_C.1_Figur_year_2023.jsonl b/data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Appendix_C.1_Figur_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf69060f546d9663a05e2211720385d0741e20ac
--- /dev/null
+++ b/data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Appendix_C.1_Figur_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies : Formalization , Implementation and...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/064ae24cdbb3eaacc801ee7f4fe0e4f2-Abstract-Conference.html", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Feint Behaviors and Strategies : Formalization , Implementation and...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/ACIDDnTbSJ@OpenReview", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 2, "title": "Computer Science and Game Theory Mar 2024", "date": "", "ddg_snippet": "Title: Application of Nash equilibrium for developing an optimal forest harvesting strategy in Toruń Forest District.Comments: 2 figures , 2 tables. Subjects: Computer Science and Game Theory (cs.GT).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.GT/2024-03", "content": "Title: Application of Nash equilibrium for developing an optimal forest harvesting strategy in Toruń Forest District.Comments: 2 figures , 2 tables. Subjects: Computer Science and Game Theory (cs.GT)."}
+{"idx": 3, "title": "Feint Behaviors and Strategies : Formalization , Implementation and...", "date": "", "ddg_snippet": "Figure 1 : An example of Palindrome-directed Generation Templates of Feint behaviors . The first row shows an action sequence of a cross-punch behavior .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "Figure 1 : An example of Palindrome-directed Generation Templates of Feint behaviors . The first row shows an action sequence of a cross-punch behavior ."}
+{"idx": 4, "title": "Coding Implementation to End-to-End Transformer... - MarkTechPost", "date": "", "ddg_snippet": "Meta researchers introduced a method that compresses repeated reasoning patterns into short, named procedures—“ behaviors ”— and then conditions models to use them at inference or distills...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/09/23/coding-implementation-to-end-to-end-transformer-model-optimization-with-hugging-face-optimum-onnx-runtime-and-quantization/", "content": "Meta researchers introduced a method that compresses repeated reasoning patterns into short, named procedures—“ behaviors ”— and then conditions models to use them at inference or distills..."}
+{"idx": 5, "title": "ERA5-Land hourly data from 1950 to present", "date": "", "ddg_snippet": "The intention is to improve the user experience by collecting metrics on visitor behaviour and fix issues on the website.", "subpage_snippet": "", "source": "cds.climate.copernicus.eu", "link": "https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download", "content": "The intention is to improve the user experience by collecting metrics on visitor behaviour and fix issues on the website."}
+{"idx": 6, "title": "Junyu LIU - Google Scholar", "date": "", "ddg_snippet": "Feint behaviors and strategies : formalization , implementation and evaluation .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=EiSrD1UAAAAJ&hl=en", "content": "Feint behaviors and strategies : formalization , implementation and evaluation ."}
+{"idx": 7, "title": "dblp: List of computer science publications by Xiangjun Peng", "date": "", "ddg_snippet": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization , Implementation and Evaluation . NeurIPS 2024.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/123/5256.html", "content": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization , Implementation and Evaluation . NeurIPS 2024."}
+{"idx": 8, "title": "What’s New in DeepSeek-V3. 1 -Terminus: Improved Language...", "date": "", "ddg_snippet": "Key takeaway: tighter evaluation and targeted fine-tuning can lower error rates where models previously “flip” on facts or instructions. Code agent upgrades: fewer hallucinations and better multi-step reasoning.", "subpage_snippet": "", "source": "www.remio.ai", "link": "https://www.remio.ai/post/what-s-new-in-deepseek-v3-1-terminus-improved-language-consistency-and-code-search-agents-upgrade", "content": "Key takeaway: tighter evaluation and targeted fine-tuning can lower error rates where models previously “flip” on facts or instructions. Code agent upgrades: fewer hallucinations and better multi-step reasoning."}
+{"idx": 9, "title": "Metacognitive Reuse in LLM Reasoning", "date": "", "ddg_snippet": "The behaviors are then used by a \"Student\" LLM for downstream reasoning tasks. Figure 1 . Figure 1 : The behavior curation pipeline, showing solution generation, reflection, and behavior extraction, as well as the integration of behaviors into inference and fine-tuning.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2509.13237", "content": "The behaviors are then used by a \"Student\" LLM for downstream reasoning tasks. Figure 1 . Figure 1 : The behavior curation pipeline, showing solution generation, reflection, and behavior extraction, as well as the integration of behaviors into inference and fine-tuning."}
diff --git a/data/sampled_jsons/ACL_Anthology_CVE-Bench_long_paper_'Insufficient_Exploration'_definition_year_2023-2024.jsonl b/data/sampled_jsons/ACL_Anthology_CVE-Bench_long_paper_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..59fecf44488b2df3ecfa1f81570d09be07d85bc0
--- /dev/null
+++ b/data/sampled_jsons/ACL_Anthology_CVE-Bench_long_paper_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE - Bench : Benchmarking LLM-based Software Engineering", "date": "", "ddg_snippet": "In this paper , we introduce CVE - Bench (§2), a benchmark that evaluates LLM-based agents in a realistic vulnerability -repairing setting. CVE - Bench contains three unique characteristics", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "In this paper , we introduce CVE - Bench (§2), a benchmark that evaluates LLM-based agents in a realistic vulnerability -repairing setting. CVE - Bench contains three unique characteristics"}
+{"idx": 1, "title": "Ingestion checks · Issue #5695 · acl -org/ acl - anthology · GitHub", "date": "", "ddg_snippet": "Explore . acl -org / acl - anthology Public. Notifications You must be signed in to change notification settings. Fork 355.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/acl-org/acl-anthology/issues/5695", "content": "Explore . acl -org / acl - anthology Public. Notifications You must be signed in to change notification settings. Fork 355."}
+{"idx": 2, "title": "xOffense: An AI-driven autonomous penetration testing framework with...", "date": "", "ddg_snippet": "In this paper , we present the design, implementation, and evaluation of xOffense, a lightweight, domain-adaptive, and highly effective autonomous penetration testing system. Our key contributions are as follows", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13021v1", "content": "In this paper , we present the design, implementation, and evaluation of xOffense, a lightweight, domain-adaptive, and highly effective autonomous penetration testing system. Our key contributions are as follows"}
+{"idx": 3, "title": "“I Want To Enjoy Every Minute That I Can”: For... | British Vogue", "date": "", "ddg_snippet": "While the sport has long had female enthusiasts, that fanbase has exploded: 40 per cent of F1 fans are now women, with 2.2 million attending races for their first time in 2024.", "subpage_snippet": "", "source": "www.vogue.co.uk", "link": "https://www.vogue.co.uk/article/lando-norris-interview", "content": "While the sport has long had female enthusiasts, that fanbase has exploded: 40 per cent of F1 fans are now women, with 2.2 million attending races for their first time in 2024."}
+{"idx": 4, "title": "Best Early Game Weapons in Dying Light: The Beast - Guide", "date": "", "ddg_snippet": "Common Weapon Mistakes and How to Avoid Them.Close Range (0-2 meters): Knives, fast melee weapons Medium Range (2-5 meters): Hatchets, batons, most melee weapons Long Range (5+ meters): Hunting bow, throwing weapons.", "subpage_snippet": "", "source": "gamingpromax.com", "link": "https://gamingpromax.com/dying-light-beast-best-early-game-weapons-guide/", "content": "Common Weapon Mistakes and How to Avoid Them.Close Range (0-2 meters): Knives, fast melee weapons Medium Range (2-5 meters): Hatchets, batons, most melee weapons Long Range (5+ meters): Hunting bow, throwing weapons."}
+{"idx": 5, "title": "Shared Task on Automatic Minuting", "date": "", "ddg_snippet": "AutoMin 2023 Papers in ACL Anthology ».Participants will have access to curated datasets, notably the ELITR Minuting Corpus, EuroParlMin v.1 and ELITR- Bench , which serve as foundational resources for training and evaluation purposes.", "subpage_snippet": "", "source": "ufal.github.io", "link": "https://ufal.github.io/automin-2025/", "content": "AutoMin 2023 Papers in ACL Anthology ».Participants will have access to curated datasets, notably the ELITR Minuting Corpus, EuroParlMin v.1 and ELITR- Bench , which serve as foundational resources for training and evaluation purposes."}
+{"idx": 6, "title": "Melania Meme", "date": "", "ddg_snippet": "Melania Meme token that support her initiatives.", "subpage_snippet": "", "source": "melaniameme.com", "link": "https://melaniameme.com/", "content": "Melania Meme token that support her initiatives."}
+{"idx": 7, "title": "Litematica - Minecraft Mod", "date": "", "ddg_snippet": "A client-side schematic mod with extra features for creative mode work.", "subpage_snippet": "", "source": "modrinth.com", "link": "https://modrinth.com/mod/litematica", "content": "A client-side schematic mod with extra features for creative mode work."}
+{"idx": 8, "title": "Instagram story viewer - Watch Instagram stories anonymously", "date": "", "ddg_snippet": "View Instagram profile with anonymity service. View highlights, stories, comments and posts anonymously without registering. View profiles using iPhone, Android and PC.", "subpage_snippet": "", "source": "storynavigation.com", "link": "https://storynavigation.com/", "content": "View Instagram profile with anonymity service. View highlights, stories, comments and posts anonymously without registering. View profiles using iPhone, Android and PC."}
+{"idx": 9, "title": "Растяжка подвздошно-поясничной мышцы. Упражнения — Видео...", "date": "", "ddg_snippet": "Смотрите онлайн Растяжка подвздошно-поясничной мышцы. Упражнения 3 мин 48 с. Видео от 1 июля 2022 в хорошем качестве, без регистрации в бесплатном видеокаталоге ВКонтакте! 94914 — просмотрели. 1326 — оценили.", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/video-25202791_456241177", "content": "Смотрите онлайн Растяжка подвздошно-поясничной мышцы. Упражнения 3 мин 48 с. Видео от 1 июля 2022 в хорошем качестве, без регистрации в бесплатном видеокаталоге ВКонтакте! 94914 — просмотрели. 1326 — оценили."}
diff --git a/data/sampled_jsons/ACM_Digital_Library_WWW_2024_Information_Retrieval_year_2024.jsonl b/data/sampled_jsons/ACM_Digital_Library_WWW_2024_Information_Retrieval_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..58e96a20897e9aa24786df91cc72fce67fddcac2
--- /dev/null
+++ b/data/sampled_jsons/ACM_Digital_Library_WWW_2024_Information_Retrieval_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Information Retrieval: - ACM Digital Library", "date": "", "ddg_snippet": "IR systems need to adapt to specific user characteristics and preferences, and techniques that were considered too niche a few years ago are now a matter of system design consideration. The Adaptations and Concerns section covers the following topics: conversational search, cross-language retrieval , temporal extraction and retrieval , bias in retrieval systems, and privacy in search.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3674127", "content": "IR systems need to adapt to specific user characteristics and preferences, and techniques that were considered too niche a few years ago are now a matter of system design consideration. The Adaptations and Concerns section covers the following topics: conversational search, cross-language retrieval , temporal extraction and retrieval , bias in retrieval systems, and privacy in search."}
+{"idx": 1, "title": "ACM Digital Library", "date": "", "ddg_snippet": "Please contact your library or ACM to request this access. dl-team@hq. acm .org Welcome to the ACM Digital Library A community engaged with a repository of resources to support computing research and practice Please explore and use the [Feedback] button on any page to help us shape the new site.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/", "content": "Please contact your library or ACM to request this access. dl-team@hq. acm .org Welcome to the ACM Digital Library A community engaged with a repository of resources to support computing research and practice Please explore and use the [Feedback] button on any page to help us shape the new site."}
+{"idx": 2, "title": "dblp: Information Retrieval 2024", "date": "", "ddg_snippet": "Bibliographic content of Information Retrieval 2024Omar Alonso, Ricardo Baeza-Yates: Information Retrieval : Advanced Topics and Techniques. ACM Books 60, ACM 2024 , ISBN 979-8-4007-1050-6", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/db/books/collections/AB2024", "content": "Bibliographic content of Information Retrieval 2024Omar Alonso, Ricardo Baeza-Yates: Information Retrieval : Advanced Topics and Techniques. ACM Books 60, ACM 2024 , ISBN 979-8-4007-1050-6"}
+{"idx": 3, "title": "Action First: Leveraging Preference-Aware Actions ... - ACM Digital Library", "date": "", "ddg_snippet": "Sara Kemper, Justin Cui, Kai Dicarlantonio, Kathy Lin, Danjie Tang, Anton Korikov, and Scott Sanner. 2024 . Retrieval -Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2786 ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3726302.3729885", "content": "Sara Kemper, Justin Cui, Kai Dicarlantonio, Kathy Lin, Danjie Tang, Anton Korikov, and Scott Sanner. 2024 . Retrieval -Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2786 ..."}
+{"idx": 4, "title": "Retrieval and Structuring Augmented Generation with LLMs for Web ...", "date": "", "ddg_snippet": "Using Retrieval -Augmented Generation (RAG), we ground LLM outputs in relevant external data, improving their application in search engines, chatbots, and recommendation systems. We also delve into text structuring techniques, such as taxonomy construction and taxonomy-guided retrieval , enhancing the effectiveness of information retrieval .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3701716.3715870", "content": "Using Retrieval -Augmented Generation (RAG), we ground LLM outputs in relevant external data, improving their application in search engines, chatbots, and recommendation systems. We also delve into text structuring techniques, such as taxonomy construction and taxonomy-guided retrieval , enhancing the effectiveness of information retrieval ."}
+{"idx": 5, "title": "Proceedings of the 2024 ACM SIGIR International Conference on Theory of ...", "date": "", "ddg_snippet": "Welcome to ACM ICTIR 2024 , the 10th conference with that name to be fully sponsored by the ACM Special Interest Group on Information Retrieval (SIGIR), and the 14th International Conference on the Theory of Information Retrieval .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3664190", "content": "Welcome to ACM ICTIR 2024 , the 10th conference with that name to be fully sponsored by the ACM Special Interest Group on Information Retrieval (SIGIR), and the 14th International Conference on the Theory of Information Retrieval ."}
+{"idx": 6, "title": "Information Retrieval: Advanced Topics and Techniques - ACM Digital Library", "date": "", "ddg_snippet": "The Adaptations and Concerns section covers the following topics: conversational search, cross-language retrieval , temporal extraction and retrieval , bias in retrieval systems, and privacy in search.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/book/10.1145/3674127", "content": "The Adaptations and Concerns section covers the following topics: conversational search, cross-language retrieval , temporal extraction and retrieval , bias in retrieval systems, and privacy in search."}
+{"idx": 7, "title": "Privacy in Information Retrieval - ACM Digital Library", "date": "", "ddg_snippet": "Information Retrieval (IR) research has extensively utilized personalization to advance its state-of-the-art. In this process, many IR algorithms and applications require the use of users' personal information , contextual information and other sensitive ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3674127.3674139", "content": "Information Retrieval (IR) research has extensively utilized personalization to advance its state-of-the-art. In this process, many IR algorithms and applications require the use of users' personal information , contextual information and other sensitive ..."}
+{"idx": 8, "title": "JCDL2024 Workshop: Utilizing AI/ML to Enhance ... - ACM Digital Library", "date": "", "ddg_snippet": "JCDL2024 Workshop: Utilizing AI/ML to Enhance Information Extraction, Organization, and Retrieval from Large-scale Archival Collections", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3677389.3702608", "content": "JCDL2024 Workshop: Utilizing AI/ML to Enhance Information Extraction, Organization, and Retrieval from Large-scale Archival Collections"}
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+{"idx": 1, "title": "Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "22 Apr 2025 — AERO is a deep reinforcement learning framework for efficient account migration in sharding blockchains , using a prefix-based grouping strategy.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "22 Apr 2025 — AERO is a deep reinforcement learning framework for efficient account migration in sharding blockchains , using a prefix-based grouping strategy."}
+{"idx": 2, "title": "Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "by M Song · 2025 · Cited by 2 — AERO is a deep reinforcement learning framework for efficient account migration in sharding blockchains, using a prefix-based grouping strategy. 11 pages", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "by M Song · 2025 · Cited by 2 — AERO is a deep reinforcement learning framework for efficient account migration in sharding blockchains, using a prefix-based grouping strategy. 11 pages"}
+{"idx": 3, "title": "Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "29 Jan 2025 — This paper proposes AERO, a deep reinforcement learning framework for efficient account migration in sharding blockchains, improving throughput by 31.77%.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=WcuXvn3HVk&referrer=[the+profile+of+Bohan+Zhou](/profile?id=~Bohan_Zhou1)", "content": "29 Jan 2025 — This paper proposes AERO, a deep reinforcement learning framework for efficient account migration in sharding blockchains, improving throughput by 31.77%."}
+{"idx": 4, "title": "AERO: Enhancing Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "by M Song · 2025 · Cited by 2 — AERO is a deep reinforcement learning framework for efficient account migration in sharding blockchains, using a prefix-based grouping strategy.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3696410.3714926", "content": "by M Song · 2025 · Cited by 2 — AERO is a deep reinforcement learning framework for efficient account migration in sharding blockchains, using a prefix-based grouping strategy."}
+{"idx": 5, "title": "Academic Papers on Blockchain & Smart Contracts", "date": "", "ddg_snippet": "... Blockchain Incentive Mechanisms with Deep Reinforcement Learning. ... AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hzysvilla/Academic_Smart_Contract_Papers", "content": "... Blockchain Incentive Mechanisms with Deep Reinforcement Learning. ... AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration ."}
+{"idx": 6, "title": "A survey on blockchain sharding", "date": "", "ddg_snippet": "by X Liu · 2023 · Cited by 35 — View PDF; Download full issue. Search ScienceDirect. Article ... AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0019057823002963", "content": "by X Liu · 2023 · Cited by 35 — View PDF; Download full issue. Search ScienceDirect. Article ... AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration ."}
+{"idx": 7, "title": "A new framework for prognostics in decentralized industries", "date": "", "ddg_snippet": "by TQD Pham · 2025 · Cited by 2 — The framework uses Federated Learning (FL) for localized training and Blockchain (BC) for trust, transparency, and data integrity, enhancing RUL predictions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.05725", "content": "by TQD Pham · 2025 · Cited by 2 — The framework uses Federated Learning (FL) for localized training and Blockchain (BC) for trust, transparency, and data integrity, enhancing RUL predictions."}
+{"idx": 8, "title": "Mingxuan Song's publications", "date": "", "ddg_snippet": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Request PDF. Restricted access. Profile Image. Mingxuan Song.", "subpage_snippet": "", "source": "sciprofiles.com", "link": "https://sciprofiles.com/user/publications/2109258", "content": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Request PDF. Restricted access. Profile Image. Mingxuan Song."}
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+{"idx": 0, "title": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "Blockchain , Sharding , Account migration , Reinforcement learning . Account migration protocols in sharding blockchains are essential for maintaining scalability by redistributing account states across different shards [22].", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "Blockchain , Sharding , Account migration , Reinforcement learning . Account migration protocols in sharding blockchains are essential for maintaining scalability by redistributing account states across different shards [22]."}
+{"idx": 1, "title": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "D Full Shard Transaction Distriction. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Blockchain , Sharding , Account migration , Reinforcement learning .", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "D Full Shard Transaction Distriction. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Blockchain , Sharding , Account migration , Reinforcement learning ."}
+{"idx": 2, "title": "Peking University - Cited by 70 - Deep Learning - Google Scholar", "date": "", "ddg_snippet": "AERO : Enhancing sharding blockchain via deep reinforcement learning for account migration .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=BdT5CzcAAAAJ&hl=en", "content": "AERO : Enhancing sharding blockchain via deep reinforcement learning for account migration ."}
+{"idx": 3, "title": "(PDF) Sharding for Blockchain based Mobile Edge Computing...", "date": "", "ddg_snippet": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Blockchain -Based Transformer-Assisted Multi-Agent Reinforcement Learning for Resource Allocation and Computation Offloading in 5G Private Networks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/363131667_Sharding_for_Blockchain_based_Mobile_Edge_Computing_System_A_Deep_Reinforcement_Learning_Approach", "content": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Blockchain -Based Transformer-Assisted Multi-Agent Reinforcement Learning for Resource Allocation and Computation Offloading in 5G Private Networks."}
+{"idx": 4, "title": "LB-Chain: Load-Balanced and Low-Latency Blockchain Sharding via ...", "date": "", "ddg_snippet": "Index Terms— Blockchain , blockchain sharding , load balance, account migration .Using machine learning , the allocation service then predicts the upcoming number of transactions generated by different accounts and shards . Account Allocation Algorithm.", "subpage_snippet": "", "source": "home.cse.ust.hk", "link": "https://home.cse.ust.hk/~weiwa/papers/lb-chain-tpds22.pdf", "content": "Index Terms— Blockchain , blockchain sharding , load balance, account migration .Using machine learning , the allocation service then predicts the upcoming number of transactions generated by different accounts and shards . Account Allocation Algorithm."}
+{"idx": 5, "title": "[PDF] LB-Chain: Load-Balanced and Low-Latency Blockchain ...", "date": "", "ddg_snippet": "Blockchain sharding has been increasingly used to improve blockchain systems’ performance, in which a blockchain is split into multiple smaller, disjoint shards .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/LB-Chain:-Load-Balanced-and-Low-Latency-Blockchain-Li-Wang/f78243fab1362932088f8593b898151dee65ffbf", "content": "Blockchain sharding has been increasingly used to improve blockchain systems’ performance, in which a blockchain is split into multiple smaller, disjoint shards ."}
+{"idx": 6, "title": "Enhancing Scalability in Sharding Blockchain via ... | SpringerLink", "date": "", "ddg_snippet": "(2022) BrokerChain: a cross- shard blockchain protocol for account /balance-based state sharding . In: IEEE INFOCOM 2022—IEEE conference on computer communications, London, United Kingdom, pp 1968–1977.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-97-1923-5_26", "content": "(2022) BrokerChain: a cross- shard blockchain protocol for account /balance-based state sharding . In: IEEE INFOCOM 2022—IEEE conference on computer communications, London, United Kingdom, pp 1968–1977."}
+{"idx": 7, "title": "ContribChain: A Stress-Balanced Blockchain Sharding Protocol with...", "date": "", "ddg_snippet": "[27] employs Deep Reinforcement Learn -ing (DRL) to optimize state placement. While these ap-proaches enhance account allocation, they fail to consider performance disparities among shards , and thus do not achieve stress balance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.06899", "content": "[27] employs Deep Reinforcement Learn -ing (DRL) to optimize state placement. While these ap-proaches enhance account allocation, they fail to consider performance disparities among shards , and thus do not achieve stress balance."}
+{"idx": 8, "title": "Prophet: Conflict-Free Sharding Blockchain via ... | DeepAI", "date": "", "ddg_snippet": "Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards ' independent and random scheduling for cross- shard transactions results in numerous conflicts and aborts, since...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/prophet-conflict-free-sharding-blockchain-via-byzantine-tolerant-deterministic-ordering", "content": "Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards ' independent and random scheduling for cross- shard transactions results in numerous conflicts and aborts, since..."}
+{"idx": 9, "title": "Scalability of blockchain : Review of cross- sharding with high...", "date": "", "ddg_snippet": "deep more scalable. and blockchain security and atomicity. learning with the proposed reinforcement - Blockchain transpare of cross- shard transactions. - state sharding .LB-Chain: Load-Balanced and Low-Latency Blockchain Sharding via Account Migration .", "subpage_snippet": "", "source": "www.bio-conferences.org", "link": "https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00075.pdf", "content": "deep more scalable. and blockchain security and atomicity. learning with the proposed reinforcement - Blockchain transpare of cross- shard transactions. - state sharding .LB-Chain: Load-Balanced and Low-Latency Blockchain Sharding via Account Migration ."}
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+{"idx": 0, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "Apr 22, 2025 · We propose AERO , a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains. AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "Apr 22, 2025 · We propose AERO , a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains. AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts ."}
+{"idx": 1, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "To address these scalability issues, account migration ofers a promising solution. However, existing migration solutions struggle with the high computational overhead and insuficient capture of complex transaction patterns. We propose AERO , a deep reinforcement learning framework to facilitate eficient account migration in sharding blockchains.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "To address these scalability issues, account migration ofers a promising solution. However, existing migration solutions struggle with the high computational overhead and insuficient capture of complex transaction patterns. We propose AERO , a deep reinforcement learning framework to facilitate eficient account migration in sharding blockchains."}
+{"idx": 2, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "Jan 29, 2025 · This paper proposes AERO , a deep reinforcement learning framework for efficient account migration in sharding blockchains, improving throughput by 31.77% and reducing cross- shard transactions and workload imbalances.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/zh-CN/chatpaper/paper/129864", "content": "Jan 29, 2025 · This paper proposes AERO , a deep reinforcement learning framework for efficient account migration in sharding blockchains, improving throughput by 31.77% and reducing cross- shard transactions and workload imbalances."}
+{"idx": 3, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain ...", "date": "", "ddg_snippet": "by H Huang · 2025 · Cited by 34 — We developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/sc/2025/02/10908689/24MWrrwc2Fa", "content": "by H Huang · 2025 · Cited by 34 — We developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms."}
+{"idx": 4, "title": "Reinforcement Learning: An Introduction | Guide books", "date": "", "ddg_snippet": "In Reinforcement Learning , Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/book/10.5555/3312046", "content": "In Reinforcement Learning , Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has ..."}
+{"idx": 5, "title": "Sustainable business decision modelling with blockchain ...", "date": "", "ddg_snippet": "by G Wickremasinghe · 2025 — This includes machine learning techniques such as supervised, unsupervised, reinforcement learning , partial differential equations integration ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2096720925000399", "content": "by G Wickremasinghe · 2025 — This includes machine learning techniques such as supervised, unsupervised, reinforcement learning , partial differential equations integration ..."}
+{"idx": 6, "title": "Machine Learning Apr 2025", "date": "", "ddg_snippet": "8 Apr 2025 — Title: Stability Enhancement in Reinforcement Learning via Adaptive Control Lyapunov Function . Donghe Chen, Han Wang, Lin Cheng, Shengping ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.LG/2025-04?skip=375&show=2000", "content": "8 Apr 2025 — Title: Stability Enhancement in Reinforcement Learning via Adaptive Control Lyapunov Function . Donghe Chen, Han Wang, Lin Cheng, Shengping ..."}
+{"idx": 7, "title": "Blockchain Security and Its Application in Internet of Things", "date": "", "ddg_snippet": "In Figure 1, blockchain -based transactions are verified using a machine learning model, and the prediction result shows that the transaction is legitimate ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/10958/Blockchain_Security_and_Its_Application_in_Internet_of_Things.pdf?v=1748528140", "content": "In Figure 1, blockchain -based transactions are verified using a machine learning model, and the prediction result shows that the transaction is legitimate ..."}
+{"idx": 8, "title": "Papers - Zhen Xiao", "date": "", "ddg_snippet": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Proc. of the Web Conference 2025 (WWW 2025), May 2025. Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan, Rongkai Zhang, Pengze Li, and Zhen Xiao.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/", "content": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Proc. of the Web Conference 2025 (WWW 2025), May 2025. Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan, Rongkai Zhang, Pengze Li, and Zhen Xiao."}
+{"idx": 9, "title": "Mingxuan Song", "date": "", "ddg_snippet": "WWW 2025 Oral CCF-A Mingxuan Song, Pengze Li, Bohan Zhou, Shenglin Yin, Zhen Xiao*, Jieyi Long. \" AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .\" Proceedings of the Web Conference, May 2025. [PDF] CVPR 2024 CCF-A Shenglin Yin, Zhen Xiao*, Mingxuan Song, and Jieyi Long. \"Adversarial Distillation Based on Slack Matching and Attribution Region Alignment ...", "subpage_snippet": "", "source": "www.songmingxuan.com", "link": "https://www.songmingxuan.com/", "content": "WWW 2025 Oral CCF-A Mingxuan Song, Pengze Li, Bohan Zhou, Shenglin Yin, Zhen Xiao*, Jieyi Long. \" AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .\" Proceedings of the Web Conference, May 2025. [PDF] CVPR 2024 CCF-A Shenglin Yin, Zhen Xiao*, Mingxuan Song, and Jieyi Long. \"Adversarial Distillation Based on Slack Matching and Attribution Region Alignment ..."}
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+{"idx": 0, "title": "Literature | PDF | Cryptocurrency | Machine Learning", "date": "", "ddg_snippet": "... Blockchain Sharding via Account Migration Structural Identity Representation Learning for Blockchain-Enabled Metaverse Based on Complex N Collaborative ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/752858914/literature", "content": "... Blockchain Sharding via Account Migration Structural Identity Representation Learning for Blockchain-Enabled Metaverse Based on Complex N Collaborative ..."}
+{"idx": 1, "title": "Advancement in Blockchain Technology and Applications", "date": "", "ddg_snippet": "Face Detection Using Deep Learning to Ensure a Coercion Resistant Blockchain -Based. Electronic Voting. Eng. Sci. 2021, 16, 341–353. 143. Tandon, S.; Singh, N ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/9291/Advancement_in_Blockchain_Technology_and_Applications.pdf?v=1740190107", "content": "Face Detection Using Deep Learning to Ensure a Coercion Resistant Blockchain -Based. Electronic Voting. Eng. Sci. 2021, 16, 341–353. 143. Tandon, S.; Singh, N ..."}
+{"idx": 2, "title": "Machine Learning Jan 2024", "date": "", "ddg_snippet": "8 Jan 2024 — Title: Consistency Enhancement -Based Deep Multiview Clustering via Contrastive Learning . Hao Yang, Hua Mao, Wai Lok Woo, Jie Chen, Xi Peng.", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.LG/2024-01?skip=800&show=2000", "content": "8 Jan 2024 — Title: Consistency Enhancement -Based Deep Multiview Clustering via Contrastive Learning . Hao Yang, Hua Mao, Wai Lok Woo, Jie Chen, Xi Peng."}
+{"idx": 3, "title": "Pattern Recognition and Machine Learning (Information ...", "date": "", "ddg_snippet": "Deep unsupervised clustering by information maximization on Gaussian mixture autoencoders, Information Sciences: an International Journal, 714:C, Online ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/book/10.5555/1162264", "content": "Deep unsupervised clustering by information maximization on Gaussian mixture autoencoders, Information Sciences: an International Journal, 714:C, Online ..."}
+{"idx": 4, "title": "Handbook of Blockchain Technology 1", "date": "", "ddg_snippet": "This Handbook provides an interdisciplinary investigation into the role and influence of blockchain technology in areas...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/handbook-of-blockchain-technology-1.html", "content": "This Handbook provides an interdisciplinary investigation into the role and influence of blockchain technology in areas..."}
+{"idx": 5, "title": "Blockchain Security and Its Application in Internet of Things", "date": "", "ddg_snippet": "In Figure 1, blockchain -based transactions are verified using a machine learning model, and the prediction result shows that the transaction is legitimate ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/10958/Blockchain_Security_and_Its_Application_in_Internet_of_Things.pdf?v=1748528140", "content": "In Figure 1, blockchain -based transactions are verified using a machine learning model, and the prediction result shows that the transaction is legitimate ..."}
+{"idx": 6, "title": "Machine Learning Jan 2024", "date": "", "ddg_snippet": "8 Jan 2024 — Title: Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies? Jan Rathjens, Laurenz Wiskott.", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.LG/2024-01?skip=325&show=2000", "content": "8 Jan 2024 — Title: Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies? Jan Rathjens, Laurenz Wiskott."}
+{"idx": 7, "title": "Co-Designing Cryptographic Systems with Resource", "date": "", "ddg_snippet": "by JL Watson · 2024 — We target a core bottleneck in zero-knowledge proofs, multiscalar multiplication (MSM), improve latency for large single proof execution on the ...", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-172.pdf", "content": "by JL Watson · 2024 — We target a core bottleneck in zero-knowledge proofs, multiscalar multiplication (MSM), improve latency for large single proof execution on the ..."}
+{"idx": 8, "title": "Smart Monitoring and Control in the Future Internet of Things", "date": "", "ddg_snippet": "Ethereum transactions transfer value between accounts , pass data to SC function calls, and deploy new smart contracts to the BC network. Once submitted to ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/smart-monitoring-and-control-in-the-future-internet-of-2n3wdirq99.pdf", "content": "Ethereum transactions transfer value between accounts , pass data to SC function calls, and deploy new smart contracts to the BC network. Once submitted to ..."}
+{"idx": 9, "title": "A comprehensive review on internet of things task ...", "date": "", "ddg_snippet": "by W Dayong · 2024 · Cited by 18 — This paper provides a comprehensive and in-depth understanding of the algorithms and mechanisms of multiple IoT task offloading in the MEC network.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2405844024059474", "content": "by W Dayong · 2024 · Cited by 18 — This paper provides a comprehensive and in-depth understanding of the algorithms and mechanisms of multiple IoT task offloading in the MEC network."}
diff --git a/data/sampled_jsons/AERO_blockchain_Section_5.1_experimental_setup_nodes_total_number.jsonl b/data/sampled_jsons/AERO_blockchain_Section_5.1_experimental_setup_nodes_total_number.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d6894177a9d525ce435879315b01e2a9649aa6e
--- /dev/null
+++ b/data/sampled_jsons/AERO_blockchain_Section_5.1_experimental_setup_nodes_total_number.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Book by the seat | The Private Jet Experience | Aero ™", "date": "", "ddg_snippet": "A beyond first-class experience awaits, no membership required. Aero effortlessly merges the worlds of hospitality, design, and travel. Enjoy spacious premium seats, private terminals with no lines or crowds, a dedicated concierge team, and curated amenities.", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/", "content": "A beyond first-class experience awaits, no membership required. Aero effortlessly merges the worlds of hospitality, design, and travel. Enjoy spacious premium seats, private terminals with no lines or crowds, a dedicated concierge team, and curated amenities."}
+{"idx": 1, "title": "Seats. aero - Home", "date": "", "ddg_snippet": "Seats. aero is the fastest search engine for award travel. Explore availability across entire regions, search with instant results, create free alerts and more to find the best flights for your points.", "subpage_snippet": "", "source": "seats.aero", "link": "https://seats.aero/", "content": "Seats. aero is the fastest search engine for award travel. Explore availability across entire regions, search with instant results, create free alerts and more to find the best flights for your points."}
+{"idx": 2, "title": "AERO Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of AERO is of or relating to aircraft or aeronautics. How to use aero in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/aero", "content": "The meaning of AERO is of or relating to aircraft or aeronautics. How to use aero in a sentence."}
+{"idx": 3, "title": "Mil-Spec AR Parts, Components & Accessories | Aero Precision", "date": "", "ddg_snippet": "Aero Precision manufacturers mil-spec parts, including AR15 & AR10 uppers, lowers, rifles, handguards, barrels, scope mounts & more.", "subpage_snippet": "", "source": "www.aeroprecisionusa.com", "link": "https://www.aeroprecisionusa.com/", "content": "Aero Precision manufacturers mil-spec parts, including AR15 & AR10 uppers, lowers, rifles, handguards, barrels, scope mounts & more."}
+{"idx": 4, "title": "Aero End of Support FAQs - Adobe Inc.", "date": "", "ddg_snippet": "Aug 7, 2025 · Adobe Aero will be discontinued on iOS, Android, and Creative Cloud Desktop effective November 6, 2025. Existing users can access the application and download their content until December 3, 2025.", "subpage_snippet": "", "source": "helpx.adobe.com", "link": "https://helpx.adobe.com/aero/aero-end-of-support-faq.html", "content": "Aug 7, 2025 · Adobe Aero will be discontinued on iOS, Android, and Creative Cloud Desktop effective November 6, 2025. Existing users can access the application and download their content until December 3, 2025."}
+{"idx": 5, "title": "Trailer Tarp Systems, Accessories | Aero Industries, Inc.", "date": "", "ddg_snippet": "Aero Industries, Inc. is a global leader in the manufacturing of trailer tarp systems and accessories. Founded in 1944, the company’s heritage is rooted in customer service and innovation.", "subpage_snippet": "", "source": "www.aeroindustries.com", "link": "https://www.aeroindustries.com/", "content": "Aero Industries, Inc. is a global leader in the manufacturing of trailer tarp systems and accessories. Founded in 1944, the company’s heritage is rooted in customer service and innovation."}
+{"idx": 6, "title": "Aircraft Fleet | Aero ™", "date": "", "ddg_snippet": "\"I’ve done a lot of traveling in my life, and I’ve never experienced transportation quite like Aero . Exceptional service, luxurious terminal, high quality amenities, top notch staff.\"", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/fleet", "content": "\"I’ve done a lot of traveling in my life, and I’ve never experienced transportation quite like Aero . Exceptional service, luxurious terminal, high quality amenities, top notch staff.\""}
+{"idx": 7, "title": "Explore Flights from Los Angeles | Aero ™", "date": "", "ddg_snippet": "Aero is a premium jet service that provides the time-saving convenience and world-class service of private air travel, booked by the seat with no membership required.", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/flights?adults=1&infants=0&petInSeat=0&petUnderSeat=0&serviceAnimal=0&type=roundtrip", "content": "Aero is a premium jet service that provides the time-saving convenience and world-class service of private air travel, booked by the seat with no membership required."}
+{"idx": 8, "title": "Our Story | The Private Jet Experience | Aero ™", "date": "", "ddg_snippet": "Aero was created to bring the magic back to flying. We aim to deliver an unforgettable, radically better travel experience. Learn more about how we started, our leadership, and our vision for the future.", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/our-story", "content": "Aero was created to bring the magic back to flying. We aim to deliver an unforgettable, radically better travel experience. Learn more about how we started, our leadership, and our vision for the future."}
+{"idx": 9, "title": "Flights To and From Los Angeles | The Private Jet Experience | ...", "date": "", "ddg_snippet": "Wherever you choose to wander, fly in signature Aero style. Aero ’s book-by-the-seat jet service connects travelers in Los Angeles to sought-after leisure destinations and the world's largest entertainment and sporting events.", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/destinations/na/los-angeles", "content": "Wherever you choose to wander, fly in signature Aero style. Aero ’s book-by-the-seat jet service connects travelers in Los Angeles to sought-after leisure destinations and the world's largest entertainment and sporting events."}
diff --git a/data/sampled_jsons/AERO_blockchain_WWW_2025_16_shards_nodes_experimental_configuration_year_2024.jsonl b/data/sampled_jsons/AERO_blockchain_WWW_2025_16_shards_nodes_experimental_configuration_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0afea4b96ce3fbf5e27ba5ddeb00f6124ad27e7
--- /dev/null
+++ b/data/sampled_jsons/AERO_blockchain_WWW_2025_16_shards_nodes_experimental_configuration_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "The experimental setup consists of 16 physical shards , each containing 8 nodes , amounting to a total of 128 nodes in the total network. During each epoch, the consensus phase is composed of 100 blocks, with each block containing a maximum of 1,000 transactions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "The experimental setup consists of 16 physical shards , each containing 8 nodes , amounting to a total of 128 nodes in the total network. During each epoch, the consensus phase is composed of 100 blocks, with each block containing a maximum of 1,000 transactions."}
+{"idx": 1, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts. We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts. We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle."}
+{"idx": 2, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain Sharding Protocols", "date": "", "ddg_snippet": "•In step ⃝1, before the BlockEmulator system gets started, users/developers set up the configuration parameters for the emulated blockchain .The configuration parameters mainly includethe directory of the outputof experimental results, block-generation interval,the consensus protocol adopted, block size, the number of nodes per shard , and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.03612", "content": "•In step ⃝1, before the BlockEmulator system gets started, users/developers set up the configuration parameters for the emulated blockchain .The configuration parameters mainly includethe directory of the outputof experimental results, block-generation interval,the consensus protocol adopted, block size, the number of nodes per shard , and ..."}
+{"idx": 3, "title": "A sharding blockchain protocol for enhanced scalability and performance ...", "date": "", "ddg_snippet": "Blockchain sharding technology (, ) came into being to break through this performance bottleneck. This approach divides the blockchain network into multiple shards that process transactions in parallel through the idea of divide-and-conquer, thus significantly improving the system's overall performance.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1319157824002738", "content": "Blockchain sharding technology (, ) came into being to break through this performance bottleneck. This approach divides the blockchain network into multiple shards that process transactions in parallel through the idea of divide-and-conquer, thus significantly improving the system's overall performance."}
+{"idx": 4, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement ... - OpenReview", "date": "", "ddg_snippet": "TL;DR: This paper proposes AERO , a deep reinforcement learning framework for efficient account migration in sharding blockchains , improving throughput by 31.77% and reducing cross- shard transactions and workload imbalances.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=WcuXvn3HVk", "content": "TL;DR: This paper proposes AERO , a deep reinforcement learning framework for efficient account migration in sharding blockchains , improving throughput by 31.77% and reducing cross- shard transactions and workload imbalances."}
+{"idx": 5, "title": "TbDd: A new trust-based, DRL-driven framework for blockchain sharding ...", "date": "", "ddg_snippet": "Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards , yet it is vulnerable to the 1% attacks where dishonest nodes target a shard to corrupt the entire blockchain . Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1389128624001750", "content": "Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards , yet it is vulnerable to the 1% attacks where dishonest nodes target a shard to corrupt the entire blockchain . Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL ..."}
+{"idx": 6, "title": "Experimental blockchain implementation featuring shards and ... - GitHub", "date": "", "ddg_snippet": "Chainspace is a distributed ledger platform for high-integrity and transparent processing of transactions within a decentralized system. More detailed documentation can be found in the docs folder of this repo. Chainspace is a decentralised application. This means that to use it you must instantiate a network of nodes and then communicate with ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/DECODEproject/chainspace", "content": "Chainspace is a distributed ledger platform for high-integrity and transparent processing of transactions within a decentralized system. More detailed documentation can be found in the docs folder of this repo. Chainspace is a decentralised application. This means that to use it you must instantiate a network of nodes and then communicate with ..."}
+{"idx": 7, "title": "PDF AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "blockchain network into multiple smaller, manageable segments called shards . Each shard simultaneously processes a subset of blockchain transactions and smart contracts, while periodically reassigning and maintaining shard nodes to ensure security.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "blockchain network into multiple smaller, manageable segments called shards . Each shard simultaneously processes a subset of blockchain transactions and smart contracts, while periodically reassigning and maintaining shard nodes to ensure security."}
+{"idx": 8, "title": "Deep Learning Approaches for Blockchain Scalability Through Sharding ...", "date": "", "ddg_snippet": "Sharding technology creates new difficulties even as it helps traditional blockchain networks overcome performance issues. The distribution of malicious nodes may be unequal because of random node allocation, resulting in performance variations and security threats. Current reputation-based sharding techniques frequently ignore node performance features and don't take care of leader election ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10830433", "content": "Sharding technology creates new difficulties even as it helps traditional blockchain networks overcome performance issues. The distribution of malicious nodes may be unequal because of random node allocation, resulting in performance variations and security threats. Current reputation-based sharding techniques frequently ignore node performance features and don't take care of leader election ..."}
+{"idx": 9, "title": "PDF Dynamic sharding model and performance optimization method for ...", "date": "", "ddg_snippet": "To prevent resource waste and reduce the percentage of cross- shard transactions, the model constructs the sharding structure based on nodes' computing capabilities, transaction frequencies, and transmission rates between nodes . It minimizes data interactions and consensus overhead between shards .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s11227-024-06870-8.pdf", "content": "To prevent resource waste and reduce the percentage of cross- shard transactions, the model constructs the sharding structure based on nodes' computing capabilities, transaction frequencies, and transmission rates between nodes . It minimizes data interactions and consensus overhead between shards ."}
diff --git a/data/sampled_jsons/AERO_blockchain_reward_function_equations_8-12_Rt_formula.jsonl b/data/sampled_jsons/AERO_blockchain_reward_function_equations_8-12_Rt_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cba0cc6017457190faeab22454b0a089921fe929
--- /dev/null
+++ b/data/sampled_jsons/AERO_blockchain_reward_function_equations_8-12_Rt_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blockchain . aero (MFL) - ICO Rating and Overview | ICOmarks", "date": "", "ddg_snippet": "Blockchain . aero ICO Get full information about Blockchain . aero - ICO details, Rating, (MFL) Token price, White paper, Team and more.", "subpage_snippet": "", "source": "icomarks.ai", "link": "https://icomarks.ai/ico/blockchainaero", "content": "Blockchain . aero ICO Get full information about Blockchain . aero - ICO details, Rating, (MFL) Token price, White paper, Team and more."}
+{"idx": 1, "title": "[ANN] BLOCKCHAIN . AERO : Proof-of-flight. Token Launch Phase I", "date": "", "ddg_snippet": "Nik Blockchain . aero (OP). Newbie Offline Offline. Activity: 35 Merit: 0. View Profile. [ANN] BLOCKCHAIN . AERO : Proof-of-flight. Token Launch Phase I.Each holder with at least a \"cent\" of WAVES will get 1 McFly token which roughly equals to 1 minute of flight.", "subpage_snippet": "", "source": "bitcointalk.org", "link": "https://bitcointalk.org/index.php?topic=2070063.0", "content": "Nik Blockchain . aero (OP). Newbie Offline Offline. Activity: 35 Merit: 0. View Profile. [ANN] BLOCKCHAIN . AERO : Proof-of-flight. Token Launch Phase I.Each holder with at least a \"cent\" of WAVES will get 1 McFly token which roughly equals to 1 minute of flight."}
+{"idx": 2, "title": "Logarithmic Equations Calculator & Solver - SnapXam", "date": "", "ddg_snippet": "Logarithmic Equations Calculator online with solution and steps. Detailed step by step solutions to your Logarithmic Equations problems with our math solver and online calculator.", "subpage_snippet": "", "source": "www.snapxam.com", "link": "https://www.snapxam.com/calculators/logarithmic-equations-calculator", "content": "Logarithmic Equations Calculator online with solution and steps. Detailed step by step solutions to your Logarithmic Equations problems with our math solver and online calculator."}
+{"idx": 3, "title": "Blockchain . Aero", "date": "", "ddg_snippet": "На Международном Авиасалоне МАКС-2017 в Москве в июле было объявлено о создании технологического блокчейн-консорциума для инфраструктуры массовой городской авиации и аэротакси — Blockchain . Aero .", "subpage_snippet": "", "source": "www.tadviser.ru", "link": "https://www.tadviser.ru/index.php/Компания:Blockchain.Aero", "content": "На Международном Авиасалоне МАКС-2017 в Москве в июле было объявлено о создании технологического блокчейн-консорциума для инфраструктуры массовой городской авиации и аэротакси — Blockchain . Aero ."}
+{"idx": 4, "title": "The Role of Reinforcement Learning in Optimizing Maintenance...", "date": "", "ddg_snippet": "Reward Function (R): Quantification of maintenance decision quality. Production value from continued operation. Maintenance costs (labor, parts, downtime).", "subpage_snippet": "", "source": "diogoribeiro7.github.io", "link": "https://diogoribeiro7.github.io/industrial+ai/predictive+maintenance/machine+learning/reinforcement_learning_optimizing_maintenance/", "content": "Reward Function (R): Quantification of maintenance decision quality. Production value from continued operation. Maintenance costs (labor, parts, downtime)."}
+{"idx": 5, "title": "Основная гонка Формулы 2 Гран-при Азербайджана...", "date": "", "ddg_snippet": "Видеозаписи Formula 2 | Formula 3. 30 видео. Formula 2.Round 12 .Azerbaijan.Sprint.F1TV.1080p.d22t.rus.", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/video-78194749_456240357", "content": "Видеозаписи Formula 2 | Formula 3. 30 видео. Formula 2.Round 12 .Azerbaijan.Sprint.F1TV.1080p.d22t.rus."}
+{"idx": 6, "title": "MSI в интернет-магазине DNS", "date": "", "ddg_snippet": "Отзывы на товары. 15.6\" Ноутбук MSI Modern 15 B 12 M-215XRU черный.обзор MSI Formula .", "subpage_snippet": "", "source": "www.dns-shop.ru", "link": "https://www.dns-shop.ru/brand/msi/", "content": "Отзывы на товары. 15.6\" Ноутбук MSI Modern 15 B 12 M-215XRU черный.обзор MSI Formula ."}
+{"idx": 7, "title": "Stadion.Uz", "date": "", "ddg_snippet": "12 :00 Чемпионат Сербии. \"Партизан\" - \"Црвена Звезда\".Этап 5 на автодроме \"Казань Ринг Каньон\". SMP Formula 4. Гонка 1. 15:15 \"Взойди на свой Олимп\". 15:50 Боулинг.", "subpage_snippet": "", "source": "stadion.uz", "link": "https://stadion.uz/", "content": "12 :00 Чемпионат Сербии. \"Партизан\" - \"Црвена Звезда\".Этап 5 на автодроме \"Казань Ринг Каньон\". SMP Formula 4. Гонка 1. 15:15 \"Взойди на свой Олимп\". 15:50 Боулинг."}
+{"idx": 8, "title": "3.5 Inches To Centimeters Converter | 3.5 in To cm Converter", "date": "", "ddg_snippet": "Convert 3.5 Inch to Centimeter with formula , common lengths conversion, conversion tables and more.Simply use our calculator above, or apply the formula to change the length 3.5 in to cm.", "subpage_snippet": "", "source": "inches-to-cm.appspot.com", "link": "https://inches-to-cm.appspot.com/3.5-inches-to-cm.html", "content": "Convert 3.5 Inch to Centimeter with formula , common lengths conversion, conversion tables and more.Simply use our calculator above, or apply the formula to change the length 3.5 in to cm."}
+{"idx": 9, "title": "Crypto Airdrops: Up-to-Date Airdrops List 2025, Free... | CryptoRank.io", "date": "", "ddg_snippet": "List of New Cryptocurrency Airdrops for 2025. Master the Art of Drop Hunting: Become a Contender for Free Tokens and Unlock Lucrative Opportunities in Cryptocurrency, Blockchain , and Web3.", "subpage_snippet": "", "source": "cryptorank.io", "link": "https://cryptorank.io/drophunting", "content": "List of New Cryptocurrency Airdrops for 2025. Master the Art of Drop Hunting: Become a Contender for Free Tokens and Unlock Lucrative Opportunities in Cryptocurrency, Blockchain , and Web3."}
diff --git a/data/sampled_jsons/AERO_paper_reward_function_Rt_equations_CST_IST_formula_calculation.jsonl b/data/sampled_jsons/AERO_paper_reward_function_Rt_equations_CST_IST_formula_calculation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..42d0c0e6bd61ccee219876deaeeda77dc0eac622
--- /dev/null
+++ b/data/sampled_jsons/AERO_paper_reward_function_Rt_equations_CST_IST_formula_calculation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Aerodynamics Formulas - Aerostudents", "date": "", "ddg_snippet": "An equation which looks a bit like the previous equation , is the Euler equation : dp = −ρV(dV) (2) So, if we integrate this equation , we find bernoulli’s equation : p+ 1 2 ρV2= C (3) Where C is a constant. So p+1 2 ρV 2is constant for any 2 points along a streamline. Using this formula , the airspeed can be calculated: V 0= r 2 p t −p 0", "subpage_snippet": "", "source": "www.aerostudents.com", "link": "http://www.aerostudents.com/courses/intro-to-aerospace-1/aerodynamics-and-aircraft-limits.pdf", "content": "An equation which looks a bit like the previous equation , is the Euler equation : dp = −ρV(dV) (2) So, if we integrate this equation , we find bernoulli’s equation : p+ 1 2 ρV2= C (3) Where C is a constant. So p+1 2 ρV 2is constant for any 2 points along a streamline. Using this formula , the airspeed can be calculated: V 0= r 2 p t −p 0"}
+{"idx": 1, "title": "Essential Formulae – Introduction to Aerospace Flight Vehicles", "date": "", "ddg_snippet": "The overarching concept of this eTextbook is to give students a broad-based introduction to the aerospace field, emphasizing technical content while making the material attractive and digestible. This eTextbook is structured and split into lessons centered around a 50-minute lecture period. Each lesson includes text content with detailed illustrations, application problems, a self ...", "subpage_snippet": "", "source": "eaglepubs.erau.edu", "link": "https://eaglepubs.erau.edu/introductiontoaerospaceflightvehicles/back-matter/essential-formulae/", "content": "The overarching concept of this eTextbook is to give students a broad-based introduction to the aerospace field, emphasizing technical content while making the material attractive and digestible. This eTextbook is structured and split into lessons centered around a 50-minute lecture period. Each lesson includes text content with detailed illustrations, application problems, a self ..."}
+{"idx": 2, "title": "Isentropic Flow Equations - NASA", "date": "", "ddg_snippet": "May 13, 2021 · The \" t \" subscript used in many of these equations stands for \"total conditions\". (You probably already have some idea of total conditions from experience with Bernoulli's equation ). Eq #3: p / r ^gam = constant = pt / rt ^gam Using the equation of state, we can easily derive the following relations from equation (3): Eq #4:", "subpage_snippet": "", "source": "www.grc.nasa.gov", "link": "https://www.grc.nasa.gov/WWW/K-12/airplane/isentrop.html", "content": "May 13, 2021 · The \" t \" subscript used in many of these equations stands for \"total conditions\". (You probably already have some idea of total conditions from experience with Bernoulli's equation ). Eq #3: p / r ^gam = constant = pt / rt ^gam Using the equation of state, we can easily derive the following relations from equation (3): Eq #4:"}
+{"idx": 3, "title": "Dynamical Equations for Flight Vehicles", "date": "", "ddg_snippet": "Dynamical Equations for Flight Vehicles These notes provide a systematic background of the derivation of the equations of motion for a flight vehicle, and their linearization. The relationship between dimensional stability derivatives and dimensionless aerodynamic coefficients is presented, and the principal contributions to all important stability derivatives for flight vehicles having left ...", "subpage_snippet": "", "source": "courses.cit.cornell.edu", "link": "https://courses.cit.cornell.edu/mae5070/DynamicEquations.pdf", "content": "Dynamical Equations for Flight Vehicles These notes provide a systematic background of the derivation of the equations of motion for a flight vehicle, and their linearization. The relationship between dimensional stability derivatives and dimensionless aerodynamic coefficients is presented, and the principal contributions to all important stability derivatives for flight vehicles having left ..."}
+{"idx": 4, "title": "Aerodynamics Formula Overview - Aerodynamics Formula Overview ...", "date": "", "ddg_snippet": "AERODYNAMICS FORMULAS aerodynamics formula overview curved flow v2 dp dr (13) thermodynamics enthalpy: equation of state pv rt ρrt (14) first law of", "subpage_snippet": "", "source": "www.studocu.com", "link": "https://www.studocu.com/ph/document/patts-college-of-aeronautics/aeronautical-engineering/aerodynamics-formula-overview/46023044", "content": "AERODYNAMICS FORMULAS aerodynamics formula overview curved flow v2 dp dr (13) thermodynamics enthalpy: equation of state pv rt ρrt (14) first law of"}
+{"idx": 5, "title": "Reward — Reinforcement Learning - GitHub Pages", "date": "", "ddg_snippet": "Reward Hypothesis All goals can be described by the maximisation of expected cumulative reward . \\ [max \\, \\mathbb {E} \\left [ \\sum_ {i = 0}^ {\\infty} R _ { t +i+1} \\right]\\] Our goal is to sequentially perform actions which maximize expected cumulative reward . However, Any action may have long-term consequences. Reward may be delayed.", "subpage_snippet": "", "source": "sarthdubey.in", "link": "https://sarthdubey.in/Reinforcement_Learning/Sections/003_01_Reward.html", "content": "Reward Hypothesis All goals can be described by the maximisation of expected cumulative reward . \\ [max \\, \\mathbb {E} \\left [ \\sum_ {i = 0}^ {\\infty} R _ { t +i+1} \\right]\\] Our goal is to sequentially perform actions which maximize expected cumulative reward . However, Any action may have long-term consequences. Reward may be delayed."}
+{"idx": 6, "title": "How to Make a Reward Function in Reinforcement Learning?", "date": "", "ddg_snippet": "Jul 23, 2025 · Crafting a proper reward function is essential to ensure that the agent learns the correct behavior. In this article, we’ll explore how to make a reward function in reinforcement learning. Understanding the Role of the Reward Function In reinforcement learning, an agent’s goal is to maximize the cumulative reward over time, known as the return.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/how-to-make-a-reward-function-in-reinforcement-learning/", "content": "Jul 23, 2025 · Crafting a proper reward function is essential to ensure that the agent learns the correct behavior. In this article, we’ll explore how to make a reward function in reinforcement learning. Understanding the Role of the Reward Function In reinforcement learning, an agent’s goal is to maximize the cumulative reward over time, known as the return."}
+{"idx": 7, "title": "Subsonic/Transonic Configuration Aerodynamics. - DTIC", "date": "", "ddg_snippet": "The Jameson-Caughey finite-volume method is used to solve the governing equations in conservative form. ... paper is limited to computation of inviscid ... 459 pages", "subpage_snippet": "", "source": "apps.dtic.mil", "link": "https://apps.dtic.mil/sti/tr/pdf/ADA094086.pdf", "content": "The Jameson-Caughey finite-volume method is used to solve the governing equations in conservative form. ... paper is limited to computation of inviscid ... 459 pages"}
+{"idx": 8, "title": "Robert D. Blevins - Formulas For Natural Frequency and ...", "date": "", "ddg_snippet": "Robert D. Blevins - Formulas for natural frequency and mode shape-Van Nostrand Reinhold (1979).pdf - Free download as PDF File (.pdf) or view presentation ...", "subpage_snippet": "", "source": "pt.scribd.com", "link": "https://pt.scribd.com/document/440735558/Robert-D-Blevins-Formulas-for-natural-frequency-and-mode-shape-Van-Nostrand-Reinhold-1979-pdf", "content": "Robert D. Blevins - Formulas for natural frequency and mode shape-Van Nostrand Reinhold (1979).pdf - Free download as PDF File (.pdf) or view presentation ..."}
+{"idx": 9, "title": "Aviation Whitepaper Draft_v7_Clean.docx", "date": "", "ddg_snippet": "Hydrogen liquefaction would also add energy to this equation with an additional 2MW needed. (assuming 13 kwh/kg H2). This may require a significant increase in ... 67 pages", "subpage_snippet": "", "source": "archesh2.org", "link": "https://archesh2.org/wp-content/uploads/2025/05/ARCHES-Aviation-Whitepaper-May-2025-1.pdf", "content": "Hydrogen liquefaction would also add energy to this equation with an additional 2MW needed. (assuming 13 kwh/kg H2). This may require a significant increase in ... 67 pages"}
diff --git a/data/sampled_jsons/AERO_sharding_blockchain_section_5.1_experimental_setup_total_nodes_network_year_2024.jsonl b/data/sampled_jsons/AERO_sharding_blockchain_section_5.1_experimental_setup_total_nodes_network_year_2024.jsonl
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+{"idx": 0, "title": "TON ( blockchain ) - Wikipedia", "date": "", "ddg_snippet": "TON, also known as The Open Network , is a decentralized layer-1 blockchain . TON was originally developed by Nikolai Durov who is also known for his role in creating the messaging platform, Telegram.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/TON_(blockchain)", "content": "TON, also known as The Open Network , is a decentralized layer-1 blockchain . TON was originally developed by Nikolai Durov who is also known for his role in creating the messaging platform, Telegram."}
+{"idx": 1, "title": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement...", "date": "", "ddg_snippet": "C Experiment Settings and Overhead Analysis. D Full Shard Transaction Distriction. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "C Experiment Settings and Overhead Analysis. D Full Shard Transaction Distriction. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration."}
+{"idx": 2, "title": "The authoritative guide to Blockchain Sharding , part 1 | NEAR", "date": "", "ddg_snippet": "Sharding is often advertised as a solution that scales infinitely with the number of nodes participating in the network operation. While it is in theory possible to design such a sharding solution, any solution that has the concept of a Beacon chain doesn’t have infinite scalability.", "subpage_snippet": "", "source": "www.near.org", "link": "https://www.near.org/blog/the-authoritative-guide-to-blockchain-sharding-part-1", "content": "Sharding is often advertised as a solution that scales infinitely with the number of nodes participating in the network operation. While it is in theory possible to design such a sharding solution, any solution that has the concept of a Beacon chain doesn’t have infinite scalability."}
+{"idx": 3, "title": "(PDF) EquiFlowShard: A Blockchain Sharding Protocol with...", "date": "", "ddg_snippet": "into three types: network sharding , transaction sharding , and state sharding . Among. these, state sharding allows each node to store only the ledger of its shard , significantly. blockchain . (3) CTR: The proportion of cross- shard transactions to the total transactions.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388469805_EquiFlowShard_A_Blockchain_Sharding_Protocol_with_Optimized_Account_Distribution", "content": "into three types: network sharding , transaction sharding , and state sharding . Among. these, state sharding allows each node to store only the ledger of its shard , significantly. blockchain . (3) CTR: The proportion of cross- shard transactions to the total transactions."}
+{"idx": 4, "title": "Sharding FAQ", "date": "", "ddg_snippet": "Could sharded blockchains do a better job of dealing with network partitions? The schemes described in this document would offer no improvement over non-sharded blockchains ; realistically, every shard would end up with some nodes on both sides of the partition.", "subpage_snippet": "", "source": "vitalik.eth.limo", "link": "https://vitalik.eth.limo/general/2017/12/31/sharding_faq.html", "content": "Could sharded blockchains do a better job of dealing with network partitions? The schemes described in this document would offer no improvement over non-sharded blockchains ; realistically, every shard would end up with some nodes on both sides of the partition."}
+{"idx": 5, "title": "Estuary: A Low Cross- Shard Blockchain Sharding ... | IEEE Xplore", "date": "", "ddg_snippet": "Sharding is one of the most promising technologies for significantly increasing blockchain transaction throughput. However, as the number of shards increases, the ratio of cross- shard transactions in existing blockchain sharding protocols gradually approaches 100%.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10384778", "content": "Sharding is one of the most promising technologies for significantly increasing blockchain transaction throughput. However, as the number of shards increases, the ratio of cross- shard transactions in existing blockchain sharding protocols gradually approaches 100%."}
+{"idx": 6, "title": "Ethereum PoS: Ethereum Blockchain ’s 2.0 Upgrade | Gemini", "date": "", "ddg_snippet": "This decreases the workload of the network ’s validators by enabling each validator to only store and manage one shard of the network instead of the whole blockchain . Through this process the Ethereum blockchain will be separated into 64 shards , of which Ethereum 1.0 will be one .", "subpage_snippet": "", "source": "www.gemini.com", "link": "https://www.gemini.com/en-GB/cryptopedia/ethereum-blockchain-pos-proof-of-stake", "content": "This decreases the workload of the network ’s validators by enabling each validator to only store and manage one shard of the network instead of the whole blockchain . Through this process the Ethereum blockchain will be separated into 64 shards , of which Ethereum 1.0 will be one ."}
+{"idx": 7, "title": "TOP AI Network Presale: What is $TOP, the Decentralized AI Token", "date": "", "ddg_snippet": "Sharding Technology Integration: AI shard operates parallel to existing TOP blockchain infrastructure, enabling high-speed transactions while inheriting proven network security and scalability. Understanding TOP AI Network ($TOP): The Technology Behind It.", "subpage_snippet": "", "source": "web3.bitget.com", "link": "https://web3.bitget.com/en/academy/top-ai-network-presale-guide-what-is-top-token-the-decentralized-ai-infrastructure-with-sharding-and-zero-knowledge-privacy", "content": "Sharding Technology Integration: AI shard operates parallel to existing TOP blockchain infrastructure, enabling high-speed transactions while inheriting proven network security and scalability. Understanding TOP AI Network ($TOP): The Technology Behind It."}
+{"idx": 8, "title": "A Blockchain That Finally Feels Like Home for... - Indie Hackers", "date": "", "ddg_snippet": "It got me wondering: what if the blockchain adjusted to developers, instead of developers adjusting to the blockchain ? That’s the idea behind Haveto: a Layer-1 designed to feel familiar, flexible, and efficient for people who actually create things. What Makes Haveto Different.", "subpage_snippet": "", "source": "www.indiehackers.com", "link": "https://www.indiehackers.com/post/a-blockchain-that-finally-feels-like-home-for-developers-48d75360c9", "content": "It got me wondering: what if the blockchain adjusted to developers, instead of developers adjusting to the blockchain ? That’s the idea behind Haveto: a Layer-1 designed to feel familiar, flexible, and efficient for people who actually create things. What Makes Haveto Different."}
+{"idx": 9, "title": "Что такое Sharding - Как работает шардинг в базах данных - Tproger", "date": "", "ddg_snippet": "Как работает Sharding в базах данных?При помощи команды sh.enableSharding() разрешаем шардинг в этой БД. sh.shardCollection(\"socialApp.users\", { \"user_id\": 1 }) — применяем sharding к коллекции users.", "subpage_snippet": "", "source": "tproger.ru", "link": "https://tproger.ru/articles/kak-rabotaet-sharding-v-bazah-dannyh-", "content": "Как работает Sharding в базах данных?При помощи команды sh.enableSharding() разрешаем шардинг в этой БД. sh.shardCollection(\"socialApp.users\", { \"user_id\": 1 }) — применяем sharding к коллекции users."}
diff --git a/data/sampled_jsons/ATA-_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_Equa.jsonl b/data/sampled_jsons/ATA-_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_Equa.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b9db09b5379fc7ec29f0868b6b3f281a318e28d1
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+++ b/data/sampled_jsons/ATA-_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_Equa.jsonl
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+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Feb 2, 2025 · Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources . However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "Feb 2, 2025 · Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources . However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ..."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "May 1, 2025 · The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "May 1, 2025 · The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods."}
+{"idx": 2, "title": "[PDF] ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ATA:-Adaptive-Task-Allocation-for-Efficient-in-Maranjyan-Saad/871e17b70c5c31985b6ecb1d61960ad5ee7d1cbd", "content": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully ..."}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "#1 ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning [PDF 1] [Copy] [Kimi 1] [REL] Authors: Artavazd Maranjyan, El Mehdi Saad, Peter Richtarik, Francesco Orabona Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources . However ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/1BaC3AdG1i@OpenReview", "content": "#1 ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning [PDF 1] [Copy] [Kimi 1] [REL] Authors: Artavazd Maranjyan, El Mehdi Saad, Peter Richtarik, Francesco Orabona Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources . However ..."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Abstract summary: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning .We propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of computation times.We show that ATA identifies the optimal task allocation and performs comparably to methods with ...", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2502.00775v2_enmode", "content": "Abstract summary: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning .We propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of computation times.We show that ATA identifies the optimal task allocation and performs comparably to methods with ..."}
+{"idx": 5, "title": "Unlocking Efficiency: How Adaptive Task Allocation ...", "date": "", "ddg_snippet": "May 24, 2025 · In the rapidly evolving world of artificial intelligence, distributed machine learning stands as a cornerstone for future developments. Yet, as industries adopt parallelized systems to enhance machine learning operations, the challenge of inefficient resource allocation becomes glaring. Enter Adaptive Task Allocation ( ATA ), a sophisticated solution designed to address this very problem.", "subpage_snippet": "", "source": "www.lumafeed.com", "link": "https://www.lumafeed.com/article/9d4d8fe1-d044-4756-99ca-33ab376e4e05", "content": "May 24, 2025 · In the rapidly evolving world of artificial intelligence, distributed machine learning stands as a cornerstone for future developments. Yet, as industries adopt parallelized systems to enhance machine learning operations, the challenge of inefficient resource allocation becomes glaring. Enter Adaptive Task Allocation ( ATA ), a sophisticated solution designed to address this very problem."}
+{"idx": 6, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "18 Jun 2025 — TL;DR: We propose a method that learns machine speeds on the fly to assign tasks more efficiently in parallel computing. Abstract: Asynchronous ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i&noteId=hkH8Wi9zZm", "content": "18 Jun 2025 — TL;DR: We propose a method that learns machine speeds on the fly to assign tasks more efficiently in parallel computing. Abstract: Asynchronous ..."}
+{"idx": 7, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "2 Feb 2025 — Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "2 Feb 2025 — Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by ..."}
+{"idx": 8, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning to minimize the expected completion time subject to the.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/86bb01c56f97fb6ecf7e661151ca6ae354faf473.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning to minimize the expected completion time subject to the."}
+{"idx": 9, "title": "Efficient Task Allocation in Internet of Things Using Lévy ...", "date": "", "ddg_snippet": "Optimizing the efficient use of resources in terms of energy consumption is critical when considering IoT device resource -constrained environments. This study ...", "subpage_snippet": "", "source": "thesai.org", "link": "https://thesai.org/Downloads/Volume16No5/Paper_55-Efficient_Task_Allocation_in_Internet_of_Things.pdf", "content": "Optimizing the efficient use of resources in terms of energy consumption is critical when considering IoT device resource -constrained environments. This study ..."}
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+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Feb 2, 2025 · Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources . However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "Feb 2, 2025 · Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources . However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ..."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "May 1, 2025 · The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "May 1, 2025 · The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods."}
+{"idx": 2, "title": "[PDF] ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ATA:-Adaptive-Task-Allocation-for-Efficient-in-Maranjyan-Saad/871e17b70c5c31985b6ecb1d61960ad5ee7d1cbd", "content": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully ..."}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "#1 ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning [PDF 1] [Copy] [Kimi 1] [REL] Authors: Artavazd Maranjyan, El Mehdi Saad, Peter Richtarik, Francesco Orabona Asynchronous methods are fundamental for parallelizing computations in distributed machine learning .", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/1BaC3AdG1i@OpenReview", "content": "#1 ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning [PDF 1] [Copy] [Kimi 1] [REL] Authors: Artavazd Maranjyan, El Mehdi Saad, Peter Richtarik, Francesco Orabona Asynchronous methods are fundamental for parallelizing computations in distributed machine learning ."}
+{"idx": 4, "title": "Unlocking Efficiency: How Adaptive Task Allocation ...", "date": "", "ddg_snippet": "May 24, 2025 · In the rapidly evolving world of artificial intelligence, distributed machine learning stands as a cornerstone for future developments. Yet, as industries adopt parallelized systems to enhance machine learning operations, the challenge of inefficient resource allocation becomes glaring. Enter Adaptive Task Allocation ( ATA ), a sophisticated solution designed to address this very problem.", "subpage_snippet": "", "source": "www.lumafeed.com", "link": "https://www.lumafeed.com/article/9d4d8fe1-d044-4756-99ca-33ab376e4e05", "content": "May 24, 2025 · In the rapidly evolving world of artificial intelligence, distributed machine learning stands as a cornerstone for future developments. Yet, as industries adopt parallelized systems to enhance machine learning operations, the challenge of inefficient resource allocation becomes glaring. Enter Adaptive Task Allocation ( ATA ), a sophisticated solution designed to address this very problem."}
+{"idx": 5, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Abstract summary: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning .We propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of computation times.We show that ATA identifies the optimal task allocation and performs comparably to methods with ...", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2502.00775v2_enmode", "content": "Abstract summary: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning .We propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of computation times.We show that ATA identifies the optimal task allocation and performs comparably to methods with ..."}
+{"idx": 6, "title": "ICML Poster ATA : Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to..."}
+{"idx": 7, "title": "ATA : Adaptive Task Allocation", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning .", "subpage_snippet": "", "source": "artomaranjyan.github.io", "link": "https://artomaranjyan.github.io/assets/pdf/posters/ATA_SNSL.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ."}
+{"idx": 8, "title": "Postdoc, KAUST - Cited by 12 - Online learning - Bandits theory", "date": "", "ddg_snippet": "Ata : Adaptive task allocation for efficient resource management in distributed machine learning .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Jih_bwsAAAAJ&hl=en", "content": "Ata : Adaptive task allocation for efficient resource management in distributed machine learning ."}
+{"idx": 9, "title": "( PDF ) Adaptive Task Allocation For Iot-Driven Robotics Using...", "date": "", "ddg_snippet": "Improve resource utilization and efficiency . Methodology: This combines NP-complexity models with real-time data from IoT devices and cloud computing for dynamic task allocation . The approach involves machine learning in the processing of data and scheduling tasks .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/129858531/Adaptive_Task_Allocation_For_Iot_Driven_Robotics_Using_NP_Complexity_Models_And_Cloud_Manufacturing", "content": "Improve resource utilization and efficiency . Methodology: This combines NP-complexity models with real-time data from IoT devices and cloud computing for dynamic task allocation . The approach involves machine learning in the processing of data and scheduling tasks ."}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Equation_7_conf(i,k)_formula.jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Equation_7_conf(i,k)_formula.jsonl
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index 0000000000000000000000000000000000000000..3e42b708efd35e2afe99d7f02d8c8ed17ec2b6e3
--- /dev/null
+++ b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Equation_7_conf(i,k)_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00775] ATA : Adaptive Task Allocation for Efficient Resource...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 1, "title": "ICML Poster ATA : Adaptive Task Allocation for Efficient Resource...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 2, "title": "(PDF) A Spatio-Temporal Task Allocation Model in Mobile...", "date": "", "ddg_snippet": "accuracy of task allocation , examining how many tasks of interest to the workers are. included in the top K allocation list. Therefore, ACC@ K can help us understand the. performance of the model under different lengths of allocation task lists.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373078649_A_Spatio-Temporal_Task_Allocation_Model_in_Mobile_Crowdsensing_Based_on_Knowledge_Graph", "content": "accuracy of task allocation , examining how many tasks of interest to the workers are. included in the top K allocation list. Therefore, ACC@ K can help us understand the. performance of the model under different lengths of allocation task lists."}
+{"idx": 3, "title": "ATA : Adaptive Task Allocation", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona.", "subpage_snippet": "", "source": "artomaranjyan.github.io", "link": "https://artomaranjyan.github.io/assets/pdf/posters/ATA_ICML.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona."}
+{"idx": 4, "title": "A quantitative model for adaptive task allocation in human-computer...", "date": "", "ddg_snippet": "An explicit task allocation for both servers is calculated, based on the predicted human and system performances. Tasks are then allocated to both servers and their performances measured while they service their tasks .", "subpage_snippet": "", "source": "elibrary.ru", "link": "https://elibrary.ru/item.asp?id=6664206", "content": "An explicit task allocation for both servers is calculated, based on the predicted human and system performances. Tasks are then allocated to both servers and their performances measured while they service their tasks ."}
+{"idx": 5, "title": "Articles by Artavazd Maranjyan | Synthical", "date": "", "ddg_snippet": "Optimization and Control, Distributed, Parallel, and Cluster Computing. ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/a7a2366f-21e0-41d8-ac86-8a155fcddc77/articles", "content": "Optimization and Control, Distributed, Parallel, and Cluster Computing. ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning."}
+{"idx": 6, "title": "Peter Richtarik", "date": "", "ddg_snippet": "[271] Egor Shulgin and Peter Richtárik On the convergence of DP-SGD with adaptive clipping [arXiv] [method: QC-SGD, DP-QC-SGD].", "subpage_snippet": "", "source": "richtarik.org", "link": "https://richtarik.org/i_papers.html", "content": "[271] Egor Shulgin and Peter Richtárik On the convergence of DP-SGD with adaptive clipping [arXiv] [method: QC-SGD, DP-QC-SGD]."}
+{"idx": 7, "title": "Arto Maranjyan - Google Scholar", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=93WEFj8AAAAJ&hl=en", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning."}
+{"idx": 8, "title": "MindFlayer SGD: Efficient Parallel SGD in the Presence of", "date": "", "ddg_snippet": "ATA : Adaptive task allocation for efficient resource management in distributed machine learning. In International Conference on Machine Learn-ing, 2025a.For Rennala SGD, the time complexity is modeled as an approximation of the random variable. K i =1.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=RNpvu3MSvm", "content": "ATA : Adaptive task allocation for efficient resource management in distributed machine learning. In International Conference on Machine Learn-ing, 2025a.For Rennala SGD, the time complexity is modeled as an approximation of the random variable. K i =1."}
+{"idx": 9, "title": "Поиск авиабилетов авиакомпании «Аэрофлот»", "date": "", "ddg_snippet": "Стоимость перелетов, онлайн бронирование билетов на самолет от компании «Аэрофлот»...", "subpage_snippet": "", "source": "www.aeroflot.ru", "link": "https://www.aeroflot.ru/sb/app/ru-ru", "content": "Стоимость перелетов, онлайн бронирование билетов на самолет от компании «Аэрофлот»..."}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Theorem_6.1_regret_bound_O(sqrt(T_log_T)).jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Theorem_6.1_regret_bound_O(sqrt(T_log_T)).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..098956493cc77902976488ce05e5000e4b32c5c1
--- /dev/null
+++ b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Theorem_6.1_regret_bound_O(sqrt(T_log_T)).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "We will give its full specifics in the regret upper bound of Theorem 6.1 . ... The following theorem provides an upper bound on the regret of ATA -Empirical.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "We will give its full specifics in the regret upper bound of Theorem 6.1 . ... The following theorem provides an upper bound on the regret of ATA -Empirical."}
+{"idx": 1, "title": "Finite-Time Logarithmic Bayes Regret Upper Bounds", "date": "", "ddg_snippet": "by A Atsidakou · 2023 · Cited by 2 — We derive the first finite-time logarithmic Bayes regret upper bounds for Bayesian bandits. In a multi-armed bandit, we obtain O (c∆ log n) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.09136", "content": "by A Atsidakou · 2023 · Cited by 2 — We derive the first finite-time logarithmic Bayes regret upper bounds for Bayesian bandits. In a multi-armed bandit, we obtain O (c∆ log n) ..."}
+{"idx": 2, "title": "Data-driven Population Tracking in Service Systems", "date": "", "ddg_snippet": "Return to Proof of Theorem 6 . For the proof of Theorem 6 , we consider disjoint subsets of the time horizon, which we call episodes. We define episodes ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/4748063.pdf?abstractid=4748063&mirid=1", "content": "Return to Proof of Theorem 6 . For the proof of Theorem 6 , we consider disjoint subsets of the time horizon, which we call episodes. We define episodes ..."}
+{"idx": 3, "title": "Asymptotically Efficient Distributed Experimentation", "date": "", "ddg_snippet": "The seminal work of Lai and Robbins [1985] showed that any optimal policy must sample each arm at least Θ ( log T ) times to achieve the optimal regret in finite.", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5266599.pdf?abstractid=5266599&mirid=1", "content": "The seminal work of Lai and Robbins [1985] showed that any optimal policy must sample each arm at least Θ ( log T ) times to achieve the optimal regret in finite."}
+{"idx": 4, "title": "Second Order Methods for Bandit Optimization and Control", "date": "", "ddg_snippet": "by A Suggala · 2024 · Cited by 6 — We show that our algorithm achieves optimal (in terms of horizon) regret bounds for a large class of convex functions that satisfy a condition we call κ- ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/suggala24a/suggala24a.pdf", "content": "by A Suggala · 2024 · Cited by 6 — We show that our algorithm achieves optimal (in terms of horizon) regret bounds for a large class of convex functions that satisfy a condition we call κ- ..."}
+{"idx": 5, "title": "An Online Learning Approach to Dynamic Pricing and ...", "date": "", "ddg_snippet": "by X Chen · Cited by 48 — In Section. 5, we give the detailed description of GOLiQ and establish a logarithmic regret bound by appropriately selecting our algorithm ...", "subpage_snippet": "", "source": "yunanliu.wordpress.ncsu.edu", "link": "https://yunanliu.wordpress.ncsu.edu/files/2023/05/Main_paper_final.pdf", "content": "by X Chen · Cited by 48 — In Section. 5, we give the detailed description of GOLiQ and establish a logarithmic regret bound by appropriately selecting our algorithm ..."}
+{"idx": 6, "title": "Towards Efficient and Reproducible Natural Language ...", "date": "", "ddg_snippet": "by J Dodge · 2020 · Cited by 2 — sampling from a k-DPP has better regret bounds than a number of other approaches. ... since the entropy of a Gaussian is proportional to the log ...", "subpage_snippet": "", "source": "www.lti.cs.cmu.edu", "link": "https://www.lti.cs.cmu.edu/people/alumni/alumni-thesis/dodge-jesse-thesis.pdf", "content": "by J Dodge · 2020 · Cited by 2 — sampling from a k-DPP has better regret bounds than a number of other approaches. ... since the entropy of a Gaussian is proportional to the log ..."}
+{"idx": 7, "title": "An Online Learning Approach to Dynamic Pricing and ...", "date": "", "ddg_snippet": "12 Jun 2023 — Toward this, we develop a new way to treat and bound the transient queueing performance in the regret analysis of our online learning algorithm ...", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/10.1287/opre.2020.0612", "content": "12 Jun 2023 — Toward this, we develop a new way to treat and bound the transient queueing performance in the regret analysis of our online learning algorithm ..."}
+{"idx": 8, "title": "learning, games and optimality: algorithms for decision- ...", "date": "", "ddg_snippet": "by ER Arunachaleswaran · 2025 — A central question we address is what kind of algorithm should be employed to play a repeated game against an unknown but rational opponent with consistent ...", "subpage_snippet": "", "source": "repository.upenn.edu", "link": "https://repository.upenn.edu/bitstreams/e4cfc8ad-1584-448a-8428-68b3d24a99fa/download", "content": "by ER Arunachaleswaran · 2025 — A central question we address is what kind of algorithm should be employed to play a repeated game against an unknown but rational opponent with consistent ..."}
+{"idx": 9, "title": "Tractable and Near-Optimal Adversarial Algorithms for ...", "date": "", "ddg_snippet": "by Z Wang · 2023 · Cited by 2 — Abstract. Consider the problem of simultaneous estimation of location and variance matrix under. Huber's contaminated Gaussian model. 112 pages", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume24/22-1053/22-1053.pdf", "content": "by Z Wang · 2023 · Cited by 2 — Abstract. Consider the problem of simultaneous estimation of location and variance matrix under. Huber's contaminated Gaussian model. 112 pages"}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Theorem_6.1_regret_bound_year_2023.jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Theorem_6.1_regret_bound_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c46037ed3ae14e540f1931a27bc00b3a47a2fb83
--- /dev/null
+++ b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Theorem_6.1_regret_bound_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 1, "title": "ICML Poster ATA : Adaptive Task Allocation for Efficient Resource...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 2, "title": "Reviews: Achieving budget-optimality with adaptive schemes in...", "date": "", "ddg_snippet": "This paper provides bounds on the gain of adaptive task allocation in crowdsourcing against non- adaptive allocation schemes.Finally, the authors propose an adaptive task assignment scheme that matches the bounds introduced.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2016/file/03e7ef47cee6fa4ae7567394b99912b7-Reviews.html", "content": "This paper provides bounds on the gain of adaptive task allocation in crowdsourcing against non- adaptive allocation schemes.Finally, the authors propose an adaptive task assignment scheme that matches the bounds introduced."}
+{"idx": 3, "title": "Adaptive Monte Carlo Integration", "date": "", "ddg_snippet": "5.2 Regret curves for the different stochastic MAB algorithms for adaptive -AMCS on the kidnapped robot task .", "subpage_snippet": "", "source": "era.library.ualberta.ca", "link": "https://era.library.ualberta.ca/items/b77abfb0-e07d-4131-bee2-153dcbaea18f/download/b18cccb8-de27-4768-9d6c-123a013df91e", "content": "5.2 Regret curves for the different stochastic MAB algorithms for adaptive -AMCS on the kidnapped robot task ."}
+{"idx": 4, "title": "Learning the task allocation game", "date": "", "ddg_snippet": "The distributed task allocation problem occurs in domains like web services, the grid, and other distributed systems. In this problem, the system consists of servers and mediators. Servers execute tasks and may differ in their capabilities, e.g. one.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/14400115/Learning_the_task_allocation_game", "content": "The distributed task allocation problem occurs in domains like web services, the grid, and other distributed systems. In this problem, the system consists of servers and mediators. Servers execute tasks and may differ in their capabilities, e.g. one."}
+{"idx": 5, "title": "(Open Access) Towards Multi Robot Task Allocation and Navigation...", "date": "", "ddg_snippet": "read more. Abstract: Developing algorithms for multi robot systems to reach target positions and navigate safely in the environment is an open field of research. Most systems treat Multi Robot Task Allocation (MRTA) and Multi Robot Path Planning (MRPP)...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/towards-multi-robot-task-allocation-and-navigation-using-4basum9x2q", "content": "read more. Abstract: Developing algorithms for multi robot systems to reach target positions and navigate safely in the environment is an open field of research. Most systems treat Multi Robot Task Allocation (MRTA) and Multi Robot Path Planning (MRPP)..."}
+{"idx": 6, "title": "ATA : Adaptive Task Allocation", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona.", "subpage_snippet": "", "source": "artomaranjyan.github.io", "link": "https://artomaranjyan.github.io/assets/pdf/posters/ATA_SNSL.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona."}
+{"idx": 7, "title": "Postdoc, KAUST - Cited by 12 - Online learning - Bandits theory", "date": "", "ddg_snippet": "Ata : Adaptive task allocation for efficient resource management in distributed machine learning.New Lower Bounds for Stochastic Non-Convex Optimization through Divergence Composition.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Jih_bwsAAAAJ&hl=en", "content": "Ata : Adaptive task allocation for efficient resource management in distributed machine learning.New Lower Bounds for Stochastic Non-Convex Optimization through Divergence Composition."}
+{"idx": 8, "title": "Articles by Peter Richtárik | Synthical", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.On the Convergence of DP-SGD with Adaptive Clipping. 27 December 2024 by Egor Shulgin and Peter Richtárik.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/search/by_author/Peter+Richtárik", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.On the Convergence of DP-SGD with Adaptive Clipping. 27 December 2024 by Egor Shulgin and Peter Richtárik."}
+{"idx": 9, "title": "Local Differential Privacy for Bayesian Optimization", "date": "", "ddg_snippet": "the regret lower bounds in Theorem 1 have an additional. factor of 1/ in front of the lower bounds for non-private case in (Chowdhury and Gopalan 2019)3.Although the LDP- ATA -GP-UCB algorithm achieves al-most optimal regret bound , it might be a ‘overkill’ for the LDP setting.", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/17330/17330-13-20824-1-2-20210518.pdf", "content": "the regret lower bounds in Theorem 1 have an additional. factor of 1/ in front of the lower bounds for non-private case in (Chowdhury and Gopalan 2019)3.Although the LDP- ATA -GP-UCB algorithm achieves al-most optimal regret bound , it might be a ‘overkill’ for the LDP setting."}
diff --git a/data/sampled_jsons/ATA_paper_GTA_performs_poorly_total_worker_time_page_8_year_2025.jsonl b/data/sampled_jsons/ATA_paper_GTA_performs_poorly_total_worker_time_page_8_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ddb77c6c0c20392d0f79d6a1b53f6965425c99a1
--- /dev/null
+++ b/data/sampled_jsons/ATA_paper_GTA_performs_poorly_total_worker_time_page_8_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "The total worker time ratio increases because GTA becomes less eficient, using more resources than necessary. The runtime ratio grows for ATA and ATA -Empirical since a larger number of workers requires more exploration.", "subpage_snippet": "", "source": "repository.kaust.edu.sa", "link": "https://repository.kaust.edu.sa/bitstreams/884560fb-34b9-42e5-a5ee-88334b809ebb/download", "content": "The total worker time ratio increases because GTA becomes less eficient, using more resources than necessary. The runtime ratio grows for ATA and ATA -Empirical since a larger number of workers requires more exploration."}
+{"idx": 1, "title": "Computation Time 2 . 5 To t a l w o r k e r t i m e 1 e 1 0", "date": "", "ddg_snippet": "ATA ATA : Empirical FTA: Optimal GTA UTA 1.0 1.5 2.0 Total worker time 2.5 1e10", "subpage_snippet": "", "source": "artomaranjyan.github.io", "link": "https://artomaranjyan.github.io/assets/pdf/posters/ATA_SNSL.pdf", "content": "ATA ATA : Empirical FTA: Optimal GTA UTA 1.0 1.5 2.0 Total worker time 2.5 1e10"}
+{"idx": 2, "title": "webTA 3.8 - Timekeeper Procedure Manual", "date": "", "ddg_snippet": "Jun 9, 2017 · The Work Time section of the T&A Data page has daily time entry fields for each week in a pay period. Daily work time totals are displayed in the Work Time Total row.", "subpage_snippet": "", "source": "www.dcms.uscg.mil", "link": "https://www.dcms.uscg.mil/Portals/10/CG-1/cg121/docs/webTA/webTA_Timekeeper_Guide.pdf?ver=2017-06-09-070800-483", "content": "Jun 9, 2017 · The Work Time section of the T&A Data page has daily time entry fields for each week in a pay period. Daily work time totals are displayed in the Work Time Total row."}
+{"idx": 3, "title": "total_worker_time in sys.dm_exec_query_stats - SQLServerCentral", "date": "", "ddg_snippet": "Sep 25, 2008 · Is total worker time dependent on the # of CPU's? Like if I have 8 CPU's, and I assume that everything is evenly spread across processors, would i divide total _ worker _ time /4 to get the aprroximate ...", "subpage_snippet": "", "source": "www.sqlservercentral.com", "link": "https://www.sqlservercentral.com/forums/topic/total_worker_time-in-sys-dm_exec_query_stats", "content": "Sep 25, 2008 · Is total worker time dependent on the # of CPU's? Like if I have 8 CPU's, and I assume that everything is evenly spread across processors, would i divide total _ worker _ time /4 to get the aprroximate ..."}
+{"idx": 4, "title": "high 'total_worker_time' for stored proc using OPENQUERY in ...", "date": "", "ddg_snippet": "0 total_worker_time in sys.dm_exec_query_stats is cumulative - it is the total execution time for all the executions of the currently compiled version of the query - see execution_count for the number of executions this represents. See last_ worker _ time , min_ worker _ time or max_ worker _ time for timing of individual executions.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/8979195/high-total-worker-time-for-stored-proc-using-openquery-in-sql-server-2005", "content": "0 total_worker_time in sys.dm_exec_query_stats is cumulative - it is the total execution time for all the executions of the currently compiled version of the query - see execution_count for the number of executions this represents. See last_ worker _ time , min_ worker _ time or max_ worker _ time for timing of individual executions."}
+{"idx": 5, "title": "ATA and RTA Calculation Every Task - ResearchGate", "date": "", "ddg_snippet": "Download scientific diagram | ATA and RTA Calculation Every Task from publication: Assessment Of Worker Posture In Herbicides Production And Break Time Determination Using OCRA Index Method ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/ATA-and-RTA-Calculation-Every-Task_tbl1_337314882", "content": "Download scientific diagram | ATA and RTA Calculation Every Task from publication: Assessment Of Worker Posture In Herbicides Production And Break Time Determination Using OCRA Index Method ..."}
+{"idx": 6, "title": "cherryATA", "date": "", "ddg_snippet": "As expected, GTA is the fastest in terms of runtime (first column), but it performs poorly in terms of total worker time. (second column). This is because it ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "As expected, GTA is the fastest in terms of runtime (first column), but it performs poorly in terms of total worker time. (second column). This is because it ..."}
+{"idx": 7, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "19 Jun 2025 — The paper claims that ATA dynamically learns worker speeds and allocates tasks so as to minimize total computation time without prior knowledge, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i&noteId=On98HLkOqs", "content": "19 Jun 2025 — The paper claims that ATA dynamically learns worker speeds and allocates tasks so as to minimize total computation time without prior knowledge, ..."}
+{"idx": 8, "title": "Real-Time Reverse Transcription–Polymerase Chain ...", "date": "", "ddg_snippet": "by SL Emery · 2004 · Cited by 431 — A real- time reverse transcription–polymerase chain reaction (RT-PCR) assay was developed to rapidly detect the severe acute respiratory syndrome–associated ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC3322901/", "content": "by SL Emery · 2004 · Cited by 431 — A real- time reverse transcription–polymerase chain reaction (RT-PCR) assay was developed to rapidly detect the severe acute respiratory syndrome–associated ..."}
+{"idx": 9, "title": "ATA / GTA Case Studies - Brunel University London", "date": "", "ddg_snippet": "Case Study 1 Two second year students are trying to work out their grade-point average for their first year studies to put on their placement application. They don’t know how to calculate their GPA and are arguing about it. One of them is suggesting to use excel, but the other doesn’t know how to use it.", "subpage_snippet": "", "source": "www.brunel.ac.uk", "link": "https://www.brunel.ac.uk/study/BEEC/documents/Case-Studies.pdf", "content": "Case Study 1 Two second year students are trying to work out their grade-point average for their first year studies to put on their placement application. They don’t know how to calculate their GPA and are arguing about it. One of them is suggesting to use excel, but the other doesn’t know how to use it."}
diff --git a/data/sampled_jsons/ATA_paper_Table_1_Total_Worker_Time_Ratio_n=153_year_2023.jsonl b/data/sampled_jsons/ATA_paper_Table_1_Total_Worker_Time_Ratio_n=153_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b02e333b6ab17f3cad9c62c9d79b7a15119595ac
--- /dev/null
+++ b/data/sampled_jsons/ATA_paper_Table_1_Total_Worker_Time_Ratio_n=153_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Data center - Wikipedia", "date": "", "ddg_snippet": "During the microcomputer industry boom of the 1980s, users started to deploy computers everywhere, in many cases with little or no care about ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Data_center", "content": "During the microcomputer industry boom of the 1980s, users started to deploy computers everywhere, in many cases with little or no care about ..."}
+{"idx": 1, "title": "Taxation in the State of Palestine - Wikipedia", "date": "", "ddg_snippet": "... notably the Protocol on Economic Relations also called the Paris Protocol, which was signed in 1994 by the Palestine Liberation Organization (PLO) and ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Taxation_in_the_State_of_Palestine", "content": "... notably the Protocol on Economic Relations also called the Paris Protocol, which was signed in 1994 by the Palestine Liberation Organization (PLO) and ..."}
+{"idx": 2, "title": "Water Resources Data", "date": "", "ddg_snippet": "TABLE A1. 1 Actual Renewable Water Resources per Capita, by Region Region Australia and New Zealand Latin America and the Caribbean North America ...", "subpage_snippet": "", "source": "paperzz.com", "link": "https://paperzz.com/doc/9019557/water-resources-data", "content": "TABLE A1. 1 Actual Renewable Water Resources per Capita, by Region Region Australia and New Zealand Latin America and the Caribbean North America ..."}
+{"idx": 3, "title": "(PDF) Brain scaling in ants: body to brain size ratio", "date": "", "ddg_snippet": "Within the leaf-cutting ants of the neotropics, the genus Atta exhibits a large degree of physical polymorphism, with workers ranging in size from ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/267892224_Brain_scaling_in_ants_body_to_brain_size_ratio", "content": "Within the leaf-cutting ants of the neotropics, the genus Atta exhibits a large degree of physical polymorphism, with workers ranging in size from ..."}
+{"idx": 4, "title": "Taxi1500: A Multilingual Dataset for Text Classification in", "date": "", "ddg_snippet": "One reason for this is that evaluation datasets do not yet cover a wide range of languages, including low-resource and endangered ones .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.08487v2", "content": "One reason for this is that evaluation datasets do not yet cover a wide range of languages, including low-resource and endangered ones ."}
+{"idx": 5, "title": "How unemployment insurance savings accounts affect employment", "date": "", "ddg_snippet": "The paper finds that workers affiliated to the scheme show an increased hazard ratio of leaving employment, or accelerated time to employment ...", "subpage_snippet": "", "source": "izajold.springeropen.com", "link": "https://izajold.springeropen.com/articles/10.1186/2193-9020-2-9", "content": "The paper finds that workers affiliated to the scheme show an increased hazard ratio of leaving employment, or accelerated time to employment ..."}
+{"idx": 6, "title": "⇉Sorghum: Production, Processing, Marketing and Utilization", "date": "", "ddg_snippet": "The authors are very much indebted to the management of NAERLS, ABU Zaria providing support and the Agricultural Transformation Agenda ( ATA ) for ...", "subpage_snippet": "", "source": "graduateway.com", "link": "https://graduateway.com/sorghum-production-processing-marketing-and-utilization/", "content": "The authors are very much indebted to the management of NAERLS, ABU Zaria providing support and the Agricultural Transformation Agenda ( ATA ) for ..."}
+{"idx": 7, "title": "(PDF) Collaborating with People Like Me: Ethnic Coauthorship", "date": "", "ddg_snippet": "By examining the ethnic identity of authors in over 2.5 million scientific papers written by US-based authors from 1985 to 2008, we find that persons ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/280445835_Collaborating_with_People_Like_Me_Ethnic_Coauthorship_within_the_United_States", "content": "By examining the ethnic identity of authors in over 2.5 million scientific papers written by US-based authors from 1985 to 2008, we find that persons ..."}
+{"idx": 8, "title": "Changes in the Prevalence and Correlates of Weight-Control", "date": "", "ddg_snippet": "Parameters derived from multivariate linear regression models in Table 3 with exposure × cohort interaction and sex-stratified analyses ...", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamapediatrics/fullarticle/2773003", "content": "Parameters derived from multivariate linear regression models in Table 3 with exposure × cohort interaction and sex-stratified analyses ..."}
+{"idx": 9, "title": "Form S-1", "date": "", "ddg_snippet": "... the information in this prospectus assumes ( 1 ) completion of the reorganization transaction (as defined below) and (2) that the underwriters will not ...", "subpage_snippet": "", "source": "www.sec.gov", "link": "https://www.sec.gov/Archives/edgar/data/1495320/000119312510152576/ds1.htm", "content": "... the information in this prospectus assumes ( 1 ) completion of the reorganization transaction (as defined below) and (2) that the underwriters will not ..."}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_Section_3.2_four-step_self-supervised_learning_process_behaviors_emo_year_2023.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_Section_3.2_four-step_self-supervised_learning_process_behaviors_emo_year_2023.jsonl
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index 0000000000000000000000000000000000000000..4db64ad86115c84b15cc7b87cd9eff00d16412cd
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+++ b/data/sampled_jsons/A_Checks-and-Balances_Framework_Section_3.2_four-step_self-supervised_learning_process_behaviors_emo_year_2023.jsonl
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+{"idx": 0, "title": "RoSe: Robust Self-supervised Stereo Matching under Adverse", "date": "", "ddg_snippet": "This approach consists of two key steps : (1) self - supervised scene correspondence learning and ( 2 ) adverse weather distillation, both aimed at ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.19165v1", "content": "This approach consists of two key steps : (1) self - supervised scene correspondence learning and ( 2 ) adverse weather distillation, both aimed at ..."}
+{"idx": 1, "title": "ConCISE: Confidence-guided Compression in Step-by-step", "date": "", "ddg_snippet": "... step E fficient Reasoning), a framework designed to generate concise reasoning chains, integrating Confidence Injection to boost reasoning confidence, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04881v2", "content": "... step E fficient Reasoning), a framework designed to generate concise reasoning chains, integrating Confidence Injection to boost reasoning confidence, ..."}
+{"idx": 2, "title": "AgentFly: Extensible and Scalable Reinforcement Learning for LM", "date": "", "ddg_snippet": "In addition to the general framework design and implementation, we empirically validate AgentFly, by integrating four widely used RL algorithms and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14897v1", "content": "In addition to the general framework design and implementation, we empirically validate AgentFly, by integrating four widely used RL algorithms and ..."}
+{"idx": 3, "title": "Cache-of-Thought: Master-Apprentice Framework for", "date": "", "ddg_snippet": "... a general master-apprentice framework for multi-VLM inference with a cache, ( 2 ) an effective multi-modal retrieval and in-context learning approach ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20587v2", "content": "... a general master-apprentice framework for multi-VLM inference with a cache, ( 2 ) an effective multi-modal retrieval and in-context learning approach ..."}
+{"idx": 4, "title": "Deep Learning With TensorFlow: From Setup To Data Preparation |", "date": "", "ddg_snippet": "... If you want to deep dive into Machine Learning and learn how to use these tools even further, then check out my complete Machine Learning and Data ...", "subpage_snippet": "", "source": "zerotomastery.io", "link": "https://zerotomastery.io/blog/deep-learning-with-tensorflow/", "content": "... If you want to deep dive into Machine Learning and learn how to use these tools even further, then check out my complete Machine Learning and Data ..."}
+{"idx": 5, "title": "How to Improve Self Esteem: Proven Strategies That Work | Blog", "date": "", "ddg_snippet": "One study showed a correlation coefficient of 0.446 between internal attributions for success and failure and self -esteem.", "subpage_snippet": "", "source": "therapytalk.io", "link": "https://therapytalk.io/blogs/how-to-improve-self-esteem-proven-strategies-that-work", "content": "One study showed a correlation coefficient of 0.446 between internal attributions for success and failure and self -esteem."}
+{"idx": 6, "title": "Mobile-Agent-v3: Foundamental Agents for GUI Automation", "date": "", "ddg_snippet": "... a general-purpose GUI agent framework , Mobile-Agent-v3, which further enhances GUI-Owl’s performance (73. 3 on AndroidWorld and 37.7 on OSWorld), ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15144v1", "content": "... a general-purpose GUI agent framework , Mobile-Agent-v3, which further enhances GUI-Owl’s performance (73. 3 on AndroidWorld and 37.7 on OSWorld), ..."}
+{"idx": 7, "title": "Using Spot VMs or reservations to deploy a Vertex AI Llama-3.1", "date": "", "ddg_snippet": "Tutorial: Deploy Llamma 3 models with SpotVM and Reservations ... Tutorial: Optimize model performance with advanced features in Model Garden", "subpage_snippet": "", "source": "cloud.google.com", "link": "https://cloud.google.com/vertex-ai/generative-ai/docs/open-models/spotvm-reservations/use-spotvm-reservations", "content": "Tutorial: Deploy Llamma 3 models with SpotVM and Reservations ... Tutorial: Optimize model performance with advanced features in Model Garden"}
+{"idx": 8, "title": "Frontiers | ULBERT: a domain-adapted BERT model for bilingual", "date": "", "ddg_snippet": "... information but are limited to certain legal domains, e.g., CourtListener, 1 FindLaw, 2 Justia s, 3 Legal Information Institutes, 4 Fastcase, 5 and ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1448785/full", "content": "... information but are limited to certain legal domains, e.g., CourtListener, 1 FindLaw, 2 Justia s, 3 Legal Information Institutes, 4 Fastcase, 5 and ..."}
+{"idx": 9, "title": "CVPR 2023 Papers", "date": "", "ddg_snippet": "... Across Modalities, Tasks and Stages for ... A New Path: Scaling Vision- and -Language Navigation With Synthetic Instructions and Imitation Learning", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/papers.html", "content": "... Across Modalities, Tasks and Stages for ... A New Path: Scaling Vision- and -Language Navigation With Synthetic Instructions and Imitation Learning"}
diff --git a/data/sampled_jsons/A_Geometric_Approach_to_Personalized_Recommendation_Table_1_dataset_statistics_ML-1M_Amazon_Beauty_A.jsonl b/data/sampled_jsons/A_Geometric_Approach_to_Personalized_Recommendation_Table_1_dataset_statistics_ML-1M_Amazon_Beauty_A.jsonl
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index 0000000000000000000000000000000000000000..0664a105fa1b74060aebecb0efb347362b1381bf
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+{"idx": 0, "title": "A Geometric Approach to Personalized Recommendation with Set-Theoretic ...", "date": "", "ddg_snippet": "In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To capture this set-theoretic dependence we represent each user and attribute by a hyper-rectangle or box (i.e. a Cartesian product of intervals).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10875", "content": "In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To capture this set-theoretic dependence we represent each user and attribute by a hyper-rectangle or box (i.e. a Cartesian product of intervals)."}
+{"idx": 1, "title": "A Geometric Approach to Personalized Recommendation With Set T C U B ...", "date": "", "ddg_snippet": "1 INTRODUCTION 033 032 Recommendation systems are a standard component of most online platforms, providing personalized 034 suggestions for products, movies, articles, and more. In addition to generic recommendation , these 035 platforms often present the option for the user to search for items, either via natural language or 036 structured queries. While collaborative filtering methods like ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0HWAbWgI3T", "content": "1 INTRODUCTION 033 032 Recommendation systems are a standard component of most online platforms, providing personalized 034 suggestions for products, movies, articles, and more. In addition to generic recommendation , these 035 platforms often present the option for the user to search for items, either via natural language or 036 structured queries. While collaborative filtering methods like ..."}
+{"idx": 2, "title": "dblp: A Geometric Approach to Personalized Recommendation with Set ...", "date": "", "ddg_snippet": "Bibliographic details on A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-10875", "content": "Bibliographic details on A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings."}
+{"idx": 3, "title": "[PDF] A Geometric Approach to Personalized Recommendation with Set ...", "date": "", "ddg_snippet": "This work formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent, and empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30 \\\\% overall. Personalized item recommendation typically suffers from data sparsity, which is most ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Geometric-Approach-to-Personalized-Recommendation-Dasgupta-Boratko/07df63a8f1aab135768c58da100c740a3fa574f1", "content": "This work formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent, and empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30 \\\\% overall. Personalized item recommendation typically suffers from data sparsity, which is most ..."}
+{"idx": 4, "title": "Geometric Approach to Personalized Recommendation with Set-Theoretic ...", "date": "", "ddg_snippet": "1 . Introduction Recommendation systems are a standard component of most online platforms, providing personalized suggestions for products, movies, articles, and more. In addition to generic recommendation , these platforms often present the option 1Manning College of Information & Computer Sciences, UMass Amherst.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10875", "content": "1 . Introduction Recommendation systems are a standard component of most online platforms, providing personalized suggestions for products, movies, articles, and more. In addition to generic recommendation , these platforms often present the option 1Manning College of Information & Computer Sciences, UMass Amherst."}
+{"idx": 5, "title": "A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To capture this set-theoretic dependence we represent each user and attribute by a hyperrectangle or box (i.e. a Cartesian product of intervals).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0HWAbWgI3T", "content": "In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To capture this set-theoretic dependence we represent each user and attribute by a hyperrectangle or box (i.e. a Cartesian product of intervals)."}
+{"idx": 6, "title": "A Geometric Approach to Personalized Recommendation with Set-Theoretic ...", "date": "", "ddg_snippet": "This paper aims to advance the field of Machine Learn-ing by introducing a geometric approach to personalized recommendation under set-theoretic constraints. Our pri-mary contribution is methodological, focusing on improving representation learning for structured preference modeling.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=27tMzmzDjO", "content": "This paper aims to advance the field of Machine Learn-ing by introducing a geometric approach to personalized recommendation under set-theoretic constraints. Our pri-mary contribution is methodological, focusing on improving representation learning for structured preference modeling."}
+{"idx": 7, "title": "The effect is less for the non-compounding error. - ResearchGate", "date": "", "ddg_snippet": "Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/The-effect-is-less-for-the-non-compounding-error_fig4_389091382", "content": "Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization."}
+{"idx": 8, "title": "arXiv:2401.08217v2 [cs.IR] 29 Mar 2024", "date": "", "ddg_snippet": "- 1M dataset compared to Amazon Beauty . The elab-orate sequential patterns and multifaceted movie interests in ML-1M impose greater complexity -increasing dependency on interest angle guidance and structure optimiza", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.08217", "content": "- 1M dataset compared to Amazon Beauty . The elab-orate sequential patterns and multifaceted movie interests in ML-1M impose greater complexity -increasing dependency on interest angle guidance and structure optimiza"}
+{"idx": 9, "title": "LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable ...", "date": "", "ddg_snippet": "Table 2: Performance comparison on three benchmark datasets , i.e., ML-1M , Amazon Beauty , and Amazon Toys . We set the original models as baselines to compare with our proposed LLMHG model based on GPT3.5 or GPT4.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.08217v2", "content": "Table 2: Performance comparison on three benchmark datasets , i.e., ML-1M , Amazon Beauty , and Amazon Toys . We set the original models as baselines to compare with our proposed LLMHG model based on GPT3.5 or GPT4."}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_equation_12_theoretical_convergence_rate.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_equation_12_theoretical_convergence_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..507f2a5c12a74063d556a06b2a5251c7256aa0a5
--- /dev/null
+++ b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_equation_12_theoretical_convergence_rate.jsonl
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+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "This paper presents a theoretical analysis of conditional deep generative models, focusing on estimating high-dimensional conditional distributions that concentrate around manifolds. The authors provide statistical guarantees and convergence rates for different network architectures and present empirical results to support their theoretical claims. The main strength of the paper lies in its ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=V6hhhXoTSq", "content": "This paper presents a theoretical analysis of conditional deep generative models, focusing on estimating high-dimensional conditional distributions that concentrate around manifolds. The authors provide statistical guarantees and convergence rates for different network architectures and present empirical results to support their theoretical claims. The main strength of the paper lies in its ..."}
+{"idx": 1, "title": "Regression Analysis Course - Enroll Now & Start Learning Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Learn Regression Analysis online at your own pace. Start today with a special offer. Join millions of learners from around the world already learning on Udemy.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=NDV5rY-scjJIbYEkZJPukxEFW9IJEQ0Jz-itbYnXsSXN5TDryJESFSUM2Fc-cX9o-2gKYcCsFeTZQXOhW0KWTPBFmRu4Yb3J4k3szEgxqcUuPZPx_sbC9GlcBX-DKS6n.tdhOjDQ1CyAwUrNDX183XQ&rut=fd5a081676f11ad4b54b4c8011137c792adf84ad43f87069828518d3d55ad7a6&u3=https://www.bing.com/aclick?ld=e8MK_CkA8K5Pms4s5I08aASDVUCUywU354dlRt2yWlA3ndlQEHdO5w_yXpo5symTDQcAUxnsODBCsKoIdUHxOe3xxrkq5bPwp57bfJ-s2TtJl7IwhRX6wcYDYhmBd5GG0IumZDmIf9Utx3QTDXSigFWOEaZd9ZbmhgKRi4l2U2lAh3n085b7JLlDU7OvDICQhWzkruQw&u=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&rlid=86835c8731ff1922250d3448fd65a8f4&vqd=4-77253116738524875911717959411415126481&iurl={1}IG=1CD8013C393346708A23B91A62410F6A&CID=3CE2E54902F1696032EEF3270394686D&ID=DevEx,5038.1", "content": "Learn Regression Analysis online at your own pace. Start today with a special offer. Join millions of learners from around the world already learning on Udemy."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high ..."}
+{"idx": 5, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Abstract In this work, we explore the theoretical proper-ties of conditional deep generative models un-der the statistical framework of distribution re-gression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample prop-erties of a likelihood-based approach for estimat ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "Abstract In this work, we explore the theoretical proper-ties of conditional deep generative models un-der the statistical framework of distribution re-gression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample prop-erties of a likelihood-based approach for estimat ..."}
+{"idx": 6, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend solely on the intrinsic dimension and smoothness of the true conditional distribution.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2410.02025v1_enmode", "content": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend solely on the intrinsic dimension and smoothness of the true conditional distribution."}
+{"idx": 7, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space …"}
+{"idx": 8, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Overview This paper presents a likelihood-based approach for distribution regression using conditional deep generative models. It introduces a novel framework for learning conditional distributions of target variables given input variables. The model is trained to maximize the likelihood of the observed data, allowing for principled uncertainty quantification. Plain English Explanation The ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "Overview This paper presents a likelihood-based approach for distribution regression using conditional deep generative models. It introduces a novel framework for learning conditional distributions of target variables given input variables. The model is trained to maximize the likelihood of the observed data, allowing for principled uncertainty quantification. Plain English Explanation The ..."}
+{"idx": 9, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models ... theoretical convergence rates depend on noise and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1IyPRv1A0r&noteId=LRN425LsSb", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models ... theoretical convergence rates depend on noise and ..."}
diff --git a/data/sampled_jsons/A_Reduction_of_Imitation_Learning_and_Structured_Prediction_to_No-Regret_Online_Learning_Ross_Gordon.jsonl b/data/sampled_jsons/A_Reduction_of_Imitation_Learning_and_Structured_Prediction_to_No-Regret_Online_Learning_Ross_Gordon.jsonl
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+{"idx": 2, "title": "A Reduction of Imitation Learning and Structured Prediction to", "date": "", "ddg_snippet": "... A Reduction of Imitation Learning and Structured Prediction to No - Regret Online Learning AU - Stephane Ross AU - Geoffrey Gordon AU - Drew Bagnell BT ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v15/ross11a.html", "content": "... A Reduction of Imitation Learning and Structured Prediction to No - Regret Online Learning AU - Stephane Ross AU - Geoffrey Gordon AU - Drew Bagnell BT ..."}
+{"idx": 3, "title": "[1011.0686] A Reduction of Imitation Learning and Structured", "date": "", "ddg_snippet": "View a PDF of the paper titled A Reduction of Imitation Learning and Structured Prediction to No - Regret Online Learning , by Stephane Ross and 2 other ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1011.0686", "content": "View a PDF of the paper titled A Reduction of Imitation Learning and Structured Prediction to No - Regret Online Learning , by Stephane Ross and 2 other ..."}
+{"idx": 4, "title": "Imitation Learning | Dibbla.Space", "date": "", "ddg_snippet": "... to HG-DAgger and more recent advances DAgger Dataset Aggregation (DAgger) is a imitation learning algorithm proposed in AISTAT11 paper A Reduction of ...", "subpage_snippet": "", "source": "dibbla.space", "link": "https://dibbla.space/tags/imitation-learning/", "content": "... to HG-DAgger and more recent advances DAgger Dataset Aggregation (DAgger) is a imitation learning algorithm proposed in AISTAT11 paper A Reduction of ..."}
+{"idx": 5, "title": "Intervention-Based Imitation Learning | Dibbla.Space", "date": "", "ddg_snippet": "... to HG-DAgger and more recent advances DAgger Dataset Aggregation (DAgger) is a imitation learning algorithm proposed in AISTAT11 paper A Reduction of ...", "subpage_snippet": "", "source": "dibbla.space", "link": "https://dibbla.space/series/intervention-based-imitation-learning/", "content": "... to HG-DAgger and more recent advances DAgger Dataset Aggregation (DAgger) is a imitation learning algorithm proposed in AISTAT11 paper A Reduction of ..."}
+{"idx": 6, "title": "Apprenticeship Learning Task - GM-RKB", "date": "", "ddg_snippet": "A Reduction of Imitation Learning and Structured Prediction to No - Regret Online Learning .” In: Proceedings of AISTATS-2011.", "subpage_snippet": "", "source": "www.gabormelli.com", "link": "http://www.gabormelli.com/RKB/Apprenticeship_Learning_Task", "content": "A Reduction of Imitation Learning and Structured Prediction to No - Regret Online Learning .” In: Proceedings of AISTATS-2011."}
+{"idx": 7, "title": "Stéphane Ross - Robotics Institute", "date": "", "ddg_snippet": "A Reduction of Imitation Learning and Structured Prediction to No - Regret Online Learning . ... of the 13th International Conference on Artificial ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "http://www.cs.cmu.edu/~sross1/publications.html", "content": "A Reduction of Imitation Learning and Structured Prediction to No - Regret Online Learning . ... of the 13th International Conference on Artificial ..."}
+{"idx": 8, "title": "J. Andrew Bagnell - Google Scholar", "date": "", "ddg_snippet": "... includes citations to the following articles in Scholar. ... A reduction of imitation learning and structured prediction to no - regret online learning", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=7t4jbPQAAAAJ&hl=en", "content": "... includes citations to the following articles in Scholar. ... A reduction of imitation learning and structured prediction to no - regret online learning"}
+{"idx": 9, "title": "CS 159 Spring 2018 - References", "date": "", "ddg_snippet": "A Reduction of Imitation Learning and Structured Prediction to No - Regret Online Learning , by Stephane Ross , Geoff Gordon , and Drew Bagnell .", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/view/cs-159-spring-2018/references", "content": "A Reduction of Imitation Learning and Structured Prediction to No - Regret Online Learning , by Stephane Ross , Geoff Gordon , and Drew Bagnell ."}
diff --git a/data/sampled_jsons/A_relationship_between_arbitrary_positive_matrices_and_doubly_stochastic_matrices_1964_author.jsonl b/data/sampled_jsons/A_relationship_between_arbitrary_positive_matrices_and_doubly_stochastic_matrices_1964_author.jsonl
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+{"idx": 0, "title": "Sinkhorn's theorem - Wikipedia", "date": "", "ddg_snippet": "^ Sinkhorn, Richard. ( 1964 ). \" A relationship between arbitrary positive matrices and doubly stochastic matrices .\"(1967). \"Concerning nonnegative matrices and doubly stochastic matrices \". Pacific J. Math.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Sinkhorn's_theorem", "content": "^ Sinkhorn, Richard. ( 1964 ). \" A relationship between arbitrary positive matrices and doubly stochastic matrices .\"(1967). \"Concerning nonnegative matrices and doubly stochastic matrices \". Pacific J. Math."}
+{"idx": 1, "title": "A Relationship Between Arbitrary Positive Matrices and Doubly ...", "date": "", "ddg_snippet": "1 June 1964 . journal article. Published by Institute of Mathematical Statistics in The Annals of Mathematical Statistics.", "subpage_snippet": "", "source": "www.scilit.com", "link": "https://www.scilit.com/publications/f78016a4908c46e4690e1550598bc28f", "content": "1 June 1964 . journal article. Published by Institute of Mathematical Statistics in The Annals of Mathematical Statistics."}
+{"idx": 2, "title": "A Relationship Between Arbitrary Positive Matrices and Doubly ...", "date": "", "ddg_snippet": "Vol.35 • No. 2 • June, 1964 . Institute of Mathematical Statistics.All Fields Abstract Author Name Affiliation DOI/ISSN/ISBN Figure & Table Captions Keywords Title.", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-35/issue-2/A-Relationship-Between-Arbitrary-Positive-Matrices-and-Doubly-Stochastic-Matrices/10.1214/aoms/1177703591.short", "content": "Vol.35 • No. 2 • June, 1964 . Institute of Mathematical Statistics.All Fields Abstract Author Name Affiliation DOI/ISSN/ISBN Figure & Table Captions Keywords Title."}
+{"idx": 3, "title": "Sinkhorn and circular law – Libres pensées d'un mathématicien ordinaire", "date": "", "ddg_snippet": "Richard Sinkhorn A relationship between arbitrary positive matrices and doubly stochastic matrices Ann.", "subpage_snippet": "", "source": "djalil.chafai.net", "link": "https://djalil.chafai.net/blog/2021/08/28/sinkhorn-and-circular-law/", "content": "Richard Sinkhorn A relationship between arbitrary positive matrices and doubly stochastic matrices Ann."}
+{"idx": 4, "title": "Zur überführung beliebiger positiver Matrizen in stochastische und...", "date": "", "ddg_snippet": "R. Sinkhorn, A relationship between arbitrary positive matrices and doubly stochastic matrices .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/BF01304907", "content": "R. Sinkhorn, A relationship between arbitrary positive matrices and doubly stochastic matrices ."}
+{"idx": 5, "title": "Alternating projection method for solving doubly stochastic inverse...", "date": "", "ddg_snippet": "R. Sinkhorn, A relationship between arbitrary positive matrices and doubly stochastic matrices , Ann.", "subpage_snippet": "", "source": "www.jaac-online.com", "link": "https://www.jaac-online.com/article/doi/10.11948/20220424", "content": "R. Sinkhorn, A relationship between arbitrary positive matrices and doubly stochastic matrices , Ann."}
+{"idx": 6, "title": "Concerning nonnegative matrices and doubly stochastic matrices", "date": "", "ddg_snippet": "6. R. Sinkhorn, A relationship between arbitrary positive matrices and doubly stochastic matrices , Ann. Math. Statist.", "subpage_snippet": "", "source": "msp.org", "link": "https://msp.org/pjm/1967/21-2/pjm-v21-n2-p14-s.pdf", "content": "6. R. Sinkhorn, A relationship between arbitrary positive matrices and doubly stochastic matrices , Ann. Math. Statist."}
+{"idx": 7, "title": "(PDF) A Unified Treatment of Some Theorems on Positive Matrices", "date": "", "ddg_snippet": "A Relationship Between Arbitrary Positive Matrices and Doubly Stochastic Matrices .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/265238677_A_Unified_Treatment_of_Some_Theorems_on_Positive_Matrices", "content": "A Relationship Between Arbitrary Positive Matrices and Doubly Stochastic Matrices ."}
+{"idx": 8, "title": "Manifold Optimization Over the Set of Doubly", "date": "", "ddg_snippet": "[32] R. Sinkhorn, “ A relationship between arbitrary positive matrices and doubly stochastic matrices ,” The annals of mathematical statistics, vol. 35, no. 2, pp. 876–879, 1964 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1802.02628", "content": "[32] R. Sinkhorn, “ A relationship between arbitrary positive matrices and doubly stochastic matrices ,” The annals of mathematical statistics, vol. 35, no. 2, pp. 876–879, 1964 ."}
+{"idx": 9, "title": "Perron-Frobenius Theory for Quaternion Doubly", "date": "", "ddg_snippet": "Keywords : quaternion doubly stochastic matrices , signature matrices , Perron-Frobenius theory, irreducible matrices , infinity norm, 1-norm, Gershgorindisc.309. [6] R. Sinkhorn, “ A relationship between arbitrary positive matrices and doubly stochastic matrices ” , Ann.", "subpage_snippet": "", "source": "ijream.org", "link": "https://ijream.org/papers/IJREAMV04I1044021.pdf", "content": "Keywords : quaternion doubly stochastic matrices , signature matrices , Perron-Frobenius theory, irreducible matrices , infinity norm, 1-norm, Gershgorindisc.309. [6] R. Sinkhorn, “ A relationship between arbitrary positive matrices and doubly stochastic matrices ” , Ann."}
diff --git a/data/sampled_jsons/A_task_is_worth_one_word_Learning_with_task_prompts_for_high-quality_versatile_image_inpainting_Zhua.jsonl b/data/sampled_jsons/A_task_is_worth_one_word_Learning_with_task_prompts_for_high-quality_versatile_image_inpainting_Zhua.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fbbf94edb56a4b8d770dfa163bd384097b5ef0ba
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+{"idx": 0, "title": "[2312.03594] A Task is Worth One Word", "date": "", "ddg_snippet": "by J Zhuang · 2023 · Cited by 107 — A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting . Authors:Junhao Zhuang , Yanhong Zeng, Wenran ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03594", "content": "by J Zhuang · 2023 · Cited by 107 — A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting . Authors:Junhao Zhuang , Yanhong Zeng, Wenran ..."}
+{"idx": 1, "title": "A Task Is Worth One Word: Learning with Task Prompts for ...", "date": "", "ddg_snippet": "5 Nov 2024 — ... A Task Is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting . Authors: Junhao Zhuang . Junhao Zhuang .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/978-3-031-73636-0_12", "content": "5 Nov 2024 — ... A Task Is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting . Authors: Junhao Zhuang . Junhao Zhuang ."}
+{"idx": 2, "title": "Learning with Task Prompts for High-Quality Versatile ...", "date": "", "ddg_snippet": "23 Jul 2024 — We introduce PowerPaint, the first high-quality and versatile inpainting model that excels in multiple inpainting tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03594v4", "content": "23 Jul 2024 — We introduce PowerPaint, the first high-quality and versatile inpainting model that excels in multiple inpainting tasks."}
+{"idx": 3, "title": "Diffree: Text-Guided Shape Free Object Inpainting with ...", "date": "", "ddg_snippet": "by L Zhao · Cited by 7 — [1] Zhuang , Junhao, et al . \" A task is worth one word: Learning with task prompts for high-quality versatile image inpainting .\" ECCV 2025. [2] Li, Yuheng, et al .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JT53iXH7eO", "content": "by L Zhao · Cited by 7 — [1] Zhuang , Junhao, et al . \" A task is worth one word: Learning with task prompts for high-quality versatile image inpainting .\" ECCV 2025. [2] Li, Yuheng, et al ."}
+{"idx": 4, "title": "OpenImages-v6 Dataset", "date": "", "ddg_snippet": "A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting . Yanhong Zeng, Kai Chen, Chun Yuan, Junhao Zhuang , Wenran Liu.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/dataset/openimages-v6?ref=www1.labellerr.com", "content": "A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting . Yanhong Zeng, Kai Chen, Chun Yuan, Junhao Zhuang , Wenran Liu."}
+{"idx": 5, "title": "InferEdit: An instruction-based system with a multimodal ...", "date": "", "ddg_snippet": "by Z Huang · 2025 — Zhuang , Y. Zeng, W. Liu, C. Yuan, K. Chen. A task is worth one word: Learning with task prompts for high-quality versatile image inpainting .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2468502X25000488", "content": "by Z Huang · 2025 — Zhuang , Y. Zeng, W. Liu, C. Yuan, K. Chen. A task is worth one word: Learning with task prompts for high-quality versatile image inpainting ."}
+{"idx": 6, "title": "Junhao Zhuang: About Me", "date": "", "ddg_snippet": "dise, A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting Junhao Zhuang , Yanhong Zeng, Wenran Liu, Chun Yuan, Kai ...", "subpage_snippet": "", "source": "zhuang2002.github.io", "link": "https://zhuang2002.github.io/", "content": "dise, A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting Junhao Zhuang , Yanhong Zeng, Wenran Liu, Chun Yuan, Kai ..."}
+{"idx": 7, "title": "Instruction-based Fine-Grained Image Editing at Scale", "date": "", "ddg_snippet": "13 Nov 2024 — [8] Zhuang J, Zeng Y, Liu W, et al . A task is worth one word: Learning with task prompts for high-quality versatile image inpainting [J].", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9ZDdlgH6O8&referrer=[the+profile+of+Shuzheng+Si](/profile?id=~Shuzheng_Si1)", "content": "13 Nov 2024 — [8] Zhuang J, Zeng Y, Liu W, et al . A task is worth one word: Learning with task prompts for high-quality versatile image inpainting [J]."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "... A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting · Achieving high-quality versatile image inpainting, where ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=background+painting+module", "content": "... A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting · Achieving high-quality versatile image inpainting, where ..."}
+{"idx": 9, "title": "Computer Vision – ECCV 2024", "date": "", "ddg_snippet": "4 Oct 2024 — ... A Task Is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting . Junhao Zhuang , Yanhong Zeng, Wenran Liu ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/book/10.1007/978-3-031-73636-0", "content": "4 Oct 2024 — ... A Task Is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting . Junhao Zhuang , Yanhong Zeng, Wenran Liu ..."}
diff --git a/data/sampled_jsons/A_tutorial_on_spectral_clustering_von_Luxburg_abstract.jsonl b/data/sampled_jsons/A_tutorial_on_spectral_clustering_von_Luxburg_abstract.jsonl
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index 0000000000000000000000000000000000000000..bce6c5e4bc9f43f86978cf6dde5074b41be7b204
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+++ b/data/sampled_jsons/A_tutorial_on_spectral_clustering_von_Luxburg_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[0711.0189] A Tutorial on Spectral Clustering - arXiv.org [PDF] A tutorial on spectral clustering | Semantic Scholar Luxburg2007_Article_ATutorialOnSpectralClustering.pdf Images A tutorial on spectral clustering | Statistics and Computing A Tutorial on Spectral Clustering - ADS A Tutorial on Spectral Clustering - uni-tuebingen.de [0711.0189] A Tutorial on Spectral Clustering - arXiv.org [PDF] A tutorial on spectral clustering | Semantic Scholar [PDF] A tutorial on spectral clustering | Semantic Scholar [PDF] A tutorial on spectral clustering | Semantic Scholar A Tutorial on Spectral Clustering - uni-tuebingen.de A Tutorial on Spectral Clustering - uni-tuebingen.de A Tutorial on Spectral Clustering : Ulrike von Luxburg : Free ...", "date": "", "ddg_snippet": "Nov 1, 2007 · View a PDF of the paper titled A Tutorial on Spectral Clustering, by Ulrike von Luxburg Nov 1, 2007 · This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ... A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. View all Aug 22, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ... In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ... A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany ulrike. luxburg @tuebingen.mpg.de This article appears in Statistics and Computing, 17 (4), 2007. The original publication is available at www.springer.com. Abstract What is spectral clustering? In recent years, spectral clustering has become one of the most popular modern clustering algorithms . It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Can a randomized spectral algorithm find a clustering solution? A bound for choosing a correct number of eigenvectors in a randomized spectral algorithm able to find a clustering solution is shown and the efficacy of the algorithm is shown with experiments on real world graphs. Can spectral clustering be implemented using MATLAB? A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well. Is spectral clustering faster than other clustering algorithms? A simple spectral clustering algorithm based on a vertex embedding with $O (\\log (k))$ vectors computed by the power method is presented, finding that it is significantly faster than alternative clustering algorithms, while producing results with approximately the same clustering accuracy. What are the advantages of spectral clustering? Compared to the “traditional algorithms” such as k-means or single linkage, spectral clustering has many fundamental advantages. Results ob-tained by spectral clustering often outperform the traditional approaches, spectral clustering is very simple to implement and can be solved efficiently by standard linear algebra methods . Does a normalized spectral clustering algorithm converge? For both normalized spectral clustering algorithms, it can be proved that this is indeed the case (von Luxburg, Bousquet, and Belkin, 2004, 2005; von Luxburg, Belkin, and Bousquet, to appear). Mathe-matically, one proves that as we take the limit n → ∞, the matrix Lsym converges in a strong sense Nov 1, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/0711.0189", "content": "Nov 1, 2007 · View a PDF of the paper titled A Tutorial on Spectral Clustering, by Ulrike von Luxburg Nov 1, 2007 · This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ... A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. View all Aug 22, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ... In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ... A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany ulrike. luxburg @tuebingen.mpg.de This article appears in Statistics and Computing, 17 (4), 2007. The original publication is available at www.springer.com. Abstract What is spectral clustering? In recent years, spectral clustering has become one of the most popular modern clustering algorithms . It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Can a randomized spectral algorithm find a clustering solution? A bound for choosing a correct number of eigenvectors in a randomized spectral algorithm able to find a clustering solution is shown and the efficacy of the algorithm is shown with experiments on real world graphs. Can spectral clustering be implemented using MATLAB? A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well. Is spectral clustering faster than other clustering algorithms? A simple spectral clustering algorithm based on a vertex embedding with $O (\\log (k))$ vectors computed by the power method is presented, finding that it is significantly faster than alternative clustering algorithms, while producing results with approximately the same clustering accuracy. What are the advantages of spectral clustering? Compared to the “traditional algorithms” such as k-means or single linkage, spectral clustering has many fundamental advantages. Results ob-tained by spectral clustering often outperform the traditional approaches, spectral clustering is very simple to implement and can be solved efficiently by standard linear algebra methods . Does a normalized spectral clustering algorithm converge? For both normalized spectral clustering algorithms, it can be proved that this is indeed the case (von Luxburg, Bousquet, and Belkin, 2004, 2005; von Luxburg, Belkin, and Bousquet, to appear). Mathe-matically, one proves that as we take the limit n → ∞, the matrix Lsym converges in a strong sense Nov 1, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by..."}
+{"idx": 1, "title": "A tutorial on spectral clustering | Statistics and Computing A Tutorial on Spectral Clustering - ADS A Tutorial on Spectral Clustering - uni-tuebingen.de [0711.0189] A Tutorial on Spectral Clustering - arXiv.org [PDF] A tutorial on spectral clustering | Semantic Scholar [PDF] A tutorial on spectral clustering | Semantic Scholar [PDF] A tutorial on spectral clustering | Semantic Scholar A Tutorial on Spectral Clustering - uni-tuebingen.de A Tutorial on Spectral Clustering - uni-tuebingen.de A Tutorial on Spectral Clustering : Ulrike von Luxburg : Free ...", "date": "", "ddg_snippet": "Aug 22, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ... In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ... A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany ulrike. luxburg @tuebingen.mpg.de This article appears in Statistics and Computing, 17 (4), 2007. The original publication is available at www.springer.com. Abstract What is spectral clustering? In recent years, spectral clustering has become one of the most popular modern clustering algorithms . It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Can a randomized spectral algorithm find a clustering solution? A bound for choosing a correct number of eigenvectors in a randomized spectral algorithm able to find a clustering solution is shown and the efficacy of the algorithm is shown with experiments on real world graphs. Can spectral clustering be implemented using MATLAB? A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well. Is spectral clustering faster than other clustering algorithms? A simple spectral clustering algorithm based on a vertex embedding with $O (\\log (k))$ vectors computed by the power method is presented, finding that it is significantly faster than alternative clustering algorithms, while producing results with approximately the same clustering accuracy. What are the advantages of spectral clustering? Compared to the “traditional algorithms” such as k-means or single linkage, spectral clustering has many fundamental advantages. Results ob-tained by spectral clustering often outperform the traditional approaches, spectral clustering is very simple to implement and can be solved efficiently by standard linear algebra methods . Does a normalized spectral clustering algorithm converge? For both normalized spectral clustering algorithms, it can be proved that this is indeed the case (von Luxburg, Bousquet, and Belkin, 2004, 2005; von Luxburg, Belkin, and Bousquet, to appear). Mathe-matically, one proves that as we take the limit n → ∞, the matrix Lsym converges in a strong sense Nov 1, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11222-007-9033-z", "content": "Aug 22, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ... In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ... A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany ulrike. luxburg @tuebingen.mpg.de This article appears in Statistics and Computing, 17 (4), 2007. The original publication is available at www.springer.com. Abstract What is spectral clustering? In recent years, spectral clustering has become one of the most popular modern clustering algorithms . It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Can a randomized spectral algorithm find a clustering solution? A bound for choosing a correct number of eigenvectors in a randomized spectral algorithm able to find a clustering solution is shown and the efficacy of the algorithm is shown with experiments on real world graphs. Can spectral clustering be implemented using MATLAB? A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well. Is spectral clustering faster than other clustering algorithms? A simple spectral clustering algorithm based on a vertex embedding with $O (\\log (k))$ vectors computed by the power method is presented, finding that it is significantly faster than alternative clustering algorithms, while producing results with approximately the same clustering accuracy. What are the advantages of spectral clustering? Compared to the “traditional algorithms” such as k-means or single linkage, spectral clustering has many fundamental advantages. Results ob-tained by spectral clustering often outperform the traditional approaches, spectral clustering is very simple to implement and can be solved efficiently by standard linear algebra methods . Does a normalized spectral clustering algorithm converge? For both normalized spectral clustering algorithms, it can be proved that this is indeed the case (von Luxburg, Bousquet, and Belkin, 2004, 2005; von Luxburg, Belkin, and Bousquet, to appear). Mathe-matically, one proves that as we take the limit n → ∞, the matrix Lsym converges in a strong sense Nov 1, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by..."}
+{"idx": 2, "title": "[PDF] A tutorial on spectral clustering | Semantic Scholar", "date": "", "ddg_snippet": "Nov 1, 2007 · This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-tutorial-on-spectral-clustering-Luxburg/eda90bd43f4256986688e525b45b833a3addab97", "content": "Nov 1, 2007 · This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ..."}
+{"idx": 3, "title": "Luxburg2007_Article_ATutorialOnSpectralClustering.pdf", "date": "", "ddg_snippet": "A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms.", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/courses/cs6241/2020sp/readings/vonLuxburg-2007-spectral.pdf", "content": "A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms."}
+{"idx": 4, "title": "A Tutorial on Spectral Clustering - ADS", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2007arXiv0711.0189V/abstract", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 5, "title": "A Tutorial on Spectral Clustering - uni-tuebingen.de", "date": "", "ddg_snippet": "A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany ulrike. luxburg @tuebingen.mpg.de This article appears in Statistics and Computing, 17 (4), 2007. The original publication is available at www.springer.com. Abstract", "subpage_snippet": "", "source": "www.tml.cs.uni-tuebingen.de", "link": "http://www.tml.cs.uni-tuebingen.de/team/luxburg/publications/Luxburg07_tutorial.pdf", "content": "A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany ulrike. luxburg @tuebingen.mpg.de This article appears in Statistics and Computing, 17 (4), 2007. The original publication is available at www.springer.com. Abstract"}
+{"idx": 6, "title": "A Tutorial on Spectral Clustering : Ulrike von Luxburg : Free ...", "date": "", "ddg_snippet": "Nov 1, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by...", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/arxiv-0711.0189", "content": "Nov 1, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by..."}
+{"idx": 7, "title": "A Tutorial on Spectral Clustering - People | MIT CSAIL", "date": "", "ddg_snippet": "by U von Luxburg · Cited by 14149 — A Tutorial on Spectral Clustering . Ulrike von Luxburg . Max Planck Institute for Biological Cybernetics. Spemannstr. 38, 72076 Tübingen, Germany ulrike.luxburg ... 32 pages", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/dsontag/courses/ml14/notes/Luxburg07_tutorial_spectral_clustering.pdf", "content": "by U von Luxburg · Cited by 14149 — A Tutorial on Spectral Clustering . Ulrike von Luxburg . Max Planck Institute for Biological Cybernetics. Spemannstr. 38, 72076 Tübingen, Germany ulrike.luxburg ... 32 pages"}
+{"idx": 8, "title": "A Tutorial on Spectral Clustering", "date": "", "ddg_snippet": "by U von Luxburg · Cited by 14149 — A Tutorial on Spectral Clustering . Ulrike von Luxburg . Max Planck Institute for Biological Cybernetics. Spemannstr. 38, 72076 Tübingen, Germany ulrike.luxburg ... 32 pages", "subpage_snippet": "", "source": "graphics.stanford.edu", "link": "http://graphics.stanford.edu/courses/cs233-25-spring/ReferencedPapers/Luxburg07_spectral_clustering_tutorial_4488.pdf", "content": "by U von Luxburg · Cited by 14149 — A Tutorial on Spectral Clustering . Ulrike von Luxburg . Max Planck Institute for Biological Cybernetics. Spemannstr. 38, 72076 Tübingen, Germany ulrike.luxburg ... 32 pages"}
+{"idx": 9, "title": "A Tutorial on Spectral Clustering", "date": "", "ddg_snippet": "by U von Luxburg · 2006 · Cited by 14149 — A Tutorial on Spectral Clustering . Ulrike von Luxburg1. August 2006. 1 Department for Empirical Inference, email: ulrike. luxburg @tuebingen.mpg.de. Page 2. A ... 26 pages", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~aarti/Class/10701/readings/Luxburg06_TR.pdf", "content": "by U von Luxburg · 2006 · Cited by 14149 — A Tutorial on Spectral Clustering . Ulrike von Luxburg1. August 2006. 1 Department for Empirical Inference, email: ulrike. luxburg @tuebingen.mpg.de. Page 2. A ... 26 pages"}
diff --git a/data/sampled_jsons/A_very_simple_way_to_improve_the_performance_of_almost_any_machine_learning_algorithm_is_to_train_ma_year_2015.jsonl b/data/sampled_jsons/A_very_simple_way_to_improve_the_performance_of_almost_any_machine_learning_algorithm_is_to_train_ma_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..323b443d22f3ff093148afca9e08b82f56765936
--- /dev/null
+++ b/data/sampled_jsons/A_very_simple_way_to_improve_the_performance_of_almost_any_machine_learning_algorithm_is_to_train_ma_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Geoffrey Hinton publications", "date": "", "ddg_snippet": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to ...", "subpage_snippet": "", "source": "www.thejournal.club", "link": "https://www.thejournal.club/c/author/94647/", "content": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to ..."}
+{"idx": 1, "title": "Distilling the Knowledge in a Neural Network | DeepAI", "date": "", "ddg_snippet": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/distilling-the-knowledge-in-a-neural-network", "content": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to ..."}
+{"idx": 2, "title": "[1503.02531] Distilling the Knowledge in a Neural Network", "date": "", "ddg_snippet": "Abstract: A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1503.02531", "content": "Abstract: A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data ..."}
+{"idx": 3, "title": "Distilling the Knowledge in a Neural Network « Another", "date": "", "ddg_snippet": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to ...", "subpage_snippet": "", "source": "tm.durusau.net", "link": "http://tm.durusau.net/?p=61104", "content": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to ..."}
+{"idx": 4, "title": "Training Neural Nets: a Hacker’s Perspective", "date": "", "ddg_snippet": "Now that trained network is very likely to fail if it is tested on the right- side images as the network never got to see any image of ...", "subpage_snippet": "", "source": "floydhub.ghost.io", "link": "https://floydhub.ghost.io/training-neural-nets-a-hackers-perspective/", "content": "Now that trained network is very likely to fail if it is tested on the right- side images as the network never got to see any image of ..."}
+{"idx": 5, "title": "(PDF) Faster Segment Anything: Towards Lightweight SAM for", "date": "", "ddg_snippet": "A naive way to train such a new SAM as in the original SAM paper leads to unsatisfactory performance , especially when limited training sources are ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371851844_Faster_Segment_Anything_Towards_Lightweight_SAM_for_Mobile_Applications", "content": "A naive way to train such a new SAM as in the original SAM paper leads to unsatisfactory performance , especially when limited training sources are ..."}
+{"idx": 6, "title": "How To Improve Deep Learning Performance -", "date": "", "ddg_snippet": "... a new framing of your problem or more data is often going to give you more payoff than tuning the parameters of your best performing algorithm .", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/improve-deep-learning-performance/", "content": "... a new framing of your problem or more data is often going to give you more payoff than tuning the parameters of your best performing algorithm ."}
+{"idx": 7, "title": "A Machine Learning Approach to Algorithm Selection for Exact", "date": "", "ddg_snippet": "... articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1999-4893/12/10/200", "content": "... articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ..."}
+{"idx": 8, "title": "Mixture-of-Experts | Papers With Code", "date": "", "ddg_snippet": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/mixture-of-experts", "content": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to ..."}
+{"idx": 9, "title": "Knowledge Distillation Explained | Papers With Code", "date": "", "ddg_snippet": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/method/knowledge-distillation", "content": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to ..."}
diff --git a/data/sampled_jsons/Abdin_et_al_2024_feature_localization_large_language_model_year_2024.jsonl b/data/sampled_jsons/Abdin_et_al_2024_feature_localization_large_language_model_year_2024.jsonl
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index 0000000000000000000000000000000000000000..232b2fc02b0b80c38254688197d0de86a50a7945
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+{"idx": 0, "title": "Blink of an eye: a simple theory for feature localization ... - OpenReview", "date": "", "ddg_snippet": "Already, practitioners have applied critical windows to make LLMs safer (Qi et al ., 2024 ) and reason better ( Abdin et al.,2024;Lin et al.,2024 ). Our theory significantly streamlines our understanding of critical windows and provides concrete insights for practi- tioners.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QvqnPVGWAN", "content": "Already, practitioners have applied critical windows to make LLMs safer (Qi et al ., 2024 ) and reason better ( Abdin et al.,2024;Lin et al.,2024 ). Our theory significantly streamlines our understanding of critical windows and provides concrete insights for practi- tioners."}
+{"idx": 1, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "In large language models (LLMs), undesirable behavior can often emerge very suddenly. For example, • Claude transitioned from coding to browsing pictures of Yellowstone while using a computer (Anthropic, 2024 ). Figure 1. Examples of critical windows for different data modalities and samplers, including reasoning ( Abdin et al ., 2024 ; Qi et al ., 2024 ) and certain jailbreaks (Haize Labs, 2024b ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/45312/paper", "content": "In large language models (LLMs), undesirable behavior can often emerge very suddenly. For example, • Claude transitioned from coding to browsing pictures of Yellowstone while using a computer (Anthropic, 2024 ). Figure 1. Examples of critical windows for different data modalities and samplers, including reasoning ( Abdin et al ., 2024 ; Qi et al ., 2024 ) and certain jailbreaks (Haize Labs, 2024b ..."}
+{"idx": 2, "title": "dblp: Phi-3 Technical Report: A Highly Capable Language Model Locally ...", "date": "", "ddg_snippet": "Bibliographic details on Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2404-14219", "content": "Bibliographic details on Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone."}
+{"idx": 3, "title": "[2404.14219] Phi-3 Technical Report: A Highly Capable Language Model ...", "date": "", "ddg_snippet": "We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. Our training dataset is a scaled-up ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.14219", "content": "We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. Our training dataset is a scaled-up ..."}
+{"idx": 4, "title": "Exploring Large Language Models for Feature Selection:", "date": "", "ddg_snippet": "Abstract The rapid advancement of Large Language Models (LLMs) has significantly influenced various domains, leveraging their exceptional few-shot and zero-shot learning capabilities. In this work, we aim to explore and understand the LLMs-based feature selection methods from a data-centric perspective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.12025v1", "content": "Abstract The rapid advancement of Large Language Models (LLMs) has significantly influenced various domains, leveraging their exceptional few-shot and zero-shot learning capabilities. In this work, we aim to explore and understand the LLMs-based feature selection methods from a data-centric perspective."}
+{"idx": 5, "title": "Contextual feature extraction hierarchies converge in large language ...", "date": "", "ddg_snippet": "Why brain-like feature extraction emerges in large language models (LLMs) remains elusive. Mischler, Li and colleagues demonstrate that high-performing LLMs not only predict neural responses more ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-024-00925-4", "content": "Why brain-like feature extraction emerges in large language models (LLMs) remains elusive. Mischler, Li and colleagues demonstrate that high-performing LLMs not only predict neural responses more ..."}
+{"idx": 6, "title": "(PDF) Exploring Large Language Models for Feature Selection: A Data ...", "date": "", "ddg_snippet": "Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for time series forecasting are often specialized, necessitating ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383307911_Exploring_Large_Language_Models_for_Feature_Selection_A_Data-centric_Perspective", "content": "Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for time series forecasting are often specialized, necessitating ..."}
+{"idx": 7, "title": "BLINK OF AN EYE A SIMPLE THEORY FOR FEATURE LOCALIZATION ... - OpenReview", "date": "", "ddg_snippet": "1 INTRODUCTION In large language models (LLMs), undesirable behavior can often emerge very suddenly. For example, Claude transitioned from coding to browsing pictures of Yellowstone while using a computer (An- thropic, 2024 ); the Phi-4 team reported that the probability of correctly answering a math problem can plummet with a single token ( Abdin et al ., 2024 ; Lin et al ., 2024 ); Gemini abruptly ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=nodyP4FLrM", "content": "1 INTRODUCTION In large language models (LLMs), undesirable behavior can often emerge very suddenly. For example, Claude transitioned from coding to browsing pictures of Yellowstone while using a computer (An- thropic, 2024 ); the Phi-4 team reported that the probability of correctly answering a math problem can plummet with a single token ( Abdin et al ., 2024 ; Lin et al ., 2024 ); Gemini abruptly ..."}
+{"idx": 8, "title": "Exploring Large Language Models for Feature Selection: A Data-centric ...", "date": "", "ddg_snippet": "Abstract The rapid advancement of Large Language Models (LLMs) has significantly influenced various domains, leveraging their exceptional few-shot and zero-shot learning capabilities. In this work, we aim to explore and understand the LLMs-based feature selection methods from a data-centric perspective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.12025", "content": "Abstract The rapid advancement of Large Language Models (LLMs) has significantly influenced various domains, leveraging their exceptional few-shot and zero-shot learning capabilities. In this work, we aim to explore and understand the LLMs-based feature selection methods from a data-centric perspective."}
+{"idx": 9, "title": "[2412.08905] Phi-4 Technical Report - arXiv.org", "date": "", "ddg_snippet": "We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models , where pre-training is based primarily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic data throughout the training process. While previous models in the Phi family largely distill the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.08905", "content": "We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models , where pre-training is based primarily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic data throughout the training process. While previous models in the Phi family largely distill the ..."}
diff --git a/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity_GELU_ReLU_Loih_year_2024.jsonl b/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity_GELU_ReLU_Loih_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8ed7fc47f2708593772f1063b60641ecefb87152
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+{"idx": 0, "title": "Accelerating Linear Recurrent Neural Networks for the Edge", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01330v1", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption."}
+{"idx": 1, "title": "S W LINEAR RNNS ARE AT THE E -P PARETO FRONT - OpenReview", "date": "", "ddg_snippet": "ABSTRACT Linear recurrent neural networks enable powerful long-range sequence model-ing with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deploy-ment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption. In this paper, we investigate the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KwyLszIdbB", "content": "ABSTRACT Linear recurrent neural networks enable powerful long-range sequence model-ing with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deploy-ment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption. In this paper, we investigate the ..."}
+{"idx": 2, "title": "Accelerating Linear Recurrent Neural Networks for the ...", "date": "", "ddg_snippet": "by A Pierro · 2025 · Cited by 2 — The event-driven neuromorphic architecture of Loihi 2 is inherently suited to take advan- tage of the unstructured sparsity in both connections ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01330", "content": "by A Pierro · 2025 · Cited by 2 — The event-driven neuromorphic architecture of Loihi 2 is inherently suited to take advan- tage of the unstructured sparsity in both connections ..."}
+{"idx": 3, "title": "Synthesis Lectures on Computer Architecture", "date": "", "ddg_snippet": "(MLPs), convolution neural networks (CNNs), recurrent neural networks (RNNs), and trans- former networks. These topologies are introduced below and detailed ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-01769-8.pdf", "content": "(MLPs), convolution neural networks (CNNs), recurrent neural networks (RNNs), and trans- former networks. These topologies are introduced below and detailed ..."}
+{"idx": 4, "title": "Sparse and Wide Linear RNNs Are at the Efficiency-Performance...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KwyLszIdbB", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption."}
+{"idx": 5, "title": "EdgeDRNN: Recurrent Neural Network Accelerator for Edge Inference", "date": "", "ddg_snippet": "Low-latency, low-power portable recurrent neural network (RNN) accelerators offer powerful inference capabilities for real-time applications such as IoT, robotics, and human-machine interaction. We propose a lightweight Gated Recurrent Unit (GRU)-based RNN accelerator called EdgeDRNN that is optimized for low-latency edge RNN inference with batch size of 1. EdgeDRNN adopts the spiking neural ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9268992", "content": "Low-latency, low-power portable recurrent neural network (RNN) accelerators offer powerful inference capabilities for real-time applications such as IoT, robotics, and human-machine interaction. We propose a lightweight Gated Recurrent Unit (GRU)-based RNN accelerator called EdgeDRNN that is optimized for low-latency edge RNN inference with batch size of 1. EdgeDRNN adopts the spiking neural ..."}
+{"idx": 6, "title": "Recurrent Neural Networks for Edge Intelligence:", "date": "", "ddg_snippet": "Recurrent Neural Networks are ubiquitous and pervasive in many artificial intelligence applications such as speech recognition, predictive healthcare, creative art, and so on. Although they provide accurate superior solutions, they pose a massive challenge \"training havoc.\" Current expansion of IoT demands intelligent models to be deployed at the edge . This is precisely to handle ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3448974", "content": "Recurrent Neural Networks are ubiquitous and pervasive in many artificial intelligence applications such as speech recognition, predictive healthcare, creative art, and so on. Although they provide accurate superior solutions, they pose a massive challenge \"training havoc.\" Current expansion of IoT demands intelligent models to be deployed at the edge . This is precisely to handle ..."}
+{"idx": 7, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with ...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption. Unstructured sparsity offers a compelling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01330", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption. Unstructured sparsity offers a compelling ..."}
+{"idx": 8, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with ...", "date": "", "ddg_snippet": "Article \" Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search ...", "subpage_snippet": "", "source": "jglobal.jst.go.jp", "link": "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202502203259049665", "content": "Article \" Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search ..."}
+{"idx": 9, "title": "(PDF) EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge ...", "date": "", "ddg_snippet": "This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing. EdgeDRNN adopts the spiking neural network ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/338228638_EdgeDRNN_Enabling_Low-latency_Recurrent_Neural_Network_Edge_Inference", "content": "This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing. EdgeDRNN adopts the spiking neural network ..."}
diff --git a/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity_Table_1_Loihi__year_2023.jsonl b/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity_Table_1_Loihi__year_2023.jsonl
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+{"idx": 0, "title": "ACCELERATING Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of ACCELERATING is increasing in speed or rate of occurrence. How to use accelerating in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/accelerating", "content": "The meaning of ACCELERATING is increasing in speed or rate of occurrence. How to use accelerating in a sentence."}
+{"idx": 1, "title": "ACCELERATING | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "Astronomers reported new evidence to support the theory that the universe is expanding at an accelerating rate. Accelerating change in technology has increased our capacity to supply the market with new products and services.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/accelerating", "content": "Astronomers reported new evidence to support the theory that the universe is expanding at an accelerating rate. Accelerating change in technology has increased our capacity to supply the market with new products and services."}
+{"idx": 2, "title": "Accelerating - definition of accelerating by The Free Dictionary", "date": "", "ddg_snippet": "Define accelerating . accelerating synonyms, accelerating pronunciation, accelerating translation, English dictionary definition of accelerating . hasten the occurrence of: accelerate reforms; to move or go faster; to progress faster: accelerate educational programs Not to be confused with: exhilarate...", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/accelerating", "content": "Define accelerating . accelerating synonyms, accelerating pronunciation, accelerating translation, English dictionary definition of accelerating . hasten the occurrence of: accelerate reforms; to move or go faster; to progress faster: accelerate educational programs Not to be confused with: exhilarate..."}
+{"idx": 3, "title": "30 Synonyms & Antonyms for ACCELERATING - Thesaurus.com", "date": "", "ddg_snippet": "Find 30 different ways to say ACCELERATING , along with antonyms, related words, and example sentences at Thesaurus.com.", "subpage_snippet": "", "source": "www.thesaurus.com", "link": "https://www.thesaurus.com/browse/accelerating", "content": "Find 30 different ways to say ACCELERATING , along with antonyms, related words, and example sentences at Thesaurus.com."}
+{"idx": 4, "title": "Accelerating Linear Recurrent Neural Networks for the ...", "date": "", "ddg_snippet": "The results show competitive performance compared to an edge GPU ( Jetson Orin Nano ) in terms of latency and energy efficiency. Weaknesses: The study focuses ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=UNrfYfbLZ3&noteId=RwdeaYNOyu", "content": "The results show competitive performance compared to an edge GPU ( Jetson Orin Nano ) in terms of latency and energy efficiency. Weaknesses: The study focuses ..."}
+{"idx": 5, "title": "Accelerating Linear Recurrent Neural Networks for the ...", "date": "", "ddg_snippet": "3 Feb 2025 — Table 1 : Power and performance results ∗ . The Loihi 2 is running a sparse and quantized S5 model, while the Jetson Orin Nano is running a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01330v1", "content": "3 Feb 2025 — Table 1 : Power and performance results ∗ . The Loihi 2 is running a sparse and quantized S5 model, while the Jetson Orin Nano is running a ..."}
+{"idx": 6, "title": "Accelerating Linear Recurrent Neural Networks for the ...", "date": "", "ddg_snippet": "by A Pierro · 2025 · Cited by 2 — When quantized and deployed on Loihi 2 , sparse models deliver 42× lower latency and 149× lower energy consumption in token -by- token processing, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01330", "content": "by A Pierro · 2025 · Cited by 2 — When quantized and deployed on Loihi 2 , sparse models deliver 42× lower latency and 149× lower energy consumption in token -by- token processing, ..."}
+{"idx": 7, "title": "SPARSE AND WIDE LINEAR RNNS ARE AT THE ...", "date": "", "ddg_snippet": "by A Pierro — We see that Loihi 2 processes a single STFT frame 35× faster and with 1200× less energy than the. Jetson Orin Nano ( Token -by- token ; Loihi 2 Fall-Through and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KwyLszIdbB", "content": "by A Pierro — We see that Loihi 2 processes a single STFT frame 35× faster and with 1200× less energy than the. Jetson Orin Nano ( Token -by- token ; Loihi 2 Fall-Through and ..."}
+{"idx": 8, "title": "N P E L L M I L 2: Euromorphic Rinciples For Fficient Arge ...", "date": "", "ddg_snippet": "This document presents a novel architecture for large language models (LLMs) optimized for Intel's neuromorphic processor, Loihi 2 , which significantly ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/880502264/2503-18002v2", "content": "This document presents a novel architecture for large language models (LLMs) optimized for Intel's neuromorphic processor, Loihi 2 , which significantly ..."}
+{"idx": 9, "title": "Acceleration | Definition, Facts, & Units | Britannica", "date": "", "ddg_snippet": "Aug 29, 2025 · acceleration , rate at which velocity changes with time, in terms of both speed and direction. A point or an object moving in a straight line is accelerated if it speeds up or slows down. Motion on a circle is accelerated even if the speed is constant, because the direction is continually changing.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/science/acceleration", "content": "Aug 29, 2025 · acceleration , rate at which velocity changes with time, in terms of both speed and direction. A point or an object moving in a straight line is accelerated if it speeds up or slows down. Motion on a circle is accelerated even if the speed is constant, because the direction is continually changing."}
diff --git a/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity_filetypepdf.jsonl b/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity_filetypepdf.jsonl
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+{"idx": 0, "title": "Accelerating Linear Recurrent Neural Networks for the Edge ...", "date": "", "ddg_snippet": "In this paper, we conduct a scaling study to investigate the Pareto front of performance and eficiency across inference compute budgets. We find that highly sparse linear RNNs consistently achieve better eficiency-performance trade-offs than dense baselines, with 2x less compute and 36% less memory at iso-accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01330", "content": "In this paper, we conduct a scaling study to investigate the Pareto front of performance and eficiency across inference compute budgets. We find that highly sparse linear RNNs consistently achieve better eficiency-performance trade-offs than dense baselines, with 2x less compute and 36% less memory at iso-accuracy."}
+{"idx": 1, "title": "( PDF ) Accelerating Linear Recurrent Neural Networks for the Edge ...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage. and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685255_Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage. and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but."}
+{"idx": 2, "title": "Model Compression | Papers With Code", "date": "", "ddg_snippet": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity . Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/model-compression/codeless?page=7", "content": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity . Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference."}
+{"idx": 3, "title": "[ PDF ] On the quantization of recurrent neural networks", "date": "", "ddg_snippet": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity . Alessandro PierroSteven AbreuJonathan TimcheckPhilipp StratmannAndreas WildS.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-the-quantization-of-recurrent-neural-networks-Li-Álvarez/3c964c58cd47aa64c9be520c18efd364c514ac29", "content": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity . Alessandro PierroSteven AbreuJonathan TimcheckPhilipp StratmannAndreas WildS."}
+{"idx": 4, "title": "Fugu-MT 論文翻訳(概要): Accelerating Deep Learning Inference via...", "date": "", "ddg_snippet": "関連論文リスト. Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity [39.483346492111515] 線形リカレントニューラルネットワークは...", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2101.07344v1", "content": "関連論文リスト. Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity [39.483346492111515] 線形リカレントニューラルネットワークは..."}
+{"idx": 5, "title": "Weekly Group Sync - icml.cc", "date": "", "ddg_snippet": "Can fixed-point quantization compress sparse linear RNNs without damaging the network’s performance? Can unstructured sparsity and fixed-point quantization be translated into latency and energy advantages on neuromorphic hardware?", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/45121.pdf", "content": "Can fixed-point quantization compress sparse linear RNNs without damaging the network’s performance? Can unstructured sparsity and fixed-point quantization be translated into latency and energy advantages on neuromorphic hardware?"}
+{"idx": 6, "title": "untitled [idus.us.es]", "date": "", "ddg_snippet": "Abstract—Low-latency, low-power portable recurrent neural network (RNN) accelerators offer powerful inference capabilities for real-time applications such as IoT, robotics, and human-machine interaction. We propose a lightweight Gated Recurrent Unit (GRU)-based RNN accelerator called EdgeDRNN that is optimized for low-latency edge RNN inference with batch size of 1. EdgeDRNN adopts the ...", "subpage_snippet": "", "source": "idus.us.es", "link": "https://idus.us.es/bitstream/handle/11441/143929/EdgeDRNN_Recurrent_Neural_Network_Accelerator_for_Edge_Inference.pdf?sequence=1", "content": "Abstract—Low-latency, low-power portable recurrent neural network (RNN) accelerators offer powerful inference capabilities for real-time applications such as IoT, robotics, and human-machine interaction. We propose a lightweight Gated Recurrent Unit (GRU)-based RNN accelerator called EdgeDRNN that is optimized for low-latency edge RNN inference with batch size of 1. EdgeDRNN adopts the ..."}
+{"idx": 7, "title": "Philipp Stratmann - Google Akademik", "date": "", "ddg_snippet": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity .2025. Sparse and Wide Linear RNNs Are at the Efficiency-Performance Pareto Front.", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=Xb4HwHAAAAAJ&hl=tr", "content": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity .2025. Sparse and Wide Linear RNNs Are at the Efficiency-Performance Pareto Front."}
+{"idx": 8, "title": "GitHub - Dengyu-Wu/neuromorphics-daily-arxiv: Neuromorphic paper...", "date": "", "ddg_snippet": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity .Gradient-Free Training of Recurrent Neural Networks using Random Perturbations.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Dengyu-Wu/neuromorphics-daily-arxiv", "content": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity .Gradient-Free Training of Recurrent Neural Networks using Random Perturbations."}
+{"idx": 9, "title": "A Diagonal State Space Model", "date": "", "ddg_snippet": "\" Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity .\" arXiv preprint arXiv:2502.01330 (2025). - Go alternative routes such as MatMul-Free LLMs Abreu, Steven, et al.", "subpage_snippet": "", "source": "flagship.kip.uni-heidelberg.de", "link": "https://flagship.kip.uni-heidelberg.de/jss/HBPm?m=displayPresentation&mI=263&mEID=9685", "content": "\" Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity .\" arXiv preprint arXiv:2502.01330 (2025). - Go alternative routes such as MatMul-Free LLMs Abreu, Steven, et al."}
diff --git a/data/sampled_jsons/ActSVD_5%_model_parameters_modified_Wei_et_al_2024_year_2024.jsonl b/data/sampled_jsons/ActSVD_5%_model_parameters_modified_Wei_et_al_2024_year_2024.jsonl
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+{"idx": 0, "title": "Wei et al., 2024 | PDF | Fungus | Gene - Scribd", "date": "", "ddg_snippet": "Wei et al ., 2024 - Free download as PDF File (.pdf), Text File (.txt) or read online for free. Loss of the accessory chromosome converts a pathogenic tree-root fungus into a mutualistic endophyte", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/804428819/Wei-et-al-2024", "content": "Wei et al ., 2024 - Free download as PDF File (.pdf), Text File (.txt) or read online for free. Loss of the accessory chromosome converts a pathogenic tree-root fungus into a mutualistic endophyte"}
+{"idx": 1, "title": "Advanced computational modelling of composite materials", "date": "", "ddg_snippet": "Yuan et al . [107]proposed a modified temperature-dependent cohesive zone model by introducing the temperature term into the original cohesive zone model for considering the effect of thermal expansion.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0013794424002832", "content": "Yuan et al . [107]proposed a modified temperature-dependent cohesive zone model by introducing the temperature term into the original cohesive zone model for considering the effect of thermal expansion."}
+{"idx": 2, "title": "[2402.05162] Assessing the Brittleness of Safety Alignment via Pruning ...", "date": "", "ddg_snippet": "Surprisingly, the isolated regions we find are sparse, comprising about 3% at the parameter level and 2.5% at the rank level. Removing these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.05162", "content": "Surprisingly, the isolated regions we find are sparse, comprising about 3% at the parameter level and 2.5% at the rank level. Removing these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mechanisms."}
+{"idx": 3, "title": "alignment-attribution-code/README.md at main - GitHub", "date": "", "ddg_snippet": "Surprisingly, the isolated regions we find are sparse, comprising about $3$ % at the parameter level and $2.5$ % at the rank level. Removing these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mechanisms.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/boyiwei/alignment-attribution-code/blob/main/README.md", "content": "Surprisingly, the isolated regions we find are sparse, comprising about $3$ % at the parameter level and $2.5$ % at the rank level. Removing these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mechanisms."}
+{"idx": 4, "title": "PDF ASSESSING THE BRITTLENESS OF SAFETY ALIGNMENT VIA PRUNING ... - OpenReview", "date": "", "ddg_snippet": "Our study examines the model weights and disentangles safety and utility from two perspectives: individual neurons and specific ranks within the model . For neuron attribution, we follow two widely adopted and effective methods from the previous works on pruning transformer models (Lee et al ., 2019; Sun et al ., 2024 ) to calculate a behavior-specific importance score for each neuron in an LLM ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=niBPvgJIHB&name=pdf", "content": "Our study examines the model weights and disentangles safety and utility from two perspectives: individual neurons and specific ranks within the model . For neuron attribution, we follow two widely adopted and effective methods from the previous works on pruning transformer models (Lee et al ., 2019; Sun et al ., 2024 ) to calculate a behavior-specific importance score for each neuron in an LLM ..."}
+{"idx": 5, "title": "Assessing the Brittleness of Safety Alignment", "date": "", "ddg_snippet": "Surprisingly, the isolated regions we find are sparse, comprising about percent 3 3\\% 3 % at the parameter level and percent 2.5 2.5\\% 2.5 % at the rank level. Removing these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.05162v1", "content": "Surprisingly, the isolated regions we find are sparse, comprising about percent 3 3\\% 3 % at the parameter level and percent 2.5 2.5\\% 2.5 % at the rank level. Removing these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mechanisms."}
+{"idx": 6, "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ...", "date": "", "ddg_snippet": "Surprisingly, the isolated re-gions we find are sparse, comprising about 3% at the parameter level and 2.5% at the rank level. Re-moving these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mecha-nisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.05162v1", "content": "Surprisingly, the isolated re-gions we find are sparse, comprising about 3% at the parameter level and 2.5% at the rank level. Re-moving these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mecha-nisms."}
+{"idx": 7, "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ...", "date": "", "ddg_snippet": "Surprisingly, the isolated regions we find are sparse, comprising about 3 % percent 3 3\\% 3 % at the parameter level and 2.5 % percent 2.5 2.5\\% 2.5 % at the rank level. Removing these regions compromises safety while only mildly impacting utility, corroborating the inherent brittleness of the model's safety mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.05162?_immersive_translate_auto_translate=1", "content": "Surprisingly, the isolated regions we find are sparse, comprising about 3 % percent 3 3\\% 3 % at the parameter level and 2.5 % percent 2.5 2.5\\% 2.5 % at the rank level. Removing these regions compromises safety while only mildly impacting utility, corroborating the inherent brittleness of the model's safety mechanisms."}
+{"idx": 8, "title": "2025 ACC/AHA/ACEP/NAEMSP/SCAI Guideline for the Management of Patients ...", "date": "", "ddg_snippet": "A comprehensive literature search was conducted from July 2023 to April 2024 . Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline.", "subpage_snippet": "", "source": "www.ahajournals.org", "link": "https://www.ahajournals.org/doi/10.1161/CIR.0000000000001309", "content": "A comprehensive literature search was conducted from July 2023 to April 2024 . Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline."}
+{"idx": 9, "title": "Simplified Lipid Nanoparticles for Tissue‐ And Cell‐Targeted mRNA ...", "date": "", "ddg_snippet": "The SELECT platform (Simplified LNP with Engineered mRNA for Cell-type Targeting) is designed to achieve organ- and cell- specific mRNA delivery by integrating LNP-based targeting and mRNA sequence-c...", "subpage_snippet": "", "source": "advanced.onlinelibrary.wiley.com", "link": "https://advanced.onlinelibrary.wiley.com/doi/full/10.1002/adma.202409812", "content": "The SELECT platform (Simplified LNP with Engineered mRNA for Cell-type Targeting) is designed to achieve organ- and cell- specific mRNA delivery by integrating LNP-based targeting and mRNA sequence-c..."}
diff --git a/data/sampled_jsons/ActSVD_Wei_et_al_2024b_modifying_model_parameters.jsonl b/data/sampled_jsons/ActSVD_Wei_et_al_2024b_modifying_model_parameters.jsonl
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index 0000000000000000000000000000000000000000..d5b9b15a60d638ea77404e7265f81f07836f5404
--- /dev/null
+++ b/data/sampled_jsons/ActSVD_Wei_et_al_2024b_modifying_model_parameters.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Model Editing Harms General Abilities of Large Language Models ...", "date": "", "ddg_snippet": "While current model editing methods can effectively modify a model's behavior within a specific area of interest, they often overlook the potential unintended side effects on the general abilities of LLMs such as reasoning, natural language inference, and question answering.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.934/", "content": "While current model editing methods can effectively modify a model's behavior within a specific area of interest, they often overlook the potential unintended side effects on the general abilities of LLMs such as reasoning, natural language inference, and question answering."}
+{"idx": 1, "title": "PDF Model Surgery: Modulating LLM's Behavior via Simple Parameter Editing", "date": "", "ddg_snippet": "An alternative is machine unlearning, which uses methods like gradient ascent to remove previously learned unde- sirable behaviors (Zhang et al.,2024b;Liu et al ., 2024;Zhang et al.,2024a). While these techniques are effective in promot- ing non-toxicity and safety, they necessitate the training of a LLM.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.321.pdf", "content": "An alternative is machine unlearning, which uses methods like gradient ascent to remove previously learned unde- sirable behaviors (Zhang et al.,2024b;Liu et al ., 2024;Zhang et al.,2024a). While these techniques are effective in promot- ing non-toxicity and safety, they necessitate the training of a LLM."}
+{"idx": 2, "title": "The Mirage of Model Editing: Revisiting Evaluation in the Wild", "date": "", "ddg_snippet": "These methods update LLMs by adding trainable parameters to encode new knowledge, e.g., additional neurons in FFN (Dong et al ., 2022; Huang et al ., 2023) or specialized mem-ory modules (Hartvigsen et al ., 2023; Wang et al ., 2024b ), while preserving pretrained weights.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.11177", "content": "These methods update LLMs by adding trainable parameters to encode new knowledge, e.g., additional neurons in FFN (Dong et al ., 2022; Huang et al ., 2023) or specialized mem-ory modules (Hartvigsen et al ., 2023; Wang et al ., 2024b ), while preserving pretrained weights."}
+{"idx": 3, "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ...", "date": "", "ddg_snippet": "Surprisingly, the isolated re-gions we find are sparse, comprising about 3% at the parameter level and 2.5% at the rank level. Re-moving these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mecha-nisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.05162v1", "content": "Surprisingly, the isolated re-gions we find are sparse, comprising about 3% at the parameter level and 2.5% at the rank level. Re-moving these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mecha-nisms."}
+{"idx": 4, "title": "alignment-attribution-code/README.md at main - GitHub", "date": "", "ddg_snippet": "Most of the parameters are similar to the prune neurons situation. Important parameters are: --prune_method: To specify the pruning method, in this case we choose low_rank, which corresponds to ActSVD in our paper. --prune_data: To specify the dataset used to identify the safety/utility projection matrix.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/boyiwei/alignment-attribution-code/blob/main/README.md", "content": "Most of the parameters are similar to the prune neurons situation. Important parameters are: --prune_method: To specify the pruning method, in this case we choose low_rank, which corresponds to ActSVD in our paper. --prune_data: To specify the dataset used to identify the safety/utility projection matrix."}
+{"idx": 5, "title": "Assessing the Brittleness of Safety Alignment", "date": "", "ddg_snippet": "This observation aligns with Lee et al . 's hypothesis that fine-tuning attacks may create alternative pathways in the original model . Given that safety-critical neurons are sparse, these new routes could bypass the existing safety mechanisms easily, and therefore we need more robust defenses against fine-tuning attacks.", "subpage_snippet": "", "source": "boyiwei.com", "link": "https://boyiwei.com/alignment-attribution/", "content": "This observation aligns with Lee et al . 's hypothesis that fine-tuning attacks may create alternative pathways in the original model . Given that safety-critical neurons are sparse, these new routes could bypass the existing safety mechanisms easily, and therefore we need more robust defenses against fine-tuning attacks."}
+{"idx": 6, "title": "Assessing the Brittleness of Safety Alignment", "date": "", "ddg_snippet": "Surprisingly, the isolated regions we find are sparse, comprising about percent 3 3\\% 3 % at the parameter level and percent 2.5 2.5\\% 2.5 % at the rank level. Removing these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.05162v1", "content": "Surprisingly, the isolated regions we find are sparse, comprising about percent 3 3\\% 3 % at the parameter level and percent 2.5 2.5\\% 2.5 % at the rank level. Removing these regions compromises safety without significantly impacting utility, corroborating the inherent brittleness of the model's safety mechanisms."}
+{"idx": 7, "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ...", "date": "", "ddg_snippet": "Recent studies show that fine-tuning an aligned LLM, even on a non-malicious dataset, can inadvertently weaken a model's safety mechanisms (Qi et al ., 2024b ; Yang et al ., 2023; Zhan et al ., 2023). Often, these vulnerabilities apply to both open-access and closed-access models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.05162v4", "content": "Recent studies show that fine-tuning an aligned LLM, even on a non-malicious dataset, can inadvertently weaken a model's safety mechanisms (Qi et al ., 2024b ; Yang et al ., 2023; Zhan et al ., 2023). Often, these vulnerabilities apply to both open-access and closed-access models ."}
+{"idx": 8, "title": "arXiv:2410.13708v1 [cs.CL] 17 Oct 2024", "date": "", "ddg_snippet": "(e.g., Llama-2-7b-chat) to respond to 16× ↑ more harmful queries, while only modifying 0.006% ↓ of the parameters , in contrast to the ∼ 5% modifica-tion required in previous studies. More importantly, we demonstrate that attention heads primarily function as feature extractors for safety and models fine-tuned from the same base model exhibit overlapping safety heads through comprehen ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.13708v1", "content": "(e.g., Llama-2-7b-chat) to respond to 16× ↑ more harmful queries, while only modifying 0.006% ↓ of the parameters , in contrast to the ∼ 5% modifica-tion required in previous studies. More importantly, we demonstrate that attention heads primarily function as feature extractors for safety and models fine-tuned from the same base model exhibit overlapping safety heads through comprehen ..."}
+{"idx": 9, "title": "arXiv:2410.13708v2 [cs.CL] 24 Feb 2025", "date": "", "ddg_snippet": "Currently, revealing the black-box LLM safety is typically achieved through mechanism interpre-tation methods. Specifically, these methods (Geiger et al ., 2021; Stolfo et al ., 2023; Gurnee et al ., 2023) granularly analyze features, neurons, layers, and parameters to assist humans in understand-ing model behavior and capabilities. Recent studies (Zou et al ., 2023a; Templeton, 2024; Arditi et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.13708", "content": "Currently, revealing the black-box LLM safety is typically achieved through mechanism interpre-tation methods. Specifically, these methods (Geiger et al ., 2021; Stolfo et al ., 2023; Gurnee et al ., 2023) granularly analyze features, neurons, layers, and parameters to assist humans in understand-ing model behavior and capabilities. Recent studies (Zou et al ., 2023a; Templeton, 2024; Arditi et al ..."}
diff --git a/data/sampled_jsons/Active_Learning_machine_learning_definition_framework.jsonl b/data/sampled_jsons/Active_Learning_machine_learning_definition_framework.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4be79e53de990f7f1d8f846adc735bbda6b934fe
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+++ b/data/sampled_jsons/Active_Learning_machine_learning_definition_framework.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Active learning (machine learning) - Wikipedia", "date": "", "ddg_snippet": "Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Active_learning_(machine_learning)", "content": "Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs."}
+{"idx": 1, "title": "Active Learning in Machine Learning [Guide & Examples]", "date": "", "ddg_snippet": "31 Aug 2022 — Active Learning is a type of machine learning where the model is trained on only the most relevant data. Explore the benefits and limitations of the framework.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/active-learning-guide", "content": "31 Aug 2022 — Active Learning is a type of machine learning where the model is trained on only the most relevant data. Explore the benefits and limitations of the framework."}
+{"idx": 2, "title": "Active Learning in Machine Learning Explained | Towards Data ...", "date": "", "ddg_snippet": "Mar 28, 2022 · Active learning is a branch of machine learning in which a model can have access to newly labelled data points. It’s a common tactic used when there is an abundance of unlabelled data and the manual annotation process is expensive [1].", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/active-learning-in-machine-learning-explained-777c42bd52fa/", "content": "Mar 28, 2022 · Active learning is a branch of machine learning in which a model can have access to newly labelled data points. It’s a common tactic used when there is an abundance of unlabelled data and the manual annotation process is expensive [1]."}
+{"idx": 3, "title": "ML | Active Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Aug 6, 2025 · Active Learning is a special case of Supervised Machine Learning . This approach is used to construct a high-performance classifier while keeping the size of the training dataset to a minimum by actively selecting the valuable data points. Active Learning in Machine Learning A subset of machine learning known as \"active learning \" allows a learning algorithm to interactively query a user to ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/ml-active-learning/", "content": "Aug 6, 2025 · Active Learning is a special case of Supervised Machine Learning . This approach is used to construct a high-performance classifier while keeping the size of the training dataset to a minimum by actively selecting the valuable data points. Active Learning in Machine Learning A subset of machine learning known as \"active learning \" allows a learning algorithm to interactively query a user to ..."}
+{"idx": 4, "title": "Machine Learning: Active Learning | Baeldung on Computer Science", "date": "", "ddg_snippet": "Feb 28, 2025 · This framework is active learning , a subfield of machine learning that postulates query learning . 3. Active Learning Active learning , also known as query learning , revolves around the idea that the learning system can choose the data to learn from.", "subpage_snippet": "", "source": "www.baeldung.com", "link": "https://www.baeldung.com/cs/ml-active-learning", "content": "Feb 28, 2025 · This framework is active learning , a subfield of machine learning that postulates query learning . 3. Active Learning Active learning , also known as query learning , revolves around the idea that the learning system can choose the data to learn from."}
+{"idx": 5, "title": "Active Learning in Machine Learning Guide [Full Guide]", "date": "", "ddg_snippet": "14 Sept 2023 — Active learning is a supervised machine learning approach that aims to optimize annotation using a few small training samples.", "subpage_snippet": "", "source": "encord.com", "link": "https://encord.com/blog/active-learning-machine-learning-guide/", "content": "14 Sept 2023 — Active learning is a supervised machine learning approach that aims to optimize annotation using a few small training samples."}
+{"idx": 6, "title": "Data Science: modAL — Active Learning Framework", "date": "", "ddg_snippet": "What is Active Learning? Active Learning is a machine learning technique where the model actively selects the most informative data points to ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@abhilashagulhane111/data-science-modal-active-learning-framework-93763d3f843b", "content": "What is Active Learning? Active Learning is a machine learning technique where the model actively selects the most informative data points to ..."}
+{"idx": 7, "title": "Active Learning: Strategies, Tools, and Real-World Use ...", "date": "", "ddg_snippet": "Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label ...", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/active-learning-strategies-tools-use-cases", "content": "Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label ..."}
+{"idx": 8, "title": "ALBench: A Framework for Evaluating Active Learning in ...", "date": "", "ddg_snippet": "by Z Feng · 2022 · Cited by 8 — Active learning alleviates the expensive data annotation issue through incrementally training models powered with efficient data selection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.13339", "content": "by Z Feng · 2022 · Cited by 8 — Active learning alleviates the expensive data annotation issue through incrementally training models powered with efficient data selection."}
+{"idx": 9, "title": "Active Learning Approaches: Strategies, Deep Learning ...", "date": "", "ddg_snippet": "Oct 1, 2023 · Recent developments are dedicated to multi-label active learning , hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of machine learning (e.g. conflict and ignorance) with adaptive, incremental learning policies in the field of online machine learning .", "subpage_snippet": "", "source": "younsess-elbrag.medium.com", "link": "https://younsess-elbrag.medium.com/active-learning-approaches-strategies-deep-learning-integration-and-essential-tools-6ff2bdfe5cb", "content": "Oct 1, 2023 · Recent developments are dedicated to multi-label active learning , hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of machine learning (e.g. conflict and ignorance) with adaptive, incremental learning policies in the field of online machine learning ."}
diff --git a/data/sampled_jsons/Active_Learning_machine_learning_query_strategy.jsonl b/data/sampled_jsons/Active_Learning_machine_learning_query_strategy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8ddfad0a464a839f95fb40cdb12a3c4e8cef7edf
--- /dev/null
+++ b/data/sampled_jsons/Active_Learning_machine_learning_query_strategy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Active Learning in Machine Learning Guide [Full Guide] | Encord", "date": "", "ddg_snippet": "Learn more Active Learning in Machine Learning : What is it? Active learning is an approach that strategically selects data points for labeling to optimize the learning process. Unlike traditional supervised learning , where a fixed dataset with labeled examples is used for training, active learning algorithms actively query for the most informative data points to label. The primary objective is ...", "subpage_snippet": "", "source": "encord.com", "link": "https://encord.com/blog/active-learning-machine-learning-guide/", "content": "Learn more Active Learning in Machine Learning : What is it? Active learning is an approach that strategically selects data points for labeling to optimize the learning process. Unlike traditional supervised learning , where a fixed dataset with labeled examples is used for training, active learning algorithms actively query for the most informative data points to label. The primary objective is ..."}
+{"idx": 1, "title": "Active Learning in Classification — Query Strategies - Medium", "date": "", "ddg_snippet": "The most unique and charming part of active learning is query strategies choice. Query strategy is the approach to select the most informative examples which will be considered the next incoming ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@rongqianhui/active-learning-in-classification-query-strategies-69cc9fe70938", "content": "The most unique and charming part of active learning is query strategies choice. Query strategy is the approach to select the most informative examples which will be considered the next incoming ..."}
+{"idx": 2, "title": "Active Learning Scenarios and Query Strategies", "date": "", "ddg_snippet": "17 Mar 2024 — One of the most widely used query strategies in active learning is uncertainty sampling . The idea behind this strategy is to select the samples ...", "subpage_snippet": "", "source": "gpttutorpro.com", "link": "https://gpttutorpro.com/active-learning-scenarios-and-query-strategies/", "content": "17 Mar 2024 — One of the most widely used query strategies in active learning is uncertainty sampling . The idea behind this strategy is to select the samples ..."}
+{"idx": 3, "title": "Active Learning in Machine Learning [Guide & Examples]", "date": "", "ddg_snippet": "31 Aug 2022 — Active Learning Query Strategies · Stream-based Selective Sampling · Pool-based Sampling · Query Synthesis Methods.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/active-learning-guide", "content": "31 Aug 2022 — Active Learning Query Strategies · Stream-based Selective Sampling · Pool-based Sampling · Query Synthesis Methods."}
+{"idx": 4, "title": "Query Strategy", "date": "", "ddg_snippet": "Query strategies in active learning refer to methods used to select informative samples to be labeled by a human annotator, with the goal of improving the ...", "subpage_snippet": "", "source": "encord.com", "link": "https://encord.com/glossary/query-strategies/", "content": "Query strategies in active learning refer to methods used to select informative samples to be labeled by a human annotator, with the goal of improving the ..."}
+{"idx": 5, "title": "Demystifying Active Learning", "date": "", "ddg_snippet": "Query strategy · Given the features of an unlabeled instance, it generates M dropout variants of the input (e.g., with a dropout rate ρ = 0.75).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@parklize/demystifying-active-learning-dea6175779f1", "content": "Query strategy · Given the features of an unlabeled instance, it generates M dropout variants of the input (e.g., with a dropout rate ρ = 0.75)."}
+{"idx": 6, "title": "AutoAL: Automated Active Learning with Differentiable ...", "date": "", "ddg_snippet": "by Y Wang · 2024 — This work presents the first differentiable AL strategy search method, named AutoAL, which is designed on top of existing AL sampling strategies .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.13853", "content": "by Y Wang · 2024 — This work presents the first differentiable AL strategy search method, named AutoAL, which is designed on top of existing AL sampling strategies ."}
+{"idx": 7, "title": "Revisiting Uncertainty-based Query Strategies for Active ...", "date": "", "ddg_snippet": "by C Schröder · 2022 · Cited by 109 — This paper revisits uncertainty-based query strategies for active learning with transformers, which are computationally inexpensive and ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.findings-acl.172.pdf", "content": "by C Schröder · 2022 · Cited by 109 — This paper revisits uncertainty-based query strategies for active learning with transformers, which are computationally inexpensive and ..."}
+{"idx": 8, "title": "A Review Of Active Learning Strategies", "date": "", "ddg_snippet": "by HBML Engineer — Active learning strategies include stream-based, pool-based, uncertainty sampling, diversity sampling, and expected model change.", "subpage_snippet": "", "source": "www.sagacify.com", "link": "https://www.sagacify.com/news/what-is-active-learning", "content": "by HBML Engineer — Active learning strategies include stream-based, pool-based, uncertainty sampling, diversity sampling, and expected model change."}
+{"idx": 9, "title": "ML | Active Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Active Learning is a special case of Supervised Machine Learning . This approach is used to construct a high-performance classifier while keeping the size of the training dataset to a minimum by actively selecting the valuable data points. Active Learning in Machine Learning A subset of machine learning known as \" active learning \" allows a learning algorithm to interactively query a user to ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/ml-active-learning/", "content": "Active Learning is a special case of Supervised Machine Learning . This approach is used to construct a high-performance classifier while keeping the size of the training dataset to a minimum by actively selecting the valuable data points. Active Learning in Machine Learning A subset of machine learning known as \" active learning \" allows a learning algorithm to interactively query a user to ..."}
diff --git a/data/sampled_jsons/Adcock_&_Collier_2001_Measurement_validity_A_shared_standard_for_qualitative_and_quantitative_resear.jsonl b/data/sampled_jsons/Adcock_&_Collier_2001_Measurement_validity_A_shared_standard_for_qualitative_and_quantitative_resear.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5c4a0757513f7e2a048fdbe3fdb682be731b9ac
--- /dev/null
+++ b/data/sampled_jsons/Adcock_&_Collier_2001_Measurement_validity_A_shared_standard_for_qualitative_and_quantitative_resear.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Measurement Validity : A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Vol. 95, No. 3 September 2001 . Measurement Validity : A Shared Standard for Qualitative and Quantitative Research .September 2001 . of measurement validity — an issue that arises when a measure that is valid in one context is invalid in another.", "subpage_snippet": "", "source": "www.theisrm.org", "link": "https://www.theisrm.org/documents/Adcock+&+Collier+(2001)+Measurement+Validity+-+A+Shared+Standard+for+Qualitative+and+Quantitative+Research.pdf", "content": "Vol. 95, No. 3 September 2001 . Measurement Validity : A Shared Standard for Qualitative and Quantitative Research .September 2001 . of measurement validity — an issue that arises when a measure that is valid in one context is invalid in another."}
+{"idx": 1, "title": "(PDF) Measurement Validity : A Shared Standard For Qualitative ...", "date": "", "ddg_snippet": "... Validity Measures can be reliable but still not be valid . Researchers must also consider whether their evaluation techniques meaningfully capture the intended construct ( Adcock and Collier , 2001 ).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228277142_Measurement_Validity_A_Shared_Standard_For_Qualitative_and_Quantitative_Research", "content": "... Validity Measures can be reliable but still not be valid . Researchers must also consider whether their evaluation techniques meaningfully capture the intended construct ( Adcock and Collier , 2001 )."}
+{"idx": 2, "title": "(PDF) Measurement Validity : A Shared Standard for Qualitative ...", "date": "", "ddg_snippet": "First page of “ Measurement Validity : A Shared Standard for Qualitative and Quantitative Research ” PDF Icon. download.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/21470767/Measurement_Validity_A_Shared_Standard_for_Qualitative_and_Quantitative_Research", "content": "First page of “ Measurement Validity : A Shared Standard for Qualitative and Quantitative Research ” PDF Icon. download."}
+{"idx": 3, "title": "Measurement Validity : A Shared Standard for Qualitative and", "date": "", "ddg_snippet": "Vol. 95, No. 3 September 2001 Measurement Validity : A Shared Standard for Qualitative and Quantitative Research . ROBERT ADCOCK and DAVID COLLIER University of California, Berkeley cholars routinely make claims that presuppose the validity ofthe observations and...", "subpage_snippet": "", "source": "polisci.berkeley.edu", "link": "https://polisci.berkeley.edu/sites/default/files/people/u3827/APSR2001-Validity.pdf", "content": "Vol. 95, No. 3 September 2001 Measurement Validity : A Shared Standard for Qualitative and Quantitative Research . ROBERT ADCOCK and DAVID COLLIER University of California, Berkeley cholars routinely make claims that presuppose the validity ofthe observations and..."}
+{"idx": 4, "title": "Measurement Validity : A Shared Standard for Qualitative ... | Scilit", "date": "", "ddg_snippet": "Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts.", "subpage_snippet": "", "source": "www.scilit.com", "link": "https://www.scilit.com/publications/9dd30fc5aebd3fba0a89375267c7903f", "content": "Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts."}
+{"idx": 5, "title": "Measurement Validity : A Shared Standard for Qualitative ... :: SSRN", "date": "", "ddg_snippet": "Adcock , Robert and Collier , David, Measurement Validity : A Shared Standard for Qualitative and Quantitative Research (September 1, 2001 ).", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1677697", "content": "Adcock , Robert and Collier , David, Measurement Validity : A Shared Standard for Qualitative and Quantitative Research (September 1, 2001 )."}
+{"idx": 6, "title": "Measurement Validity : A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Vol. 95, No. 3 September 2001 . Measurement Validity : A Shared Standard for Qualitative and Quantitative Research . ROBERT ADCOCK and DAVID COLLIER University of California, Berkeley. S. cholars routinely make claims that presuppose the validity of the observations and...", "subpage_snippet": "", "source": "www.jakebowers.org", "link": "http://www.jakebowers.org/ITVExperiments/Adcock2001.pdf", "content": "Vol. 95, No. 3 September 2001 . Measurement Validity : A Shared Standard for Qualitative and Quantitative Research . ROBERT ADCOCK and DAVID COLLIER University of California, Berkeley. S. cholars routinely make claims that presuppose the validity of the observations and..."}
+{"idx": 7, "title": "[PDF] Measurement Validity : A Shared Standard for Qualitative ...", "date": "", "ddg_snippet": "R. Adcock , D. Collier .First, we seek to establish a shared framework that allows quantitative and qualitative scholars to assess more effectively, and communicate about, issues of valid measurement .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Measurement-Validity:-A-Shared-Standard-for-and-Adcock-Collier/230929efa285855cad2dc6e21dc7850f7a81d8e9", "content": "R. Adcock , D. Collier .First, we seek to establish a shared framework that allows quantitative and qualitative scholars to assess more effectively, and communicate about, issues of valid measurement ."}
+{"idx": 8, "title": "Measurement Validity : A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "540 COLLIER & ADCOCK 2 2For example, among advocates of a graded approach, Bollen (1993) and Bollen & Paxton (2000) begin with eight ordered scales (each involving between two and seven categories)...", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/uc/item/945280s6", "content": "540 COLLIER & ADCOCK 2 2For example, among advocates of a graded approach, Bollen (1993) and Bollen & Paxton (2000) begin with eight ordered scales (each involving between two and seven categories)..."}
+{"idx": 9, "title": "Measurement Validity | Measuring Democracy", "date": "", "ddg_snippet": "I found Adcock and Collier ’s piece “ Measurement Validity : A shared standard for qualitative and quantitative research to be the most worthy of note this week.", "subpage_snippet": "", "source": "blogs.ubc.ca", "link": "https://blogs.ubc.ca/louiselupoli423/2013/01/22/measurement-validity/", "content": "I found Adcock and Collier ’s piece “ Measurement Validity : A shared standard for qualitative and quantitative research to be the most worthy of note this week."}
diff --git a/data/sampled_jsons/Adcock_Collier_2001_measurement_validity_year_2001.jsonl b/data/sampled_jsons/Adcock_Collier_2001_measurement_validity_year_2001.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1e66176d9b9090c98fa5d3e6c0ed86366ac974a6
--- /dev/null
+++ b/data/sampled_jsons/Adcock_Collier_2001_measurement_validity_year_2001.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "ROBERT ADCOCK and DAVID COLLIER University of California, Berkeley Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts. Yet, despite recent advances in political science methods, surprisingly little attention has been devoted to measurement validity .", "subpage_snippet": "", "source": "www.theisrm.org", "link": "https://www.theisrm.org/documents/Adcock+&+Collier+(2001)+Measurement+Validity+-+A+Shared+Standard+for+Qualitative+and+Quantitative+Research.pdf", "content": "ROBERT ADCOCK and DAVID COLLIER University of California, Berkeley Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts. Yet, despite recent advances in political science methods, surprisingly little attention has been devoted to measurement validity ."}
+{"idx": 1, "title": "Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts. Yet, despite recent advances in political science methods, surprisingly little attention has been devoted to measurement validity . We address this gap by exploring four themes. First, we seek to establish a shared framework that allows quantitative and ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/american-political-science-review/article/abs/measurement-validity-a-shared-standard-for-qualitative-and-quantitative-research/91C7A9800DB26A76EBBABC5889A50C8B", "content": "Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts. Yet, despite recent advances in political science methods, surprisingly little attention has been devoted to measurement validity . We address this gap by exploring four themes. First, we seek to establish a shared framework that allows quantitative and ..."}
+{"idx": 2, "title": "Adcock y Collier (2001) - Measurement Validity. A Shared ...", "date": "", "ddg_snippet": "Measurement validity : A shared standard for qualitative and quantitative research Adcock , Robert; Collier , David The American Political Science Review; Sep 2001 ; 95, 3; ProQuest Central pg. 529 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. fReproduced with permission of the copyright owner. Further reproduction prohibited without ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/520421528/Adcock-y-Collier-2001-Measurement-Validity-a-Shared-Standart-for-Qualitative-and-Quantitative-Research", "content": "Measurement validity : A shared standard for qualitative and quantitative research Adcock , Robert; Collier , David The American Political Science Review; Sep 2001 ; 95, 3; ProQuest Central pg. 529 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. fReproduced with permission of the copyright owner. Further reproduction prohibited without ..."}
+{"idx": 3, "title": "Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Robert Adcock (adcockr@uclink4.berkeley.edu) is a Ph.D candi- This literature provides an opportunity to identify date, Department of Political Science, and David Collier parallel concerns about validity as well as differences in (dcollier@socrates.berkeley.edu) is Professor of Political Science, specific practices.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/3118231", "content": "Robert Adcock (adcockr@uclink4.berkeley.edu) is a Ph.D candi- This literature provides an opportunity to identify date, Department of Political Science, and David Collier parallel concerns about validity as well as differences in (dcollier@socrates.berkeley.edu) is Professor of Political Science, specific practices."}
+{"idx": 4, "title": "(PDF) Measurement Validity: A Shared Standard For Qualitative ...", "date": "", "ddg_snippet": "Sep 1, 2001 · The literature has pointed to three different strategies for measurement validity ( Adcock and Collier 2001 ): content validity (the measurement captures the full content of the definition ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228277142_Measurement_Validity_A_Shared_Standard_For_Qualitative_and_Quantitative_Research", "content": "Sep 1, 2001 · The literature has pointed to three different strategies for measurement validity ( Adcock and Collier 2001 ): content validity (the measurement captures the full content of the definition ..."}
+{"idx": 5, "title": "Measurement validity: A shared standard for - ProQuest", "date": "", "ddg_snippet": "Measurement validity : A shared standard for qualitative and quantitative research Adcock , Robert; Collier , David. The American Political Science Review; Washington Vol. 95, Iss. 3, (Sep 2001 ): 529-546.", "subpage_snippet": "", "source": "www.proquest.com", "link": "https://www.proquest.com/docview/214424808", "content": "Measurement validity : A shared standard for qualitative and quantitative research Adcock , Robert; Collier , David. The American Political Science Review; Washington Vol. 95, Iss. 3, (Sep 2001 ): 529-546."}
+{"idx": 6, "title": "Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Measurement Validity: A Shared Standard for Qualitative and Quantitative Research Robert Adcock and David Collier American Political Science Review, 2001 , vol. 95, issue 3, 529-546 Abstract: Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts.", "subpage_snippet": "", "source": "econpapers.repec.org", "link": "https://econpapers.repec.org/RePEc:cup:apsrev:v:95:y:2001:i:03:p:529-546_00", "content": "Measurement Validity: A Shared Standard for Qualitative and Quantitative Research Robert Adcock and David Collier American Political Science Review, 2001 , vol. 95, issue 3, 529-546 Abstract: Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts."}
+{"idx": 7, "title": "Measurement Validity : A Shared Standard for Qualitative and", "date": "", "ddg_snippet": "Vol. 95, No. 3 September 2001 Measurement Validity : A Shared Standard for Qualitative and Quantitative Research. ROBERT ADCOCK and DAVID COLLIER University of California, Berkeley cholars routinely make claims that presuppose the validity ofthe observations and...", "subpage_snippet": "", "source": "polisci.berkeley.edu", "link": "https://polisci.berkeley.edu/sites/default/files/people/u3827/APSR2001-Validity.pdf", "content": "Vol. 95, No. 3 September 2001 Measurement Validity : A Shared Standard for Qualitative and Quantitative Research. ROBERT ADCOCK and DAVID COLLIER University of California, Berkeley cholars routinely make claims that presuppose the validity ofthe observations and..."}
+{"idx": 8, "title": "[PDF] Measurement Validity : A Shared Standard... | Semantic Scholar", "date": "", "ddg_snippet": "Measurement Validity : A Shared Standard for Qualitative and Quantitative Research.R. Adcock , D. Collier . Published in American Political Science…1 September 2001 .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Measurement-Validity:-A-Shared-Standard-for-and-Adcock-Collier/230929efa285855cad2dc6e21dc7850f7a81d8e9", "content": "Measurement Validity : A Shared Standard for Qualitative and Quantitative Research.R. Adcock , D. Collier . Published in American Political Science…1 September 2001 ."}
+{"idx": 9, "title": "Measurement Validity : A Shared Standard for Qualitative and...", "date": "", "ddg_snippet": "2001 .to Choices about Concepts David Collier and Robert Adcock Department of Political Science, University of California, Berkeley, California 94720- 1950; e-mail: dcollier@socrates.berkeley.edu KEY WORDS: methodology, concept formation, validity , levels of measurement , regimes...", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/uc/item/945280s6", "content": "2001 .to Choices about Concepts David Collier and Robert Adcock Department of Political Science, University of California, Berkeley, California 94720- 1950; e-mail: dcollier@socrates.berkeley.edu KEY WORDS: methodology, concept formation, validity , levels of measurement , regimes..."}
diff --git a/data/sampled_jsons/Algorithm_1_Deliberate_Practice_Synthetic_Data_Generation_DP_trigger_new_synthetic_data.jsonl b/data/sampled_jsons/Algorithm_1_Deliberate_Practice_Synthetic_Data_Generation_DP_trigger_new_synthetic_data.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0648437c260766a10ab46a1a5d6d08cb8ffa5167
--- /dev/null
+++ b/data/sampled_jsons/Algorithm_1_Deliberate_Practice_Synthetic_Data_Generation_DP_trigger_new_synthetic_data.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation ( DP ), a novel framework that improves sample efficiency through dynamic synthetic data generation . Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.15588", "content": "Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation ( DP ), a novel framework that improves sample efficiency through dynamic synthetic data generation . Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been ..."}
+{"idx": 1, "title": "GitHub - microsoft/DPSDA: Private Evolution: Generating DP Synthetic ...", "date": "", "ddg_snippet": "This repo is a Python library to generate differentially private ( DP ) synthetic data without the need of any ML model training. It is based on the following papers that proposed Private Evolution (PE), a new DP synthetic data framework that only utilizes the blackbox inference APIs of foundation models (e.g., Stable Diffusion, GPT models).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/microsoft/DPSDA", "content": "This repo is a Python library to generate differentially private ( DP ) synthetic data without the need of any ML model training. It is based on the following papers that proposed Private Evolution (PE), a new DP synthetic data framework that only utilizes the blackbox inference APIs of foundation models (e.g., Stable Diffusion, GPT models)."}
+{"idx": 2, "title": "Generating synthetic data with differentially private LLM inference", "date": "", "ddg_snippet": "In \" Private prediction for large-scale synthetic text generation \", we present an inference-only approach for generating DP synthetic data . The approach works by prompting an off-the-shelf LLM with many sensitive examples in parallel, and aggregating their predictions with differential privacy.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/generating-synthetic-data-with-differentially-private-llm-inference/", "content": "In \" Private prediction for large-scale synthetic text generation \", we present an inference-only approach for generating DP synthetic data . The approach works by prompting an off-the-shelf LLM with many sensitive examples in parallel, and aggregating their predictions with differential privacy."}
+{"idx": 3, "title": "Synthetic Data Generation: A Comprehensive Guide", "date": "", "ddg_snippet": "Explore Synthetic Data Generation for AI: Overcome data limits, ensure privacy, and boost ethics with practical Python examples.", "subpage_snippet": "", "source": "letsdatascience.com", "link": "https://letsdatascience.com/synthetic-data-generation/", "content": "Explore Synthetic Data Generation for AI: Overcome data limits, ensure privacy, and boost ethics with practical Python examples."}
+{"idx": 4, "title": "Synthetic Text Generation with Differential Privacy: A Simple and ...", "date": "", "ddg_snippet": "Generating synthetic versions of such data with a formal privacy guarantee, such as differential privacy ( DP ), provides a promising path to mitigating these privacy concerns, but previous approaches in this direction have typically failed to produce synthetic data of high quality.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.acl-long.74/", "content": "Generating synthetic versions of such data with a formal privacy guarantee, such as differential privacy ( DP ), provides a promising path to mitigating these privacy concerns, but previous approaches in this direction have typically failed to produce synthetic data of high quality."}
+{"idx": 5, "title": "Synthetic Data Generation in Python: A Hands-On Guide", "date": "", "ddg_snippet": "Learn about synthetic data generation using Python in this hands-on guide. Explore techniques, tools, and code examples to enhance AI and machine learning models.", "subpage_snippet": "", "source": "www.datacamp.com", "link": "https://www.datacamp.com/tutorial/synthetic-data-generation", "content": "Learn about synthetic data generation using Python in this hands-on guide. Explore techniques, tools, and code examples to enhance AI and machine learning models."}
+{"idx": 6, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "Inspired by the principle of deliberate prac - tice in human learning, we propose Deliberate Practice for Synthetic Data Generation ( DP ), a novel framework that improves sample efficiency through dynamic synthetic data generation . Prior work has shown that scaling synthetic data is in- herently challenging, as naively adding new data leads to diminishing returns. To address this, prun- ing has ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0LZRtvK871", "content": "Inspired by the principle of deliberate prac - tice in human learning, we propose Deliberate Practice for Synthetic Data Generation ( DP ), a novel framework that improves sample efficiency through dynamic synthetic data generation . Prior work has shown that scaling synthetic data is in- herently challenging, as naively adding new data leads to diminishing returns. To address this, prun- ing has ..."}
+{"idx": 7, "title": "Synthetic Data Generation - GeeksforGeeks", "date": "", "ddg_snippet": "Synthetic data generation is the process of creating artificial data that mimics the statistical properties of real-world data . Synthetic data can be used for training machine learning models, testing algorithms , and more.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/artificial-intelligence/synthetic-data-generation/", "content": "Synthetic data generation is the process of creating artificial data that mimics the statistical properties of real-world data . Synthetic data can be used for training machine learning models, testing algorithms , and more."}
+{"idx": 8, "title": "Best Practices and Lessons Learned on Synthetic Data", "date": "", "ddg_snippet": "Abstract The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns. This paper provides an overview of synthetic data research, discussing its ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.07503", "content": "Abstract The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns. This paper provides an overview of synthetic data research, discussing its ..."}
+{"idx": 9, "title": "SYNTHETIC-1: Scaling Distributed Synthetic Data Generation for Verified ...", "date": "", "ddg_snippet": "Today, we are excited to introduce SYNTHETIC-1, a collaborative effort to create the largest open-source dataset of verified reasoning traces for math, coding and science, leveraging DeepSeek-R1. Our dataset consists of 1.4 million high-quality tasks and verifiers, designed to advance reasoning model training.", "subpage_snippet": "", "source": "www.primeintellect.ai", "link": "https://www.primeintellect.ai/blog/synthetic-1", "content": "Today, we are excited to introduce SYNTHETIC-1, a collaborative effort to create the largest open-source dataset of verified reasoning traces for math, coding and science, leveraging DeepSeek-R1. Our dataset consists of 1.4 million high-quality tasks and verifiers, designed to advance reasoning model training."}
diff --git "a/data/sampled_jsons/Algorithm_1_sensitivity_\317\2041_\317\2042_block_optimization_SPD_sync-point_drop_LLM.jsonl" "b/data/sampled_jsons/Algorithm_1_sensitivity_\317\2041_\317\2042_block_optimization_SPD_sync-point_drop_LLM.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..1985757a0cb6488f51627421dc5b96fe2d9532c5
--- /dev/null
+++ "b/data/sampled_jsons/Algorithm_1_sensitivity_\317\2041_\317\2042_block_optimization_SPD_sync-point_drop_LLM.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dying Light The Beast Frame Drops Fix Guide for PC", "date": "", "ddg_snippet": "The problem often starts mild but can get worse the longer you play, leading many to suspect a memory leak or optimization issue. While we wait for patches, there are some steps you can take right now to improve performance. How to fix Dying Light The Beast frame drops .", "subpage_snippet": "", "source": "gamerblurb.com", "link": "https://gamerblurb.com/articles/dying-light-the-beast-fov-guide-how-to-change-field-of-view-9kydz", "content": "The problem often starts mild but can get worse the longer you play, leading many to suspect a memory leak or optimization issue. While we wait for patches, there are some steps you can take right now to improve performance. How to fix Dying Light The Beast frame drops ."}
+{"idx": 1, "title": "Find the point on the parabola y^2 = 2x that is closest to... - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=-cnt9Q0Rqn4", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 2, "title": "Q-ROAR: Outlier-Aware Rescaling for RoPE Position Interpolation in...", "date": "", "ddg_snippet": "We provide the first systematic analysis of PI plus PTQ and introduce two diagnostics: Interpolation Pressure (per-band phase scaling sensitivity ) and Tail Inflation Ratios (outlier shift from short to long contexts). τ 1 +log(ωb,med /ωmin ). and and.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14391", "content": "We provide the first systematic analysis of PI plus PTQ and introduce two diagnostics: Interpolation Pressure (per-band phase scaling sensitivity ) and Tail Inflation Ratios (outlier shift from short to long contexts). τ 1 +log(ωb,med /ωmin ). and and."}
+{"idx": 3, "title": "Writing my own communications library - a worklog of creating Penny...", "date": "", "ddg_snippet": "Choosing LLM inference makes things simpler as it almost only relies on AllReduce so this is the first algorithm that I ’ll try to implement. This will be the first part of a worklog on it, showing my progress.", "subpage_snippet": "", "source": "szymonozog.github.io", "link": "https://szymonozog.github.io/posts/2025-09-21-Penny-worklog-1.html", "content": "Choosing LLM inference makes things simpler as it almost only relies on AllReduce so this is the first algorithm that I ’ll try to implement. This will be the first part of a worklog on it, showing my progress."}
+{"idx": 4, "title": "Kimi K2: Open Agentic Intelligence", "date": "", "ddg_snippet": "Post-training combines large-scale synthetic tool-use data with a unified RL framework using both verifiable rewards and self-critic feedbacks. Kimi K2 sets new state-of-the-art on agentic and reasoning benchmarks, establishing itself as the most capable open-weight LLM to date.", "subpage_snippet": "", "source": "agenticai-learning.org", "link": "https://agenticai-learning.org/slides/d11.pdf", "content": "Post-training combines large-scale synthetic tool-use data with a unified RL framework using both verifiable rewards and self-critic feedbacks. Kimi K2 sets new state-of-the-art on agentic and reasoning benchmarks, establishing itself as the most capable open-weight LLM to date."}
+{"idx": 5, "title": "Dying Light: The Beast: Chain Reaction Sidequest Guide", "date": "", "ddg_snippet": "Selby pointing a gun at Lucy Dying Light The Beast.Funnily enough, Lucy and Selby are in the first room to your left as soon as you enter the house, right before the sofa blocking your path. Making The Decision And Quest Completion. Choosing whether to save Lucy or let Selby kill Lucy.", "subpage_snippet": "", "source": "www.dualshockers.com", "link": "https://www.dualshockers.com/dying-light-the-beast-chain-reaction-sidequest-guide/", "content": "Selby pointing a gun at Lucy Dying Light The Beast.Funnily enough, Lucy and Selby are in the first room to your left as soon as you enter the house, right before the sofa blocking your path. Making The Decision And Quest Completion. Choosing whether to save Lucy or let Selby kill Lucy."}
+{"idx": 6, "title": "La-MAML: Look-ahead Meta Learning for", "date": "", "ddg_snippet": "In this work, we propose Look-ahead MAML (La-MAML), a fast optimisation -based meta-learning algorithm for online-continual learning, aided by a small episodic memory.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2020/file/85b9a5ac91cd629bd3afe396ec07270a-Paper.pdf", "content": "In this work, we propose Look-ahead MAML (La-MAML), a fast optimisation -based meta-learning algorithm for online-continual learning, aided by a small episodic memory."}
+{"idx": 7, "title": "WAN 2.2 VACE: Object Swap with an Image in ComfyUI (GGUF Q5)", "date": "", "ddg_snippet": "Uses point -based masking, a 3-sampler chain, and gentle LoRA weights. Works with FP8 and... Two loaders are present: one High-Noise and one Low-Noise.You can choose FP8 or Q5 GGUF builds here.", "subpage_snippet": "", "source": "aistudynow.com", "link": "https://aistudynow.com/wan-2-2-vace-object-swap-with-an-image-in-comfyui-gguf-q5/", "content": "Uses point -based masking, a 3-sampler chain, and gentle LoRA weights. Works with FP8 and... Two loaders are present: one High-Noise and one Low-Noise.You can choose FP8 or Q5 GGUF builds here."}
+{"idx": 8, "title": "PartialCorrelationFunction—Wolfram Documentation", "date": "", "ddg_snippet": "Model-Based Design. Algorithm Development. Wolfram|Alpha for Business. Blockchain Technology.use explicit { τ 1 , τ 2 ,…} Examples.", "subpage_snippet": "", "source": "reference.wolfram.com", "link": "https://reference.wolfram.com/language/ref/PartialCorrelationFunction.html.en?source=footer", "content": "Model-Based Design. Algorithm Development. Wolfram|Alpha for Business. Blockchain Technology.use explicit { τ 1 , τ 2 ,…} Examples."}
+{"idx": 9, "title": "zen Rocket League settings (2025): Configuration, controller binds...", "date": "", "ddg_snippet": "Steering Sensitivity : 1 .50.Resolution: 1920 x 1080 (16:9). Display Mode: Fullscreen. V- Sync : Disabled. Anti-Aliasing: Off. Basic Settings.", "subpage_snippet": "", "source": "www.sportskeeda.com", "link": "https://www.sportskeeda.com/esports/zen-rocket-league-settings-configuration-controller-binds-sensitivity", "content": "Steering Sensitivity : 1 .50.Resolution: 1920 x 1080 (16:9). Display Mode: Fullscreen. V- Sync : Disabled. Anti-Aliasing: Off. Basic Settings."}
diff --git "a/data/sampled_jsons/Algorithm_1_\316\264_tilde_Statistical_Collusion_by_Collectives_on_Learning_Platforms.jsonl" "b/data/sampled_jsons/Algorithm_1_\316\264_tilde_Statistical_Collusion_by_Collectives_on_Learning_Platforms.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..1f35e615ae190d4501f089bff2ea9b2cb435d2bc
--- /dev/null
+++ "b/data/sampled_jsons/Algorithm_1_\316\264_tilde_Statistical_Collusion_by_Collectives_on_Learning_Platforms.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Risk Preferences of Learning Algorithms1footnote 11footnote 1We", "date": "", "ddg_snippet": "... the homogenization of the content on these platforms , as more divergent content gets screened out by the algorithm ’s decision to not recommend it.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2205.04619v3", "content": "... the homogenization of the content on these platforms , as more divergent content gets screened out by the algorithm ’s decision to not recommend it."}
+{"idx": 1, "title": "Robust Performance Incentivizing Algorithms for Multi-Armed", "date": "", "ddg_snippet": "We do this by identifying a collection of intuitive properties that a bandit algorithm has to satisfy to achieve these objectives.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.07929v2", "content": "We do this by identifying a collection of intuitive properties that a bandit algorithm has to satisfy to achieve these objectives."}
+{"idx": 2, "title": "Regulation of Algorithmic Collusion", "date": "", "ddg_snippet": "The definition allows a regulator to empirically audit algorithms by applying a statistical test to the data that they collect.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.15794v2", "content": "The definition allows a regulator to empirically audit algorithms by applying a statistical test to the data that they collect."}
+{"idx": 3, "title": "1 Introduction", "date": "", "ddg_snippet": "Online collaborative medical prediction platforms offer convenience and real-time feedback by leveraging massive electronic health records.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11187v1", "content": "Online collaborative medical prediction platforms offer convenience and real-time feedback by leveraging massive electronic health records."}
+{"idx": 4, "title": "Downloads", "date": "", "ddg_snippet": "2nd Workshop on Machine Learning on the Phone ... Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2018", "content": "2nd Workshop on Machine Learning on the Phone ... Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced"}
+{"idx": 5, "title": "Kai-Min CHUNG | Academia Sinica, Taipei | Institute of", "date": "", "ddg_snippet": "We study online routing problems with predictions, inspired by recent exciting results emerged from the area of learning -augmented algorithms .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Kai-Min-Chung", "content": "We study online routing problems with predictions, inspired by recent exciting results emerged from the area of learning -augmented algorithms ."}
+{"idx": 6, "title": "A secure and scalable IoT access control framework with dynamic", "date": "", "ddg_snippet": "For example, by designing a lightweight blockchain structure model and efficient consensus algorithms , the performance of blockchain can be improved ...", "subpage_snippet": "", "source": "societyartrock.org", "link": "https://societyartrock.org/article/a-secure-and-scalable-iot-access-control-framework-with-dynamic-attribute-updates-and-policy-hiding", "content": "For example, by designing a lightweight blockchain structure model and efficient consensus algorithms , the performance of blockchain can be improved ..."}
+{"idx": 7, "title": "INDY Lab - Publications", "date": "", "ddg_snippet": "By doing so, BO algorithms typically introduce additional restrictive assumptions on the additive structure that reduce their applicability domain.", "subpage_snippet": "", "source": "indy.epfl.ch", "link": "https://indy.epfl.ch/publications", "content": "By doing so, BO algorithms typically introduce additional restrictive assumptions on the additive structure that reduce their applicability domain."}
+{"idx": 8, "title": "1 Introduction", "date": "", "ddg_snippet": "... 129 billion dollars on digital advertising, surpassing for the first time the combined amount spent via traditional advertising channels by 20 billion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.02796v1", "content": "... 129 billion dollars on digital advertising, surpassing for the first time the combined amount spent via traditional advertising channels by 20 billion ..."}
+{"idx": 9, "title": "ACT6100 | Freakonometrics", "date": "", "ddg_snippet": "... exemple bien connu étant la norme de Mahalanobis , ou la distance associée, où on prend pour \\boldsymbol{M} la matrice \\boldsymbol{\\Sigma}^{- 1 ...", "subpage_snippet": "", "source": "freakonometrics.hypotheses.org", "link": "https://freakonometrics.hypotheses.org/category/courses/act6100", "content": "... exemple bien connu étant la norme de Mahalanobis , ou la distance associée, où on prend pour \\boldsymbol{M} la matrice \\boldsymbol{\\Sigma}^{- 1 ..."}
diff --git a/data/sampled_jsons/AltUp_Baykal_et_al.,_2023_abstract.jsonl b/data/sampled_jsons/AltUp_Baykal_et_al.,_2023_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f130b79ae5a58672ad1b7647d8d438f15967fe5f
--- /dev/null
+++ b/data/sampled_jsons/AltUp_Baykal_et_al.,_2023_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NeurIPS 2023 Spotlight Posters", "date": "", "ddg_snippet": "Although the mapping between sound and meaning in human language is assumed to be largely arbitrary, research in cognitive science has shown that ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/events/spotlight-posters-2023", "content": "Although the mapping between sound and meaning in human language is assumed to be largely arbitrary, research in cognitive science has shown that ..."}
+{"idx": 1, "title": "NeurIPS 2023 Spotlight Posters", "date": "", "ddg_snippet": "Although the mapping between sound and meaning in human language is assumed to be largely arbitrary, research in cognitive science has shown that ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/events/spotlight-posters-2023", "content": "Although the mapping between sound and meaning in human language is assumed to be largely arbitrary, research in cognitive science has shown that ..."}
+{"idx": 2, "title": "PDF NeurIPS 2023 Presentation", "date": "", "ddg_snippet": "Can we get the benefits of increased representation dimension without the full computational cost? Performance for a word2vec model with varying representation dimension (figure from Yang et al., 2022).", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2023/Slides/72994.pdf", "content": "Can we get the benefits of increased representation dimension without the full computational cost? Performance for a word2vec model with varying representation dimension (figure from Yang et al., 2022)."}
+{"idx": 3, "title": "[2301.13310] Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.13310", "content": "It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening ..."}
+{"idx": 4, "title": "Alternating Updates for Efficient Transformers - NIPS", "date": "", "ddg_snippet": "Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/hash/f2059277ac6ce66e7e5543001afa8bb5-Abstract-Conference.html", "content": "Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ..."}
+{"idx": 5, "title": "Alternating updates for efficient transformers | Proceedings of the ...", "date": "", "ddg_snippet": "We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3669474", "content": "We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency."}
+{"idx": 6, "title": "Alternating Updates for Efficient Transformers - Semantic Scholar", "date": "", "ddg_snippet": "This work introduces Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden, and demonstrates how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Alternating-Updates-for-Efficient-Transformers-Baykal-Cutler/c9d46cfcf0211d11356c295ecd9584c84c19c8f8/figure/0", "content": "This work introduces Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden, and demonstrates how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity."}
+{"idx": 7, "title": "Alternating updates for efficient transformers - Google Research", "date": "", "ddg_snippet": "In \" Alternating Updates for Efficient Transformers \", accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost. AltUp is easy to implement, widely applicable to any transformer architecture, and requires minimal hyperparameter tuning.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/alternating-updates-for-efficient-transformers/", "content": "In \" Alternating Updates for Efficient Transformers \", accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost. AltUp is easy to implement, widely applicable to any transformer architecture, and requires minimal hyperparameter tuning."}
+{"idx": 8, "title": "Paper page - Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "Abstract Alternating Updates ( AltUp ) increases transformer model capacity without significantly impacting latency, achieving up to 87% speedup on benchmarks while maintaining accuracy.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2301.13310", "content": "Abstract Alternating Updates ( AltUp ) increases transformer model capacity without significantly impacting latency, achieving up to 87% speedup on benchmarks while maintaining accuracy."}
+{"idx": 9, "title": "PDF Alternating Updates for Efficient Transforme - papers.nips.cc", "date": "", "ddg_snippet": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2023/file/f2059277ac6ce66e7e5543001afa8bb5-Paper-Conference.pdf", "content": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ..."}
diff --git a/data/sampled_jsons/An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_Section_2.4_parameter_in.jsonl b/data/sampled_jsons/An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_Section_2.4_parameter_in.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..50e69ca1ab87a199cf8b701902c85c7729e45a48
--- /dev/null
+++ b/data/sampled_jsons/An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_Section_2.4_parameter_in.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Spurious Signals: Debiasing Multimodal Large Language", "date": "", "ddg_snippet": "To address this “superficial correlation” bias , causal mediation analysis (Pearl, 2022 ) offers a powerful framework for enhancing the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15361v1", "content": "To address this “superficial correlation” bias , causal mediation analysis (Pearl, 2022 ) offers a powerful framework for enhancing the ..."}
+{"idx": 1, "title": "Training Large Language Models to Reason in a Continuous Latent", "date": "", "ddg_snippet": "... reasoning including ProntoQA (Saparov and He, 2022 ) , and our newly proposed ProsQA ( Section 4 .1 ) which requires stronger planning ability, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06769v2", "content": "... reasoning including ProntoQA (Saparov and He, 2022 ) , and our newly proposed ProsQA ( Section 4 .1 ) which requires stronger planning ability, ..."}
+{"idx": 2, "title": "Efficient Compositional Multi-tasking for On-device Large", "date": "", "ddg_snippet": "... performance with a single inference pass by combining already-available task-specific adapters and learning a small number of additional parameters ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16083v1", "content": "... performance with a single inference pass by combining already-available task-specific adapters and learning a small number of additional parameters ..."}
+{"idx": 3, "title": "Cache-of-Thought: Master-Apprentice Framework for", "date": "", "ddg_snippet": "Recent Vision Language Models (VLMs) (OpenAI, 2024b ; Anthropic, 2024 ; Gemini, 2024 ) have shown tremendous promise in a wide range of real-world ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20587v2", "content": "Recent Vision Language Models (VLMs) (OpenAI, 2024b ; Anthropic, 2024 ; Gemini, 2024 ) have shown tremendous promise in a wide range of real-world ..."}
+{"idx": 4, "title": "Acquiescence Bias in Large Language Models", "date": "", "ddg_snippet": "... for this bias range from the tendency to present socially acceptable behaviour by not disagreeing with an assumed authority of the questionnaire (see ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08480v1", "content": "... for this bias range from the tendency to present socially acceptable behaviour by not disagreeing with an assumed authority of the questionnaire (see ..."}
+{"idx": 5, "title": "Do models say what they learn? — LessWrong", "date": "", "ddg_snippet": "... this model is representative of medium-sized open-source chat models and has become a popular option for RL experimentation within the community.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/abtegBoDfnCzewndm/do-models-say-what-they-learn", "content": "... this model is representative of medium-sized open-source chat models and has become a popular option for RL experimentation within the community."}
+{"idx": 6, "title": "Group-wise normalization in differential abundance analysis of", "date": "", "ddg_snippet": "... of DAA methods, which we call “compositional data analysis methods,\" uses advanced statistical de- biasing procedures to correct model estimates ...", "subpage_snippet": "", "source": "bmcbioinformatics.biomedcentral.com", "link": "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-025-06235-9", "content": "... of DAA methods, which we call “compositional data analysis methods,\" uses advanced statistical de- biasing procedures to correct model estimates ..."}
+{"idx": 7, "title": "Large Language Models for Detecting Bias in Job Descriptions", "date": "", "ddg_snippet": "... another study used part- of -speech tags, sentiment analysis , and the identification of verbs often associated with biased expressions to identify ...", "subpage_snippet": "", "source": "journals.openedition.org", "link": "https://journals.openedition.org/ijcol/1454", "content": "... another study used part- of -speech tags, sentiment analysis , and the identification of verbs often associated with biased expressions to identify ..."}
+{"idx": 8, "title": "Google's new 540 billion parameter language model - LessWrong", "date": "", "ddg_snippet": "We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale.", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/mLuQfS7gmfr4nwTdv/google-s-new-540-billion-parameter-language-model", "content": "We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale."}
+{"idx": 9, "title": "Bayesian estimation in multiple comparisons – Guilherme D.", "date": "", "ddg_snippet": "... models typically estimate parameters for a factor F by designating one level as a reference (intercept) and calculating slopes for other levels of F ...", "subpage_snippet": "", "source": "gdgarcia.ca", "link": "https://gdgarcia.ca/multcomp", "content": "... models typically estimate parameters for a factor F by designating one level as a reference (intercept) and calculating slopes for other levels of F ..."}
diff --git a/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_Cui_2023_year_2023.jsonl b/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_Cui_2023_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f3dcbf72bd63b832b87c4b3112fa46641d550920
--- /dev/null
+++ b/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_Cui_2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A first look at License Variants in the PyPI Ecosystem", "date": "", "ddg_snippet": "... empirical study into license variants in software packaging ecosystem but also equips developers and organizations with practical tools for navigating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14594v1", "content": "... empirical study into license variants in software packaging ecosystem but also equips developers and organizations with practical tools for navigating ..."}
+{"idx": 1, "title": "How Robust are LLM-Generated Library Imports? An Empirical", "date": "", "ddg_snippet": "In this paper, we conduct an empirical study of six state- of -the-art LLMs, both proprietary and open - source , by prompting them to solve real-world ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10818v1", "content": "In this paper, we conduct an empirical study of six state- of -the-art LLMs, both proprietary and open - source , by prompting them to solve real-world ..."}
+{"idx": 2, "title": "E-Cigarette and Cannabis Social Media Posts and Adolescent", "date": "", "ddg_snippet": "Multinomial logistic regression analysis of exposure to e-cigarette and cannabis posts and substance use initiation ( study 1)", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2835516", "content": "Multinomial logistic regression analysis of exposure to e-cigarette and cannabis posts and substance use initiation ( study 1)"}
+{"idx": 3, "title": "On Evaluating the Efficiency of Source Code Generated by LLMs |", "date": "", "ddg_snippet": "The present systematic survey comprehensively analyses studies published between 2021 and 2024, focusing on utilizing LLMs in the code generation ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381388109_On_Evaluating_the_Efficiency_of_Source_Code_Generated_by_LLMs", "content": "The present systematic survey comprehensively analyses studies published between 2021 and 2024, focusing on utilizing LLMs in the code generation ..."}
+{"idx": 4, "title": "The Effectiveness of Software Designed to Detect AI-Generated", "date": "", "ddg_snippet": "Quite a few websites and blogs claim to evaluate the accuracy of various AI text detectors (e.g., Abdullahi, 2023 ; Andrews, 2023 ; Aw, 2023 ...", "subpage_snippet": "", "source": "www.degruyter.com", "link": "https://www.degruyter.com/document/doi/10.1515/opis-2022-0158/html", "content": "Quite a few websites and blogs claim to evaluate the accuracy of various AI text detectors (e.g., Abdullahi, 2023 ; Andrews, 2023 ; Aw, 2023 ..."}
+{"idx": 5, "title": "A randomized placebo-controlled trial to investigate the effect", "date": "", "ddg_snippet": "... studies include an open -label study of 30 men with idiopathic infertility allocated to 4 mg lycopene daily for 12 weeks, where improvements were ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00394-019-02091-5", "content": "... studies include an open -label study of 30 men with idiopathic infertility allocated to 4 mg lycopene daily for 12 weeks, where improvements were ..."}
+{"idx": 6, "title": "US20050216898A1 - System for software source code comparison -", "date": "", "ddg_snippet": "open source software is free of any licensing restriction, such as open source software that is in fact committed to the public domain, the ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20050216898A1/en", "content": "open source software is free of any licensing restriction, such as open source software that is in fact committed to the public domain, the ..."}
+{"idx": 7, "title": "DeepQMC: An open-source software suite for variational", "date": "", "ddg_snippet": "Noé; DeepQMC: An open - source software suite for variational optimization of deep-learning molecular wave functions.", "subpage_snippet": "", "source": "pubs.aip.org", "link": "https://pubs.aip.org/aip/jcp/article/159/9/094108/2909731/DeepQMC-An-open-source-software-suite-for", "content": "Noé; DeepQMC: An open - source software suite for variational optimization of deep-learning molecular wave functions."}
+{"idx": 8, "title": "Xiang Ling - Google Scholar", "date": "", "ddg_snippet": "This \"Cited by\" count includes citations to the following articles in ... An empirical study of license conflict in free and open source software", "subpage_snippet": "", "source": "scholar.google.com.hk", "link": "https://scholar.google.com.hk/citations?user=5gaFkzAAAAAJ&hl=en", "content": "This \"Cited by\" count includes citations to the following articles in ... An empirical study of license conflict in free and open source software"}
+{"idx": 9, "title": "Natural language processing for analyzing online customer", "date": "", "ddg_snippet": "... an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-2203/", "content": "... an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, ..."}
diff --git a/data/sampled_jsons/Anvith_Thudi_2024_per-instance_privacy_machine_learning.jsonl b/data/sampled_jsons/Anvith_Thudi_2024_per-instance_privacy_machine_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d66f388b9e7442a2cbe6e9affd1acc47bf82a87
--- /dev/null
+++ b/data/sampled_jsons/Anvith_Thudi_2024_per-instance_privacy_machine_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CleverHans Lab - Anvith", "date": "", "ddg_snippet": "@article{ anvith 2024 fromjournal, author = { Thudi , Anvith and Shumailov, Ilia and Boenisch, Franziska and Papernot, Nicolas}, title = {From Differential Privacy to Bounds on Membership Inference: Less can be More}, year = { 2024 } }.", "subpage_snippet": "", "source": "cleverhans.io", "link": "https://cleverhans.io/members/anvith.html", "content": "@article{ anvith 2024 fromjournal, author = { Thudi , Anvith and Shumailov, Ilia and Boenisch, Franziska and Papernot, Nicolas}, title = {From Differential Privacy to Bounds on Membership Inference: Less can be More}, year = { 2024 } }."}
+{"idx": 1, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "( 2024 ) and a per - instance Re´nyi-differential privacy analysis by Thudi et al. An - vith Thudi is also supported by a Vanier Fellowship from NSERC. Daniel M. Roy is supported by the funding through.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "( 2024 ) and a per - instance Re´nyi-differential privacy analysis by Thudi et al. An - vith Thudi is also supported by a Vanier Fellowship from NSERC. Daniel M. Roy is supported by the funding through."}
+{"idx": 2, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "View a PDF of the paper titled Leveraging Per - Instance Privacy for Machine Unlearning, by Nazanin Mohammadi Sepahvand and 7 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786v1", "content": "View a PDF of the paper titled Leveraging Per - Instance Privacy for Machine Unlearning, by Nazanin Mohammadi Sepahvand and 7 other authors."}
+{"idx": 3, "title": "Anvith Thudi", "date": "", "ddg_snippet": "Transactions on Machine Learning Research. \"Training Private Models That Know What They Don't Know\" Stephan Rabanser, Anvith Thudi , Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot. Proceedings of the 37th Conference on Neural Information Processing Systems.", "subpage_snippet": "", "source": "www.anvith.com", "link": "https://www.anvith.com/", "content": "Transactions on Machine Learning Research. \"Training Private Models That Know What They Don't Know\" Stephan Rabanser, Anvith Thudi , Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot. Proceedings of the 37th Conference on Neural Information Processing Systems."}
+{"idx": 4, "title": "Anvith Thudi - Google Scholar", "date": "", "ddg_snippet": "Transactions on Machine Learning Research, 2024 .Leveraging Per - Instance Privacy for Machine Unlearning. NM Sepahvand, A Thudi , B Isik, A Bhattacharyya, N Papernot", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=bTEybH0AAAAJ&hl=en", "content": "Transactions on Machine Learning Research, 2024 .Leveraging Per - Instance Privacy for Machine Unlearning. NM Sepahvand, A Thudi , B Isik, A Bhattacharyya, N Papernot"}
+{"idx": 5, "title": "Nicolas PAPERNOT | Professor (Assistant) | Doctor of Philosophy", "date": "", "ddg_snippet": "Anvith Thudi Anvith Thudi .When training a machine learning model with differential privacy , one sets a privacy budget. This budget represents a maximal privacy violation that any user is willing to face by contributing their data to the training set.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Nicolas-Papernot", "content": "Anvith Thudi Anvith Thudi .When training a machine learning model with differential privacy , one sets a privacy budget. This budget represents a maximal privacy violation that any user is willing to face by contributing their data to the training set."}
+{"idx": 6, "title": "Leveraging Per -Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Anvith Thudi . Berivan Isik.Our results show that per - instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and practical way to assess unlearning performance.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "Anvith Thudi . Berivan Isik.Our results show that per - instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and practical way to assess unlearning performance."}
+{"idx": 7, "title": "Secure and Trustworthy ML 2024 : A home for machine learning ...", "date": "", "ddg_snippet": "These and related topics were at the centre of the 2024 Secure and Trustworthy Machine Learning (SaTML) co.This year, the conference published 34 papers ranging in topics from protecting user privacy and fairness to proposing new security attacks on ML systems.", "subpage_snippet": "", "source": "srinstitute.utoronto.ca", "link": "https://srinstitute.utoronto.ca/news/satml-secure-trustworthy-machine-learning-24", "content": "These and related topics were at the centre of the 2024 Secure and Trustworthy Machine Learning (SaTML) co.This year, the conference published 34 papers ranging in topics from protecting user privacy and fairness to proposing new security attacks on ML systems."}
+{"idx": 8, "title": "Fairness, transparency, privacy | The Alan Turing Institute", "date": "", "ddg_snippet": "His research interests span Trustworthy Machine Learning , with a particular interest in unlearning and privacy and their connections to the performance of models. Anvith Thudi is supported by a Vanier Canada Graduate Scholarship in the Natural Sciences and Engineering and is currently...", "subpage_snippet": "", "source": "www.turing.ac.uk", "link": "https://www.turing.ac.uk/research/interest-groups/fairness-transparency-privacy", "content": "His research interests span Trustworthy Machine Learning , with a particular interest in unlearning and privacy and their connections to the performance of models. Anvith Thudi is supported by a Vanier Canada Graduate Scholarship in the Natural Sciences and Engineering and is currently..."}
+{"idx": 9, "title": "On the Necessity of Auditable Algorithmic Definitions for Machine ...", "date": "", "ddg_snippet": "Machine unlearning, i.e. having a model forget about some of its training data, has become increasingly more important as privacy legislation promotes variants of the right-to-be-forgotten. Anvith Thudi , Hengrui Jia, Ilia Shumailov, Nicolas Papernot. arXiv_AI.", "subpage_snippet": "", "source": "paperreading.club", "link": "https://paperreading.club/page?id=98102", "content": "Machine unlearning, i.e. having a model forget about some of its training data, has become increasingly more important as privacy legislation promotes variants of the right-to-be-forgotten. Anvith Thudi , Hengrui Jia, Ilia Shumailov, Nicolas Papernot. arXiv_AI."}
diff --git a/data/sampled_jsons/Appendix_A.8_regularization_ablation_study_lambda_0.00_coordination_performance_Hanabi.jsonl b/data/sampled_jsons/Appendix_A.8_regularization_ablation_study_lambda_0.00_coordination_performance_Hanabi.jsonl
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index 0000000000000000000000000000000000000000..d135aab2bbd057027653f4f7031e5e5ebdfd0a0c
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination ...", "date": "", "ddg_snippet": "We introduce the LLM- Coordination Benchmark for evaluating and analyzing LLMs in Pure Coordination Games, covering multi-turn Agentic Coordination and single-turn Coordination QA tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.03903v2", "content": "We introduce the LLM- Coordination Benchmark for evaluating and analyzing LLMs in Pure Coordination Games, covering multi-turn Agentic Coordination and single-turn Coordination QA tasks."}
+{"idx": 1, "title": "CorrSteer: Steering Improves Task Performance and Safety in LLMs ...", "date": "", "ddg_snippet": "Only positively correlated features are selected to ensure steering induces positive activation, as our ablation study confirms that negative correlation features consistently degrade performance 6. All methods are fully automated based on observed activations without hyperparameter tuning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.12535", "content": "Only positively correlated features are selected to ensure steering induces positive activation, as our ablation study confirms that negative correlation features consistently degrade performance 6. All methods are fully automated based on observed activations without hyperparameter tuning."}
+{"idx": 2, "title": "PDF Supplementary Appendix - The New England Journal of Medicine", "date": "", "ddg_snippet": "Supplementary Appendix Supplement to: Wazni OM, Saliba WI, Nair DG, et al. Left atrial appendage closure after ablation for atrial fibril-lation. N Engl J Med 2024;392:1277-87. DOI: 10.1056 ...", "subpage_snippet": "", "source": "www.nejm.org", "link": "https://www.nejm.org/doi/suppl/10.1056/NEJMoa2408308/suppl_file/nejmoa2408308_appendix.pdf", "content": "Supplementary Appendix Supplement to: Wazni OM, Saliba WI, Nair DG, et al. Left atrial appendage closure after ablation for atrial fibril-lation. N Engl J Med 2024;392:1277-87. DOI: 10.1056 ..."}
+{"idx": 3, "title": "Computational Intelligence and Tikhonov Regularization with Reduced ...", "date": "", "ddg_snippet": "In this chapter we present a method to predict the optimal value of the Tikhonov’s regularization parameter by solving simplified versions of the inverse problems considered. This can be of great benefit since methods such as the L-curve and the Fixed Point...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-97344-5_8", "content": "In this chapter we present a method to predict the optimal value of the Tikhonov’s regularization parameter by solving simplified versions of the inverse problems considered. This can be of great benefit since methods such as the L-curve and the Fixed Point..."}
+{"idx": 4, "title": "Language Instructed Reinforcement Learning for Human-AI Coordination", "date": "", "ddg_snippet": "Finally, we show that knowing the language instruction significantly boosts human-AI coordination performance in human evaluations in Hanabi . InstructQ.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370070312_Language_Instructed_Reinforcement_Learning_for_Human-AI_Coordination", "content": "Finally, we show that knowing the language instruction significantly boosts human-AI coordination performance in human evaluations in Hanabi . InstructQ."}
+{"idx": 5, "title": "Agreement Between Predicted and Actual Measured Ablation Depth After FS ...", "date": "", "ddg_snippet": "Abstract Purpose To compare the predicted ablation depth (AD) with the postoperatively measured corneal ablation depth (postop-AD) at central, paracentral, and midperipheral locations using two rotating Scheimpflug analyzers and a Fourier-domain optical coherence tomographer in eyes that underwent femtosecond laser-assisted LASIK (FS-LASIK).", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9160334/", "content": "Abstract Purpose To compare the predicted ablation depth (AD) with the postoperatively measured corneal ablation depth (postop-AD) at central, paracentral, and midperipheral locations using two rotating Scheimpflug analyzers and a Fourier-domain optical coherence tomographer in eyes that underwent femtosecond laser-assisted LASIK (FS-LASIK)."}
+{"idx": 6, "title": "Efficacy and safety of ultrasound-guided radiofrequency ablation for ...", "date": "", "ddg_snippet": "Background We comprehensively evaluate the efficacy and safety of US-guided radiofrequency ablation (RFA) in the treatment of papillary thyroid microcarcinoma (PTMC) via a systematic review and meta-analysis.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/02656736.2022.2129101", "content": "Background We comprehensively evaluate the efficacy and safety of US-guided radiofrequency ablation (RFA) in the treatment of papillary thyroid microcarcinoma (PTMC) via a systematic review and meta-analysis."}
+{"idx": 7, "title": "Comparison of radiofrequency ablation and microwave ablation for benign ...", "date": "", "ddg_snippet": "Abstract Purpose To compare the effectiveness and safety of radiofrequency ablation (RFA) and microwave ablation (MWA) for the treatment of benign thyroid nodules (BTNs).", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1111/cen.14438", "content": "Abstract Purpose To compare the effectiveness and safety of radiofrequency ablation (RFA) and microwave ablation (MWA) for the treatment of benign thyroid nodules (BTNs)."}
+{"idx": 8, "title": "An Introduction to `glmnet`", "date": "", "ddg_snippet": "Introduction Glmnet is a package that fits generalized linear and similar models via penalized maximum likelihood. The regularization path is computed for the lasso or elastic net penalty at a grid of values (on the log scale) for the regularization parameter lambda . The algorithm is extremely fast, and can exploit sparsity in the input matrix x. It fits linear, logistic and multinomial ...", "subpage_snippet": "", "source": "glmnet.stanford.edu", "link": "https://glmnet.stanford.edu/articles/glmnet.html", "content": "Introduction Glmnet is a package that fits generalized linear and similar models via penalized maximum likelihood. The regularization path is computed for the lasso or elastic net penalty at a grid of values (on the log scale) for the regularization parameter lambda . The algorithm is extremely fast, and can exploit sparsity in the input matrix x. It fits linear, logistic and multinomial ..."}
+{"idx": 9, "title": "Samarth0710/neurips-2024-peer-reviews-test-10 - Hugging Face", "date": "", "ddg_snippet": "Samarth0710/neurips-2024-peer-reviews-test-10 · Datasets at Hugging Facetrain · 4.24k rows", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/Samarth0710/neurips-2024-peer-reviews-test-10/viewer", "content": "Samarth0710/neurips-2024-peer-reviews-test-10 · Datasets at Hugging Facetrain · 4.24k rows"}
diff --git a/data/sampled_jsons/Application_of_improved_grey_wolf_model_collaborative_trajectory_planning_reinforcement_learning_202.jsonl b/data/sampled_jsons/Application_of_improved_grey_wolf_model_collaborative_trajectory_planning_reinforcement_learning_202.jsonl
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index 0000000000000000000000000000000000000000..aa9c9fd6c1db9e78ec3037ae99082252f00c1c8a
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Application of improved grey wolf model in collaborative ...", "date": "", "ddg_snippet": "by J Chen · 2024 · Cited by 6 — The article aims to provide more efficient path planning solutions for UAV swarm in fields such as disaster relief and terrain exploration.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-65383-9", "content": "by J Chen · 2024 · Cited by 6 — The article aims to provide more efficient path planning solutions for UAV swarm in fields such as disaster relief and terrain exploration."}
+{"idx": 1, "title": "Application of improved grey wolf model in collaborative ...", "date": "", "ddg_snippet": "by J Chen · 2024 · Cited by 6 — The improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/39068161/", "content": "by J Chen · 2024 · Cited by 6 — The improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms."}
+{"idx": 2, "title": "Application of an Improved Grey Wolf Optimization Algorithm ...", "date": "", "ddg_snippet": "3 Aug 2024 — The testing results indicated that compared with GWO and GA, success rate of planning optimal path by IGWO is increased by 50% and 40%, ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3662739.3665983", "content": "3 Aug 2024 — The testing results indicated that compared with GWO and GA, success rate of planning optimal path by IGWO is increased by 50% and 40%, ..."}
+{"idx": 3, "title": "Grey wolf optimization enhanced collaborative path ...", "date": "", "ddg_snippet": "by H Xu · 2025 · Cited by 3 — This study proposes a novel three-dimensional (3D) collaborative path planning framework for UUV swarms, grounded in an advanced grey wolf optimization (GWO) ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0029801825007954", "content": "by H Xu · 2025 · Cited by 3 — This study proposes a novel three-dimensional (3D) collaborative path planning framework for UUV swarms, grounded in an advanced grey wolf optimization (GWO) ..."}
+{"idx": 4, "title": "An Improved Grey Wolf Optimizer Inspired by Advanced ...", "date": "", "ddg_snippet": "by Z Teng · 2025 · Cited by 1 — Subsequently,. IGWO is applied to UAV shortest path planning in various obstacle-laden environments. Simulation results show that the paths ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.03663", "content": "by Z Teng · 2025 · Cited by 1 — Subsequently,. IGWO is applied to UAV shortest path planning in various obstacle-laden environments. Simulation results show that the paths ..."}
+{"idx": 5, "title": "The HSGWO-MPIO algorithm based on improved search ...", "date": "", "ddg_snippet": "24 Apr 2023 — Article 16 August 2024. Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11227-023-05246-8", "content": "24 Apr 2023 — Article 16 August 2024. Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm."}
+{"idx": 6, "title": "Application of uniform experimental design theory to multi ...", "date": "", "ddg_snippet": "by L Cheng · 2024 · Cited by 10 — This paper develops a multi-strategy improved SSA (ISSA) based on uniform experimental design theory.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0957417424017160", "content": "by L Cheng · 2024 · Cited by 10 — This paper develops a multi-strategy improved SSA (ISSA) based on uniform experimental design theory."}
+{"idx": 7, "title": "Multi-UAV collaborative trajectory planning for non- ...", "date": "", "ddg_snippet": "15 Apr 2025 — This paper proposes a novel collaborative trajectory planning method for non- cooperative target tracking using multiple UAVs in complex ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/09544100251335357?int.sj-full-text.similar-articles.6=", "content": "15 Apr 2025 — This paper proposes a novel collaborative trajectory planning method for non- cooperative target tracking using multiple UAVs in complex ..."}
+{"idx": 8, "title": "Improved Grey Wolf Optimization Algorithm and Application", "date": "", "ddg_snippet": "This paper proposed an improved Grey Wolf Optimizer (GWO) to resolve the problem of instability and convergence accuracy when GWO is used as a ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Improved-Grey-Wolf-Optimization-Algorithm-and-Hou-Gao/71f5cd7d92961d03150a155645e79a8fcd124013", "content": "This paper proposed an improved Grey Wolf Optimizer (GWO) to resolve the problem of instability and convergence accuracy when GWO is used as a ..."}
+{"idx": 9, "title": "Multi‐UAV Cooperative Search for Moving Targets With ...", "date": "", "ddg_snippet": "16 Sept 2024 — An improved gray wolf optimizer (IGWO) combined with MPC is proposed, which can solve the current optimal path for UAVs at each time step and ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1155/2024/5876393", "content": "16 Sept 2024 — An improved gray wolf optimizer (IGWO) combined with MPC is proposed, which can solve the current optimal path for UAVs at each time step and ..."}
diff --git a/data/sampled_jsons/Archetypal_SAE_Adaptive_Stable_Dictionary_Learning_Concept_Extraction_Large_Vision_Models_equation_2.jsonl b/data/sampled_jsons/Archetypal_SAE_Adaptive_Stable_Dictionary_Learning_Concept_Extraction_Large_Vision_Models_equation_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5dd059bc237eb5827c6fff28a125a02727dfc5c7
--- /dev/null
+++ b/data/sampled_jsons/Archetypal_SAE_Adaptive_Stable_Dictionary_Learning_Concept_Extraction_Large_Vision_Models_equation_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts . However, we reveal a fundamental limitation: existing SAEs exhibit severe instability, as identical models trained on similar datasets can produce sharply different ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts . However, we reveal a fundamental limitation: existing SAEs exhibit severe instability, as identical models trained on similar datasets can produce sharply different ..."}
+{"idx": 1, "title": "Archetypal SAEs: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "In recent years, the field of interpretability has focused heavily on concept -based approaches. As deep learning models advance to unprecedented scales in vision , language, and multimodal domains, the ability to automatically decompose learned representations into meaningful \" concepts \" has become one of the most promising interpretability strategies. Concept extraction attempts to identify ...", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "In recent years, the field of interpretability has focused heavily on concept -based approaches. As deep learning models advance to unprecedented scales in vision , language, and multimodal domains, the ability to automatically decompose learned representations into meaningful \" concepts \" has become one of the most promising interpretability strategies. Concept extraction attempts to identify ..."}
+{"idx": 2, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "To rigorously assess dictionary quality learned by SAEs , we introduce two new benchmarks that test (i) plausibility, if dictionaries recover \"true\" classification directions and (ii) identifiability, if dictionaries disentangle synthetic concept mixtures.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9v1eW8HgMU", "content": "To rigorously assess dictionary quality learned by SAEs , we introduce two new benchmarks that test (i) plausibility, if dictionaries recover \"true\" classification directions and (ii) identifiability, if dictionaries disentangle synthetic concept mixtures."}
+{"idx": 3, "title": "Archetypal SAEs: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "As deep learning models advance to unprecedented scales in vision , language, and multimodal domains, the ability to automatically decompose learned representations into meaningful \" concepts ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@kempnerinstitute/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-acf95010c691", "content": "As deep learning models advance to unprecedented scales in vision , language, and multimodal domains, the ability to automatically decompose learned representations into meaningful \" concepts ..."}
+{"idx": 4, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "Join the discussion on this paper pageArchetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.12892", "content": "Join the discussion on this paper pageArchetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models"}
+{"idx": 5, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A-SAE), wherein dictionary atoms are constrained to the convex hull of data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v1", "content": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A-SAE), wherein dictionary atoms are constrained to the convex hull of data."}
+{"idx": 6, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "These approaches build upon archetypal analysis to enhance stability and consistency in concept extraction . The A-SAE model constrains each dictionary atom to reside strictly within the convex hull of the training data, which imposes a geometric constraint that improves stability across different training runs.", "subpage_snippet": "", "source": "phdstudio.org", "link": "https://phdstudio.org/2025/03/17/archetypal-sae-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models-sajjad-ansari-artificial-intelligence-category-marktechpost/", "content": "These approaches build upon archetypal analysis to enhance stability and consistency in concept extraction . The A-SAE model constrains each dictionary atom to reside strictly within the convex hull of the training data, which imposes a geometric constraint that improves stability across different training runs."}
+{"idx": 7, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts . However, we reveal a fundamental limitation: existing SAEs exhibit severe instability, as identical models trained on similar ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.12892v1", "content": "View recent discussion. Abstract: Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts . However, we reveal a fundamental limitation: existing SAEs exhibit severe instability, as identical models trained on similar ..."}
+{"idx": 8, "title": "PDF Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "Our results provide sub- stantial evidence that A-SAEs find more structured and coherent concepts . Further, to enable reproduction, we open-source our extensive codebase for large -scale SAE training on modern vision models . 2 . Related Work Sparse Coding & Dictionary Learning .", "subpage_snippet": "", "source": "konklab.fas.harvard.edu", "link": "https://konklab.fas.harvard.edu/Papers/Fel_2025_ICML.pdf", "content": "Our results provide sub- stantial evidence that A-SAEs find more structured and coherent concepts . Further, to enable reproduction, we open-source our extensive codebase for large -scale SAE training on modern vision models . 2 . Related Work Sparse Coding & Dictionary Learning ."}
+{"idx": 9, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models", "subpage_snippet": "", "source": "animadversio.github.io", "link": "https://animadversio.github.io/publication/fel-2025-archetypal/", "content": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models"}
diff --git a/data/sampled_jsons/Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_for_Concept_Extraction_in_Large_Vision_Models_year_2024.jsonl b/data/sampled_jsons/Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_for_Concept_Extraction_in_Large_Vision_Models_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd364af20070afde45cffdd989fa06ff4a891060
--- /dev/null
+++ b/data/sampled_jsons/Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_for_Concept_Extraction_in_Large_Vision_Models_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for", "date": "", "ddg_snippet": "... Archetypal SAE : Adaptive and Stable ... Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/03/16/archetypal-sae-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "... Archetypal SAE : Adaptive and Stable ... Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models"}
+{"idx": 1, "title": "Understanding Local Rank and Information Compression in Deep", "date": "", "ddg_snippet": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models ... large language models (LLMs) often have ...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/10/18/understanding-local-rank-and-information-compression-in-deep-neural-networks/", "content": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models ... large language models (LLMs) often have ..."}
+{"idx": 2, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary", "date": "", "ddg_snippet": "View a PDF of the paper titled Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models , by Thomas Fel ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "View a PDF of the paper titled Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models , by Thomas Fel ..."}
+{"idx": 3, "title": "Evaluating the Vulnerabilities of Unlearning Techniques in", "date": "", "ddg_snippet": "... for developing more robust unlearning techniques and implementing thorough evaluation protocols to ensure the safe deployment of large language ...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/10/03/evaluating-the-vulnerabilities-of-unlearning-techniques-in-large-language-models-a-comprehensive-white-box-analysis/", "content": "... for developing more robust unlearning techniques and implementing thorough evaluation protocols to ensure the safe deployment of large language ..."}
+{"idx": 4, "title": "Machine Learning Category - Page 341 of 516 - MarkTechPost", "date": "", "ddg_snippet": "Large language models have transformed how machines comprehend and generate text, especially in complex problem-solving areas like mathematical ...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/category/technology/artificial-intelligence/machine-learning/page/341/", "content": "Large language models have transformed how machines comprehend and generate text, especially in complex problem-solving areas like mathematical ..."}
+{"idx": 5, "title": "This AI Paper Introduces FoundationStereo: A Zero-Shot Stereo", "date": "", "ddg_snippet": "... Archetypal SAE : Adaptive and Stable ... Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/03/16/this-ai-paper-introduces-foundationstereo-a-zero-shot-stereo-matching-model-for-robust-depth-estimation/", "content": "... Archetypal SAE : Adaptive and Stable ... Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models"}
+{"idx": 6, "title": "A Coding Guide to Build an Optical Character Recognition (OCR)", "date": "", "ddg_snippet": "... Archetypal SAE : Adaptive and Stable ... Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/03/17/a-coding-guide-to-build-an-optical-character-recognition-ocr-app-in-google-colab-using-opencv-and-tesseract-ocr/", "content": "... Archetypal SAE : Adaptive and Stable ... Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models"}
+{"idx": 7, "title": "This AI Paper from Columbia University Introduces Manify: A", "date": "", "ddg_snippet": "... integrated framework, the library simplifies ... Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/03/17/this-ai-paper-from-columbia-university-introduces-manify-a-python-library-for-non-euclidean-representation-learning/", "content": "... integrated framework, the library simplifies ... Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models"}
+{"idx": 8, "title": "Isabel Papadimitriou", "date": "", "ddg_snippet": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models - Thomas Fel, Ekdeep Singh Lubana, Jacob S.", "subpage_snippet": "", "source": "www.isabelpapad.com", "link": "https://www.isabelpapad.com/", "content": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models - Thomas Fel, Ekdeep Singh Lubana, Jacob S."}
+{"idx": 9, "title": "Editors Pick Category - Page 523 of 918 - MarkTechPost", "date": "", "ddg_snippet": "Large vision -language models (LVLMs) integrate large language models with image processing capabilities, enabling them to interpret images and ...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/category/editors-pick/page/523/", "content": "Large vision -language models (LVLMs) integrate large language models with image processing capabilities, enabling them to interpret images and ..."}
diff --git a/data/sampled_jsons/Archetypal_SAE_paper_Section_6_text_norm_constraint_explicitly_stated_year_2024.jsonl b/data/sampled_jsons/Archetypal_SAE_paper_Section_6_text_norm_constraint_explicitly_stated_year_2024.jsonl
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+{"idx": 0, "title": "SAE International®", "date": "", "ddg_snippet": "10 Sept 2015 — and technology constraints are formally stated , architecture ... Section 6 presents our concluding remarks. COLLISION DETECTION AT. 339 pages", "subpage_snippet": "", "source": "euro.ecom.cmu.edu", "link": "http://euro.ecom.cmu.edu/resources/elibrary/auto/PT-148.pdf", "content": "10 Sept 2015 — and technology constraints are formally stated , architecture ... Section 6 presents our concluding remarks. COLLISION DETECTION AT. 339 pages"}
+{"idx": 1, "title": "Interpreting the Linear Structure of Vision-language Model ...", "date": "", "ddg_snippet": "10 Aug 2025 — Leveraging VLM-Explore ( Section 6 ) we observe that many low-energy concepts correspond to coherent but rare patterns, such as “yaks” or “ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11695v3", "content": "10 Aug 2025 — Leveraging VLM-Explore ( Section 6 ) we observe that many low-energy concepts correspond to coherent but rare patterns, such as “yaks” or “ ..."}
+{"idx": 2, "title": "A neural network approach for population synthesis", "date": "", "ddg_snippet": "11 Mar 2024 — ... section 6 ; section 7 concludes the paper and presents our plans for further work. 2. Related work. The topic of population synthesis is one ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/00375497241233597?int.sj-full-text.similar-articles.2", "content": "11 Mar 2024 — ... section 6 ; section 7 concludes the paper and presents our plans for further work. 2. Related work. The topic of population synthesis is one ..."}
+{"idx": 3, "title": "Cybersecurity framework for connected and automated ...", "date": "", "ddg_snippet": "by SK Khan · 2025 · Cited by 9 — Furthermore, Section 6 discusses the findings of the simulations, followed by a discussion and policy recommendations. Finally, in Section 8 ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0967070X24003561", "content": "by SK Khan · 2025 · Cited by 9 — Furthermore, Section 6 discusses the findings of the simulations, followed by a discussion and policy recommendations. Finally, in Section 8 ..."}
+{"idx": 4, "title": "How I Met Your V2X Sensor Data: Analysis of Projection ...", "date": "", "ddg_snippet": "by PA Kara · 2023 · Cited by 9 — The paper is concluded in Section 6 , which also summarizes the next steps toward such solutions. 2. V2X Protocols and Use Cases. 2.1. General Overview of ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9919924/", "content": "by PA Kara · 2023 · Cited by 9 — The paper is concluded in Section 6 , which also summarizes the next steps toward such solutions. 2. V2X Protocols and Use Cases. 2.1. General Overview of ..."}
+{"idx": 5, "title": "Graphical security modelling for Autonomous Vehicles", "date": "", "ddg_snippet": "by NH Nguyen · 2025 · Cited by 2 — Section 6 outlines the limitations of our work and suggests future directions. Finally, Section 7 concludes the paper . 2. Related work. The field of ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167404824005352", "content": "by NH Nguyen · 2025 · Cited by 2 — Section 6 outlines the limitations of our work and suggests future directions. Finally, Section 7 concludes the paper . 2. Related work. The field of ..."}
+{"idx": 6, "title": "An Approach for Handling Uncertainties Related to ...", "date": "", "ddg_snippet": "by J Olstam · 2020 · Cited by 38 — Section 6 ends the article with conclusions and needs for future research. 2. Approaches for Traffic Simulation including Automated Vehicles.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1155/2020/8850591", "content": "by J Olstam · 2020 · Cited by 38 — Section 6 ends the article with conclusions and needs for future research. 2. Approaches for Traffic Simulation including Automated Vehicles."}
+{"idx": 7, "title": "Lawful and Accountable Personal Data Processing with ...", "date": "", "ddg_snippet": "by LT van Binsbergen · 2025 — The implementation of the semantics in Section 5, given in Section 6 , uses the eFLINT language. The next section informally introduces the main ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.07172", "content": "by LT van Binsbergen · 2025 — The implementation of the semantics in Section 5, given in Section 6 , uses the eFLINT language. The next section informally introduces the main ..."}
+{"idx": 8, "title": "The Functional Reasoning Design Language (FRDL)", "date": "", "ddg_snippet": "by D Hulse · 2025 — Finally, conclusions will be presented in. Section 6 , along with lessons and avenues for further work. 2 Background. This section discusses the state of ... 16 pages", "subpage_snippet": "", "source": "ntrs.nasa.gov", "link": "https://ntrs.nasa.gov/api/citations/20250006622/downloads/twocolumn_formatted.pdf", "content": "by D Hulse · 2025 — Finally, conclusions will be presented in. Section 6 , along with lessons and avenues for further work. 2 Background. This section discusses the state of ... 16 pages"}
+{"idx": 9, "title": "An Overview of Strategies to Design Networks for Time-Critical ...", "date": "", "ddg_snippet": "by V Gavriluţ · 2022 · Cited by 23 — ... Section 6 , which discusses the achievements of the analyzed strategies and proposes future research directions. Finally, Section 7 concludes ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3501294?af=R&utm_source=researcher_app&utm_medium=referral&utm_campaign=RESR_MRKT_Researcher_inbound", "content": "by V Gavriluţ · 2022 · Cited by 23 — ... Section 6 , which discusses the achievements of the analyzed strategies and proposes future research directions. Finally, Section 7 concludes ..."}
diff --git a/data/sampled_jsons/Archetypal_SAE_paper_Table_1_stability_values_0.95_0.85_RA-SAE_TopK.jsonl b/data/sampled_jsons/Archetypal_SAE_paper_Table_1_stability_values_0.95_0.85_RA-SAE_TopK.jsonl
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index 0000000000000000000000000000000000000000..349075cc4cbd855739aa7b4bd5653c530fc3e51d
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+++ b/data/sampled_jsons/Archetypal_SAE_paper_Table_1_stability_values_0.95_0.85_RA-SAE_TopK.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2502.12892: Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "Abstract page for arXiv paper 2502.12892: Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models"}
+{"idx": 1, "title": "PDF BAR DATA HANDBOOK AISI/SAE - sdi-pit.com", "date": "", "ddg_snippet": "The first four numerals usually describe the AISI- SAE steel grade designation. The last numeral is typically a zero, unless the steel is modified as follows: a 1 in the last digit signifies boron; a 4 in the last digit signifies lead; a 6 in the last digit indicates Electric Furnace practice with reduced levels of phosphorus and sulfur.", "subpage_snippet": "", "source": "www.sdi-pit.com", "link": "http://www.sdi-pit.com/docs/Steel-Dynamics-Bar-Book-Rev-2-New-Cover.pdf", "content": "The first four numerals usually describe the AISI- SAE steel grade designation. The last numeral is typically a zero, unless the steel is modified as follows: a 1 in the last digit signifies boron; a 4 in the last digit signifies lead; a 6 in the last digit indicates Electric Furnace practice with reduced levels of phosphorus and sulfur."}
+{"idx": 2, "title": "PDF SAE steel grades.pdf", "date": "", "ddg_snippet": "The Society of Automotive Engineers ( SAE ) designates SAE steel grades. These are four digit numbers which represent chemical composition standards for steel specifications. The American Iron and Steel Institute (AISI) originally started a very similar system. Over time they used the same numbers to refer to the same alloy, but the AISI system used a letter prefix to denote the steelmaking ...", "subpage_snippet": "", "source": "assets.website-files.com", "link": "https://assets.website-files.com/60941e9e3ce588528a28cf21/61fc14bb88a7a1f60f3b6a69_SAE+steel+grades.pdf", "content": "The Society of Automotive Engineers ( SAE ) designates SAE steel grades. These are four digit numbers which represent chemical composition standards for steel specifications. The American Iron and Steel Institute (AISI) originally started a very similar system. Over time they used the same numbers to refer to the same alloy, but the AISI system used a letter prefix to denote the steelmaking ..."}
+{"idx": 3, "title": "SAE AISI Plain Carbon Steel Grade - CalQlata", "date": "", "ddg_snippet": "A completed SAE -AISI material grading table for the principal physical properties of all recognised and estimated plain carbon steels.", "subpage_snippet": "", "source": "www.calqlata.com", "link": "https://www.calqlata.com/Materials/Plain-Carbon-Steel.html", "content": "A completed SAE -AISI material grading table for the principal physical properties of all recognised and estimated plain carbon steels."}
+{"idx": 4, "title": "AISI/SAE Steel and Alloys - Designation System - The Engineering ToolBox", "date": "", "ddg_snippet": "Alloy steels and carbon steels can be designated with specific grades by a four-digit AISI/ SAE numerical index system. The system is based on the chemical compositions of the steels and alloys.", "subpage_snippet": "", "source": "www.engineeringtoolbox.com", "link": "https://www.engineeringtoolbox.com/aisi-sae-steel-numbering-system-d_1449.html", "content": "Alloy steels and carbon steels can be designated with specific grades by a four-digit AISI/ SAE numerical index system. The system is based on the chemical compositions of the steels and alloys."}
+{"idx": 5, "title": "SAE AISI Alloy Carbon Steels | physical properties | CalQlata", "date": "", "ddg_snippet": "Chemical composition of all recognised SAE -AISI (low) alloy carbon steels to be used in conjunction with CalQlata's Table of carbon steel properties", "subpage_snippet": "", "source": "www.calqlata.com", "link": "https://www.calqlata.com/Materials/Alloy-Steel.html", "content": "Chemical composition of all recognised SAE -AISI (low) alloy carbon steels to be used in conjunction with CalQlata's Table of carbon steel properties"}
+{"idx": 6, "title": "Technical Papers - Publications - Publications - SAE International", "date": "", "ddg_snippet": "Navigate Engineering Challenges with The Latest Advances in Mobility Research Supporting the automotive, aerospace, and commercial vehicle sectors, SAE Technical Papers help guide engineers, designers, and researchers through their project challenges and establish leadership in a competitive landscape. Utilize basic and applied research side-by-side to guide design, implementation, and safety ...", "subpage_snippet": "", "source": "www.sae.org", "link": "https://www.sae.org/publications/technical-papers", "content": "Navigate Engineering Challenges with The Latest Advances in Mobility Research Supporting the automotive, aerospace, and commercial vehicle sectors, SAE Technical Papers help guide engineers, designers, and researchers through their project challenges and establish leadership in a competitive landscape. Utilize basic and applied research side-by-side to guide design, implementation, and safety ..."}
+{"idx": 7, "title": "SAE AISI Steel Grade Numbering System | Alloy | CalQlata", "date": "", "ddg_snippet": "SAE -AISI steel grading and numbering system for all generally recognised steels and their alloys.", "subpage_snippet": "", "source": "www.calqlata.com", "link": "https://www.calqlata.com/Materials/SAE-numbering.html", "content": "SAE -AISI steel grading and numbering system for all generally recognised steels and their alloys."}
+{"idx": 8, "title": "PDF STANDARD CARBON STEELS ASTM A 576 90b (Reapproved 2006) - Steel Data", "date": "", "ddg_snippet": "The maximum limits for P and S are 0.035% and 0.040% respectively, unless otherwise agreed upon between purchaser and supplier. Grade designations correspond to the respective AISI and SAE designations. Grade compositions correspond to the respective AISI compositions. The max S and P for Electric Furnace Quality Steels (grades preceded by an 'E') is 0.025 max.", "subpage_snippet": "", "source": "www.steeldata.info", "link": "https://www.steeldata.info/hard/demo/data/images/sae.pdf", "content": "The maximum limits for P and S are 0.035% and 0.040% respectively, unless otherwise agreed upon between purchaser and supplier. Grade designations correspond to the respective AISI and SAE designations. Grade compositions correspond to the respective AISI compositions. The max S and P for Electric Furnace Quality Steels (grades preceded by an 'E') is 0.025 max."}
+{"idx": 9, "title": "Surface Finish Chart for Ra Roughness - AMF Technologies", "date": "", "ddg_snippet": "The surface finish of a product can significantly affect its performance and durability. Surface roughness measures the texture of a surface.", "subpage_snippet": "", "source": "www.amftechnologies.com", "link": "https://www.amftechnologies.com/surface-finish-chart/", "content": "The surface finish of a product can significantly affect its performance and durability. Surface roughness measures the texture of a surface."}
diff --git a/data/sampled_jsons/Attention_Is_All_You_Need_abstract_quadratic_OR_linear_complexity.jsonl b/data/sampled_jsons/Attention_Is_All_You_Need_abstract_quadratic_OR_linear_complexity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f43e2b3dc7277017f8bc1ce8016e1dd40dd0bffd
--- /dev/null
+++ b/data/sampled_jsons/Attention_Is_All_You_Need_abstract_quadratic_OR_linear_complexity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear Attention with Global Context: A Multipole Attention", "date": "", "ddg_snippet": "... is characterized by the use of the self- attention mechanism [ 2 ] that allows modeling global contextual information across the tokens of a text or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02748v1", "content": "... is characterized by the use of the self- attention mechanism [ 2 ] that allows modeling global contextual information across the tokens of a text or ..."}
+{"idx": 1, "title": "Attentions Under the Microscope: A Comparative Study of", "date": "", "ddg_snippet": "... or optimized self- attention mechanisms, ... However, the original attention formulation incurs quadratic complexity in both time and memory [ 20 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07247v1", "content": "... or optimized self- attention mechanisms, ... However, the original attention formulation incurs quadratic complexity in both time and memory [ 20 ] ."}
+{"idx": 2, "title": "Fast and Simplex: 2-Simplicial Attention in Triton", "date": "", "ddg_snippet": "... after was to reduce the quadratic complexity of attention with ... The other end of the spectrum is going from quadratic to higher order attention .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02754v1", "content": "... after was to reduce the quadratic complexity of attention with ... The other end of the spectrum is going from quadratic to higher order attention ."}
+{"idx": 3, "title": "Low-Cost FlashAttention with Fused Exponential and", "date": "", "ddg_snippet": "In practice, the attention mechanism is applied across multiple heads in parallel [ 3 ] , allowing the model to comprehend complex relationships ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14314v1", "content": "In practice, the attention mechanism is applied across multiple heads in parallel [ 3 ] , allowing the model to comprehend complex relationships ..."}
+{"idx": 4, "title": "Fastformer: Additive Attention Can Be All You Need | Papers", "date": "", "ddg_snippet": "PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/fastformer-additive- attention - is - all - you - need /news-recommendation-on ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/fastformer-additive-attention-is-all-you-need", "content": "PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/fastformer-additive- attention - is - all - you - need /news-recommendation-on ..."}
+{"idx": 5, "title": "\"Attention\", \"Transformers\", in Neural", "date": "", "ddg_snippet": "While in Google Research, I worked with two of the authors of the \" Attention is All you Need \" paper, including the gentleman who chose ...", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=38756888", "content": "While in Google Research, I worked with two of the authors of the \" Attention is All you Need \" paper, including the gentleman who chose ..."}
+{"idx": 6, "title": "Efficient Attention: Breaking The Quadratic Transformer", "date": "", "ddg_snippet": "... dense Transformers remain surprisingly competitive, and the many proposed variants all have their own drawbacks; none have superseded standard GPT or ...", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/transformer/attention/abstract", "content": "... dense Transformers remain surprisingly competitive, and the many proposed variants all have their own drawbacks; none have superseded standard GPT or ..."}
+{"idx": 7, "title": "Beyond Attention: Toward Machines with Intrinsic Higher Mental", "date": "", "ddg_snippet": "One of the most prominent computational frameworks for a dual-stream information flow is based on the principle of free energy minimization [ 24 ] or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06257v1", "content": "One of the most prominent computational frameworks for a dual-stream information flow is based on the principle of free energy minimization [ 24 ] or ..."}
+{"idx": 8, "title": "On the Tradeoffs of SSMs and Transformers | Goomba Lab", "date": "", "ddg_snippet": "When a self- attention layer receives a new token, it needs to compare it to all the previously seen elements of the sequence, which means that it ...", "subpage_snippet": "", "source": "goombalab.github.io", "link": "https://goombalab.github.io/blog/2025/tradeoffs/", "content": "When a self- attention layer receives a new token, it needs to compare it to all the previously seen elements of the sequence, which means that it ..."}
+{"idx": 9, "title": "The Transformer Algorithm with the Lowest Optimal Time", "date": "", "ddg_snippet": "Everyone is familiar with the seminal 2017 Attention is all you need paper, but I am going to summarize it anyway so that newcomers will have a ...", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/the-transformer-algorithm-with-the-lowest-optimal-time-complexity-possible", "content": "Everyone is familiar with the seminal 2017 Attention is all you need paper, but I am going to summarize it anyway so that newcomers will have a ..."}
diff --git a/data/sampled_jsons/Attention_Is_All_You_Need_abstract_year_2017.jsonl b/data/sampled_jsons/Attention_Is_All_You_Need_abstract_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..37b742927c8853e49c5880512be27c2b639b030b
--- /dev/null
+++ b/data/sampled_jsons/Attention_Is_All_You_Need_abstract_year_2017.jsonl
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+{"idx": 0, "title": "Attention - Wikipedia", "date": "", "ddg_snippet": "Attention or focus, is the concentration of awareness on some phenomenon to the exclusion of other stimuli. [1] It is the selective concentration on discrete information, either subjectively or objectively.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Attention", "content": "Attention or focus, is the concentration of awareness on some phenomenon to the exclusion of other stimuli. [1] It is the selective concentration on discrete information, either subjectively or objectively."}
+{"idx": 1, "title": "ATTENTION Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of ATTENTION is the act or state of applying the mind to something. How to use attention in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/attention", "content": "The meaning of ATTENTION is the act or state of applying the mind to something. How to use attention in a sentence."}
+{"idx": 2, "title": "ATTENTION | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "ATTENTION definition: 1. notice, thought, or interest: 2. to make someone notice you: 3. to watch, listen to, or think…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/attention", "content": "ATTENTION definition: 1. notice, thought, or interest: 2. to make someone notice you: 3. to watch, listen to, or think…. Learn more."}
+{"idx": 3, "title": "ATTENTION Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Attention definition: the act or faculty of attending, especially by directing the mind to an object.. See examples of ATTENTION used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/attention", "content": "Attention definition: the act or faculty of attending, especially by directing the mind to an object.. See examples of ATTENTION used in a sentence."}
+{"idx": 4, "title": "Attention ( Stanford Encyclopedia of Philosophy )", "date": "", "ddg_snippet": "Sep 8, 2009 · Attention is involved in the selective directedness of our mental lives. The nature of this selectivity is one of the principal points of disagreement between the extant theories of attention . Some of the most influential theories treat the selectivity of attention as resulting from limitations in the brain’s capacity to process the complex properties of multiple perceivable stimuli. Other ...", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/attention/", "content": "Sep 8, 2009 · Attention is involved in the selective directedness of our mental lives. The nature of this selectivity is one of the principal points of disagreement between the extant theories of attention . Some of the most influential theories treat the selectivity of attention as resulting from limitations in the brain’s capacity to process the complex properties of multiple perceivable stimuli. Other ..."}
+{"idx": 5, "title": "Attention - definition of attention by The Free Dictionary", "date": "", "ddg_snippet": "attention If you give someone or something your attention , you look at them, listen to them, or think about them carefully. When he had their attention , he began his lecture. He turned his attention back to his magazine. You can also say that someone pays attention to something.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/attention", "content": "attention If you give someone or something your attention , you look at them, listen to them, or think about them carefully. When he had their attention , he began his lecture. He turned his attention back to his magazine. You can also say that someone pays attention to something."}
+{"idx": 6, "title": "Attention - Psychology Today", "date": "", "ddg_snippet": "Attention can help us focus our awareness on a particular aspect of our environment, important decisions, or the thoughts in our head.", "subpage_snippet": "", "source": "www.psychologytoday.com", "link": "https://www.psychologytoday.com/us/basics/attention", "content": "Attention can help us focus our awareness on a particular aspect of our environment, important decisions, or the thoughts in our head."}
+{"idx": 7, "title": "Attention | Definition, Theories, Aspects, & Facts | Britannica", "date": "", "ddg_snippet": "Attention is awareness of the here and now in a focal and perceptive way. For early psychologists, such as Edward Bradford Titchener, attention determined the content of consciousness and influenced the quality of conscious experience.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/science/attention", "content": "Attention is awareness of the here and now in a focal and perceptive way. For early psychologists, such as Edward Bradford Titchener, attention determined the content of consciousness and influenced the quality of conscious experience."}
+{"idx": 8, "title": "How Psychologists Define Attention - Verywell Mind", "date": "", "ddg_snippet": "Oct 31, 2024 · Attention is the ability to actively process specific information in the environment while tuning out other details. It's like a highlighter or spotlight and makes what we focus on stand out.", "subpage_snippet": "", "source": "www.verywellmind.com", "link": "https://www.verywellmind.com/what-is-attention-2795009", "content": "Oct 31, 2024 · Attention is the ability to actively process specific information in the environment while tuning out other details. It's like a highlighter or spotlight and makes what we focus on stand out."}
+{"idx": 9, "title": "Attention - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "When you ask that question, you are asking people to focus their mental powers on you. Whether they do or not depends on your next words. You'll have their full attention if you say, \"Here's $100.\" The noun attention can also refer to an interest in something or someone.", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/attention", "content": "When you ask that question, you are asking people to focus their mental powers on you. Whether they do or not depends on your next words. You'll have their full attention if you say, \"Here's $100.\" The noun attention can also refer to an interest in something or someone."}
diff --git a/data/sampled_jsons/AuxK_loss_sparse_autoencoder_dead_neurons.jsonl b/data/sampled_jsons/AuxK_loss_sparse_autoencoder_dead_neurons.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..00ac064a7fc1b9885e6c4301ad5fcd9d138dfc94
--- /dev/null
+++ b/data/sampled_jsons/AuxK_loss_sparse_autoencoder_dead_neurons.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2406.04093] Scaling and evaluating sparse autoencoders", "date": "", "ddg_snippet": "However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents. We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04093", "content": "However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents. We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier."}
+{"idx": 1, "title": "Sparse Autoencoders in Deep Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Sparse autoencoders are a specific form of autoencoder that's been trained for feature learning and dimensionality reduction. As opposed to regular autoencoders , which are trained to reconstruct the input data in the output, sparse autoencoders add a sparsity penalty that encourages the hidden layer to only use a limited number of neurons at ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/deep-learning/sparse-autoencoders-in-deep-learning/", "content": "Sparse autoencoders are a specific form of autoencoder that's been trained for feature learning and dimensionality reduction. As opposed to regular autoencoders , which are trained to reconstruct the input data in the output, sparse autoencoders add a sparsity penalty that encourages the hidden layer to only use a limited number of neurons at ..."}
+{"idx": 2, "title": "ai-safety-foundation/sparse_autoencoder - GitHub", "date": "", "ddg_snippet": "This library contains: A sparse autoencoder model, along with all the underlying PyTorch components you need to customise and/or build your own: Encoder, constrained unit norm decoder and tied bias PyTorch modules in autoencoder . L1 and L2 loss modules in loss . Adam module with helper method to reset state in optimizer. Activations data generator using TransformerLens, with the underlying ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ai-safety-foundation/sparse_autoencoder", "content": "This library contains: A sparse autoencoder model, along with all the underlying PyTorch components you need to customise and/or build your own: Encoder, constrained unit norm decoder and tied bias PyTorch modules in autoencoder . L1 and L2 loss modules in loss . Adam module with helper method to reset state in optimizer. Activations data generator using TransformerLens, with the underlying ..."}
+{"idx": 3, "title": "Sparse Autoencoder Library - Sparse Autoencoder - GitHub Pages", "date": "", "ddg_snippet": "Over the course of training, a subset of autoencoder neurons will have zero activity across a large number of datapoints. The authors of Towards Monosemanticity: Decomposing Language Models With Dictionary Learning found that \"resampling\" these dead neurons during training improves the number of likely-interpretable features (i.e., those in the high density cluster) and reduces total loss ...", "subpage_snippet": "", "source": "ai-safety-foundation.github.io", "link": "https://ai-safety-foundation.github.io/sparse_autoencoder/reference/", "content": "Over the course of training, a subset of autoencoder neurons will have zero activity across a large number of datapoints. The authors of Towards Monosemanticity: Decomposing Language Models With Dictionary Learning found that \"resampling\" these dead neurons during training improves the number of likely-interpretable features (i.e., those in the high density cluster) and reduces total loss ..."}
+{"idx": 4, "title": "(PDF) Scaling and evaluating sparse autoencoders - ResearchGate", "date": "", "ddg_snippet": "Methods that reduce the number of dead latents (gpt2sm 2M, k=32). With AuxK and/or tied initialization, number of dead latents generally decreases over the course of training, after an early spike.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381227195_Scaling_and_evaluating_sparse_autoencoders", "content": "Methods that reduce the number of dead latents (gpt2sm 2M, k=32). With AuxK and/or tied initialization, number of dead latents generally decreases over the course of training, after an early spike."}
+{"idx": 5, "title": "PDF Dead Feature Counts in Sparse Autoencoders Predict Underlying Deep Q ...", "date": "", "ddg_snippet": "This is achieved by training a sparse autoencoder , a network trained to make its output equal to its input, on the neuron activations of an underlying model. Non- sparse autoencoders are often used to learn eficient codings of arbitrary data, by making their hidden size smaller than their input and output size.", "subpage_snippet": "", "source": "math.mit.edu", "link": "https://math.mit.edu/research/highschool/primes/materials/2025/DuPlessie.pdf", "content": "This is achieved by training a sparse autoencoder , a network trained to make its output equal to its input, on the neuron activations of an underlying model. Non- sparse autoencoders are often used to learn eficient codings of arbitrary data, by making their hidden size smaller than their input and output size."}
+{"idx": 6, "title": "PDF Scaling and evaluating sparse autoencoders - OpenAI", "date": "", "ddg_snippet": "However, studying the proper-ties of autoencoder scaling is dificult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents. We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier.", "subpage_snippet": "", "source": "cdn.openai.com", "link": "https://cdn.openai.com/papers/sparse-autoencoders.pdf", "content": "However, studying the proper-ties of autoencoder scaling is dificult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents. We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier."}
+{"idx": 7, "title": "Sparse Autoencoders for More Interpretable RLHF", "date": "", "ddg_snippet": "In an attempt to improve autoencoder performance, Anthropic resampled dead neurons to the feature directions in which the sparse autoencoder performed worst. For simplicity, we resample dead neurons by setting their corresponding rows of W e n c W enc and W d e c W dec to Kaiming uniform random vectors.", "subpage_snippet": "", "source": "www.lakernewhouse.com", "link": "https://www.lakernewhouse.com/writing/sparse-autoencoders-for-rlhf.html", "content": "In an attempt to improve autoencoder performance, Anthropic resampled dead neurons to the feature directions in which the sparse autoencoder performed worst. For simplicity, we resample dead neurons by setting their corresponding rows of W e n c W enc and W d e c W dec to Kaiming uniform random vectors."}
+{"idx": 8, "title": "K-sparse autoencoders example | ksae - Weights & Biases", "date": "", "ddg_snippet": "The fraction of dead neurons appears to be more unstable in the later layers. In all layers, there is an initial peak; in the earlier layers, there is a second peak before a slower decline.", "subpage_snippet": "", "source": "wandb.ai", "link": "https://wandb.ai/tslwn/ksae/reports/K-sparse-autoencoders-example--Vmlldzo4Mzg1OTYx", "content": "The fraction of dead neurons appears to be more unstable in the later layers. In all layers, there is an initial peak; in the earlier layers, there is a second peak before a slower decline."}
+{"idx": 9, "title": "Scaling and evaluating sparse autoencoders - arXiv.org", "date": "", "ddg_snippet": "However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents. We propose using k-sparse autoencoders (Makhzani and Frey, 2013) to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04093v1", "content": "However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents. We propose using k-sparse autoencoders (Makhzani and Frey, 2013) to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier."}
diff --git "a/data/sampled_jsons/BA-Cycle_vs_previous_inverse_folding_\342\210\206\342\210\206G_unbound_state_difference.jsonl" "b/data/sampled_jsons/BA-Cycle_vs_previous_inverse_folding_\342\210\206\342\210\206G_unbound_state_difference.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..03813176fc167a843f36522b2fea8d049f4426a0
--- /dev/null
+++ "b/data/sampled_jsons/BA-Cycle_vs_previous_inverse_folding_\342\210\206\342\210\206G_unbound_state_difference.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "ize the log-likelihood provided by protein inverse folding models for ∆∆G estimation. Compared to previous inverse folding -based methods, our method explicitly accounts for the unbound state of protein complex in the ∆∆G thermodynamic cycle, introducing a physical inductiv", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "ize the log-likelihood provided by protein inverse folding models for ∆∆G estimation. Compared to previous inverse folding -based methods, our method explicitly accounts for the unbound state of protein complex in the ∆∆G thermodynamic cycle, introducing a physical inductiv"}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 2, "title": "Inverse folding of protein complexes with a structure ...", "date": "", "ddg_snippet": "Dec 19, 2023 · Our inverse folding framework for protein design does not model an explicit protein function or definition of protein fitness. Rather, using a structure-guided paradigm, we indirectly explore the underlying fitness landscape by focusing exploration to regions where the backbone fold of the protein is preserved.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10769282/", "content": "Dec 19, 2023 · Our inverse folding framework for protein design does not model an explicit protein function or definition of protein fitness. Rather, using a structure-guided paradigm, we indirectly explore the underlying fitness landscape by focusing exploration to regions where the backbone fold of the protein is preserved."}
+{"idx": 3, "title": "Advancing protein evolution with inverse folding models ...", "date": "", "ddg_snippet": "Jul 7, 2025 · An AI-informed approach integrates generic protein inverse folding models with structural and evolutionary constraints to efficiently identify high-fitness mutations, enabling the development of advanced base editors and demonstrating broad scalability for artificial protein evolution.", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell/fulltext/S0092-8674(25)00680-4", "content": "Jul 7, 2025 · An AI-informed approach integrates generic protein inverse folding models with structural and evolutionary constraints to efficiently identify high-fitness mutations, enabling the development of advanced base editors and demonstrating broad scalability for artificial protein evolution."}
+{"idx": 4, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsuper-vised state -of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coeficients of 0.3201 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsuper-vised state -of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coeficients of 0.3201 ..."}
+{"idx": 5, "title": "Improving Inverse Folding models at Protein Stability ...", "date": "", "ddg_snippet": "Jun 15, 2024 · The effect of point mutations on stability was best predicted by inverse folding models which are trained to recover the native sequence of a protein using its backbone structure and partial sequence context.", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2024.06.15.599145v1.full.pdf", "content": "Jun 15, 2024 · The effect of point mutations on stability was best predicted by inverse folding models which are trained to recover the native sequence of a protein using its backbone structure and partial sequence context."}
+{"idx": 6, "title": "Unlocking the potential of willow condensed tannins: effects on", "date": "", "ddg_snippet": "The decrease was even greater for TN with unbound and total CT content decreasing by 94% ( P < 0.05; Table 1 ) and 78% ( P < 0 ...", "subpage_snippet": "", "source": "animalmicrobiome.biomedcentral.com", "link": "https://animalmicrobiome.biomedcentral.com/articles/10.1186/s42523-025-00444-6", "content": "The decrease was even greater for TN with unbound and total CT content decreasing by 94% ( P < 0.05; Table 1 ) and 78% ( P < 0 ..."}
+{"idx": 7, "title": "Activation Ratios For Reconstruction Of Signal Transduction", "date": "", "ddg_snippet": "Several groups have performed both steady- state and dynamic studies of isolated cycles and pathways[3-7]. ... previous models in literature and then ...", "subpage_snippet": "", "source": "123dok.net", "link": "https://123dok.net/document/y96399dy-activation-ratios-for-reconstruction-of-signal-transduction-networks.html", "content": "Several groups have performed both steady- state and dynamic studies of isolated cycles and pathways[3-7]. ... previous models in literature and then ..."}
+{"idx": 8, "title": "Evolutionary divergence in the conformational landscapes of", "date": "", "ddg_snippet": "... more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/83368", "content": "... more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs ..."}
+{"idx": 9, "title": "入选ICLR 2025！浙大沈春华等人提出玻尔兹曼对齐技术，蛋白质结合自由...", "date": "", "ddg_snippet": "Mar 3, 2025 · 结合自由能 ( ∆G ，即结合态和未结合态之间吉布斯自由能的差值) 等参数能够对蛋白质间互相作用的动态过程进行定量表征， 但如何准确的预测结合自由能的变化 ( ∆∆G ，也称突变效应) 成为了科研界了解或调节蛋白质-蛋白质相互作用的前提之一。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/HyperAI/article/details/145991467", "content": "Mar 3, 2025 · 结合自由能 ( ∆G ，即结合态和未结合态之间吉布斯自由能的差值) 等参数能够对蛋白质间互相作用的动态过程进行定量表征， 但如何准确的预测结合自由能的变化 ( ∆∆G ，也称突变效应) 成为了科研界了解或调节蛋白质-蛋白质相互作用的前提之一。"}
diff --git a/data/sampled_jsons/BA-DDG_Boltzmann_Aligned_inverse_folding_limitations_conclusion_side-chain_conformational_flexibilit.jsonl b/data/sampled_jsons/BA-DDG_Boltzmann_Aligned_inverse_folding_limitations_conclusion_side-chain_conformational_flexibilit.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e8de5a472af176b0bbcdb74afa9882cf6db7df64
--- /dev/null
+++ b/data/sampled_jsons/BA-DDG_Boltzmann_Aligned_inverse_folding_limitations_conclusion_side-chain_conformational_flexibilit.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Compared to previous inverse folding -based methods, our method explicitly considers the unbound state of the protein complex, enabling fine-tuning of inverse folding models in a manner consistent with statistical thermodynamics. Our contributions can be summarized as follows: •", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "Compared to previous inverse folding -based methods, our method explicitly considers the unbound state of the protein complex, enabling fine-tuning of inverse folding models in a manner consistent with statistical thermodynamics. Our contributions can be summarized as follows: •"}
+{"idx": 2, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "h Boltzmann Alignment . BA-DDG employs a forward process identical to that of BA -Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆G and the predicted ∆∆G ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "h Boltzmann Alignment . BA-DDG employs a forward process identical to that of BA -Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆G and the predicted ∆∆G ..."}
+{"idx": 3, "title": "aim-uofa/BA-DDG | DeepWiki", "date": "", "ddg_snippet": "This document provides an overview of the BA-DDG ( Boltzmann - Aligned ΔΔG) system, a deep learning framework for predicting changes in binding affinity (ΔΔG) between protein-protein interactions upon mu", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/aim-uofa/BA-DDG/1-overview", "content": "This document provides an overview of the BA-DDG ( Boltzmann - Aligned ΔΔG) system, a deep learning framework for predicting changes in binding affinity (ΔΔG) between protein-protein interactions upon mu"}
+{"idx": 4, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values."}
+{"idx": 5, "title": "BA-DDG/README.md at master · aim-uofa/BA-DDG · GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/blob/master/README.md", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 6, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\\Delta\\Delta G$.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\\Delta\\Delta G$."}
+{"idx": 7, "title": "Boltzmann - Aligned Inverse Folding Model as... | OpenReview", "date": "", "ddg_snippet": "However, the protein conformational distribution is intractable.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "However, the protein conformational distribution is intractable."}
+{"idx": 8, "title": "An end-to-end deep learning method for protein side - chain packing...", "date": "", "ddg_snippet": "Protein side - chain packing (PSCP), the task of determining amino acid side - chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371164369_An_end-to-end_deep_learning_method_for_protein_side-chain_packing_and_inverse_folding", "content": "Protein side - chain packing (PSCP), the task of determining amino acid side - chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design."}
+{"idx": 9, "title": "FlowPacker: Protein side - chain packing with torsional", "date": "", "ddg_snippet": "Accurate prediction of protein side - chain conformations is necessary to under-stand protein folding , protein-protein interactions and facilitate de novo protein design.", "subpage_snippet": "", "source": "www.mlsb.io", "link": "https://www.mlsb.io/papers_2024/FlowPacker:_protein_side-chain_packing_with_torsional_flow_matching.pdf", "content": "Accurate prediction of protein side - chain conformations is necessary to under-stand protein folding , protein-protein interactions and facilitate de novo protein design."}
diff --git a/data/sampled_jsons/BA-DDG_README.md_hardware_GPU_configuration.jsonl b/data/sampled_jsons/BA-DDG_README.md_hardware_GPU_configuration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e915449fe64dca5cb67cfe03b281dd2d2dc7a7fa
--- /dev/null
+++ b/data/sampled_jsons/BA-DDG_README.md_hardware_GPU_configuration.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Thinking Machines: Machine Learning and Its Hardware ...", "date": "", "ddg_snippet": "GPUs are used for applications with a huge DLP, and FPGAs are used for applications with a complex control flow and requiring a higher execution performance.", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/thinking-machines-machine-learning-and-its-hardware-implementation-0128182792-9780128182796.html", "content": "GPUs are used for applications with a huge DLP, and FPGAs are used for applications with a complex control flow and requiring a higher execution performance."}
+{"idx": 1, "title": "ProteinZero: Self-Improving Protein Generation via Online ...", "date": "", "ddg_snippet": "9 Jun 2025 — We propose a fast thermodynamic stability ddG estimator based on inverse folding sequence likelihoods and unconditional sequence priors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07459v1", "content": "9 Jun 2025 — We propose a fast thermodynamic stability ddG estimator based on inverse folding sequence likelihoods and unconditional sequence priors."}
+{"idx": 2, "title": "boltzmann-aligned inverse folding model as", "date": "", "ddg_snippet": "Upon BA-Cycle, we introduce a method named BA - DDG (Sec. 3.3) to fine-tune the inverse folding model using ∆∆G-labeled data, introducing an inductive bias from.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/69e9689e53a10509760846b53b77823430a0c523.pdf", "content": "Upon BA-Cycle, we introduce a method named BA - DDG (Sec. 3.3) to fine-tune the inverse folding model using ∆∆G-labeled data, introducing an inductive bias from."}
+{"idx": 3, "title": "De novo protein design by inversion of the AlphaFold ...", "date": "", "ddg_snippet": "by CA Goverde · 2023 · Cited by 86 — Hardware settings . For the design pipeline one Nvidia Tesla V100 (32GB) was used. The prediction of a protein with a sequence length of 92 (top7) and ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10204179/", "content": "by CA Goverde · 2023 · Cited by 86 — Hardware settings . For the design pipeline one Nvidia Tesla V100 (32GB) was used. The prediction of a protein with a sequence length of 92 (top7) and ..."}
+{"idx": 4, "title": "Track: Poster Session 6", "date": "", "ddg_snippet": "26 Apr 2025 — MagicPIG can improve decoding throughput by up to 5 × across various GPU hardware and achieve 54ms decoding latency on a single RTX 4090 for ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31976", "content": "26 Apr 2025 — MagicPIG can improve decoding throughput by up to 5 × across various GPU hardware and achieve 54ms decoding latency on a single RTX 4090 for ..."}
+{"idx": 5, "title": "A Systematic Literature Review on Mining LTL Specifications", "date": "", "ddg_snippet": "by S Germiniani · 2025 — The main contribution of this paper is a technique that uses Dynamic Dependency Graphs. ( DDGs ) to infer properties about hardware designs from a. 49 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/10926195.pdf", "content": "by S Germiniani · 2025 — The main contribution of this paper is a technique that uses Dynamic Dependency Graphs. ( DDGs ) to infer properties about hardware designs from a. 49 pages"}
+{"idx": 6, "title": "notomorrow/hyperion-engine", "date": "", "ddg_snippet": "Hyperion Engine is a 3D game engine written in C++. Currently, it targets Windows, macOS and Linux and has support for C# scripting via .NET Core.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/notomorrow/hyperion-engine", "content": "Hyperion Engine is a 3D game engine written in C++. Currently, it targets Windows, macOS and Linux and has support for C# scripting via .NET Core."}
+{"idx": 7, "title": "r/RedditEng", "date": "", "ddg_snippet": "We save and configure them through Github providing version control. We have views for “all the fields” + views for “the important fields”. They make it easy ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/RedditEng/", "content": "We save and configure them through Github providing version control. We have views for “all the fields” + views for “the important fields”. They make it easy ..."}
+{"idx": 8, "title": "Decoupled neural network training with re-computation ...", "date": "", "ddg_snippet": "by J Peng · 2023 · Cited by 1 — This section presents the proposed decoupled network training with re-computation and weight prediction (DTRP) method in detail.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9949630/", "content": "by J Peng · 2023 · Cited by 1 — This section presents the proposed decoupled network training with re-computation and weight prediction (DTRP) method in detail."}
+{"idx": 9, "title": "Accelerating Biocatalysis Discovery with Machine Learning", "date": "", "ddg_snippet": "by B Markus · 2023 · Cited by 55 — Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines.", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acscatal.3c03417", "content": "by B Markus · 2023 · Cited by 55 — Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines."}
diff --git a/data/sampled_jsons/Bad_Example_online_matching_probability_1n_offline_server_t.jsonl b/data/sampled_jsons/Bad_Example_online_matching_probability_1n_offline_server_t.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dd6de7669f5c061606e85460b2b710011f802c23
--- /dev/null
+++ b/data/sampled_jsons/Bad_Example_online_matching_probability_1n_offline_server_t.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "La BAD : les premiers 50 ans - Banque africaine de développement", "date": "", "ddg_snippet": "Le 4 novembre 1964, les ministres de 23 États africains indépendants se rencontraient à Lagos, au Nigeria, pour la réunion inaugurale du Conseil des gouverneurs de la Banque africaine de développement ( BAD ).", "subpage_snippet": "", "source": "www.afdb.org", "link": "https://www.afdb.org/fr/propos/vue-densemble/historique/la-bad-les-premiers-50-ans", "content": "Le 4 novembre 1964, les ministres de 23 États africains indépendants se rencontraient à Lagos, au Nigeria, pour la réunion inaugurale du Conseil des gouverneurs de la Banque africaine de développement ( BAD )."}
+{"idx": 1, "title": "Organigramme approuvé Banque africaine de développement (BAD)", "date": "", "ddg_snippet": "Jun 23, 2025 · Organigramme approuvé Banque africaine de développement ( BAD ) Mai 2022 (Mis à jour au 31 mai 2025) Groupe de la Banque africaine de développement", "subpage_snippet": "", "source": "www.afdb.org", "link": "https://www.afdb.org/sites/default/files/2025/06/23/nouvelle_structure_approuvee_mise_a_jour_au_31_mai_2025.pdf?e=1&page=1&embedInfo=theme,293042,151b26,ffffff,ffe358,ffffff", "content": "Jun 23, 2025 · Organigramme approuvé Banque africaine de développement ( BAD ) Mai 2022 (Mis à jour au 31 mai 2025) Groupe de la Banque africaine de développement"}
+{"idx": 2, "title": "La Banque africaine de développement", "date": "", "ddg_snippet": "La Banque africaine de développement ( BAD ) est l’institution mère du Groupe. L’accord portant création de la banque a é t é adopté et ouvert à la signature à l’occasion de la Conférence de Khartoum tenue le 4 aoû t 1963. Cet accord est entré en vigueur le 10 septembre 1964 à Khartoum, au Soudan.", "subpage_snippet": "", "source": "www.afdb.org", "link": "https://www.afdb.org/fr/propos/vue-densemble/la-banque-africaine-de-developpement", "content": "La Banque africaine de développement ( BAD ) est l’institution mère du Groupe. L’accord portant création de la banque a é t é adopté et ouvert à la signature à l’occasion de la Conférence de Khartoum tenue le 4 aoû t 1963. Cet accord est entré en vigueur le 10 septembre 1964 à Khartoum, au Soudan."}
+{"idx": 3, "title": "Appel d’offres pour la fourniture des services lors des...", "date": "", "ddg_snippet": "Apr 10, 2025 · Appel d’offres pour la fourniture des services lors des Assemblées Annuelles 2025 de la Bad Publication Date: 10-Apr-2025 Deadline Date:", "subpage_snippet": "", "source": "www.afdb.org", "link": "https://www.afdb.org/en/corporate-procurement/appel-doffres-pour-la-fourniture-des-services-lors-des-assemblees-annuelles-2025-de-la-bad-82710", "content": "Apr 10, 2025 · Appel d’offres pour la fourniture des services lors des Assemblées Annuelles 2025 de la Bad Publication Date: 10-Apr-2025 Deadline Date:"}
+{"idx": 4, "title": "Accueil | IDEV", "date": "", "ddg_snippet": "IDEV, ou l’Évaluation indépendante du développement de la Banque Africaine de Développement ( BAD ) est une fonction indépendante avec pour mission de renforcer l'efficacité du développement de la Banque dans ses pays membres régionaux.", "subpage_snippet": "", "source": "idev.afdb.org", "link": "https://idev.afdb.org/fr", "content": "IDEV, ou l’Évaluation indépendante du développement de la Banque Africaine de Développement ( BAD ) est une fonction indépendante avec pour mission de renforcer l'efficacité du développement de la Banque dans ses pays membres régionaux."}
+{"idx": 5, "title": "Banque africaine de développement | Faire la différence", "date": "", "ddg_snippet": "Sep 17, 2025 · Le Groupe de la Banque africaine de développement est une institution financière de développement multilatérale régionale créée pour contribuer au développement économique et au progrès social des pays africains qui sont membres de l'institution dans la région.", "subpage_snippet": "", "source": "www.afdb.org", "link": "https://www.afdb.org/fr", "content": "Sep 17, 2025 · Le Groupe de la Banque africaine de développement est une institution financière de développement multilatérale régionale créée pour contribuer au développement économique et au progrès social des pays africains qui sont membres de l'institution dans la région."}
+{"idx": 6, "title": "African Development Bank Group | Making a Difference", "date": "", "ddg_snippet": "5 days ago · The African Development Bank Group is a regional multilateral development finance institution established to contribute to the economic development and social progress of African countries that are the institution's Regional Member Countries.", "subpage_snippet": "", "source": "www.afdb.org", "link": "https://www.afdb.org/en", "content": "5 days ago · The African Development Bank Group is a regional multilateral development finance institution established to contribute to the economic development and social progress of African countries that are the institution's Regional Member Countries."}
+{"idx": 7, "title": "Postes vacants | Banque africaine de développement", "date": "", "ddg_snippet": "La Banque africaine de développement propose divers flux RSS pour vous tenir informé de nos activités, opportunités et initiatives. Abonnez-vous à nos flux pour recevoir automatiquement les mises à jour lorsque de nouveaux contenus sont publiés.", "subpage_snippet": "", "source": "www.afdb.org", "link": "https://www.afdb.org/fr/propos/carrieres/postes-vacants", "content": "La Banque africaine de développement propose divers flux RSS pour vous tenir informé de nos activités, opportunités et initiatives. Abonnez-vous à nos flux pour recevoir automatiquement les mises à jour lorsque de nouveaux contenus sont publiés."}
+{"idx": 8, "title": "Historique | Banque africaine de développement", "date": "", "ddg_snippet": "Mamoun Beheiry (Soudan), premier président de la Banque africaine de développement Khartoum (Soudan), septembre 1964. Un groupe d'hommes, des Africains, se réunit à Khartoum au Soudan, pour ratifier l'accord multinational qui porte sur la création de la Banque africaine de développement. Ils représentent vingt-cinq gouvernements du continent. Ils sont tous investis de la même mission ...", "subpage_snippet": "", "source": "www.afdb.org", "link": "https://www.afdb.org/fr/propos-information-dentreprise/historique", "content": "Mamoun Beheiry (Soudan), premier président de la Banque africaine de développement Khartoum (Soudan), septembre 1964. Un groupe d'hommes, des Africains, se réunit à Khartoum au Soudan, pour ratifier l'accord multinational qui porte sur la création de la Banque africaine de développement. Ils représentent vingt-cinq gouvernements du continent. Ils sont tous investis de la même mission ..."}
+{"idx": 9, "title": "Banque africaine de développement - Assemblées Annuelles", "date": "", "ddg_snippet": "The Annual Meetings of the African Development Bank Group provide a unique platform for knowledge exchange among high-level decision-makers in Africa, key officials from bilateral and multilateral development agencies, top academics, NGOs, civil society, and private sector leaders, fostering dialogue on economic growth, investment, and sustainable development.", "subpage_snippet": "", "source": "am.afdb.org", "link": "https://am.afdb.org/fr", "content": "The Annual Meetings of the African Development Bank Group provide a unique platform for knowledge exchange among high-level decision-makers in Africa, key officials from bilateral and multilateral development agencies, top academics, NGOs, civil society, and private sector leaders, fostering dialogue on economic growth, investment, and sustainable development."}
diff --git a/data/sampled_jsons/Beimel_et_al._2022_differential_privacy_adaptive_queries_year_2022.jsonl b/data/sampled_jsons/Beimel_et_al._2022_differential_privacy_adaptive_queries_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..891ba8548f2cc480968e49bef17873d8ca486052
--- /dev/null
+++ b/data/sampled_jsons/Beimel_et_al._2022_differential_privacy_adaptive_queries_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Differential Privacy for Adaptively Solving Search Problems via ...", "date": "", "ddg_snippet": "Abstract Recently differential privacy has been used for a number of streaming, data structure, and dynamic graph problems as a means of hiding the internal randomness of the data structure, so that multi-ple possibly adaptive queries can be made with-out sacrificing the correctness of the responses. Although these works use differential privacy to show that for some problems it is possible to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kEn7Wt6Yj2", "content": "Abstract Recently differential privacy has been used for a number of streaming, data structure, and dynamic graph problems as a means of hiding the internal randomness of the data structure, so that multi-ple possibly adaptive queries can be made with-out sacrificing the correctness of the responses. Although these works use differential privacy to show that for some problems it is possible to ..."}
+{"idx": 1, "title": "Advancing Differential Privacy: Where We Are Now and Future Directions ...", "date": "", "ddg_snippet": "For example, in the context of language models, should a token, a paragraph, or the complete interaction of a single user be considered as a single unit for privacy protection (H. Brown et al ., 2022 )?", "subpage_snippet": "", "source": "hdsr.mitpress.mit.edu", "link": "https://hdsr.mitpress.mit.edu/pub/sl9we8gh", "content": "For example, in the context of language models, should a token, a paragraph, or the complete interaction of a single user be considered as a single unit for privacy protection (H. Brown et al ., 2022 )?"}
+{"idx": 2, "title": "Differentially Private Deep Learning With Dynamic Privacy Budget ...", "date": "", "ddg_snippet": "Finally, we integrate the adaptive optimizer into the gradient descent. In addition to improving the model utility, we also leverage the leading Sinh-Normal noise addition mechanism to achieve truncated concentrated differential privacy (tCDP) - as demonstrated by our rigorous analysis.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10179278", "content": "Finally, we integrate the adaptive optimizer into the gradient descent. In addition to improving the model utility, we also leverage the leading Sinh-Normal noise addition mechanism to achieve truncated concentrated differential privacy (tCDP) - as demonstrated by our rigorous analysis."}
+{"idx": 3, "title": "An Adaptive Mechanism for Accurate Query Answering under Differential ...", "date": "", "ddg_snippet": "ABSTRACT We propose a novel mechanism for answering sets of count-ing queries under di erential privacy . Given a workload of counting queries , the mechanism automatically selects a dif-ferent set of \\strategy\" queries to answer privately, using those answers to derive answers to the workload. The main algorithm proposed in this paper approximates the optimal strategy for any workload of linear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1202.3807", "content": "ABSTRACT We propose a novel mechanism for answering sets of count-ing queries under di erential privacy . Given a workload of counting queries , the mechanism automatically selects a dif-ferent set of \\strategy\" queries to answer privately, using those answers to derive answers to the workload. The main algorithm proposed in this paper approximates the optimal strategy for any workload of linear ..."}
+{"idx": 4, "title": "Computational Differential Privacy for Encrypted Databases Supporting ...", "date": "", "ddg_snippet": "Therefore, the appropriate privacy notion for encrypted databases that we use is computational differential privacy , which was introduced by Beimel et al. at CRYPTO '08. In our work, we focus on the case of functional encryption, which is an extensively studied primitive permitting some authorized computation over encrypted data.", "subpage_snippet": "", "source": "eprint.iacr.org", "link": "https://eprint.iacr.org/2024/048", "content": "Therefore, the appropriate privacy notion for encrypted databases that we use is computational differential privacy , which was introduced by Beimel et al. at CRYPTO '08. In our work, we focus on the case of functional encryption, which is an extensively studied primitive permitting some authorized computation over encrypted data."}
+{"idx": 5, "title": "Scenario-based Adaptations of Differential Privacy: A Technical Survey", "date": "", "ddg_snippet": "In this work, we summarize differential privacy adaptations in specific scenarios and analyze the correlations between data characteristics and differential privacy design. We mainly present them in two lines including differential privacy adaptations in local data privacy and differential privacy adaptations in statistical dataset privacy .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3651153", "content": "In this work, we summarize differential privacy adaptations in specific scenarios and analyze the correlations between data characteristics and differential privacy design. We mainly present them in two lines including differential privacy adaptations in local data privacy and differential privacy adaptations in statistical dataset privacy ."}
+{"idx": 6, "title": "PDF Composition Theorems for Interactive Differential Privacy", "date": "", "ddg_snippet": "Hence, interactivity is a feature that differential privacy grants us for free. Concurrently and independently of our work, Vadhan and Zhang [ 2022 ] proved an optimal concurrent composition theorem for f-DP [Dong et al ., 2022 ], which implies our result for the approximate DP case.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2022/file/3f52b555967a95ee850fcecbd29ee52d-Paper-Conference.pdf", "content": "Hence, interactivity is a feature that differential privacy grants us for free. Concurrently and independently of our work, Vadhan and Zhang [ 2022 ] proved an optimal concurrent composition theorem for f-DP [Dong et al ., 2022 ], which implies our result for the approximate DP case."}
+{"idx": 7, "title": "PDF Advancing Differential Privacy: Where We Are Now and Future Directions ...", "date": "", "ddg_snippet": "This fits into a broader line of work in private ML that makes substitutions to components of traditional nonprivate training pipelines, ranging from activation functions, to pooling functions, and normalization layers (Cheng et al ., 2022 ; Nasirigerdeh et al ., 2023; Papernot et al ., 2021).", "subpage_snippet": "", "source": "s3.amazonaws.com", "link": "https://s3.amazonaws.com/assets.pubpub.org/ki90cxv0c1p0h148qvqipnk3mxcxy2e2.pdf", "content": "This fits into a broader line of work in private ML that makes substitutions to components of traditional nonprivate training pipelines, ranging from activation functions, to pooling functions, and normalization layers (Cheng et al ., 2022 ; Nasirigerdeh et al ., 2023; Papernot et al ., 2021)."}
+{"idx": 8, "title": "On Differential Privacy and Adaptive Data Analysis with Bounded Space", "date": "", "ddg_snippet": "Computational differential privacy was defined by Beimel et al [3] and Mironov et al. [29]. Let \\ (\\mathcal A\\) be a randomized algorithm (mechanism) that operates on datasets.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-30620-4_2", "content": "Computational differential privacy was defined by Beimel et al [3] and Mironov et al. [29]. Let \\ (\\mathcal A\\) be a randomized algorithm (mechanism) that operates on datasets."}
+{"idx": 9, "title": "More Than Privacy: Applying Differential Privacy in Key Areas of ...", "date": "", "ddg_snippet": "Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy , as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9158374", "content": "Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy , as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason ..."}
diff --git a/data/sampled_jsons/Beyond_Optimism_Exploration_Partially_Observable_Rewards_Limitations_Future_Work_continuous_MDPs_neu.jsonl b/data/sampled_jsons/Beyond_Optimism_Exploration_Partially_Observable_Rewards_Limitations_Future_Work_continuous_MDPs_neu.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f6c6f423be29816682057f08385f4c1377eef0c7
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Optimism_Exploration_Partially_Observable_Rewards_Limitations_Future_Work_continuous_MDPs_neu.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causality-informed Anomaly Detection in Partially Observable", "date": "", "ddg_snippet": "... partially observable networks , there has been growing interest in developing anomaly detection techniques that can operate effectively under limited ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09742v1", "content": "... partially observable networks , there has been growing interest in developing anomaly detection techniques that can operate effectively under limited ..."}
+{"idx": 1, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory"}
+{"idx": 2, "title": "Downloads", "date": "", "ddg_snippet": "... Workshop on practical ML for ... Bundle Networks : Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "... Workshop on practical ML for ... Bundle Networks : Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps"}
+{"idx": 3, "title": "ICLR 2021 Schedule", "date": "", "ddg_snippet": "Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/calendar", "content": "Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms"}
+{"idx": 4, "title": "ICLR 2023 Schedule", "date": "", "ddg_snippet": "2:00] CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations ... Error from Tensor Decomposition for Neural Network ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2023/calendar", "content": "2:00] CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations ... Error from Tensor Decomposition for Neural Network ..."}
+{"idx": 5, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? ... Neural Networks for Equivariance to Lie Groups on ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? ... Neural Networks for Equivariance to Lie Groups on ..."}
+{"idx": 6, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? ... Neural Networks for Equivariance to Lie Groups on ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? ... Neural Networks for Equivariance to Lie Groups on ..."}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning ... Learning Based Optimization ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning ... Learning Based Optimization ..."}
+{"idx": 8, "title": "NeurIPS 2021 Papers", "date": "", "ddg_snippet": "... Dynamics: Learning Dynamics of Normalized Neural Network ... The Limitations of Large Width in Neural Networks : A Deep Gaussian Process Perspective", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2021/papers.html", "content": "... Dynamics: Learning Dynamics of Normalized Neural Network ... The Limitations of Large Width in Neural Networks : A Deep Gaussian Process Perspective"}
+{"idx": 9, "title": "Position: AI Safety Must Embrace an Antifragile Perspective", "date": "", "ddg_snippet": "... observed in natural systems—is equally relevant in data-driven AI, where over-optimization for narrow tasks leads to brittle capabilities that fail ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13339v1", "content": "... observed in natural systems—is equally relevant in data-driven AI, where over-optimization for narrow tasks leads to brittle capabilities that fail ..."}
diff --git a/data/sampled_jsons/Beyond_Optimism_Exploration_Partially_Observable_Rewards_continuous_MDP_neural_network_S-functions_i_year_2024.jsonl b/data/sampled_jsons/Beyond_Optimism_Exploration_Partially_Observable_Rewards_continuous_MDP_neural_network_S-functions_i_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..61d1d7a5c18d429441ea1e9de33a1243e42f4269
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Optimism_Exploration_Partially_Observable_Rewards_continuous_MDP_neural_network_S-functions_i_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v2", "content": "RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism ."}
+{"idx": 1, "title": "(PDF) Beyond Optimism : Exploration With Partially Observable ...", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 2, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/hash/784fd5a46dfe303e5b51c8621b84cf3f-Abstract-Conference.html", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 3, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2406.13909v2", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 4, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Keywords : reinforcement learning, partial observability , exploration , successor representations. TL;DR : Directed exploration with the successor representation for MDPs with partially observable rewards .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=k6ZHvF1vkg", "content": "Keywords : reinforcement learning, partial observability , exploration , successor representations. TL;DR : Directed exploration with the successor representation for MDPs with partially observable rewards ."}
+{"idx": 5, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "This paper explores exploration strategies in reinforcement learning (RL) problems with partially observable rewards . It introduces a new approach called \" Optimism Beyond Optimism \" (OBO) that aims to improve exploration in these challenging scenarios.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-optimism-exploration-partially-observable-rewards", "content": "This paper explores exploration strategies in reinforcement learning (RL) problems with partially observable rewards . It introduces a new approach called \" Optimism Beyond Optimism \" (OBO) that aims to improve exploration in these challenging scenarios."}
+{"idx": 6, "title": "GitHub - AmiiThinks/mon_ mdp _neurips24", "date": "", "ddg_snippet": "Explore .Source code of the paper \" Beyond Optimism : Exploration With Partially Observable Rewards \" (Arxiv, NeurIPS). Install and Examples.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AmiiThinks/mon_mdp_neurips24", "content": "Explore .Source code of the paper \" Beyond Optimism : Exploration With Partially Observable Rewards \" (Arxiv, NeurIPS). Install and Examples."}
+{"idx": 7, "title": "A New Approach to Exploration in Reinforcement Learning", "date": "", "ddg_snippet": "Title: Beyond Optimism : Exploration With Partially Observable Rewards . Abstract: Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not...", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-26-a-new-approach-to-exploration-in-reinforcement-learning--ak5d6mx", "content": "Title: Beyond Optimism : Exploration With Partially Observable Rewards . Abstract: Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not..."}
+{"idx": 8, "title": "Chapter 1: Markov Decision Processes and Partially Observable ...", "date": "", "ddg_snippet": "Partially Observable Markov Decision Processes extend the MDP framework to address the fundamental limitation of assuming complete state observability . In most real-world scenarios, agents operate with incomplete information about their environment.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@haitaowang076/chapter-1-markov-decision-processes-and-partially-observable-markov-decision-processes-18cbc8c55311", "content": "Partially Observable Markov Decision Processes extend the MDP framework to address the fundamental limitation of assuming complete state observability . In most real-world scenarios, agents operate with incomplete information about their environment."}
+{"idx": 9, "title": "Advanced Exploration Strategies | DeepWiki", "date": "", "ddg_snippet": "Partially Observable MDPs. Reward shaping modifies the reward function to encourage exploration behavior while preserving the optimal policy.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/yandexdataschool/Practical_RL/5.2-advanced-exploration-strategies", "content": "Partially Observable MDPs. Reward shaping modifies the reward function to encourage exploration behavior while preserving the optimal policy."}
diff --git a/data/sampled_jsons/Beyond_Optimism_Exploration_Section_2.2_Lattimore_Szepesvari_argued_against_optimism.jsonl b/data/sampled_jsons/Beyond_Optimism_Exploration_Section_2.2_Lattimore_Szepesvari_argued_against_optimism.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4cb81acf03c83d55eeaeece005dacf3813b542cf
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Optimism_Exploration_Section_2.2_Lattimore_Szepesvari_argued_against_optimism.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "1 Introduction", "date": "", "ddg_snippet": "Figure 1 : Traditional implementation of active exploration algorithms such as Thompson sampling requires on probabilistic models over latent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19466v3", "content": "Figure 1 : Traditional implementation of active exploration algorithms such as Thompson sampling requires on probabilistic models over latent ..."}
+{"idx": 1, "title": "Bandits & RL", "date": "", "ddg_snippet": "Csaba Szepesvari and Tor Lattimore , Bandit Algorithms , University of Alberta (Fall 2016) and Indiana University(Spring 2017).", "subpage_snippet": "", "source": "alekhagarwal.net", "link": "https://alekhagarwal.net/bandits_and_rl/", "content": "Csaba Szepesvari and Tor Lattimore , Bandit Algorithms , University of Alberta (Fall 2016) and Indiana University(Spring 2017)."}
+{"idx": 2, "title": "No-Regret M♮-Concave Function Maximization: Stochastic Bandit", "date": "", "ddg_snippet": "We first consider the stochastic optimization setting and provide an O ( T − 1 / 2 ) O(T^{-1/ 2 }) -simple regret algorithm ( Theorem ˜ 4. 2 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.12439v2", "content": "We first consider the stochastic optimization setting and provide an O ( T − 1 / 2 ) O(T^{-1/ 2 }) -simple regret algorithm ( Theorem ˜ 4. 2 ..."}
+{"idx": 3, "title": "Replicable Reinforcement Learning with Linear Function", "date": "", "ddg_snippet": "... optimization landscapes [ BGW22 ] , agents exploring different parts of the state space [ PAED17 ] , or the non-stationarity of the data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08660v1", "content": "... optimization landscapes [ BGW22 ] , agents exploring different parts of the state space [ PAED17 ] , or the non-stationarity of the data ..."}
+{"idx": 4, "title": "Controlling Large Language Model Agents with Entropic", "date": "", "ddg_snippet": "Indeed, recent work has shown that this process frequently goes awry, causing in-context LLM agents to fail to produce sensible exploratory behavior ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.00244v2", "content": "Indeed, recent work has shown that this process frequently goes awry, causing in-context LLM agents to fail to produce sensible exploratory behavior ..."}
+{"idx": 5, "title": "Optimization via Strategic Law of Large Numbers11footnote", "date": "", "ddg_snippet": "We establish in our StLLN that, under the optimal strategy for the two -armed decision problem (which chooses which distribution of the two to sample ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.05604v2", "content": "We establish in our StLLN that, under the optimal strategy for the two -armed decision problem (which chooses which distribution of the two to sample ..."}
+{"idx": 6, "title": "The Learning-Theoretic Agenda: Status 2023 - LessWrong 2.0", "date": "", "ddg_snippet": "For any aspect of intelligent agency that we don’t understand and any AI that we design, one of the two will hold: either the AI lacks this aspect ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/ZwshvqiqCvXPsZEct/the-learning-theoretic-agenda-status-2023", "content": "For any aspect of intelligent agency that we don’t understand and any AI that we design, one of the two will hold: either the AI lacks this aspect ..."}
+{"idx": 7, "title": "The Learning-Theoretic Agenda: Status 2023 - LessWrong 2.0", "date": "", "ddg_snippet": "For any aspect of intelligent agency that we don’t understand and any AI that we design, one of the two will hold: either the AI lacks this aspect ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/ZwshvqiqCvXPsZEct/the-learning-theoretic-agenda-status-2023?comments=false", "content": "For any aspect of intelligent agency that we don’t understand and any AI that we design, one of the two will hold: either the AI lacks this aspect ..."}
+{"idx": 8, "title": "AAAI-18 Tutorial Forum - AAAI", "date": "", "ddg_snippet": "The Tutorial Forum provides an opportunity for researchers and practitioners to spend two days each year exploring exciting advances in disciplines ...", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/conference/aaai/aaai-18/aaai18tutorials/", "content": "The Tutorial Forum provides an opportunity for researchers and practitioners to spend two days each year exploring exciting advances in disciplines ..."}
+{"idx": 9, "title": "A Computational Model of Inclusive Pedagogy: From Understanding", "date": "", "ddg_snippet": "... development appears first on the social plane, between two or more individuals, and then on the individual plane (Vygotsky and Cole, 1978 , Eun, 2010 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.02853v1", "content": "... development appears first on the social plane, between two or more individuals, and then on the individual plane (Vygotsky and Cole, 1978 , Eun, 2010 ..."}
diff --git a/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Limitations_Future_Work_neural_network_year_2024.jsonl b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Limitations_Future_Work_neural_network_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0e78027a16a777ea0e3e891ef1c229f62ade90a3
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_Limitations_Future_Work_neural_network_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism: Exploration With Partially Observable ...", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=k6ZHvF1vkg&referrer=[the+profile+of+Michael+Bowling](/profile?id=~Michael_Bowling1)", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial ..."}
+{"idx": 1, "title": "Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Limitations and Future Work . First, we considered tabular MDPs, thus we plan to follow up on continuous MDPs. This will require extending the S-function and ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/784fd5a46dfe303e5b51c8621b84cf3f-Paper-Conference.pdf", "content": "Limitations and Future Work . First, we considered tabular MDPs, thus we plan to follow up on continuous MDPs. This will require extending the S-function and ..."}
+{"idx": 2, "title": "Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Limitations and Future Work . First, we considered tabular MDPs, thus we plan to follow up on continuous MDPs. This will require extending the S-function and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v1", "content": "Limitations and Future Work . First, we considered tabular MDPs, thus we plan to follow up on continuous MDPs. This will require extending the S-function and ..."}
+{"idx": 3, "title": "Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "by S Parisi · 2024 · Cited by 4 — In this paper, we present a novel exploration strategy based on the successor representation to tackle this question. Note that Lattimore and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.13909?", "content": "by S Parisi · 2024 · Cited by 4 — In this paper, we present a novel exploration strategy based on the successor representation to tackle this question. Note that Lattimore and ..."}
+{"idx": 4, "title": "Awesome Exploration Methods in Reinforcement Learning", "date": "", "ddg_snippet": "26 Jun 2025 — A non-exhaustive, but useful taxonomy of methods in Exploration RL. We provide some example methods for each of the different categories.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/opendilab/awesome-exploration-rl", "content": "26 Jun 2025 — A non-exhaustive, but useful taxonomy of methods in Exploration RL. We provide some example methods for each of the different categories."}
+{"idx": 5, "title": "A deep reinforcement learning control framework for a ...", "date": "", "ddg_snippet": "This paper puts forward a novel deep reinforcement learning control framework to realise continuous action control for a partially observable system.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/00207721.2025.2468870?af=R", "content": "This paper puts forward a novel deep reinforcement learning control framework to realise continuous action control for a partially observable system."}
+{"idx": 6, "title": "Which Rewards Matter? Reward Selection for ...", "date": "", "ddg_snippet": "by S Chaudhari — Beyond optimism : Exploration with partially observable rewards . arXiv ... while large-scale domains rely on neural network approximators for Q-values, given their ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=h8UQlBDV0P", "content": "by S Chaudhari — Beyond optimism : Exploration with partially observable rewards . arXiv ... while large-scale domains rely on neural network approximators for Q-values, given their ..."}
+{"idx": 7, "title": "Investigating the properties of neural network ...", "date": "", "ddg_snippet": "by H Wang · 2024 · Cited by 37 — In this paper we investigate the properties of representations learned by deep reinforcement learning systems.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0004370224000365", "content": "by H Wang · 2024 · Cited by 37 — In this paper we investigate the properties of representations learned by deep reinforcement learning systems."}
+{"idx": 8, "title": "Research - Simone Parisi", "date": "", "ddg_snippet": "In our NeurIPS follow-up, we presented a novel exploration paradigm to overcome the limitations of classic optimistic strategies, known to fail under partial ...", "subpage_snippet": "", "source": "sparisi.github.io", "link": "https://sparisi.github.io/assets/research.html", "content": "In our NeurIPS follow-up, we presented a novel exploration paradigm to overcome the limitations of classic optimistic strategies, known to fail under partial ..."}
+{"idx": 9, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers, paper visualization, competitions, datasets & benchmarks.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers, paper visualization, competitions, datasets & benchmarks."}
diff --git a/data/sampled_jsons/Beyond_Optimism_Parisi_2024_UVFA_neural_network_input_state_action_goal_output_year_2024.jsonl b/data/sampled_jsons/Beyond_Optimism_Parisi_2024_UVFA_neural_network_input_state_action_goal_output_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..da573b5065a71b2806cf12a22faeb949091f74eb
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Optimism_Parisi_2024_UVFA_neural_network_input_state_action_goal_output_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.13909", "content": "Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits ..."}
+{"idx": 1, "title": "Simone Parisi", "date": "", "ddg_snippet": "Selected Papers Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi , Alireza Kazemipour, Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi , Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling", "subpage_snippet": "", "source": "sparisi.github.io", "link": "https://sparisi.github.io/", "content": "Selected Papers Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi , Alireza Kazemipour, Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi , Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling"}
+{"idx": 2, "title": "Beyond optimism | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740005", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 3, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "For discrete actions , the network would take the (current state , goal state ) pair and output the value for all ( action , goal action ) pairs, similarly to how deep Q-networks [48] work.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v2", "content": "For discrete actions , the network would take the (current state , goal state ) pair and output the value for all ( action , goal action ) pairs, similarly to how deep Q-networks [48] work."}
+{"idx": 4, "title": "Research - Simone Parisi", "date": "", "ddg_snippet": "Simone Parisi , Victoria Dean, Deepak Pathak, Abhinav Gupta Neural Information Processing Systems (NeurIPS), 2021 Learning and Transfer of State Representations In computer vision and natural language processing, recent advances have allowed researchers to exploit massive amounts of data to pre-train perception models.", "subpage_snippet": "", "source": "sparisi.github.io", "link": "https://sparisi.github.io/assets/research.html", "content": "Simone Parisi , Victoria Dean, Deepak Pathak, Abhinav Gupta Neural Information Processing Systems (NeurIPS), 2021 Learning and Transfer of State Representations In computer vision and natural language processing, recent advances have allowed researchers to exploit massive amounts of data to pre-train perception models."}
+{"idx": 5, "title": "PDF Unbiased Methods for Multi-Goal RL - arXiv.org", "date": "", "ddg_snippet": "Universal Value Function Approximators ( UVFA ) (Schaul et al., 2015) extend the classical Q-learning and Temporal Difference (TD) algorithms to the multi- goal setting. It learns the goal -conditioned value-function (, ) or -function *(, , ) for every state - goal pair, with function approximation, via a TD algorithm. Still, no learning occurs until a reward is observed, and UVFA fails in many high ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2106.08863.pdf", "content": "Universal Value Function Approximators ( UVFA ) (Schaul et al., 2015) extend the classical Q-learning and Temporal Difference (TD) algorithms to the multi- goal setting. It learns the goal -conditioned value-function (, ) or -function *(, , ) for every state - goal pair, with function approximation, via a TD algorithm. Still, no learning occurs until a reward is observed, and UVFA fails in many high ..."}
+{"idx": 6, "title": "Solvable neural network model for input-output associations: Optimal ...", "date": "", "ddg_snippet": "In neural information processing, inputs modulate neural dynamics to generate desired outputs . To unravel the dynamics and underlying neural connectivity enabling such input - output association, we propose an exactly solvable neural-network model with a connectivity matrix explicitly consisting of inputs and required outputs .", "subpage_snippet": "", "source": "journals.aps.org", "link": "https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.5.043221", "content": "In neural information processing, inputs modulate neural dynamics to generate desired outputs . To unravel the dynamics and underlying neural connectivity enabling such input - output association, we propose an exactly solvable neural-network model with a connectivity matrix explicitly consisting of inputs and required outputs ."}
+{"idx": 7, "title": "opendilab/awesome-exploration-rl - GitHub", "date": "", "ddg_snippet": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi , Alireza Kazemipour, Michael Bowling Key: Reinforcement Learning, Partial Observability, Optimism , Exploration ExpEnv: Tabular Environments (with and without unobservable rewards) Exploring the Edges of Latent State Clusters for Goal -Conditioned Reinforcement Learning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/opendilab/awesome-exploration-rl", "content": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi , Alireza Kazemipour, Michael Bowling Key: Reinforcement Learning, Partial Observability, Optimism , Exploration ExpEnv: Tabular Environments (with and without unobservable rewards) Exploring the Edges of Latent State Clusters for Goal -Conditioned Reinforcement Learning"}
+{"idx": 8, "title": "PDF Universal Value Function Approximators - site44", "date": "", "ddg_snippet": "In large problems, the value function is typically repre-sented by a function approximator V (s; ), such as a linear combination of features or a neural network with param-eters . The function approximator exploits the structure in the state space to efficiently learn the value of observed states and generalise to the value of similar, unseen states . However, the goal space often contains just ...", "subpage_snippet": "", "source": "schaul.site44.com", "link": "https://schaul.site44.com/publications/uvfa.pdf", "content": "In large problems, the value function is typically repre-sented by a function approximator V (s; ), such as a linear combination of features or a neural network with param-eters . The function approximator exploits the structure in the state space to efficiently learn the value of observed states and generalise to the value of similar, unseen states . However, the goal space often contains just ..."}
+{"idx": 9, "title": "Amii researchers present on RL advancements, survival prediction, LLMs ...", "date": "", "ddg_snippet": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi , Alireza Kazemipour, Michael Bowling Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all.", "subpage_snippet": "", "source": "www.amii.ca", "link": "https://www.amii.ca/updates-insights/amii-researchers-present-on-rl-advancements-survival-prediction-llms-and-drug-discovery-at-neurips-2024", "content": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi , Alireza Kazemipour, Michael Bowling Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all."}
diff --git a/data/sampled_jsons/Beyond_Self-Repellent_Kernels_Section_4.5_LRU_cache_Equation_15.jsonl b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_Section_4.5_LRU_cache_Equation_15.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LRU Cache - Complete Tutorial - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 23, 2025 · Please refer LRU cache implementation using Doubly Linked List and Hashing for details Complexity Analysis of the Efficient Solution Time Complexity: put () operation: O (1) i.e. time required to insert or update new key-value pair is constant get () operation: O (1) i.e. time required to get the value of a key is constant Auxiliary Space: O (c) where c is the capacity of the Cache . Advantages ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/system-design/lru-cache-implementation/", "content": "Jul 23, 2025 · Please refer LRU cache implementation using Doubly Linked List and Hashing for details Complexity Analysis of the Efficient Solution Time Complexity: put () operation: O (1) i.e. time required to insert or update new key-value pair is constant get () operation: O (1) i.e. time required to get the value of a key is constant Auxiliary Space: O (c) where c is the capacity of the Cache . Advantages ..."}
+{"idx": 1, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "Least Recently Used ( LRU ) cache scheme Essential idea: track only recently visited states, discarding the least-recently used when capacity in cache is reached, whose size = ( acts as the compression ratio) Leverages temporal locality: non-neighboring states do not affect self -repellency", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/47270.pdf", "content": "Least Recently Used ( LRU ) cache scheme Essential idea: track only recently visited states, discarding the least-recently used when capacity in cache is reached, whose size = ( acts as the compression ratio) Leverages temporal locality: non-neighboring states do not affect self -repellency"}
+{"idx": 2, "title": "[2505.18300] Beyond Self-Repellent Kernels: History-Driven ... Modeling Cache Performance Beyond LRU LRU Cache - Complete Tutorial - GeeksforGeeks ICML Poster Beyond Self-Repellent Kernels: History-Driven ... TOMPECS0304-20 - SJTU LRU Cache - Complete Tutorial - GeeksforGeeks LRU Cache - Complete Tutorial - GeeksforGeeks Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of LRU Cache - Complete Tutorial - GeeksforGeeks Modeling Cache Performance Beyond LRU - Massachusetts Institute of LRU Cache under Stationary Requests - arXiv.org", "date": "", "ddg_snippet": "May 23, 2025 · We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ... We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec . 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4 ), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Jul 23, 2025 · Please refer LRU cache implementation using Doubly Linked List and Hashing for details Complexity Analysis of the Efficient Solution Time Complexity: put () operation: O (1) i.e. time required to insert or update new key-value pair is constant get () operation: O (1) i.e. time required to get the value of a key is constant Auxiliary Space: O (c) where c is the capacity of the Cache . Advantages ... Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. A variety of cache replacement algorithms have been introduced and analyzed over the past few decades, mostly based on the least recently used algorithm ( LRU ). Considerable work has focused on analyzing these policies [5, 6, 10, 15 , 21, 22, 24]. What is LRU cache? Cache replacement algorithms are efficiently designed to replace the cache when the space is full. The Least Recently Used (LRU) is one of those algorithms. As the name suggests when the cache memory is full, LRU picks the data that is least recently used and removes it in order to make space for the new data. How to implement a LRU cache using a key-value pair? The basic idea behind implementing an LRU (Least Recently Used) cache using a key-value pair approach is to manage element access and removal efficiently through a combination of a doubly linked list and a hash map. When adding a new key-value pair, insert it as a new node at the head of the doubly linked list . Do modern processors use high-performance cache replacement policies? Modern processors use high-performance cache replacement policies that outperform traditional alternatives like least-recently used (LRU). Unfortunately, current cache models do not capture these high-performance policies as most use stack distances, which are inherently tied to or its vari- LRU ants. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. How do I update the priority of data in the LRU cache? Also updates the priority of data in the LRU cache. put (key, value): Update the value of the key if that key exists, Otherwise, add a key-value pair to the cache. If the number of keys exceeds the capacity of the LRU cache then dismiss the least recently used key. Does a cache model predict high-performance replacement policies? We present a cache model that accurately predicts the be-havior of high-performance replacement policies on modern LLCs. Our model leverages two key observations: First, each core’s private cache hierarchy filters accesses before they reach the LLC, capturing successive references to the same address [5,28,30]. Apr 24, 2022 · A variety of cache replacement algorithms has been introduced and analyzed over the last few decades, mostly based on the least recently used algorithm ( LRU ). Considerable work has focused on analyzing these policies [ 4, 5 , 11, 17] for iid requests (the so-called independent reference model - IRM) and for Markov-modulated requests [9, 15 , 16].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "content": "May 23, 2025 · We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ... We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec . 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4 ), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Jul 23, 2025 · Please refer LRU cache implementation using Doubly Linked List and Hashing for details Complexity Analysis of the Efficient Solution Time Complexity: put () operation: O (1) i.e. time required to insert or update new key-value pair is constant get () operation: O (1) i.e. time required to get the value of a key is constant Auxiliary Space: O (c) where c is the capacity of the Cache . Advantages ... Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. A variety of cache replacement algorithms have been introduced and analyzed over the past few decades, mostly based on the least recently used algorithm ( LRU ). Considerable work has focused on analyzing these policies [5, 6, 10, 15 , 21, 22, 24]. What is LRU cache? Cache replacement algorithms are efficiently designed to replace the cache when the space is full. The Least Recently Used (LRU) is one of those algorithms. As the name suggests when the cache memory is full, LRU picks the data that is least recently used and removes it in order to make space for the new data. How to implement a LRU cache using a key-value pair? The basic idea behind implementing an LRU (Least Recently Used) cache using a key-value pair approach is to manage element access and removal efficiently through a combination of a doubly linked list and a hash map. When adding a new key-value pair, insert it as a new node at the head of the doubly linked list . Do modern processors use high-performance cache replacement policies? Modern processors use high-performance cache replacement policies that outperform traditional alternatives like least-recently used (LRU). Unfortunately, current cache models do not capture these high-performance policies as most use stack distances, which are inherently tied to or its vari- LRU ants. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. How do I update the priority of data in the LRU cache? Also updates the priority of data in the LRU cache. put (key, value): Update the value of the key if that key exists, Otherwise, add a key-value pair to the cache. If the number of keys exceeds the capacity of the LRU cache then dismiss the least recently used key. Does a cache model predict high-performance replacement policies? We present a cache model that accurately predicts the be-havior of high-performance replacement policies on modern LLCs. Our model leverages two key observations: First, each core’s private cache hierarchy filters accesses before they reach the LLC, capturing successive references to the same address [5,28,30]. Apr 24, 2022 · A variety of cache replacement algorithms has been introduced and analyzed over the last few decades, mostly based on the least recently used algorithm ( LRU ). Considerable work has focused on analyzing these policies [ 4, 5 , 11, 17] for iid requests (the so-called independent reference model - IRM) and for Markov-modulated requests [9, 15 , 16]."}
+{"idx": 3, "title": "Modeling Cache Performance Beyond LRU LRU Cache - Complete Tutorial - GeeksforGeeks ICML Poster Beyond Self-Repellent Kernels: History-Driven ... TOMPECS0304-20 - SJTU LRU Cache - Complete Tutorial - GeeksforGeeks LRU Cache - Complete Tutorial - GeeksforGeeks Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of LRU Cache - Complete Tutorial - GeeksforGeeks Modeling Cache Performance Beyond LRU - Massachusetts Institute of LRU Cache under Stationary Requests - arXiv.org", "date": "", "ddg_snippet": "We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec . 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4 ), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Jul 23, 2025 · Please refer LRU cache implementation using Doubly Linked List and Hashing for details Complexity Analysis of the Efficient Solution Time Complexity: put () operation: O (1) i.e. time required to insert or update new key-value pair is constant get () operation: O (1) i.e. time required to get the value of a key is constant Auxiliary Space: O (c) where c is the capacity of the Cache . Advantages ... Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. A variety of cache replacement algorithms have been introduced and analyzed over the past few decades, mostly based on the least recently used algorithm ( LRU ). Considerable work has focused on analyzing these policies [5, 6, 10, 15 , 21, 22, 24]. What is LRU cache? Cache replacement algorithms are efficiently designed to replace the cache when the space is full. The Least Recently Used (LRU) is one of those algorithms. As the name suggests when the cache memory is full, LRU picks the data that is least recently used and removes it in order to make space for the new data. How to implement a LRU cache using a key-value pair? The basic idea behind implementing an LRU (Least Recently Used) cache using a key-value pair approach is to manage element access and removal efficiently through a combination of a doubly linked list and a hash map. When adding a new key-value pair, insert it as a new node at the head of the doubly linked list . Do modern processors use high-performance cache replacement policies? Modern processors use high-performance cache replacement policies that outperform traditional alternatives like least-recently used (LRU). Unfortunately, current cache models do not capture these high-performance policies as most use stack distances, which are inherently tied to or its vari- LRU ants. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. How do I update the priority of data in the LRU cache? Also updates the priority of data in the LRU cache. put (key, value): Update the value of the key if that key exists, Otherwise, add a key-value pair to the cache. If the number of keys exceeds the capacity of the LRU cache then dismiss the least recently used key. Does a cache model predict high-performance replacement policies? We present a cache model that accurately predicts the be-havior of high-performance replacement policies on modern LLCs. Our model leverages two key observations: First, each core’s private cache hierarchy filters accesses before they reach the LLC, capturing successive references to the same address [5,28,30]. Apr 24, 2022 · A variety of cache replacement algorithms has been introduced and analyzed over the last few decades, mostly based on the least recently used algorithm ( LRU ). Considerable work has focused on analyzing these policies [ 4, 5 , 11, 17] for iid requests (the so-called independent reference model - IRM) and for Markov-modulated requests [9, 15 , 16].", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/sanchez/papers/2016.model.hpca.pdf", "content": "We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec . 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4 ), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Jul 23, 2025 · Please refer LRU cache implementation using Doubly Linked List and Hashing for details Complexity Analysis of the Efficient Solution Time Complexity: put () operation: O (1) i.e. time required to insert or update new key-value pair is constant get () operation: O (1) i.e. time required to get the value of a key is constant Auxiliary Space: O (c) where c is the capacity of the Cache . Advantages ... Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. A variety of cache replacement algorithms have been introduced and analyzed over the past few decades, mostly based on the least recently used algorithm ( LRU ). Considerable work has focused on analyzing these policies [5, 6, 10, 15 , 21, 22, 24]. What is LRU cache? Cache replacement algorithms are efficiently designed to replace the cache when the space is full. The Least Recently Used (LRU) is one of those algorithms. As the name suggests when the cache memory is full, LRU picks the data that is least recently used and removes it in order to make space for the new data. How to implement a LRU cache using a key-value pair? The basic idea behind implementing an LRU (Least Recently Used) cache using a key-value pair approach is to manage element access and removal efficiently through a combination of a doubly linked list and a hash map. When adding a new key-value pair, insert it as a new node at the head of the doubly linked list . Do modern processors use high-performance cache replacement policies? Modern processors use high-performance cache replacement policies that outperform traditional alternatives like least-recently used (LRU). Unfortunately, current cache models do not capture these high-performance policies as most use stack distances, which are inherently tied to or its vari- LRU ants. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. How do I update the priority of data in the LRU cache? Also updates the priority of data in the LRU cache. put (key, value): Update the value of the key if that key exists, Otherwise, add a key-value pair to the cache. If the number of keys exceeds the capacity of the LRU cache then dismiss the least recently used key. Does a cache model predict high-performance replacement policies? We present a cache model that accurately predicts the be-havior of high-performance replacement policies on modern LLCs. Our model leverages two key observations: First, each core’s private cache hierarchy filters accesses before they reach the LLC, capturing successive references to the same address [5,28,30]. Apr 24, 2022 · A variety of cache replacement algorithms has been introduced and analyzed over the last few decades, mostly based on the least recently used algorithm ( LRU ). Considerable work has focused on analyzing these policies [ 4, 5 , 11, 17] for iid requests (the so-called independent reference model - IRM) and for Markov-modulated requests [9, 15 , 16]."}
+{"idx": 4, "title": "ICML Poster Beyond Self-Repellent Kernels: History-Driven ...", "date": "", "ddg_snippet": "Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46659", "content": "Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands."}
+{"idx": 5, "title": "LRU Cache under Stationary Requests - arXiv.org", "date": "", "ddg_snippet": "Apr 24, 2022 · A variety of cache replacement algorithms has been introduced and analyzed over the last few decades, mostly based on the least recently used algorithm ( LRU ). Considerable work has focused on analyzing these policies [ 4, 5 , 11, 17] for iid requests (the so-called independent reference model - IRM) and for Markov-modulated requests [9, 15 , 16].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1707.06204v1", "content": "Apr 24, 2022 · A variety of cache replacement algorithms has been introduced and analyzed over the last few decades, mostly based on the least recently used algorithm ( LRU ). Considerable work has focused on analyzing these policies [ 4, 5 , 11, 17] for iid requests (the so-called independent reference model - IRM) and for Markov-modulated requests [9, 15 , 16]."}
+{"idx": 6, "title": "LRU Cache - LeetCode", "date": "", "ddg_snippet": "Can you solve this real interview question? LRU Cache - Design a data structure that follows the constraints of a Least Recently Used ( LRU ) cache [https://en.wikipedia.org/wiki/ Cache _replacement_policies# LRU ]. Implement the...", "subpage_snippet": "", "source": "leetcode.com", "link": "https://leetcode.com/problems/lru-cache/", "content": "Can you solve this real interview question? LRU Cache - Design a data structure that follows the constraints of a Least Recently Used ( LRU ) cache [https://en.wikipedia.org/wiki/ Cache _replacement_policies# LRU ]. Implement the..."}
+{"idx": 7, "title": "LRU , метод вытеснения из кэша / Хабр", "date": "", "ddg_snippet": "Поэтому я решил написать небольшую статью, где расскажу как быстро реализовать метод LRU , и не вынуждать коллег вручную сбрасывать кэш там, где не требуется. Мы будем под кэшированием понимать сохранение результатов вычислений в ответ на некоторые...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/136758/", "content": "Поэтому я решил написать небольшую статью, где расскажу как быстро реализовать метод LRU , и не вынуждать коллег вручную сбрасывать кэш там, где не требуется. Мы будем под кэшированием понимать сохранение результатов вычислений в ответ на некоторые..."}
+{"idx": 8, "title": "Mastering the LRU Cache Interview Question... | Stackademic", "date": "", "ddg_snippet": "🔎 Why LRU Cache Is a Popular Interview QuestionReal-World Relevance → Used in browsers, databases, and operating systems....if key not in self . cache : return -1 node = self . cache [key] self ._remove(node) self ._add_front...", "subpage_snippet": "", "source": "blog.stackademic.com", "link": "https://blog.stackademic.com/mastering-the-lru-cache-interview-question-java-c-python-implementations-with-real-world-475ba5e58011", "content": "🔎 Why LRU Cache Is a Popular Interview QuestionReal-World Relevance → Used in browsers, databases, and operating systems....if key not in self . cache : return -1 node = self . cache [key] self ._remove(node) self ._add_front..."}
+{"idx": 9, "title": "Implementing LRU cache using std::map and std::list... - Stack Overflow", "date": "", "ddg_snippet": "The cache uses Least Recently Used policy explained by the behaviour: If the cache has a capacity to store 5 keys like 5 3 2 1 4 then if next key=1 comes as a hit, the cache order becomes 1 5 3 2 4.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/79765807/implementing-lru-cache-using-stdmap-and-stdlist-in-c-cant-get-required-o", "content": "The cache uses Least Recently Used policy explained by the behaviour: If the cache has a capacity to store 5 keys like 5 3 2 1 4 then if next key=1 comes as a hit, the cache order becomes 1 5 3 2 4."}
diff --git a/data/sampled_jsons/Beyond_a_Gaussian_Denoiser_Residual_Learning_of_Deep_CNN_for_Image_Denoising_Zhang_2017_architecture.jsonl b/data/sampled_jsons/Beyond_a_Gaussian_Denoiser_Residual_Learning_of_Deep_CNN_for_Image_Denoising_Zhang_2017_architecture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..97e4c1b63fed22bad5468fa086a084341cff830a
--- /dev/null
+++ b/data/sampled_jsons/Beyond_a_Gaussian_Denoiser_Residual_Learning_of_Deep_CNN_for_Image_Denoising_Zhang_2017_architecture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image ...", "date": "", "ddg_snippet": "The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/7839189", "content": "The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into ..."}
+{"idx": 1, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image ...", "date": "", "ddg_snippet": "With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising , single image super-resolution and JPEG image deblocking.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1608.03981v1", "content": "With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising , single image super-resolution and JPEG image deblocking."}
+{"idx": 2, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image ...", "date": "", "ddg_snippet": "Network Architecture Batch normalization and residual learning are beneficial to Gaussian denoising (especially for a single noise level). The residual of a noisy image corrupted by additive white Gaussian noise (AWGN) follows a constant Gaussian distribution which stablizes batch normalization during training. Histogram of noisy patches, clean patches, and residual (noise) patches from a ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN", "content": "Network Architecture Batch normalization and residual learning are beneficial to Gaussian denoising (especially for a single noise level). The residual of a noisy image corrupted by additive white Gaussian noise (AWGN) follows a constant Gaussian distribution which stablizes batch normalization during training. Histogram of noisy patches, clean patches, and residual (noise) patches from a ..."}
+{"idx": 3, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image ...", "date": "", "ddg_snippet": "The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into ...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/28166495/", "content": "The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into ..."}
+{"idx": 4, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image ...", "date": "", "ddg_snippet": "In 2016, Zhang et al. [10] proposed the DnCNN model for image denoising , introduced the idea of residual learning , and used the network model to predict the residual image , which can achieve blind ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/306187437_Beyond_a_Gaussian_Denoiser_Residual_Learning_of_Deep_CNN_for_Image_Denoising", "content": "In 2016, Zhang et al. [10] proposed the DnCNN model for image denoising , introduced the idea of residual learning , and used the network model to predict the residual image , which can achieve blind ..."}
+{"idx": 5, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image ...", "date": "", "ddg_snippet": "Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into image ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1608.03981", "content": "Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into image ..."}
+{"idx": 6, "title": "DeepInMiniscope: Deep learning-powered physics-informed ... - Science", "date": "", "ddg_snippet": "DeepInMiniscope, a miniaturized, compact, and thin integrated microscope, performs high-resolution 3D imaging over a large volume.", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/sciadv.adr6687", "content": "DeepInMiniscope, a miniaturized, compact, and thin integrated microscope, performs high-resolution 3D imaging over a large volume."}
+{"idx": 7, "title": "Beyond a Gaussian denoiser: Residual learning of deep CNN for image ...", "date": "", "ddg_snippet": "In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into image denoising .", "subpage_snippet": "", "source": "scholarshare.temple.edu", "link": "https://scholarshare.temple.edu/items/eddedcf8-1479-40b5-b4e1-108d242b774e", "content": "In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into image denoising ."}
+{"idx": 8, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image ...", "date": "", "ddg_snippet": "The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1109/TIP.2017.2662206", "content": "The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising ..."}
+{"idx": 9, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image ...", "date": "", "ddg_snippet": "This paper investigates the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into image Denoising , and uses residual learning and batch normalization to speed up the training process as well as boost theDenoising performance. The discriminative model learning for image ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Beyond-a-Gaussian-Denoiser:-Residual-Learning-of-Zhang-Zuo/0c00a328fa7cd56ee60338c54e89bd48310db80b", "content": "This paper investigates the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into image Denoising , and uses residual learning and batch normalization to speed up the training process as well as boost theDenoising performance. The discriminative model learning for image ..."}
diff --git a/data/sampled_jsons/Black_et_al._2023_diffusion_model_alignment_prompts_year_2023.jsonl b/data/sampled_jsons/Black_et_al._2023_diffusion_model_alignment_prompts_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a9298d864e06f843807dad2d688b91b8140fdbd
--- /dev/null
+++ b/data/sampled_jsons/Black_et_al._2023_diffusion_model_alignment_prompts_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "D-Fusion: Direct Preference Optimization for Aligning Diffusion Models ...", "date": "", "ddg_snippet": "Figure 1. (Misalignment) Diffusion models (e.g., Stable Diffusion (SD) (Rombach et al ., 2022)) often encounter the issue that the generated images do not accurately match the given prompts . Ex-isting RL-based fine-tuning methods (e.g., DPO (Wallace et al ., 2023 )) have limited effectiveness in improving the alignment . For each set of images above, we use the same seed for sampling.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.22002", "content": "Figure 1. (Misalignment) Diffusion models (e.g., Stable Diffusion (SD) (Rombach et al ., 2022)) often encounter the issue that the generated images do not accurately match the given prompts . Ex-isting RL-based fine-tuning methods (e.g., DPO (Wallace et al ., 2023 )) have limited effectiveness in improving the alignment . For each set of images above, we use the same seed for sampling."}
+{"idx": 1, "title": "PDF Diffusion Model Alignment Using Direct Preference Optimization", "date": "", "ddg_snippet": "Training diffusion models with reinforce-ment learning. arXiv preprint arXiv:2305.13301, 2023 . 1, 3, 4, 5 [8] Kevin Black et al. Training diffusion models with reinforce-ment learning. arXiv preprint arXiv:2305.13301, 2023 . 2 [9] Kevin Clark, Paul Vicol, Kevin Swersky, and David J Fleet.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Wallace_Diffusion_Model_Alignment_Using_Direct_Preference_Optimization_CVPR_2024_paper.pdf", "content": "Training diffusion models with reinforce-ment learning. arXiv preprint arXiv:2305.13301, 2023 . 1, 3, 4, 5 [8] Kevin Black et al. Training diffusion models with reinforce-ment learning. arXiv preprint arXiv:2305.13301, 2023 . 2 [9] Kevin Clark, Paul Vicol, Kevin Swersky, and David J Fleet."}
+{"idx": 2, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Interestingly, the aesthetic quality model (top right) tends towards minimalist black -and-white line drawings, revealing the kinds of images that the LAION aesthetic predictor considers \"more aesthetic\". 1 Next, we demonstrate DDPO on the more complex prompt -image alignment task.", "subpage_snippet": "", "source": "bair.berkeley.edu", "link": "https://bair.berkeley.edu/blog/2023/07/14/ddpo/", "content": "Interestingly, the aesthetic quality model (top right) tends towards minimalist black -and-white line drawings, revealing the kinds of images that the LAION aesthetic predictor considers \"more aesthetic\". 1 Next, we demonstrate DDPO on the more complex prompt -image alignment task."}
+{"idx": 3, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Figure 1 (Reinforcement learning for diffusion models ) We propose a reinforcement learning algorithm, DDPO, for optimizing diffusion models on downstream objectives such as compressibility, aesthetic quality, and prompt -image alignment as determined by vision-language models . Each row shows a progression of samples for the same prompt and random seed over the course of training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=pBN8CgrDhE", "content": "Figure 1 (Reinforcement learning for diffusion models ) We propose a reinforcement learning algorithm, DDPO, for optimizing diffusion models on downstream objectives such as compressibility, aesthetic quality, and prompt -image alignment as determined by vision-language models . Each row shows a progression of samples for the same prompt and random seed over the course of training."}
+{"idx": 4, "title": "PDF 1 I Training Diffusion Models With Reinforcement Lear", "date": "", "ddg_snippet": "hm. Like Lee et al. ( 2023 ), DPOK only considers a single preference-based reward function (Xu et al ., 2023 ); additionally, their work studies KL-regularization and primarily focuses on training a different diffusion model for each prompt . 1 In contrast, we train on many prompts at once (up to 398) and demonstrate generalization to many more ...", "subpage_snippet": "", "source": "rl-diffusion.github.io", "link": "https://rl-diffusion.github.io/files/paper.pdf", "content": "hm. Like Lee et al. ( 2023 ), DPOK only considers a single preference-based reward function (Xu et al ., 2023 ); additionally, their work studies KL-regularization and primarily focuses on training a different diffusion model for each prompt . 1 In contrast, we train on many prompts at once (up to 398) and demonstrate generalization to many more ..."}
+{"idx": 5, "title": "(PDF) D-Fusion: Direct Preference Optimization for Aligning Diffusion ...", "date": "", "ddg_snippet": "Existing RL-based fine-tuning methods (e.g., DPO (Wallace et al ., 2023 )) have limited effectiveness in improving the alignment . For each set of images above, we use the same seed for sampling.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392167909_D-Fusion_Direct_Preference_Optimization_for_Aligning_Diffusion_Models_with_Visually_Consistent_Samples", "content": "Existing RL-based fine-tuning methods (e.g., DPO (Wallace et al ., 2023 )) have limited effectiveness in improving the alignment . For each set of images above, we use the same seed for sampling."}
+{"idx": 6, "title": "Preference Alignment on Diffusion Model: A Comprehensive", "date": "", "ddg_snippet": "Several studies (Uehara et al ., 2024b; Du et al ., 2023 ; Cao et al ., 2023 ) have reviewed the integration of text-to-image models with RL; however, they tend to primarily concentrate on the applications of RL to diffusion processes or offer overarching assessments of generative AI applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.07829v1", "content": "Several studies (Uehara et al ., 2024b; Du et al ., 2023 ; Cao et al ., 2023 ) have reviewed the integration of text-to-image models with RL; however, they tend to primarily concentrate on the applications of RL to diffusion processes or offer overarching assessments of generative AI applications."}
+{"idx": 7, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Prompt -Image Alignment We also optimize a more ambitious reward function: prompt -image alignment as determined by the LLaVA vision-language model . We use animals doing human-like activites, since the base Stable Diffusion model tends to struggle with these kinds of prompts .", "subpage_snippet": "", "source": "rl-diffusion.github.io", "link": "https://rl-diffusion.github.io/", "content": "Prompt -Image Alignment We also optimize a more ambitious reward function: prompt -image alignment as determined by the LLaVA vision-language model . We use animals doing human-like activites, since the base Stable Diffusion model tends to struggle with these kinds of prompts ."}
+{"idx": 8, "title": "PDF Diffusion Model Alignment Using Direct Preference Optimization", "date": "", "ddg_snippet": "2022. 2 [7] Kevin Black , Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning. arXiv preprint arXiv:2305.13301, 2023 . 1, 3, 4, 5 [8] Kevin Black et al. Training diffusion models with reinforcement learning. arXiv preprint ar", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wallace_Diffusion_Model_Alignment_CVPR_2024_supplemental.pdf", "content": "2022. 2 [7] Kevin Black , Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning. arXiv preprint arXiv:2305.13301, 2023 . 1, 3, 4, 5 [8] Kevin Black et al. Training diffusion models with reinforcement learning. arXiv preprint ar"}
+{"idx": 9, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with diverse user preferences is an ongoing and critical area of research. One approach to aligning diffusion models with user preferences is to fine-tune using reinforcement learning (RL) to optimize the models based on rewards signals that reflect the user preferences ( Black et al ., 2023 ; Fan et al ., 2023 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v1", "content": "Aligning diffusion models with diverse user preferences is an ongoing and critical area of research. One approach to aligning diffusion models with user preferences is to fine-tune using reinforcement learning (RL) to optimize the models based on rewards signals that reflect the user preferences ( Black et al ., 2023 ; Fan et al ., 2023 )."}
diff --git a/data/sampled_jsons/Black_et_al._2023_diffusion_model_prompts_evaluation_year_2023.jsonl b/data/sampled_jsons/Black_et_al._2023_diffusion_model_prompts_evaluation_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fd56278ed09d9265b75aec1ff201b73409bc3ea3
--- /dev/null
+++ b/data/sampled_jsons/Black_et_al._2023_diffusion_model_prompts_evaluation_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Prompt Mixing in Diffusion Models using the Black Scholes Algorithm", "date": "", "ddg_snippet": "Specifically, the parallels between diffusion models and the Black -Scholes model enable us to leverage properties related to the dynamics of the Markovian model derived in the Black -Scholes algorithm. Our prompt -mixing algorithm is data-efficient, meaning it does not need additional training.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.13685v1", "content": "Specifically, the parallels between diffusion models and the Black -Scholes model enable us to leverage properties related to the dynamics of the Markovian model derived in the Black -Scholes algorithm. Our prompt -mixing algorithm is data-efficient, meaning it does not need additional training."}
+{"idx": 1, "title": "Reverse Stable Diffusion: What prompt was used to ... - ScienceDirect", "date": "", "ddg_snippet": "To this end, we study the task of predicting the prompt embedding given an image generated by a generative diffusion model . We consider a series of white-box and black -box models (with and without access to the weights of the diffusion network) to deal with the proposed task.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1077314224002911", "content": "To this end, we study the task of predicting the prompt embedding given an image generated by a generative diffusion model . We consider a series of white-box and black -box models (with and without access to the weights of the diffusion network) to deal with the proposed task."}
+{"idx": 2, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Here, we show several snapshots from the training process: each series of three images shows samples for the same prompt and random seed over time, with the first sample coming from vanilla Stable Diffusion . Interestingly, the model shifts towards a more cartoon-like style, which was not intentional.", "subpage_snippet": "", "source": "bair.berkeley.edu", "link": "https://bair.berkeley.edu/blog/2023/07/14/ddpo/", "content": "Here, we show several snapshots from the training process: each series of three images shows samples for the same prompt and random seed over time, with the first sample coming from vanilla Stable Diffusion . Interestingly, the model shifts towards a more cartoon-like style, which was not intentional."}
+{"idx": 3, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Figure 1 (Reinforcement learning for diffusion models ) We propose a reinforcement learning algorithm, DDPO, for optimizing diffusion models on downstream objectives such as compressibility, aesthetic quality, and prompt -image alignment as determined by vision-language models . Each row shows a progression of samples for the same prompt and random seed over the course of training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=pBN8CgrDhE", "content": "Figure 1 (Reinforcement learning for diffusion models ) We propose a reinforcement learning algorithm, DDPO, for optimizing diffusion models on downstream objectives such as compressibility, aesthetic quality, and prompt -image alignment as determined by vision-language models . Each row shows a progression of samples for the same prompt and random seed over the course of training."}
+{"idx": 4, "title": "PDF 1 I Training Diffusion Models With Reinforcement Lear", "date": "", "ddg_snippet": "hm. Like Lee et al. ( 2023 ), DPOK only considers a single preference-based reward function (Xu et al ., 2023 ); additionally, their work studies KL-regularization and primarily focuses on training a different diffusion model for each prompt . 1 In contrast, we train on many prompts at once (up to 398) and demonstrate generalization to many more ...", "subpage_snippet": "", "source": "rl-diffusion.github.io", "link": "https://rl-diffusion.github.io/files/paper.pdf", "content": "hm. Like Lee et al. ( 2023 ), DPOK only considers a single preference-based reward function (Xu et al ., 2023 ); additionally, their work studies KL-regularization and primarily focuses on training a different diffusion model for each prompt . 1 In contrast, we train on many prompts at once (up to 398) and demonstrate generalization to many more ..."}
+{"idx": 5, "title": "GitHub - Zhendong-Wang/Prompt-Diffusion: Official PyTorch ...", "date": "", "ddg_snippet": "The resulting Prompt Diffusion model becomes the first diffusion -based vision-language foundation model capable of in-context learning. It demonstrates high-quality in-context generation for the trained tasks and effectively generalizes to new, unseen vision tasks using their respective prompts .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Zhendong-Wang/Prompt-Diffusion", "content": "The resulting Prompt Diffusion model becomes the first diffusion -based vision-language foundation model capable of in-context learning. It demonstrates high-quality in-context generation for the trained tasks and effectively generalizes to new, unseen vision tasks using their respective prompts ."}
+{"idx": 6, "title": "Advances in diffusion models for image data augmentation: a review of ...", "date": "", "ddg_snippet": "Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; thereby, improving the performance and robustness of machine learning models in downstream tasks. In parallel, augmentation approaches can also be used for editing/modifying a given image in a context- and ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11116-x", "content": "Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; thereby, improving the performance and robustness of machine learning models in downstream tasks. In parallel, augmentation approaches can also be used for editing/modifying a given image in a context- and ..."}
+{"idx": 7, "title": "Prompt Mixing in Diffusion Models using the Black Scholes Algorithm", "date": "", "ddg_snippet": "Specifically, the parallels between diffusion models and the Black -Scholes model enable us to leverage properties related to the dynamics of the Markovian model derived in the Black -Scholes algorithm.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380821169_Prompt_Mixing_in_Diffusion_Models_using_the_Black_Scholes_Algorithm", "content": "Specifically, the parallels between diffusion models and the Black -Scholes model enable us to leverage properties related to the dynamics of the Markovian model derived in the Black -Scholes algorithm."}
+{"idx": 8, "title": "On Discrete Prompt Optimization for Diffusion Models - arXiv.org", "date": "", "ddg_snippet": "Abstract This paper introduces the first gradient-based framework for prompt optimization in text-to-image diffusion models . We formulate prompt engineering as a discrete optimization problem over the language space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.01606v1", "content": "Abstract This paper introduces the first gradient-based framework for prompt optimization in text-to-image diffusion models . We formulate prompt engineering as a discrete optimization problem over the language space."}
+{"idx": 9, "title": "On Discrete Prompt Optimization for Diffusion Models - OpenReview", "date": "", "ddg_snippet": "Empirical evaluation on prompts collected from diverse sources (DiffusionDB, ChatGPT, COCO) suggests that our method can discover prompts that substantially improve ( prompt enhancement) or destroy (adversarial attack) the faithfulness of images generated by the text-to-image diffusion model .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Fw4fBE2rqW", "content": "Empirical evaluation on prompts collected from diverse sources (DiffusionDB, ChatGPT, COCO) suggests that our method can discover prompts that substantially improve ( prompt enhancement) or destroy (adversarial attack) the faithfulness of images generated by the text-to-image diffusion model ."}
diff --git a/data/sampled_jsons/Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models_github.jsonl b/data/sampled_jsons/Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models_github.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d90d85a59f5695ce751c50d7eaf991f7b1c0603f
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models_github.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink Home - Wikipedia", "date": "", "ddg_snippet": "Immedia Semiconductor LLC, [1] doing business as Blink , is an American home automation company which produces home security cameras. The company was founded in 2009 by Peter Besen, Don Shulsinger, Dan Grunberg, Stephen Gordon, and Doug Chin.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Blink_Home", "content": "Immedia Semiconductor LLC, [1] doing business as Blink , is an American home automation company which produces home security cameras. The company was founded in 2009 by Peter Besen, Don Shulsinger, Dan Grunberg, Stephen Gordon, and Doug Chin."}
+{"idx": 1, "title": "Blink Smart Security", "date": "", "ddg_snippet": "Affordable wireless and plug-in smart home security cameras and systems from Blink , an Amazon company.", "subpage_snippet": "", "source": "blinkforhome.com", "link": "https://blinkforhome.com/", "content": "Affordable wireless and plug-in smart home security cameras and systems from Blink , an Amazon company."}
+{"idx": 2, "title": "Blink Home Monitor - Apps on Google Play", "date": "", "ddg_snippet": "See and speak to people and pets, right from the Blink app with features like HD live view, infrared night vision, and crisp two-way audio. Connect to an Alexa-enabled device to engage live view,...", "subpage_snippet": "", "source": "play.google.com", "link": "https://play.google.com/store/apps/details?id=com.immediasemi.android.blink&hl=en", "content": "See and speak to people and pets, right from the Blink app with features like HD live view, infrared night vision, and crisp two-way audio. Connect to an Alexa-enabled device to engage live view,..."}
+{"idx": 3, "title": "Blink Whole Home Security Camera System Bundle | Costco", "date": "", "ddg_snippet": "Blink Whole Home Security Camera System Bundle Easy Setup, No Wiring Required Up to Two-years of Battery (Batteries Included) 360° Coverage With Mini Pan-Tilt Camera Monitor Your Home Anywhere From the Blink App", "subpage_snippet": "", "source": "www.costco.com", "link": "https://www.costco.com/blink-whole-home-security-camera-system-bundle-.product.4000215015.html", "content": "Blink Whole Home Security Camera System Bundle Easy Setup, No Wiring Required Up to Two-years of Battery (Batteries Included) 360° Coverage With Mini Pan-Tilt Camera Monitor Your Home Anywhere From the Blink App"}
+{"idx": 4, "title": "Blink Home Monitor on the App Store", "date": "", "ddg_snippet": "Connect to an Alexa-enabled device to engage live view, arm and disarm your system, and more using your voice. Plus, use the Blink app to customize motion alerts, and set activity and privacy zones, so you only get notified about the activity you care about.", "subpage_snippet": "", "source": "apps.apple.com", "link": "https://apps.apple.com/us/app/blink-home-monitor/id1013961111", "content": "Connect to an Alexa-enabled device to engage live view, arm and disarm your system, and more using your voice. Plus, use the Blink app to customize motion alerts, and set activity and privacy zones, so you only get notified about the activity you care about."}
+{"idx": 5, "title": "Blink Mini Indoor Pan-Tilt Camera in White | Amazon", "date": "", "ddg_snippet": "Shop the Blink Mini Pan-Tilt Camera. This rotating indoor plug-in smart security camera has two-way audio, HD video, motion detection, and works with Alexa.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/Blink-Mini-Pan-Tilt-Camera-White/dp/B09N6YCT3Y", "content": "Shop the Blink Mini Pan-Tilt Camera. This rotating indoor plug-in smart security camera has two-way audio, HD video, motion detection, and works with Alexa."}
+{"idx": 6, "title": "Blink Home Monitor App — Blink Smart Security", "date": "", "ddg_snippet": "The app connects your home to your phone in HD video so you can see and protect what matters most. With multi-system support, you can use Blink to watch your home, vacation home, or business all at the same time. Plus, you can control multiple camera systems within one single app!", "subpage_snippet": "", "source": "blinkforhome.com", "link": "https://blinkforhome.com/blink-app", "content": "The app connects your home to your phone in HD video so you can see and protect what matters most. With multi-system support, you can use Blink to watch your home, vacation home, or business all at the same time. Plus, you can control multiple camera systems within one single app!"}
+{"idx": 7, "title": "Account and Login — Blink Support", "date": "", "ddg_snippet": "Blink Support Center helps you to find FAQ, how-to guides and step-by-step tutorials.", "subpage_snippet": "", "source": "support.blinkforhome.com", "link": "https://support.blinkforhome.com/en_US/account-and-login", "content": "Blink Support Center helps you to find FAQ, how-to guides and step-by-step tutorials."}
+{"idx": 8, "title": "Setting up your Blink devices - Blink Support", "date": "", "ddg_snippet": "Blink Support Center helps you to find FAQ, how-to guides and step-by-step tutorials.", "subpage_snippet": "", "source": "support.blinkforhome.com", "link": "https://support.blinkforhome.com/en_US/how-to-setup-devices", "content": "Blink Support Center helps you to find FAQ, how-to guides and step-by-step tutorials."}
+{"idx": 9, "title": "Sign in to your Blink account", "date": "", "ddg_snippet": "You can sign in to your account using your new password. Log in to update your payment method.", "subpage_snippet": "", "source": "blink.com", "link": "https://blink.com/users/sign_in", "content": "You can sign in to your account using your new password. Log in to update your payment method."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Appendix_A.3.2_GPU_hardware_configuration.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Appendix_A.3.2_GPU_hardware_configuration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..39c431e5a03954f9101fb363779fd8b871605863
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Appendix_A.3.2_GPU_hardware_configuration.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "12 Oct 2024 — In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to Δ Δ G Δ Δ G \\ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "12 Oct 2024 — In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to Δ Δ G Δ Δ G \\ ..."}
+{"idx": 1, "title": "Enhancing Physical Understanding of Protein Inverse Folding ...", "date": "", "ddg_snippet": "More details are illustrated in Appendix A. 4.3 Conventional inverse - folding model modeling . In this subsection, we will briefly describe the IFNN used in ...", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2023.12.05.570298v2.full.pdf", "content": "More details are illustrated in Appendix A. 4.3 Conventional inverse - folding model modeling . In this subsection, we will briefly describe the IFNN used in ..."}
+{"idx": 2, "title": "Energy-Based Models for Predicting Mutational Effects on ...", "date": "", "ddg_snippet": "by P Soga · 2025 — Boltzmann - Aligned Inverse Folding Model as a Predictor of Mu- tational Effects on Protein-Protein Interactions. In The Thirteenth ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3711896.3736931", "content": "by P Soga · 2025 — Boltzmann - Aligned Inverse Folding Model as a Predictor of Mu- tational Effects on Protein-Protein Interactions. In The Thirteenth ..."}
+{"idx": 3, "title": "GPU acceleration of FSI simulations by the immersed ...", "date": "", "ddg_snippet": "by J Wu · 2019 · Cited by 33 — This paper proposes an approach to accelerate the simulations of fluid structure interaction (FSI) by implementing the immersed boundary-lattice Boltzmann ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0898122116305636", "content": "by J Wu · 2019 · Cited by 33 — This paper proposes an approach to accelerate the simulations of fluid structure interaction (FSI) by implementing the immersed boundary-lattice Boltzmann ..."}
+{"idx": 4, "title": "ProteinZero: Self-Improving Protein Generation via Online", "date": "", "ddg_snippet": "We present ProteinZero, a novel framework that enables scalable, automated, and continuous self-improvement of the inverse folding model through ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07459v2", "content": "We present ProteinZero, a novel framework that enables scalable, automated, and continuous self-improvement of the inverse folding model through ..."}
+{"idx": 5, "title": "CLARA: A Modular Framework for Unsupervised Transit Detection", "date": "", "ddg_snippet": "... 3 ) investigates how synthetic light curve construction influences the performance of Unsupervised Random Forest (URF) models for anomaly detection in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.04722v1", "content": "... 3 ) investigates how synthetic light curve construction influences the performance of Unsupervised Random Forest (URF) models for anomaly detection in ..."}
+{"idx": 6, "title": "SageNet: Fast Neural Network Emulation of the Stiff-amplified", "date": "", "ddg_snippet": "While the above PTA analyses yielded correct posterior probability distributions for the model parameters, the computational efficiency was however ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.04054v1", "content": "While the above PTA analyses yielded correct posterior probability distributions for the model parameters, the computational efficiency was however ..."}
+{"idx": 7, "title": "Molecular dynamics-based refinement and validation for sub-5 Å", "date": "", "ddg_snippet": "However, seminal advances in detection hardware and programs over the past three years ( Li et al., 2013 ; Milazzo et al., 2011 ) have enabled now ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/16105", "content": "However, seminal advances in detection hardware and programs over the past three years ( Li et al., 2013 ; Milazzo et al., 2011 ) have enabled now ..."}
+{"idx": 8, "title": "Designing ecosystems of intelligence from first principles -", "date": "", "ddg_snippet": "Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/26339137231222481", "content": "Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model ..."}
+{"idx": 9, "title": "Predicting mutational effects on protein binding from ...", "date": "", "ddg_snippet": "The key idea is to parameterize the binding energy as the difference between the folding energy of the protein complex and the sum of the folding energies of ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45926", "content": "The key idea is to parameterize the binding energy as the difference between the folding energy of the protein complex and the sum of the folding energies of ..."}
diff --git "a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_BA-Cycle_\342\210\206\342\210\206G_calculation_previous_inverse_folding-based_meth_year_2024.jsonl" "b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_BA-Cycle_\342\210\206\342\210\206G_calculation_previous_inverse_folding-based_meth_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..06485d2f5137b168ef40c52563150a77343b887f
--- /dev/null
+++ "b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_BA-Cycle_\342\210\206\342\210\206G_calculation_previous_inverse_folding-based_meth_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Is Inverse Folding & How To Practically Apply It", "date": "", "ddg_snippet": "Inverse Folding models can be used to create alternative versions of the above binder that might have more desirable properties than the binder above. Tips & Tricks for Inverse Folding .", "subpage_snippet": "", "source": "neurosnap.ai", "link": "https://neurosnap.ai/blog/post/what-is-inverse-folding-how-to-practically-apply-it/65908e76104e7841a40c3187", "content": "Inverse Folding models can be used to create alternative versions of the above binder that might have more desirable properties than the binder above. Tips & Tricks for Inverse Folding ."}
+{"idx": 1, "title": "Benchmarking inverse folding models for antibody CDR... | PLOS One", "date": "", "ddg_snippet": "Historically, inverse folding models have been evaluated using amino acid recovery rates, which measure how accurately a model reproduces the exact native sequences of the designed regions [11,13–16]. Methods . Evaluation tasks for antibody inverse folding models .", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0324566", "content": "Historically, inverse folding models have been evaluated using amino acid recovery rates, which measure how accurately a model reproduces the exact native sequences of the designed regions [11,13–16]. Methods . Evaluation tasks for antibody inverse folding models ."}
+{"idx": 2, "title": "Inverse Folding ICML 2022", "date": "", "ddg_snippet": "Inverse folding on partially masked structures. Inverse folding as a pre-training task has diverse use cases.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16886.pdf", "content": "Inverse folding on partially masked structures. Inverse folding as a pre-training task has diverse use cases."}
+{"idx": 3, "title": "(PDF) Protein Inverse Folding From Structure Feedback", "date": "", "ddg_snippet": "The inverse folding problem, aiming to design amino acid sequences that fold into desired three -dimensional structures, is pivotal for various biotechnological applications.date sequences from the inverse - folding model , then predict the three -dimensional.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392372040_Protein_Inverse_Folding_From_Structure_Feedback", "content": "The inverse folding problem, aiming to design amino acid sequences that fold into desired three -dimensional structures, is pivotal for various biotechnological applications.date sequences from the inverse - folding model , then predict the three -dimensional."}
+{"idx": 4, "title": "Advancing protein evolution with inverse folding models integrating...", "date": "", "ddg_snippet": "Protein engineering enables artificial protein evolution through iterative sequence changes, but current methods often suffer from low success rates and limited cost effectiveness.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/40628259/", "content": "Protein engineering enables artificial protein evolution through iterative sequence changes, but current methods often suffer from low success rates and limited cost effectiveness."}
+{"idx": 5, "title": "RNA inverse folding can be solved in linear time for structures without...", "date": "", "ddg_snippet": "12 Inverse folding is a classic instance of negative RNA design which consists in finding a sequence that 13 uniquely folds into a target secondary structure with respect to energy minimization.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04589901/document", "content": "12 Inverse folding is a classic instance of negative RNA design which consists in finding a sequence that 13 uniquely folds into a target secondary structure with respect to energy minimization."}
+{"idx": 6, "title": "Learning inverse folding from millions of predicted structures | bioRxiv", "date": "", "ddg_snippet": "Augmenting inverse folding with predicted structures.An autoregressive inverse folding model is trained to perform fixed-backbone protein sequence design. Train and test sets are partitioned at the topology level, so that the model is evaluated on structurally held-out backbones.", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2022.04.10.487779v1.full", "content": "Augmenting inverse folding with predicted structures.An autoregressive inverse folding model is trained to perform fixed-backbone protein sequence design. Train and test sets are partitioned at the topology level, so that the model is evaluated on structurally held-out backbones."}
+{"idx": 7, "title": "Inverse Folding with ESM-IF1.ipynb - Colab", "date": "", "ddg_snippet": "close. Inverse Folding with ESM-IF1.ipynb_. File. Edit.The ESM-IF1 inverse folding model is built for predicting protein sequences from their backbone atom coordinates.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/facebookresearch/esm/blob/main/examples/inverse_folding/notebook.ipynb", "content": "close. Inverse Folding with ESM-IF1.ipynb_. File. Edit.The ESM-IF1 inverse folding model is built for predicting protein sequences from their backbone atom coordinates."}
+{"idx": 8, "title": "Boltzmann - Aligned Inverse Folding Model as... | OpenReview", "date": "", "ddg_snippet": "Furthermore, we demonstrate the capability of our method in binding energy prediction, protein-protein docking, and antibody optimization tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "Furthermore, we demonstrate the capability of our method in binding energy prediction, protein-protein docking, and antibody optimization tasks."}
+{"idx": 9, "title": "aim-uofa/ BA -DDG: [ICLR 2025 Spotlight] Boltzmann - Aligned Inverse ...", "date": "", "ddg_snippet": "aim-uofa/ BA -DDG. empty. Folders and files.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "aim-uofa/ BA -DDG. empty. Folders and files."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_BA-DDG_Table_1_Spearman_year_2023.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_BA-DDG_Table_1_Spearman_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aebd9f60c701ca332a6d7dc336ba6fd5438c67e8
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_BA-DDG_Table_1_Spearman_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "12 Oct 2024 — According to Table 1 , our BA - DDG outperforms all the baselines across all evaluation metrics. Notably, it demonstrates a significant ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "12 Oct 2024 — According to Table 1 , our BA - DDG outperforms all the baselines across all evaluation metrics. Notably, it demonstrates a significant ..."}
+{"idx": 1, "title": "Energy-Based Models for Predicting Mutational Effects on ...", "date": "", "ddg_snippet": "14 Aug 2025 — Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions. In The Thirteenth International ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10629v1", "content": "14 Aug 2025 — Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions. In The Thirteenth International ..."}
+{"idx": 2, "title": "Funneling modulatory peptide design with generative models", "date": "", "ddg_snippet": "by J Tubiana · 2023 · Cited by 8 — After fitting the polarization values to a single site inhibition model , the corresponding IC50 values were extracted and reported in Table 1 .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9928118/", "content": "by J Tubiana · 2023 · Cited by 8 — After fitting the polarization values to a single site inhibition model , the corresponding IC50 values were extracted and reported in Table 1 ."}
+{"idx": 3, "title": "ICLR 2025 - Bird's-eye views of conference proceedings", "date": "", "ddg_snippet": "Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions. Xiaoran Jiao, Weian Mao, Wengong Jin, Peiyuan ...", "subpage_snippet": "", "source": "www.confviews.com", "link": "https://www.confviews.com/iclr2025", "content": "Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions. Xiaoran Jiao, Weian Mao, Wengong Jin, Peiyuan ..."}
+{"idx": 4, "title": "Protein Melting Temperature Cannot Fully Assess Whether ...", "date": "", "ddg_snippet": "by RM Razban · 2019 · Cited by 24 — The parameter b is the inverse energy of the environment and is equal to 1 /(kbT), where kb is the Boltzmann constant ... Table 1 . Pearson ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/mbe/article-pdf/36/9/1955/29208984/msz119.pdf", "content": "by RM Razban · 2019 · Cited by 24 — The parameter b is the inverse energy of the environment and is equal to 1 /(kbT), where kb is the Boltzmann constant ... Table 1 . Pearson ..."}
+{"idx": 5, "title": "Computational Approaches in Discovery & Design of ...", "date": "", "ddg_snippet": "Table 1 . Summary of the most relevant ML approaches, from the classical to the emerging ones, for assisting the discovery of bioactive peptides from AMPs ...", "subpage_snippet": "", "source": "unglueit-files.s3.amazonaws.com", "link": "https://unglueit-files.s3.amazonaws.com/ebf/f50401507a764bdb9773abf2ec51516a.pdf", "content": "Table 1 . Summary of the most relevant ML approaches, from the classical to the emerging ones, for assisting the discovery of bioactive peptides from AMPs ..."}
+{"idx": 6, "title": "© Copyright 2022 Naozumi Hiranuma", "date": "", "ddg_snippet": "by N Hiranuma · 2022 · Cited by 1 — The GAAP model shows superior performance to others for the Boltzmann score metric. For. Spearman -R, the GAAP model performs as strongly as the Rosetta ...", "subpage_snippet": "", "source": "digital.lib.washington.edu", "link": "https://digital.lib.washington.edu/bitstreams/7a617e61-a425-41cb-836d-15cf2abbcfa4/download", "content": "by N Hiranuma · 2022 · Cited by 1 — The GAAP model shows superior performance to others for the Boltzmann score metric. For. Spearman -R, the GAAP model performs as strongly as the Rosetta ..."}
+{"idx": 7, "title": "Schedule for All", "date": "", "ddg_snippet": "This is the first study using AlphaFold models to investigate stress-resistance mutations in plants, providing insights into their functional impact and ...", "subpage_snippet": "", "source": "www.iscb.org", "link": "https://www.iscb.org/cms_addon/conferences/ismbeccb2025/schedule/detailed", "content": "This is the first study using AlphaFold models to investigate stress-resistance mutations in plants, providing insights into their functional impact and ..."}
+{"idx": 8, "title": "Limitations and challenges in protein stability prediction ...", "date": "", "ddg_snippet": "by T Sanavia · 2020 · Cited by 155 — Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2001037020303433", "content": "by T Sanavia · 2020 · Cited by 155 — Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems."}
+{"idx": 9, "title": "Track: Poster Session 6", "date": "", "ddg_snippet": "26 Apr 2025 — We propose a novel approach that reformulates molecular property prediction as a node classification problem, introducing two innovative tasks ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31976", "content": "26 Apr 2025 — We propose a novel approach that reformulates molecular property prediction as a node classification problem, introducing two innovative tasks ..."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Equation_8_delta_delta_G_formula.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Equation_8_delta_delta_G_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e0c68ce85c3d9b9eb226cba0fb268e9f23ea11b
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Equation_8_delta_delta_G_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "12 Oct 2024 — In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to Δ Δ G Δ Δ G \\ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "12 Oct 2024 — In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to Δ Δ G Δ Δ G \\ ..."}
+{"idx": 1, "title": "Energy-Based Models for Predicting Mutational Effects on ...", "date": "", "ddg_snippet": "14 Aug 2025 — Predicting changes in binding free energy ( Δ Δ G \\ Delta \\ Delta G ) is a vital task in protein engineering and protein-protein ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10629v1", "content": "14 Aug 2025 — Predicting changes in binding free energy ( Δ Δ G \\ Delta \\ Delta G ) is a vital task in protein engineering and protein-protein ..."}
+{"idx": 2, "title": "Predicting mutational effects on protein binding from folding energy", "date": "", "ddg_snippet": "Boltzmann - Aligned inverse folding model as a predic- tor of mutational effects on protein-protein interactions. arXiv preprint arXiv:2410.09543, 2024. Jin ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/eed2826f97366555ba6ad58d07a1709bc2ca3172.pdf", "content": "Boltzmann - Aligned inverse folding model as a predic- tor of mutational effects on protein-protein interactions. arXiv preprint arXiv:2410.09543, 2024. Jin ..."}
+{"idx": 3, "title": "Evaluation of an Inverse Molecular Design Algorithm in a ...", "date": "", "ddg_snippet": "by DJ Huggins · 2009 · Cited by 13 — We have recently developed a new inverse approach to drug design based on the dead-end elimination and A* algorithms employing a physical potential function.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC2700139/", "content": "by DJ Huggins · 2009 · Cited by 13 — We have recently developed a new inverse approach to drug design based on the dead-end elimination and A* algorithms employing a physical potential function."}
+{"idx": 4, "title": "Deterministic Solvers for the Boltzmann Transport Equation ...", "date": "", "ddg_snippet": "The book covers all aspects from the expansion of the Boltzmann transport equation with harmonic functions to applicatio...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/deterministic-solvers-for-the-boltzmann-transport-equation-3709107776-9783709107775.html", "content": "The book covers all aspects from the expansion of the Boltzmann transport equation with harmonic functions to applicatio..."}
+{"idx": 5, "title": "Temperature-dependent funnel-like DNA folding landscapes", "date": "", "ddg_snippet": "by M Rico-Pasto · 2025 — Here, we study the FEL of DNA hairpins at different temperatures using calorimetric tweezers by deriving ΔS0(T) from the Clausius–Clapeyron equation with force ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12276014/", "content": "by M Rico-Pasto · 2025 — Here, we study the FEL of DNA hairpins at different temperatures using calorimetric tweezers by deriving ΔS0(T) from the Clausius–Clapeyron equation with force ..."}
+{"idx": 6, "title": "Efficient generative modeling of protein sequences using ...", "date": "", "ddg_snippet": "by J Trinquier · 2021 · Cited by 102 — The resulting models are mathematically equivalent to Potts models in statistical physics, or to Boltzmann machines in statistical learning.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-021-25756-4", "content": "by J Trinquier · 2021 · Cited by 102 — The resulting models are mathematically equivalent to Potts models in statistical physics, or to Boltzmann machines in statistical learning."}
+{"idx": 7, "title": "Exploring the unfolding pathways of protein families using ...", "date": "", "ddg_snippet": "by R Kumar · 2024 · Cited by 4 — We explore how a protein's native structure determines its unfolding process. We examine how the local structural features, like shear, and the global ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-75436-8", "content": "by R Kumar · 2024 · Cited by 4 — We explore how a protein's native structure determines its unfolding process. We examine how the local structural features, like shear, and the global ..."}
+{"idx": 8, "title": "Exact hydrodynamic manifolds for the linear Boltzmann ...", "date": "", "ddg_snippet": "by F Kogelbauer · 2025 · Cited by 5 — We give an explicit description of the spectral closure for the three-dimensional linear Boltzmann -BGK equation in terms of the macroscopic fields, density, ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00161-025-01379-8", "content": "by F Kogelbauer · 2025 · Cited by 5 — We give an explicit description of the spectral closure for the three-dimensional linear Boltzmann -BGK equation in terms of the macroscopic fields, density, ..."}
+{"idx": 9, "title": "Molecular free energy profiles from force spectroscopy ...", "date": "", "ddg_snippet": "G (q) can be obtained from the observed trajectory by Boltzmann inversion. If the linker spring-constant is known, then G o(x) can, in principle, be obtained from ...", "subpage_snippet": "", "source": "pubs.aip.org", "link": "https://pubs.aip.org/aip/jcp/article/151/15/154115/1074919/Molecular-free-energy-profiles-from-force", "content": "G (q) can be obtained from the observed trajectory by Boltzmann inversion. If the linker spring-constant is known, then G o(x) can, in principle, be obtained from ..."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Figure_2_BA-DDG_scatter_plot_Spearman_year_2023.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Figure_2_BA-DDG_scatter_plot_Spearman_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bafe3334f623a6cc9a9dd3a1b356c0837f59409f
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Figure_2_BA-DDG_scatter_plot_Spearman_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann constant - Wikipedia", "date": "", "ddg_snippet": "1. 2 Role in Boltzmann factors1.3 Role in the statistical definition of entropy...root-mean-square speed of the atoms, which turns out to be inversely proportional to the...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Boltzmann_constant", "content": "1. 2 Role in Boltzmann factors1.3 Role in the statistical definition of entropy...root-mean-square speed of the atoms, which turns out to be inversely proportional to the..."}
+{"idx": 1, "title": "Boltzmann - Aligned Inverse Folding Model as a Predictor of...", "date": "", "ddg_snippet": "3. 2 ) that integrates the inverse folding model into Boltzmann alignment . This method is named BA-Cycle and uses the inverse folding model to evaluate. BA - DDG employs a forward process identical to that of BA-Cycle. During training, the parameters. θ𝜃\\thetaitalic_θ.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "3. 2 ) that integrates the inverse folding model into Boltzmann alignment . This method is named BA-Cycle and uses the inverse folding model to evaluate. BA - DDG employs a forward process identical to that of BA-Cycle. During training, the parameters. θ𝜃\\thetaitalic_θ."}
+{"idx": 2, "title": "Boltzmann - Aligned Inverse Folding Model as... | OpenReview", "date": "", "ddg_snippet": "Experimental results on SKEMPI v 2 indicate that our method achieves Spearman coefficients of 0.3201 (unsupervised) and 0.5134 (supervised) on SKEMPI v 2 , significantly surpassing the previously reported %SoTA values SoTA results of 0.2632 and 0.4324, respectively.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "Experimental results on SKEMPI v 2 indicate that our method achieves Spearman coefficients of 0.3201 (unsupervised) and 0.5134 (supervised) on SKEMPI v 2 , significantly surpassing the previously reported %SoTA values SoTA results of 0.2632 and 0.4324, respectively."}
+{"idx": 3, "title": "aim-uofa/ BA - DDG : [ICLR 2025 Spotlight] Boltzmann - Aligned Inverse ...", "date": "", "ddg_snippet": "aim-uofa/ BA - DDG . empty. Folders and files.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "aim-uofa/ BA - DDG . empty. Folders and files."}
+{"idx": 4, "title": "Inverse Folding ICML 2022", "date": "", "ddg_snippet": "Learning inverse folding from millions of predicted structures. ICML 2022. Chloe Hsu‡, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer*, Alexander Rives* Fundamental AI Research (FAIR) at Meta AI. ‡", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16886.pdf", "content": "Learning inverse folding from millions of predicted structures. ICML 2022. Chloe Hsu‡, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer*, Alexander Rives* Fundamental AI Research (FAIR) at Meta AI. ‡"}
+{"idx": 5, "title": "Scatterplot это: ключевые особенности и применение диаграммы...", "date": "", "ddg_snippet": "Scatterplot это: определение и базовая концепция. Диаграмма рассеяния ( scatterplot ) представляет собой двумерную графическую визуализацию, где каждая точка отображает значения двух переменных — по оси X и по оси Y...", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/wiki/analytics/scatterplot-eto-klyuchevye-osobennosti-i-primenenie-diagrammy-rasseyaniya/", "content": "Scatterplot это: определение и базовая концепция. Диаграмма рассеяния ( scatterplot ) представляет собой двумерную графическую визуализацию, где каждая точка отображает значения двух переменных — по оси X и по оси Y..."}
+{"idx": 6, "title": "Solving the Boltzmann transport equation using Trixi.jl - Modelling ...", "date": "", "ddg_snippet": "I need to solve the two spatial dimensional Boltzmann transport equation of the formusing SummationByPartsOperators, OrdinaryDiffEq, ProgressLogging using LinearAlgebra: norm. using Plots : Plots , scatter , @animate, gif using Printf #.", "subpage_snippet": "", "source": "discourse.julialang.org", "link": "https://discourse.julialang.org/t/solving-the-boltzmann-transport-equation-using-trixi-jl/132509", "content": "I need to solve the two spatial dimensional Boltzmann transport equation of the formusing SummationByPartsOperators, OrdinaryDiffEq, ProgressLogging using LinearAlgebra: norm. using Plots : Plots , scatter , @animate, gif using Printf #."}
+{"idx": 7, "title": "Plots the relationship between two variables using a Spearman Plot", "date": "", "ddg_snippet": "spearman . plot : Spearman plot . Description.Should the spearman correlation be outputted in the plot ? ... arguments passed to plot . Details. Often data are not normally distributed, requiring the use of a spearman correlation to determine their relationship.", "subpage_snippet": "", "source": "www.rdocumentation.org", "link": "https://www.rdocumentation.org/packages/fifer/versions/1.1/topics/spearman.plot", "content": "spearman . plot : Spearman plot . Description.Should the spearman correlation be outputted in the plot ? ... arguments passed to plot . Details. Often data are not normally distributed, requiring the use of a spearman correlation to determine their relationship."}
+{"idx": 8, "title": "Python Matplotlib Scatter Plot Tutorial: Complete Guide", "date": "", "ddg_snippet": "Learn how to create scatter plots using Matplotlib's plt. scatter () function in Python. Master visualization techniques with detailed examples and customization options.", "subpage_snippet": "", "source": "pytutorial.com", "link": "https://pytutorial.com/python-matplotlib-scatter-plot-tutorial-complete-guide/", "content": "Learn how to create scatter plots using Matplotlib's plt. scatter () function in Python. Master visualization techniques with detailed examples and customization options."}
+{"idx": 9, "title": "Библиотека Matplotlib для построения графиков, как установить...", "date": "", "ddg_snippet": "Библиотека matplotlib предоставляет метод scatter (), который помогает проиллюстрировать взаимозависимость между переменными и то, как изменения одной переменной могут повлиять на другую.", "subpage_snippet": "", "source": "practicum.yandex.ru", "link": "https://practicum.yandex.ru/blog/biblioteka-matplotlib-dlya-vizualizacii-dannyh/", "content": "Библиотека matplotlib предоставляет метод scatter (), который помогает проиллюстрировать взаимозависимость между переменными и то, как изменения одной переменной могут повлиять на другую."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_experimental_setup_GPU_model_Appendix_A.3.2.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_experimental_setup_GPU_model_Appendix_A.3.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a22412753b2faad62fe5050ee5d64e326459b2b8
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_experimental_setup_GPU_model_Appendix_A.3.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ..."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ... - GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 2, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 3, "title": "B -a Inverse Folding Model As a Predictor of Mutational Effects on ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsuper-vised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coeficients of 0.3201 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsuper-vised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coeficients of 0.3201 ..."}
+{"idx": 4, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values."}
+{"idx": 5, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Next, we present a method (Sec. 3.2) that integrates the inverse folding model into Boltzmann alignment. This method is named BA-Cycle and uses the inverse folding model to evaluate Δ Δ 𝐺 \\Delta\\Delta G roman_Δ roman_Δ italic_G by predicting the likelihoods of protein sequences, as shown on the left side of Figure 1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "Next, we present a method (Sec. 3.2) that integrates the inverse folding model into Boltzmann alignment. This method is named BA-Cycle and uses the inverse folding model to evaluate Δ Δ 𝐺 \\Delta\\Delta G roman_Δ roman_Δ italic_G by predicting the likelihoods of protein sequences, as shown on the left side of Figure 1."}
+{"idx": 6, "title": "Portal Weekly #67: MoML 2024, data-driven discovery, boltzmann-aligned ...", "date": "", "ddg_snippet": "Since experimental ∆∆G data is limited, most existing methods focus on pretraining but often overlook the significance of aligning data. To address this, researchers have introduced a new technique called Boltzmann Alignment, which transfers knowledge from pre-trained inverse folding models to improve ∆∆G predictions.", "subpage_snippet": "", "source": "m2d2.substack.com", "link": "https://m2d2.substack.com/p/portal-weekly-67-moml-2024-data-driven", "content": "Since experimental ∆∆G data is limited, most existing methods focus on pretraining but often overlook the significance of aligning data. To address this, researchers have introduced a new technique called Boltzmann Alignment, which transfers knowledge from pre-trained inverse folding models to improve ∆∆G predictions."}
+{"idx": 7, "title": "dblp: Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Bibliographic details on Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/JiaoMJY0S25", "content": "Bibliographic details on Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions."}
+{"idx": 8, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "简介蛋白质-蛋白质相互作用（PPIs）是在所有生物体中执行多种和必要的生物功能的基础，比如蛋白质药物与蛋白质靶点的结合可以治愈疾病，因此如何设计蛋白质-蛋白质复合物是许多科研人员的研究重点。而在制药过程中…", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "简介蛋白质-蛋白质相互作用（PPIs）是在所有生物体中执行多种和必要的生物功能的基础，比如蛋白质药物与蛋白质靶点的结合可以治愈疾病，因此如何设计蛋白质-蛋白质复合物是许多科研人员的研究重点。而在制药过程中…"}
+{"idx": 9, "title": "BA-DDG/README.md at master · aim-uofa/BA-DDG · GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/blob/master/README.md", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_hardware_configuration_GPU_model_NVIDIA.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_hardware_configuration_GPU_model_NVIDIA.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..131b841a8cb666fc1ec22495a3ee87511068adb6
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_hardware_configuration_GPU_model_NVIDIA.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "12 Oct 2024 — In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to Δ Δ G Δ Δ G \\ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "12 Oct 2024 — In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to Δ Δ G Δ Δ G \\ ..."}
+{"idx": 1, "title": "Aligning protein generative models with experimental ...", "date": "", "ddg_snippet": "21 May 2024 — Models were each trained until validation loss convergence with the AdamW [29] optimizer on a single NVIDIA H100 GPU with 80GB of memory.", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2024.05.20.595026v1.full-text", "content": "21 May 2024 — Models were each trained until validation loss convergence with the AdamW [29] optimizer on a single NVIDIA H100 GPU with 80GB of memory."}
+{"idx": 2, "title": "Predicting mutational effects on protein binding from folding energy", "date": "", "ddg_snippet": "Boltzmann - Aligned inverse folding model as a predic- tor of mutational effects on protein-protein interactions. arXiv preprint arXiv:2410.09543, 2024. Jin ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/eed2826f97366555ba6ad58d07a1709bc2ca3172.pdf", "content": "Boltzmann - Aligned inverse folding model as a predic- tor of mutational effects on protein-protein interactions. arXiv preprint arXiv:2410.09543, 2024. Jin ..."}
+{"idx": 3, "title": "boltzmann inversion method: Topics by ...", "date": "", "ddg_snippet": "Lattice Boltzmann (LB) Method is a relatively new method for flow simulations. The start point of LB method is statistic mechanics and Boltzmann equation. The ...", "subpage_snippet": "", "source": "www.science.gov", "link": "https://www.science.gov/topicpages/b/boltzmann+inversion+method", "content": "Lattice Boltzmann (LB) Method is a relatively new method for flow simulations. The start point of LB method is statistic mechanics and Boltzmann equation. The ..."}
+{"idx": 4, "title": "Energy-Based Models for Predicting Mutational Effects on ...", "date": "", "ddg_snippet": "by P Soga · 2025 — All experiments were conducted using one NVIDIA A100 80GB GPU on a server ... Boltzmann - Aligned Inverse Folding Model as a Predictor of Mu-.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3711896.3736931", "content": "by P Soga · 2025 — All experiments were conducted using one NVIDIA A100 80GB GPU on a server ... Boltzmann - Aligned Inverse Folding Model as a Predictor of Mu-."}
+{"idx": 5, "title": "Enhancing Physical Understanding of Protein Inverse Folding ...", "date": "", "ddg_snippet": "3.2 Denoising training for chemical and biology system modeling ... inverse - folding model modeling . In this subsection ... All models are trained on 1 NVIDIA A100s ...", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2023.12.05.570298v2.full.pdf", "content": "3.2 Denoising training for chemical and biology system modeling ... inverse - folding model modeling . In this subsection ... All models are trained on 1 NVIDIA A100s ..."}
+{"idx": 6, "title": "Predicting mutational effects on protein binding from ...", "date": "", "ddg_snippet": "7 Jul 2025 — Boltzmann-Aligned invers e folding model as a predictor of mutational effects on protein-protein interactions. arXiv preprint arXiv ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.05502v1", "content": "7 Jul 2025 — Boltzmann-Aligned invers e folding model as a predictor of mutational effects on protein-protein interactions. arXiv preprint arXiv ..."}
+{"idx": 7, "title": "Differentiable partition function calculation for RNA", "date": "", "ddg_snippet": "by MC Matthies · 2024 · Cited by 11 — For example, end-to-end differentiable models of protein structure enable new methods for inverse folding . ... NVIDIA A100 GPU . As such ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/nar/article/52/3/e14/7457012", "content": "by MC Matthies · 2024 · Cited by 11 — For example, end-to-end differentiable models of protein structure enable new methods for inverse folding . ... NVIDIA A100 GPU . As such ..."}
+{"idx": 8, "title": "A Meticulous Guide to Advances in Deep Learning Efficiency ...", "date": "", "ddg_snippet": "30 Oct 2024 — A very long and thorough guide how deep learning algorithms, hardware , libraries, compilers, and more have become more efficient.", "subpage_snippet": "", "source": "alexzhang13.github.io", "link": "https://alexzhang13.github.io/blog/2024/efficient-dl/", "content": "30 Oct 2024 — A very long and thorough guide how deep learning algorithms, hardware , libraries, compilers, and more have become more efficient."}
+{"idx": 9, "title": "Context-aware geometric deep learning for protein ...", "date": "", "ddg_snippet": "by LF Krapp · 2024 · Cited by 26 — We trained our neural network architecture for 16 days on a single NVIDIA V100 (32 GB) GPU . ... Learning inverse folding from millions of ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-024-50571-y", "content": "by LF Krapp · 2024 · Cited by 26 — We trained our neural network architecture for 16 days on a single NVIDIA V100 (32 GB) GPU . ... Learning inverse folding from millions of ..."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_limitation_discussion_conclusion_side-chain_flexibility.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_limitation_discussion_conclusion_side-chain_flexibility.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b9259d829ed582a094c1189a46a04f7cf228bdc2
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_limitation_discussion_conclusion_side-chain_flexibility.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "B -a Inverse Folding Model As a Predictor of Mutational Effects on ...", "date": "", "ddg_snippet": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models . However, the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models . However, the ..."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ... - GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 2, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "SidechainDiff utilizes a Riemannian diffusion model to learn the generative process of side-chain conformations and can also give the structural context representations of mutations on the protein ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384930266_Boltzmann-Aligned_Inverse_Folding_Model_as_a_Predictor_of_Mutational_Effects_on_Protein-Protein_Interactions", "content": "SidechainDiff utilizes a Riemannian diffusion model to learn the generative process of side-chain conformations and can also give the structural context representations of mutations on the protein ..."}
+{"idx": 3, "title": "B -a Inverse Folding Model As a Predictor of Mutational Effects on ...", "date": "", "ddg_snippet": "h Boltzmann Alignment. BA-DDG employs a forward process identical to that of BA-Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆G and the predicted ∆∆G ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "h Boltzmann Alignment. BA-DDG employs a forward process identical to that of BA-Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆G and the predicted ∆∆G ..."}
+{"idx": 4, "title": "Portal Weekly #67: MoML 2024, data-driven discovery, boltzmann-aligned ...", "date": "", "ddg_snippet": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Predicting changes in binding free energy (∆∆G) is essential for understanding and modifying protein-protein interactions, which are important in drug design.", "subpage_snippet": "", "source": "m2d2.substack.com", "link": "https://m2d2.substack.com/p/portal-weekly-67-moml-2024-data-driven", "content": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Predicting changes in binding free energy (∆∆G) is essential for understanding and modifying protein-protein interactions, which are important in drug design."}
+{"idx": 5, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values."}
+{"idx": 6, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to ΔΔG prediction. We begin by analyzing the thermodynamic definition of ΔΔG and introducing the Boltzmann distribution to connect energy with protein conformational distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to ΔΔG prediction. We begin by analyzing the thermodynamic definition of ΔΔG and introducing the Boltzmann distribution to connect energy with protein conformational distribution."}
+{"idx": 7, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 8, "title": "dblp: Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Bibliographic details on Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/JiaoMJY0S25", "content": "Bibliographic details on Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions."}
+{"idx": 9, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "直接估计条件概率是比较困难的，因为直接通过序列生成结构的模型，通常不是预测一个状态的概率，而是在预测各种扭转角，这个状态空间就太大了。而一些概率生成模型，又是在估计条件概率的梯度 ∇_x\\log p (X|S) ，并不能直接使用。因此，让我们思考一下， 直接估计 P (X|S) 很困难，但是因为 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "直接估计条件概率是比较困难的，因为直接通过序列生成结构的模型，通常不是预测一个状态的概率，而是在预测各种扭转角，这个状态空间就太大了。而一些概率生成模型，又是在估计条件概率的梯度 ∇_x\\log p (X|S) ，并不能直接使用。因此，让我们思考一下， 直接估计 P (X|S) 很困难，但是因为 ..."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_side-chain_flexibility_limitation_final_section.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_side-chain_flexibility_limitation_final_section.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9386921fb601b03f245d6aa92ba3854e0caf7233
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_side-chain_flexibility_limitation_final_section.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann - Aligned Inverse Folding Model as... | OpenReview", "date": "", "ddg_snippet": "Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity. No Acknowledgement Section : I certify that there is no acknowledgement section in this submission for double blind review. Submission Number: 4282.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity. No Acknowledgement Section : I certify that there is no acknowledgement section in this submission for double blind review. Submission Number: 4282."}
+{"idx": 1, "title": "aim-uofa/BA-DDG: [ICLR 2025 Spotlight] Boltzmann - Aligned Inverse ...", "date": "", "ddg_snippet": "[ICLR 2025 Spotlight] Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "[ICLR 2025 Spotlight] Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions."}
+{"idx": 2, "title": "PackDock: a Diffusion Based Side Chain Packing Model for Flexible ...", "date": "", "ddg_snippet": "This model predicts the various conformational changes of protein side chains in“free” and ligand “bound” states, and streamlined the problem of side - chain flexibility modeling by predicting the side chain torsional angles.", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2024.01.31.578200v1.full.pdf", "content": "This model predicts the various conformational changes of protein side chains in“free” and ligand “bound” states, and streamlined the problem of side - chain flexibility modeling by predicting the side chain torsional angles."}
+{"idx": 3, "title": "Prediction and evaluation of side ‐ chain conformations for protein...", "date": "", "ddg_snippet": "finally , side chains are added and. the whole structure is subjected to energy minimi-. zation.” Other methods of protein tertiary-structure pre-. diction are based the “ inverse folding ” con. Use of Rotamers to Model Side Chain Conformations.", "subpage_snippet": "", "source": "farid.berkeley.edu", "link": "https://farid.berkeley.edu/downloads/publications/proteins96.pdf", "content": "finally , side chains are added and. the whole structure is subjected to energy minimi-. zation.” Other methods of protein tertiary-structure pre-. diction are based the “ inverse folding ” con. Use of Rotamers to Model Side Chain Conformations."}
+{"idx": 4, "title": "From Isotropic to Anisotropic Side Chain Representations... | PLOS One", "date": "", "ddg_snippet": "However, the spatially anisotropic nature of the side chain determines that it is challenging to identify the contact pairs. This study compares three side chain contact models : the Atom Distance criteria (ADC) model , the Isotropic Sphere Side chain (ISS)...", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0019238", "content": "However, the spatially anisotropic nature of the side chain determines that it is challenging to identify the contact pairs. This study compares three side chain contact models : the Atom Distance criteria (ADC) model , the Isotropic Sphere Side chain (ISS)..."}
+{"idx": 5, "title": "P rotein I nteractions", "date": "", "ddg_snippet": "Boltzmann - aligned inverse folding model as a predictor of mutational effects on protein-protein interactions.evaluate these two chains using the inverse folding model .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "Boltzmann - aligned inverse folding model as a predictor of mutational effects on protein-protein interactions.evaluate these two chains using the inverse folding model ."}
+{"idx": 6, "title": "Selected for ICLR 2025! Zhejiang University Shen Chunhua... | HyperAI", "date": "", "ddg_snippet": "Professor Shen Chunhua's team from Zhejiang University, in collaboration with teams from several foreign universities, proposed the Boltzmann alignment technique, which transfers knowledge from the pre-trained inverse folding model to the prediction of ∆∆G.", "subpage_snippet": "", "source": "hyper.ai", "link": "https://hyper.ai/en/news/38092", "content": "Professor Shen Chunhua's team from Zhejiang University, in collaboration with teams from several foreign universities, proposed the Boltzmann alignment technique, which transfers knowledge from the pre-trained inverse folding model to the prediction of ∆∆G."}
+{"idx": 7, "title": "A New Method for Ligand-supported Homology", "date": "", "ddg_snippet": "The best-scored side - chain orientations of the individual models were merged in a combinatorial fashion. Finally , the model was selected that yielded the best total DrugScore value avoiding any unfavourable intramolecular contacts among individual amino acid side - chains .", "subpage_snippet": "", "source": "archiv.ub.uni-marburg.de", "link": "https://archiv.ub.uni-marburg.de/diss/z2004/0019/pdf/dae.pdf", "content": "The best-scored side - chain orientations of the individual models were merged in a combinatorial fashion. Finally , the model was selected that yielded the best total DrugScore value avoiding any unfavourable intramolecular contacts among individual amino acid side - chains ."}
+{"idx": 8, "title": "Computational Protein Design with Deep Learning and Automated...", "date": "", "ddg_snippet": "Side chains are also flexible : they can move in 3-dimensional (3D) space through rotations around the side - chain covalent bonds.Most KBPs are based on the inverse Boltzmann equation", "subpage_snippet": "", "source": "theses.hal.science", "link": "https://theses.hal.science/tel-04396156/document", "content": "Side chains are also flexible : they can move in 3-dimensional (3D) space through rotations around the side - chain covalent bonds.Most KBPs are based on the inverse Boltzmann equation"}
+{"idx": 9, "title": "IMPROVEMENTS", "date": "", "ddg_snippet": "Our final submitted model for this target had a GDT_TS score of 31.3. Most of the targets folded with BCL:: Fold had this attrition pattern.BCL:: Fold uses a reduced representation when assembling protein models that does not contain loop regions or side chains .", "subpage_snippet": "", "source": "ir.vanderbilt.edu", "link": "https://ir.vanderbilt.edu/bitstream/handle/1803/12144/Heinze.pdf?sequence=1&isAllowed=y", "content": "Our final submitted model for this target had a GDT_TS score of 31.3. Most of the targets folded with BCL:: Fold had this attrition pattern.BCL:: Fold uses a reduced representation when assembling protein models that does not contain loop regions or side chains ."}
diff --git a/data/sampled_jsons/Buchholz_et_al.,_2023_Yao_et_al.,_2024c_Gaussian_independent_distribution_causal_factors.jsonl b/data/sampled_jsons/Buchholz_et_al.,_2023_Yao_et_al.,_2024c_Gaussian_independent_distribution_causal_factors.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7ad45851db0d0d0ad738e5084e1094cfc7f266b6
--- /dev/null
+++ b/data/sampled_jsons/Buchholz_et_al.,_2023_Yao_et_al.,_2024c_Gaussian_independent_distribution_causal_factors.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "2024; Yao et al ., 2024 c ; Xu et al ., 2024 ) . This practice provides further validation for the theoretical foundations of these methods but yields limited insight into their applicability to real-world problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v1", "content": "2024; Yao et al ., 2024 c ; Xu et al ., 2024 ) . This practice provides further validation for the theoretical foundations of these methods but yields limited insight into their applicability to real-world problems."}
+{"idx": 1, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "As opposed to CCRL, the method from Yao et al. (2024c ) places more flexible assumptions on the distribution of the underlying causal factors , where any smooth,.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "As opposed to CCRL, the method from Yao et al. (2024c ) places more flexible assumptions on the distribution of the underlying causal factors , where any smooth,."}
+{"idx": 2, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "tive Gaussian noise ( Buchholz et al., 2023 , Assumption 2). The underlying causal factors are transformed into observa- tions through a nonlinear and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f49eb15381f8ea150aedc07d26d294f4b7c91ada.pdf", "content": "tive Gaussian noise ( Buchholz et al., 2023 , Assumption 2). The underlying causal factors are transformed into observa- tions through a nonlinear and ..."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Buchholz_et_al_2023_contrastive_causal_representation_learning_assumptions.jsonl b/data/sampled_jsons/Buchholz_et_al_2023_contrastive_causal_representation_learning_assumptions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a503b12a0d560dee8e717b5f749f0610a4c5a18d
--- /dev/null
+++ b/data/sampled_jsons/Buchholz_et_al_2023_contrastive_causal_representation_learning_assumptions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How is Buchholz score calculated in a Swiss tournament?", "date": "", "ddg_snippet": "Jul 18, 2019 · I would like to know how the Buchholz tiebreak system works, and how it is calculated, taking into consideration other factors such as bye, no show (default win), etc.", "subpage_snippet": "", "source": "chess.stackexchange.com", "link": "https://chess.stackexchange.com/questions/24915/how-is-buchholz-score-calculated-in-a-swiss-tournament", "content": "Jul 18, 2019 · I would like to know how the Buchholz tiebreak system works, and how it is calculated, taking into consideration other factors such as bye, no show (default win), etc."}
+{"idx": 1, "title": "Learning Robust Intervention Representations with Delta", "date": "", "ddg_snippet": "This fundamental problem falls under the category of Causal Representation Learning (CRL) (Schölkopf et al . ... causal factor recovery under ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.04492v1", "content": "This fundamental problem falls under the category of Causal Representation Learning (CRL) (Schölkopf et al . ... causal factor recovery under ..."}
+{"idx": 2, "title": "Towards Causal Representation Learning with Observable Sources", "date": "", "ddg_snippet": "... systems like robotic arms governed by physical laws can often be represented using causal graphs (Mooij, Janzing, and Schölkopf 2013 ; Baumann et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.19058v1", "content": "... systems like robotic arms governed by physical laws can often be represented using causal graphs (Mooij, Janzing, and Schölkopf 2013 ; Baumann et al ..."}
+{"idx": 3, "title": "Do-PFN: In-Context Learning for Causal Effect Estimation", "date": "", "ddg_snippet": "Estimating causal effects from observational data alone can be challenging or even impossible without strict assumptions (Spirtes et al .,, 1993 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06039v1", "content": "Estimating causal effects from observational data alone can be challenging or even impossible without strict assumptions (Spirtes et al .,, 1993 ) ."}
+{"idx": 4, "title": "Identifying Weight-Variant Latent Causal Models", "date": "", "ddg_snippet": "... representation learning can be viewed as a special case of causal representation learning where the latent variables have no causal influences ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2208.14153v6", "content": "... representation learning can be viewed as a special case of causal representation learning where the latent variables have no causal influences ..."}
+{"idx": 5, "title": "On the Origins of Linear Representations in Large Language", "date": "", "ddg_snippet": "... show that linear representations emerge when learning from data matching the latent variable model, confirming that this simple structure already ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.03867v1", "content": "... show that linear representations emerge when learning from data matching the latent variable model, confirming that this simple structure already ..."}
+{"idx": 6, "title": "NeurIPS 2023 Orals", "date": "", "ddg_snippet": "... the global convergence of policy gradient (PG) methods depends on inter-related properties between the policy update and the representation ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/events/oral", "content": "... the global convergence of policy gradient (PG) methods depends on inter-related properties between the policy update and the representation ..."}
+{"idx": 7, "title": "Epistemology and Theory of Machine Learning (23-24 March 2023)", "date": "", "ddg_snippet": "But challenges are also mounting from machine learning and machine learning -adjacent fields such as reinforcement learning and causal discovery.", "subpage_snippet": "", "source": "www.mcmp.philosophie.uni-muenchen.de", "link": "https://www.mcmp.philosophie.uni-muenchen.de/events/archive/2023_workshops_conferences/ml_2023/index.html", "content": "But challenges are also mounting from machine learning and machine learning -adjacent fields such as reinforcement learning and causal discovery."}
+{"idx": 8, "title": "Newest 'tie-breaks' Questions - Chess Stack Exchange", "date": "", "ddg_snippet": "How is Buchholz score calculated in a Swiss tournament? I would like to know how the Buchholz tiebreak system works, and how it is calculated, taking into consideration other factors such as bye, no show (default win), etc.", "subpage_snippet": "", "source": "chess.stackexchange.com", "link": "https://chess.stackexchange.com/questions/tagged/tie-breaks", "content": "How is Buchholz score calculated in a Swiss tournament? I would like to know how the Buchholz tiebreak system works, and how it is calculated, taking into consideration other factors such as bye, no show (default win), etc."}
+{"idx": 9, "title": "What does the annotation symbol \"TR\" mean? - Chess Stack Exchange", "date": "", "ddg_snippet": "Feb 17, 2017 · Truncated Buchholz (1st tie-breaking system used here) The truncated Buchholz corresponds to the sum of the points scored by the opponents encountered by removing the score of the opponent who scored the fewest points.", "subpage_snippet": "", "source": "chess.stackexchange.com", "link": "https://chess.stackexchange.com/questions/16685/what-does-the-annotation-symbol-tr-mean", "content": "Feb 17, 2017 · Truncated Buchholz (1st tie-breaking system used here) The truncated Buchholz corresponds to the sum of the points scored by the opponents encountered by removing the score of the opponent who scored the fewest points."}
diff --git a/data/sampled_jsons/C-Proxy_machine_unlearning.jsonl b/data/sampled_jsons/C-Proxy_machine_unlearning.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "30 Oct 2024 — Additionally, our results with the C - proxy demonstrate that significant performance improvements in unlearning can be achieved with minimal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01257v2", "content": "30 Oct 2024 — Additionally, our results with the C - proxy demonstrate that significant performance improvements in unlearning can be achieved with minimal ..."}
+{"idx": 1, "title": "What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "Together, these results suggest that C-proxy is a practical and compute-efficient alternative , delivering significant performance gains comparable to those ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95259", "content": "Together, these results suggest that C-proxy is a practical and compute-efficient alternative , delivering significant performance gains comparable to those ..."}
+{"idx": 2, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "24 May 2025 — Recall that C - Proxy has been used in prior work by Zhao et al. (2024) to identify difficult to unlearn forget sets for certain unlearning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "24 May 2025 — Recall that C - Proxy has been used in prior work by Zhao et al. (2024) to identify difficult to unlearn forget sets for certain unlearning ..."}
+{"idx": 3, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "25 Jun 2025 — We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0A4Y9qRnu9&noteId=Zd6KsMzKb8", "content": "25 Jun 2025 — We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}
+{"idx": 4, "title": "A Contrastive Approach to Machine Unlearning", "date": "", "ddg_snippet": "Since we do not have access to the retrained model, we use a proxy criteria which requires the accuracy of the unlearning samples to be similar to the test ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0830.pdf", "content": "Since we do not have access to the retrained model, we use a proxy criteria which requires the accuracy of the unlearning samples to be similar to the test ..."}
+{"idx": 5, "title": "What makes unlearning hard and what to do about it - Liner", "date": "", "ddg_snippet": "The research introduces a confidence-based memorization metric, C-proxy , as a computationally efficient alternative for practical deployment of the RUM ...", "subpage_snippet": "", "source": "liner.com", "link": "https://liner.com/review/what-makes-unlearning-hard-and-what-to-do-about-it", "content": "The research introduces a confidence-based memorization metric, C-proxy , as a computationally efficient alternative for practical deployment of the RUM ..."}
+{"idx": 6, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}
+{"idx": 7, "title": "Towards Source-Free Machine Unlearning - CVF Open Access", "date": "", "ddg_snippet": "by SM Ahmed · 2025 — To address this challenge, we focus on the source-free unlearning scenario, where an unlearning algorithm must be capable of removing spe- cific data from a ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Ahmed_Towards_Source-Free_Machine_Unlearning_CVPR_2025_paper.pdf", "content": "by SM Ahmed · 2025 — To address this challenge, we focus on the source-free unlearning scenario, where an unlearning algorithm must be capable of removing spe- cific data from a ..."}
+{"idx": 8, "title": "Machine Unlearning: The Right to be Forgotten", "date": "", "ddg_snippet": "This survey aims to provide a comprehensive examination of machine unlearning , including its concepts, scenarios, methods, and applications.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/code/tamlhp/machine-unlearning-the-right-to-be-forgotten", "content": "This survey aims to provide a comprehensive examination of machine unlearning , including its concepts, scenarios, methods, and applications."}
+{"idx": 9, "title": "Breaking the Trilemma of Privacy, Utility, Efficiency via ...", "date": "", "ddg_snippet": "by Z Liu · Cited by 28 — The objective of machine unlearning is to align the output distri- bution of the unlearned model closely with that of the retrained model — a model never ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i5KPb9Bsjz", "content": "by Z Liu · Cited by 28 — The objective of machine unlearning is to align the output distri- bution of the unlearned model closely with that of the retrained model — a model never ..."}
diff --git a/data/sampled_jsons/CCRL_Causal_Contrastive_Representation_Learning_sensitivity_noise_mixing_function.jsonl b/data/sampled_jsons/CCRL_Causal_Contrastive_Representation_Learning_sensitivity_noise_mixing_function.jsonl
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index 0000000000000000000000000000000000000000..a97173e8f203504b1b3bb713258ddeb25a9086d8
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+++ b/data/sampled_jsons/CCRL_Causal_Contrastive_Representation_Learning_sensitivity_noise_mixing_function.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Learning Contrastive Representations for Learning with Noisy Labels", "date": "", "ddg_snippet": "To address this issue, we focus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss. We propose a novel contrastive regularization function to learn such representations over noisy data where label noise does not dominate the representation learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2203.01785", "content": "To address this issue, we focus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss. We propose a novel contrastive regularization function to learn such representations over noisy data where label noise does not dominate the representation learning ."}
+{"idx": 1, "title": "PDF On Learning Contrastive Representations for Learning with Noisy Labels", "date": "", "ddg_snippet": "To address this issue, we fo-cus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss. We propose a novel contrastive regu-larization function to learn such representations over noisy data where label noise does not dominate the representation learning .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Yi_On_Learning_Contrastive_Representations_for_Learning_With_Noisy_Labels_CVPR_2022_paper.pdf", "content": "To address this issue, we fo-cus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss. We propose a novel contrastive regu-larization function to learn such representations over noisy data where label noise does not dominate the representation learning ."}
+{"idx": 2, "title": "PDF Connectivity-Contrastive Learning: Combining Causal Discovery and ...", "date": "", "ddg_snippet": "To analyze such data, we propose a new causal representation learning framework called connectivity- contrastive learn-ing (CCL). CCL disentangles the observational mixing and extracts a set of mutually independent latent components, each having a separate causal structure between the nodes.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/morioka23a/morioka23a.pdf", "content": "To analyze such data, we propose a new causal representation learning framework called connectivity- contrastive learn-ing (CCL). CCL disentangles the observational mixing and extracts a set of mutually independent latent components, each having a separate causal structure between the nodes."}
+{"idx": 3, "title": "Contrastive Learning Improves Model Robustness Under Label Noise", "date": "", "ddg_snippet": "Deep neural network-based classifiers trained with the categorical cross-entropy (CCE) loss are sensitive to label noise in the training data. One common type of method that can mitigate the impact of label noise can be viewed as supervised robust methods; one can simply replace the CCE loss with a loss that is robust to label noise , or re-weight training samples and down-weight those with ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9522768", "content": "Deep neural network-based classifiers trained with the categorical cross-entropy (CCE) loss are sensitive to label noise in the training data. One common type of method that can mitigate the impact of label noise can be viewed as supervised robust methods; one can simply replace the CCE loss with a loss that is robust to label noise , or re-weight training samples and down-weight those with ..."}
+{"idx": 4, "title": "Contrastive learning of graphs under label noise - ScienceDirect", "date": "", "ddg_snippet": "However, most current strategies that are grounded in contrastive learning adopt a rather straightforward integration approach, neglecting an in-depth examination of the latent potential of contrastive learning and lacking a fine-grained consideration regarding graph homophily.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608024000273", "content": "However, most current strategies that are grounded in contrastive learning adopt a rather straightforward integration approach, neglecting an in-depth examination of the latent potential of contrastive learning and lacking a fine-grained consideration regarding graph homophily."}
+{"idx": 5, "title": "Counterfactual contrastive learning: robust representations via causal ...", "date": "", "ddg_snippet": "Contrastive pretraining is well-known to improve downstream task performance and model generalisation, especially in limited label settings. However, it is sensitive to the choice of augmentation pipeline. Positive pairs should preserve semantic information while destroying domain-specific information. Standard augmentation pipelines emulate domain-specific changes with pre-defined photometric ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09605", "content": "Contrastive pretraining is well-known to improve downstream task performance and model generalisation, especially in limited label settings. However, it is sensitive to the choice of augmentation pipeline. Positive pairs should preserve semantic information while destroying domain-specific information. Standard augmentation pipelines emulate domain-specific changes with pre-defined photometric ..."}
+{"idx": 6, "title": "PDF Robust Contrastive Learning against Noisy Views - CVF Open Access", "date": "", "ddg_snippet": "Noisy View age or co-occurring multimodal signals of a video. What if this assumption is violated? The literature suggests that contrastive learning produces suboptimal representations in the presence of noisy views, e.g., false positive pairs with no apparent shared information. In this work, we pro-pose a new contrastive loss function that is robust against noisy views. We provide rigorous ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Chuang_Robust_Contrastive_Learning_Against_Noisy_Views_CVPR_2022_paper.pdf", "content": "Noisy View age or co-occurring multimodal signals of a video. What if this assumption is violated? The literature suggests that contrastive learning produces suboptimal representations in the presence of noisy views, e.g., false positive pairs with no apparent shared information. In this work, we pro-pose a new contrastive loss function that is robust against noisy views. We provide rigorous ..."}
+{"idx": 7, "title": "Adaptive Contrastive Learning for Learning Robust Representations under ...", "date": "", "ddg_snippet": "As a result, diverse pairs are correctly selected for contrastive learning to induce discriminative representations robust to various types of label noise . Extensive experimental results on several standard benchmarks and real-world datasets indicate the superiority of ACL, especially in extremely noisy scenarios.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3581783.3612491", "content": "As a result, diverse pairs are correctly selected for contrastive learning to induce discriminative representations robust to various types of label noise . Extensive experimental results on several standard benchmarks and real-world datasets indicate the superiority of ACL, especially in extremely noisy scenarios."}
+{"idx": 8, "title": "On Learning Contrastive Representations for Learning With Noisy Labels ...", "date": "", "ddg_snippet": "To address this issue, we focus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss. We propose a novel contrastive regularization function to learn such representations over noisy data where the label noise does not dominate the representation learning .", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10342150-learning-contrastive-representations-learning-noisy-labels", "content": "To address this issue, we focus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss. We propose a novel contrastive regularization function to learn such representations over noisy data where the label noise does not dominate the representation learning ."}
+{"idx": 9, "title": "PDF Investigating Why Contrastive Learning Benefits Robustness against ...", "date": "", "ddg_snippet": "The above properties enable a linear layer trained on such representations to ef-fectively learn the clean labels without overfitting the noise . We further show that the low-rank struc-ture of the Jacobian of deep networks pre-trained with contrastive learning allows them to achieve a superior performance initially, when fine-tuned on noisy labels.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/xue22a/xue22a.pdf", "content": "The above properties enable a linear layer trained on such representations to ef-fectively learn the clean labels without overfitting the noise . We further show that the low-rank struc-ture of the Jacobian of deep networks pre-trained with contrastive learning allows them to achieve a superior performance initially, when fine-tuned on noisy labels."}
diff --git a/data/sampled_jsons/CLIP_embedding_space_semantic_vs_VAE_latent_space_continuous.jsonl b/data/sampled_jsons/CLIP_embedding_space_semantic_vs_VAE_latent_space_continuous.jsonl
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index 0000000000000000000000000000000000000000..2256af69eeb8b91adac8a0c7df7b477ec5c13a84
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Controlling Latent Diffusion Using Latent CLIP - arXiv.org", "date": "", "ddg_snippet": "However, while the diffusion process has moved to the latent space , the contrastive language-image pre-training ( CLIP ) models, as used in many image processing tasks, still operate in pixel space . Doing so requires costly VAE -decoding of latent images before they can be processed.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.08455v1", "content": "However, while the diffusion process has moved to the latent space , the contrastive language-image pre-training ( CLIP ) models, as used in many image processing tasks, still operate in pixel space . Doing so requires costly VAE -decoding of latent images before they can be processed."}
+{"idx": 1, "title": "Combining CLIP and VAE for Image Generation - GitHub Latent Space Representations in Variational Autoencoders ... Latent Space and Latent Encoding - SugiV Blog Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE Discrete embedding in latent space | Xiaozhi Zhu Combining CLIP and VAE for Image Generation - GitHub Interpreting CLIP with Sparse Linear Concept Embeddings Latent Space Representations in Variational Autoencoders (VAEs) Combining CLIP and VAE for Image Generation - GitHub Latent Space Representations in Variational Autoencoders (VAEs) Latent Space Representations in Variational Autoencoders (VAEs) [D] Should I use VAE or CLIP as embedding for a diffusion ...", "date": "", "ddg_snippet": "CLIP - VAE : Combining CLIP and VAE for Image Generation This repository offers the implementation code for CLIP - VAE , an innovative model that merges OpenAI's CLIP [1] and Variational Autoencoder ( VAE )[2] for image generation. The model is composed of two essential components. Firstly, CLIP , a robust vision model developed by OpenAI, is employed for the purpose of comprehending and categorizing images. The original CLIP model was designed to acquire knowledge about the connection between images and text, whereas in this context, we aim to learn the association between images and labels. Secondly, the Variational Autoencoder ( VAE ) is an adept generative model utilized to encode and decode data efficiently within a latent space , thereby generating novel data. The VAE is designed to learn the underlying distribution of the data, facilitating the production of a wide range of samples by means of continuous interpolation within the latent space . CLIP - VAE integrates these two components to tackle the task of image generation. The VAE takes on the responsibility of generating images within the latent space , while CLIP is employed to assess their alignment with specific concepts. This combined approach ensures the preservation of desired features and enhances the overall quality of the generated images. This repository encompasses all the essential code and example datasets, such as MNIST, required for training the CLIP - VAE model and generating images. Furthermore, it offers tools and example code that facilitate the visualization of the training process for the model. By leveraging this codebase and the accompanying examples, you can implement and train your very own CLIP - VAE model, ultimately enabling you to generate images according to your specific preferences. See full list on github.com The model architecture of CLIP - VAE closely resembles that of a conventional VAE . However, it introduces a significant distinction by incorporating an additional loss term that quantifies the difference between the latent vectors of images and their respective labels. This augmented loss term incentivizes the model to acquire a latent space representation that not only captures image reconstruction capabilities but also maintains the semantic relationship between an image and its corresponding label. By integrating this loss term, CLIP - VAE achieves a holistic understanding of both visual and semantic aspects, enabling it to generate images that align with their intended labels while preserving their inherent image characteristics. The overall loss term is defined as follows: $$ \\begin{aligned} \\mathcal L (x, y) &= \\mathcal L_{ CLIP } (z_x, z_y) \\\\ &+ \\mathcal L_{KLD} (z_x, \\mathcal N (0, I)) + \\mathcal L_{KLD}(z_y, \\mathcal N (0, I)) \\\\ &+ \\mathcal L_{REC}(x, \\hat x) + \\mathcal L_{REC}(y, \\hat y) \\end{aligned} $$ Here, $\\mathcal L_{ CLIP }$ represents the CLIP loss, which is defined by [1]. $\\mathcal L_{KLD}$ refers to the Kullback-Leibler divergence between the latent vector $z$ and a standard normal distribution $\\mathcal N (0, I)$. $\\mathcal L_{REC}$ denotes the reconstruction loss. In this loss formulation, the CLIP loss term encourages alignment between the latent vectors $z_x$ and $z_y$, capturing the semantic relationship between the image $x$ and its associated label $y$. The KLD terms regularize the latent vectors by ensuring they adhere to a standard normal distribution. Finally, the reconstruction loss terms $\\mathcal L_{REC}$ measure the dissimilarity between the original inputs ($x$ and $y$) and their corresponding reconstructions ($\\hat x$ and $\\hat y$). See full list on github.com The CLIP - VAE has the capability to generate images from random Gaussian noise. This is achieved by sampling random points from the latent space and inputting them into the decoder of the CLIP - VAE model. When using the latent vectors obtained through the label encoder, it becomes possible to generate images corresponding to specific desired labels. This resembles the concept of conditional VAE , where the generation process is conditioned on specific input information. By leveraging the label encoder, CLIP - VAE enables the generation of images that align with the intended labels, thereby allowing for targeted image synthesis. See full list on github.com 1.Radford, Alec, et al. \"Learning transferable visual models from natural language supervision.\" International conference on machine learning. PMLR (2021) 2.Kingma, Diederik P., and Max Welling. \"Auto-encoding variational bayes.\" arXiv preprint arXiv:1312.6114 (2013) See full list on github.com Dec 9, 2024 · For instance, blending two points in latent space can morph one image into another, and style transfer leverages latent representations to combine content and style in new ways. Jun 17, 2025 · In your old approach, you took a CLIP text embedding and projected it into the VAE latent space using a randomly initialized linear layer. The VAE was trained only to encode and decode images, not to understand or reconstruct from random vectors or text projections. CLIP embeddings into human-interpretable representations of the semantic concepts they encode? This can provide insight into the types of tasks CLIP can solve, the biases it may contain, and the manner through which downstream predictions are made. Oct 21, 2023 · In recent reading, I noticed a model, called VQ- VAE , frequently used in latent diffusion model. It is under the framework of auto-encoder and, instead of using a continuous latent space , use a discrete latent encoding dictionary. In this post, I am going to review why it is popular and how it works. What is the latent space and what makes a good latent space ? Latent space refers to a lower ... What is the difference between VAE & clip? The VAE takes on the responsibility of generating images within the latent space , while CLIP is employed to assess their alignment with specific concepts. This combined approach ensures the preservation of desired features and enhances the overall quality of the generated images. Can clip's latent space be leveraged to provide interpretability? d its use in downstream applications that require transparency. In this work, we show that the semantic structure of CLIP’s latent space can be leveraged to provide interpretability , allowing f What is latent space? Latent space is where the magic of VAEs truly comes alive. It’s not just a compressed representation — it’s a structured space that reflects the underlying patterns in your data . I’ve spent countless hours experimenting with different latent dimensions and visualizations, and let me tell you, it’s both fascinating and humbling. What is clip-VAE? This repository offers the implementation code for CLIP - VAE , an innovative model that merges OpenAI's CLIP and Variational Autoencoder ( VAE ) for image generation. The model is composed of two essential components. Firstly, CLIP , a robust vision model developed by OpenAI, is employed for the purpose of comprehending and categorizing images. What is a latent space representation? Exploring Latent Space Representations Latent space is where the magic of VAEs truly comes alive. It’s not just a compressed representation — it’s a structured space that reflects the underlying patterns in your data . What are some examples of latent space interpolation & style transfer? Some of the most exciting applications I’ve explored involve latent space interpolation and style transfer. For instance, blending two points in latent space can morph one image into another , and style transfer leverages latent representations to combine content and style in new ways. z_interp = alpha * z_start + (1 - alpha) * z_end VAE latents for stable diffusion 1.5 are just ways to represent 3x8x8 patches (the shapes vary a bit because of self attention) using 4 latent variables (means of gaussian distributions), you can think of these numbers as being continuous parameters that you can deform to transform one image patch into another.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/phykn/clip-vae", "content": "CLIP - VAE : Combining CLIP and VAE for Image Generation This repository offers the implementation code for CLIP - VAE , an innovative model that merges OpenAI's CLIP [1] and Variational Autoencoder ( VAE )[2] for image generation. The model is composed of two essential components. Firstly, CLIP , a robust vision model developed by OpenAI, is employed for the purpose of comprehending and categorizing images. The original CLIP model was designed to acquire knowledge about the connection between images and text, whereas in this context, we aim to learn the association between images and labels. Secondly, the Variational Autoencoder ( VAE ) is an adept generative model utilized to encode and decode data efficiently within a latent space , thereby generating novel data. The VAE is designed to learn the underlying distribution of the data, facilitating the production of a wide range of samples by means of continuous interpolation within the latent space . CLIP - VAE integrates these two components to tackle the task of image generation. The VAE takes on the responsibility of generating images within the latent space , while CLIP is employed to assess their alignment with specific concepts. This combined approach ensures the preservation of desired features and enhances the overall quality of the generated images. This repository encompasses all the essential code and example datasets, such as MNIST, required for training the CLIP - VAE model and generating images. Furthermore, it offers tools and example code that facilitate the visualization of the training process for the model. By leveraging this codebase and the accompanying examples, you can implement and train your very own CLIP - VAE model, ultimately enabling you to generate images according to your specific preferences. See full list on github.com The model architecture of CLIP - VAE closely resembles that of a conventional VAE . However, it introduces a significant distinction by incorporating an additional loss term that quantifies the difference between the latent vectors of images and their respective labels. This augmented loss term incentivizes the model to acquire a latent space representation that not only captures image reconstruction capabilities but also maintains the semantic relationship between an image and its corresponding label. By integrating this loss term, CLIP - VAE achieves a holistic understanding of both visual and semantic aspects, enabling it to generate images that align with their intended labels while preserving their inherent image characteristics. The overall loss term is defined as follows: $$ \\begin{aligned} \\mathcal L (x, y) &= \\mathcal L_{ CLIP } (z_x, z_y) \\\\ &+ \\mathcal L_{KLD} (z_x, \\mathcal N (0, I)) + \\mathcal L_{KLD}(z_y, \\mathcal N (0, I)) \\\\ &+ \\mathcal L_{REC}(x, \\hat x) + \\mathcal L_{REC}(y, \\hat y) \\end{aligned} $$ Here, $\\mathcal L_{ CLIP }$ represents the CLIP loss, which is defined by [1]. $\\mathcal L_{KLD}$ refers to the Kullback-Leibler divergence between the latent vector $z$ and a standard normal distribution $\\mathcal N (0, I)$. $\\mathcal L_{REC}$ denotes the reconstruction loss. In this loss formulation, the CLIP loss term encourages alignment between the latent vectors $z_x$ and $z_y$, capturing the semantic relationship between the image $x$ and its associated label $y$. The KLD terms regularize the latent vectors by ensuring they adhere to a standard normal distribution. Finally, the reconstruction loss terms $\\mathcal L_{REC}$ measure the dissimilarity between the original inputs ($x$ and $y$) and their corresponding reconstructions ($\\hat x$ and $\\hat y$). See full list on github.com The CLIP - VAE has the capability to generate images from random Gaussian noise. This is achieved by sampling random points from the latent space and inputting them into the decoder of the CLIP - VAE model. When using the latent vectors obtained through the label encoder, it becomes possible to generate images corresponding to specific desired labels. This resembles the concept of conditional VAE , where the generation process is conditioned on specific input information. By leveraging the label encoder, CLIP - VAE enables the generation of images that align with the intended labels, thereby allowing for targeted image synthesis. See full list on github.com 1.Radford, Alec, et al. \"Learning transferable visual models from natural language supervision.\" International conference on machine learning. PMLR (2021) 2.Kingma, Diederik P., and Max Welling. \"Auto-encoding variational bayes.\" arXiv preprint arXiv:1312.6114 (2013) See full list on github.com Dec 9, 2024 · For instance, blending two points in latent space can morph one image into another, and style transfer leverages latent representations to combine content and style in new ways. Jun 17, 2025 · In your old approach, you took a CLIP text embedding and projected it into the VAE latent space using a randomly initialized linear layer. The VAE was trained only to encode and decode images, not to understand or reconstruct from random vectors or text projections. CLIP embeddings into human-interpretable representations of the semantic concepts they encode? This can provide insight into the types of tasks CLIP can solve, the biases it may contain, and the manner through which downstream predictions are made. Oct 21, 2023 · In recent reading, I noticed a model, called VQ- VAE , frequently used in latent diffusion model. It is under the framework of auto-encoder and, instead of using a continuous latent space , use a discrete latent encoding dictionary. In this post, I am going to review why it is popular and how it works. What is the latent space and what makes a good latent space ? Latent space refers to a lower ... What is the difference between VAE & clip? The VAE takes on the responsibility of generating images within the latent space , while CLIP is employed to assess their alignment with specific concepts. This combined approach ensures the preservation of desired features and enhances the overall quality of the generated images. Can clip's latent space be leveraged to provide interpretability? d its use in downstream applications that require transparency. In this work, we show that the semantic structure of CLIP’s latent space can be leveraged to provide interpretability , allowing f What is latent space? Latent space is where the magic of VAEs truly comes alive. It’s not just a compressed representation — it’s a structured space that reflects the underlying patterns in your data . I’ve spent countless hours experimenting with different latent dimensions and visualizations, and let me tell you, it’s both fascinating and humbling. What is clip-VAE? This repository offers the implementation code for CLIP - VAE , an innovative model that merges OpenAI's CLIP and Variational Autoencoder ( VAE ) for image generation. The model is composed of two essential components. Firstly, CLIP , a robust vision model developed by OpenAI, is employed for the purpose of comprehending and categorizing images. What is a latent space representation? Exploring Latent Space Representations Latent space is where the magic of VAEs truly comes alive. It’s not just a compressed representation — it’s a structured space that reflects the underlying patterns in your data . What are some examples of latent space interpolation & style transfer? Some of the most exciting applications I’ve explored involve latent space interpolation and style transfer. For instance, blending two points in latent space can morph one image into another , and style transfer leverages latent representations to combine content and style in new ways. z_interp = alpha * z_start + (1 - alpha) * z_end VAE latents for stable diffusion 1.5 are just ways to represent 3x8x8 patches (the shapes vary a bit because of self attention) using 4 latent variables (means of gaussian distributions), you can think of these numbers as being continuous parameters that you can deform to transform one image patch into another."}
+{"idx": 2, "title": "Latent Space Representations in Variational Autoencoders ...", "date": "", "ddg_snippet": "Dec 9, 2024 · For instance, blending two points in latent space can morph one image into another, and style transfer leverages latent representations to combine content and style in new ways.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@whyamit101/latent-space-representations-in-variational-autoencoders-vaes-e74076eda77b", "content": "Dec 9, 2024 · For instance, blending two points in latent space can morph one image into another, and style transfer leverages latent representations to combine content and style in new ways."}
+{"idx": 3, "title": "Latent Space and Latent Encoding - SugiV Blog", "date": "", "ddg_snippet": "Jun 17, 2025 · In your old approach, you took a CLIP text embedding and projected it into the VAE latent space using a randomly initialized linear layer. The VAE was trained only to encode and decode images, not to understand or reconstruct from random vectors or text projections.", "subpage_snippet": "", "source": "blog.sugiv.fyi", "link": "https://blog.sugiv.fyi/latent-space", "content": "Jun 17, 2025 · In your old approach, you took a CLIP text embedding and projected it into the VAE latent space using a randomly initialized linear layer. The VAE was trained only to encode and decode images, not to understand or reconstruct from random vectors or text projections."}
+{"idx": 4, "title": "Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE", "date": "", "ddg_snippet": "CLIP embeddings into human-interpretable representations of the semantic concepts they encode? This can provide insight into the types of tasks CLIP can solve, the biases it may contain, and the manner through which downstream predictions are made.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/996bef37d8a638f37bdfcac2789e835d-Paper-Conference.pdf", "content": "CLIP embeddings into human-interpretable representations of the semantic concepts they encode? This can provide insight into the types of tasks CLIP can solve, the biases it may contain, and the manner through which downstream predictions are made."}
+{"idx": 5, "title": "Discrete embedding in latent space | Xiaozhi Zhu", "date": "", "ddg_snippet": "Oct 21, 2023 · In recent reading, I noticed a model, called VQ- VAE , frequently used in latent diffusion model. It is under the framework of auto-encoder and, instead of using a continuous latent space , use a discrete latent encoding dictionary. In this post, I am going to review why it is popular and how it works. What is the latent space and what makes a good latent space ? Latent space refers to a lower ...", "subpage_snippet": "", "source": "xiaozhi-alan-zhu.github.io", "link": "https://xiaozhi-alan-zhu.github.io/blog/2023/Discrete-embedding/", "content": "Oct 21, 2023 · In recent reading, I noticed a model, called VQ- VAE , frequently used in latent diffusion model. It is under the framework of auto-encoder and, instead of using a continuous latent space , use a discrete latent encoding dictionary. In this post, I am going to review why it is popular and how it works. What is the latent space and what makes a good latent space ? Latent space refers to a lower ..."}
+{"idx": 6, "title": "[D] Should I use VAE or CLIP as embedding for a diffusion ...", "date": "", "ddg_snippet": "VAE latents for stable diffusion 1.5 are just ways to represent 3x8x8 patches (the shapes vary a bit because of self attention) using 4 latent variables (means of gaussian distributions), you can think of these numbers as being continuous parameters that you can deform to transform one image patch into another.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MLQuestions/comments/1buxfhc/d_should_i_use_vae_or_clip_as_embedding_for_a/", "content": "VAE latents for stable diffusion 1.5 are just ways to represent 3x8x8 patches (the shapes vary a bit because of self attention) using 4 latent variables (means of gaussian distributions), you can think of these numbers as being continuous parameters that you can deform to transform one image patch into another."}
+{"idx": 7, "title": "Reconstruct images with CLIP image embedding", "date": "", "ddg_snippet": "CLIP embedding space is different from VAE's embedding space . VAE decoder should only work on embeddings encoded by VAE's encoder.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/computervision/comments/1jfoojt/reconstruct_images_with_clip_image_embedding/", "content": "CLIP embedding space is different from VAE's embedding space . VAE decoder should only work on embeddings encoded by VAE's encoder."}
+{"idx": 8, "title": "Explainable embeddings with Distance Explainer", "date": "", "ddg_snippet": "21 May 2025 — By the term “ embedded space ” we refer to a multi-dimensional vector space which original data can be projected or encoded into. “ Latent space ” ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.15516v1", "content": "21 May 2025 — By the term “ embedded space ” we refer to a multi-dimensional vector space which original data can be projected or encoded into. “ Latent space ” ..."}
+{"idx": 9, "title": "Learning semantic similarity in a continuous space", "date": "", "ddg_snippet": "by M Deudon · Cited by 46 — We address the problem of learning semantic representation of questions to measure similarity between pairs as a continuous distance metric. 12 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "http://papers.neurips.cc/paper/7377-learning-semantic-similarity-in-a-continuous-space.pdf", "content": "by M Deudon · Cited by 46 — We address the problem of learning semantic representation of questions to measure similarity between pairs as a continuous distance metric. 12 pages"}
diff --git a/data/sampled_jsons/CLIP_encoder_semantic_embedding_vs_VAE_latent_space_technical_differences.jsonl b/data/sampled_jsons/CLIP_encoder_semantic_embedding_vs_VAE_latent_space_technical_differences.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Variational autoencoder - Wikipedia", "date": "", "ddg_snippet": "The basic scheme of a variational autoencoder. The model receives. as input. The encoder compresses it into the latent space . The decoder receives as input the information sampled from the latent space and produces. as similar as possible to. .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Variational_autoencoder", "content": "The basic scheme of a variational autoencoder. The model receives. as input. The encoder compresses it into the latent space . The decoder receives as input the information sampled from the latent space and produces. as similar as possible to. ."}
+{"idx": 1, "title": "Variational Autoencoder ( VAE ) | Personal blog of Boris Burkov", "date": "", "ddg_snippet": "The difference in length between the optimal encoding and sub-optimal encoding is then.Sampling from nearby points of VAE latent space produces similar output images. Illustration from the original paper. Variational autoencoder ( VAE ).", "subpage_snippet": "", "source": "borisburkov.net", "link": "https://borisburkov.net/2022-12-31-1/", "content": "The difference in length between the optimal encoding and sub-optimal encoding is then.Sampling from nearby points of VAE latent space produces similar output images. Illustration from the original paper. Variational autoencoder ( VAE )."}
+{"idx": 2, "title": "A Deep Dive into Variational Autoencoders with... - PyImageSearch", "date": "", "ddg_snippet": "VAE : The latent space in a VAE is probabilistic. The encoder produces a distribution’s parameters (mean and variance), and the actual latent representation is sampled from this distribution.When comparing its latent space with those of our VAEs , distinct differences emerge.", "subpage_snippet": "", "source": "pyimagesearch.com", "link": "https://pyimagesearch.com/2023/10/02/a-deep-dive-into-variational-autoencoders-with-pytorch/", "content": "VAE : The latent space in a VAE is probabilistic. The encoder produces a distribution’s parameters (mean and variance), and the actual latent representation is sampled from this distribution.When comparing its latent space with those of our VAEs , distinct differences emerge."}
+{"idx": 3, "title": "GAN vs VAE : Interpreting and Visualizing Latent Spaces", "date": "", "ddg_snippet": "The latent space in GAN and VAE models serves as a compact representation of input data, with each dimension capturing different aspects of variation. For instance, in face generation, latent dimensions might represent attributes like gender or facial expression.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/advice/1/how-do-you-interpret-visualize-latent-space-gan-vae", "content": "The latent space in GAN and VAE models serves as a compact representation of input data, with each dimension capturing different aspects of variation. For instance, in face generation, latent dimensions might represent attributes like gender or facial expression."}
+{"idx": 4, "title": "GAN vs VAE : Differences , Similarities, Examples - Analytics Yogi", "date": "", "ddg_snippet": "Smooth Latent Space : VAEs ensure a continuous, smooth latent space , making them well-suited for tasks that require interpolation or exploration of variations along certain dimensions in the data.", "subpage_snippet": "", "source": "vitalflux.com", "link": "https://vitalflux.com/gan-vs-vae-differences-similarities-examples/", "content": "Smooth Latent Space : VAEs ensure a continuous, smooth latent space , making them well-suited for tasks that require interpolation or exploration of variations along certain dimensions in the data."}
+{"idx": 5, "title": "(PDF) LatentVis: Investigating and Comparing Variational...", "date": "", "ddg_snippet": "Comparing the semantic directions of different features allows us to compare the feature similarity encoded in the latent space , and thus to better understand the encoding process of the corresponding VAE .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/344753155_LatentVis_Investigating_and_Comparing_Variational_Auto-Encoders_via_Their_Latent_Space", "content": "Comparing the semantic directions of different features allows us to compare the feature similarity encoded in the latent space , and thus to better understand the encoding process of the corresponding VAE ."}
+{"idx": 6, "title": "Compressing Human Faces with VAE vs VQ- VAE ... - DEV Community", "date": "", "ddg_snippet": "Architecture 2: Vector Quantized VAE (VQ- VAE ). VQ- VAE replaces the continuous latent space with discrete codebook vectors: Encoder outputs feature map → quantized to nearest embedding .", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/ertugrulmutlu/compressing-human-faces-with-vae-vs-vq-vae-a-deep-dive-into-autoencoder-design-j59", "content": "Architecture 2: Vector Quantized VAE (VQ- VAE ). VQ- VAE replaces the continuous latent space with discrete codebook vectors: Encoder outputs feature map → quantized to nearest embedding ."}
+{"idx": 7, "title": "Embeddings vs . Latent Space : Unlocking... - Freedium", "date": "", "ddg_snippet": "Embeddings and latent space are key for AI to understand text and images. Learn their differences and uses in this article.", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/43a36faa2e4f", "content": "Embeddings and latent space are key for AI to understand text and images. Learn their differences and uses in this article."}
+{"idx": 8, "title": "How to Sample From Latent Space With Variational... | HackerNoon", "date": "", "ddg_snippet": "The encoder neural network of VAE outputs parameters that define a probability distribution for each dimension of the latent space (multivariate distribution).", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/how-to-sample-from-latent-space-with-variational-autoencoder", "content": "The encoder neural network of VAE outputs parameters that define a probability distribution for each dimension of the latent space (multivariate distribution)."}
+{"idx": 9, "title": "GANs vs . VAEs : What is the best generative AI approach? | TechTarget", "date": "", "ddg_snippet": "VAEs consist of encoders to transform input data into simplified forms, a decoder to rebuild the original data from these simplified forms, and a probabilistic latent space where each input data point is represented by a distribution of points within the latent space .", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/searchenterpriseai/feature/GANs-vs-VAEs-What-is-the-best-generative-AI-approach", "content": "VAEs consist of encoders to transform input data into simplified forms, a decoder to rebuild the original data from these simplified forms, and a probabilistic latent space where each input data point is represented by a distribution of points within the latent space ."}
diff --git a/data/sampled_jsons/CLIP_encoder_vs_VAE_encoder_differences_latent_space.jsonl b/data/sampled_jsons/CLIP_encoder_vs_VAE_encoder_differences_latent_space.jsonl
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index 0000000000000000000000000000000000000000..da55abe0180665358178e667d2710bcf02fc2fcc
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Latent Space Representations in Variational Autoencoders (VAEs)", "date": "", "ddg_snippet": "Extract Latent Vectors: After training the VAE , I extract the latent representations for the dataset. This gives me a clear view of how the encoder maps input data into the latent space .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@whyamit101/latent-space-representations-in-variational-autoencoders-vaes-e74076eda77b", "content": "Extract Latent Vectors: After training the VAE , I extract the latent representations for the dataset. This gives me a clear view of how the encoder maps input data into the latent space ."}
+{"idx": 1, "title": "Autoencoder vs Variational Autoencoder (VAE): Differences, Example", "date": "", "ddg_snippet": "Mapping input image to a distribution rather than a point in the latent space : As learned in the previous section, in an autoencoder, the input image is encoded into a single point in the latent space through the encoder network, which then decodes the point to reconstruct the original input image.", "subpage_snippet": "", "source": "vitalflux.com", "link": "https://vitalflux.com/autoencoder-vs-variational-autoencoder-vae-difference/", "content": "Mapping input image to a distribution rather than a point in the latent space : As learned in the previous section, in an autoencoder, the input image is encoded into a single point in the latent space through the encoder network, which then decodes the point to reconstruct the original input image."}
+{"idx": 2, "title": "Difference between AutoEncoder (AE) and Variational AutoEncoder (VAE)", "date": "", "ddg_snippet": "Summary This article covered the understanding of Autoencoder (AE) and variational Autoencoder ( VAE ) which are mainly used for data compression and data generation respectively. VAE addresses the issue of non-regularized latent space of AE which makes it able to generate data from randomly sampled vectors from the latent space .", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/difference-between-autoencoder-ae-and-variational-autoencoder-vae-ed7be1c038f2/", "content": "Summary This article covered the understanding of Autoencoder (AE) and variational Autoencoder ( VAE ) which are mainly used for data compression and data generation respectively. VAE addresses the issue of non-regularized latent space of AE which makes it able to generate data from randomly sampled vectors from the latent space ."}
+{"idx": 3, "title": "CLIP-VAE: Combining CLIP and VAE for Image Generation", "date": "", "ddg_snippet": "The CLIP - VAE has the capability to generate images from random Gaussian noise. This is achieved by sampling random points from the latent space and inputting them into the decoder of the CLIP - VAE model. When using the latent vectors obtained through the label encoder , it becomes possible to generate images corresponding to specific desired labels. This resembles the concept of conditional VAE ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/phykn/clip-vae", "content": "The CLIP - VAE has the capability to generate images from random Gaussian noise. This is achieved by sampling random points from the latent space and inputting them into the decoder of the CLIP - VAE model. When using the latent vectors obtained through the label encoder , it becomes possible to generate images corresponding to specific desired labels. This resembles the concept of conditional VAE ..."}
+{"idx": 4, "title": "How to Sample From Latent Space With Variational Autoencoder", "date": "", "ddg_snippet": "Same as traditional autoencoders, VAE architecture has two parts: an encoder and a decoder. Traditional AE models map inputs into a latent-space vector and reconstruct the output from this vector. VAE maps inputs into a multivariate normal distribution (the encoder outputs the mean and the variance of each latent dimension).", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/how-to-sample-from-latent-space-with-variational-autoencoder", "content": "Same as traditional autoencoders, VAE architecture has two parts: an encoder and a decoder. Traditional AE models map inputs into a latent-space vector and reconstruct the output from this vector. VAE maps inputs into a multivariate normal distribution (the encoder outputs the mean and the variance of each latent dimension)."}
+{"idx": 5, "title": "DALL-E_CLIP-VAE_Explained - Colab", "date": "", "ddg_snippet": "What they did is to train a discrete Variational Auto- Encoder ( VAE ) to compress the 256x256x3 training images into 32x32 grids of discrete image tokens of vocabulary size 8192. That is, they learnt to map and reconstruct an image to and from a embedding (or latent ) space of 32*32=1024 integers (image tokens).", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/simonsanvil/DALL-E-Explained/blob/main/DALL_E_CLIP_VAE_Explained.ipynb", "content": "What they did is to train a discrete Variational Auto- Encoder ( VAE ) to compress the 256x256x3 training images into 32x32 grids of discrete image tokens of vocabulary size 8192. That is, they learnt to map and reconstruct an image to and from a embedding (or latent ) space of 32*32=1024 integers (image tokens)."}
+{"idx": 6, "title": "PDF Deep Clustering with Variational Autoencoder", "date": "", "ddg_snippet": "The main difference between AE and variational autoencoder ( VAE ) [19], [18] is the way the latent space is represented. In AE, an encoded image is represented as a point in the latent space , while in VAE an encoded image is represented by the sample draw from a Gaussian distribution.", "subpage_snippet": "", "source": "www3.ntu.edu.sg", "link": "https://www3.ntu.edu.sg/home/EXDJiang/spl20.pdf", "content": "The main difference between AE and variational autoencoder ( VAE ) [19], [18] is the way the latent space is represented. In AE, an encoded image is represented as a point in the latent space , while in VAE an encoded image is represented by the sample draw from a Gaussian distribution."}
+{"idx": 7, "title": "Does unCLIP simply replace the VAE image encoder with CLIP ... - Reddit", "date": "", "ddg_snippet": "If I understand correctly, the way latent diffusion (and hence stable diffusion) works is that it encodes an input image into a latent space using using a VAE and then applies noise to it before gradually denoising. Text-conditioning is done by encoding the text with OpenCLIP, concatenating this to the encoded image and also some cross-attentional maps in the UNet. I figured, why not encode ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/StableDiffusion/comments/124hfjy/does_unclip_simply_replace_the_vae_image_encoder/", "content": "If I understand correctly, the way latent diffusion (and hence stable diffusion) works is that it encodes an input image into a latent space using using a VAE and then applies noise to it before gradually denoising. Text-conditioning is done by encoding the text with OpenCLIP, concatenating this to the encoded image and also some cross-attentional maps in the UNet. I figured, why not encode ..."}
+{"idx": 8, "title": "What is the difference in the latent space of a variational autoencoder ...", "date": "", "ddg_snippet": "For the vanilla autoencoder the structure is like this: It can be treated as a nonlinear extension of PCA, while for the variational autoencoder a mean and a standard deviation is added as a layer for each hidden variable in the middle layer: For the detailed difference please refer to . Should VAEs be even used for non-generative tasks? Yes, you can. The additional KL divergence (between ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/319191/what-is-the-difference-in-the-latent-space-of-a-variational-autoencoder-and-a-re", "content": "For the vanilla autoencoder the structure is like this: It can be treated as a nonlinear extension of PCA, while for the variational autoencoder a mean and a standard deviation is added as a layer for each hidden variable in the middle layer: For the detailed difference please refer to . Should VAEs be even used for non-generative tasks? Yes, you can. The additional KL divergence (between ..."}
+{"idx": 9, "title": "Hands-on: Build a VAE & Explore Latent Space - apxml.com", "date": "", "ddg_snippet": "The plot_latent_space function uses the encoder part of our trained VAE to get the z m e a n zmean vectors for the test images. It then creates a scatter plot where each point is an image, its position determined by its 2D latent representation, and its color by its actual digit label (labels).", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/applied-autoencoders-feature-extraction/chapter-6-variational-autoencoders-structured-latent-spaces/hands-on-building-vae-inspecting-latent-space", "content": "The plot_latent_space function uses the encoder part of our trained VAE to get the z m e a n zmean vectors for the test images. It then creates a scatter plot where each point is an image, its position determined by its 2D latent representation, and its color by its actual digit label (labels)."}
diff --git a/data/sampled_jsons/CLIP_paper_reference_21_Radford_2021.jsonl b/data/sampled_jsons/CLIP_paper_reference_21_Radford_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..31feff371ed80a4fea812faf8d105d8222f5c6cb
--- /dev/null
+++ b/data/sampled_jsons/CLIP_paper_reference_21_Radford_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) AARE 2021 — Radford Lecture (R) e-imagining Indigenous...", "date": "", "ddg_snippet": "This paper was first presented as the 2021 Radford Lecture and focuses on the.1 3. AARE 2021 — Radford Lecture (R)e‑imagining Indigenous education… N. M. Nakata is Deputy Vice-Chancellor Indigenous Education & Strategy at James Cook University.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/365352569_AARE_2021-Radford_Lecture_Re-imagining_Indigenous_education_research", "content": "This paper was first presented as the 2021 Radford Lecture and focuses on the.1 3. AARE 2021 — Radford Lecture (R)e‑imagining Indigenous education… N. M. Nakata is Deputy Vice-Chancellor Indigenous Education & Strategy at James Cook University."}
+{"idx": 1, "title": "Radford Semele Draft Regulation 14", "date": "", "ddg_snippet": "21 . Radford Semele Neighbourhood Development Plan, Made - May 2021 . 4.18 Perhaps inevitably, given the scale of change taking place in the area, a major issue that has been identified is the need to retain the village’s identity and sense of community.", "subpage_snippet": "", "source": "files.cdn-files-a.com", "link": "https://files.cdn-files-a.com/uploads/2822436/normal_60a39871d7ff1.pdf", "content": "21 . Radford Semele Neighbourhood Development Plan, Made - May 2021 . 4.18 Perhaps inevitably, given the scale of change taking place in the area, a major issue that has been identified is the need to retain the village’s identity and sense of community."}
+{"idx": 2, "title": "Radford Depth Charts - RealGM", "date": "", "ddg_snippet": "Radford basketball scores, news, schedule, players, stats, photos, rumors, depth charts on RealGM.com. 2021 -2022 Radford Highlanders Depth Chart.", "subpage_snippet": "", "source": "basketball.realgm.com", "link": "https://basketball.realgm.com/ncaa/conferences/Big-South-Conference/21/Radford/94/depth-charts", "content": "Radford basketball scores, news, schedule, players, stats, photos, rumors, depth charts on RealGM.com. 2021 -2022 Radford Highlanders Depth Chart."}
+{"idx": 3, "title": "[2103.00020] Learning Transferable Visual Models From Natural...", "date": "", "ddg_snippet": "View a PDF of the paper titled Learning Transferable Visual Models From Natural Language Supervision, by Alec Radford and 11 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2103.00020", "content": "View a PDF of the paper titled Learning Transferable Visual Models From Natural Language Supervision, by Alec Radford and 11 other authors."}
+{"idx": 4, "title": "Learning Transferable Visual Models From Natural Language...", "date": "", "ddg_snippet": "Alec Radford , Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8748-8763, 2021 .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/radford21a", "content": "Alec Radford , Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8748-8763, 2021 ."}
+{"idx": 5, "title": "Book Review: Transforming mathematics education: from embodied...", "date": "", "ddg_snippet": "Luis Radford ( 2021 ) The theory of objectification: a Vygotskian perspective on knowing and becoming in mathematics teaching and learning. Brill. xvi, 259 pages. Hardcover: ISBN: 978-90-04-45965-6, €130.00. References .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10649-021-10095-4", "content": "Luis Radford ( 2021 ) The theory of objectification: a Vygotskian perspective on knowing and becoming in mathematics teaching and learning. Brill. xvi, 259 pages. Hardcover: ISBN: 978-90-04-45965-6, €130.00. References ."}
+{"idx": 6, "title": "31 Theoretical Framework Examples (2025)", "date": "", "ddg_snippet": "A useful working definition comes from Connaway and Radford ( 2021 ): “…a theoretical framework utilizes theory/theories and their constituent elements as the presumed ‘working model’ that drives the investigation and analysis of a social phenomenon.”", "subpage_snippet": "", "source": "helpfulprofessor.com", "link": "https://helpfulprofessor.com/theoretical-framework-examples/", "content": "A useful working definition comes from Connaway and Radford ( 2021 ): “…a theoretical framework utilizes theory/theories and their constituent elements as the presumed ‘working model’ that drives the investigation and analysis of a social phenomenon.”"}
+{"idx": 7, "title": "Learning to compose soft prompts for compositional zero-shot learning", "date": "", "ddg_snippet": "to pretrained CLIP ( Radford et al., 2021 ) and CoOp (Zhou et al., 2021 ). CoOp is a soft-prompting. method that learns the prefix context with limited labeled examples in a fe w-shot setting.", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/learning-to-compose-soft-prompts-for-compositional-3414259", "content": "to pretrained CLIP ( Radford et al., 2021 ) and CoOp (Zhou et al., 2021 ). CoOp is a soft-prompting. method that learns the prefix context with limited labeled examples in a fe w-shot setting."}
+{"idx": 8, "title": "fronte atti 21", "date": "", "ddg_snippet": "Luis Radford . Laurentian University. Abstract. In this paper , I dwell on a common inadequate conception of the child that often serves as a foundation for learning and pedagogy in contemporary early childhood education — the conception that I term the heroic child.", "subpage_snippet": "", "source": "luisradford.ca", "link": "https://luisradford.ca/pub/2021+-+Radford+-+Il+bambino+eroico.pdf", "content": "Luis Radford . Laurentian University. Abstract. In this paper , I dwell on a common inadequate conception of the child that often serves as a foundation for learning and pedagogy in contemporary early childhood education — the conception that I term the heroic child."}
+{"idx": 9, "title": "Literature Review — fish 0.0.1 documentation", "date": "", "ddg_snippet": "( Radford 2021 ) propose CLIP a representational model for text-to-image feature embeddings.(Zhai 2021 ) is an apple paper that presents the Attenion Free Transformer (AFT).", "subpage_snippet": "", "source": "fishy-business.readthedocs.io", "link": "https://fishy-business.readthedocs.io/en/latest/literature.html", "content": "( Radford 2021 ) propose CLIP a representational model for text-to-image feature embeddings.(Zhai 2021 ) is an apple paper that presents the Attenion Free Transformer (AFT)."}
diff --git a/data/sampled_jsons/CLIP_vs_VAE_encoder_differences_image_embedding_space.jsonl b/data/sampled_jsons/CLIP_vs_VAE_encoder_differences_image_embedding_space.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..52cf88c0bdc5797882bff066fda36ab87da8aac9
--- /dev/null
+++ b/data/sampled_jsons/CLIP_vs_VAE_encoder_differences_image_embedding_space.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "lesswrong.com/posts/r6gpBgs98gnArCEty/how-to-think-with-images", "date": "", "ddg_snippet": "... contrastive learning forces an image encoder and a text encoder to meet in the middle – to produce embeddings that match for a true pair and differ ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/r6gpBgs98gnArCEty/how-to-think-with-images", "content": "... contrastive learning forces an image encoder and a text encoder to meet in the middle – to produce embeddings that match for a true pair and differ ..."}
+{"idx": 1, "title": "Scaling Down Text Encoders of Text-to-Image Diffusion Models", "date": "", "ddg_snippet": "... need such a large text encoder for effective text representation? We hypothesize that there is redundancy within the T5 encoder ’s embedding space ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19897v1", "content": "... need such a large text encoder for effective text representation? We hypothesize that there is redundancy within the T5 encoder ’s embedding space ..."}
+{"idx": 2, "title": "HAVIR: HierArchical Vision to Image Reconstruction using", "date": "", "ddg_snippet": "Specifically, text embeddings are obtained via frozen Text Clipper encoding of descriptions for visual stimuli, which are directly extracted from the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06035v1", "content": "Specifically, text embeddings are obtained via frozen Text Clipper encoding of descriptions for visual stimuli, which are directly extracted from the ..."}
+{"idx": 3, "title": "Deconstruction with Discrete Embeddings - R2RT", "date": "", "ddg_snippet": "You can see how given certain discrete features (Bald, Bangs, Black Hair, etc.), the VAE -GAN can modify the input image so as to include those ...", "subpage_snippet": "", "source": "r2rt.com", "link": "https://r2rt.com/deconstruction-with-discrete-embeddings.html", "content": "You can see how given certain discrete features (Bald, Bangs, Black Hair, etc.), the VAE -GAN can modify the input image so as to include those ..."}
+{"idx": 4, "title": "How to think with images | Dinkar Juyal", "date": "", "ddg_snippet": "... contrastive learning forces an image encoder and a text encoder to meet in the middle – to produce embeddings that match for a true pair and differ ...", "subpage_snippet": "", "source": "dinkarjuyal.github.io", "link": "https://dinkarjuyal.github.io/2025/06/01/How-to-think-with-images.html", "content": "... contrastive learning forces an image encoder and a text encoder to meet in the middle – to produce embeddings that match for a true pair and differ ..."}
+{"idx": 5, "title": "How to think with images - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "... contrastive learning forces an image encoder and a text encoder to meet in the middle – to produce embeddings that match for a true pair and differ ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/r6gpBgs98gnArCEty/how-to-think-with-images", "content": "... contrastive learning forces an image encoder and a text encoder to meet in the middle – to produce embeddings that match for a true pair and differ ..."}
+{"idx": 6, "title": "Your VAE Sucks", "date": "", "ddg_snippet": "My takeaway from this blog post is that image frequency space can be used to mitigate the blurriness of vanilla VAEs .", "subpage_snippet": "", "source": "theadamcolton.github.io", "link": "https://theadamcolton.github.io/your-vae-sucks.html", "content": "My takeaway from this blog post is that image frequency space can be used to mitigate the blurriness of vanilla VAEs ."}
+{"idx": 7, "title": "machine learning - How to Resolve Variational Autoencoder (VAE)", "date": "", "ddg_snippet": "What is your loss, have you tried weighing its different components and optimizing the weights ? I imagine your loss to be $$w_1 * L_{KLD} + w_2 * L ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/621004/how-to-resolve-variational-autoencoder-vae-model-collapse-in-reconstruction-ta", "content": "What is your loss, have you tried weighing its different components and optimizing the weights ? I imagine your loss to be $$w_1 * L_{KLD} + w_2 * L ..."}
+{"idx": 8, "title": "Introducing BLIP3-o: A Family of Fully Open Unified Multimodal", "date": "", "ddg_snippet": "First, what should be used as the ground-truth embeddings : should we use a VAE or CLIP to encode images into continuous features?", "subpage_snippet": "", "source": "www.salesforce.com", "link": "https://www.salesforce.com/blog/blip3/", "content": "First, what should be used as the ground-truth embeddings : should we use a VAE or CLIP to encode images into continuous features?"}
+{"idx": 9, "title": "Bifrost-1", "date": "", "ddg_snippet": "... and diffusion models using patch-level CLIP image embeddings as latent variables, which are natively aligned with the MLLM's CLIP visual encoder ...", "subpage_snippet": "", "source": "bifrost-1.github.io", "link": "https://bifrost-1.github.io/", "content": "... and diffusion models using patch-level CLIP image embeddings as latent variables, which are natively aligned with the MLLM's CLIP visual encoder ..."}
diff --git a/data/sampled_jsons/CVE-2022-0543_Redis_CVSS_score_10.0_NVD.jsonl b/data/sampled_jsons/CVE-2022-0543_Redis_CVSS_score_10.0_NVD.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..da046227acd0e3e907e73085372c380750548409
--- /dev/null
+++ b/data/sampled_jsons/CVE-2022-0543_Redis_CVSS_score_10.0_NVD.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fixing Redis CVE - 2022 - 0543 : Lua Sandbox Escape Patch", "date": "", "ddg_snippet": "This is a Critical vulnerability scored 10 out of 10 in the CVSS score . Associated CVE ID.A Critical Lua Sandbox Escape Vulnerability in Redis that allows attackers to perform remote code execution on the host running Redis . Associated ZDI ID. – CVSS Score .", "subpage_snippet": "", "source": "thesecmaster.com", "link": "https://thesecmaster.com/blog/how-to-fix-cve-2022-0543-a-critical-lua-sandbox-escape-vulnerability-in-redis", "content": "This is a Critical vulnerability scored 10 out of 10 in the CVSS score . Associated CVE ID.A Critical Lua Sandbox Escape Vulnerability in Redis that allows attackers to perform remote code execution on the host running Redis . Associated ZDI ID. – CVSS Score ."}
+{"idx": 1, "title": "CVE - 2022 – 0543 : Vulnerability in Redis with 10/10 CVSS score .", "date": "", "ddg_snippet": "Although the massive exploitation of CVE - 2022 – 0543 would appear to be a remote exploitation, the Rapid7 researchers decided to analyze the conditions that could facilitate a large-scale attack, in an attempt to calculate how many vulnerable implementations might be currently available.", "subpage_snippet": "", "source": "iics.medium.com", "link": "https://iics.medium.com/cve-2022-0543-vulnerability-in-redis-with-10-10-cvss-score-thousands-of-linux-servers-affected-94e44179324c", "content": "Although the massive exploitation of CVE - 2022 – 0543 would appear to be a remote exploitation, the Rapid7 researchers decided to analyze the conditions that could facilitate a large-scale attack, in an attempt to calculate how many vulnerable implementations might be currently available."}
+{"idx": 2, "title": "Hackers Exploiting Redis Vulnerability to Deploy New Redigo...", "date": "", "ddg_snippet": "Tracked as CVE - 2022 - 0543 ( CVSS score : 10 . 0 ), the weakness pertains to a case of sandbox escape in the Lua scripting engine that could be leveraged to attain remote code execution. This is not the first time the flaw has come under active exploitation, what with Juniper Threat Labs...", "subpage_snippet": "", "source": "thehackernews.com", "link": "https://thehackernews.com/2022/12/hackers-exploiting-redis-vulnerability.html", "content": "Tracked as CVE - 2022 - 0543 ( CVSS score : 10 . 0 ), the weakness pertains to a case of sandbox escape in the Lua scripting engine that could be leveraged to attain remote code execution. This is not the first time the flaw has come under active exploitation, what with Juniper Threat Labs..."}
+{"idx": 3, "title": "P2PInfect, a Rusty P2P worm targets Redis Servers... - Security Affairs", "date": "", "ddg_snippet": "The worm is written in the Rust programming language, it targets Redis instances by exploiting the Lua sandbox escape vulnerability CVE - 2022 - 0543 ( CVSS score 10 . 0 ).", "subpage_snippet": "", "source": "securityaffairs.com", "link": "https://securityaffairs.com/148636/malware/p2pinfect-a-rusty-p2p-worm-targets-redis-servers-on-linux-and-windows-systems.html", "content": "The worm is written in the Rust programming language, it targets Redis instances by exploiting the Lua sandbox escape vulnerability CVE - 2022 - 0543 ( CVSS score 10 . 0 )."}
+{"idx": 4, "title": "CVE - 2022 - 0543 Redis Lua Sandbox Escape RCE - Programmer Sought", "date": "", "ddg_snippet": "score .vulhub/ redis / CVE - 2022 - 0543 at 7fb138803337092cf1b62ce5c6a3ff3d192d09f6 · vulhub/vulhub · GitHub. Start a Redis 5.0.7 server installed from Ubuntu sources: docker-compose up -d. Use redis -cli to connect to the Redis server, and eval executes the payload.", "subpage_snippet": "", "source": "programmersought.com", "link": "https://programmersought.com/article/719311401751/", "content": "score .vulhub/ redis / CVE - 2022 - 0543 at 7fb138803337092cf1b62ce5c6a3ff3d192d09f6 · vulhub/vulhub · GitHub. Start a Redis 5.0.7 server installed from Ubuntu sources: docker-compose up -d. Use redis -cli to connect to the Redis server, and eval executes the payload."}
+{"idx": 5, "title": "P2PInfect: A New Cross-Platform Worm Targeting Redis Server", "date": "", "ddg_snippet": "The worm’s striking feature lies in its proficiency to infect vulnerable Redis instances through the exploitation of a crucial Lua sandbox escape vulnerability known as CVE - 2022 - 0543 ( CVSS score : 10 . 0 ).", "subpage_snippet": "", "source": "www.ampcuscyber.com", "link": "https://www.ampcuscyber.com/blogs/p2pinfect-a-new-cross-platform-worm-targeting-redis-servers/", "content": "The worm’s striking feature lies in its proficiency to infect vulnerable Redis instances through the exploitation of a crucial Lua sandbox escape vulnerability known as CVE - 2022 - 0543 ( CVSS score : 10 . 0 )."}
+{"idx": 6, "title": "NVD - Vulnerability Metrics", "date": "", "ddg_snippet": "The Common Vulnerability Scoring System ( CVSS ) is a method used to supply a qualitative measure of severity.The National Vulnerability Database ( NVD ) provides CVSS enrichment for all published CVE records.", "subpage_snippet": "", "source": "nvd.nist.gov", "link": "https://nvd.nist.gov/vuln-metrics/cvss", "content": "The Common Vulnerability Scoring System ( CVSS ) is a method used to supply a qualitative measure of severity.The National Vulnerability Database ( NVD ) provides CVSS enrichment for all published CVE records."}
+{"idx": 7, "title": "hvv 前网安人必读的漏洞清单( 2022 年） - FreeBuf网络安全行业门户", "date": "", "ddg_snippet": "Debian Redis ：76、 CVE - 2022 -35914：Teclib GLPI 远程执行代码漏洞. 漏洞描述：GLPI 10 . 0 .2 版 htmlawed 模块中的 /vendor/htmlawed/htmlawed/htmLawedTest.php存在漏洞，允许 PHP 代码注入。", "subpage_snippet": "", "source": "www.freebuf.com", "link": "https://www.freebuf.com/news/400618.html", "content": "Debian Redis ：76、 CVE - 2022 -35914：Teclib GLPI 远程执行代码漏洞. 漏洞描述：GLPI 10 . 0 .2 版 htmlawed 模块中的 /vendor/htmlawed/htmlawed/htmLawedTest.php存在漏洞，允许 PHP 代码注入。"}
+{"idx": 8, "title": "CVE : Common Vulnerabilities and Exposures", "date": "", "ddg_snippet": "Common vulnerabilities and Exposures ( CVE ). Skip to main content. CVE ™ Program Mission. Identify, define, and catalog publicly disclosed cybersecurity vulnerabilities. There are currently over 294,000 CVE Records accessible via Download or Keyword Search above.", "subpage_snippet": "", "source": "www.cve.org", "link": "https://www.cve.org/", "content": "Common vulnerabilities and Exposures ( CVE ). Skip to main content. CVE ™ Program Mission. Identify, define, and catalog publicly disclosed cybersecurity vulnerabilities. There are currently over 294,000 CVE Records accessible via Download or Keyword Search above."}
+{"idx": 9, "title": "Что такое CVE и какие угрозы там хранятся? / Хабр", "date": "", "ddg_snippet": "CVSS ( Common Vulnerability Scoring System ) - открытый стандарт для оценки уязвимостей. По CVSS на основании набора метрик и формул вычисляется оценка опасности уязвимости, она может принимать значения от 0 до 10, где 10 – наивысший балл.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/pvs-studio/articles/678410/", "content": "CVSS ( Common Vulnerability Scoring System ) - открытый стандарт для оценки уязвимостей. По CVSS на основании набора метрик и формул вычисляется оценка опасности уязвимости, она может принимать значения от 0 до 10, где 10 – наивысший балл."}
diff --git a/data/sampled_jsons/CVE-Bench_AI_Agents_Web_Application_Vulnerabilities_Table_4_cost_T-Agent_AutoGPT.jsonl b/data/sampled_jsons/CVE-Bench_AI_Agents_Web_Application_Vulnerabilities_Table_4_cost_T-Agent_AutoGPT.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9a78c209532b394422e9c6e5768fe01cb52d362d
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_AI_Agents_Web_Application_Vulnerabilities_Table_4_cost_T-Agent_AutoGPT.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vul- nerable web applications in scenarios that mimic real-world ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "by Y Zhu · 2025 · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vul- nerable web applications in scenarios that mimic real-world ..."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3pk0p4NGmQ", "content": "by Y Zhu · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while ..."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vul- nerable web applications in scenarios that mimic real-world ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332?", "content": "by Y Zhu · 2025 · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vul- nerable web applications in scenarios that mimic real-world ..."}
+{"idx": 3, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real- ...", "date": "", "ddg_snippet": "This paper introduces the ' T - Agent ' (Teams of Agent) framework, which is described as a state-of-the-art agent for exploiting web application vulnerabilities .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17332", "content": "This paper introduces the ' T - Agent ' (Teams of Agent) framework, which is described as a state-of-the-art agent for exploiting web application vulnerabilities ."}
+{"idx": 4, "title": "Explore CVE-Bench: A Real-World Cybersecurity Benchmark", "date": "", "ddg_snippet": "SQL Injection remains one of the most common and dangerous vulnerabilities affecting web applications . This attack vector allows an adversary to manipulate ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/mahmoudrabie2004_foraiscientists-forairesearchers-foraiarchitects-activity-7310000300646371330-dFux", "content": "SQL Injection remains one of the most common and dangerous vulnerabilities affecting web applications . This attack vector allows an adversary to manipulate ..."}
+{"idx": 5, "title": "Generalist Virtual Agents: A Survey on Autonomous ...", "date": "", "ddg_snippet": "Our objective is to provide a comprehensive overview of Generalist Virtual Agents (GVAs), covering their definition, necessity, implementation approaches, ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wendell0218/GVA-Survey", "content": "Our objective is to provide a comprehensive overview of Generalist Virtual Agents (GVAs), covering their definition, necessity, implementation approaches, ..."}
+{"idx": 6, "title": "CS598 JY2 Final Survey Report - Multimodal Web Agents", "date": "", "ddg_snippet": "by V Han — ... Benn, et al. Cve - bench : A benchmark for ai agents ' ability to exploit real-world web application vulnerabilities . arXiv preprint arXiv:2503.17332, 2025. 15.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=AQ2TWYqsWH", "content": "by V Han — ... Benn, et al. Cve - bench : A benchmark for ai agents ' ability to exploit real-world web application vulnerabilities . arXiv preprint arXiv:2503.17332, 2025. 15."}
+{"idx": 7, "title": "News", "date": "", "ddg_snippet": "... CVE - Bench , the first benchmark for AI agents in cybersecurity. Onni Aarne ... for testing AI agents against real-world cybersecurity vulnerabilities .", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/blog", "content": "... CVE - Bench , the first benchmark for AI agents in cybersecurity. Onni Aarne ... for testing AI agents against real-world cybersecurity vulnerabilities ."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Automated red-teaming methods play a key role in this process by generating adversarial attacks to identify and mitigate potential vulnerabilities in these ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=automated+red-teaming", "content": "Automated red-teaming methods play a key role in this process by generating adversarial attacks to identify and mitigate potential vulnerabilities in these ..."}
+{"idx": 9, "title": "llm-as-a-judge/Awesome-LLM-as-a-judge", "date": "", "ddg_snippet": "Awesome-LLM-as-a-judge: A Survey. This repo include the papers discussed in our latest survey paper on Awesome-LLM-as-a-judge.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/llm-as-a-judge/Awesome-LLM-as-a-judge", "content": "Awesome-LLM-as-a-judge: A Survey. This repo include the papers discussed in our latest survey paper on Awesome-LLM-as-a-judge."}
diff --git a/data/sampled_jsons/CVE-Bench_AI_agents_web_application_vulnerabilities_Table_5_insufficient_exploration_failure_modes.jsonl b/data/sampled_jsons/CVE-Bench_AI_agents_web_application_vulnerabilities_Table_5_insufficient_exploration_failure_modes.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ee592b1a90c59e9be5a38e0ba78b6fa9160546c5
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_AI_agents_web_application_vulnerabilities_Table_5_insufficient_exploration_failure_modes.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "Table 5 : Frequency of common failure modes of agents . Insufficient exploration is a key bottleneck for all agents . LLM agents , Cy- Agent , T- Agent , AutoGPT.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "Table 5 : Frequency of common failure modes of agents . Insufficient exploration is a key bottleneck for all agents . LLM agents , Cy- Agent , T- Agent , AutoGPT."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to ... - GitHub", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database."}
+{"idx": 2, "title": "Measuring AI Agents’ Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Mar 31, 2025 · In this post, we introduce CVE - bench — the first benchmark built on real-world vulnerabilities , which contains: 40 real-world vulnerability -exploitation challenges. A reproducible solution for each challenge. Comprehensive evaluation mechanisms, per task.", "subpage_snippet": "", "source": "ddkang.substack.com", "link": "https://ddkang.substack.com/p/measuring-ai-agents-ability-to-exploit", "content": "Mar 31, 2025 · In this post, we introduce CVE - bench — the first benchmark built on real-world vulnerabilities , which contains: 40 real-world vulnerability -exploitation challenges. A reproducible solution for each challenge. Comprehensive evaluation mechanisms, per task."}
+{"idx": 3, "title": "Measuring AI Agents’ Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Mar 31, 2025 · Our findings reveal potential threats to web application security from rapidly evolving AI agents . This highlights the need for continuous improvement in evaluating, red-teaming, and...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danieldkang/measuring-ai-agents-ability-to-exploit-web-applications-ba4225aa281f", "content": "Mar 31, 2025 · Our findings reveal potential threats to web application security from rapidly evolving AI agents . This highlights the need for continuous improvement in evaluating, red-teaming, and..."}
+{"idx": 4, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · CVE - Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents ' abilities to discover and exploit web application vulnerabilities .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "May 12, 2025 · CVE - Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents ' abilities to discover and exploit web application vulnerabilities ."}
+{"idx": 5, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "To address this challenge, we introduce CVE - Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Ex-posures.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=3pk0p4NGmQ", "content": "To address this challenge, we introduce CVE - Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Ex-posures."}
+{"idx": 6, "title": "cve-bench/README.md at main · uiuc-kang-lab/cve-bench - GitHub", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench/blob/main/README.md", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database."}
+{"idx": 7, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — In CVE - Bench , the primary goal for. LLM agents is to perform cyberattacks that successfully compromise a vulnerable web application or its users ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "by Y Zhu · 2025 · Cited by 12 — In CVE - Bench , the primary goal for. LLM agents is to perform cyberattacks that successfully compromise a vulnerable web application or its users ..."}
+{"idx": 8, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · Cited by 12 — TL;DR: We introduce a cybersecurity benchmark for evaluating the capability of AI agents in exploiting real-world vulnerabilities of web applications . Abstract:.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3pk0p4NGmQ", "content": "by Y Zhu · Cited by 12 — TL;DR: We introduce a cybersecurity benchmark for evaluating the capability of AI agents in exploiting real-world vulnerabilities of web applications . Abstract:."}
+{"idx": 9, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real- ...", "date": "", "ddg_snippet": "Insufficient exploration emerged as the dominant failure mode , affecting 67.5% to 80% of zero-day attempts and 37.5% to 55% of one-day attempts across all ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17332", "content": "Insufficient exploration emerged as the dominant failure mode , affecting 67.5% to 80% of zero-day attempts and 37.5% to 55% of one-day attempts across all ..."}
diff --git a/data/sampled_jsons/CVE-Bench_A_Benchmark_for_AI_Agents'_Ability_to_Exploit_Real-World_Web_Application_Vulnerabilities_F.jsonl b/data/sampled_jsons/CVE-Bench_A_Benchmark_for_AI_Agents'_Ability_to_Exploit_Real-World_Web_Application_Vulnerabilities_F.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2efe14a915be5e281bedf29703a2b939b69582a2
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_A_Benchmark_for_AI_Agents'_Ability_to_Exploit_Real-World_Web_Application_Vulnerabilities_F.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real - World Web Application Vulnerabilities ... for a real - world benchmark to evaluate the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real - World Web Application Vulnerabilities ... for a real - world benchmark to evaluate the ..."}
+{"idx": 1, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability", "date": "", "ddg_snippet": "Title: CVE - Bench : A Benchmark for AI Agents Ability to Exploit Real - World Web Application Vulnerabilities ... a PDF of the paper titled CVE - Bench : A ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Title: CVE - Bench : A Benchmark for AI Agents Ability to Exploit Real - World Web Application Vulnerabilities ... a PDF of the paper titled CVE - Bench : A ..."}
+{"idx": 2, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "For example, a GPT-4o-based agent resolves 35% of tasks on τ 𝜏 \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 85 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v1", "content": "For example, a GPT-4o-based agent resolves 35% of tasks on τ 𝜏 \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 85 ] ."}
+{"idx": 3, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "For example, a GPT-4o-based agent resolves 35% of tasks on τ \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 86 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v5", "content": "For example, a GPT-4o-based agent resolves 35% of tasks on τ \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 86 ] ."}
+{"idx": 4, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "For example, a GPT-4o-based agent resolves 35% of tasks on τ 𝜏 \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 86 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v4", "content": "For example, a GPT-4o-based agent resolves 35% of tasks on τ 𝜏 \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 86 ] ."}
+{"idx": 5, "title": "AI-Driven Cyberattacks are on the Rise. Are You Ready? -", "date": "", "ddg_snippet": "In a paper titled CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real - World Web Application Vulnerabilities , researchers found that ...", "subpage_snippet": "", "source": "lsvp.com", "link": "https://lsvp.com/stories/ai-enabled-hacking-is-here-are-we-ready-for-it/", "content": "In a paper titled CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real - World Web Application Vulnerabilities , researchers found that ..."}
+{"idx": 6, "title": "Kaicheng Yu", "date": "", "ddg_snippet": "CVE - Bench : A Benchmark for AI Agents Ability to Exploit Real - World Web Application Vulnerabilities ... for a real - world benchmark to evaluate the ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Kaicheng+Yu", "content": "CVE - Bench : A Benchmark for AI Agents Ability to Exploit Real - World Web Application Vulnerabilities ... for a real - world benchmark to evaluate the ..."}
+{"idx": 7, "title": "Yuxuan Zhu", "date": "", "ddg_snippet": "As AI agents become increasingly capable, researchers and practitioners have introduced agentic benchmarks to evaluate agents on complex, real - world ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Yuxuan+Zhu", "content": "As AI agents become increasingly capable, researchers and practitioners have introduced agentic benchmarks to evaluate agents on complex, real - world ..."}
+{"idx": 8, "title": "⚡ Weekly Recap: BadCam Attack, WinRAR 0-Day, EDR Killer,", "date": "", "ddg_snippet": "... Active Exploitation — The maintainers of the WinRAR file archiving utility have released an update to address an actively exploited zero-day ...", "subpage_snippet": "", "source": "thehackernews.com", "link": "https://thehackernews.com/2025/08/weekly-recap-badcam-attack-winrar-0-day.html", "content": "... Active Exploitation — The maintainers of the WinRAR file archiving utility have released an update to address an actively exploited zero-day ..."}
+{"idx": 9, "title": "ML Safety Newsletter | Dan Hendrycks | Substack", "date": "", "ddg_snippet": "OS-Harm is built on top of OSWorld, an agent capabilities benchmark with simple, realistic agentic tasks such as coding, email management, and web ...", "subpage_snippet": "", "source": "newsletter.mlsafety.org", "link": "https://newsletter.mlsafety.org/", "content": "OS-Harm is built on top of OSWorld, an agent capabilities benchmark with simple, realistic agentic tasks such as coding, email management, and web ..."}
diff --git a/data/sampled_jsons/CVE-Bench_T-Agent_AutoGPT_DB_access_zero-day_success_rate_comparison.jsonl b/data/sampled_jsons/CVE-Bench_T-Agent_AutoGPT_DB_access_zero-day_success_rate_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..690955dc0af822ab17d461ea7a9d9fe1af94b230
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_T-Agent_AutoGPT_DB_access_zero-day_success_rate_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "In CVE - Bench , we simulate the zero - day and one-day scenarios. In the zero - day scenario, LLM agents must compromise the application without further in-formation about the vulnerability . AutoGPT T - Agent Cy-Agent. 0. Denial of service DB access .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "In CVE - Bench , we simulate the zero - day and one-day scenarios. In the zero - day scenario, LLM agents must compromise the application without further in-formation about the vulnerability . AutoGPT T - Agent Cy-Agent. 0. Denial of service DB access ."}
+{"idx": 1, "title": "How to build your own AutoGPT agent with Forge and test it with...", "date": "", "ddg_snippet": "AutoGPT is a groundbreaking AI agent leveraging OpenAI's GPT -4 or GPT -3.5 APIs to autonomously tackle tasks through natural language interaction. Unlike traditional systems, it initiates tasks without constant human guidance, adapting its strategies based on evolving information.", "subpage_snippet": "", "source": "lablab.ai", "link": "https://lablab.ai/t/auto-gpt-forge-tutorial", "content": "AutoGPT is a groundbreaking AI agent leveraging OpenAI's GPT -4 or GPT -3.5 APIs to autonomously tackle tasks through natural language interaction. Unlike traditional systems, it initiates tasks without constant human guidance, adapting its strategies based on evolving information."}
+{"idx": 2, "title": "GitHub - Significant-Gravitas/ AutoGPT : AutoGPT is the vision of...", "date": "", "ddg_snippet": "AutoGPT : Build, Deploy, and Run AI Agents .This includes the original stand-alone AutoGPT Agent , along with projects such as Forge, agbenchmark and the AutoGPT Classic GUI.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Significant-Gravitas/AutoGPT", "content": "AutoGPT : Build, Deploy, and Run AI Agents .This includes the original stand-alone AutoGPT Agent , along with projects such as Forge, agbenchmark and the AutoGPT Classic GUI."}
+{"idx": 3, "title": "Taskade vs. AutoGPT : Compare AI workspaces & autonomous agents .", "date": "", "ddg_snippet": "Taskade vs. AutoGPT represent two distinct approaches to harnessing AI for productivity. Taskade offers a unified platform for team collaboration with integrated AI assistance, while AutoGPT pushes the boundaries of autonomous AI agents .", "subpage_snippet": "", "source": "smythos.com", "link": "https://smythos.com/developers/agent-comparisons/taskade-vs-autogpt/", "content": "Taskade vs. AutoGPT represent two distinct approaches to harnessing AI for productivity. Taskade offers a unified platform for team collaboration with integrated AI assistance, while AutoGPT pushes the boundaries of autonomous AI agents ."}
+{"idx": 4, "title": "AI Agents : AutoGPT architecture & breakdown | by George... | Medium", "date": "", "ddg_snippet": "Hopefully this helps those who are curious about how AutoGPT works. Also, AutoGPT can serve as a reference design for those who are building their own agentic AI systems. Note: I analyzed the code from AutoGPT v 0 .2.1, which I downloaded a week ago.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@georgesung/ai-agents-autogpt-architecture-breakdown-ba37d60db944", "content": "Hopefully this helps those who are curious about how AutoGPT works. Also, AutoGPT can serve as a reference design for those who are building their own agentic AI systems. Note: I analyzed the code from AutoGPT v 0 .2.1, which I downloaded a week ago."}
+{"idx": 5, "title": "Guide 101 to AI Agent AutoGPT & Its Comparison with ChatGPT...", "date": "", "ddg_snippet": "Discover the first AI agent that rose to prominence in our comprehensive guide to AutoGPT and how it differs from ChatGPT, the sibling model.", "subpage_snippet": "", "source": "klizos.com", "link": "https://klizos.com/guide-101-to-ai-agent-autogpt/", "content": "Discover the first AI agent that rose to prominence in our comprehensive guide to AutoGPT and how it differs from ChatGPT, the sibling model."}
+{"idx": 6, "title": "AutoGPT Agents Want to Automate ChatGPT... - The New Stack", "date": "", "ddg_snippet": "But What Is AutoGPT ? AutoGPTs “are designed to automate GPT -4 tasks, enabling the creation of agents that complete tasks for you without any intervention,” explained Nathan Lands , founder of generative AI-focused Lore.com , via Tweet .", "subpage_snippet": "", "source": "thenewstack.io", "link": "https://thenewstack.io/autogpt-agents-want-to-automate-chatgpt-overrun-the-internet/", "content": "But What Is AutoGPT ? AutoGPTs “are designed to automate GPT -4 tasks, enabling the creation of agents that complete tasks for you without any intervention,” explained Nathan Lands , founder of generative AI-focused Lore.com , via Tweet ."}
+{"idx": 7, "title": "AutoGPT Agent -Free expert AI assistant for any task", "date": "", "ddg_snippet": "AutoGPT Agent is your all-in-one AI expert, dynamically connecting you to specialized agents for writing, coding, marketing, research, and more—free to use, no login required.", "subpage_snippet": "", "source": "plz.ai", "link": "https://plz.ai/tools/autogpt-agent-ZxX6Wsn3", "content": "AutoGPT Agent is your all-in-one AI expert, dynamically connecting you to specialized agents for writing, coding, marketing, research, and more—free to use, no login required."}
+{"idx": 8, "title": "AutoGPT Agent -Free, Multi-Functional AI Assistant", "date": "", "ddg_snippet": "Discover AutoGPT Agent , your AI-powered virtual assistant for diverse tasks. Efficiently handling web research, image generation, and data analysis, AutoGPT Agent offers seamless, precise, and user-focused solutions.", "subpage_snippet": "", "source": "www.yeschat.ai", "link": "https://www.yeschat.ai/gpts-2OToJMDtFZ-AutoGPT-Agent", "content": "Discover AutoGPT Agent , your AI-powered virtual assistant for diverse tasks. Efficiently handling web research, image generation, and data analysis, AutoGPT Agent offers seamless, precise, and user-focused solutions."}
+{"idx": 9, "title": "AutoGPT Agent & GPTs for Automation & Integration Like AutoGPT ...", "date": "", "ddg_snippet": "Chat with AutoGPT Agent : Your personal AI agent will plan, research, strategize and work to complete tasks semi-autonomously using multi-modal tools as needed. Complete tasks with just a few keystrokes. v1.3 Explore the best Automation & Integrat...", "subpage_snippet": "", "source": "www.whatplugin.ai", "link": "https://www.whatplugin.ai/gpts/autogpt-agent", "content": "Chat with AutoGPT Agent : Your personal AI agent will plan, research, strategize and work to complete tasks semi-autonomously using multi-modal tools as needed. Complete tasks with just a few keystrokes. v1.3 Explore the best Automation & Integrat..."}
diff --git a/data/sampled_jsons/CVE-Bench_T-Agent_one-day_Success@5_Figure_3_arxiv_year_2025.jsonl b/data/sampled_jsons/CVE-Bench_T-Agent_one-day_Success@5_Figure_3_arxiv_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aac4c625cd4f3dbc9e5dbbe43987ffd063eca1e6
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_T-Agent_one-day_Success@5_Figure_3_arxiv_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "Figure 1 : Illustration of the sandbox framework in ... We apply CVE - Bench to evaluate various LLM agents under both zero- day and one - day settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "Figure 1 : Illustration of the sandbox framework in ... We apply CVE - Bench to evaluate various LLM agents under both zero- day and one - day settings."}
+{"idx": 1, "title": "ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat", "date": "", "ddg_snippet": "... advancement of Large Language Models (LLMs) has enabled astonishing achievements in complex tasks [ 54 , 14 , 45 , 47 , 46 ] , that LLM agents can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14201v1", "content": "... advancement of Large Language Models (LLMs) has enabled astonishing achievements in complex tasks [ 54 , 14 , 45 , 47 , 46 ] , that LLM agents can ..."}
+{"idx": 2, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "To evaluate AI agents , researchers and practitioners have built agentic benchmarks with realistic tasks to track progress and assist decision-making [ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v5", "content": "To evaluate AI agents , researchers and practitioners have built agentic benchmarks with realistic tasks to track progress and assist decision-making [ ..."}
+{"idx": 3, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "To evaluate AI agents , researchers and practitioners have built agentic benchmarks with realistic tasks to track progress and assist decision-making [ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v1", "content": "To evaluate AI agents , researchers and practitioners have built agentic benchmarks with realistic tasks to track progress and assist decision-making [ ..."}
+{"idx": 4, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "To evaluate AI agents , researchers and practitioners have built agentic benchmarks with realistic tasks to track progress and assist decision-making [ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v4", "content": "To evaluate AI agents , researchers and practitioners have built agentic benchmarks with realistic tasks to track progress and assist decision-making [ ..."}
+{"idx": 5, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "To evaluate AI agents , researchers and practitioners have built agentic benchmarks with realistic tasks to track progress and assist decision-making [ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v3", "content": "To evaluate AI agents , researchers and practitioners have built agentic benchmarks with realistic tasks to track progress and assist decision-making [ ..."}
+{"idx": 6, "title": "Micro Welding Science — Sunstone Permanent Jewelry", "date": "", "ddg_snippet": "In this welding regime, the two materials to be joined are pinched between two large electrodes and put under tremendous pressures (10’s of ...", "subpage_snippet": "", "source": "permanentjewelry.sunstonewelders.com", "link": "https://permanentjewelry.sunstonewelders.com/pages/micro-welding-science", "content": "In this welding regime, the two materials to be joined are pinched between two large electrodes and put under tremendous pressures (10’s of ..."}
+{"idx": 7, "title": "Micro Welding Science — Sunstone Welders", "date": "", "ddg_snippet": "The purpose of this paper is to discuss in detail four major welding technologies (laser, pulse-arc, resistance, solder) and how they join various ...", "subpage_snippet": "", "source": "www.sunstonewelders.com", "link": "https://www.sunstonewelders.com/pages/micro-welding-science", "content": "The purpose of this paper is to discuss in detail four major welding technologies (laser, pulse-arc, resistance, solder) and how they join various ..."}
+{"idx": 8, "title": "Home Routers a Big Consumer Cyberthreat? |", "date": "", "ddg_snippet": "All to get through the security checks is to change the user agent string of your web browser tool to a special value to access the router’s Web ...", "subpage_snippet": "", "source": "www.epanorama.net", "link": "https://www.epanorama.net/blog/2014/02/20/home-routers-a-big-consumer-cyberthreat/", "content": "All to get through the security checks is to change the user agent string of your web browser tool to a special value to access the router’s Web ..."}
+{"idx": 9, "title": "Fabien's Shortform - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "The list is very biased by my taste, by my views, by the people that had time to argue that their work is important to me, and by the papers that ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/nAsMfmxDv6Qp7cfHh/fabien-s-shortform", "content": "The list is very biased by my taste, by my views, by the people that had time to argue that their work is important to me, and by the papers that ..."}
diff --git a/data/sampled_jsons/CVE-Bench_T-Agent_sqlmap_DB_access_Figure_4.jsonl b/data/sampled_jsons/CVE-Bench_T-Agent_sqlmap_DB_access_Figure_4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d8f5153c70409dd9d3d34b5a4ff1efaf9358c05
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_T-Agent_sqlmap_DB_access_Figure_4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for AI Agents ...", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 1, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ..."}
+{"idx": 2, "title": "TryHackMe — SQLMAP. In this tryhackme room (sqlmap), we ... - Medium", "date": "", "ddg_snippet": "In this tryhackme room ( sqlmap ), we will explore sqlmap and its capabilities for exploiting SQL Injection Vulnerabilities.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@tayyabanoor1201/tryhackme-sqlmap-edba2131ccfa", "content": "In this tryhackme room ( sqlmap ), we will explore sqlmap and its capabilities for exploiting SQL Injection Vulnerabilities."}
+{"idx": 3, "title": "SQLmap Essentials - HackTheBox | Ibrahim Ng`ang`a", "date": "", "ddg_snippet": "SQLmap Essentials Hello there, welcome to my first blog of the year. This lab series is part of the Bug Bounty Hunter Path of HackTheBox. This module aims to teach you the basics of using SQLMap to discover various types of SQL Injection vulnerabilities, all the way to the advanced enumeration of databases to retrieve all data of interest. While I wait for the final skill assessment payload to ...", "subpage_snippet": "", "source": "ibrahimnganga.github.io", "link": "https://ibrahimnganga.github.io/posts/SQLmap-Essentials-HackTheBox/", "content": "SQLmap Essentials Hello there, welcome to my first blog of the year. This lab series is part of the Bug Bounty Hunter Path of HackTheBox. This module aims to teach you the basics of using SQLMap to discover various types of SQL Injection vulnerabilities, all the way to the advanced enumeration of databases to retrieve all data of interest. While I wait for the final skill assessment payload to ..."}
+{"idx": 4, "title": "SQLMap - CheatSheet - HackTricks", "date": "", "ddg_snippet": "-u \"\" -p \"\" --user- agent=SQLMAP --random- agent --threads=10 --risk=3 #MAX --level=5 #MAX --dbms= \"\" --os= \"\" --technique= \"UB\" #Use only techniques UNION and BLIND in that order (default \"BEUSTQ\") --batch #Non interactive mode, usually Sqlmap will ask you questions, this accepts the default answers --auth-type= \"\" #HTTP authentication type (Basic ...", "subpage_snippet": "", "source": "book.hacktricks.wiki", "link": "https://book.hacktricks.wiki/en/pentesting-web/sql-injection/sqlmap/index.html", "content": "-u \"\" -p \"\" --user- agent=SQLMAP --random- agent --threads=10 --risk=3 #MAX --level=5 #MAX --dbms= \"\" --os= \"\" --technique= \"UB\" #Use only techniques UNION and BLIND in that order (default \"BEUSTQ\") --batch #Non interactive mode, usually Sqlmap will ask you questions, this accepts the default answers --auth-type= \"\" #HTTP authentication type (Basic ..."}
+{"idx": 5, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities."}
+{"idx": 6, "title": "SQL Injection Attacks - How To Use SQLMap To Find Database ...", "date": "", "ddg_snippet": "SQL injection attacks allow hackers to insert malicious SQL code into application queries in order to access or destroy sensitive data in databases. According to security experts, SQL injection is one of the most common and dangerous application vulnerabilities. In this comprehensive 2600+ word guide, we will understand what SQL injection is, learn how to use the powerful SQLMap tool to find ...", "subpage_snippet": "", "source": "expertbeacon.com", "link": "https://expertbeacon.com/sql-injection-attacks-how-to-use-sqlmap-to-find-database-vulnerabilities/", "content": "SQL injection attacks allow hackers to insert malicious SQL code into application queries in order to access or destroy sensitive data in databases. According to security experts, SQL injection is one of the most common and dangerous application vulnerabilities. In this comprehensive 2600+ word guide, we will understand what SQL injection is, learn how to use the powerful SQLMap tool to find ..."}
+{"idx": 7, "title": "GitHub - sqlmapproject/sqlmap: Automatic SQL injection and database ...", "date": "", "ddg_snippet": "sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database servers. It comes with a powerful detection engine, many niche features for the ultimate penetration tester, and a broad range of switches including database fingerprinting, over data fetching from the database, accessing the underlying file ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sqlmapproject/sqlmap", "content": "sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database servers. It comes with a powerful detection engine, many niche features for the ultimate penetration tester, and a broad range of switches including database fingerprinting, over data fetching from the database, accessing the underlying file ..."}
+{"idx": 8, "title": "Measuring AI Agents' Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Introducing CVE-bench : The First Real-World Vulnerability Benchmark for AI Agents After exploring the dangerous potential of AI agents in autonomously penetrating web applications in our previous studies, we found an urgent need for standardized evaluation.", "subpage_snippet": "", "source": "ddkang.substack.com", "link": "https://ddkang.substack.com/p/measuring-ai-agents-ability-to-exploit", "content": "Introducing CVE-bench : The First Real-World Vulnerability Benchmark for AI Agents After exploring the dangerous potential of AI agents in autonomously penetrating web applications in our previous studies, we found an urgent need for standardized evaluation."}
+{"idx": 9, "title": "SQLMap Lab Walkthrough — TryHackMe | by Shahin Raza | Medium", "date": "", "ddg_snippet": "Learn how to use SQLMap in a TryHackMe lab to automate SQL injection, enumerate databases, and extract data from a vulnerable web application.", "subpage_snippet": "", "source": "5r4z4.medium.com", "link": "https://5r4z4.medium.com/sqlmap-lab-walkthrough-tryhackme-9c041ca46ed5", "content": "Learn how to use SQLMap in a TryHackMe lab to automate SQL injection, enumerate databases, and extract data from a vulnerable web application."}
diff --git a/data/sampled_jsons/CVE-Bench_highest_CVSS_score_10.0_9.9_critical_CVE_Table_6_year_2025.jsonl b/data/sampled_jsons/CVE-Bench_highest_CVSS_score_10.0_9.9_critical_CVE_Table_6_year_2025.jsonl
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index 0000000000000000000000000000000000000000..9ecadeaf8b844225f805c1e7cb0f38f090a6aeff
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_highest_CVSS_score_10.0_9.9_critical_CVE_Table_6_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Microsoft Fixes 80 Flaws — Including SMB PrivEsc and Azure CVSS ...", "date": "", "ddg_snippet": "The CVE with the highest CVSS score for this month, but not listed in the Release Notes, is CVE -2025-54914 ( CVSS score : 10 . 0 ), a critical flaw impacting Azure Networking that could result in privilege escalation.", "subpage_snippet": "", "source": "thehackernews.com", "link": "https://thehackernews.com/2025/09/microsoft-fixes-80-flaws-including-smb.html", "content": "The CVE with the highest CVSS score for this month, but not listed in the Release Notes, is CVE -2025-54914 ( CVSS score : 10 . 0 ), a critical flaw impacting Azure Networking that could result in privilege escalation."}
+{"idx": 1, "title": "CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "shown in Table 2, CVEs in CVE - Bench have a minimum of 9.0 severity score measured by the Common Vulnerability Scoring System ( CVSS ) version 3.1 base score . Table 6 . Details of reproduced CVEs . CVSS 3. x Rating Affected Web Application. CRITICAL (9.6).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "shown in Table 2, CVEs in CVE - Bench have a minimum of 9.0 severity score measured by the Common Vulnerability Scoring System ( CVSS ) version 3.1 base score . Table 6 . Details of reproduced CVEs . CVSS 3. x Rating Affected Web Application. CRITICAL (9.6)."}
+{"idx": 2, "title": "CVE - Bench : Benchmarking LLM-based Software Engineering", "date": "", "ddg_snippet": "5. Critical : 9.0 - 10 . 0 . The CVSS score is calculated based on several metrics, which fall into three main groups CVE severity& CVE score . The severity of CVEs (Common Vulnerabilities and Exposures) is classi-fied using the CVSS (Common Vulnerability Scor - ing System).", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "5. Critical : 9.0 - 10 . 0 . The CVSS score is calculated based on several metrics, which fall into three main groups CVE severity& CVE score . The severity of CVEs (Common Vulnerabilities and Exposures) is classi-fied using the CVSS (Common Vulnerability Scor - ing System)."}
+{"idx": 3, "title": "How to prioritize vulnerabilities with Tufin and CVSS | LinkedIn", "date": "", "ddg_snippet": "Fact: A high CVSS score does not always mean high risk. Severity alone can’t tell you if a vulnerability is reachable, active, or already blocked by existing controls. We’ve seen environments where critical CVEs were sitting on inactive systems or behind multiple enforcement...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/jeff-wilmot-3b54486_fact-a-high-cvss-score-does-not-always-mean-activity-7368687184268242945-r17a", "content": "Fact: A high CVSS score does not always mean high risk. Severity alone can’t tell you if a vulnerability is reachable, active, or already blocked by existing controls. We’ve seen environments where critical CVEs were sitting on inactive systems or behind multiple enforcement..."}
+{"idx": 4, "title": "Fixing Redis CVE -2022-0543: Lua Sandbox Escape Patch", "date": "", "ddg_snippet": "Summary Of CVE -2022-0543- A Critical Lua Sandbox Escape Vulnerability In Redis. This is a Critical vulnerability scored 10 out of 10 in the CVSS score .", "subpage_snippet": "", "source": "thesecmaster.com", "link": "https://thesecmaster.com/blog/how-to-fix-cve-2022-0543-a-critical-lua-sandbox-escape-vulnerability-in-redis", "content": "Summary Of CVE -2022-0543- A Critical Lua Sandbox Escape Vulnerability In Redis. This is a Critical vulnerability scored 10 out of 10 in the CVSS score ."}
+{"idx": 5, "title": "CVE -2025-10035 - Overview, Insights & Trends", "date": "", "ddg_snippet": "CVSS critical 10 . 0 . Overview. Scores . A CVSS score of 10 . 0 ?! The recent CVE -2025-10035 vulnerability in GoAnywhere MFT is a ticking time bomb.", "subpage_snippet": "", "source": "cvemon.intruder.io", "link": "https://cvemon.intruder.io/cves/CVE-2025-10035", "content": "CVSS critical 10 . 0 . Overview. Scores . A CVSS score of 10 . 0 ?! The recent CVE -2025-10035 vulnerability in GoAnywhere MFT is a ticking time bomb."}
+{"idx": 6, "title": "CVE -2024-4701 - Exploits & Severity - Feedly", "date": "", "ddg_snippet": "CVSS 9 . 9 EPSS 0.04% Critical .The vulnerability has a CVSS v3.1 base score of 9 . 9 ( Critical ), with the following vector: CVSS :3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:L. This indicates a severe impact on confidentiality and integrity, with a slightly lower impact on availability.", "subpage_snippet": "", "source": "feedly.com", "link": "https://feedly.com/cve/CVE-2024-4701", "content": "CVSS 9 . 9 EPSS 0.04% Critical .The vulnerability has a CVSS v3.1 base score of 9 . 9 ( Critical ), with the following vector: CVSS :3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:L. This indicates a severe impact on confidentiality and integrity, with a slightly lower impact on availability."}
+{"idx": 7, "title": "CVE -2024–29847 : CVSS Score 10 : Deserialization of... - Medium", "date": "", "ddg_snippet": "CRITICAL : CVE -2024–43044 : CVSS Score : 9.1 :: Path Transversal vulnerability in Jenkins. This could create havoc on successful exploitation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@patchnow/cve-2024-29847-cvss-score-10-deserialization-of-unstrusted-data-vulnerability-in-ivanti-02f690b4e1f2", "content": "CRITICAL : CVE -2024–43044 : CVSS Score : 9.1 :: Path Transversal vulnerability in Jenkins. This could create havoc on successful exploitation."}
+{"idx": 8, "title": "NVD - Vulnerability Metrics", "date": "", "ddg_snippet": "The NVD supports Common Vulnerability Scoring System ( CVSS ) v2.0, v3.x and v4.0 standards. However, per the NVD CVSS v2.0 Retirement announcement, we no longer provide CVSS v2.0 assessments for newly published CVE records. High . 7.0-8. 9 . Critical . 9.0- 10 . 0 .", "subpage_snippet": "", "source": "nvd.nist.gov", "link": "https://nvd.nist.gov/vuln-metrics/cvss", "content": "The NVD supports Common Vulnerability Scoring System ( CVSS ) v2.0, v3.x and v4.0 standards. However, per the NVD CVSS v2.0 Retirement announcement, we no longer provide CVSS v2.0 assessments for newly published CVE records. High . 7.0-8. 9 . Critical . 9.0- 10 . 0 ."}
+{"idx": 9, "title": "25+ Cyber Security Vulnerability Statistics and Facts", "date": "", "ddg_snippet": "The vulnerabilities received the highest CVSS score of 10 . 0 . The qualitative severity ranking of a score of 9.0- 10 . 0 is “ critical .”According to CVE Details, out of roughly 176,000 vulnerabilities, more than 19,000 have a CVSS score of 9.0– 10 . 0 .", "subpage_snippet": "", "source": "www.comparitech.com", "link": "https://www.comparitech.com/blog/information-security/cybersecurity-vulnerability-statistics/", "content": "The vulnerabilities received the highest CVSS score of 10 . 0 . The qualitative severity ranking of a score of 9.0- 10 . 0 is “ critical .”According to CVE Details, out of roughly 176,000 vulnerabilities, more than 19,000 have a CVSS score of 9.0– 10 . 0 ."}
diff --git a/data/sampled_jsons/CVE-Bench_paper_results_T-Agent_vs_AutoGPT_database_access_zero-day_success_rate.jsonl b/data/sampled_jsons/CVE-Bench_paper_results_T-Agent_vs_AutoGPT_database_access_zero-day_success_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a308c8c4766fc90770a7810e40d956bc8e385dba
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_paper_results_T-Agent_vs_AutoGPT_database_access_zero-day_success_rate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "As shown, among successful exploits, T-Agent performs 68% and 30% database access under zero-day and one- day settings, respectively, while the percentage of database access is smaller for AutoGPT : 0% in the both zero-day and one- day settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "As shown, among successful exploits, T-Agent performs 68% and 30% database access under zero-day and one- day settings, respectively, while the percentage of database access is smaller for AutoGPT : 0% in the both zero-day and one- day settings."}
+{"idx": 1, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for ...", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 2, "title": "NVD - CVE-2025-53944", "date": "", "ddg_snippet": "Jul 30, 2025 · Information Technology Laboratory National Vulnerability Database Vulnerabilities", "subpage_snippet": "", "source": "nvd.nist.gov", "link": "https://nvd.nist.gov/vuln/detail/CVE-2025-53944", "content": "Jul 30, 2025 · Information Technology Laboratory National Vulnerability Database Vulnerabilities"}
+{"idx": 3, "title": "CVE-Bench: A Real-World Cybersecurity Benchmark for AI Agents", "date": "", "ddg_snippet": "In contrast, our agent performs quite well in the one- day setting, and we built another agent based on AutoGPT , which performs just as well as our agent in the zero-day setting as well, but the point here is that in the zero-day setting, the success rate is about 10%, which is much less than the 60% I showed earlier, also highlighting that we ...", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/events/sessions/daniel-kang-cve-bench-a-real-world-cybersecurity-benchmark-for-ai-agents", "content": "In contrast, our agent performs quite well in the one- day setting, and we built another agent based on AutoGPT , which performs just as well as our agent in the zero-day setting as well, but the point here is that in the zero-day setting, the success rate is about 10%, which is much less than the 60% I showed earlier, also highlighting that we ..."}
+{"idx": 4, "title": "Measuring AI Agents’ Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Mar 31, 2025 · Success rates of different AI agents on CVE-bench in the zero-day or one- day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danieldkang/measuring-ai-agents-ability-to-exploit-web-applications-ba4225aa281f", "content": "Mar 31, 2025 · Success rates of different AI agents on CVE-bench in the zero-day or one- day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ..."}
+{"idx": 5, "title": "CVE-Bench: Benchmarking LLM-based Software Engineering Agent ...", "date": "", "ddg_snippet": "3 days ago · CVE-Bench : Benchmarking LLM-based Software Engineering Agent ’s Ability to Repair Real-World CVE Vulnerabilities. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4207–4224, Albuquerque, New Mexico.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212/", "content": "3 days ago · CVE-Bench : Benchmarking LLM-based Software Engineering Agent ’s Ability to Repair Real-World CVE Vulnerabilities. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4207–4224, Albuquerque, New Mexico."}
+{"idx": 6, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "Mar 23, 2025 · In contrast, command injection vulnerabilities proved much more challenging, with success rates below 30%. Some specific vulnerabilities weren' t successfully exploited by any tested AI system. Technical Explanation CVE-Bench employs a novel architecture designed to evaluate AI models' ability to identify and exploit real-world web vulnerabilities.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/cve-bench-benchmark-ai-agents-ability-to", "content": "Mar 23, 2025 · In contrast, command injection vulnerabilities proved much more challenging, with success rates below 30%. Some specific vulnerabilities weren' t successfully exploited by any tested AI system. Technical Explanation CVE-Bench employs a novel architecture designed to evaluate AI models' ability to identify and exploit real-world web vulnerabilities."}
+{"idx": 7, "title": "Agent GPT vs AutoGPT : Which One Shall You Choose? – Kanaries", "date": "", "ddg_snippet": "Agent GPT vs Auto GPT in 2025: Evolution, Limitations, and the Future of AI Agents . Auto - GPT vs Agent GPT: An Unflinching Comparison. Auto - GPT : The Autonomous Dream (and Its Nightmares). Agent GPT: Collaboration Over Autonomy.", "subpage_snippet": "", "source": "docs.kanaries.net", "link": "https://docs.kanaries.net/articles/agent-gpt-vs-autogpt", "content": "Agent GPT vs Auto GPT in 2025: Evolution, Limitations, and the Future of AI Agents . Auto - GPT vs Agent GPT: An Unflinching Comparison. Auto - GPT : The Autonomous Dream (and Its Nightmares). Agent GPT: Collaboration Over Autonomy."}
+{"idx": 8, "title": "Baby AGI Vs AutoGPT : A Comparison Of AI Giants - Dataconomy", "date": "", "ddg_snippet": "Autonomous AI agents are here and AutoGPT is one of the first to arrive. AutoGPT ’s unique capabilities. On the other hand, in the Baby AGI vs AutoGPT comparison, there are aspects where AutoGPT outshines Baby AGI.", "subpage_snippet": "", "source": "dataconomy.com", "link": "https://dataconomy.com/2023/07/17/baby-agi-vs-autogpt/", "content": "Autonomous AI agents are here and AutoGPT is one of the first to arrive. AutoGPT ’s unique capabilities. On the other hand, in the Baby AGI vs AutoGPT comparison, there are aspects where AutoGPT outshines Baby AGI."}
+{"idx": 9, "title": "AutoGPT Vs . BabyAGI: Which Is Better? - The Nature Hero", "date": "", "ddg_snippet": "Auto - GPT is an experimental open-source application based on the GPT-4 model. Additionally, it has access to the Internet, making it an ideal tool for market research, optimizing your business, sending emails and debugging codes. What Are The Features Of AutoGPT ?", "subpage_snippet": "", "source": "thenaturehero.com", "link": "https://thenaturehero.com/autogpt-vs-baby-agi/", "content": "Auto - GPT is an experimental open-source application based on the GPT-4 model. Additionally, it has access to the Internet, making it an ideal tool for market research, optimizing your business, sending emails and debugging codes. What Are The Features Of AutoGPT ?"}
diff --git a/data/sampled_jsons/CVPR_2025_accepted_papers_list_human_motion_synthesis.jsonl b/data/sampled_jsons/CVPR_2025_accepted_papers_list_human_motion_synthesis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1b37ae25d099a60c22e3ee4220934f186be07e88
--- /dev/null
+++ b/data/sampled_jsons/CVPR_2025_accepted_papers_list_human_motion_synthesis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR 2025 Accepted Papers", "date": "", "ddg_snippet": "CVPR 2025 Accepted Papers . This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/AcceptedPapers", "content": "CVPR 2025 Accepted Papers . This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here."}
+{"idx": 1, "title": "CVPR 2025 Papers", "date": "", "ddg_snippet": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis · Distinguish Then Exploit: Source-free Open Set Domain Adaptation via ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/papers.html", "content": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis · Distinguish Then Exploit: Source-free Open Set Domain Adaptation via ..."}
+{"idx": 2, "title": "Text-Driven Shape-Aware Synthesis of Human Motions", "date": "", "ddg_snippet": "by TH Liao · 2025 · Cited by 3 — We explore how body shapes influence human motion syn- thesis, an aspect often overlooked in existing text-to-motion generation methods due to the ease of ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_Shape_My_Moves_Text-Driven_Shape-Aware_Synthesis_of_Human_Motions_CVPR_2025_paper.pdf", "content": "by TH Liao · 2025 · Cited by 3 — We explore how body shapes influence human motion syn- thesis, an aspect often overlooked in existing text-to-motion generation methods due to the ease of ..."}
+{"idx": 3, "title": "HuMoGen Workshop@CVPR25", "date": "", "ddg_snippet": "[May 28, 2025 ] Paper list : Link . [Mar 11, 2025 ] Paper submission ... Advances in generative human motion synthesis models; Advances in generation ...", "subpage_snippet": "", "source": "humogen.github.io", "link": "https://humogen.github.io/", "content": "[May 28, 2025 ] Paper list : Link . [Mar 11, 2025 ] Paper submission ... Advances in generative human motion synthesis models; Advances in generation ..."}
+{"idx": 4, "title": "CVPR 2025 Workshop List - The Computer Vision Foundation", "date": "", "ddg_snippet": "CVPR 2025 Accepted Workshops · 3D Capture and Reconstruction · 3D Scene Understanding · Accessibility · Analysis of Foundation Models · Autonomous Driving.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/workshop-list", "content": "CVPR 2025 Accepted Workshops · 3D Capture and Reconstruction · 3D Scene Understanding · Accessibility · Analysis of Foundation Models · Autonomous Driving."}
+{"idx": 5, "title": "Paper Digest: CVPR 2025 Papers & Highlights", "date": "", "ddg_snippet": "7 Jun 2025 — ... Synthesis : Insights from CVPR 2025 Papers . We encourage ... Motions As Queries: One-Stage Multi-Person Holistic Human Motion Capture", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2025/06/cvpr-2025-papers-highlights/", "content": "7 Jun 2025 — ... Synthesis : Insights from CVPR 2025 Papers . We encourage ... Motions As Queries: One-Stage Multi-Person Holistic Human Motion Capture"}
+{"idx": 6, "title": "All Papers - CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "... Human Motion . Changan Chen,. Juze Zhang,. Shrinidhi K. Lakshmikanth,. Yusu Fang ... Synthesis for Multimodal Large Language Models. Qirui Jiao,. Daoyuan Chen ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/CVPR2025?day=all", "content": "... Human Motion . Changan Chen,. Juze Zhang,. Shrinidhi K. Lakshmikanth,. Yusu Fang ... Synthesis for Multimodal Large Language Models. Qirui Jiao,. Daoyuan Chen ..."}
+{"idx": 7, "title": "Computer Vision and Pattern Recognition May 2025", "date": "", "ddg_snippet": "Comments: Accepted as a spotlight presentation paper at the VAND Workshop, CVPR 2025 . 10 pages, 6 figures. Subjects: Computer Vision and Pattern Recognition ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CV/2025-05?skip=25&show=500", "content": "Comments: Accepted as a spotlight presentation paper at the VAND Workshop, CVPR 2025 . 10 pages, 6 figures. Subjects: Computer Vision and Pattern Recognition ..."}
+{"idx": 8, "title": "Paper2Chinese/CVPR-2025-reading-papers-with-code", "date": "", "ddg_snippet": "TIMotion: Temporal and Interactive Framework for Efficient Human-Human Motion Generation . Link：https://arxiv.org/abs/2408.17135; Code：https://aigc-explorer ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Paper2Chinese/CVPR-2025-reading-papers-with-code", "content": "TIMotion: Temporal and Interactive Framework for Efficient Human-Human Motion Generation . Link：https://arxiv.org/abs/2408.17135; Code：https://aigc-explorer ..."}
+{"idx": 9, "title": "Dynamic Contextual Human Motion Generation with Semantic ...", "date": "", "ddg_snippet": "by P Cong · 2025 · Cited by 3 — In this paper , we introduce an effective method, SemGeoMo, for dynamic contextual human motion generation, which fully leverages the text-affordance-joint multi ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Cong_SemGeoMo_Dynamic_Contextual_Human_Motion_Generation_with_Semantic_and_Geometric_CVPR_2025_paper.pdf", "content": "by P Cong · 2025 · Cited by 3 — In this paper , we introduce an effective method, SemGeoMo, for dynamic contextual human motion generation, which fully leverages the text-affordance-joint multi ... 10 pages"}
diff --git a/data/sampled_jsons/CVPR_2025_spatiotemporal_reasoning_remote_sensing_papers.jsonl b/data/sampled_jsons/CVPR_2025_spatiotemporal_reasoning_remote_sensing_papers.jsonl
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index 0000000000000000000000000000000000000000..270cd640a28215c64d3050930b231e4da6ac6b4c
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Paper Digest: CVPR 2025 Papers & Highlights", "date": "", "ddg_snippet": "Jun 7, 2025 · Interested users can choose to read All 2,800 CVPR - 2025 papers in a separate page. To search for papers presented at CVPR - 2025 on a specific topic, please make use of the search by venue ( CVPR - 2025 ) service.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/06/cvpr-2025-papers-highlights/", "content": "Jun 7, 2025 · Interested users can choose to read All 2,800 CVPR - 2025 papers in a separate page. To search for papers presented at CVPR - 2025 on a specific topic, please make use of the search by venue ( CVPR - 2025 ) service."}
+{"idx": 1, "title": "Jasper0122/Remote-Sensing-in-CVPR2025 - GitHub", "date": "", "ddg_snippet": "Contribute to Jasper0122/ Remote - Sensing -in- CVPR2025 development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Jasper0122/Remote-Sensing-in-CVPR2025", "content": "Contribute to Jasper0122/ Remote - Sensing -in- CVPR2025 development by creating an account on GitHub."}
+{"idx": 2, "title": "CVPR 2025 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "How to use the paper list below: - Overview: This table presents papers from the CVPR conference, year 2025 . - Filtering: By default, the table loads the first 100 records. You can use the filter box under each column header to search within these loaded entries.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/cvpr-paper-list/cvpr-2025-paper-list/", "content": "How to use the paper list below: - Overview: This table presents papers from the CVPR conference, year 2025 . - Filtering: By default, the table loads the first 100 records. You can use the filter box under each column header to search within these loaded entries."}
+{"idx": 3, "title": "CVPR 2025 Accepted Papers", "date": "", "ddg_snippet": "CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/AcceptedPapers", "content": "CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here."}
+{"idx": 4, "title": "Kangsan-Y/-CVPR-2025-in-remote-sensing - GitHub", "date": "", "ddg_snippet": "[XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?] Later, I will update this collection of papers and open source projects，as well as my understanding of the ideas in these articles.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Kangsan-Y/-CVPR-2025-in-remote-sensing", "content": "[XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?] Later, I will update this collection of papers and open source projects，as well as my understanding of the ideas in these articles."}
+{"idx": 5, "title": "CVPR 2025 Accepted Papers | MMLab@NTU", "date": "", "ddg_snippet": "The team has a total of 20 papers (including 2 orals and 3 highlights) accepted to CVPR 2025 . J. Guan, K. Wang, Z. Xu, Q. Yang, Y. Sun, S. He, B. Liang, Y. Cao, Y. Li, H. Feng, E. Ding, J. Wang, Y. Zhao, H. Zhou, Z. Liu. Z. He, T. Wang, X. Huang, X. Pan, Z. Liu.", "subpage_snippet": "", "source": "www.mmlab-ntu.com", "link": "https://www.mmlab-ntu.com/conference/cvpr2025/index.html", "content": "The team has a total of 20 papers (including 2 orals and 3 highlights) accepted to CVPR 2025 . J. Guan, K. Wang, Z. Xu, Q. Yang, Y. Sun, S. He, B. Liang, Y. Cao, Y. Li, H. Feng, E. Ding, J. Wang, Y. Zhao, H. Zhou, Z. Liu. Z. He, T. Wang, X. Huang, X. Pan, Z. Liu."}
+{"idx": 6, "title": "Meet Seed at CVPR 2025 : 12 Papers Accepted and 2 Talks", "date": "", "ddg_snippet": "The IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ) 2025 was held from June 11 to 15 in Nashville, Tennessee, USA. At this year's conference, the ByteDance Seed team had 12 papers accepted, 4 of which are highlight papers , spanning...", "subpage_snippet": "", "source": "research.doubao.com", "link": "https://research.doubao.com/en/blog/meet-seed-at-cvpr-2025-12-papers-accepted-and-2-talks", "content": "The IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ) 2025 was held from June 11 to 15 in Nashville, Tennessee, USA. At this year's conference, the ByteDance Seed team had 12 papers accepted, 4 of which are highlight papers , spanning..."}
+{"idx": 7, "title": "GitHub - Kai-Liu001/Awesome- Remote - Sensing", "date": "", "ddg_snippet": "Papers . CVPR 2025 Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Kai-Liu001/Awesome-Remote-Sensing", "content": "Papers . CVPR 2025 Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space."}
+{"idx": 8, "title": "Spatiotemporal Extraction of Aquaculture Ponds Under Complex...", "date": "", "ddg_snippet": "The extraction of water surfaces and aquaculture targets from remote sensing imagery has been challenging for operations under different regions and conditions, especially since the model parameters must be optimized manually.", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/a/gam/jsusta/v17y2025i16p7201-d1720798.html", "content": "The extraction of water surfaces and aquaculture targets from remote sensing imagery has been challenging for operations under different regions and conditions, especially since the model parameters must be optimized manually."}
+{"idx": 9, "title": "A General Adaptive Dual-level Weighting Mechanism for Remote ...", "date": "", "ddg_snippet": "Currently, deep learning-based methods for remote sensing pansharpening have advanced rapidly. However, many existing methods struggle to fully leverage feature heterogeneity and redundancy, thereby limiting their effectiveness.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Huang_A_General_Adaptive_Dual-level_Weighting_Mechanism_for_Remote_Sensing_Pansharpening@CVPR2025@CVF", "content": "Currently, deep learning-based methods for remote sensing pansharpening have advanced rapidly. However, many existing methods struggle to fully leverage feature heterogeneity and redundancy, thereby limiting their effectiveness."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_NeurIPS_2024_proceedings_experimenta.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_NeurIPS_2024_proceedings_experimenta.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "NeurIPS Proceedings .Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/2718a032d15e0b80cd164b240220df89-Abstract-Conference.html", "content": "NeurIPS Proceedings .Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?"}
+{"idx": 1, "title": "FreqDebias: Towards Generalizable Deepfake Detection via...", "date": "", "ddg_snippet": "Can we leave deepfake data . behind in training deepfake detector ?Frequency shortcut learning in neural networks. In NeurIPS Workshop on Distribution Shifts: Con-necting Methods and Applications, 2022.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kashiani_FreqDebias_Towards_Generalizable_Deepfake_Detection_via_Consistency-Driven_Frequency_Debiasing_CVPR_2025_paper.pdf", "content": "Can we leave deepfake data . behind in training deepfake detector ?Frequency shortcut learning in neural networks. In NeurIPS Workshop on Distribution Shifts: Con-necting Methods and Applications, 2022."}
+{"idx": 2, "title": "SCLBD/DeepfakeBench - Githubissues", "date": "", "ddg_snippet": "DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection ( NeurIPS 2023 D&B).", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/SCLBD/DeepfakeBench/readme", "content": "DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection ( NeurIPS 2023 D&B)."}
+{"idx": 3, "title": "Top 10 Deepfake Audio Detection Tools for 2025 | Resemble AI", "date": "", "ddg_snippet": "Explore 2025’s leading deepfake audio detection tools designed to identify fake voices with AI, GAN analysis, and biometric security.", "subpage_snippet": "", "source": "www.resemble.ai", "link": "https://www.resemble.ai/audio-deepfake-detection-tools/", "content": "Explore 2025’s leading deepfake audio detection tools designed to identify fake voices with AI, GAN analysis, and biometric security."}
+{"idx": 4, "title": "DF40: Toward Next-Generation Deepfake Detection ( 2024 )", "date": "", "ddg_snippet": "(DOI: 10.48550/arxiv.2406.13495) We propose a new comprehensive benchmark to revolutionize the current deepfake detection field to the next generation.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/df40-toward-next-generation-deepfake-detection-273mzyvz9x", "content": "(DOI: 10.48550/arxiv.2406.13495) We propose a new comprehensive benchmark to revolutionize the current deepfake detection field to the next generation."}
+{"idx": 5, "title": "GitHub - Daisy-Zhang/Awesome- Deepfakes - Detection : A list of tools...", "date": "", "ddg_snippet": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ?WaveFake: A Data Set to Facilitate Audio Deepfake Detection , NeurIPS 2021: Paper Github.", "subpage_snippet": "", "source": "git.jl-k.com", "link": "https://git.jl-k.com/Daisy-Zhang/Awesome-Deepfakes-Detection", "content": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ?WaveFake: A Data Set to Facilitate Audio Deepfake Detection , NeurIPS 2021: Paper Github."}
+{"idx": 6, "title": "Blendfake Data : A New Way to Detect Deepfakes - Simple Science", "date": "", "ddg_snippet": "Exploring blendfake data 's effectiveness in deepfake detection methods. Deepfake technology has raised serious concerns about privacy and security.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-19-blendfake-data-a-new-way-to-detect-deepfakes--akxyq01", "content": "Exploring blendfake data 's effectiveness in deepfake detection methods. Deepfake technology has raised serious concerns about privacy and security."}
+{"idx": 7, "title": "Deepfake Phishing: The Next Evolution in Cyber Deception", "date": "", "ddg_snippet": "In 2025, deepfake phishing scams use AI to clone voices and videos of executives. See how these attacks operate and learn how to train your team to resist them.", "subpage_snippet": "", "source": "www.adaptivesecurity.com", "link": "https://www.adaptivesecurity.com/blog/deepfake-phishing", "content": "In 2025, deepfake phishing scams use AI to clone voices and videos of executives. See how these attacks operate and learn how to train your team to resist them."}
+{"idx": 8, "title": "Trust Is Dead. Can the 'Chief Trust Officer' Revive It? - Business Ins...", "date": "", "ddg_snippet": "Companies are hiring new execs to stop deepfakes , data leaks, and disinformation.", "subpage_snippet": "", "source": "www.businessinsider.com", "link": "https://www.businessinsider.com/chief-trust-officer-ai-deepfakes-data-leaks-2025-9", "content": "Companies are hiring new execs to stop deepfakes , data leaks, and disinformation."}
+{"idx": 9, "title": "Sensity AI: Best Deepfake Detection Software in 2025", "date": "", "ddg_snippet": "All-In-One Deepfake Detection . Deepfake Detection for videos, images, audio. Boost your investigations with our deepfake detection hub. Sensity is a user friendly application with API access, cloud-based and onpremise.", "subpage_snippet": "", "source": "sensity.ai", "link": "https://sensity.ai/", "content": "All-In-One Deepfake Detection . Deepfake Detection for videos, images, audio. Boost your investigations with our deepfake detection hub. Sensity is a user friendly application with API access, cloud-based and onpremise."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Section_4.1_Equation_8_arXiv.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Section_4.1_Equation_8_arXiv.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AuthGuard: Generalizable Deepfake Detection via Language", "date": "", "ddg_snippet": "One approach, as seen in [ 59 , 46 ] , enhances the training data by generating synthetic samples that combine source and target images, thus ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.04501v1", "content": "One approach, as seen in [ 59 , 46 ] , enhances the training data by generating synthetic samples that combine source and target images, thus ..."}
+{"idx": 1, "title": "AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake", "date": "", "ddg_snippet": "We are witnessing rapid progress in the domain of content generation technology, i.e., models trained on massive amounts of data that can produce ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.15308v2", "content": "We are witnessing rapid progress in the domain of content generation technology, i.e., models trained on massive amounts of data that can produce ..."}
+{"idx": 2, "title": "Wavelet-packets for deepfake image analysis and detection |", "date": "", "ddg_snippet": "... can extract useful representations from data , translate textual descriptions into images , transfer scene and style information between images or ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-022-06225-5", "content": "... can extract useful representations from data , translate textual descriptions into images , transfer scene and style information between images or ..."}
+{"idx": 3, "title": "Comprehensive Layer-wise Analysis of SSL Models for Audio", "date": "", "ddg_snippet": "This indicates that we can reduce computational cost and increase the inference speed of detecting deepfakes by utilizing only a few lower layers.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.03559v2", "content": "This indicates that we can reduce computational cost and increase the inference speed of detecting deepfakes by utilizing only a few lower layers."}
+{"idx": 4, "title": "Diverse Misinformation: Impacts of Human Biases on Detection of", "date": "", "ddg_snippet": "We also recognize that data used to train models that create deepfakes may introduce algorithmic biases in the quality of the videos themselves, which ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2210.10026v3", "content": "We also recognize that data used to train models that create deepfakes may introduce algorithmic biases in the quality of the videos themselves, which ..."}
+{"idx": 5, "title": "Towards Reliable Identification of Diffusion-based Image", "date": "", "ddg_snippet": "We propose a novel data generation pipeline and construct BBC-PAIR, a new benchmark for training and evaluating methods for inpainting-based IFDL.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05466v1", "content": "We propose a novel data generation pipeline and construct BBC-PAIR, a new benchmark for training and evaluating methods for inpainting-based IFDL."}
+{"idx": 6, "title": "Synthetic speech detection through short-term and long-term", "date": "", "ddg_snippet": "Indeed, synthetic speech can be obtained by simple cut-and-paste techniques performing waveform concatenation [ 10 ], in some cases available as open ...", "subpage_snippet": "", "source": "jis-eurasipjournals.springeropen.com", "link": "https://jis-eurasipjournals.springeropen.com/articles/10.1186/s13635-021-00116-3", "content": "Indeed, synthetic speech can be obtained by simple cut-and-paste techniques performing waveform concatenation [ 10 ], in some cases available as open ..."}
+{"idx": 7, "title": "Fake speech detection using VGGish with attention block |", "date": "", "ddg_snippet": "... our model on the ASV2019 dataset is trained and cross-validated over the ASVSpoof 2021 dataset, which is comprised of an additional deepfake speech ...", "subpage_snippet": "", "source": "asmp-eurasipjournals.springeropen.com", "link": "https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-024-00348-4", "content": "... our model on the ASV2019 dataset is trained and cross-validated over the ASVSpoof 2021 dataset, which is comprised of an additional deepfake speech ..."}
+{"idx": 8, "title": "(PDF) TextureCrop: Enhancing Synthetic Image Detection through", "date": "", "ddg_snippet": "... in AUC across various detectors by 5.7% compared to center cropping and by 14% compared to resizing, across high-resolution images from the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382459460_TextureCrop_Enhancing_Synthetic_Image_Detection_through_Texture-based_Cropping", "content": "... in AUC across various detectors by 5.7% compared to center cropping and by 14% compared to resizing, across high-resolution images from the ..."}
+{"idx": 9, "title": "Shaping AI’s Impact on Billions of Lives", "date": "", "ddg_snippet": "Despite tremendous productivity gains in computing and airline travel, the United States in 2020 had 11 times more programmers and 8 times more ...", "subpage_snippet": "", "source": "shapingai.com", "link": "https://shapingai.com/", "content": "Despite tremendous productivity gains in computing and airline travel, the United States in 2020 had 11 times more programmers and 8 times more ..."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_equation_8_beta_gamma_hyperparameter.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_equation_8_beta_gamma_hyperparameter.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "In recent years, the development of deepfake 1 has aroused significant concerns regarding privacy and security among the public. Deepfake detection aims to identify whether a face from an unknown source has been manipulated by deepfake techniques. Most detection methods perform promisingly when trained and tested on identical manipulations. However, given the unpredictability and complexity of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17052v1", "content": "In recent years, the development of deepfake 1 has aroused significant concerns regarding privacy and security among the public. Deepfake detection aims to identify whether a face from an unknown source has been manipulated by deepfake techniques. Most detection methods perform promisingly when trained and tested on identical manipulations. However, given the unpredictability and complexity of ..."}
+{"idx": 1, "title": "PDF Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "1 Introduction In recent years, the development of deepfake3 has aroused significant concerns regarding privacy and security among the public. Deepfake detection aims to identify whether a face from an unknown source has been manipulated by deepfake techniques. Most detection methods perform promis-ingly when trained and tested on identical manipulations. However, given the unpredictability ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/2718a032d15e0b80cd164b240220df89-Paper-Conference.pdf", "content": "1 Introduction In recent years, the development of deepfake3 has aroused significant concerns regarding privacy and security among the public. Deepfake detection aims to identify whether a face from an unknown source has been manipulated by deepfake techniques. Most detection methods perform promis-ingly when trained and tested on identical manipulations. However, given the unpredictability ..."}
+{"idx": 2, "title": "Can we leave deepfake data behind in training deepfake detector ...", "date": "", "ddg_snippet": "The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed \"blendfake\", encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without incorporating any ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738607", "content": "The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed \"blendfake\", encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without incorporating any ..."}
+{"idx": 3, "title": "Can We Leave Deepfake Data Behind in Training Deep", "date": "", "ddg_snippet": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ? Jikang Cheng1∗, Zhiyuan Yan2 , Ying Zhang3 , Yuhao Luo2 , Zhongyuan Wang1†, Chen Li3 1 School of Computer Science, Wuhan University 2 The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) arXiv:2408.17052v1 [cs.CV] 30 Aug 2024 3 WeChat, Tencent Abstract The generalization ability of deepfake detectors is vital for ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/790279987/Can-We-Leave-Deepfake-Data-Behind-in-Training-Deep", "content": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ? Jikang Cheng1∗, Zhiyuan Yan2 , Ying Zhang3 , Yuhao Luo2 , Zhongyuan Wang1†, Chen Li3 1 School of Computer Science, Wuhan University 2 The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) arXiv:2408.17052v1 [cs.CV] 30 Aug 2024 3 WeChat, Tencent Abstract The generalization ability of deepfake detectors is vital for ..."}
+{"idx": 4, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ...", "date": "", "ddg_snippet": "AI Summary This paper addresses the suboptimal performance of deepfake detectors trained on both deepfake and blendfake data. It proposes an Oriented Progressive Regularizor (OPR) to organize the latent space progressively from real to blendfake to deepfake, enabling effective utilization of forgery information from both data types, thus improving generalization ability.", "subpage_snippet": "", "source": "deepfake-total.com", "link": "https://deepfake-total.com/related_work/2408.17052", "content": "AI Summary This paper addresses the suboptimal performance of deepfake detectors trained on both deepfake and blendfake data. It proposes an Oriented Progressive Regularizor (OPR) to organize the latent space progressively from real to blendfake to deepfake, enabling effective utilization of forgery information from both data types, thus improving generalization ability."}
+{"idx": 5, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Authors Jikang Cheng, Zhiyuan Yan, Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed ''blendfake'', encouraging models to learn generic forgery artifacts like blending ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/2718a032d15e0b80cd164b240220df89-Abstract-Conference.html", "content": "Authors Jikang Cheng, Zhiyuan Yan, Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed ''blendfake'', encouraging models to learn generic forgery artifacts like blending ..."}
+{"idx": 6, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "arXiv:2408.17052v1 Announce Type: new Abstract: The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed blendfake, encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods ...", "subpage_snippet": "", "source": "golden.com", "link": "https://golden.com/wiki/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector?-PPRM5RD", "content": "arXiv:2408.17052v1 Announce Type: new Abstract: The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed blendfake, encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods ..."}
+{"idx": 7, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648453_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector", "content": "The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually ..."}
+{"idx": 8, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "With progressively organized latent space (ours), information in both deepfake and blendfake is effectively leveraged, and deepfake samples become easier to distinguish from the real. See Fig. 4a and 6 for experimental actual latent-space distribution. - \" Can We Leave Deepfake Data Behind in Training Deepfake Detector ?\"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Can-We-Leave-Deepfake-Data-Behind-in-Training-Cheng-Yan/6b186896a5b2c15ea07a1e516c41ce01f2c15772/figure/0", "content": "With progressively organized latent space (ours), information in both deepfake and blendfake is effectively leveraged, and deepfake samples become easier to distinguish from the real. See Fig. 4a and 6 for experimental actual latent-space distribution. - \" Can We Leave Deepfake Data Behind in Training Deepfake Detector ?\""}
+{"idx": 9, "title": "ProDet - GitHub", "date": "", "ddg_snippet": "The official code for paper \" Can We Leave Deepfake Data Behind in Training Deepfake Detector \" (NIPS2024 poster) ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well. You may find the overall-best checkpoint of our method from Google Drive, which is recommended for ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "The official code for paper \" Can We Leave Deepfake Data Behind in Training Deepfake Detector \" (NIPS2024 poster) ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well. You may find the overall-best checkpoint of our method from Google Drive, which is recommended for ..."}
diff --git a/data/sampled_jsons/Capturing_Dynamics_of_Time-Varying_Data_via_Topology_Lu_Xian_publication_venue.jsonl b/data/sampled_jsons/Capturing_Dynamics_of_Time-Varying_Data_via_Topology_Lu_Xian_publication_venue.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ce25d0dc3374460c77faa2a87764e22ef8795e56
--- /dev/null
+++ b/data/sampled_jsons/Capturing_Dynamics_of_Time-Varying_Data_via_Topology_Lu_Xian_publication_venue.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Foundations of Data Science", "date": "", "ddg_snippet": "Capturing dynamics of time-varying data via topology . Lu Xian , Henry Adams, Chad M. Topaz and Lori Ziegelmeier. 2022, 4(1): 1-36. doi: 10.3934/fods.2021033.", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/fods", "content": "Capturing dynamics of time-varying data via topology . Lu Xian , Henry Adams, Chad M. Topaz and Lori Ziegelmeier. 2022, 4(1): 1-36. doi: 10.3934/fods.2021033."}
+{"idx": 1, "title": "NeurIPS Poster Neural Persistence Dynamics", "date": "", "ddg_snippet": "Xian , H. Adams, C. M. Topaz, and L. Ziegelmeier. “ Capturing dynamics of time-varying data via topology ”. In: Found. Data Sci. 4.1 (2022), pp. 1–36. [58] S ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93451", "content": "Xian , H. Adams, C. M. Topaz, and L. Ziegelmeier. “ Capturing dynamics of time-varying data via topology ”. In: Found. Data Sci. 4.1 (2022), pp. 1–36. [58] S ..."}
+{"idx": 2, "title": "Topology Applied to Machine Learning: From Global to Local", "date": "", "ddg_snippet": "by H Adams · 2021 · Cited by 33 — Xian , L., Adams, H., Topaz, C. M., and Ziegelmeier, L. (2020). Capturing dynamics of time-varying data via topology . arXiv preprint arXiv:2010.05780. Google ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.668302/full", "content": "by H Adams · 2021 · Cited by 33 — Xian , L., Adams, H., Topaz, C. M., and Ziegelmeier, L. (2020). Capturing dynamics of time-varying data via topology . arXiv preprint arXiv:2010.05780. Google ..."}
+{"idx": 3, "title": "Capturing dynamics of time-varying data via topology", "date": "", "ddg_snippet": "One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time - varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ...", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/id/2acaee54-6688-46a4-b35d-447f84c4c691", "content": "One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time - varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ..."}
+{"idx": 4, "title": "CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY", "date": "", "ddg_snippet": "CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY Lu Xian School of Information University of Michigan Ann Arbor, MI 48109, USA", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2010.05780", "content": "CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY Lu Xian School of Information University of Michigan Ann Arbor, MI 48109, USA"}
+{"idx": 5, "title": "Capturing dynamics of time-varying data via topology", "date": "", "ddg_snippet": "“ Capturing dynamics of time-varying data via topology ” is a paper by Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier published in 2022. It has an Open Access status of “gold”.", "subpage_snippet": "", "source": "oa.mg", "link": "https://oa.mg/work/10.3934/fods.2021033", "content": "“ Capturing dynamics of time-varying data via topology ” is a paper by Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier published in 2022. It has an Open Access status of “gold”."}
+{"idx": 6, "title": "Capturing Dynamics of Time-Varying Data via Topology", "date": "", "ddg_snippet": "Lu Xian , Henry Adams, +1 author Lori Ziegelmeier Published in Foundations of Data Science7 October 2020 Mathematics, Computer Science TLDR", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Capturing-Dynamics-of-Time-Varying-Data-via-Xian-Adams/156e87ce1a99116bfdce512d2ebdd54a1f4e9709/figure/0", "content": "Lu Xian , Henry Adams, +1 author Lori Ziegelmeier Published in Foundations of Data Science7 October 2020 Mathematics, Computer Science TLDR"}
+{"idx": 7, "title": "CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY", "date": "", "ddg_snippet": "(2022) Xian et al. Foundations of Data Science. One approach to understanding complex data is to study its shape through the lens of algebraic topology. While the early development of topological d...", "subpage_snippet": "", "source": "www.mendeley.com", "link": "https://www.mendeley.com/catalogue/95899e1a-7e46-3b07-8c8f-6c89016e5e78/", "content": "(2022) Xian et al. Foundations of Data Science. One approach to understanding complex data is to study its shape through the lens of algebraic topology. While the early development of topological d..."}
+{"idx": 8, "title": "donut.topology.rocks", "date": "", "ddg_snippet": "@article{ xian _capturing_2020, While the early development of topological data analysis focused primarily on static data, in recent years, theoretical and applied studies have turned to data that varies in time.", "subpage_snippet": "", "source": "donut.topology.rocks", "link": "https://donut.topology.rocks/export/310", "content": "@article{ xian _capturing_2020, While the early development of topological data analysis focused primarily on static data, in recent years, theoretical and applied studies have turned to data that varies in time."}
+{"idx": 9, "title": "replication code for \"Capturing dynamics of time-varying data ...", "date": "", "ddg_snippet": "About replication code for \" Capturing dynamics of time - varying data via topology \"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lxiancode/tda-crocker", "content": "About replication code for \" Capturing dynamics of time - varying data via topology \""}
diff --git "a/data/sampled_jsons/Catoni-OFUL_algorithm_regret_bound_O(d\342\210\232T_+_d\316\272\342\210\232T_log(T\316\264)).jsonl" "b/data/sampled_jsons/Catoni-OFUL_algorithm_regret_bound_O(d\342\210\232T_+_d\316\272\342\210\232T_log(T\316\264)).jsonl"
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index 0000000000000000000000000000000000000000..d62e53f906dfc15b854d991b424239580a7133d3
--- /dev/null
+++ "b/data/sampled_jsons/Catoni-OFUL_algorithm_regret_bound_O(d\342\210\232T_+_d\316\272\342\210\232T_log(T\316\264)).jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Catoni - OFUL approach in Algorithm 1. Given failure prob-abilities δ and confidence parameters βˆt, the algorithm chooses the action xt with the highest optimistic reward by maximizing across all functions in a confidence set Ft, as in the standard OFUL approach.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "Catoni - OFUL approach in Algorithm 1. Given failure prob-abilities δ and confidence parameters βˆt, the algorithm chooses the action xt with the highest optimistic reward by maximizing across all functions in a confidence set Ft, as in the standard OFUL approach."}
+{"idx": 1, "title": "Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "expected regret bound , O (( d log n)/∆), as Auer (2002) has shown for UCB. For the general linear stochastic bandit problem, we improve regret of the CONFIDENCEBALL.The regret of OFUL can be upper bounded in terms of (∆¯ n)n as follows.", "subpage_snippet": "", "source": "sites.ualberta.ca", "link": "https://sites.ualberta.ca/~szepesva/papers/linear-bandits-NeurIPS2011.pdf", "content": "expected regret bound , O (( d log n)/∆), as Auer (2002) has shown for UCB. For the general linear stochastic bandit problem, we improve regret of the CONFIDENCEBALL.The regret of OFUL can be upper bounded in terms of (∆¯ n)n as follows."}
+{"idx": 2, "title": "Improved Regret Bounds of Bilinear Bandits using Action Space...", "date": "", "ddg_snippet": "5. Practical algorithms . Although the Algorithm 1 shows better regret bound than the previous studies, it is not tractable to apply Algorithm 1 in practice since the cardinality of X and Z grows in the order of O (( 1 ) d ) = O ( T d /2) in general, which is spatially intractable.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/jang21a/jang21a.pdf", "content": "5. Practical algorithms . Although the Algorithm 1 shows better regret bound than the previous studies, it is not tractable to apply Algorithm 1 in practice since the cardinality of X and Z grows in the order of O (( 1 ) d ) = O ( T d /2) in general, which is spatially intractable."}
+{"idx": 3, "title": "Uniform-PAC Bounds for Reinforcement Learning", "date": "", "ddg_snippet": "A OFUL Algorithm is not Uniform-PAC. Algorithms that are uniform-PAC converge to an optimal policy with high probability, and yield both PAC and high probability regret bounds . In addition, they proposed a UBEV algorithm for learning tabular MDPs, which is uniform-PAC.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2106.11612", "content": "A OFUL Algorithm is not Uniform-PAC. Algorithms that are uniform-PAC converge to an optimal policy with high probability, and yield both PAC and high probability regret bounds . In addition, they proposed a UBEV algorithm for learning tabular MDPs, which is uniform-PAC."}
+{"idx": 4, "title": "Enjoying Non-linearity in Multinomial Logistic Bandits", "date": "", "ddg_snippet": "Table 1: Comparison of regret bounds for logistics and multinomial bandits, with respect to K, d , κ, κ∗ and T . For simplicity we omit logarithmic terms and other constants. For the computation cost of each algorithm we only provide the dependence in t , - signies untractable.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-05145114v1/document", "content": "Table 1: Comparison of regret bounds for logistics and multinomial bandits, with respect to K, d , κ, κ∗ and T . For simplicity we omit logarithmic terms and other constants. For the computation cost of each algorithm we only provide the dependence in t , - signies untractable."}
+{"idx": 5, "title": "Variance-Aware Regret Bounds for Stochastic Contextual", "date": "", "ddg_snippet": "Our regret bound naturally aligns with the intuitive expectation — in scenarios where the. comparison is deterministic, the algorithm only suffers from an O ( d ) regret .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/variance-aware-regret-bounds-for-stochastic-contextual-2eheu2tdwt.pdf", "content": "Our regret bound naturally aligns with the intuitive expectation — in scenarios where the. comparison is deterministic, the algorithm only suffers from an O ( d ) regret ."}
+{"idx": 6, "title": "Variance-Aware Regret Bounds for Undiscounted", "date": "", "ddg_snippet": "Variance-Aware Regret Bound for KL-Ucrl.s. The resulting bound improves upon the best previously known regret bound O (DS AT ) for that algorithm , where A and D respectively denote the maximum number of actions (per state) and the diameter of MDP.", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/conferences/alt2018/A/Talebi18Paper.pdf", "content": "Variance-Aware Regret Bound for KL-Ucrl.s. The resulting bound improves upon the best previously known regret bound O (DS AT ) for that algorithm , where A and D respectively denote the maximum number of actions (per state) and the diameter of MDP."}
+{"idx": 7, "title": "No Weighted- Regret Learning", "date": "", "ddg_snippet": "have no weighted- regret even for dt = O ( t log t ). Therefore, algorithms with no weighted- regret . can be used to approximate a CCE of a nite or convex unknown game that can only be simulated.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume23/20-411/20-411.pdf", "content": "have no weighted- regret even for dt = O ( t log t ). Therefore, algorithms with no weighted- regret . can be used to approximate a CCE of a nite or convex unknown game that can only be simulated."}
+{"idx": 8, "title": "EconPapers: Online Instrumental Variable Regression: Regret Analysis...", "date": "", "ddg_snippet": "Specifically, we analyse and upper bound regret of Two-Stage Least Squares (2SLS) approach to IV regression in the online setting. Our analysis shows that Online 2SLS ( O 2SLS) achieves $ O ( d ^2 \\ log ^2 T )$ regret after $ T $ interactions, where d is the dimension of covariates.", "subpage_snippet": "", "source": "econpapers.repec.org", "link": "https://econpapers.repec.org/paper/halwpaper/hal-03831210.htm", "content": "Specifically, we analyse and upper bound regret of Two-Stage Least Squares (2SLS) approach to IV regression in the online setting. Our analysis shows that Online 2SLS ( O 2SLS) achieves $ O ( d ^2 \\ log ^2 T )$ regret after $ T $ interactions, where d is the dimension of covariates."}
+{"idx": 9, "title": "An Optimization-based Algorithm for Non-stationary Kernel Bandits...", "date": "", "ddg_snippet": "Table 1: Regret Bound Comparison of Algorithms for Non-stationary Kernel/Linear Bandits.", "subpage_snippet": "", "source": "www.ambujtewari.com", "link": "https://www.ambujtewari.com/research/hong23optimization.pdf", "content": "Table 1: Regret Bound Comparison of Algorithms for Non-stationary Kernel/Linear Bandits."}
diff --git a/data/sampled_jsons/Catoni-OFUL_algorithm_regret_bound_contextual_bandits_year_2024.jsonl b/data/sampled_jsons/Catoni-OFUL_algorithm_regret_bound_contextual_bandits_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f69cf8eb107e6e1f449cefe8c338933e083edb56
--- /dev/null
+++ b/data/sampled_jsons/Catoni-OFUL_algorithm_regret_bound_contextual_bandits_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "The authors propose two algorithms : Catoni - OFUL for the known-variance setting, and VACB, for the unknown-variance setting. Both algorithms achieve regret ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5IpVe9PH14&noteId=J3K6uYfoM5", "content": "The authors propose two algorithms : Catoni - OFUL for the known-variance setting, and VACB, for the unknown-variance setting. Both algorithms achieve regret ..."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — Table 1: Comparison between different algorithms for stochastic contextual bandits , where d denotes the ... regret of any contextual bandit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — Table 1: Comparison between different algorithms for stochastic contextual bandits , where d denotes the ... regret of any contextual bandit ..."}
+{"idx": 2, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One- ...", "date": "", "ddg_snippet": "by J Wang · Cited by 2 — The SOTA method (HEAVY- OFUL ) [Huang et al., 2024] adopts an offline MLE estimator and achieves optimal regret for heavy-tailed linear bandits .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9B5pBbzCwQ", "content": "by J Wang · Cited by 2 — The SOTA method (HEAVY- OFUL ) [Huang et al., 2024] adopts an offline MLE estimator and achieves optimal regret for heavy-tailed linear bandits ."}
+{"idx": 3, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One- ...", "date": "", "ddg_snippet": "... OFUL algorithm achieves the optimal and instance-dependent regret bound . ... No-regret algorithms for heavy- tailed linear bandits . In Proceedings of the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46246", "content": "... OFUL algorithm achieves the optimal and instance-dependent regret bound . ... No-regret algorithms for heavy- tailed linear bandits . In Proceedings of the ..."}
+{"idx": 4, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One- ...", "date": "", "ddg_snippet": "by J Wang · 2025 · Cited by 2 — ... OFUL algorithm achieves the optimal and instance-dependent regret bound . ... No-regret algorithms for heavy- tailed linear bandits . In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.00419", "content": "by J Wang · 2025 · Cited by 2 — ... OFUL algorithm achieves the optimal and instance-dependent regret bound . ... No-regret algorithms for heavy- tailed linear bandits . In ..."}
+{"idx": 5, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ..."}
+{"idx": 6, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This paper develops an algorithmic approach building on Catoni's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Catoni-Contextual-Bandits-are-Robust-to-Rewards-Ye-Jin/125154c306493f2c7af8e8c09c0e58c22106e6fe", "content": "This paper develops an algorithmic approach building on Catoni's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ..."}
+{"idx": 7, "title": "PDF Open Problem: First-Order Regret Bounds for Contextual Bandits", "date": "", "ddg_snippet": "The classic algorithm for contextual bandits p is EXP4 (Auer et al., 2002), which enjoys a worst-case optimal regret bound E[R( ?)] = O( T K ln N) in the adversarial setting, but requires main-taining a weight for each policy and is thus inefficient. To circumvent this computational obstacle, many existing works assume access to an optimization oracle:", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v65/agarwal17a/agarwal17a.pdf", "content": "The classic algorithm for contextual bandits p is EXP4 (Auer et al., 2002), which enjoys a worst-case optimal regret bound E[R( ?)] = O( T K ln N) in the adversarial setting, but requires main-taining a weight for each policy and is thus inefficient. To circumvent this computational obstacle, many existing works assume access to an optimization oracle:"}
+{"idx": 8, "title": "PDF Proportional Response: Contextual Bandits for Simple and ... - NeurIPS", "date": "", "ddg_snippet": "It is the first contextual bandit algorithm capable of trading-off worst-case cumulative regret guarantees with instance-dependent simple regret guarantees. The versatility of our algorithm allows for general reward models, handles misspecification, extends to finite and continuous arm settings, and allows us to choose the trade-off between ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/6058d0c628a03fd95dfe5c72cbdf9e64-Paper-Conference.pdf", "content": "It is the first contextual bandit algorithm capable of trading-off worst-case cumulative regret guarantees with instance-dependent simple regret guarantees. The versatility of our algorithm allows for general reward models, handles misspecification, extends to finite and continuous arm settings, and allows us to choose the trade-off between ..."}
+{"idx": 9, "title": "Taking a hint: How to leverage loss predictors in contextual bandits?", "date": "", "ddg_snippet": "3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds , in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni's robust mean ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10207168", "content": "3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds , in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni's robust mean ..."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Pacchiano_2024_year_2024.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Pacchiano_2024_year_2024.jsonl
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index 0000000000000000000000000000000000000000..51a6de35b1520a7fd531e54dd8b6fa1da8d6b26c
--- /dev/null
+++ b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Pacchiano_2024_year_2024.jsonl
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+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Catoni Contextual Bandits are Robust to Heavy-tailed Rewards ... Yujia Jin - catalyzex.com Contextual Bandits Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Chenlu Ye", "date": "", "ddg_snippet": "Feb 4, 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation. This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ... However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. Recall: Contextual bandit environment Context at time t encoded into a variable xt that we see before choosing our action xt is drawn i.i.d. at each time point from a distribution νx on sample space xt Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent re-gret bounds. Advances in Neural Information Processing Sys-tems, 36. Feb 28, 2025 · We also connect our theoretical findings with practical algorithms (e.g. DPO, RSO), offering new tools and insights for the algorithmic design of alignment algorithms. Theory of decision making porblems Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Feb 4, 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation. This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ... However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. Recall: Contextual bandit environment Context at time t encoded into a variable xt that we see before choosing our action xt is drawn i.i.d. at each time point from a distribution νx on sample space xt Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent re-gret bounds. Advances in Neural Information Processing Sys-tems, 36. Feb 28, 2025 · We also connect our theoretical findings with practical algorithms (e.g. DPO, RSO), offering new tools and insights for the algorithmic design of alignment algorithms. Theory of decision making porblems Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards ...", "date": "", "ddg_snippet": "This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46438/paper", "content": "This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ..."}
+{"idx": 3, "title": "Contextual Bandits", "date": "", "ddg_snippet": "Recall: Contextual bandit environment Context at time t encoded into a variable xt that we see before choosing our action xt is drawn i.i.d. at each time point from a distribution νx on sample space xt", "subpage_snippet": "", "source": "lucasjanson.fas.harvard.edu", "link": "https://lucasjanson.fas.harvard.edu/courses/22.pdf", "content": "Recall: Contextual bandit environment Context at time t encoded into a variable xt that we see before choosing our action xt is drawn i.i.d. at each time point from a distribution νx on sample space xt"}
+{"idx": 4, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent re-gret bounds. Advances in Neural Information Processing Sys-tems, 36.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46438.pdf", "content": "Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent re-gret bounds. Advances in Neural Information Processing Sys-tems, 36."}
+{"idx": 5, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "Unknown-Variance OLS ( Pacchiano , 2024 ) VACB (Theorem 3).This work considers a dierent route for robustness to heavy - tailed rewards , building on the well-studied Catoni ’s mean estimator from the robust statistics literature. We design a contextual bandit (CB) algorithm that uses...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Unknown-Variance OLS ( Pacchiano , 2024 ) VACB (Theorem 3).This work considers a dierent route for robustness to heavy - tailed rewards , building on the well-studied Catoni ’s mean estimator from the robust statistics literature. We design a contextual bandit (CB) algorithm that uses..."}
+{"idx": 6, "title": "ICML Poster Catoni Contextual Bandits are Robust to Heavy - tailed ...", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy - tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "However, many practical scenarios naturally involve heavy - tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics..."}
+{"idx": 7, "title": "Multi-Armed Bandits | Papers With Code", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/multi-armed-bandits/codeless?page=4", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics."}
+{"idx": 8, "title": "Bandits With Heavy Tail | Request PDF", "date": "", "ddg_snippet": "Robustness to heavy - tailed reward distributions has been extensively explored in the stochastic multi-armed setting, from the initial work of Bubeck et al.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/258727169_Bandits_With_Heavy_Tail", "content": "Robustness to heavy - tailed reward distributions has been extensively explored in the stochastic multi-armed setting, from the initial work of Bubeck et al."}
+{"idx": 9, "title": "Cooperative Multi-Agent Bandits with Heavy Tails", "date": "", "ddg_snippet": "Cooperative Multiagent Bandits with Heavy Tails . tailed effects, which is the central theme of this paper.For the specic case of (1 + ε)- heavy tailed rewards , the single-agent lower bound provided by (Bubeck et al., 2013) can be easily extended to the cooperative multi-agent case.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/dubey20a/dubey20a.pdf", "content": "Cooperative Multiagent Bandits with Heavy Tails . tailed effects, which is the central theme of this paper.For the specic case of (1 + ε)- heavy tailed rewards , the single-agent lower bound provided by (Bubeck et al., 2013) can be easily extended to the cooperative multi-agent case."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Table_1.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_Table_1.jsonl
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index 0000000000000000000000000000000000000000..005faf1f683a53c8ad20c909102b1070ab6f8c6b
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Feb 4, 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Feb 4, 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards ...", "date": "", "ddg_snippet": "This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46438/paper", "content": "This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ..."}
+{"idx": 3, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "The Problem: Heavy-Tailed Rewards in Contextual Bandits : [0, R] Standard Assumption: rewards are bounded within a fixed range", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46438.pdf", "content": "The Problem: Heavy-Tailed Rewards in Contextual Bandits : [0, R] Standard Assumption: rewards are bounded within a fixed range"}
+{"idx": 4, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Catoni Cont", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ..."}
+{"idx": 5, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "Extensive comparisons (see Table 1 ) with existing algorithms highlight the advantages of the proposed methods in heavy - tailed reward settings. The concentration ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5IpVe9PH14&noteId=J3K6uYfoM5", "content": "Extensive comparisons (see Table 1 ) with existing algorithms highlight the advantages of the proposed methods in heavy - tailed reward settings. The concentration ..."}
+{"idx": 6, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — Table 1: Comparison between different algorithms for stochastic contextual bandits ... A Notation Table and Additional Related Works. A.1 Notation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — Table 1: Comparison between different algorithms for stochastic contextual bandits ... A Notation Table and Additional Related Works. A.1 Notation ..."}
+{"idx": 7, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "Table 1 . Comparison between different algorithms for stochastic contextual bandits , where d denotes the dimension for linear function approximation, dF, ˜dF ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "Table 1 . Comparison between different algorithms for stochastic contextual bandits , where d denotes the dimension for linear function approximation, dF, ˜dF ..."}
+{"idx": 8, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165093", "content": "In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general ..."}
+{"idx": 9, "title": "Efficient Algorithms for Generalized Linear Bandits with ...", "date": "", "ddg_snippet": "by B Xue · 2023 · Cited by 12 — For the sake of clarity, the presented regret bounds in Table 1 are under the assumption that the rewards have finite variance. The computational complexity ... 12 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/e0982cbc81401df3430ee1ff780dc7a2-Paper-Conference.pdf", "content": "by B Xue · 2023 · Cited by 12 — For the sake of clarity, the presented regret bounds in Table 1 are under the assumption that the rewards have finite variance. The computational complexity ... 12 pages"}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_conclusion_OFUL.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_conclusion_OFUL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fc2347612ea80a36d780e796319590de7ae815df
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "This work considers a dierent route for robustness to heavy - tailed rewards , building on the well-studied Catoni ’s mean estimator from the robust statistics literature. We design a contextual bandit (CB)...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "This work considers a dierent route for robustness to heavy - tailed rewards , building on the well-studied Catoni ’s mean estimator from the robust statistics literature. We design a contextual bandit (CB)..."}
+{"idx": 1, "title": "Multi-Armed Bandits | Papers With Code", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/multi-armed-bandits/codeless?page=4", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics."}
+{"idx": 2, "title": "(PDF) Asymptotically Optimal Contextual Bandit Algorithm Using...", "date": "", "ddg_snippet": "The authors propose an online algorithm for sequential learning in the contextual multiarmed bandit setting.. Their approach is to partition the context space and, then, optimally combine all of the possible mappings between the partition regions and...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/asymptotically-optimal-contextual-bandit-algorithm-using-1ojkf3fbn1", "content": "The authors propose an online algorithm for sequential learning in the contextual multiarmed bandit setting.. Their approach is to partition the context space and, then, optimally combine all of the possible mappings between the partition regions and..."}
+{"idx": 3, "title": "On Private and Robust Bandits", "date": "", "ddg_snippet": "We study private and robust multi-armed bandits (MABs), where the agent receives Huber’s contaminated heavy - tailed rewards and meanwhile needs to ensure dif-ferential privacy.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/6d13e085b79d454da5910e4ca82a3d9d-Paper-Conference.pdf", "content": "We study private and robust multi-armed bandits (MABs), where the agent receives Huber’s contaminated heavy - tailed rewards and meanwhile needs to ensure dif-ferential privacy."}
+{"idx": 4, "title": "Online and Distribution-Free Robustness : Regression and Contextual ...", "date": "", "ddg_snippet": "We study private and robust multi-armed bandits (MABs), where the agent receives Huber's contaminated heavy - tailed rewards and meanwhile needs to ensure differential privacy.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/359040908_Online_and_Distribution-Free_Robustness_Regression_and_Contextual_Bandits_with_Huber_Contamination", "content": "We study private and robust multi-armed bandits (MABs), where the agent receives Huber's contaminated heavy - tailed rewards and meanwhile needs to ensure differential privacy."}
+{"idx": 5, "title": "Bandits Corrupted by Nature: Lower Bounds on Regret and Robust ...", "date": "", "ddg_snippet": "8 Conclusion . In this paper, we study the setting of Bandits with Stochastic Corruption that encompasses both the heavy - tailed rewards with bounded variance and unbounded corruptions in rewards .", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04615733v1/document", "content": "8 Conclusion . In this paper, we study the setting of Bandits with Stochastic Corruption that encompasses both the heavy - tailed rewards with bounded variance and unbounded corruptions in rewards ."}
+{"idx": 6, "title": "Optimal Algorithms for Lipschitz Bandits with Heavy - tailed Rewards", "date": "", "ddg_snippet": "6. Conclusion and Future Work. We have proposed two adaptive algorithms for Lipschitz bandits with heavy - tailed rewards . Our algorithms only re-quire the existence of nite (1 + )-th moments of rewards for some ∈ (0, 1], and are optimal in the sense that the regret bounds match the lower...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v97/lu19c/lu19c.pdf", "content": "6. Conclusion and Future Work. We have proposed two adaptive algorithms for Lipschitz bandits with heavy - tailed rewards . Our algorithms only re-quire the existence of nite (1 + )-th moments of rewards for some ∈ (0, 1], and are optimal in the sense that the regret bounds match the lower..."}
+{"idx": 7, "title": "Heavy - Tailed Linear Bandits", "date": "", "ddg_snippet": "heavy - tailed bandits . Nonetheless, these methods rely on specific noise assumptions or bandit .for heavy - tailed bandits are not updated in a one-pass manner. One-Pass Band √it Learning.", "subpage_snippet": "", "source": "www.pengzhao-ml.com", "link": "https://www.pengzhao-ml.com/publication/arXiv'25_huberBandits.pdf", "content": "heavy - tailed bandits . Nonetheless, these methods rely on specific noise assumptions or bandit .for heavy - tailed bandits are not updated in a one-pass manner. One-Pass Band √it Learning."}
+{"idx": 8, "title": "On Private and Robust Bandits | DeepAI", "date": "", "ddg_snippet": "We study private and robust multi-armed bandits (MABs), where the agent receives Huber's contaminated heavy - tailed rewards and meanwhile needs to ensure differential privacy.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/on-private-and-robust-bandits", "content": "We study private and robust multi-armed bandits (MABs), where the agent receives Huber's contaminated heavy - tailed rewards and meanwhile needs to ensure differential privacy."}
+{"idx": 9, "title": "Chenlu Ye - Google Akademik", "date": "", "ddg_snippet": "Corruption- Robust Offline Reinforcement Learning with General Function Approximation. Catoni contextual bandits are robust to heavy - tailed rewards .", "subpage_snippet": "", "source": "scholar.google.com.eg", "link": "https://scholar.google.com.eg/citations?user=c8yK5XsAAAAJ&hl=tr", "content": "Corruption- Robust Offline Reinforcement Learning with General Function Approximation. Catoni contextual bandits are robust to heavy - tailed rewards ."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_sitearxiv.org.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_sitearxiv.org.jsonl
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index 0000000000000000000000000000000000000000..ff4cf650353c2656f8776a10fc7de07eef503bf5
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+++ b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Single Index Bandits: Generalized Linear Contextual Bandits ...", "date": "", "ddg_snippet": "Feb 4, 2025 · View a PDF of the paper titled Catoni Contextual Bandits are Robust to Heavy - tailed Rewards , by Chenlu Ye and 3 other authors Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Catoni Contextual Bandits are Robust to Heavy - tailed Rewards Catoni contextual bandits are robust to heavy - tailed rewards . arXiv preprint arXiv:2502.02486, 2025. [56]Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, and Zhi-Hua Zhou. Online stochastic linear optimization under one-bit feedback. In International Conference on Machine Learning, pages 392–401. PMLR, 2016.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Feb 4, 2025 · View a PDF of the paper titled Catoni Contextual Bandits are Robust to Heavy - tailed Rewards , by Chenlu Ye and 3 other authors Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Catoni Contextual Bandits are Robust to Heavy - tailed Rewards Catoni contextual bandits are robust to heavy - tailed rewards . arXiv preprint arXiv:2502.02486, 2025. [56]Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, and Zhi-Hua Zhou. Online stochastic linear optimization under one-bit feedback. In International Conference on Machine Learning, pages 392–401. PMLR, 2016."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "In this work, we consider contextual bandits under heavy - tailed rewards (rewards with a large range R) with general function approximation.Tackling heavy - tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent regret bounds.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "In this work, we consider contextual bandits under heavy - tailed rewards (rewards with a large range R) with general function approximation.Tackling heavy - tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent regret bounds."}
+{"idx": 2, "title": "Single Index Bandits: Generalized Linear Contextual Bandits ...", "date": "", "ddg_snippet": "Catoni contextual bandits are robust to heavy - tailed rewards . arXiv preprint arXiv:2502.02486, 2025. [56]Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, and Zhi-Hua Zhou. Online stochastic linear optimization under one-bit feedback. In International Conference on Machine Learning, pages 392–401. PMLR, 2016.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.12751v1", "content": "Catoni contextual bandits are robust to heavy - tailed rewards . arXiv preprint arXiv:2502.02486, 2025. [56]Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, and Zhi-Hua Zhou. Online stochastic linear optimization under one-bit feedback. In International Conference on Machine Learning, pages 392–401. PMLR, 2016."}
+{"idx": 3, "title": "Single Index Bandits: Generalized Linear Contextual Bandits with...", "date": "", "ddg_snippet": "Catoni contextual bandits are robust to heavy - tailed rewards . arXiv preprint arXiv:2502.02486, 2025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.12751", "content": "Catoni contextual bandits are robust to heavy - tailed rewards . arXiv preprint arXiv:2502.02486, 2025."}
+{"idx": 4, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "Table 1: Comparisons of our regret bounds and computational complexity to previous best-known results for heavy - tailed linear bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "Table 1: Comparisons of our regret bounds and computational complexity to previous best-known results for heavy - tailed linear bandits ."}
+{"idx": 5, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Chenlu Ye∗†. Yujia Jin‡. Alekh Agarwal§. Tong Zhang¶. Abstract. Typical contextual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Chenlu Ye∗†. Yujia Jin‡. Alekh Agarwal§. Tong Zhang¶. Abstract. Typical contextual ..."}
+{"idx": 6, "title": "Machine Learning Feb 2025", "date": "", "ddg_snippet": "Title: Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy - Tailed Noise. Thanh Dang, Melih Barsbey, A K M Rokonuzzaman Sonet, Mert Gurbuzbalaban, Umut Simsekli, Lingjiong Zhu.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/stat.ML/2025-02", "content": "Title: Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy - Tailed Noise. Thanh Dang, Melih Barsbey, A K M Rokonuzzaman Sonet, Mert Gurbuzbalaban, Umut Simsekli, Lingjiong Zhu."}
+{"idx": 7, "title": "Machine Learning", "date": "", "ddg_snippet": "Title: Efficient Distributed Optimization under Heavy - Tailed Noise. Su Hyeong Lee, Manzil Zaheer, Tian Li.Title: Optimal Control of Fluid Restless Multi-armed Bandits : A Machine Learning Approach.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.LG/pastweek?show=597", "content": "Title: Efficient Distributed Optimization under Heavy - Tailed Noise. Su Hyeong Lee, Manzil Zaheer, Tian Li.Title: Optimal Control of Fluid Restless Multi-armed Bandits : A Machine Learning Approach."}
+{"idx": 8, "title": "Extended UCB Policies for Multi-Armed Bandit Problems", "date": "", "ddg_snippet": "... all reward distributions are light- tailed , i.e., the sample mean converges to the true mean faster so learning becomes easier compared to the heavy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1112.1768v5", "content": "... all reward distributions are light- tailed , i.e., the sample mean converges to the true mean faster so learning becomes easier compared to the heavy ..."}
+{"idx": 9, "title": "Machine Learning Feb 2025", "date": "", "ddg_snippet": "Title: Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang. Subjects: Machine Learning (stat.ML) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.LG/2025-02?skip=2650&show=50", "content": "Title: Catoni Contextual Bandits are Robust to Heavy-tailed Rewards . Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang. Subjects: Machine Learning (stat.ML) ..."}
diff --git a/data/sampled_jsons/Catoni_estimator_contextual_bandits_implementation_complexity_OFUL_year_2023.jsonl b/data/sampled_jsons/Catoni_estimator_contextual_bandits_implementation_complexity_OFUL_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb83bd00cee6d0d7b6516485731f62d4d734b47e
--- /dev/null
+++ b/data/sampled_jsons/Catoni_estimator_contextual_bandits_implementation_complexity_OFUL_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.02486] Catoni Contextual Bandits are Robust to Heavy ... Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Nearly Optimal Catoni’s M-estimator for Infinite Variance Contextual Bandits - GitHub Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Nearly Optimal Catoni ’s M- estimator for Infinite Variance Contextual Bandits - GitHub Nearly Optimal Catoni ’s M- estimator for Infinite Variance Nearly Optimal Catoni ’s M- estimator for Infinite Variance Contextual Bandits - GitHub Nearly Optimal Catoni ’s M- estimator for Infinite Variance Taking a hint: How to leverage loss predictors in contextual ...", "date": "", "ddg_snippet": "Feb 4, 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. Algorithm for Known Variance combines the OFUL framework with a variance-weighted regression ap-proach Uses the Catoni estimator to construct a robust confidence set for the true reward function. Result: This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ... In the context of these L−sub-Gaussian estimators, the estimator proposed in Catoni (2012), which is henceforth referred to as Catoni ’s estimator , is significant owing to the fact that it is a (nearly) √ optimal sub-Gaussian estimator of the mean with L = 2 + o(1). This Python package contains implementations of methods from different papers dealing with contextual bandit problems, as well as adaptations from typical multi-armed bandits strategies. It aims to provide an easy way to prototype and compare ideas, to reproduce research papers that don't provide easily-available implementations of their proposed algorithms, and to serve as a guide in learning about contextual bandits . For details about the implementations, or if you would like to cite this in your research, see \"Adapting multi-armed bandits policies to contextual bandits scenarios\". See full list on github.com Note: requires C/C++ compilers configured for Python. See this guide for instructions. Package is available on PyPI, can be installed with: pip install contextualbandits or if that fails: Fedora Linux users can install package using dnf package manager: dnf install python3-contextualbandits See full list on github.com Contextual bandits , also known as multi-armed bandits with covariates or associative reinforcement learning, is a problem similar to multi-armed bandits , but with the difference that side information or covariates are available at each iteration and can be used to select an arm, whose rewards are also dependent on the covariates. The problem comes from an iterative process generating data as follows: •At each round, the world creates an observation consisting of a set of covariates (features) of fixed dimension, and a reward (which is stochastic but dependent on the covariates) for each arm/choice/label. •An agent must choose an arm or label for the observation. •The world reveals the reward for the arm chosen by the agent, but not for the other arms. The aim is to create a policy that would maximize the rewards obtained by the agent. The arms might also expire over time and new arms might appear too, leading to the same exploration-exploitation dilemma faced in multi-armed bandits . See full list on github.com You can find detailed usage examples with public datasets in the following IPython notebooks: •Online Contextual Bandits •Off-policy Learning in Contextual Bandits •Policy Evaluation in Contextual Bandits See full list on github.com Package documentation is available in readthedocs: http:// contextual - bandits .readthedocs.io Documentation is also internally available through docstrings (e.g. you can try help(contextualbandits.online.BootstrappedUCB), help(contextualbandits.online.BootstrappedUCB.fit), etc.). See full list on github.com •Can now pass per-arm smoothing and beta_prior hyperparameters. •Can now work with sparse matrices in CSR format. •Added functionality for ranking top-N arms instead of always picking the single best one. •Added tree-based partitioned UCB and TS. •Added new online method ParametricTS. •Added option for ExploreFirst to make choices using active learning. See full list on github.com Methods in this package include: Online linear models: •LinUCB (see [2] and [11]) •Linear Thompson Sampling (see [4]) •Logistic UCB and Thompson sampling (see [1]) Adaptations from multi-armed bandits taking arbitrary classifiers: See full list on github.com Using pickle to serialize objects from this library is likely to fail. Use cloudpickle or dill instead, which have the same syntax as pickle, e.g.: See full list on github.com Many of the algorithms here oftentimes don't manage to beat simpler benchmarks (e.g. Offset Tree vs. a naïve One-Vs-Rest using only subsets of the data for each classifier), and I wouldn't recommend relying on them. They are nevertheless provided for comparison purposes. If in doubt of where to start or which method to choose, BootstrappedUCB is the safest bet for online methods, and OffsetTree is the safest bet for off-policy methods, when considering using methods without tuning any hyperparameters. Many of this package's methods assume that the binary classification algorithms used have probabilistic outputs (e.g. DoublyRobustEstimator), ideally with a predict_proba method, or with a decision_function method to which it will apply a sigmoid transformation (otherwise will assume the outputs from predict are bounded between zero and one). Under some of the online algorithms (e.g. SoftmaxExplorer, AdaptiveGreedy) or if using smoothing, this will not work very well with e.g. SVM, in which case you'll need to programmatically define a new class that performs a recalibration within its fit method, and outputs the calibrated numbers through its predict_proba (see reference [12]). Be aware that this is a research-oriented package, and is meant to provide flexibility at the expense of speed. See full list on github.com •[1] Cortes, D. (2018). Adapting multi-armed bandits policies to contextual bandits scenarios. arXiv preprint arXiv:1811.04383. •[2] Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010, April). A contextual -bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web (pp. 661-670). ACM. •[3] Chapelle, O., & Li, L. (2011). An empirical evaluation of thompson sampling. In Advances in neural information processing systems (pp. 2249-2257). •[4] Agrawal, S., & Goyal, N. (2013, February). Thompson sampling for contextual bandits with linear payoffs. In International Conference on Machine Learning (pp. 127-135). •[5] Chakrabarti, D., Kumar, R., Radlinski, F., & Upfal, E. (2009). Mortal multi-armed bandits . In Advances in neural information processing systems (pp. 273-280). •[6] Vermorel, J., & Mohri, M. (2005, October). Multi-armed bandit algorithms and empirical evaluation. In European conference on machine learning (pp. 437-448). Springer, Berlin, Heidelberg. See full list on github.com However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation. How can Catoni estimators be extended to a more general setting? driven estimator output by Algorithm 1. Suppose that The extension of Catoni estimators to this more general setting is based on the application of standard martingale analysis (Freedman, 1975; Seldin et al., 2012) to establish the bounds for bounded functions of real-valued random variables. Can a continuous reward be used for a contextual bandits problem? While, in general, algorithms for the contextual bandits problem assume continuous rewards in the range [0,1], this package deals only with the case of discrete rewards {0,1}, and only with the case of arms that all see the same covariates. Some methods might still work fine for continuous rewards, but they are not meant to be used with them. Is Catoni's estimator based on a non-trivial extension? This is based on the non-trivial extension of Catoni’s estimator proposed in Catoni (2012) for the case of infinite variance. We also provided an algorithm to adapt to the situ-ation of unknown moment bound using classical Lepskii’s adaptive estimation method. Why is Catoni's estimator important? In the context of these L−sub-Gaussian estimators, the estimator proposed in Catoni (2012), which is henceforth referred to as Catoni’s estimator, is significant owing to the fact that it is a (nearly) √ optimal sub-Gaussian estimator of the mean with L = 2 + o(1 ). What is a contextual bandit? Contextual bandits, also known as multi-armed bandits with covariates or associative reinforcement learning , is a problem similar to multi-armed bandits, but with the difference that side information or covariates are available at each iteration and can be used to select an arm, whose rewards are also dependent on the covariates. Is Catoni a phase-based elimination algorithm? Phase-based Elimination with Catoni It is clear that in Algorithm 2, the Catoni ’s estimate is waste-fully computed at every step and the initial exploration phase is larger than necessary due to the union bound. We next provide a phase-based algorithm and achieve better sample complexity in terms of ∆i. 3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds, in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni ’s robust mean ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Feb 4, 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. Algorithm for Known Variance combines the OFUL framework with a variance-weighted regression ap-proach Uses the Catoni estimator to construct a robust confidence set for the true reward function. Result: This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ... In the context of these L−sub-Gaussian estimators, the estimator proposed in Catoni (2012), which is henceforth referred to as Catoni ’s estimator , is significant owing to the fact that it is a (nearly) √ optimal sub-Gaussian estimator of the mean with L = 2 + o(1). This Python package contains implementations of methods from different papers dealing with contextual bandit problems, as well as adaptations from typical multi-armed bandits strategies. It aims to provide an easy way to prototype and compare ideas, to reproduce research papers that don't provide easily-available implementations of their proposed algorithms, and to serve as a guide in learning about contextual bandits . For details about the implementations, or if you would like to cite this in your research, see \"Adapting multi-armed bandits policies to contextual bandits scenarios\". See full list on github.com Note: requires C/C++ compilers configured for Python. See this guide for instructions. Package is available on PyPI, can be installed with: pip install contextualbandits or if that fails: Fedora Linux users can install package using dnf package manager: dnf install python3-contextualbandits See full list on github.com Contextual bandits , also known as multi-armed bandits with covariates or associative reinforcement learning, is a problem similar to multi-armed bandits , but with the difference that side information or covariates are available at each iteration and can be used to select an arm, whose rewards are also dependent on the covariates. The problem comes from an iterative process generating data as follows: •At each round, the world creates an observation consisting of a set of covariates (features) of fixed dimension, and a reward (which is stochastic but dependent on the covariates) for each arm/choice/label. •An agent must choose an arm or label for the observation. •The world reveals the reward for the arm chosen by the agent, but not for the other arms. The aim is to create a policy that would maximize the rewards obtained by the agent. The arms might also expire over time and new arms might appear too, leading to the same exploration-exploitation dilemma faced in multi-armed bandits . See full list on github.com You can find detailed usage examples with public datasets in the following IPython notebooks: •Online Contextual Bandits •Off-policy Learning in Contextual Bandits •Policy Evaluation in Contextual Bandits See full list on github.com Package documentation is available in readthedocs: http:// contextual - bandits .readthedocs.io Documentation is also internally available through docstrings (e.g. you can try help(contextualbandits.online.BootstrappedUCB), help(contextualbandits.online.BootstrappedUCB.fit), etc.). See full list on github.com •Can now pass per-arm smoothing and beta_prior hyperparameters. •Can now work with sparse matrices in CSR format. •Added functionality for ranking top-N arms instead of always picking the single best one. •Added tree-based partitioned UCB and TS. •Added new online method ParametricTS. •Added option for ExploreFirst to make choices using active learning. See full list on github.com Methods in this package include: Online linear models: •LinUCB (see [2] and [11]) •Linear Thompson Sampling (see [4]) •Logistic UCB and Thompson sampling (see [1]) Adaptations from multi-armed bandits taking arbitrary classifiers: See full list on github.com Using pickle to serialize objects from this library is likely to fail. Use cloudpickle or dill instead, which have the same syntax as pickle, e.g.: See full list on github.com Many of the algorithms here oftentimes don't manage to beat simpler benchmarks (e.g. Offset Tree vs. a naïve One-Vs-Rest using only subsets of the data for each classifier), and I wouldn't recommend relying on them. They are nevertheless provided for comparison purposes. If in doubt of where to start or which method to choose, BootstrappedUCB is the safest bet for online methods, and OffsetTree is the safest bet for off-policy methods, when considering using methods without tuning any hyperparameters. Many of this package's methods assume that the binary classification algorithms used have probabilistic outputs (e.g. DoublyRobustEstimator), ideally with a predict_proba method, or with a decision_function method to which it will apply a sigmoid transformation (otherwise will assume the outputs from predict are bounded between zero and one). Under some of the online algorithms (e.g. SoftmaxExplorer, AdaptiveGreedy) or if using smoothing, this will not work very well with e.g. SVM, in which case you'll need to programmatically define a new class that performs a recalibration within its fit method, and outputs the calibrated numbers through its predict_proba (see reference [12]). Be aware that this is a research-oriented package, and is meant to provide flexibility at the expense of speed. See full list on github.com •[1] Cortes, D. (2018). Adapting multi-armed bandits policies to contextual bandits scenarios. arXiv preprint arXiv:1811.04383. •[2] Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010, April). A contextual -bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web (pp. 661-670). ACM. •[3] Chapelle, O., & Li, L. (2011). An empirical evaluation of thompson sampling. In Advances in neural information processing systems (pp. 2249-2257). •[4] Agrawal, S., & Goyal, N. (2013, February). Thompson sampling for contextual bandits with linear payoffs. In International Conference on Machine Learning (pp. 127-135). •[5] Chakrabarti, D., Kumar, R., Radlinski, F., & Upfal, E. (2009). Mortal multi-armed bandits . In Advances in neural information processing systems (pp. 273-280). •[6] Vermorel, J., & Mohri, M. (2005, October). Multi-armed bandit algorithms and empirical evaluation. In European conference on machine learning (pp. 437-448). Springer, Berlin, Heidelberg. See full list on github.com However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation. How can Catoni estimators be extended to a more general setting? driven estimator output by Algorithm 1. Suppose that The extension of Catoni estimators to this more general setting is based on the application of standard martingale analysis (Freedman, 1975; Seldin et al., 2012) to establish the bounds for bounded functions of real-valued random variables. Can a continuous reward be used for a contextual bandits problem? While, in general, algorithms for the contextual bandits problem assume continuous rewards in the range [0,1], this package deals only with the case of discrete rewards {0,1}, and only with the case of arms that all see the same covariates. Some methods might still work fine for continuous rewards, but they are not meant to be used with them. Is Catoni's estimator based on a non-trivial extension? This is based on the non-trivial extension of Catoni’s estimator proposed in Catoni (2012) for the case of infinite variance. We also provided an algorithm to adapt to the situ-ation of unknown moment bound using classical Lepskii’s adaptive estimation method. Why is Catoni's estimator important? In the context of these L−sub-Gaussian estimators, the estimator proposed in Catoni (2012), which is henceforth referred to as Catoni’s estimator, is significant owing to the fact that it is a (nearly) √ optimal sub-Gaussian estimator of the mean with L = 2 + o(1 ). What is a contextual bandit? Contextual bandits, also known as multi-armed bandits with covariates or associative reinforcement learning , is a problem similar to multi-armed bandits, but with the difference that side information or covariates are available at each iteration and can be used to select an arm, whose rewards are also dependent on the covariates. Is Catoni a phase-based elimination algorithm? Phase-based Elimination with Catoni It is clear that in Algorithm 2, the Catoni ’s estimate is waste-fully computed at every step and the initial exploration phase is larger than necessary due to the union bound. We next provide a phase-based algorithm and achieve better sample complexity in terms of ∆i. 3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds, in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni ’s robust mean ..."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Algorithm for Known Variance combines the OFUL framework with a variance-weighted regression ap-proach Uses the Catoni estimator to construct a robust confidence set for the true reward function. Result:", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46438.pdf", "content": "Algorithm for Known Variance combines the OFUL framework with a variance-weighted regression ap-proach Uses the Catoni estimator to construct a robust confidence set for the true reward function. Result:"}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Catoni-Contextual-Bandits-are-Robust-to-Rewards-Ye-Jin/125154c306493f2c7af8e8c09c0e58c22106e6fe", "content": "This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ..."}
+{"idx": 3, "title": "Nearly Optimal Catoni’s M-estimator for Infinite Variance", "date": "", "ddg_snippet": "In the context of these L−sub-Gaussian estimators, the estimator proposed in Catoni (2012), which is henceforth referred to as Catoni ’s estimator , is significant owing to the fact that it is a (nearly) √ optimal sub-Gaussian estimator of the mean with L = 2 + o(1).", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/bhatt22b/bhatt22b.pdf", "content": "In the context of these L−sub-Gaussian estimators, the estimator proposed in Catoni (2012), which is henceforth referred to as Catoni ’s estimator , is significant owing to the fact that it is a (nearly) √ optimal sub-Gaussian estimator of the mean with L = 2 + o(1)."}
+{"idx": 4, "title": "Contextual Bandits - GitHub Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Nearly Optimal Catoni ’s M- estimator for Infinite Variance Contextual Bandits - GitHub Nearly Optimal Catoni ’s M- estimator for Infinite Variance Nearly Optimal Catoni ’s M- estimator for Infinite Variance Contextual Bandits - GitHub Nearly Optimal Catoni ’s M- estimator for Infinite Variance Taking a hint: How to leverage loss predictors in contextual ...", "date": "", "ddg_snippet": "This Python package contains implementations of methods from different papers dealing with contextual bandit problems, as well as adaptations from typical multi-armed bandits strategies. It aims to provide an easy way to prototype and compare ideas, to reproduce research papers that don't provide easily-available implementations of their proposed algorithms, and to serve as a guide in learning about contextual bandits . For details about the implementations, or if you would like to cite this in your research, see \"Adapting multi-armed bandits policies to contextual bandits scenarios\". See full list on github.com Note: requires C/C++ compilers configured for Python. See this guide for instructions. Package is available on PyPI, can be installed with: pip install contextualbandits or if that fails: Fedora Linux users can install package using dnf package manager: dnf install python3-contextualbandits See full list on github.com Contextual bandits , also known as multi-armed bandits with covariates or associative reinforcement learning, is a problem similar to multi-armed bandits , but with the difference that side information or covariates are available at each iteration and can be used to select an arm, whose rewards are also dependent on the covariates. The problem comes from an iterative process generating data as follows: •At each round, the world creates an observation consisting of a set of covariates (features) of fixed dimension, and a reward (which is stochastic but dependent on the covariates) for each arm/choice/label. •An agent must choose an arm or label for the observation. •The world reveals the reward for the arm chosen by the agent, but not for the other arms. The aim is to create a policy that would maximize the rewards obtained by the agent. The arms might also expire over time and new arms might appear too, leading to the same exploration-exploitation dilemma faced in multi-armed bandits . See full list on github.com You can find detailed usage examples with public datasets in the following IPython notebooks: •Online Contextual Bandits •Off-policy Learning in Contextual Bandits •Policy Evaluation in Contextual Bandits See full list on github.com Package documentation is available in readthedocs: http:// contextual - bandits .readthedocs.io Documentation is also internally available through docstrings (e.g. you can try help(contextualbandits.online.BootstrappedUCB), help(contextualbandits.online.BootstrappedUCB.fit), etc.). See full list on github.com •Can now pass per-arm smoothing and beta_prior hyperparameters. •Can now work with sparse matrices in CSR format. •Added functionality for ranking top-N arms instead of always picking the single best one. •Added tree-based partitioned UCB and TS. •Added new online method ParametricTS. •Added option for ExploreFirst to make choices using active learning. See full list on github.com Methods in this package include: Online linear models: •LinUCB (see [2] and [11]) •Linear Thompson Sampling (see [4]) •Logistic UCB and Thompson sampling (see [1]) Adaptations from multi-armed bandits taking arbitrary classifiers: See full list on github.com Using pickle to serialize objects from this library is likely to fail. Use cloudpickle or dill instead, which have the same syntax as pickle, e.g.: See full list on github.com Many of the algorithms here oftentimes don't manage to beat simpler benchmarks (e.g. Offset Tree vs. a naïve One-Vs-Rest using only subsets of the data for each classifier), and I wouldn't recommend relying on them. They are nevertheless provided for comparison purposes. If in doubt of where to start or which method to choose, BootstrappedUCB is the safest bet for online methods, and OffsetTree is the safest bet for off-policy methods, when considering using methods without tuning any hyperparameters. Many of this package's methods assume that the binary classification algorithms used have probabilistic outputs (e.g. DoublyRobustEstimator), ideally with a predict_proba method, or with a decision_function method to which it will apply a sigmoid transformation (otherwise will assume the outputs from predict are bounded between zero and one). Under some of the online algorithms (e.g. SoftmaxExplorer, AdaptiveGreedy) or if using smoothing, this will not work very well with e.g. SVM, in which case you'll need to programmatically define a new class that performs a recalibration within its fit method, and outputs the calibrated numbers through its predict_proba (see reference [12]). Be aware that this is a research-oriented package, and is meant to provide flexibility at the expense of speed. See full list on github.com •[1] Cortes, D. (2018). Adapting multi-armed bandits policies to contextual bandits scenarios. arXiv preprint arXiv:1811.04383. •[2] Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010, April). A contextual -bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web (pp. 661-670). ACM. •[3] Chapelle, O., & Li, L. (2011). An empirical evaluation of thompson sampling. In Advances in neural information processing systems (pp. 2249-2257). •[4] Agrawal, S., & Goyal, N. (2013, February). Thompson sampling for contextual bandits with linear payoffs. In International Conference on Machine Learning (pp. 127-135). •[5] Chakrabarti, D., Kumar, R., Radlinski, F., & Upfal, E. (2009). Mortal multi-armed bandits . In Advances in neural information processing systems (pp. 273-280). •[6] Vermorel, J., & Mohri, M. (2005, October). Multi-armed bandit algorithms and empirical evaluation. In European conference on machine learning (pp. 437-448). Springer, Berlin, Heidelberg. See full list on github.com However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation. How can Catoni estimators be extended to a more general setting? driven estimator output by Algorithm 1. Suppose that The extension of Catoni estimators to this more general setting is based on the application of standard martingale analysis (Freedman, 1975; Seldin et al., 2012) to establish the bounds for bounded functions of real-valued random variables. Can a continuous reward be used for a contextual bandits problem? While, in general, algorithms for the contextual bandits problem assume continuous rewards in the range [0,1], this package deals only with the case of discrete rewards {0,1}, and only with the case of arms that all see the same covariates. Some methods might still work fine for continuous rewards, but they are not meant to be used with them. Is Catoni's estimator based on a non-trivial extension? This is based on the non-trivial extension of Catoni’s estimator proposed in Catoni (2012) for the case of infinite variance. We also provided an algorithm to adapt to the situ-ation of unknown moment bound using classical Lepskii’s adaptive estimation method. Why is Catoni's estimator important? In the context of these L−sub-Gaussian estimators, the estimator proposed in Catoni (2012), which is henceforth referred to as Catoni’s estimator, is significant owing to the fact that it is a (nearly) √ optimal sub-Gaussian estimator of the mean with L = 2 + o(1 ). What is a contextual bandit? Contextual bandits, also known as multi-armed bandits with covariates or associative reinforcement learning , is a problem similar to multi-armed bandits, but with the difference that side information or covariates are available at each iteration and can be used to select an arm, whose rewards are also dependent on the covariates. Is Catoni a phase-based elimination algorithm? Phase-based Elimination with Catoni It is clear that in Algorithm 2, the Catoni ’s estimate is waste-fully computed at every step and the initial exploration phase is larger than necessary due to the union bound. We next provide a phase-based algorithm and achieve better sample complexity in terms of ∆i. 3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds, in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni ’s robust mean ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/david-cortes/contextualbandits", "content": "This Python package contains implementations of methods from different papers dealing with contextual bandit problems, as well as adaptations from typical multi-armed bandits strategies. It aims to provide an easy way to prototype and compare ideas, to reproduce research papers that don't provide easily-available implementations of their proposed algorithms, and to serve as a guide in learning about contextual bandits . For details about the implementations, or if you would like to cite this in your research, see \"Adapting multi-armed bandits policies to contextual bandits scenarios\". See full list on github.com Note: requires C/C++ compilers configured for Python. See this guide for instructions. Package is available on PyPI, can be installed with: pip install contextualbandits or if that fails: Fedora Linux users can install package using dnf package manager: dnf install python3-contextualbandits See full list on github.com Contextual bandits , also known as multi-armed bandits with covariates or associative reinforcement learning, is a problem similar to multi-armed bandits , but with the difference that side information or covariates are available at each iteration and can be used to select an arm, whose rewards are also dependent on the covariates. The problem comes from an iterative process generating data as follows: •At each round, the world creates an observation consisting of a set of covariates (features) of fixed dimension, and a reward (which is stochastic but dependent on the covariates) for each arm/choice/label. •An agent must choose an arm or label for the observation. •The world reveals the reward for the arm chosen by the agent, but not for the other arms. The aim is to create a policy that would maximize the rewards obtained by the agent. The arms might also expire over time and new arms might appear too, leading to the same exploration-exploitation dilemma faced in multi-armed bandits . See full list on github.com You can find detailed usage examples with public datasets in the following IPython notebooks: •Online Contextual Bandits •Off-policy Learning in Contextual Bandits •Policy Evaluation in Contextual Bandits See full list on github.com Package documentation is available in readthedocs: http:// contextual - bandits .readthedocs.io Documentation is also internally available through docstrings (e.g. you can try help(contextualbandits.online.BootstrappedUCB), help(contextualbandits.online.BootstrappedUCB.fit), etc.). See full list on github.com •Can now pass per-arm smoothing and beta_prior hyperparameters. •Can now work with sparse matrices in CSR format. •Added functionality for ranking top-N arms instead of always picking the single best one. •Added tree-based partitioned UCB and TS. •Added new online method ParametricTS. •Added option for ExploreFirst to make choices using active learning. See full list on github.com Methods in this package include: Online linear models: •LinUCB (see [2] and [11]) •Linear Thompson Sampling (see [4]) •Logistic UCB and Thompson sampling (see [1]) Adaptations from multi-armed bandits taking arbitrary classifiers: See full list on github.com Using pickle to serialize objects from this library is likely to fail. Use cloudpickle or dill instead, which have the same syntax as pickle, e.g.: See full list on github.com Many of the algorithms here oftentimes don't manage to beat simpler benchmarks (e.g. Offset Tree vs. a naïve One-Vs-Rest using only subsets of the data for each classifier), and I wouldn't recommend relying on them. They are nevertheless provided for comparison purposes. If in doubt of where to start or which method to choose, BootstrappedUCB is the safest bet for online methods, and OffsetTree is the safest bet for off-policy methods, when considering using methods without tuning any hyperparameters. Many of this package's methods assume that the binary classification algorithms used have probabilistic outputs (e.g. DoublyRobustEstimator), ideally with a predict_proba method, or with a decision_function method to which it will apply a sigmoid transformation (otherwise will assume the outputs from predict are bounded between zero and one). Under some of the online algorithms (e.g. SoftmaxExplorer, AdaptiveGreedy) or if using smoothing, this will not work very well with e.g. SVM, in which case you'll need to programmatically define a new class that performs a recalibration within its fit method, and outputs the calibrated numbers through its predict_proba (see reference [12]). Be aware that this is a research-oriented package, and is meant to provide flexibility at the expense of speed. See full list on github.com •[1] Cortes, D. (2018). Adapting multi-armed bandits policies to contextual bandits scenarios. arXiv preprint arXiv:1811.04383. •[2] Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010, April). A contextual -bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web (pp. 661-670). ACM. •[3] Chapelle, O., & Li, L. (2011). An empirical evaluation of thompson sampling. In Advances in neural information processing systems (pp. 2249-2257). •[4] Agrawal, S., & Goyal, N. (2013, February). Thompson sampling for contextual bandits with linear payoffs. In International Conference on Machine Learning (pp. 127-135). •[5] Chakrabarti, D., Kumar, R., Radlinski, F., & Upfal, E. (2009). Mortal multi-armed bandits . In Advances in neural information processing systems (pp. 273-280). •[6] Vermorel, J., & Mohri, M. (2005, October). Multi-armed bandit algorithms and empirical evaluation. In European conference on machine learning (pp. 437-448). Springer, Berlin, Heidelberg. See full list on github.com However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation. How can Catoni estimators be extended to a more general setting? driven estimator output by Algorithm 1. Suppose that The extension of Catoni estimators to this more general setting is based on the application of standard martingale analysis (Freedman, 1975; Seldin et al., 2012) to establish the bounds for bounded functions of real-valued random variables. Can a continuous reward be used for a contextual bandits problem? While, in general, algorithms for the contextual bandits problem assume continuous rewards in the range [0,1], this package deals only with the case of discrete rewards {0,1}, and only with the case of arms that all see the same covariates. Some methods might still work fine for continuous rewards, but they are not meant to be used with them. Is Catoni's estimator based on a non-trivial extension? This is based on the non-trivial extension of Catoni’s estimator proposed in Catoni (2012) for the case of infinite variance. We also provided an algorithm to adapt to the situ-ation of unknown moment bound using classical Lepskii’s adaptive estimation method. Why is Catoni's estimator important? In the context of these L−sub-Gaussian estimators, the estimator proposed in Catoni (2012), which is henceforth referred to as Catoni’s estimator, is significant owing to the fact that it is a (nearly) √ optimal sub-Gaussian estimator of the mean with L = 2 + o(1 ). What is a contextual bandit? Contextual bandits, also known as multi-armed bandits with covariates or associative reinforcement learning , is a problem similar to multi-armed bandits, but with the difference that side information or covariates are available at each iteration and can be used to select an arm, whose rewards are also dependent on the covariates. Is Catoni a phase-based elimination algorithm? Phase-based Elimination with Catoni It is clear that in Algorithm 2, the Catoni ’s estimate is waste-fully computed at every step and the initial exploration phase is larger than necessary due to the union bound. We next provide a phase-based algorithm and achieve better sample complexity in terms of ∆i. 3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds, in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni ’s robust mean ..."}
+{"idx": 5, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation."}
+{"idx": 6, "title": "Taking a hint: How to leverage loss predictors in contextual ...", "date": "", "ddg_snippet": "3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds, in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni ’s robust mean ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10207168", "content": "3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds, in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni ’s robust mean ..."}
+{"idx": 7, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — Additionally, while we obtain information-theoretic results in this paper, the algorithms are not easy to implement , both because OFUL -style ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — Additionally, while we obtain information-theoretic results in this paper, the algorithms are not easy to implement , both because OFUL -style ..."}
+{"idx": 8, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general ..."}
+{"idx": 9, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One- ...", "date": "", "ddg_snippet": "by J Wang · Cited by 2 — Soft version truncation, such as. Catoni's M- estimator ( Catoni , 2012), reduces the impact of outliers by assigning them lower weights. This ensures ro- bust ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9B5pBbzCwQ", "content": "by J Wang · Cited by 2 — Soft version truncation, such as. Catoni's M- estimator ( Catoni , 2012), reduces the impact of outliers by assigning them lower weights. This ensures ro- bust ..."}
diff --git a/data/sampled_jsons/Catoni_estimator_implementation.jsonl b/data/sampled_jsons/Catoni_estimator_implementation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0c2fd1ee229f6b105418f8b1d903524e6cc439a
--- /dev/null
+++ b/data/sampled_jsons/Catoni_estimator_implementation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Catoni-Giulini estimator", "date": "", "ddg_snippet": "Mar 7, 2024 · We’ve previously discussed the median-of-means estimator for estimating the mean of random vectors. While median-of-means has sub-Gaussian performance assuming only the existence of the covariance matrix, it’s a complicated estimator to implement in the multivariate setting.", "subpage_snippet": "", "source": "benchugg.com", "link": "https://benchugg.com/research_notes/catoni_giulini/", "content": "Mar 7, 2024 · We’ve previously discussed the median-of-means estimator for estimating the mean of random vectors. While median-of-means has sub-Gaussian performance assuming only the existence of the covariance matrix, it’s a complicated estimator to implement in the multivariate setting."}
+{"idx": 1, "title": "Catoni-Giulini M-estimator - The Stats Map", "date": "", "ddg_snippet": "Jan 5, 2025 · In 2017, Catoni and Giulini proposed an approach to multivariate concentration based on M- estimation . Let ψ be any symmetric “influence function” such that −log(1−x+x2/2)≤ ψ(x)≤ log(1+x+x2/2), ∀x∈ R. The motivation behind this condition is to choose a function ψ such that eψ is bounded by polynomials. The estimator is then", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/Catoni-Giulini-M-estimator", "content": "Jan 5, 2025 · In 2017, Catoni and Giulini proposed an approach to multivariate concentration based on M- estimation . Let ψ be any symmetric “influence function” such that −log(1−x+x2/2)≤ ψ(x)≤ log(1+x+x2/2), ∀x∈ R. The motivation behind this condition is to choose a function ψ such that eψ is bounded by polynomials. The estimator is then"}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This is a robust estimator proposed by Audibert & Catoni (2011)(see also (Lugosi & Mendelson, 2019)) to estimate random variables with bounded variance and unbounded range.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "This is a robust estimator proposed by Audibert & Catoni (2011)(see also (Lugosi & Mendelson, 2019)) to estimate random variables with bounded variance and unbounded range."}
+{"idx": 3, "title": "On Catoni 's M- Estimation | DeepAI", "date": "", "ddg_snippet": "On Catoni 's M- Estimation . 10/15/2022. Catoni proposed a robust M- estimator and gave the deviation inequality for one fixed test function.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/on-catoni-s-m-estimation", "content": "On Catoni 's M- Estimation . 10/15/2022. Catoni proposed a robust M- estimator and gave the deviation inequality for one fixed test function."}
+{"idx": 4, "title": "Sub-Gaussian mean estimators", "date": "", "ddg_snippet": "Empirical mean as a sub-Gaussian estimator . Median of means. Catoni 's estimators . Main results.", "subpage_snippet": "", "source": "luc.devroye.org", "link": "https://luc.devroye.org/Devroye+Lerasle+Lugosi+Oliveira-SubgaussianEstimators-2016.pdf", "content": "Empirical mean as a sub-Gaussian estimator . Median of means. Catoni 's estimators . Main results."}
+{"idx": 5, "title": "A generalized Catoni 's ${\\rm M}$- estimator under finite {$\\alpha$-th...", "date": "", "ddg_snippet": "We focus on estimators based on median-of-means techniques, but other methods such as the trimmed-mean and Catoni ’s estimators are also reviewed.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/344622193_A_generalized_Catoni's_rm_M-estimator_under_finite_alpha-th_moment_assumption_with_alpha_in_12", "content": "We focus on estimators based on median-of-means techniques, but other methods such as the trimmed-mean and Catoni ’s estimators are also reviewed."}
+{"idx": 6, "title": "On Catoni's M-Estimation - arXiv.org", "date": "", "ddg_snippet": "Catoni proposed a robust M- estimator and gave the deviation inequality for one fixed test function. The present paper is devoted to the uniform concentration inequality for a family of test functions. As an application, we consider empirical risk minimization for heavy-tailed losses.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.08211", "content": "Catoni proposed a robust M- estimator and gave the deviation inequality for one fixed test function. The present paper is devoted to the uniform concentration inequality for a family of test functions. As an application, we consider empirical risk minimization for heavy-tailed losses."}
+{"idx": 7, "title": "Nearly Optimal Catoni’s M-estimator for Infinite Variance", "date": "", "ddg_snippet": "The proposed estimator has the same order of magnitude and the same asymptotic constant as in Catoni (2012), but for the case of bounded moments. We further propose a version of the estimator that does not require even the knowledge of υε, but adapts the moment bound in a data-driven manner.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/bhatt22b/bhatt22b.pdf", "content": "The proposed estimator has the same order of magnitude and the same asymptotic constant as in Catoni (2012), but for the case of bounded moments. We further propose a version of the estimator that does not require even the knowledge of υε, but adapts the moment bound in a data-driven manner."}
+{"idx": 8, "title": "A generalized Catoni's M-estimator under finite -th moment ...", "date": "", "ddg_snippet": "Abstract: We generalize Catoni ’ s M- estimator , put forward in [3] by Ca- toni under finite variance assumption, to the case in which distributions can have finite α-th moment with α (1,2).", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journalArticle/Download?urlid=10.1214/21-EJS1911", "content": "Abstract: We generalize Catoni ’ s M- estimator , put forward in [3] by Ca- toni under finite variance assumption, to the case in which distributions can have finite α-th moment with α (1,2)."}
+{"idx": 9, "title": "stat-map/Catoni-Giulini M-estimator.md at main - GitHub", "date": "", "ddg_snippet": "This is the estimate of $\\la \\theta, \\E X\\ra$. An approximation $\\xi (\\theta)$ is computationally tractable for certain choices of $\\psi$, but still doesn't give a closed-form bound, since you can't compute it for all $\\theta$.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bchugg/stat-map/blob/main/Catoni-Giulini+M-estimator.md", "content": "This is the estimate of $\\la \\theta, \\E X\\ra$. An approximation $\\xi (\\theta)$ is computationally tractable for certain choices of $\\psi$, but still doesn't give a closed-form bound, since you can't compute it for all $\\theta$."}
diff --git "a/data/sampled_jsons/Catoni_estimator_influence_function_\316\250(x)_log(1_+_x_+_x\302\2622)_heavy-tailed.jsonl" "b/data/sampled_jsons/Catoni_estimator_influence_function_\316\250(x)_log(1_+_x_+_x\302\2622)_heavy-tailed.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..8c5ade61d9ac105a35ea50e1cfa29c041edb1747
--- /dev/null
+++ "b/data/sampled_jsons/Catoni_estimator_influence_function_\316\250(x)_log(1_+_x_+_x\302\2622)_heavy-tailed.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Stats Map · Catoni -Giulini M- Estimator", "date": "", "ddg_snippet": "Catoni -Giulini M- estimator . Modified Jan 05, 20252 min read. In 2017, Catoni and Giulini proposed. an approach to multivariate concentration based on M- estimation .", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/Catoni-Giulini-M-estimator", "content": "Catoni -Giulini M- estimator . Modified Jan 05, 20252 min read. In 2017, Catoni and Giulini proposed. an approach to multivariate concentration based on M- estimation ."}
+{"idx": 1, "title": "Beyond Catoni : Sharper Rates for Heavy - Tailed and Robust Mean", "date": "", "ddg_snippet": "Keywords: Mean Estimation , Heavy - Tailed Estimation , Robust Estimation , High-Dimensional Statistics. 1 − δ failure probability; the influence of such elements on the estimator (4) is negligible.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/gupta24a/gupta24a.pdf", "content": "Keywords: Mean Estimation , Heavy - Tailed Estimation , Robust Estimation , High-Dimensional Statistics. 1 − δ failure probability; the influence of such elements on the estimator (4) is negligible."}
+{"idx": 2, "title": "Beyond Catoni : Sharper", "date": "", "ddg_snippet": "Vanilla One -Dimensional Catoni Estimator . Improved Heavy - Tailed Estimator .• Failure probability δ, Two -dimensional iid samples x 1 , . . . , xn, ψ function , Scaling parameter.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.13010", "content": "Vanilla One -Dimensional Catoni Estimator . Improved Heavy - Tailed Estimator .• Failure probability δ, Two -dimensional iid samples x 1 , . . . , xn, ψ function , Scaling parameter."}
+{"idx": 3, "title": "Concentration study of M- estimators using the influence function", "date": "", "ddg_snippet": "Tail probabilities of M- estimator and influence function .value of β. In this context, in Section 5, we show that T ( X 1 n) is suitable to estimate the. mean in high dimension in a heavy - tailed and corrupted setting (even though. our estimators are not minimax in corrupted setting).", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03757720/document", "content": "Tail probabilities of M- estimator and influence function .value of β. In this context, in Section 5, we show that T ( X 1 n) is suitable to estimate the. mean in high dimension in a heavy - tailed and corrupted setting (even though. our estimators are not minimax in corrupted setting)."}
+{"idx": 4, "title": "Different chooses of influence function . | Download Scientific Diagram", "date": "", "ddg_snippet": "Estimating the expected value of a random variable by data-driven methods is one of the most fundamental problems in statistics. In this study, we present an extension of Olivier Catoni ’s classical M- estimators of the empirical mean, which focus on the heavy - tailed data by imposing...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Different-chooses-of-influence-function_fig1_360682529", "content": "Estimating the expected value of a random variable by data-driven methods is one of the most fundamental problems in statistics. In this study, we present an extension of Olivier Catoni ’s classical M- estimators of the empirical mean, which focus on the heavy - tailed data by imposing..."}
+{"idx": 5, "title": "Catoni -style confidence sequences", "date": "", "ddg_snippet": "Catoni -style confidence sequences for heavy - tailed mean estimation .Theorem 9 ( Catoni -style confidence sequence). Let {λt}t∈N+ be any predictable process, and let ϕ be a Catoni -type influence function .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/catoni-style-confidence-sequences-for-heavy-tailed-mean-1fw1oynx.pdf", "content": "Catoni -style confidence sequences for heavy - tailed mean estimation .Theorem 9 ( Catoni -style confidence sequence). Let {λt}t∈N+ be any predictable process, and let ϕ be a Catoni -type influence function ."}
+{"idx": 6, "title": "GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for...", "date": "", "ddg_snippet": "GL-LowPopArt consists of two stages: the first stage provides a rough, initial estimate , and the second stage refines it via matrix Catoni estima - tor (Minsker, 2018). It takes two designs π 1 and π 2 as inputs for Stage I and II , respectively.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TyArXyYnvz", "content": "GL-LowPopArt consists of two stages: the first stage provides a rough, initial estimate , and the second stage refines it via matrix Catoni estima - tor (Minsker, 2018). It takes two designs π 1 and π 2 as inputs for Stage I and II , respectively."}
+{"idx": 7, "title": "Sub-Gaussian Estimators of the Mean of a Random Matrix with...", "date": "", "ddg_snippet": "log ( 1 /α) n. Simple question: how to estimate the mean?Extensions to higher dimensions. Naive approach: apply the \"median trick\" (or Catoni ’s estimator ) coordinatewise. Makes the bound dimension-dependent.", "subpage_snippet": "", "source": "icerm.brown.edu", "link": "https://icerm.brown.edu/materials/Slides/tw-16-5/Sub-Gaussian_estimators_of_the_mean_of_a_random_matrix_with_entries_possessing_only_two_moments_]_Stanislav_Minsker,_University_of_Southern_California.pdf", "content": "log ( 1 /α) n. Simple question: how to estimate the mean?Extensions to higher dimensions. Naive approach: apply the \"median trick\" (or Catoni ’s estimator ) coordinatewise. Makes the bound dimension-dependent."}
+{"idx": 8, "title": "Beyond Catoni : Sharper Rates for Heavy - Tailed and Robust Mean...", "date": "", "ddg_snippet": "d -Dimensional Heavy - Tailed Estimation . • In d-dimensional estimation , we are given iid samples x 1 , . . . , xn ∈ Rd , with Cov (xi ) ≼ σ 2 Id and want to compute an estimate of the mean µ. • For simplicity, we will focus on the σ = 1 case.", "subpage_snippet": "", "source": "shivamgupta2.github.io", "link": "https://shivamgupta2.github.io/Beyond_Catoni_Slides.pdf", "content": "d -Dimensional Heavy - Tailed Estimation . • In d-dimensional estimation , we are given iid samples x 1 , . . . , xn ∈ Rd , with Cov (xi ) ≼ σ 2 Id and want to compute an estimate of the mean µ. • For simplicity, we will focus on the σ = 1 case."}
+{"idx": 9, "title": "Statistica Sinica Preprint No: SS-2024-0249", "date": "", "ddg_snippet": "Consider an increasing Catoni -type influence function ψ such that for x ∈ R.where Y ∈ R, X ∈ Rd, and β is the parameter vector to be estimated . Both X and Y are possibly heavy - tailed .", "subpage_snippet": "", "source": "www3.stat.sinica.edu.tw", "link": "https://www3.stat.sinica.edu.tw/ss_newpaper/SS-2024-0249_na.pdf", "content": "Consider an increasing Catoni -type influence function ψ such that for x ∈ R.where Y ∈ R, X ∈ Rd, and β is the parameter vector to be estimated . Both X and Y are possibly heavy - tailed ."}
diff --git a/data/sampled_jsons/Catoni_psi_function_log(1+x+x^22)_definition.jsonl b/data/sampled_jsons/Catoni_psi_function_log(1+x+x^22)_definition.jsonl
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index 0000000000000000000000000000000000000000..398fe602d7b16b44f98f2e7d4ca012ba26c19014
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Stats Map · Anisotropic Distribution", "date": "", "ddg_snippet": "Modified Sep 02, 2024 1 min read ... Mathematically, the definition is that P is anisotropic if there exists some orthogonal matrix Q such that", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/anisotropic-distribution", "content": "Modified Sep 02, 2024 1 min read ... Mathematically, the definition is that P is anisotropic if there exists some orthogonal matrix Q such that"}
+{"idx": 1, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "... log T ) 𝒪 𝑡 𝑇 \\mathcal{O}(t\\ log T) caligraphic_O ( italic_t roman_ log italic_T ) to 𝒪 ( 1 ) 𝒪 1 \\mathcal{O}( 1 ) caligraphic_O ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "... log T ) 𝒪 𝑡 𝑇 \\mathcal{O}(t\\ log T) caligraphic_O ( italic_t roman_ log italic_T ) to 𝒪 ( 1 ) 𝒪 1 \\mathcal{O}( 1 ) caligraphic_O ( ..."}
+{"idx": 2, "title": "E-detectors: A Nonparametric Framework for Sequential Change", "date": "", "ddg_snippet": "M_{n}^{\\mathrm{SR}}}=\\frac{{p_{\\mu }}({ X _{n}})}{{p_{{\\mu _{0}}}}({ X _{n}})}\\cdot \\big[{M_{n- 1 }^{\\mathrm{SR}}}+ 1 \\big],\\hspace{ 2 .5pt}\\hspace{ 2 .5pt}{M_{0 ...", "subpage_snippet": "", "source": "nejsds.nestat.org", "link": "https://nejsds.nestat.org/journal/NEJSDS/article/59/text", "content": "M_{n}^{\\mathrm{SR}}}=\\frac{{p_{\\mu }}({ X _{n}})}{{p_{{\\mu _{0}}}}({ X _{n}})}\\cdot \\big[{M_{n- 1 }^{\\mathrm{SR}}}+ 1 \\big],\\hspace{ 2 .5pt}\\hspace{ 2 .5pt}{M_{0 ..."}
+{"idx": 3, "title": "E-detectors: A Nonparametric Framework for Sequential Change", "date": "", "ddg_snippet": "M_{n}^{\\mathrm{SR}}}=\\frac{{p_{\\mu }}({ X _{n}})}{{p_{{\\mu _{0}}}}({ X _{n}})}\\cdot \\big[{M_{n- 1 }^{\\mathrm{SR}}}+ 1 \\big],\\hspace{ 2 .5pt}\\hspace{ 2 .5pt}{M_{0 ...", "subpage_snippet": "", "source": "nejsds.nestat.org", "link": "https://nejsds.nestat.org/journal/NEJSDS/article/59/read", "content": "M_{n}^{\\mathrm{SR}}}=\\frac{{p_{\\mu }}({ X _{n}})}{{p_{{\\mu _{0}}}}({ X _{n}})}\\cdot \\big[{M_{n- 1 }^{\\mathrm{SR}}}+ 1 \\big],\\hspace{ 2 .5pt}\\hspace{ 2 .5pt}{M_{0 ..."}
+{"idx": 4, "title": "The Stats Map · Asymptotic Confidence Sequences", "date": "", "ddg_snippet": "A ( 1 − α )-asymptotic CS for a parameter μ is a sequence of intervals ([ θ t − L t , θ t + R t ]) such that there exists a ...", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/asymptotic-confidence-sequences", "content": "A ( 1 − α )-asymptotic CS for a parameter μ is a sequence of intervals ([ θ t − L t , θ t + R t ]) such that there exists a ..."}
+{"idx": 5, "title": "1 Introduction", "date": "", "ddg_snippet": "Figure 1 : Robust Universal Confidence set for the L 2 L_{ 2 } projection θ ~ L 2 = ( [ 0 , 1 ] , 0.19 , 0.19 ) \\widetilde{\\theta}_{L_{ 2 }}=([0, 1 ],0.19 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.04034v4", "content": "Figure 1 : Robust Universal Confidence set for the L 2 L_{ 2 } projection θ ~ L 2 = ( [ 0 , 1 ] , 0.19 , 0.19 ) \\widetilde{\\theta}_{L_{ 2 }}=([0, 1 ],0.19 ..."}
+{"idx": 6, "title": "Data-driven distributionally robust optimization using the", "date": "", "ddg_snippet": "The Wasserstein distance of two distributions \\(\\mathbb {Q}_ 1 \\) and \\(\\mathbb {Q}_ 2 \\) can be viewed as the minimum transportation cost for moving the ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10107-017-1172-1", "content": "The Wasserstein distance of two distributions \\(\\mathbb {Q}_ 1 \\) and \\(\\mathbb {Q}_ 2 \\) can be viewed as the minimum transportation cost for moving the ..."}
+{"idx": 7, "title": "Aryana-bs: context-aware alignment of bisulfite-sequencing", "date": "", "ddg_snippet": "Bismark [ 10 ] produces two indices from the reference genome by converting Cs to Ts and Gs to As and then aligns the reads to each of them using the ...", "subpage_snippet": "", "source": "bmcbioinformatics.biomedcentral.com", "link": "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-025-06182-5", "content": "Bismark [ 10 ] produces two indices from the reference genome by converting Cs to Ts and Gs to As and then aligns the reads to each of them using the ..."}
+{"idx": 8, "title": "The variational approach to concentration", "date": "", "ddg_snippet": "We apply the master theorem with the product distribution \\(\\mu_u\\times \\mu_v\\) for \\( u,v\\in\\Sigma^{ 1 / 2 }\\sd\\) where \\(\\sd = \\{ x :\\norm{ x }= 1 \\}\\) is ...", "subpage_snippet": "", "source": "benchugg.com", "link": "https://benchugg.com/research_notes/variational_approach_to_concentration/", "content": "We apply the master theorem with the product distribution \\(\\mu_u\\times \\mu_v\\) for \\( u,v\\in\\Sigma^{ 1 / 2 }\\sd\\) where \\(\\sd = \\{ x :\\norm{ x }= 1 \\}\\) is ..."}
+{"idx": 9, "title": "Extended UCB Policies for Multi-Armed Bandit Problems", "date": "", "ddg_snippet": "exp ( u ( X − 𝔼 X ) ) ] ≤ exp ( ζ u 2 / 2 ) , ∀ | u | ≤ u 0 , ∀ ζ ≥ sup | u | ≤ u 0 M ( 2 ) ( u ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1112.1768v5", "content": "exp ( u ( X − 𝔼 X ) ) ] ≤ exp ( ζ u 2 / 2 ) , ∀ | u | ≤ u 0 , ∀ ζ ≥ sup | u | ≤ u 0 M ( 2 ) ( u ..."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit.jsonl
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index 0000000000000000000000000000000000000000..e9d2100b3091ff9013a223233f032bcf22ebaa8c
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "In this 16 work, we develop a comprehensive causal model of how and why 17 Reddit users engage with activist communities driving mass climate 18 protests (mainly the 2019 Earth Strike, Fridays for Future, and Ex- 19 tinction Rebellion). Our framework, based on Stochastic Variational 20 Inference applied to Bayesian Networks, learns the causal ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "In this 16 work, we develop a comprehensive causal model of how and why 17 Reddit users engage with activist communities driving mass climate 18 protests (mainly the 2019 Earth Strike, Fridays for Future, and Ex- 19 tinction Rebellion). Our framework, based on Stochastic Variational 20 Inference applied to Bayesian Networks, learns the causal ..."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714684", "content": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion)."}
+{"idx": 2, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
+{"idx": 3, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "TL;DR: Causal model to study the interplay between media coverage, online interactions and sociodemographic features in the engagement in climate activism communities in Reddit .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6yBhoJn6qy", "content": "TL;DR: Causal model to study the interplay between media coverage, online interactions and sociodemographic features in the engagement in climate activism communities in Reddit ."}
+{"idx": 4, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "We developed a rich and comprehensive causal model to study the interplay between diferent determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "We developed a rich and comprehensive causal model to study the interplay between diferent determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data."}
+{"idx": 5, "title": "r/climatechange on Reddit: To all the climate activists here on Reddit ...", "date": "", "ddg_snippet": "To all the climate activists here on Reddit , what drives you towards this cause? What made you a climate activist? This is for research purposes. Really keen to know a bit of your story.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/climatechange/comments/11s32ot/to_all_the_climate_activists_here_on_reddit_what/", "content": "To all the climate activists here on Reddit , what drives you towards this cause? What made you a climate activist? This is for research purposes. Really keen to know a bit of your story."}
+{"idx": 6, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "We developed a rich and comprehensive causal model to study the interplay between different determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "We developed a rich and comprehensive causal model to study the interplay between different determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data."}
+{"idx": 7, "title": "Causal Modeling of Climate Activism on Reddit - Semantic Scholar", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Causal-Modeling-of-Climate-Activism-on-Reddit-Lenti-Aiello/c4c7c3972ba102db37738c082f27ebfcd3983057", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
+{"idx": 8, "title": "Causal Modeling of Climate Activism on Reddit - ScienceOpen", "date": "", "ddg_snippet": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).", "subpage_snippet": "", "source": "www.scienceopen.com", "link": "https://www.scienceopen.com/document?vid=e07fdb2b-4dc5-465e-bddc-88fff36d5a28", "content": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion)."}
+{"idx": 9, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2410.10562", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_sitearxiv.org_'Section_4.4'_'Sympathy'_'activation'_year_2024.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_sitearxiv.org_'Section_4.4'_'Sympathy'_'activation'_year_2024.jsonl
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index 0000000000000000000000000000000000000000..b0b8ff839732a366f44370e54e3c2b1c0488f019
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_sitearxiv.org_'Section_4.4'_'Sympathy'_'activation'_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causality - Wikipedia", "date": "", "ddg_snippet": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Causality", "content": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future."}
+{"idx": 1, "title": "Sentiment and Social Signals in the Climate Crisis: A Survey on", "date": "", "ddg_snippet": "As climate instability intensifies, societies worldwide are grappling not only with the physical consequences of extreme weather events but also with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.18837v3", "content": "As climate instability intensifies, societies worldwide are grappling not only with the physical consequences of extreme weather events but also with ..."}
+{"idx": 2, "title": "CAUSAL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/causal", "content": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence."}
+{"idx": 3, "title": "CAUSAL | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/causal", "content": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more."}
+{"idx": 4, "title": "CAUSAL Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/causal", "content": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence."}
+{"idx": 5, "title": "Causal - definition of causal by The Free Dictionary", "date": "", "ddg_snippet": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/causal", "content": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause."}
+{"idx": 6, "title": "CAUSAL definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "If there is a causal relationship between two things, one thing is responsible for causing the other thing.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/causal", "content": "If there is a causal relationship between two things, one thing is responsible for causing the other thing."}
+{"idx": 7, "title": "causal adjective - Definition, pictures, pronunciation and usage...", "date": "", "ddg_snippet": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/causal", "content": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 8, "title": "causal , n. & adj. meanings, etymology and more | Oxford English...", "date": "", "ddg_snippet": "causal , n. & adj. meanings, etymology, pronunciation and more in the Oxford English Dictionary", "subpage_snippet": "", "source": "www.oed.com", "link": "https://www.oed.com/dictionary/causal_n", "content": "causal , n. & adj. meanings, etymology, pronunciation and more in the Oxford English Dictionary"}
+{"idx": 9, "title": "causal - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark.", "subpage_snippet": "", "source": "en.m.wiktionary.org", "link": "https://en.m.wiktionary.org/wiki/causal", "content": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_six_subreddits_activation.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_six_subreddits_activation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0b2bdcf7460da5af726b9e571c7fd4ea311ef800
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_six_subreddits_activation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — Summary: This paper proposes a causal model to measure the effects of media, demographic features and user interaction on activation in climate activism groups ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — Summary: This paper proposes a causal model to measure the effects of media, demographic features and user interaction on activation in climate activism groups ..."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests ."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — These characteris- tics of the subreddits increase the probability of interacting with some activists , thus raising the probability of activation . All four.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — These characteris- tics of the subreddits increase the probability of interacting with some activists , thus raising the probability of activation . All four."}
+{"idx": 3, "title": "arXiv:2502.05049v1 [cs.SI] 7 Feb 2025", "date": "", "ddg_snippet": "by F Cinus · 2025 — To maximize coverage of. Reddit's user base, we use the most popular subreddits as the feature space. ... Causal Modeling of Climate Activism on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05049", "content": "by F Cinus · 2025 — To maximize coverage of. Reddit's user base, we use the most popular subreddits as the feature space. ... Causal Modeling of Climate Activism on ..."}
+{"idx": 4, "title": "A Collective Field Experiment Disentangling Participation ...", "date": "", "ddg_snippet": "by L Oswald · 2025 — After screening out low-quality responses (see SI), we randomly assigned participants to one of our six subreddits : two incentivized subreddits , ...", "subpage_snippet": "", "source": "osf.io", "link": "https://osf.io/p2jaq_v1/download/?format=pdf", "content": "by L Oswald · 2025 — After screening out low-quality responses (see SI), we randomly assigned participants to one of our six subreddits : two incentivized subreddits , ..."}
+{"idx": 5, "title": "Emergent structures of attention on social media are driven by ...", "date": "", "ddg_snippet": "by AH Smith · 2025 — We leverage a unique combination of data and methods to demonstrate a causal link between amplification and triad transitivity in a directed social network.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/pnasnexus/article/4/4/pgaf106/8102872", "content": "by AH Smith · 2025 — We leverage a unique combination of data and methods to demonstrate a causal link between amplification and triad transitivity in a directed social network."}
+{"idx": 6, "title": "large-scale investigation of everyday moral dilemmas", "date": "", "ddg_snippet": "by DA Yudkin · 2025 · Cited by 4 — Reddit is a decentralized online platform designed to facilitate user-driven discussions across a vast array of topics, organized into ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/pnasnexus/article/4/5/pgaf119/8124984", "content": "by DA Yudkin · 2025 · Cited by 4 — Reddit is a decentralized online platform designed to facilitate user-driven discussions across a vast array of topics, organized into ..."}
+{"idx": 7, "title": "What Makes People Join Conspiracy Communities?: Role ...", "date": "", "ddg_snippet": "5 Jan 2021 — We leverage longitudinal data from 56 conspiracy communities on Reddit to compare individual and social factors determining which users join the communities.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3432922", "content": "5 Jan 2021 — We leverage longitudinal data from 56 conspiracy communities on Reddit to compare individual and social factors determining which users join the communities."}
+{"idx": 8, "title": "Framing, Emotions, Salience: The Future Of Ai As Seen By ...", "date": "", "ddg_snippet": "by A Ocal · 2023 · Cited by 1 — On the grounds of framing theory and affective intelligence theory, this work investigates technological frames expressed in social media ...", "subpage_snippet": "", "source": "surface.syr.edu", "link": "https://surface.syr.edu/cgi/viewcontent.cgi?article=2897&context=etd", "content": "by A Ocal · 2023 · Cited by 1 — On the grounds of framing theory and affective intelligence theory, this work investigates technological frames expressed in social media ..."}
+{"idx": 9, "title": "Looking for theories for sustainable world peace", "date": "", "ddg_snippet": "I would like to have your opinions on: a) Do you know of a theory that seems viable to avoid wars in the future? b) Maybe the question is ill posed.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/PoliticalScience/comments/1c0m9z2/looking_for_theories_for_sustainable_world_peace/", "content": "I would like to have your opinions on: a) Do you know of a theory that seems viable to avoid wars in the future? b) Maybe the question is ill posed."}
diff --git a/data/sampled_jsons/Characteristic_Interventional_Sum-Product_Networks_2024.jsonl b/data/sampled_jsons/Characteristic_Interventional_Sum-Product_Networks_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6655c827405c7a99a44e832bc1dcda3a4945eeb4
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+++ b/data/sampled_jsons/Characteristic_Interventional_Sum-Product_Networks_2024.jsonl
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+{"idx": 0, "title": "||chi;$SPN: Characteristic Interventional Sum-Product Networks ... Code Repository for the paper χ -SPN: Characteristic ... χSPN: Characteristic Interventional Sum-Product Networks for ... ||chi;$SPN: Characteristic Interventional Sum-Product Networks ... χSPN Characteristic Interventional SumProduct Networks for ... ΧSPN | Proceedings of the Fortieth Conference on Uncertainty ... $\\chi$SPN: Characteristic Interventional Sum-Product Networks ...", "date": "", "ddg_snippet": "Aug 14, 2024 · We take a step towards this direction and propose Characteristic Interventional Sum-Product Network (χ SPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. χ SPN uses characteristic functions in the leaves of an interventional SPN (iSPN) thereby providing a unified ... Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network ( χ -SPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. χ -SPN uses characteristic ... Abstract Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network (χSPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. χSPN uses ... Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Charac... title: χSPN Characteristic Interventional SumProduct Networks for Causal Inference in Hybrid Domains publish date: 2024 -08-14 Jan 3, 2025 · Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Ch aracteristic I nterventional Sum-Product Network (ΧSPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. ΧSPN uses characteristic ... Jun 22, 2024 · Published: 22 Jun 2024 , Last Modified: 05 Aug 2024 TPM 2024 Everyone Revisions BibTeX CC BY 4.0 Keywords: causality, hybrid domains, sum-product networks , characteristic functions TL;DR: We present the 1st causal based based on characteristic functions and sum-product networks for hybrid domains.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.07545", "content": "Aug 14, 2024 · We take a step towards this direction and propose Characteristic Interventional Sum-Product Network (χ SPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. χ SPN uses characteristic functions in the leaves of an interventional SPN (iSPN) thereby providing a unified ... Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network ( χ -SPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. χ -SPN uses characteristic ... Abstract Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network (χSPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. χSPN uses ... Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Charac... title: χSPN Characteristic Interventional SumProduct Networks for Causal Inference in Hybrid Domains publish date: 2024 -08-14 Jan 3, 2025 · Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Ch aracteristic I nterventional Sum-Product Network (ΧSPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. ΧSPN uses characteristic ... Jun 22, 2024 · Published: 22 Jun 2024 , Last Modified: 05 Aug 2024 TPM 2024 Everyone Revisions BibTeX CC BY 4.0 Keywords: causality, hybrid domains, sum-product networks , characteristic functions TL;DR: We present the 1st causal based based on characteristic functions and sum-product networks for hybrid domains."}
+{"idx": 1, "title": "χSPN: Characteristic Interventional Sum-Product Networks for ...", "date": "", "ddg_snippet": "Abstract Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network (χSPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. χSPN uses ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v244/main/assets/poonia24a/poonia24a.pdf", "content": "Abstract Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network (χSPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. χSPN uses ..."}
+{"idx": 2, "title": "$χ$SPN: Characteristic Interventional Sum-Product Networks ...", "date": "", "ddg_snippet": "by H Poonia · 2024 · Cited by 6 — (2024). $χ$SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains. Proceedings of the Fortieth Conference on Uncertainty ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v244/poonia24a.html", "content": "by H Poonia · 2024 · Cited by 6 — (2024). $χ$SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains. Proceedings of the Fortieth Conference on Uncertainty ..."}
+{"idx": 3, "title": "Code Repository for the paper χ -SPN: Characteristic ...", "date": "", "ddg_snippet": "Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network ( χ -SPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. χ -SPN uses characteristic ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/harpoonix/chi-SPN", "content": "Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network ( χ -SPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. χ -SPN uses characteristic ..."}
+{"idx": 4, "title": "χSPN Characteristic Interventional SumProduct Networks for ...", "date": "", "ddg_snippet": "title: χSPN Characteristic Interventional SumProduct Networks for Causal Inference in Hybrid Domains publish date: 2024 -08-14", "subpage_snippet": "", "source": "dataset-finder.borninsea.com", "link": "https://dataset-finder.borninsea.com/posts/χSPN_Characteristic_Interventional_SumProduct_Networks_for_Causal_Inference_in_Hybrid_Domains/", "content": "title: χSPN Characteristic Interventional SumProduct Networks for Causal Inference in Hybrid Domains publish date: 2024 -08-14"}
+{"idx": 5, "title": "$\\chi$SPN: Characteristic Interventional Sum-Product Networks ...", "date": "", "ddg_snippet": "Jun 22, 2024 · Published: 22 Jun 2024 , Last Modified: 05 Aug 2024 TPM 2024 Everyone Revisions BibTeX CC BY 4.0 Keywords: causality, hybrid domains, sum-product networks , characteristic functions TL;DR: We present the 1st causal based based on characteristic functions and sum-product networks for hybrid domains.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=bttY3ppppX", "content": "Jun 22, 2024 · Published: 22 Jun 2024 , Last Modified: 05 Aug 2024 TPM 2024 Everyone Revisions BibTeX CC BY 4.0 Keywords: causality, hybrid domains, sum-product networks , characteristic functions TL;DR: We present the 1st causal based based on characteristic functions and sum-product networks for hybrid domains."}
+{"idx": 6, "title": "$\\chi$SPN: Characteristic Interventional Sum-Product ...", "date": "", "ddg_snippet": "by H Poonia · Cited by 6 — The main strength of the proposed χ-SPN network is its ability to estimate interventional distributions in the presence of random variables drawn from mixed ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=s3kqfH5KBI", "content": "by H Poonia · Cited by 6 — The main strength of the proposed χ-SPN network is its ability to estimate interventional distributions in the presence of random variables drawn from mixed ..."}
+{"idx": 7, "title": "$χ$SPN: Characteristic Interventional Sum-Product Networks for ...", "date": "", "ddg_snippet": "The paper introduces Characteristic Interventional Sum-Product Networks (χSPN), which aim to solve the challenge of causal inference in hybrid domains— ...", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-SPN-Characteristic-Interventional-clzvrlexu2ndb01glo03vj5cf", "content": "The paper introduces Characteristic Interventional Sum-Product Networks (χSPN), which aim to solve the challenge of causal inference in hybrid domains— ..."}
+{"idx": 8, "title": "[Revisión de artículo] $χ$SPN: Characteristic Interventional Sum- ...", "date": "", "ddg_snippet": "1. Characteristics of SPNs. A Sum - Product Network (SPN) is a directed acyclic graph that consists of sum and product nodes. Each type of node has distinct roles ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/es/review/spn-characteristic-interventional-sum-product-networks-for-causal-inference-in-hybrid-domains", "content": "1. Characteristics of SPNs. A Sum - Product Network (SPN) is a directed acyclic graph that consists of sum and product nodes. Each type of node has distinct roles ..."}
+{"idx": 9, "title": "SPN: Characteristic Interventional Sum-Product Networks ...", "date": "", "ddg_snippet": "Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.07545v1", "content": "Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a ..."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_Dike_Eris_arxiv_year_2023-2024.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_Dike_Eris_arxiv_year_2023-2024.jsonl
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+{"idx": 2, "title": "ICML ICML Poster A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "May 8, 2025 - This paper introduces a ... ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "May 8, 2025 - This paper introduces a ... ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial ..."}
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+{"idx": 6, "title": "Renaissance Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Artistic scrolls and elegance grace these renaissance checks in a rich four color rotation. Coordinating renaissance address labels and checkbook cover are also available.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/834/renaissance-checks", "content": "Artistic scrolls and elegance grace these renaissance checks in a rich four color rotation. Coordinating renaissance address labels and checkbook cover are also available."}
+{"idx": 7, "title": "Modern Checks - Order Cool & Contemporary Personal Checks", "date": "", "ddg_snippet": "Show your trendy style with contemporary and cool personal checks from Checks .com! Choose a stylish check design or pattern and order checks online today!", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/274/modern-checks", "content": "Show your trendy style with contemporary and cool personal checks from Checks .com! Choose a stylish check design or pattern and order checks online today!"}
+{"idx": 8, "title": "Pretty in Pink Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Discover the perfect blend of Parisian style and elegance with our Pretty in Pink checks . Designed for those who live life with flair, these top-tear pink personal checks are available in four vibrant designs featuring stripes, polka dots and diamond patterns.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/1799/pretty-in-pink-checks", "content": "Discover the perfect blend of Parisian style and elegance with our Pretty in Pink checks . Designed for those who live life with flair, these top-tear pink personal checks are available in four vibrant designs featuring stripes, polka dots and diamond patterns."}
+{"idx": 9, "title": "Horse Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Horse Play Checks Photographs of galloping horses adorn this equine rotation of stallions, mustangs and foals. Coordinating horse return address labels are available.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/811/horse-play-checks", "content": "Horse Play Checks Photographs of galloping horses adorn this equine rotation of stallions, mustangs and foals. Coordinating horse return address labels are available."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_dataset_empirical_studies_four_reasons.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_dataset_empirical_studies_four_reasons.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90f6b4091aaedc0cedf3d4eec6e3c579aa071c8e
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_dataset_empirical_studies_four_reasons.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 2, "title": "Sustainable Good Governance, Development and ...", "date": "", "ddg_snippet": "... checks and balances' framework . In this framework, the Union government should focus on the core responsibilities, having to do with defence, external ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/sustainable-good-governance-development-and-democracy-9789352808113.html", "content": "... checks and balances' framework . In this framework, the Union government should focus on the core responsibilities, having to do with defence, external ..."}
+{"idx": 3, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment · What can large language models do for sustainable food? Exact risk curves of signSGD ...", "subpage_snippet": "", "source": "dev.icml.cc", "link": "https://dev.icml.cc/virtual/current/papers.html", "content": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment · What can large language models do for sustainable food? Exact risk curves of signSGD ..."}
+{"idx": 4, "title": "The Unified Cognitive Consciousness Theory for ...", "date": "", "ddg_snippet": "2 Jun 2025 — Empirical studies reveal the importance of semantic anchoring ... A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.02139v1", "content": "2 Jun 2025 — Empirical studies reveal the importance of semantic anchoring ... A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment."}
+{"idx": 5, "title": "Multi-LLM Agent Collaborative Intelligence: The Path to AGI", "date": "", "ddg_snippet": "... studies from various domains. This structure enables the reader to ... Checks-and-Balances Framework for Context-. Aware Ethical Alignment of Large ... 589 pages", "subpage_snippet": "", "source": "shuyuej.com", "link": "http://shuyuej.com/books/The-Path-to-Artificial-General-Intelligence.pdf", "content": "... studies from various domains. This structure enables the reader to ... Checks-and-Balances Framework for Context-. Aware Ethical Alignment of Large ... 589 pages"}
+{"idx": 6, "title": "The dynamics of military coups in the contemporary Middle ...", "date": "", "ddg_snippet": "by W Ma · 2025 — The embedded checks-and-balances framework further incentivizes institutional resistance to coup plots (Bruin 2020; Makara 2013; Albrecht 2014).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s44216-025-00050-y", "content": "by W Ma · 2025 — The embedded checks-and-balances framework further incentivizes institutional resistance to coup plots (Bruin 2020; Makara 2013; Albrecht 2014)."}
+{"idx": 7, "title": "Computer Science", "date": "", "ddg_snippet": "We provide both theoretical analysis and empirical ... This paper introduces a checks-and-balances framework ... reason without access to real-time scene data .", "subpage_snippet": "", "source": "www.arxiv.org", "link": "http://www.arxiv.org/list/cs/new?skip=875&show=500", "content": "We provide both theoretical analysis and empirical ... This paper introduces a checks-and-balances framework ... reason without access to real-time scene data ."}
+{"idx": 8, "title": "ICLM 2025 AI Safety 7847 Camera Ready3 | PDF | Emotions", "date": "", "ddg_snippet": "10 Aug 2025 — Empirical Studies risk of excessive censorship? The ethical ... work introduces a checks-and-balances framework for notators and ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/899938563/ICLM-2025-AI-Safety-7847-Camera-Ready3", "content": "10 Aug 2025 — Empirical Studies risk of excessive censorship? The ethical ... work introduces a checks-and-balances framework for notators and ..."}
+{"idx": 9, "title": "How Poor Decisions are Smoldering Within the U.S. Fire Service", "date": "", "ddg_snippet": "by CD Cavnor · 2018 · Cited by 5 — A “ checks and balances” framework has been established in high-risk occupations that verify that safety procedures are followed. Production over Safety. Work ...", "subpage_snippet": "", "source": "apps.dtic.mil", "link": "https://apps.dtic.mil/sti/tr/pdf/AD1052528.pdf", "content": "by CD Cavnor · 2018 · Cited by 5 — A “ checks and balances” framework has been established in high-risk occupations that verify that safety procedures are followed. Production over Safety. Work ..."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b872c6defc7516ac7f650db785364c1a19d28556
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment.jsonl
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+{"idx": 0, "title": "Order Personal Checks Online At Affordable Prices | Checks.com", "date": "", "ddg_snippet": "We print an exceptional collection of high-quality personal checks at cheap prices. Whether you are buying a checkbook for the first time or are reordering your favorite check design, you’ll always get the same low price - no discounts codes required.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/", "content": "We print an exceptional collection of high-quality personal checks at cheap prices. Whether you are buying a checkbook for the first time or are reordering your favorite check design, you’ll always get the same low price - no discounts codes required."}
+{"idx": 1, "title": "View Over 70 Personal Checks Designs at Low Prices", "date": "", "ddg_snippet": "Order your favorite personal checks online at low prices. Checks .com features a variety of over 70 personal check designs to fit your unique personality.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/129/personal-checks", "content": "Order your favorite personal checks online at low prices. Checks .com features a variety of over 70 personal check designs to fit your unique personality."}
+{"idx": 2, "title": "Value Checks - Order Our Best Value Checks Online", "date": "", "ddg_snippet": "Order value-priced personal checks online starting at just $8.20 per box at Checks .com! We have a variety of inexpensive check designs to choose from, so you can find the perfect one to match your style and budget.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/323/value-checks", "content": "Order value-priced personal checks online starting at just $8.20 per box at Checks .com! We have a variety of inexpensive check designs to choose from, so you can find the perfect one to match your style and budget."}
+{"idx": 3, "title": "How To Order Checks Online | Checks.com", "date": "", "ddg_snippet": "Order checks , address labels and checkbook covers online, secure, fast and easy with Checks .com. Customize and preview your checks before you order.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/how-to-order-checks", "content": "Order checks , address labels and checkbook covers online, secure, fast and easy with Checks .com. Customize and preview your checks before you order."}
+{"idx": 4, "title": "Traditional Checks - Order Affordable Personal Checks Online", "date": "", "ddg_snippet": "Checks .com's collection of traditional checks offers the widest variety of designs where you're sure to find a favorite or two. Also check out our line of Choice Checks for our most exclusive and most secure check designs, or browse our cheaper-priced checks for an incredibly low price per box.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/334/traditional-checks", "content": "Checks .com's collection of traditional checks offers the widest variety of designs where you're sure to find a favorite or two. Also check out our line of Choice Checks for our most exclusive and most secure check designs, or browse our cheaper-priced checks for an incredibly low price per box."}
+{"idx": 5, "title": "Reorder Discounted Personal Checks Online", "date": "", "ddg_snippet": "At Checks .com, whenever you reorder checks , you pay the same low price as intro customers. Reorder checks for discounted prices online today!", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/quickreorder", "content": "At Checks .com, whenever you reorder checks , you pay the same low price as intro customers. Reorder checks for discounted prices online today!"}
+{"idx": 6, "title": "Renaissance Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Artistic scrolls and elegance grace these renaissance checks in a rich four color rotation. Coordinating renaissance address labels and checkbook cover are also available.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/834/renaissance-checks", "content": "Artistic scrolls and elegance grace these renaissance checks in a rich four color rotation. Coordinating renaissance address labels and checkbook cover are also available."}
+{"idx": 7, "title": "Modern Checks - Order Cool & Contemporary Personal Checks", "date": "", "ddg_snippet": "Show your trendy style with contemporary and cool personal checks from Checks .com! Choose a stylish check design or pattern and order checks online today!", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/274/modern-checks", "content": "Show your trendy style with contemporary and cool personal checks from Checks .com! Choose a stylish check design or pattern and order checks online today!"}
+{"idx": 8, "title": "Pretty in Pink Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Discover the perfect blend of Parisian style and elegance with our Pretty in Pink checks . Designed for those who live life with flair, these top-tear pink personal checks are available in four vibrant designs featuring stripes, polka dots and diamond patterns.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/1799/pretty-in-pink-checks", "content": "Discover the perfect blend of Parisian style and elegance with our Pretty in Pink checks . Designed for those who live life with flair, these top-tear pink personal checks are available in four vibrant designs featuring stripes, polka dots and diamond patterns."}
+{"idx": 9, "title": "Horse Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Horse Play Checks Photographs of galloping horses adorn this equine rotation of stallions, mustangs and foals. Coordinating horse return address labels are available.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/811/horse-play-checks", "content": "Horse Play Checks Photographs of galloping horses adorn this equine rotation of stallions, mustangs and foals. Coordinating horse return address labels are available."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Dike_Eris_introduction_year_2023-2024.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Dike_Eris_introduction_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ba27bc55af28804d42d80f30042d22de7fc670e
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Dike_Eris_introduction_year_2023-2024.jsonl
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+{"idx": 0, "title": "Order Personal Checks Online At Affordable Prices | Checks.com", "date": "", "ddg_snippet": "We print an exceptional collection of high-quality personal checks at cheap prices. Whether you are buying a checkbook for the first time or are reordering your favorite check design, you’ll always get the same low price - no discounts codes required.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/", "content": "We print an exceptional collection of high-quality personal checks at cheap prices. Whether you are buying a checkbook for the first time or are reordering your favorite check design, you’ll always get the same low price - no discounts codes required."}
+{"idx": 1, "title": "View Over 70 Personal Checks Designs at Low Prices", "date": "", "ddg_snippet": "Order your favorite personal checks online at low prices. Checks .com features a variety of over 70 personal check designs to fit your unique personality.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/129/personal-checks", "content": "Order your favorite personal checks online at low prices. Checks .com features a variety of over 70 personal check designs to fit your unique personality."}
+{"idx": 2, "title": "Value Checks - Order Our Best Value Checks Online", "date": "", "ddg_snippet": "Order value-priced personal checks online starting at just $8.20 per box at Checks .com! We have a variety of inexpensive check designs to choose from, so you can find the perfect one to match your style and budget.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/323/value-checks", "content": "Order value-priced personal checks online starting at just $8.20 per box at Checks .com! We have a variety of inexpensive check designs to choose from, so you can find the perfect one to match your style and budget."}
+{"idx": 3, "title": "Traditional Checks - Order Affordable Personal Checks Online", "date": "", "ddg_snippet": "Checks .com's collection of traditional checks offers the widest variety of designs where you're sure to find a favorite or two. Also check out our line of Choice Checks for our most exclusive and most secure check designs, or browse our cheaper-priced checks for an incredibly low price per box.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/334/traditional-checks", "content": "Checks .com's collection of traditional checks offers the widest variety of designs where you're sure to find a favorite or two. Also check out our line of Choice Checks for our most exclusive and most secure check designs, or browse our cheaper-priced checks for an incredibly low price per box."}
+{"idx": 4, "title": "Reorder Discounted Personal Checks Online", "date": "", "ddg_snippet": "At Checks .com, whenever you reorder checks , you pay the same low price as intro customers. Reorder checks for discounted prices online today!", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/quickreorder", "content": "At Checks .com, whenever you reorder checks , you pay the same low price as intro customers. Reorder checks for discounted prices online today!"}
+{"idx": 5, "title": "Classic Checks - Order Affordable Classic Personal Checks Online", "date": "", "ddg_snippet": "Shop vintage designs and exceptional value with our classic checks collection. Order personal checks online confidently with our satisfaction guarantee.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/271/classic-checks", "content": "Shop vintage designs and exceptional value with our classic checks collection. Order personal checks online confidently with our satisfaction guarantee."}
+{"idx": 6, "title": "Order a Box of Swirl-Patterned Checks Online", "date": "", "ddg_snippet": "These swirl-patterned personal checks are just peachy & are available online from checks .com! Order coordinating address labels also available.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/1131/savvy-checks", "content": "These swirl-patterned personal checks are just peachy & are available online from checks .com! Order coordinating address labels also available."}
+{"idx": 7, "title": "Pretty in Pink Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Discover the perfect blend of Parisian style and elegance with our Pretty in Pink checks . Designed for those who live life with flair, these top-tear pink personal checks are available in four vibrant designs featuring stripes, polka dots and diamond patterns.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/1799/pretty-in-pink-checks", "content": "Discover the perfect blend of Parisian style and elegance with our Pretty in Pink checks . Designed for those who live life with flair, these top-tear pink personal checks are available in four vibrant designs featuring stripes, polka dots and diamond patterns."}
+{"idx": 8, "title": "Check Your Order Status Securely | Checks.com", "date": "", "ddg_snippet": "Track your check order online with our safe and secure order tracking system at Checks .com", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/order-status", "content": "Track your check order online with our safe and secure order tracking system at Checks .com"}
+{"idx": 9, "title": "Horse Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Horse Play Checks Photographs of galloping horses adorn this equine rotation of stallions, mustangs and foals. Coordinating horse return address labels are available.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/811/horse-play-checks", "content": "Horse Play Checks Photographs of galloping horses adorn this equine rotation of stallions, mustangs and foals. Coordinating horse return address labels are available."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_studies.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_studies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dfc166a03aa561fc45f716a3b181d7238a362b97
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_studies.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while preserv-ing the integrity of LLM knowledge without interference from the RLHF backpropagation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v3", "content": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while preserv-ing the integrity of LLM knowledge without interference from the RLHF backpropagation."}
+{"idx": 1, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 3, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet..."}
+{"idx": 4, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts ."}
+{"idx": 5, "title": "infolab.stanford.edu", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ..."}
+{"idx": 6, "title": "Benchmarking, ethical alignment, and evaluation framework for ...", "date": "", "ddg_snippet": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772485923000534", "content": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration."}
+{"idx": 7, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Three-Branch-Checks-and-Balances-Frameworkfor-of-Chang/5918a91419cf95db8599b086590facf63f124702/figure/4", "content": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 8, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 9, "title": "Ethical Guardrails for AI: A Checks-and-Balances Approach", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124", "subpage_snippet": "", "source": "www.zerna.io", "link": "https://www.zerna.io/page/security/presentation_set/security-llm-research/presentation/security-ethical-alignment-fairness/slide/security-paper-2502_00136", "content": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124"}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_limitations_and_future_work.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_limitations_and_future_work.jsonl
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index 0000000000000000000000000000000000000000..9f24621b903adf542ecae222d6aec9997b3e5cbd
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+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_limitations_and_future_work.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enforcing AI ethical framework in your testing | TestCollab Blog", "date": "", "ddg_snippet": "To conclude, a lack of an AI ethical framework and security mechanisms can have serious and far-reaching consequences for an organization, including ...", "subpage_snippet": "", "source": "testcollab.com", "link": "https://testcollab.com/blog/enforcing-ai-ethical-framework", "content": "To conclude, a lack of an AI ethical framework and security mechanisms can have serious and far-reaching consequences for an organization, including ..."}
+{"idx": 1, "title": "Analysis of Global AI Governance Strategies — AI Alignment", "date": "", "ddg_snippet": "... provides a framework for policymakers and AI governance stakeholders to evaluate and adapt their approaches as new information about alignment ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/6nNwMbdRXZDuNd4Gx/analysis-of-global-ai-governance-strategies", "content": "... provides a framework for policymakers and AI governance stakeholders to evaluate and adapt their approaches as new information about alignment ..."}
+{"idx": 2, "title": "Navigating the ethical landscape of AI... | F1000Research", "date": "", "ddg_snippet": "The comprehensive guidelines for ethical AI deployment are encapsulated in frameworks like the IEEE Global Initiative on Ethics of Autonomous and ...", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/14-299", "content": "The comprehensive guidelines for ethical AI deployment are encapsulated in frameworks like the IEEE Global Initiative on Ethics of Autonomous and ..."}
+{"idx": 3, "title": "Justified Evidence Collection for Argument-based AI Fairness", "date": "", "ddg_snippet": "The increasing investment and demand for AI -enabled systems raises critical concerns about fairness, accountability, and transparency in the design ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.08064v1", "content": "The increasing investment and demand for AI -enabled systems raises critical concerns about fairness, accountability, and transparency in the design ..."}
+{"idx": 4, "title": "Ethical Clinical Challenges and Pathways to Trustworthy AI", "date": "", "ddg_snippet": "... for this section, we prioritized studies that examine real-world challenges in AI governance, data privacy, and the limitations of current ethical ...", "subpage_snippet": "", "source": "greenberg.news", "link": "https://greenberg.news/ethical-clinical-challenges-and-pathways-to-trustworthy-ai/", "content": "... for this section, we prioritized studies that examine real-world challenges in AI governance, data privacy, and the limitations of current ethical ..."}
+{"idx": 5, "title": "AI Ethics for Boards", "date": "", "ddg_snippet": "It is based on nine broad ethical principles for making and using AI that are recognized widely in government, industry and academia.", "subpage_snippet": "", "source": "www.directorsandboards.com", "link": "https://www.directorsandboards.com/board-issues/ai/ai-ethics-for-boards-and-the-c-suite/", "content": "It is based on nine broad ethical principles for making and using AI that are recognized widely in government, industry and academia."}
+{"idx": 6, "title": "The AI Security Balancing Act: Mitigating Risks While Enabling", "date": "", "ddg_snippet": "Let ’ s dive in and discover how you can harness the power of AI while ensuring its responsible and ethical use.", "subpage_snippet": "", "source": "www.cit-net.com", "link": "https://www.cit-net.com/the-ai-security-balancing-act-mitigating-risks-while-enabling-innovation/", "content": "Let ’ s dive in and discover how you can harness the power of AI while ensuring its responsible and ethical use."}
+{"idx": 7, "title": "AI Safety Inventory | Belgian Waffle Connoisseur. Photographer.", "date": "", "ddg_snippet": "Australia’s AI Ethics Framework (2019) provides guidelines for businesses and governments to design, develop, and implement AI responsibly.", "subpage_snippet": "", "source": "halans.com", "link": "https://halans.com/posts/2024-06-02-ai-safety-inventory/", "content": "Australia’s AI Ethics Framework (2019) provides guidelines for businesses and governments to design, develop, and implement AI responsibly."}
+{"idx": 8, "title": "DomingoSenise.com", "date": "", "ddg_snippet": "Last week I have the pleasure to talk to my good friend Gregory about AI , ethics , the future of work , AI and geo-politics … and he recommended ...", "subpage_snippet": "", "source": "www.domingosenise.com", "link": "http://www.domingosenise.com/", "content": "Last week I have the pleasure to talk to my good friend Gregory about AI , ethics , the future of work , AI and geo-politics … and he recommended ..."}
+{"idx": 9, "title": "AI Governance - Tim Heath Solutions & Web Design", "date": "", "ddg_snippet": "Establishing a framework for ethical decision-making in AI development and deployment is essential to avoid unethical practices and potential harm to ...", "subpage_snippet": "", "source": "timheath.com.au", "link": "https://timheath.com.au/ai-governance/", "content": "Establishing a framework for ethical decision-making in AI development and deployment is essential to avoid unethical practices and potential harm to ..."}
diff --git a/data/sampled_jsons/Chen_2023_preference-based_reinforcement_learning_reference_trajectory.jsonl b/data/sampled_jsons/Chen_2023_preference-based_reinforcement_learning_reference_trajectory.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4acc685f257c350f5905a47df78b0d37f62906a2
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dueling RL: Reinforcement Learning with Trajectory Preferences P O PREFERENCE-BASED REINFORCE MENT L - proceedings.iclr.cc Human-in-the-loop: Provably Efficient Preference-based ... Search-Based Credit Assignment for Offline Preference-Based ... RA-PbRL: Provably Eficient Risk-Aware Preference-Based ... Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … PRIMT: Preference-based Reinforcement Learning with ...", "date": "", "ddg_snippet": "We consider the problem of preference - based reinforcement learning (PbRL), where, unlike traditional reinforcement learning (RL), an agent receives feedback only in terms of 1 bit (0/1) preferences over a trajectory pair instead of absolute rewards for it. The suc-cess of the traditional reward-based RL frame-work crucially depends on how accuratel... See full list on proceedings.mlr.press In this section, we introduce and analyze an algorithm for solving the preference - based RL problem when the transition model, P, that governs the probability of transitioning to a next state is known to the learner. In this case, it becomes possible to directly compute expected features induced by policies; however, the difficulty of learning based ... See full list on proceedings.mlr.press We now sketch the proof of Theorem 1. Details and proofs of supporting results can be found in Ap-pendix B.1. The main idea of the proof is to ensure that Πt contains only candidate policies that are predicted to be “sufficiently good” under the learned model wL t using the size of the confidence set Ct(δ). We must also verify that Πt always contai... See full list on proceedings.mlr.press In this work we addressed the problem of reinforce-ment learning from relative preference feedback where the agent does not get to see the absolute reward of actions taken at each state but instead observes the relative preferences between trajectories. We modeled the preference feedback in terms of the underlying non-Markovian linear reward model ... See full list on proceedings.mlr.press Let’s bound the term (φb Pt(π∗) − φb Pt(π))>w∗ (φb Pt(π∗) − φb Pt(π))>w∗ = (φb Pt(π∗) − φb Pt(π))>wL + (φb Pt(π∗) − φb Pt(π))>(w∗ − See full list on proceedings.mlr.press ABSTRACT In this paper, we investigate the problem of ofline Preference-based Reinforcement Learning (PbRL) with human feedback where feedback is available in the form of preference between trajectory pairs rather than explicit rewards. Our proposed algorithm consists of two main steps: (1) estimate the implicit reward using Maximum Likelihood Estimation (MLE) with general function ... Abstract We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences , where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical ... Aug 21, 2025 · In contrast, preferences are easier to collect, but it is unclear which parts of a behavior contribute most to a trajectory segment, leaving credit assignment unresolved. In this paper, we introduce a Search- Based Preference Weighting (SPW) scheme to unify these two feedback sources. Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI sys-tems with human intentions. At its core, RLHF can be viewed as a special-ized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ... How does human-in-the-loop reinforcement learn-ing work? We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences, where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. How do we learn the transition dynamics and the preference function? Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. We define the confidence sets and calculate the confidence bonuses for the transition and preference estimations, respectively. What is reinforcement learning? Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. Which algorithm is used to learn transition dynamics and preference function? Algorithm The algorithm is formally defined in Algorithm 1. Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. Can preference-based learning be used in bandit setting? Besides PbRL, preference-based learning has also been well-explored in bandit setting under the notion of “dueling bandits” (Yue et al., 2012; Falahatgar et al., 2017a;b; Busa-Fekete et al., 2018; Xu et al., 2020a; Busa-Fekete et al., 2018), which can be regarded as a special case of PbRL with single state and horizon H = 1. What is reinforcement learning (RL)? Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. This framework has achieved tremendous successes in various fields such as Atari games (Mnih et al., 2013), Go (Silver et al., 2017), and StarCraft (Vinyals et al., 2019). Poster PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models Ruiqi Wang · Dezhong Zhao · Ziqin Yuan · Tianyu Shao · Guohua Chen · Dominic Kao · Sungeun Hong · Byung-Cheol Min", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/saha23a/saha23a.pdf", "content": "We consider the problem of preference - based reinforcement learning (PbRL), where, unlike traditional reinforcement learning (RL), an agent receives feedback only in terms of 1 bit (0/1) preferences over a trajectory pair instead of absolute rewards for it. The suc-cess of the traditional reward-based RL frame-work crucially depends on how accuratel... See full list on proceedings.mlr.press In this section, we introduce and analyze an algorithm for solving the preference - based RL problem when the transition model, P, that governs the probability of transitioning to a next state is known to the learner. In this case, it becomes possible to directly compute expected features induced by policies; however, the difficulty of learning based ... See full list on proceedings.mlr.press We now sketch the proof of Theorem 1. Details and proofs of supporting results can be found in Ap-pendix B.1. The main idea of the proof is to ensure that Πt contains only candidate policies that are predicted to be “sufficiently good” under the learned model wL t using the size of the confidence set Ct(δ). We must also verify that Πt always contai... See full list on proceedings.mlr.press In this work we addressed the problem of reinforce-ment learning from relative preference feedback where the agent does not get to see the absolute reward of actions taken at each state but instead observes the relative preferences between trajectories. We modeled the preference feedback in terms of the underlying non-Markovian linear reward model ... See full list on proceedings.mlr.press Let’s bound the term (φb Pt(π∗) − φb Pt(π))>w∗ (φb Pt(π∗) − φb Pt(π))>w∗ = (φb Pt(π∗) − φb Pt(π))>wL + (φb Pt(π∗) − φb Pt(π))>(w∗ − See full list on proceedings.mlr.press ABSTRACT In this paper, we investigate the problem of ofline Preference-based Reinforcement Learning (PbRL) with human feedback where feedback is available in the form of preference between trajectory pairs rather than explicit rewards. Our proposed algorithm consists of two main steps: (1) estimate the implicit reward using Maximum Likelihood Estimation (MLE) with general function ... Abstract We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences , where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical ... Aug 21, 2025 · In contrast, preferences are easier to collect, but it is unclear which parts of a behavior contribute most to a trajectory segment, leaving credit assignment unresolved. In this paper, we introduce a Search- Based Preference Weighting (SPW) scheme to unify these two feedback sources. Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI sys-tems with human intentions. At its core, RLHF can be viewed as a special-ized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ... How does human-in-the-loop reinforcement learn-ing work? We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences, where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. How do we learn the transition dynamics and the preference function? Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. We define the confidence sets and calculate the confidence bonuses for the transition and preference estimations, respectively. What is reinforcement learning? Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. Which algorithm is used to learn transition dynamics and preference function? Algorithm The algorithm is formally defined in Algorithm 1. Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. Can preference-based learning be used in bandit setting? Besides PbRL, preference-based learning has also been well-explored in bandit setting under the notion of “dueling bandits” (Yue et al., 2012; Falahatgar et al., 2017a;b; Busa-Fekete et al., 2018; Xu et al., 2020a; Busa-Fekete et al., 2018), which can be regarded as a special case of PbRL with single state and horizon H = 1. What is reinforcement learning (RL)? Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. This framework has achieved tremendous successes in various fields such as Atari games (Mnih et al., 2013), Go (Silver et al., 2017), and StarCraft (Vinyals et al., 2019). Poster PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models Ruiqi Wang · Dezhong Zhao · Ziqin Yuan · Tianyu Shao · Guohua Chen · Dominic Kao · Sungeun Hong · Byung-Cheol Min"}
+{"idx": 1, "title": "PRIMT: Preference-based Reinforcement Learning with ...", "date": "", "ddg_snippet": "This work explores the integration of foundation models into preference-based reinforcement learning (PbRL), aiming to improve learning efficiency and robustness through multimodal feedback and trajectory synthesis.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15607", "content": "This work explores the integration of foundation models into preference-based reinforcement learning (PbRL), aiming to improve learning efficiency and robustness through multimodal feedback and trajectory synthesis."}
+{"idx": 2, "title": "P O PREFERENCE-BASED REINFORCE MENT L - proceedings.iclr.cc", "date": "", "ddg_snippet": "ABSTRACT In this paper, we investigate the problem of ofline Preference-based Reinforcement Learning (PbRL) with human feedback where feedback is available in the form of preference between trajectory pairs rather than explicit rewards. Our proposed algorithm consists of two main steps: (1) estimate the implicit reward using Maximum Likelihood Estimation (MLE) with general function ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/7efe88bb4138d602e56637cfcf713654-Paper-Conference.pdf", "content": "ABSTRACT In this paper, we investigate the problem of ofline Preference-based Reinforcement Learning (PbRL) with human feedback where feedback is available in the form of preference between trajectory pairs rather than explicit rewards. Our proposed algorithm consists of two main steps: (1) estimate the implicit reward using Maximum Likelihood Estimation (MLE) with general function ..."}
+{"idx": 3, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "Abstract We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences , where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "Abstract We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences , where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical ..."}
+{"idx": 4, "title": "Search-Based Credit Assignment for Offline Preference-Based ...", "date": "", "ddg_snippet": "Aug 21, 2025 · In contrast, preferences are easier to collect, but it is unclear which parts of a behavior contribute most to a trajectory segment, leaving credit assignment unresolved. In this paper, we introduce a Search- Based Preference Weighting (SPW) scheme to unify these two feedback sources.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15327v1", "content": "Aug 21, 2025 · In contrast, preferences are easier to collect, but it is unclear which parts of a behavior contribute most to a trajectory segment, leaving credit assignment unresolved. In this paper, we introduce a Search- Based Preference Weighting (SPW) scheme to unify these two feedback sources."}
+{"idx": 5, "title": "RA-PbRL: Provably Eficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI sys-tems with human intentions. At its core, RLHF can be viewed as a special-ized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI sys-tems with human intentions. At its core, RLHF can be viewed as a special-ized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ..."}
+{"idx": 6, "title": "PRIMT: Preference-based Reinforcement Learning with ...", "date": "", "ddg_snippet": "Poster PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models Ruiqi Wang · Dezhong Zhao · Ziqin Yuan · Tianyu Shao · Guohua Chen · Dominic Kao · Sungeun Hong · Byung-Cheol Min", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2025/poster/119903", "content": "Poster PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models Ruiqi Wang · Dezhong Zhao · Ziqin Yuan · Tianyu Shao · Guohua Chen · Dominic Kao · Sungeun Hong · Byung-Cheol Min"}
+{"idx": 7, "title": "Preference-Guided Reinforcement Learning for Efficient ...", "date": "", "ddg_snippet": "9 Jul 2024 — In this study, we propose a novel online RL algorithm called Learning Online with trajectory P reference guidancE (LOPE), an end-to-end framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06503v1", "content": "9 Jul 2024 — In this study, we propose a novel online RL algorithm called Learning Online with trajectory P reference guidancE (LOPE), an end-to-end framework ..."}
+{"idx": 8, "title": "Efficient Preference-Based Reinforcement Learning Using ...", "date": "", "ddg_snippet": "by Y Liu · 2023 · Cited by 29 — We fine-tune the learned reward function via active preference queries to a human (right), where the trajectories for queries are simulated.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2301.04741", "content": "by Y Liu · 2023 · Cited by 29 — We fine-tune the learned reward function via active preference queries to a human (right), where the trajectories for queries are simulated."}
+{"idx": 9, "title": "Provable Reward-Agnostic Preference-Based ...", "date": "", "ddg_snippet": "by W Zhan · Cited by 21 — A theoretical reward-agnostic PbRL framework where exploratory trajectories that enable accurate learning of hidden reward functions are acquired before ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=yTBXeXdbMf", "content": "by W Zhan · Cited by 21 — A theoretical reward-agnostic PbRL framework where exploratory trajectories that enable accurate learning of hidden reward functions are acquired before ..."}
diff --git a/data/sampled_jsons/Chen_et_al.,_2023_Preference-Based_Reinforcement_Learning_year_2023.jsonl b/data/sampled_jsons/Chen_et_al.,_2023_Preference-Based_Reinforcement_Learning_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48a162aadc32556314f79dd9ffd0fb748f6653cd
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al.,_2023_Preference-Based_Reinforcement_Learning_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Efficient Preference-Based Reinforcement Learning Using ...", "date": "", "ddg_snippet": "by Y Liu · 2023 · Cited by 29 — Recent work by Chen et al. [13] provides evidence of inductive bias when pre-training using noise injection and a preference-learning objective, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2301.04741", "content": "by Y Liu · 2023 · Cited by 29 — Recent work by Chen et al. [13] provides evidence of inductive bias when pre-training using noise injection and a preference-learning objective, ..."}
+{"idx": 1, "title": "Preference-Guided Reinforcement Learning for Efficient ...", "date": "", "ddg_snippet": "9 Jul 2024 — In this paper, we investigate preference-based reinforcement learning (PbRL) that allows reinforcement learning (RL) agents to learn from ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06503v1", "content": "9 Jul 2024 — In this paper, we investigate preference-based reinforcement learning (PbRL) that allows reinforcement learning (RL) agents to learn from ..."}
+{"idx": 2, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "This paper studies preference - based RL (PbRL) where instead of the expected return, the agent optimizes a risk measure based on preference feedback.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf&referrer=[the+profile+of+Huazheng+Wang](/profile?id=~Huazheng_Wang1)", "content": "This paper studies preference - based RL (PbRL) where instead of the expected return, the agent optimizes a risk measure based on preference feedback."}
+{"idx": 3, "title": "Preference learning based deep reinforcement learning for ...", "date": "", "ddg_snippet": "by X Liu · 2025 · Cited by 4 — This paper proposes a Preference-Based Mask-PPO (PBMP) algorithm , which leverages the strengths of preference learning and invalid action masking to optimize ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40747-024-01772-x", "content": "by X Liu · 2025 · Cited by 4 — This paper proposes a Preference-Based Mask-PPO (PBMP) algorithm , which leverages the strengths of preference learning and invalid action masking to optimize ..."}
+{"idx": 4, "title": "Beyond Reward: Offline Preference-guided Policy Optimization", "date": "", "ddg_snippet": "This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/kang23b/kang23b.pdf", "content": "This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning."}
+{"idx": 5, "title": "CLARIFY: Contrastive Preference Reinforcement Learning ...", "date": "", "ddg_snippet": "Preference-based reinforcement learning (PbRL ) bypasses explicit reward engineering by inferring reward functions from human preference comparisons, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43680", "content": "Preference-based reinforcement learning (PbRL ) bypasses explicit reward engineering by inferring reward functions from human preference comparisons, ..."}
+{"idx": 6, "title": "Preference Elicitation for Offline Reinforcement Learning", "date": "", "ddg_snippet": "by A Pace · Cited by 1 — Unfortu- nately, most algorithms for preference acquisition require environment interaction (Saha et al ., 2023 ; Chen et al .,. 2022; Lindner et al ., 2021) and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=umJkP7Bpiv", "content": "by A Pace · Cited by 1 — Unfortu- nately, most algorithms for preference acquisition require environment interaction (Saha et al ., 2023 ; Chen et al .,. 2022; Lindner et al ., 2021) and ..."}
+{"idx": 7, "title": "Online Preference-based Reinforcement Learning with Self ...", "date": "", "ddg_snippet": "Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3709347.3743845", "content": "Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human ..."}
+{"idx": 8, "title": "Sample-Efficient Preference-based Reinforcement Learning ...", "date": "", "ddg_snippet": "Abstract: Preference-based reinforcement learning (PbRL) aligns a robot behav- ior with human preferences via a reward function learned from binary feedback.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v229/metcalf23a/metcalf23a.pdf", "content": "Abstract: Preference-based reinforcement learning (PbRL) aligns a robot behav- ior with human preferences via a reward function learned from binary feedback."}
+{"idx": 9, "title": "Adaptive Confidence-aware Preference-based Reinforcement ...", "date": "", "ddg_snippet": "23 May 2025 — Our approach provides a standardized framework for handling noisy feedback data by incorporating confidence analysis, temporal smoothing, and an adaptive ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3701716.3717570", "content": "23 May 2025 — Our approach provides a standardized framework for handling noisy feedback data by incorporating confidence analysis, temporal smoothing, and an adaptive ..."}
diff --git a/data/sampled_jsons/Chen_et_al._2023_risk-aware_preference-based_reinforcement_learning_abstract_year_2023.jsonl b/data/sampled_jsons/Chen_et_al._2023_risk-aware_preference-based_reinforcement_learning_abstract_year_2023.jsonl
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index 0000000000000000000000000000000000000000..c82a49ba2f72e1b771dc8937ae0f3e457e3ac82b
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF RA-PbRL: Provably Eficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI sys-tems with human intentions. At its core, RLHF can be viewed as a special-ized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI sys-tems with human intentions. At its core, RLHF can be viewed as a special-ized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ..."}
+{"idx": 1, "title": "Sample-Efficient Preference-based Reinforcement Learning with...", "date": "", "ddg_snippet": "Abstract : Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that encoding environment dynamics in the reward function improves the sample efficiency of PbRL by an order of magnitude.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=i84V7i6KEMd", "content": "Abstract : Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that encoding environment dynamics in the reward function improves the sample efficiency of PbRL by an order of magnitude."}
+{"idx": 2, "title": "PDF Human-in-the-loop: Provably Efficient Preference-based Reinforcement ...", "date": "", "ddg_snippet": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ..."}
+{"idx": 3, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 4, "title": "A survey of Preference Reinforcement Learning - GitHub", "date": "", "ddg_snippet": "Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. Chen X, Zhong H, Yang Z, et al. (ICML 2022) [paper]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Kavka1/Preference-RL", "content": "Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. Chen X, Zhong H, Yang Z, et al. (ICML 2022) [paper]"}
+{"idx": 5, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Previous approaches to PbRL (Xu et al ., 2020a; Coronato et al ., 2020; Xu et al ., 2020b; Chen et al ., 2022; Zhan et al ., 2023)mainly aim to maximize the mean reward or utility, which is risk -neutral. However, there is a growing need for risk-aware strategies in various fields where PbRL has shown empirical success. For example, in autonomous driving, PbRL reduces the computational burden by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v3", "content": "Previous approaches to PbRL (Xu et al ., 2020a; Coronato et al ., 2020; Xu et al ., 2020b; Chen et al ., 2022; Zhan et al ., 2023)mainly aim to maximize the mean reward or utility, which is risk -neutral. However, there is a growing need for risk-aware strategies in various fields where PbRL has shown empirical success. For example, in autonomous driving, PbRL reduces the computational burden by ..."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Preference-based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware measures are ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.23569", "content": "Preference-based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware measures are ..."}
+{"idx": 7, "title": "Efficient Preference-Based Reinforcement Learning Using Learned ...", "date": "", "ddg_snippet": "Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10161081", "content": "Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ..."}
+{"idx": 8, "title": "Adaptive Confidence-aware Preference-based Reinforcement Learning with ...", "date": "", "ddg_snippet": "As AI systems become more integrated into society, ensuring they align with human values and exhibit responsible behavior is crucial. Preference-based Reinforcement Learning (PbRL), which learns directly from human feedback, offers a promising approach to achieving this alignment. However, PbRL faces significant challenges related to data quality, like inconsistent feedback, especially when ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3701716.3717570", "content": "As AI systems become more integrated into society, ensuring they align with human values and exhibit responsible behavior is crucial. Preference-based Reinforcement Learning (PbRL), which learns directly from human feedback, offers a promising approach to achieving this alignment. However, PbRL faces significant challenges related to data quality, like inconsistent feedback, especially when ..."}
+{"idx": 9, "title": "RA-PbRL | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3739861", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
diff --git a/data/sampled_jsons/Chen_et_al_2022_preference-based_reinforcement_learning_year_2022.jsonl b/data/sampled_jsons/Chen_et_al_2022_preference-based_reinforcement_learning_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e3925abf041b6c308c192c649b1dbb5ea7d004df
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al_2022_preference-based_reinforcement_learning_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ..."}
+{"idx": 1, "title": "Efficient Preference-Based Reinforcement Learning: Randomized ...", "date": "", "ddg_snippet": "Abstract We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in this setting is to design algorithms that select informative preference queries to identify the underlying reward while ensuring theoretical guarantees. We propose a meta-algorithm based on randomized ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.09508", "content": "Abstract We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in this setting is to design algorithms that select informative preference queries to identify the underlying reward while ensuring theoretical guarantees. We propose a meta-algorithm based on randomized ..."}
+{"idx": 2, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9945333", "content": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more ..."}
+{"idx": 3, "title": "Online Preference-based Reinforcement Learning with Self ...", "date": "", "ddg_snippet": "Jun 5, 2025 · Abstract Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human preferences . However, real-time human feedback is hard to obtain in online tasks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3709347.3743845", "content": "Jun 5, 2025 · Abstract Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human preferences . However, real-time human feedback is hard to obtain in online tasks."}
+{"idx": 4, "title": "Efficient Preference-Based Reinforcement Learning Using ...", "date": "", "ddg_snippet": "May 29, 2023 · Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10161081", "content": "May 29, 2023 · Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ..."}
+{"idx": 5, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "Abstract We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag.html", "content": "Abstract We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer."}
+{"idx": 6, "title": "Q -E OFFLINE PREFERENCE-BASED RE INFORCEMENT LEARNING VIA IN ...", "date": "", "ddg_snippet": "1 INTRODUCTION Reinforcement Learning (RL) has emerged as a powerful approach for solving a wide variety of se-quential decision-making problems, including classic games (Silver et al ., 2016), video games (Mnih et al ., 2015; Vinyals et al ., 2019), robotics (Ahn et al ., 2022 ), and plasma control (Degrave et al ., 2022 ) with supervision from just reward signals. Nevertheless, in many real-world ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=GOvTGntFNj", "content": "1 INTRODUCTION Reinforcement Learning (RL) has emerged as a powerful approach for solving a wide variety of se-quential decision-making problems, including classic games (Silver et al ., 2016), video games (Mnih et al ., 2015; Vinyals et al ., 2019), robotics (Ahn et al ., 2022 ), and plasma control (Degrave et al ., 2022 ) with supervision from just reward signals. Nevertheless, in many real-world ..."}
+{"idx": 7, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "by X Chen · 2022 · Cited by 93 — We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where instead of receiving a numeric reward at each step, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2205.11140", "content": "by X Chen · 2022 · Cited by 93 — We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where instead of receiving a numeric reward at each step, ..."}
+{"idx": 8, "title": "Preference-Guided Reinforcement Learning for Efficient ...", "date": "", "ddg_snippet": "9 Jul 2024 — Although learning a separate reward function does help in solving the sparse reward problem, continuously enforcing such preference - based ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06503v1", "content": "9 Jul 2024 — Although learning a separate reward function does help in solving the sparse reward problem, continuously enforcing such preference - based ..."}
+{"idx": 9, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "This paper studies preference - based RL (PbRL) where instead of the expected return, the agent optimizes a risk measure based on preference feedback.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf&referrer=[the+profile+of+Huazheng+Wang](/profile?id=~Huazheng_Wang1)", "content": "This paper studies preference - based RL (PbRL) where instead of the expected return, the agent optimizes a risk measure based on preference feedback."}
diff --git a/data/sampled_jsons/Chinchilla_scaling_law_exponent_dataset_size.jsonl b/data/sampled_jsons/Chinchilla_scaling_law_exponent_dataset_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..455fb177289aa256cd7ac0c10d3e174e2fb8d709
--- /dev/null
+++ b/data/sampled_jsons/Chinchilla_scaling_law_exponent_dataset_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law - Wikipedia", "date": "", "ddg_snippet": "Usually, the goal is to make the scaling law exponent larger, which means the same loss can be trained for much less compute.Subsequent studies discovered scaling laws in the overtraining regime, for dataset sizes up to 32x more than Chinchilla -optimal.[24].", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "Usually, the goal is to make the scaling law exponent larger, which means the same loss can be trained for much less compute.Subsequent studies discovered scaling laws in the overtraining regime, for dataset sizes up to 32x more than Chinchilla -optimal.[24]."}
+{"idx": 1, "title": "What is the Chinchilla Scaling Law ?", "date": "", "ddg_snippet": "The Chinchilla Scaling Law highlights an optimal balance between model size and the amount of training data . Specifically, the study found that an approximate ratio of 20 training tokens per model parameter is ideal for achieving the best performance with a given compute budget.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2024/09/chinchilla-scaling-law/", "content": "The Chinchilla Scaling Law highlights an optimal balance between model size and the amount of training data . Specifically, the study found that an approximate ratio of 20 training tokens per model parameter is ideal for achieving the best performance with a given compute budget."}
+{"idx": 2, "title": "Scaling Laws : Building Compute-Optimal AI Models | Medium", "date": "", "ddg_snippet": "The Chinchilla Scaling Law has changed the way AI practitioners think about model scaling . By optimizing model size and dataset size based on a compute budget, researchers can build more efficient and cost-effective models.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@malikzeeshan3.1417/scaling-laws-building-compute-optimal-ai-models-bfd717d36b7b", "content": "The Chinchilla Scaling Law has changed the way AI practitioners think about model scaling . By optimizing model size and dataset size based on a compute budget, researchers can build more efficient and cost-effective models."}
+{"idx": 3, "title": "Understanding the KM and Chinchilla Scaling Laws for Large...", "date": "", "ddg_snippet": "Chinchilla Scaling Law : This law , proposed by Hoffmann et al. from Google DeepMind, presents an alternative form for scaling laws .The Chinchilla law also provides an optimized way to allocate compute budget to model size and data size , defined by", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/understanding-km-chinchilla-scaling-laws-large-language-hemant-rawat", "content": "Chinchilla Scaling Law : This law , proposed by Hoffmann et al. from Google DeepMind, presents an alternative form for scaling laws .The Chinchilla law also provides an optimized way to allocate compute budget to model size and data size , defined by"}
+{"idx": 4, "title": "Chinchilla Scaling Laws - Optimizing Model and Dataset Size for...", "date": "", "ddg_snippet": "Chinchilla scaling laws provide a valuable guideline for balancing model size and dataset requirements, ensuring efficient and effective machine learning.", "subpage_snippet": "", "source": "victorleungtw.com", "link": "https://victorleungtw.com/2024/05/15/chinchilla/", "content": "Chinchilla scaling laws provide a valuable guideline for balancing model size and dataset requirements, ensuring efficient and effective machine learning."}
+{"idx": 5, "title": "Superposition Yields Robust Neural Scaling", "date": "", "ddg_snippet": "Neural scaling laws also include scaling laws with dataset size and with training steps, which we did not study. At each step, a fixed number of new data are used for optimization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.10465", "content": "Neural scaling laws also include scaling laws with dataset size and with training steps, which we did not study. At each step, a fixed number of new data are used for optimization."}
+{"idx": 6, "title": "Chinchilla data -optimal scaling laws : In plain English", "date": "", "ddg_snippet": "In plain English, Chinchilla /Hoffman scaling laws say that… 1,400B (1.4T) tokens should be used to train a data -optimal LLM of size 70B parameters So, we need around 20 text tokens per parameter.", "subpage_snippet": "", "source": "lifearchitect.ai", "link": "https://lifearchitect.ai/chinchilla/", "content": "In plain English, Chinchilla /Hoffman scaling laws say that… 1,400B (1.4T) tokens should be used to train a data -optimal LLM of size 70B parameters So, we need around 20 text tokens per parameter."}
+{"idx": 7, "title": "The Evolution of Scaling Laws for LLMs | by LM Po - Freedium", "date": "", "ddg_snippet": "Find the Chinchilla Scaling Law as: where L is the loss function, E is the irreducible loss, A and B are critical values for model size and data size , respectively, and a = 0.46 and b = 0.54 are scaling exponents . Main Findings.", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/aeb6ae64f6f1", "content": "Find the Chinchilla Scaling Law as: where L is the loss function, E is the irreducible loss, A and B are critical values for model size and data size , respectively, and a = 0.46 and b = 0.54 are scaling exponents . Main Findings."}
+{"idx": 8, "title": "Scaling Laws - End-to-End LLM Training & Alignment Course", "date": "", "ddg_snippet": "Comprehensive analysis of neural scaling laws that revolutionized large language model training. Understand how OpenAI's GPT-3, DeepMind's Chinchilla , and other frontier models determine optimal model size , dataset size , and compute allocation for maximum performance.", "subpage_snippet": "", "source": "ai-research-course.netlify.app", "link": "https://ai-research-course.netlify.app/advanced-track/pretraining-scale/scaling_laws/", "content": "Comprehensive analysis of neural scaling laws that revolutionized large language model training. Understand how OpenAI's GPT-3, DeepMind's Chinchilla , and other frontier models determine optimal model size , dataset size , and compute allocation for maximum performance."}
+{"idx": 9, "title": "Size Matters: How Big Is Too Big for An LLM? | by Dr.... | Towards AI", "date": "", "ddg_snippet": "The Chinchilla scaling law for LLMs states that there is a compute-optimal number of model parameters for a given amount of training tokens.LLM test loss decreases smoothly when compute, dataset size , and parameters are scaled up. Image from [1].", "subpage_snippet": "", "source": "pub.towardsai.net", "link": "https://pub.towardsai.net/size-matters-how-big-is-too-big-for-an-llm-289e6ff35ee6", "content": "The Chinchilla scaling law for LLMs states that there is a compute-optimal number of model parameters for a given amount of training tokens.LLM test loss decreases smoothly when compute, dataset size , and parameters are scaled up. Image from [1]."}
diff --git a/data/sampled_jsons/Christian_Gaetz_Yibo_Gao_2024.jsonl b/data/sampled_jsons/Christian_Gaetz_Yibo_Gao_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7064a33eb10277d2ac25b616a0f9f214ed2e6a1d
--- /dev/null
+++ b/data/sampled_jsons/Christian_Gaetz_Yibo_Gao_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Origin of Christianity - Biblical Archaeology Society", "date": "", "ddg_snippet": "Sep 9, 2025 · To understand the origin of Christianity, one must begin with the population of Jewish Christians who lived during Jesus’ lifetime.", "subpage_snippet": "", "source": "www.biblicalarchaeology.org", "link": "https://www.biblicalarchaeology.org/daily/biblical-topics/new-testament/the-origin-of-christianity/", "content": "Sep 9, 2025 · To understand the origin of Christianity, one must begin with the population of Jewish Christians who lived during Jesus’ lifetime."}
+{"idx": 1, "title": "Christian Forums", "date": "", "ddg_snippet": "Christian Forums is an online community for Christians around the world to find fellowship with other Christians.", "subpage_snippet": "", "source": "www.christianforums.com", "link": "https://www.christianforums.com/", "content": "Christian Forums is an online community for Christians around the world to find fellowship with other Christians."}
+{"idx": 2, "title": "Is MrBeast A Christian?", "date": "", "ddg_snippet": "Sep 19, 2023 · Is MrBeast a Christian ? We gathered as much information as we could to give the best guess answer on this topic.", "subpage_snippet": "", "source": "www.christianforums.com", "link": "https://www.christianforums.com/articles/is-mrbeast-a-christian/", "content": "Sep 19, 2023 · Is MrBeast a Christian ? We gathered as much information as we could to give the best guess answer on this topic."}
+{"idx": 3, "title": "Singles (Only*) | Christian Forums", "date": "", "ddg_snippet": "Jun 29, 2012 · Starting today August 7th, 2024 , in order to post in the Married Couples, Courting Couples, or Singles forums, you will not be allowed to post if you have your Marital status designated as private. Announcements will be made in the respective forums as well but please note that if yours is currently listed as Private, you will need to submit a ticket in the Support Area to have yours changed.", "subpage_snippet": "", "source": "www.christianforums.com", "link": "https://www.christianforums.com/forums/singles-only.137/", "content": "Jun 29, 2012 · Starting today August 7th, 2024 , in order to post in the Married Couples, Courting Couples, or Singles forums, you will not be allowed to post if you have your Marital status designated as private. Announcements will be made in the respective forums as well but please note that if yours is currently listed as Private, you will need to submit a ticket in the Support Area to have yours changed."}
+{"idx": 4, "title": "General Theology - Christian Forums", "date": "", "ddg_snippet": "Controversial Christian Theology New Discussion and debate about unorthodox Christian theology Threads 15.4K Messages 977.7K S", "subpage_snippet": "", "source": "www.christianforums.com", "link": "https://www.christianforums.com/forums/general-theology.80/", "content": "Controversial Christian Theology New Discussion and debate about unorthodox Christian theology Threads 15.4K Messages 977.7K S"}
+{"idx": 5, "title": "Liberal Denominations - Christian Forums", "date": "", "ddg_snippet": "Feb 3, 2025 · The Christian Reformed Church (Dutch Reformed) are currently having infighting over homosexuality. Many white congregations in the denomination approve of allowing same sex couples to participate fully in their churches, but a large number of Asians and Latino congregations that disapprove, as well as some older white members.", "subpage_snippet": "", "source": "www.christianforums.com", "link": "https://www.christianforums.com/threads/liberal-denominations.8319881/", "content": "Feb 3, 2025 · The Christian Reformed Church (Dutch Reformed) are currently having infighting over homosexuality. Many white congregations in the denomination approve of allowing same sex couples to participate fully in their churches, but a large number of Asians and Latino congregations that disapprove, as well as some older white members."}
+{"idx": 6, "title": "Threads - Christian Forums", "date": "", "ddg_snippet": "CF has always been a site that welcomes people from different backgrounds and beliefs to participate in discussion and even debate. That is the nature of its ministry. In view of recent events emotions are running very high. We need to remind people of some basic principles in debating on this site. We need to be civil when we express differences in opinion. No personal attacks. Avoid you ...", "subpage_snippet": "", "source": "www.christianforums.com", "link": "https://www.christianforums.com/feeds/threads", "content": "CF has always been a site that welcomes people from different backgrounds and beliefs to participate in discussion and even debate. That is the nature of its ministry. In view of recent events emotions are running very high. We need to remind people of some basic principles in debating on this site. We need to be civil when we express differences in opinion. No personal attacks. Avoid you ..."}
+{"idx": 7, "title": "Discussion and Debate - Christian Forums", "date": "", "ddg_snippet": "CF has always been a site that welcomes people from different backgrounds and beliefs to participate in discussion and even debate. That is the nature of its ministry. In view of recent events emotions are running very high. We need to remind people of some basic principles in debating on this site. We need to be civil when we express differences in opinion. No personal attacks. Avoid you ...", "subpage_snippet": "", "source": "www.christianforums.com", "link": "https://www.christianforums.com/forums/discussion-and-debate.1124/", "content": "CF has always been a site that welcomes people from different backgrounds and beliefs to participate in discussion and even debate. That is the nature of its ministry. In view of recent events emotions are running very high. We need to remind people of some basic principles in debating on this site. We need to be civil when we express differences in opinion. No personal attacks. Avoid you ..."}
+{"idx": 8, "title": "Is Oral a Sin? The TRUTH About Oral Sex in the Bible - Christian...", "date": "", "ddg_snippet": "Oct 3, 2022 · Though the Bible never explicitly mentions oral sex, the Bible is very clear about all sexual behavior. Sex of all kinds (including oral sex) is reserved for married couples. Oral sex is not sinful within marriage (assuming both spouses are comfortable with it). Any sexual contact outside of marriage, including oral sex, is considered sinful.", "subpage_snippet": "", "source": "www.christianforums.com", "link": "https://www.christianforums.com/articles/what-does-the-bible-say-about-oral-sex/", "content": "Oct 3, 2022 · Though the Bible never explicitly mentions oral sex, the Bible is very clear about all sexual behavior. Sex of all kinds (including oral sex) is reserved for married couples. Oral sex is not sinful within marriage (assuming both spouses are comfortable with it). Any sexual contact outside of marriage, including oral sex, is considered sinful."}
+{"idx": 9, "title": "Home - Biblical Archaeology Society", "date": "", "ddg_snippet": "The Biblical Archaeology Society (BAS) is a nonprofit organization dedicated to educating about archaeology in the Bible lands.", "subpage_snippet": "", "source": "www.biblicalarchaeology.org", "link": "https://www.biblicalarchaeology.org/", "content": "The Biblical Archaeology Society (BAS) is a nonprofit organization dedicated to educating about archaeology in the Bible lands."}
diff --git a/data/sampled_jsons/Circuit_Fingerprinting_Attacks_Passive_Deanonymization_of_Tor_Hidden_Services_Kwon_AlSabah_abstract.jsonl b/data/sampled_jsons/Circuit_Fingerprinting_Attacks_Passive_Deanonymization_of_Tor_Hidden_Services_Kwon_AlSabah_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5fc10fbd9ff3e2acbad2fcfd2c31ed70594fe7d9
--- /dev/null
+++ b/data/sampled_jsons/Circuit_Fingerprinting_Attacks_Passive_Deanonymization_of_Tor_Hidden_Services_Kwon_AlSabah_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Circuit - Wikipedia", "date": "", "ddg_snippet": "Circuitt , a 2023 Indian Marathi-language action thriller film starring Vaibhav Tatwawadi and Hruta Durgule in lead roles.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Circuit", "content": "Circuitt , a 2023 Indian Marathi-language action thriller film starring Vaibhav Tatwawadi and Hruta Durgule in lead roles."}
+{"idx": 1, "title": "CIRCUIT Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of CIRCUIT is a usually circular line encompassing an area. How to use circuit in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/circuit", "content": "The meaning of CIRCUIT is a usually circular line encompassing an area. How to use circuit in a sentence."}
+{"idx": 2, "title": "Electric circuit | Diagrams & Examples | Britannica", "date": "", "ddg_snippet": "Electric circuits are classified in several ways. A direct-current circuit carries current that flows only in one direction. An alternating-current circuit carries current that pulsates back and forth many times each second, as in most household circuits .", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/technology/electric-circuit", "content": "Electric circuits are classified in several ways. A direct-current circuit carries current that flows only in one direction. An alternating-current circuit carries current that pulsates back and forth many times each second, as in most household circuits ."}
+{"idx": 3, "title": "Online circuit simulator & schematic editor - CircuitLab", "date": "", "ddg_snippet": "CircuitLab provides online, in-browser tools for schematic capture and circuit simulation. These tools allow students, hobbyists, and professional engineers to design and analyze analog and digital systems before ever building a prototype.", "subpage_snippet": "", "source": "www.circuitlab.com", "link": "https://www.circuitlab.com/", "content": "CircuitLab provides online, in-browser tools for schematic capture and circuit simulation. These tools allow students, hobbyists, and professional engineers to design and analyze analog and digital systems before ever building a prototype."}
+{"idx": 4, "title": "CIRCUIT | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "circuit noun [C] (CIRCLE) something shaped like a circle, esp. a route, path, or sports track that starts and ends in the same place:", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/circuit", "content": "circuit noun [C] (CIRCLE) something shaped like a circle, esp. a route, path, or sports track that starts and ends in the same place:"}
+{"idx": 5, "title": "What is a Circuit ? - SparkFun Learn", "date": "", "ddg_snippet": "One of the first things you'll encounter when learning about electronics is the concept of a circuit . This tutorial will explain what a circuit is, as well as discuss voltage in further detail. A simple circuit , involving a button, an LED, and a resistor, built two different ways.", "subpage_snippet": "", "source": "learn.sparkfun.com", "link": "https://learn.sparkfun.com/tutorials/what-is-a-circuit/all", "content": "One of the first things you'll encounter when learning about electronics is the concept of a circuit . This tutorial will explain what a circuit is, as well as discuss voltage in further detail. A simple circuit , involving a button, an LED, and a resistor, built two different ways."}
+{"idx": 6, "title": "Electrical Circuit: Theory, Components, Working, Diagram", "date": "", "ddg_snippet": "The article explains the fundamental components of an electrical circuit , including the source, load, and conductors, and covers key concepts such as voltage, current, resistance, and the differences between AC and DC currents.", "subpage_snippet": "", "source": "electricalacademia.com", "link": "https://electricalacademia.com/basic-electrical/basic-electrical-circuit-theory-components-working-diagram/", "content": "The article explains the fundamental components of an electrical circuit , including the source, load, and conductors, and covers key concepts such as voltage, current, resistance, and the differences between AC and DC currents."}
+{"idx": 7, "title": "What Are Electric Circuits? | Basic Concepts Of Electricity ...", "date": "", "ddg_snippet": "A circuit is an unbroken loop of conductive material that allows charge carriers to flow through continuously without beginning or end. If a circuit is “broken,” that means its conductive elements no longer form a complete path, and continuous charge flow cannot occur in it.", "subpage_snippet": "", "source": "www.allaboutcircuits.com", "link": "https://www.allaboutcircuits.com/textbook/direct-current/chpt-1/electric-circuits/", "content": "A circuit is an unbroken loop of conductive material that allows charge carriers to flow through continuously without beginning or end. If a circuit is “broken,” that means its conductive elements no longer form a complete path, and continuous charge flow cannot occur in it."}
+{"idx": 8, "title": "Circuits – 25+ Examples, Types, Rules, Differences", "date": "", "ddg_snippet": "Aug 27, 2024 · Circuits are interconnected pathways that allow the flow of electric current, typically consisting of components like resistors, capacitors, inductors, and transistors. These elements are connected by conductive wires or traces through which electrons travel, creating a complete path for the current.", "subpage_snippet": "", "source": "www.examples.com", "link": "https://www.examples.com/physics/circuits.html", "content": "Aug 27, 2024 · Circuits are interconnected pathways that allow the flow of electric current, typically consisting of components like resistors, capacitors, inductors, and transistors. These elements are connected by conductive wires or traces through which electrons travel, creating a complete path for the current."}
+{"idx": 9, "title": "Cricut Design Space", "date": "", "ddg_snippet": "Set up a new Cricut product, browse projects, start designing, and more.", "subpage_snippet": "", "source": "design.cricut.com", "link": "https://design.cricut.com/", "content": "Set up a new Cricut product, browse projects, start designing, and more."}
diff --git a/data/sampled_jsons/Clause_1.v_Llama2_Llama3_license_restriction_use_with_other_models.jsonl b/data/sampled_jsons/Clause_1.v_Llama2_Llama3_license_restriction_use_with_other_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c1e4d3104d87810c0c231003f64e31c17d0db819
--- /dev/null
+++ b/data/sampled_jsons/Clause_1.v_Llama2_Llama3_license_restriction_use_with_other_models.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "They've Stolen My GPL-Licensed Model!", "date": "", "ddg_snippet": "16 Dec 2024 — ... license offers good clarity, it enforces use behavior restrictions ... Clause 1.v . of the Llama 2 license reads: ”You will not use the Llama ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11483v1", "content": "16 Dec 2024 — ... license offers good clarity, it enforces use behavior restrictions ... Clause 1.v . of the Llama 2 license reads: ”You will not use the Llama ..."}
+{"idx": 1, "title": "They've Stolen My GPL-Licensed Model!", "date": "", "ddg_snippet": "by M Duan · 2024 · Cited by 2 — 7For example, Clause 1.v . of the Llama 2 license reads: \"You will not use the Llama. Materials or any output or results of the Llama ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11483", "content": "by M Duan · 2024 · Cited by 2 — 7For example, Clause 1.v . of the Llama 2 license reads: \"You will not use the Llama. Materials or any output or results of the Llama ..."}
+{"idx": 2, "title": "Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "Similarly, merging two models licensed under Llama2 and Llama3 , respectively, is prohibited, as Clause 1.v of both licenses restricts using licensed models (and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40180", "content": "Similarly, merging two models licensed under Llama2 and Llama3 , respectively, is prohibited, as Clause 1.v of both licenses restricts using licensed models (and ..."}
+{"idx": 3, "title": "Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "clause 1.v reads: “You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model ...”. Such ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/9b804e3daca923a328ce5903280bef3a779e3436.pdf", "content": "clause 1.v reads: “You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model ...”. Such ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_Equation_8_9.jsonl b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_Equation_8_9.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..89ed5b1d24b99a37a563bd19ef1a121d9b99fdff
--- /dev/null
+++ b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_Equation_8_9.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI&noteId=Z6OLWcPple", "content": "Physics - informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations ..."}
+{"idx": 1, "title": "Brain and cognitive reserve: Translation via network control ...", "date": "", "ddg_snippet": "by JD Medaglia · 2017 · Cited by 180 — We describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0149763416302329", "content": "by JD Medaglia · 2017 · Cited by 180 — We describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function."}
+{"idx": 2, "title": "Feedback Favors the Generalization of Neural ODEs", "date": "", "ddg_snippet": "14 Oct 2024 — The feedback neural network is a novel two-DOF neural network , which possesses robust performance in unseen scenarios with no loss of accuracy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10253v1", "content": "14 Oct 2024 — The feedback neural network is a novel two-DOF neural network , which possesses robust performance in unseen scenarios with no loss of accuracy ..."}
+{"idx": 3, "title": "Cognitive Development (Part I) - New Perspectives on ...", "date": "", "ddg_snippet": "11 May 2017 — This chapter provides an overview of research using constructive neural networks to simulate phenomena in cognitive development.", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/books/new-perspectives-on-human-development/cognitive-development/8C91FE0D753BDB98264F76F4864CE044", "content": "11 May 2017 — This chapter provides an overview of research using constructive neural networks to simulate phenomena in cognitive development."}
+{"idx": 4, "title": "Intelligent vehicle trajectory tracking control based on ...", "date": "", "ddg_snippet": "23 Apr 2024 — The neural network with the same structure is selected as the BLNN ( equation ( 8 )), and the same training data is used to train the network.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/09544070241244858", "content": "23 Apr 2024 — The neural network with the same structure is selected as the BLNN ( equation ( 8 )), and the same training data is used to train the network."}
+{"idx": 5, "title": "Daily Papers", "date": "", "ddg_snippet": "Specifically, this paper introduces the third generation of the Physics - Informed Neural Network Gravity Model ( PINN -GM-III) which solves the problems of ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=differentiable+learning", "content": "Specifically, this paper introduces the third generation of the Physics - Informed Neural Network Gravity Model ( PINN -GM-III) which solves the problems of ..."}
+{"idx": 6, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "CoPINN : Cognitive Physics - Informed Neural Networks · Approximating Latent Manifolds in Neural Networks via Vanishing Ideals · GTR: A General, Multi-View, and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "CoPINN : Cognitive Physics - Informed Neural Networks · Approximating Latent Manifolds in Neural Networks via Vanishing Ideals · GTR: A General, Multi-View, and ..."}
+{"idx": 7, "title": "Construction of influencing factor segmentation and ...", "date": "", "ddg_snippet": "by Y Hong · 2024 · Cited by 9 — A machine learning-based prediction model for college students' cell phone addiction was developed, yielding a prediction accuracy of 76.68%.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11024546/", "content": "by Y Hong · 2024 · Cited by 9 — A machine learning-based prediction model for college students' cell phone addiction was developed, yielding a prediction accuracy of 76.68%."}
+{"idx": 8, "title": "Deep Learning: An Introduction for Applied Mathematicians", "date": "", "ddg_snippet": "by CF Higham · 2019 · Cited by 383 — This article provides a very brief introduction to the basic ideas that underlie deep learning from an applied mathematics perspective.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/full/10.1137/18M1165748", "content": "by CF Higham · 2019 · Cited by 383 — This article provides a very brief introduction to the basic ideas that underlie deep learning from an applied mathematics perspective."}
+{"idx": 9, "title": "Artificial intelligence and machine learning‐aided drug ...", "date": "", "ddg_snippet": "by S Vatansever · 2020 · Cited by 395 — This DNN‐based tool, namely, N2A‐SVM, consists of three steps, including extraction of vector representation of each gene in the protein–protein interaction ( ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8043990/", "content": "by S Vatansever · 2020 · Cited by 395 — This DNN‐based tool, namely, N2A‐SVM, consists of three steps, including extraction of vector representation of each gene in the protein–protein interaction ( ..."}
diff --git a/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_equation_8_equation_9_weights_easiest_hardest_samp_year_2024.jsonl b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_equation_8_equation_9_weights_easiest_hardest_samp_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3dae85d4381e14d0be20576fcfd1c02f2e1d001
--- /dev/null
+++ b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_equation_8_equation_9_weights_easiest_hardest_samp_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "The authors suggest to train PINNs via assigning high sample weights to easy samples early on in the training process and then gradually shift towards hard ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI&noteId=Z6OLWcPple", "content": "The authors suggest to train PINNs via assigning high sample weights to easy samples early on in the training process and then gradually shift towards hard ..."}
+{"idx": 1, "title": "Brain and cognitive reserve: Translation via network control ...", "date": "", "ddg_snippet": "by JD Medaglia · 2017 · Cited by 180 — We describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0149763416302329", "content": "by JD Medaglia · 2017 · Cited by 180 — We describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function."}
+{"idx": 2, "title": "Cognitive Development (Part I) - New Perspectives on ...", "date": "", "ddg_snippet": "11 May 2017 — This chapter provides an overview of research using constructive neural networks to simulate phenomena in cognitive development.", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/books/new-perspectives-on-human-development/cognitive-development/8C91FE0D753BDB98264F76F4864CE044", "content": "11 May 2017 — This chapter provides an overview of research using constructive neural networks to simulate phenomena in cognitive development."}
+{"idx": 3, "title": "GitHub - siyuancncd/CoPINN: This is the official ...", "date": "", "ddg_snippet": "Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points indiscriminately ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN", "content": "Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points indiscriminately ..."}
+{"idx": 4, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Abstract Physics-informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "Abstract Physics-informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points ..."}
+{"idx": 5, "title": "CoPINN: Cognitive Physics-Informed Neural Networks | Read ...", "date": "", "ddg_snippet": "Abstract Physics-informed neural networks (PINN) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs).", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46458/paper", "content": "Abstract Physics-informed neural networks (PINN) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs)."}
+{"idx": 6, "title": "[2405.08111] Conformalized Physics-Informed Neural Networks", "date": "", "ddg_snippet": "May 13, 2024 · Physics-informed neural networks (PINNs) are an influential method of solving differential equations and estimating their parameters given data. However, since they make use of neural networks , they provide only a point estimate of differential equation parameters, as well as the solution at any given point, without any measure of uncertainty. Ensemble and Bayesian methods have been previously ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.08111", "content": "May 13, 2024 · Physics-informed neural networks (PINNs) are an influential method of solving differential equations and estimating their parameters given data. However, since they make use of neural networks , they provide only a point estimate of differential equation parameters, as well as the solution at any given point, without any measure of uncertainty. Ensemble and Bayesian methods have been previously ..."}
+{"idx": 7, "title": "Physics-Informed Neural Networks: A Review of ... - MDPI", "date": "", "ddg_snippet": "Jul 21, 2025 · The introduction of Physics-Informed Neural Networks (PINNs) marks a paradigm shift in the relationship between machine learning and computational physics , transitioning from a loose collaboration to a deep coupling. By embedding conservation laws, such as the residuals of governing equations , boundary conditions, and initial conditions, as soft constraints within the neural network ’s loss ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/14/8092", "content": "Jul 21, 2025 · The introduction of Physics-Informed Neural Networks (PINNs) marks a paradigm shift in the relationship between machine learning and computational physics , transitioning from a loose collaboration to a deep coupling. By embedding conservation laws, such as the residuals of governing equations , boundary conditions, and initial conditions, as soft constraints within the neural network ’s loss ..."}
+{"idx": 8, "title": "CoPINN/CoPINN.pdf at main · siyuancncd/CoPINN · GitHub", "date": "", "ddg_snippet": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network \" (ICML 2025, Spotlight) - CoPINN / CoPINN .pdf at main · siyuancncd/ CoPINN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN/blob/main/CoPINN.pdf", "content": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network \" (ICML 2025, Spotlight) - CoPINN / CoPINN .pdf at main · siyuancncd/ CoPINN"}
+{"idx": 9, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "This paper introduces a framework called CoPINN , which effectively addresses the Unbalanced Prediction Problem in Physics-Informed Neural Networks for solving Partial Differential Equations .", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/165180?from=subpath-search", "content": "This paper introduces a framework called CoPINN , which effectively addresses the Unbalanced Prediction Problem in Physics-Informed Neural Networks for solving Partial Differential Equations ."}
diff --git a/data/sampled_jsons/CoPINN_vie_vih_weights_epoch_scheduler_equation_8_9_year_2024.jsonl b/data/sampled_jsons/CoPINN_vie_vih_weights_epoch_scheduler_equation_8_9_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..469f58ee7713f4e27fc16f8a597c6e030932fb82
--- /dev/null
+++ b/data/sampled_jsons/CoPINN_vie_vih_weights_epoch_scheduler_equation_8_9_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CoPINN: Cognitive Physics-Informed Neural Networks - OpenReview", "date": "", "ddg_snippet": "Compute the weight assigned to the easiest sample point in epoch i, i.e., vi e, by Equation ( 8 ). Compute the weight assigned to the hardest sample point in epoch i, i.e., vi h, by Equation ( 9 ).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "Compute the weight assigned to the easiest sample point in epoch i, i.e., vi e, by Equation ( 8 ). Compute the weight assigned to the hardest sample point in epoch i, i.e., vi h, by Equation ( 9 )."}
+{"idx": 1, "title": "GitHub - siyuancncd/CoPINN: This is the official implementation of ...", "date": "", "ddg_snippet": "Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points indiscriminately ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN", "content": "Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points indiscriminately ..."}
+{"idx": 2, "title": "A (Very Short) Visual Introduction to Learning Rate Schedulers ... - Medium", "date": "", "ddg_snippet": "The best learning rate scheduler to use can depend on the specific problem and dataset, and it is often helpful to experiment with different schedulers to see which one works best.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@theom/a-very-short-visual-introduction-to-learning-rate-schedulers-with-code-189eddffdb00", "content": "The best learning rate scheduler to use can depend on the specific problem and dataset, and it is often helpful to experiment with different schedulers to see which one works best."}
+{"idx": 3, "title": "Pytorch Change the learning rate based on number of epochs", "date": "", "ddg_snippet": "Epoch -20 lr: 0.00010000000000000003 More on How to adjust Learning Rate - torch.optim.lr_scheduler provides several methods to adjust the learning rate based on the number of epochs .", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/60050586/pytorch-change-the-learning-rate-based-on-number-of-epochs", "content": "Epoch -20 lr: 0.00010000000000000003 More on How to adjust Learning Rate - torch.optim.lr_scheduler provides several methods to adjust the learning rate based on the number of epochs ."}
+{"idx": 4, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "The paper introduces a new method called Cognitive Physics-Informed Neural Networks ( CoPINN ) to improve how computers solve complex math problems known as partial differential equations (PDEs). Un...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46458/paper", "content": "The paper introduces a new method called Cognitive Physics-Informed Neural Networks ( CoPINN ) to improve how computers solve complex math problems known as partial differential equations (PDEs). Un..."}
+{"idx": 5, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Spotlight Poster CoPINN : Cognitive Physics-Informed Neural Networks Siyuan Duan · Wenyuan Wu · Peng Hu · Zhenwen Ren · Dezhong Peng · Yuan Sun East Exhibition Hall A-B #E-2302", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46458", "content": "Spotlight Poster CoPINN : Cognitive Physics-Informed Neural Networks Siyuan Duan · Wenyuan Wu · Peng Hu · Zhenwen Ren · Dezhong Peng · Yuan Sun East Exhibition Hall A-B #E-2302"}
+{"idx": 6, "title": "12.11. Learning Rate Scheduling — Dive into Deep Learning 1.0.3 ... - D2L", "date": "", "ddg_snippet": "12.11.2. Schedulers One way of adjusting the learning rate is to set it explicitly at each step. This is conveniently achieved by the set_learning_rate method. We could adjust it downward after every epoch (or even after every minibatch), e.g., in a dynamic manner in response to how optimization is progressing.", "subpage_snippet": "", "source": "www.d2l.ai", "link": "https://www.d2l.ai/chapter_optimization/lr-scheduler.html", "content": "12.11.2. Schedulers One way of adjusting the learning rate is to set it explicitly at each step. This is conveniently achieved by the set_learning_rate method. We could adjust it downward after every epoch (or even after every minibatch), e.g., in a dynamic manner in response to how optimization is progressing."}
+{"idx": 7, "title": "[2204.11144] Competitive Physics Informed Networks - arXiv.org", "date": "", "ddg_snippet": "We observe relative errors on the order of single-precision accuracy, consistently decreasing with each epoch . To the authors' knowledge, this is the first time this level of accuracy and convergence behavior has been achieved.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2204.11144", "content": "We observe relative errors on the order of single-precision accuracy, consistently decreasing with each epoch . To the authors' knowledge, this is the first time this level of accuracy and convergence behavior has been achieved."}
+{"idx": 8, "title": "Exponential Decay & Other LR Schedules - apxml.com", "date": "", "ddg_snippet": "Inside the training loop, after each epoch completes (i.e., after processing all batches for that epoch ), we call scheduler .step(). This updates the learning rate within the optimizer according to the chosen schedule for the next epoch .", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/deep-learning-regularization-optimization/chapter-7-optimization-refinements-tuning/other-lr-schedules", "content": "Inside the training loop, after each epoch completes (i.e., after processing all batches for that epoch ), we call scheduler .step(). This updates the learning rate within the optimizer according to the chosen schedule for the next epoch ."}
+{"idx": 9, "title": "Learning Rate Scheduling - Deep Learning Wizard", "date": "", "ddg_snippet": "We try to make learning deep learning, deep bayesian learning, and deep reinforcement learning math and code easier. Open-source and used by thousands globally.", "subpage_snippet": "", "source": "www.deeplearningwizard.com", "link": "https://www.deeplearningwizard.com/deep_learning/boosting_models_pytorch/lr_scheduling/", "content": "We try to make learning deep learning, deep bayesian learning, and deep reinforcement learning math and code easier. Open-source and used by thousands globally."}
diff --git a/data/sampled_jsons/CogAgent_A_visual_language_model_for_gui_agents_abstract.jsonl b/data/sampled_jsons/CogAgent_A_visual_language_model_for_gui_agents_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..41398963c8cdd8ed29c74a8d1ad6d5cf51c7fa95
--- /dev/null
+++ b/data/sampled_jsons/CogAgent_A_visual_language_model_for_gui_agents_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2312.08914] CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "View a PDF of the paper titled CogAgent : A Visual Language Model for GUI Agents , by Wenyi Hong and 13 other authors. Abstract :People are spending an enormous amount of time on digital devices through graphical user interfaces ( GUIs ), e.g., computer or smartphone screens.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.08914", "content": "View a PDF of the paper titled CogAgent : A Visual Language Model for GUI Agents , by Wenyi Hong and 13 other authors. Abstract :People are spending an enormous amount of time on digital devices through graphical user interfaces ( GUIs ), e.g., computer or smartphone screens."}
+{"idx": 1, "title": "CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "Agents based on visual language models (VLMs) have the potential to overcome these limitations.Figure 1. Samples of visual agents generated by CogAgent . More samples are demonstrated in the Appendix.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf", "content": "Agents based on visual language models (VLMs) have the potential to overcome these limitations.Figure 1. Samples of visual agents generated by CogAgent . More samples are demonstrated in the Appendix."}
+{"idx": 2, "title": "Paper page - CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "Abstract . CogAgent , a visual language model with strong GUI understanding and navigation capabilities, outperforms LLM-based methods in both PC and Android GUI tasks using only screenshots.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2312.08914", "content": "Abstract . CogAgent , a visual language model with strong GUI understanding and navigation capabilities, outperforms LLM-based methods in both PC and Android GUI tasks using only screenshots."}
+{"idx": 3, "title": "CogAgent : Visual Language Model for GUIs", "date": "", "ddg_snippet": "CogAgent is an 18-billion-parameter visual language model that automates GUI tasks with high-resolution input and efficient dual-resolution modules.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2312.08914", "content": "CogAgent is an 18-billion-parameter visual language model that automates GUI tasks with high-resolution input and efficient dual-resolution modules."}
+{"idx": 4, "title": "CogAgent : A Visual Language Model for GUI Agents | Request PDF", "date": "", "ddg_snippet": "Graphical User Interface ( GUI ) agents show amazing abilities in assisting human-computer interaction, automating human user's navigation on digital devices. An ideal GUI agent is expected to achieve high accuracy, low latency, and compatibility for different GUI platforms.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384235870_CogAgent_A_Visual_Language_Model_for_GUI_Agents", "content": "Graphical User Interface ( GUI ) agents show amazing abilities in assisting human-computer interaction, automating human user's navigation on digital devices. An ideal GUI agent is expected to achieve high accuracy, low latency, and compatibility for different GUI platforms."}
+{"idx": 5, "title": "CogAgent : A Visual Language Model for GUI Agents | OpenReview", "date": "", "ddg_snippet": "As a general-ist visual language model , CogAgent achieves the state of the art on five text-rich and four general VQA benchmarks, including VQAv2, OK- VQA, Text- Vqa, St- Vqa, ChartQA, infoVQA, DocVQA, MM-Vet, and POPE.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=HKg8G2vzW7&referrer=[the+profile+of+Wenyi+Hong](/profile?id=~Wenyi_Hong1)", "content": "As a general-ist visual language model , CogAgent achieves the state of the art on five text-rich and four general VQA benchmarks, including VQAv2, OK- VQA, Text- Vqa, St- Vqa, ChartQA, infoVQA, DocVQA, MM-Vet, and POPE."}
+{"idx": 6, "title": "CogAgent : A Visual Language Model for GUI Agents | AI Research...", "date": "", "ddg_snippet": "Overview CogAgent is a new visual language model designed for GUI interactionUses novel cross-module architecture for better visual - language alignment", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/cogagent-visual-language-model-gui-agents", "content": "Overview CogAgent is a new visual language model designed for GUI interactionUses novel cross-module architecture for better visual - language alignment"}
+{"idx": 7, "title": "CogAgent : A Visual Language Model for GUI Agents - SuperAGI", "date": "", "ddg_snippet": "People are spending an enormous amount of time on digital devices through graphical user interfaces ( GUIs ), e.g., computer or smartphone screens. Agent Builder. Automate outreach with custom sales workflows. AI Voice Agents .", "subpage_snippet": "", "source": "superagi.com", "link": "https://superagi.com/research_papers/cogagent-a-visual-language-model-for-gui-agents/", "content": "People are spending an enormous amount of time on digital devices through graphical user interfaces ( GUIs ), e.g., computer or smartphone screens. Agent Builder. Automate outreach with custom sales workflows. AI Voice Agents ."}
+{"idx": 8, "title": "zai-org/CogVLM: a state-of-the-art-level open visual language model", "date": "", "ddg_snippet": "Paper: CogAgent : A Visual Language Model for GUI Agents . CogAgent is an open-source visual language model improved based on CogVLM.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zai-org/CogVLM", "content": "Paper: CogAgent : A Visual Language Model for GUI Agents . CogAgent is an open-source visual language model improved based on CogVLM."}
+{"idx": 9, "title": "THUDM/ cogagent -vqa-hf · Hugging Face", "date": "", "ddg_snippet": "CogAgent is an open-source visual language model improved based on CogVLM. cogagent -chat: This model has strong capabilities in GUI Agent , visual multi-turn dialogue, visual grounding, etc.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/THUDM/cogagent-vqa-hf", "content": "CogAgent is an open-source visual language model improved based on CogVLM. cogagent -chat: This model has strong capabilities in GUI Agent , visual multi-turn dialogue, visual grounding, etc."}
diff --git a/data/sampled_jsons/CogAgent_GUI_agents_Hong_et_al_visual_language_model.jsonl b/data/sampled_jsons/CogAgent_GUI_agents_Hong_et_al_visual_language_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..20ab09ee8f3963ef675fac50095d838d7a30471c
--- /dev/null
+++ b/data/sampled_jsons/CogAgent_GUI_agents_Hong_et_al_visual_language_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2312.08914] CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "View a PDF of the paper titled CogAgent : A Visual Language Model for GUI Agents , by Wenyi Hong and 13 other authors.In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.08914", "content": "View a PDF of the paper titled CogAgent : A Visual Language Model for GUI Agents , by Wenyi Hong and 13 other authors.In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation."}
+{"idx": 1, "title": "CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "Agents based on visual language models (VLMs) have the potential to overcome these limitations.The decoder, a pre-trained language model , is en-hanced with a visual expert module introduced by Wang et al .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf", "content": "Agents based on visual language models (VLMs) have the potential to overcome these limitations.The decoder, a pre-trained language model , is en-hanced with a visual expert module introduced by Wang et al ."}
+{"idx": 2, "title": "CogAgent : A Visual Language Model for GUI Agents | Request PDF", "date": "", "ddg_snippet": "Graphical User Interface ( GUI ) grounding models are crucial for enabling intelligent agents to understand and interact with complex visual interfaces.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384235870_CogAgent_A_Visual_Language_Model_for_GUI_Agents", "content": "Graphical User Interface ( GUI ) grounding models are crucial for enabling intelligent agents to understand and interact with complex visual interfaces."}
+{"idx": 3, "title": "Paper page - CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "CogAgent , a visual language model with strong GUI understanding and navigation capabilities, outperforms LLM-based methods in both PC and Android GUI tasks using only screenshots.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2312.08914", "content": "CogAgent , a visual language model with strong GUI understanding and navigation capabilities, outperforms LLM-based methods in both PC and Android GUI tasks using only screenshots."}
+{"idx": 4, "title": "A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "CogAgent is an open-source visual language model improved based on CogVLM. CogAgent -18B has 11 billion visual parameters and 7 billion language parameters.", "subpage_snippet": "", "source": "replicate.com", "link": "https://replicate.com/cjwbw/cogagent-chat", "content": "CogAgent is an open-source visual language model improved based on CogVLM. CogAgent -18B has 11 billion visual parameters and 7 billion language parameters."}
+{"idx": 5, "title": "CogAgent : A Visual Language Model for GUI Agents - SuperAGI", "date": "", "ddg_snippet": "Agent Builder. Automate outreach with custom sales workflows. AI Voice Agents .People are spending an enormous amount of time on digital devices through graphical user interfaces ( GUIs ), e.g., computer or smartphone screens.", "subpage_snippet": "", "source": "superagi.com", "link": "https://superagi.com/research_papers/cogagent-a-visual-language-model-for-gui-agents/", "content": "Agent Builder. Automate outreach with custom sales workflows. AI Voice Agents .People are spending an enormous amount of time on digital devices through graphical user interfaces ( GUIs ), e.g., computer or smartphone screens."}
+{"idx": 6, "title": "THUDM/ cogagent -vqa-hf · Hugging Face", "date": "", "ddg_snippet": "CogAgent is an open-source visual language model improved based on CogVLM. cogagent -chat: This model has strong capabilities in GUI Agent , visual multi-turn dialogue, visual grounding, etc.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/THUDM/cogagent-vqa-hf", "content": "CogAgent is an open-source visual language model improved based on CogVLM. cogagent -chat: This model has strong capabilities in GUI Agent , visual multi-turn dialogue, visual grounding, etc."}
+{"idx": 7, "title": "CogAgent : Visual Language Model for GUIs", "date": "", "ddg_snippet": "CogAgent is an 18-billion-parameter visual language model that automates GUI tasks with high-resolution input and efficient dual-resolution modules.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2312.08914", "content": "CogAgent is an 18-billion-parameter visual language model that automates GUI tasks with high-resolution input and efficient dual-resolution modules."}
+{"idx": 8, "title": "CogAgent : A Visual Language Model for GUI Agents | AI Research...", "date": "", "ddg_snippet": "Overview CogAgent is a new visual language model designed for GUI interactionUses novel cross-module architecture for better visual - language alignment", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/cogagent-visual-language-model-gui-agents", "content": "Overview CogAgent is a new visual language model designed for GUI interactionUses novel cross-module architecture for better visual - language alignment"}
+{"idx": 9, "title": "Wenyi Hong", "date": "", "ddg_snippet": "One of first GUI agents based on pre-trained VLMs. We introduce CogAgent , an open-sourced 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation.", "subpage_snippet": "", "source": "wenyihong.github.io", "link": "https://wenyihong.github.io/", "content": "One of first GUI agents based on pre-trained VLMs. We introduce CogAgent , an open-sourced 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation."}
diff --git a/data/sampled_jsons/CogAgent_Hong_et_al._visual_language_model_GUI_agent_year_2023-2024.jsonl b/data/sampled_jsons/CogAgent_Hong_et_al._visual_language_model_GUI_agent_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..50b9c4da7dfd755b9c6f9a7585fe162eb48ac66d
--- /dev/null
+++ b/data/sampled_jsons/CogAgent_Hong_et_al._visual_language_model_GUI_agent_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "Agents based on visual language models (VLMs) have the potential to overcome these limitations.The decoder, a pre-trained language model , is en-hanced with a visual expert module introduced by Wang et al . [38] to facilitate a deep fusion of visual and language features.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf", "content": "Agents based on visual language models (VLMs) have the potential to overcome these limitations.The decoder, a pre-trained language model , is en-hanced with a visual expert module introduced by Wang et al . [38] to facilitate a deep fusion of visual and language features."}
+{"idx": 1, "title": "[2312.08914] CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "View a PDF of the paper titled CogAgent : A Visual Language Model for GUI Agents , by Wenyi Hong and 13 other authors.In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.08914", "content": "View a PDF of the paper titled CogAgent : A Visual Language Model for GUI Agents , by Wenyi Hong and 13 other authors.In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation."}
+{"idx": 2, "title": "CogAgent : A Visual Language Model for GUI Agents | Request PDF", "date": "", "ddg_snippet": "Graphical User Interface ( GUI ) grounding models are crucial for enabling intelligent agents to understand and interact with complex visual interfaces.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384235870_CogAgent_A_Visual_Language_Model_for_GUI_Agents", "content": "Graphical User Interface ( GUI ) grounding models are crucial for enabling intelligent agents to understand and interact with complex visual interfaces."}
+{"idx": 3, "title": "Paper page - CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "CogAgent , a visual language model with strong GUI understanding and navigation capabilities, outperforms LLM-based methods in both PC and Android GUI tasks using only screenshots.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2312.08914", "content": "CogAgent , a visual language model with strong GUI understanding and navigation capabilities, outperforms LLM-based methods in both PC and Android GUI tasks using only screenshots."}
+{"idx": 4, "title": "CogAgent : Visual Language Model for GUIs", "date": "", "ddg_snippet": "CogAgent is an 18-billion-parameter visual language model that automates GUI tasks with high-resolution input and efficient dual-resolution modules.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2312.08914", "content": "CogAgent is an 18-billion-parameter visual language model that automates GUI tasks with high-resolution input and efficient dual-resolution modules."}
+{"idx": 5, "title": "A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "CogAgent is an open-source visual language model improved based on CogVLM. CogAgent -18B has 11 billion visual parameters and 7 billion language parameters.", "subpage_snippet": "", "source": "replicate.com", "link": "https://replicate.com/cjwbw/cogagent-chat", "content": "CogAgent is an open-source visual language model improved based on CogVLM. CogAgent -18B has 11 billion visual parameters and 7 billion language parameters."}
+{"idx": 6, "title": "THUDM/ cogagent -vqa-hf · Hugging Face", "date": "", "ddg_snippet": "CogAgent is an open-source visual language model improved based on CogVLM. cogagent -chat: This model has strong capabilities in GUI Agent , visual multi-turn dialogue, visual grounding, etc.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/THUDM/cogagent-vqa-hf", "content": "CogAgent is an open-source visual language model improved based on CogVLM. cogagent -chat: This model has strong capabilities in GUI Agent , visual multi-turn dialogue, visual grounding, etc."}
+{"idx": 7, "title": "CogAgent : A Visual Language Model for GUI Agents - SuperAGI", "date": "", "ddg_snippet": "Agent Builder. Automate outreach with custom sales workflows. AI Voice Agents .People are spending an enormous amount of time on digital devices through graphical user interfaces ( GUIs ), e.g., computer or smartphone screens.", "subpage_snippet": "", "source": "superagi.com", "link": "https://superagi.com/research_papers/cogagent-a-visual-language-model-for-gui-agents/", "content": "Agent Builder. Automate outreach with custom sales workflows. AI Voice Agents .People are spending an enormous amount of time on digital devices through graphical user interfaces ( GUIs ), e.g., computer or smartphone screens."}
+{"idx": 8, "title": "CogAgent : A Visual Language Model for GUI Agents | AI Research...", "date": "", "ddg_snippet": "Overview CogAgent is a new visual language model designed for GUI interactionUses novel cross-module architecture for better visual - language alignment", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/cogagent-visual-language-model-gui-agents", "content": "Overview CogAgent is a new visual language model designed for GUI interactionUses novel cross-module architecture for better visual - language alignment"}
+{"idx": 9, "title": "How to Get Started with CogAgent : Your Visual Language Assistant...", "date": "", "ddg_snippet": "cogagent -chat for strong GUI Agent capabilities. cogagent -vqa for enhanced single-turn visual dialogue. Setting Up Your Environment. To set up CogAgent quickly, you can utilize the following Python code snippet.", "subpage_snippet": "", "source": "fxis.ai", "link": "https://fxis.ai/edu/how-to-get-started-with-cogagent-your-visual-language-assistant/", "content": "cogagent -chat for strong GUI Agent capabilities. cogagent -vqa for enhanced single-turn visual dialogue. Setting Up Your Environment. To set up CogAgent quickly, you can utilize the following Python code snippet."}
diff --git a/data/sampled_jsons/CogAgent_Visual_Language_Model_GUI_Agents_abstract_Wenyi_Hong.jsonl b/data/sampled_jsons/CogAgent_Visual_Language_Model_GUI_Agents_abstract_Wenyi_Hong.jsonl
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index 0000000000000000000000000000000000000000..c955931aadc6c4bc412fd4670f8266a509977237
--- /dev/null
+++ b/data/sampled_jsons/CogAgent_Visual_Language_Model_GUI_Agents_abstract_Wenyi_Hong.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CogAgent: A Visual Language Model for GUI Agents | Papers With", "date": "", "ddg_snippet": "In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/cogagent-a-visual-language-model-for-gui", "content": "In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation."}
+{"idx": 1, "title": "[2312.08914] CogAgent: A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "View a PDF of the paper titled CogAgent : A Visual Language Model for GUI Agents , by Wenyi Hong and 13 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.08914", "content": "View a PDF of the paper titled CogAgent : A Visual Language Model for GUI Agents , by Wenyi Hong and 13 other authors"}
+{"idx": 2, "title": "ShowUI: One Vision-Language-Action Model for GUI Visual Agent", "date": "", "ddg_snippet": "This relationship guides the self-attention blocks within visual encoders or language models for token selection, effectively reducing computation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.17465v1", "content": "This relationship guides the self-attention blocks within visual encoders or language models for token selection, effectively reducing computation."}
+{"idx": 3, "title": "Wenyi Hong", "date": "", "ddg_snippet": "Abstract : Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Wenyi+Hong", "content": "Abstract : Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to ..."}
+{"idx": 4, "title": "Jiazheng Xu", "date": "", "ddg_snippet": "In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Jiazheng+Xu", "content": "In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation."}
+{"idx": 5, "title": "CVPR 2024 Open Access Repository", "date": "", "ddg_snippet": "... Hong _2024_CVPR, author = { Hong , Wenyi and Wang, Weihan and Lv, Qingsong and Xu, Jiazheng and Yu, Wenmeng and Ji, Junhui and Wang, Yan and Wang, Zihan ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.html", "content": "... Hong _2024_CVPR, author = { Hong , Wenyi and Wang, Weihan and Lv, Qingsong and Xu, Jiazheng and Yu, Wenmeng and Ji, Junhui and Wang, Yan and Wang, Zihan ..."}
+{"idx": 6, "title": "Most Influential CVPR Papers (2025-03 Version) –", "date": "", "ddg_snippet": "CogAgent : A Visual Language Model for GUI Agents IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/03/most-influential-cvpr-papers-2025-03-version/", "content": "CogAgent : A Visual Language Model for GUI Agents IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight ..."}
+{"idx": 7, "title": "Most Influential CVPR Papers (2024-09) – Paper Digest", "date": "", "ddg_snippet": "CogAgent : A Visual Language Model for GUI Agents IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2024/09/most-influential-cvpr-papers-2024-09/", "content": "CogAgent : A Visual Language Model for GUI Agents IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight ..."}
+{"idx": 8, "title": "Juan-Zi Li", "date": "", "ddg_snippet": "ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation.", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/juan-zi-li/", "content": "ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation."}
+{"idx": 9, "title": "DenseFusion-1M: Merging Vision Experts for Comprehensive", "date": "", "ddg_snippet": "Existing Multimodal Large Language Models (MLLMs) increasingly emphasize complex understanding of various visual elements, including multiple objects ...", "subpage_snippet": "", "source": "kingsleylo.com", "link": "https://kingsleylo.com/article/densefusion-1m-merging-vision-experts-for-comprehensive-multimodal-perception", "content": "Existing Multimodal Large Language Models (MLLMs) increasingly emphasize complex understanding of various visual elements, including multiple objects ..."}
diff --git a/data/sampled_jsons/Cognitive_Physics-Informed_Neural_Networks_Equation_8_9_easiest_hardest_samples_v_ie_v_ih_year_2023.jsonl b/data/sampled_jsons/Cognitive_Physics-Informed_Neural_Networks_Equation_8_9_easiest_hardest_samples_v_ie_v_ih_year_2023.jsonl
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index 0000000000000000000000000000000000000000..a0a8deb30c29ae762559b92e99aeca31ef0b5220
--- /dev/null
+++ b/data/sampled_jsons/Cognitive_Physics-Informed_Neural_Networks_Equation_8_9_easiest_hardest_samples_v_ie_v_ih_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Abstract Physics - informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "Abstract Physics - informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points ..."}
+{"idx": 1, "title": "Physics-informed learning of governing equations from scarce data", "date": "", "ddg_snippet": "This work introduces a novel approach called physics - informed neural network with sparse regression to discover governing partial differential equations from scarce and noisy data for nonlinear ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-021-26434-1", "content": "This work introduces a novel approach called physics - informed neural network with sparse regression to discover governing partial differential equations from scarce and noisy data for nonlinear ..."}
+{"idx": 2, "title": "Physics-Informed Neural Networks: A Review of Methodological ... - MDPI", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs' development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/14/8092", "content": "Physics - informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs' development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence."}
+{"idx": 3, "title": "Physics-informed neural network combined with characteristic-based ...", "date": "", "ddg_snippet": "Abstract This paper presents a novel approach for solving the shallow-water transport equation using a physics - informed neural network (PINN) combined with characteristic-based split (CBS). Simulation of the tide in East Coast of China is performed to verify the applicability of the present method in a practical problem.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224000110", "content": "Abstract This paper presents a novel approach for solving the shallow-water transport equation using a physics - informed neural network (PINN) combined with characteristic-based split (CBS). Simulation of the tide in East Coast of China is performed to verify the applicability of the present method in a practical problem."}
+{"idx": 4, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Abstract Physics - informed neural networks (PINN) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs).", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46458/paper", "content": "Abstract Physics - informed neural networks (PINN) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs)."}
+{"idx": 5, "title": "(PDF) A brief overview of Physics-Informed Neural Networks and some ...", "date": "", "ddg_snippet": "We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386336316_A_brief_overview_of_Physics-Informed_Neural_Networks_and_some_critical_remarks", "content": "We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear ..."}
+{"idx": 6, "title": "Complex Physics-Informed Neural Network - arXiv.org", "date": "", "ddg_snippet": "1 Introduction Physics - Informed Neural Networks (PINNs) have emerged as a powerful method for solving both forward and inverse problems involving Partial Differential Equations (PDEs) [1-4]. PINNs leverage the expressive power of neural networks to minimize a loss function that enforces the governing PDEs and boundary/initial conditions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04917v2", "content": "1 Introduction Physics - Informed Neural Networks (PINNs) have emerged as a powerful method for solving both forward and inverse problems involving Partial Differential Equations (PDEs) [1-4]. PINNs leverage the expressive power of neural networks to minimize a loss function that enforces the governing PDEs and boundary/initial conditions."}
+{"idx": 7, "title": "physics-informed neural networks - arXiv.org", "date": "", "ddg_snippet": "Perdikaris, and G. Karniadakis. Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinea partial differential equations . Journal of Computatio", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.13848", "content": "Perdikaris, and G. Karniadakis. Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinea partial differential equations . Journal of Computatio"}
+{"idx": 8, "title": "US5724987A - Neurocognitive adaptive computer-aided training", "date": "", "ddg_snippet": "... information, large brain scanners such as those used in Positron Emission Tomography or Magnetic Resonance Imaging (MRI), are not practical to use in ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US5724987A/en", "content": "... information, large brain scanners such as those used in Positron Emission Tomography or Magnetic Resonance Imaging (MRI), are not practical to use in ..."}
+{"idx": 9, "title": "GitHub - siyuancncd/CoPINN: This is the official implementation of ...", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points indiscriminately ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN", "content": "Physics - informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations , which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points indiscriminately ..."}
diff --git a/data/sampled_jsons/Concept_Bottleneck_Models_Koh_Pang_Wei_Koh_ICML_2020_abstract.jsonl b/data/sampled_jsons/Concept_Bottleneck_Models_Koh_Pang_Wei_Koh_ICML_2020_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a05b0f22336bf2f12442274e09067cfcaf2a0a23
--- /dev/null
+++ b/data/sampled_jsons/Concept_Bottleneck_Models_Koh_Pang_Wei_Koh_ICML_2020_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Concept Bottleneck Models", "date": "", "ddg_snippet": "Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang.We seek to learn models that we can interact with using high-level concepts : would the model predict severe arthritis if it thinks there is a bone spur in the x-ray?", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/poster/6816", "content": "Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang.We seek to learn models that we can interact with using high-level concepts : would the model predict severe arthritis if it thinks there is a bone spur in the x-ray?"}
+{"idx": 1, "title": "[2007.04612] Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.04612", "content": "Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 2, "title": "Concept Bottleneck Models - PMLR", "date": "", "ddg_snippet": "Proceedings of Machine Learning Research [edit] Concept Bottleneck Models Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5338-5348, 2020 .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a.html", "content": "Proceedings of Machine Learning Research [edit] Concept Bottleneck Models Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5338-5348, 2020 ."}
+{"idx": 3, "title": "Concept bottleneck models | Proceedings of the 37th ...", "date": "", "ddg_snippet": "Jul 13, 2020 · We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3524938.3525433", "content": "Jul 13, 2020 · We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction."}
+{"idx": 4, "title": "dblp: Concept Bottleneck Models. GitHub - yewsiang/ConceptBottleneck: Concept Bottleneck ... Concept Bottleneck Models - NASA/ADS ICML Poster Concept Bottleneck Models Concept Bottleneck Models - PMLR [2007.04612] Concept Bottleneck Models - arXiv.org [2007.04612] Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348 This repository contains code and scripts for the following paper: The experiments use the following datasets: •NIH Osteoarthritis Initiative (OAI) •Caltech-UCSD Birds 200 (CUB) To download the TravelingBirds dataset, which we use to test robustness to background shifts, please download the CUB_fixed folder from this CodaLab bundle by clicking on the download button. If you use this dataset, please also cite the original CUB and Places datasets. The NIH Osteoarthritis Initiative (OAI) dataset requires an application for data access, so we are unable to provide the raw data here. To access that data, please first obtain data access permission from the Osteoarthritis Initiative, and then refer to this Github repository for data processing code. If you use it, please cite the Pierson et al. paper corresponding to that repository as well. See full list on github.com We seek to learn models that we can interact with using high-level concepts : would the model predict severe arthritis if it thinks there is a bone spur in the x-ray? State-of-the-art models today do not typically support the manipulation of concepts like \"the existence of bone spurs\", as they are trained end-to-end to go directly from raw input (e.... See full list on github.com We used the same environment as Codalab's default gpu setting, please run pip install -r requirements.txt. Main packages are: •matplotlib 3.1.1 •numpy 1.17.1 •pandas 0.25.1 •Pillow 6.2.2 •scipy 1.3.1 See full list on github.com Standard task training for CUB can be ran using the scripts/experiments.sh and Codalab scripts can be ran using scripts/codalab_experiments.sh. More information about how to perform data processing and other evaluations can be found in the README in CUB/. See full list on github.com On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Poster Concept Bottleneck Models Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang Keywords: [ Accountability, Transparency and Interpretability ] Why are concept bottleneck models important? These models also allow for richer human-model interaction: accuracy improves significantly if we can correct model mistakes on concepts at test time. Koh , P.W., Nguyen, T., Tang, Y.S., Mussmann, S., Pierson, E., Kim, B. & Liang, P.. ( 2020 ). Concept Bottleneck Models . What is a concept bottleneck X-ray grading model? On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). How can we intervene on a concept bottleneck model? We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/icml/KohNTMPKL20", "content": "Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348 This repository contains code and scripts for the following paper: The experiments use the following datasets: •NIH Osteoarthritis Initiative (OAI) •Caltech-UCSD Birds 200 (CUB) To download the TravelingBirds dataset, which we use to test robustness to background shifts, please download the CUB_fixed folder from this CodaLab bundle by clicking on the download button. If you use this dataset, please also cite the original CUB and Places datasets. The NIH Osteoarthritis Initiative (OAI) dataset requires an application for data access, so we are unable to provide the raw data here. To access that data, please first obtain data access permission from the Osteoarthritis Initiative, and then refer to this Github repository for data processing code. If you use it, please cite the Pierson et al. paper corresponding to that repository as well. See full list on github.com We seek to learn models that we can interact with using high-level concepts : would the model predict severe arthritis if it thinks there is a bone spur in the x-ray? State-of-the-art models today do not typically support the manipulation of concepts like \"the existence of bone spurs\", as they are trained end-to-end to go directly from raw input (e.... See full list on github.com We used the same environment as Codalab's default gpu setting, please run pip install -r requirements.txt. Main packages are: •matplotlib 3.1.1 •numpy 1.17.1 •pandas 0.25.1 •Pillow 6.2.2 •scipy 1.3.1 See full list on github.com Standard task training for CUB can be ran using the scripts/experiments.sh and Codalab scripts can be ran using scripts/codalab_experiments.sh. More information about how to perform data processing and other evaluations can be found in the README in CUB/. See full list on github.com On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Poster Concept Bottleneck Models Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang Keywords: [ Accountability, Transparency and Interpretability ] Why are concept bottleneck models important? These models also allow for richer human-model interaction: accuracy improves significantly if we can correct model mistakes on concepts at test time. Koh , P.W., Nguyen, T., Tang, Y.S., Mussmann, S., Pierson, E., Kim, B. & Liang, P.. ( 2020 ). Concept Bottleneck Models . What is a concept bottleneck X-ray grading model? On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). How can we intervene on a concept bottleneck model? We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction ."}
+{"idx": 5, "title": "GitHub - yewsiang/ConceptBottleneck: Concept Bottleneck ... Concept Bottleneck Models - NASA/ADS ICML Poster Concept Bottleneck Models Concept Bottleneck Models - PMLR [2007.04612] Concept Bottleneck Models - arXiv.org [2007.04612] Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "This repository contains code and scripts for the following paper: The experiments use the following datasets: •NIH Osteoarthritis Initiative (OAI) •Caltech-UCSD Birds 200 (CUB) To download the TravelingBirds dataset, which we use to test robustness to background shifts, please download the CUB_fixed folder from this CodaLab bundle by clicking on the download button. If you use this dataset, please also cite the original CUB and Places datasets. The NIH Osteoarthritis Initiative (OAI) dataset requires an application for data access, so we are unable to provide the raw data here. To access that data, please first obtain data access permission from the Osteoarthritis Initiative, and then refer to this Github repository for data processing code. If you use it, please cite the Pierson et al. paper corresponding to that repository as well. See full list on github.com We seek to learn models that we can interact with using high-level concepts : would the model predict severe arthritis if it thinks there is a bone spur in the x-ray? State-of-the-art models today do not typically support the manipulation of concepts like \"the existence of bone spurs\", as they are trained end-to-end to go directly from raw input (e.... See full list on github.com We used the same environment as Codalab's default gpu setting, please run pip install -r requirements.txt. Main packages are: •matplotlib 3.1.1 •numpy 1.17.1 •pandas 0.25.1 •Pillow 6.2.2 •scipy 1.3.1 See full list on github.com Standard task training for CUB can be ran using the scripts/experiments.sh and Codalab scripts can be ran using scripts/codalab_experiments.sh. More information about how to perform data processing and other evaluations can be found in the README in CUB/. See full list on github.com On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Poster Concept Bottleneck Models Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang Keywords: [ Accountability, Transparency and Interpretability ] Why are concept bottleneck models important? These models also allow for richer human-model interaction: accuracy improves significantly if we can correct model mistakes on concepts at test time. Koh , P.W., Nguyen, T., Tang, Y.S., Mussmann, S., Pierson, E., Kim, B. & Liang, P.. ( 2020 ). Concept Bottleneck Models . What is a concept bottleneck X-ray grading model? On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). How can we intervene on a concept bottleneck model? We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yewsiang/ConceptBottleneck", "content": "This repository contains code and scripts for the following paper: The experiments use the following datasets: •NIH Osteoarthritis Initiative (OAI) •Caltech-UCSD Birds 200 (CUB) To download the TravelingBirds dataset, which we use to test robustness to background shifts, please download the CUB_fixed folder from this CodaLab bundle by clicking on the download button. If you use this dataset, please also cite the original CUB and Places datasets. The NIH Osteoarthritis Initiative (OAI) dataset requires an application for data access, so we are unable to provide the raw data here. To access that data, please first obtain data access permission from the Osteoarthritis Initiative, and then refer to this Github repository for data processing code. If you use it, please cite the Pierson et al. paper corresponding to that repository as well. See full list on github.com We seek to learn models that we can interact with using high-level concepts : would the model predict severe arthritis if it thinks there is a bone spur in the x-ray? State-of-the-art models today do not typically support the manipulation of concepts like \"the existence of bone spurs\", as they are trained end-to-end to go directly from raw input (e.... See full list on github.com We used the same environment as Codalab's default gpu setting, please run pip install -r requirements.txt. Main packages are: •matplotlib 3.1.1 •numpy 1.17.1 •pandas 0.25.1 •Pillow 6.2.2 •scipy 1.3.1 See full list on github.com Standard task training for CUB can be ran using the scripts/experiments.sh and Codalab scripts can be ran using scripts/codalab_experiments.sh. More information about how to perform data processing and other evaluations can be found in the README in CUB/. See full list on github.com On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Poster Concept Bottleneck Models Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang Keywords: [ Accountability, Transparency and Interpretability ] Why are concept bottleneck models important? These models also allow for richer human-model interaction: accuracy improves significantly if we can correct model mistakes on concepts at test time. Koh , P.W., Nguyen, T., Tang, Y.S., Mussmann, S., Pierson, E., Kim, B. & Liang, P.. ( 2020 ). Concept Bottleneck Models . What is a concept bottleneck X-ray grading model? On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). How can we intervene on a concept bottleneck model? We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction ."}
+{"idx": 6, "title": "Concept Bottleneck Models - NASA/ADS", "date": "", "ddg_snippet": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv200704612K/abstract", "content": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 7, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models . Pang Wei Koh . Thao Nguyen. Abstract . We seek to learn models that support interventions on high-level concepts : e.g., would the model would have predicted severe arthritis if it didn’t think that there was a bone spur in the x-ray?", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/concept-bottleneck-models/", "content": "Concept Bottleneck Models . Pang Wei Koh . Thao Nguyen. Abstract . We seek to learn models that support interventions on high-level concepts : e.g., would the model would have predicted severe arthritis if it didn’t think that there was a bone spur in the x-ray?"}
+{"idx": 8, "title": "Concept Bottleneck Models | DeepAI", "date": "", "ddg_snippet": "Concept Bottleneck Models . 07/09/ 2020 . ∙. by Pang Wei Koh , et al. ∙. 14.By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/concept-bottleneck-models", "content": "Concept Bottleneck Models . 07/09/ 2020 . ∙. by Pang Wei Koh , et al. ∙. 14.By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction."}
+{"idx": 9, "title": "[2007.04612v3] Concept Bottleneck Models", "date": "", "ddg_snippet": "Title: Concept Bottleneck Models . Authors: Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang.Edited for clarity from the ICML 2020 version.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.04612v3", "content": "Title: Concept Bottleneck Models . Authors: Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang.Edited for clarity from the ICML 2020 version."}
diff --git a/data/sampled_jsons/Concept_Bottleneck_Models_Koh_Pang_Wei_Koh_ICML_2020_year_2020.jsonl b/data/sampled_jsons/Concept_Bottleneck_Models_Koh_Pang_Wei_Koh_ICML_2020_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..528d7b8865b973ade96f10a3abf62164cbd54f4a
--- /dev/null
+++ b/data/sampled_jsons/Concept_Bottleneck_Models_Koh_Pang_Wei_Koh_ICML_2020_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2007.04612] Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.04612", "content": "Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 1, "title": "GitHub - yewsiang/ConceptBottleneck: Concept Bottleneck ... ICML Poster Concept Bottleneck Models dblp: Concept Bottleneck Models. Concept Bottleneck Models - Google Research [2007.04612] Concept Bottleneck Models - arXiv.org Concept Bottleneck Models - PMLR Concept Bottleneck Models - Google Research", "date": "", "ddg_snippet": "This repository contains code and scripts for the following paper: The experiments use the following datasets: •NIH Osteoarthritis Initiative (OAI) •Caltech-UCSD Birds 200 (CUB) To download the TravelingBirds dataset, which we use to test robustness to background shifts, please download the CUB_fixed folder from this CodaLab bundle by clicking on the download button. If you use this dataset, please also cite the original CUB and Places datasets. The NIH Osteoarthritis Initiative (OAI) dataset requires an application for data access, so we are unable to provide the raw data here. To access that data, please first obtain data access permission from the Osteoarthritis Initiative, and then refer to this Github repository for data processing code. If you use it, please cite the Pierson et al. paper corresponding to that repository as well. See full list on github.com We seek to learn models that we can interact with using high-level concepts : would the model predict severe arthritis if it thinks there is a bone spur in the x-ray? State-of-the-art models today do not typically support the manipulation of concepts like \"the existence of bone spurs\", as they are trained end-to-end to go directly from raw input (e.... See full list on github.com We used the same environment as Codalab's default gpu setting, please run pip install -r requirements.txt. Main packages are: •matplotlib 3.1.1 •numpy 1.17.1 •pandas 0.25.1 •Pillow 6.2.2 •scipy 1.3.1 See full list on github.com Standard task training for CUB can be ran using the scripts/experiments.sh and Codalab scripts can be ran using scripts/codalab_experiments.sh. More information about how to perform data processing and other evaluations can be found in the README in CUB/. See full list on github.com Poster Concept Bottleneck Models Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang Keywords: [ Accountability, Transparency and Interpretability ] Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348 What is a concept bottleneck model? On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models, while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”). What is a concept bottleneck X-ray grading model? On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). How can we intervene on concept bottleneck models? By construction , we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models , while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yewsiang/ConceptBottleneck", "content": "This repository contains code and scripts for the following paper: The experiments use the following datasets: •NIH Osteoarthritis Initiative (OAI) •Caltech-UCSD Birds 200 (CUB) To download the TravelingBirds dataset, which we use to test robustness to background shifts, please download the CUB_fixed folder from this CodaLab bundle by clicking on the download button. If you use this dataset, please also cite the original CUB and Places datasets. The NIH Osteoarthritis Initiative (OAI) dataset requires an application for data access, so we are unable to provide the raw data here. To access that data, please first obtain data access permission from the Osteoarthritis Initiative, and then refer to this Github repository for data processing code. If you use it, please cite the Pierson et al. paper corresponding to that repository as well. See full list on github.com We seek to learn models that we can interact with using high-level concepts : would the model predict severe arthritis if it thinks there is a bone spur in the x-ray? State-of-the-art models today do not typically support the manipulation of concepts like \"the existence of bone spurs\", as they are trained end-to-end to go directly from raw input (e.... See full list on github.com We used the same environment as Codalab's default gpu setting, please run pip install -r requirements.txt. Main packages are: •matplotlib 3.1.1 •numpy 1.17.1 •pandas 0.25.1 •Pillow 6.2.2 •scipy 1.3.1 See full list on github.com Standard task training for CUB can be ran using the scripts/experiments.sh and Codalab scripts can be ran using scripts/codalab_experiments.sh. More information about how to perform data processing and other evaluations can be found in the README in CUB/. See full list on github.com Poster Concept Bottleneck Models Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang Keywords: [ Accountability, Transparency and Interpretability ] Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348 What is a concept bottleneck model? On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models, while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”). What is a concept bottleneck X-ray grading model? On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). How can we intervene on concept bottleneck models? By construction , we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models , while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”)."}
+{"idx": 2, "title": "Concept Bottleneck Models - PMLR", "date": "", "ddg_snippet": "@InProceedings{pmlr-v119-koh20a, title = {Concept Bottleneck Models }, author = { Koh , Pang Wei and Nguyen, Thao and Tang, Yew Siang and Mussmann, Stephen and Pierson, Emma and Kim, Been and Liang, Percy}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {5338--5348}, year = { 2020 }, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a.html", "content": "@InProceedings{pmlr-v119-koh20a, title = {Concept Bottleneck Models }, author = { Koh , Pang Wei and Nguyen, Thao and Tang, Yew Siang and Mussmann, Stephen and Pierson, Emma and Kim, Been and Liang, Percy}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {5338--5348}, year = { 2020 }, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series ..."}
+{"idx": 3, "title": "Concept bottleneck models | Proceedings of the 37th ...", "date": "", "ddg_snippet": "Jul 13, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3524938.3525433", "content": "Jul 13, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 4, "title": "ICML Poster Concept Bottleneck Models dblp: Concept Bottleneck Models. Concept Bottleneck Models - Google Research [2007.04612] Concept Bottleneck Models - arXiv.org Concept Bottleneck Models - PMLR Concept Bottleneck Models - Google Research", "date": "", "ddg_snippet": "Poster Concept Bottleneck Models Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang Keywords: [ Accountability, Transparency and Interpretability ] Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348 What is a concept bottleneck model? On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models, while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”). What is a concept bottleneck X-ray grading model? On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). How can we intervene on concept bottleneck models? By construction , we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models , while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”).", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/poster/6816", "content": "Poster Concept Bottleneck Models Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang Keywords: [ Accountability, Transparency and Interpretability ] Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348 What is a concept bottleneck model? On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models, while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”). What is a concept bottleneck X-ray grading model? On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). How can we intervene on concept bottleneck models? By construction , we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models , while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”)."}
+{"idx": 5, "title": "dblp: Concept Bottleneck Models.", "date": "", "ddg_snippet": "Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/icml/KohNTMPKL20", "content": "Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348"}
+{"idx": 6, "title": "Concept Bottleneck Models - Google Research", "date": "", "ddg_snippet": "On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models , while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”).", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/concept-bottleneck-models/", "content": "On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models , while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”)."}
+{"idx": 7, "title": "If Concept Bottlenecks are the Question, are Foundation Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) [ 41 ] combine two neural modules: a concept extractor f : 𝒳 → ℝ k : 𝑓 → 𝒳 superscript ℝ 𝑘 f ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.19774v2", "content": "Concept Bottleneck Models (CBMs) [ 41 ] combine two neural modules: a concept extractor f : 𝒳 → ℝ k : 𝑓 → 𝒳 superscript ℝ 𝑘 f ..."}
+{"idx": 8, "title": "Pang Wei Koh", "date": "", "ddg_snippet": "Jacqueline He, Howard Yen, Margaret Li, Shuyue Stella Li, Zhiyuan Zeng, Weijia Shi, Yulia Tsvetkov, Danqi Chen, Pang Wei Koh , and Luke Zettlemoyer", "subpage_snippet": "", "source": "koh.pw", "link": "https://koh.pw/", "content": "Jacqueline He, Howard Yen, Margaret Li, Shuyue Stella Li, Zhiyuan Zeng, Weijia Shi, Yulia Tsvetkov, Danqi Chen, Pang Wei Koh , and Luke Zettlemoyer"}
+{"idx": 9, "title": "Pang Wei Koh - Google Scholar", "date": "", "ddg_snippet": "PW Koh *, S Sagawa*, H Marklund, SM Xie, M Zhang, A Balsubramani, ... ... S Min, K Krishna, X Lyu, M Lewis, W Yih, PW Koh , M Iyyer, L Zettlemoyer, ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Nn990CkAAAAJ&hl=en", "content": "PW Koh *, S Sagawa*, H Marklund, SM Xie, M Zhang, A Balsubramani, ... ... S Min, K Krishna, X Lyu, M Lewis, W Yih, PW Koh , M Iyyer, L Zettlemoyer, ..."}
diff --git a/data/sampled_jsons/Constructive_Algorithms_for_Discrepancy_Minimization_Bansal_FOCS_2010_SDP_entropy_method_year_2010.jsonl b/data/sampled_jsons/Constructive_Algorithms_for_Discrepancy_Minimization_Bansal_FOCS_2010_SDP_entropy_method_year_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..634865f9452d82affba7955c833099596adaf679
--- /dev/null
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+{"idx": 0, "title": "Constructive Algorithms for Discrepancy Minimization Constructive Algorithms for Discrepancy Minimization Constructive Algorithms for Discrepancy Minimization Images Constructive Discrepancy Minimization by Walking on The Edges Lecture Constructive Algorithms for Discrepancy Minimization Constructive Algorithms for Discrepancy Minimization | IEEE ... Constructive algorithms for discrepancy minimization for FOCS ...", "date": "", "ddg_snippet": "Feb 11, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . We also give a first approximation-like result for discrepancy . In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to: In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . View all Mar 26, 2012 · Recently, a breakthrough work of Bansal ( FOCS 2010 ) gave an efficient algorithm which finds such a coloring. His algorithm was based on an SDP relaxation of the discrepancy problem and a clever ... The analysis will show that there are only a few problematic sets at any step, and hence the entropy penalty will be low enough for us continue (i.e. the SDP will always remain feasible). At each step the random hops for various elements are correlated by a solution to a semidefinite program, where this program is determined by the current state and the entropy method . Published in: 2010 IEEE 51st Annual Symposium on Foundations of Computer Science Article #: Date of Conference: 23-26 October 2010 Oct 23, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . We also give a first approximation-like result for discrepancy .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1002.2259", "content": "Feb 11, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . We also give a first approximation-like result for discrepancy . In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to: In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . View all Mar 26, 2012 · Recently, a breakthrough work of Bansal ( FOCS 2010 ) gave an efficient algorithm which finds such a coloring. His algorithm was based on an SDP relaxation of the discrepancy problem and a clever ... The analysis will show that there are only a few problematic sets at any step, and hence the entropy penalty will be low enough for us continue (i.e. the SDP will always remain feasible). At each step the random hops for various elements are correlated by a solution to a semidefinite program, where this program is determined by the current state and the entropy method . Published in: 2010 IEEE 51st Annual Symposium on Foundations of Computer Science Article #: Date of Conference: 23-26 October 2010 Oct 23, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . We also give a first approximation-like result for discrepancy ."}
+{"idx": 1, "title": "Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to:", "subpage_snippet": "", "source": "ieee-focs.org", "link": "https://ieee-focs.org/FOCS-2010-Papers/Constructive-Algorithms-for-Discrepancy-Minimization.pdf", "content": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to:"}
+{"idx": 2, "title": "Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method .", "subpage_snippet": "", "source": "acm-prod.literatumonline.com", "link": "https://acm-prod.literatumonline.com/doi/abs/10.1109/FOCS.2010.7", "content": "In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method ."}
+{"idx": 3, "title": "Constructive Discrepancy Minimization by Walking on The Edges", "date": "", "ddg_snippet": "Mar 26, 2012 · Recently, a breakthrough work of Bansal ( FOCS 2010 ) gave an efficient algorithm which finds such a coloring. His algorithm was based on an SDP relaxation of the discrepancy problem and a clever ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221940942_Constructive_Discrepancy_Minimization_by_Walking_on_The_Edges", "content": "Mar 26, 2012 · Recently, a breakthrough work of Bansal ( FOCS 2010 ) gave an efficient algorithm which finds such a coloring. His algorithm was based on an SDP relaxation of the discrepancy problem and a clever ..."}
+{"idx": 4, "title": "Lecture Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "The analysis will show that there are only a few problematic sets at any step, and hence the entropy penalty will be low enough for us continue (i.e. the SDP will always remain feasible).", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/~zdvir/apx11slides/bansal-scribe.pdf", "content": "The analysis will show that there are only a few problematic sets at any step, and hence the entropy penalty will be low enough for us continue (i.e. the SDP will always remain feasible)."}
+{"idx": 5, "title": "Constructive Algorithms for Discrepancy Minimization | IEEE ...", "date": "", "ddg_snippet": "At each step the random hops for various elements are correlated by a solution to a semidefinite program, where this program is determined by the current state and the entropy method . Published in: 2010 IEEE 51st Annual Symposium on Foundations of Computer Science Article #: Date of Conference: 23-26 October 2010", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/5670954/footnotes", "content": "At each step the random hops for various elements are correlated by a solution to a semidefinite program, where this program is determined by the current state and the entropy method . Published in: 2010 IEEE 51st Annual Symposium on Foundations of Computer Science Article #: Date of Conference: 23-26 October 2010"}
+{"idx": 6, "title": "Constructive algorithms for discrepancy minimization for FOCS ...", "date": "", "ddg_snippet": "Oct 23, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . We also give a first approximation-like result for discrepancy .", "subpage_snippet": "", "source": "research.ibm.com", "link": "https://research.ibm.com/publications/constructive-algorithms-for-discrepancy-minimization", "content": "Oct 23, 2010 · In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so-called Entropy Method . We also give a first approximation-like result for discrepancy ."}
+{"idx": 7, "title": "Constructive Discrepancy Minimization by Walking on The ...", "date": "", "ddg_snippet": "by S Lovett · 2012 · Cited by 205 — Recently, a breakthrough work of Bansal (FOCS 2010) gave an efficient algorithm which finds such a coloring. His algorithm was based on an SDP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1203.5747", "content": "by S Lovett · 2012 · Cited by 205 — Recently, a breakthrough work of Bansal (FOCS 2010) gave an efficient algorithm which finds such a coloring. His algorithm was based on an SDP ..."}
+{"idx": 8, "title": "The entropy rounding method in approximation algorithms", "date": "", "ddg_snippet": "by T Rothvoß · 2012 · Cited by 25 — {Ban10} N. Bansal. Constructive algorithms for discrepancy minimization . In FOCS, pages 3--10, 2010. Digital Library · Google Scholar.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2095116.2095148", "content": "by T Rothvoß · 2012 · Cited by 25 — {Ban10} N. Bansal. Constructive algorithms for discrepancy minimization . In FOCS, pages 3--10, 2010. Digital Library · Google Scholar."}
+{"idx": 9, "title": "The Entropy Rounding Method in Approximation Algorithms", "date": "", "ddg_snippet": "by T Rothvoß · 2012 · Cited by 25 — Our result can be made constructive using the Bansal framework based on ... Constructive algorithms for discrepancy minimization . In FOCS, pages 3–10, 2010.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/pdf/10.1137/1.9781611973099.32?download=true", "content": "by T Rothvoß · 2012 · Cited by 25 — Our result can be made constructive using the Bansal framework based on ... Constructive algorithms for discrepancy minimization . In FOCS, pages 3–10, 2010."}
diff --git a/data/sampled_jsons/Critical_windows_non-asymptotic_theory_feature_emergence_diffusion_models_Li_Chen_2024_abstract.jsonl b/data/sampled_jsons/Critical_windows_non-asymptotic_theory_feature_emergence_diffusion_models_Li_Chen_2024_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..87767b2a8f5f73e8dae144cbeae207c2a276f5f1
--- /dev/null
+++ b/data/sampled_jsons/Critical_windows_non-asymptotic_theory_feature_emergence_diffusion_models_Li_Chen_2024_abstract.jsonl
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+{"idx": 0, "title": "[2403.01633] Critical windows : non - asymptotic theory for feature ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Critical windows : non - asymptotic theory for feature emergence in diffusion models , by Marvin Li and Sitan Chen .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.01633", "content": "View a PDF of the paper titled Critical windows : non - asymptotic theory for feature emergence in diffusion models , by Marvin Li and Sitan Chen ."}
+{"idx": 1, "title": "Critical windows : non - asymptotic theory for feature emergence in...", "date": "", "ddg_snippet": "Li , G., Wei, Y., Chen , Y., and Chi, Y. Towards faster non - asymptotic convergence for diffusion -based generative models . arXiv preprint arXiv:2306.09251, 2023a.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=a8ZpjLJuKk", "content": "Li , G., Wei, Y., Chen , Y., and Chi, Y. Towards faster non - asymptotic convergence for diffusion -based generative models . arXiv preprint arXiv:2306.09251, 2023a."}
+{"idx": 2, "title": "Critical windows : non - asymptotic theory for feature emergence in...", "date": "", "ddg_snippet": "This paper explores a non - asymptotic theory for the emergence of features in diffusion models , a type of machine learning model used for tasks like image generation. The researchers introduce the concept of \" critical windows \" - specific time periods during the diffusion process where...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/critical-windows-non-asymptotic-theory-feature-emergence", "content": "This paper explores a non - asymptotic theory for the emergence of features in diffusion models , a type of machine learning model used for tasks like image generation. The researchers introduce the concept of \" critical windows \" - specific time periods during the diffusion process where..."}
+{"idx": 3, "title": "Homepage: Sitan Chen", "date": "", "ddg_snippet": "Critical Windows : Non - Asymptotic Theory for Feature Emergence in Diffusion Models [pdf] Marvin Li , Sitan Chen ICML 2024 .", "subpage_snippet": "", "source": "sitanchen.com", "link": "https://sitanchen.com/", "content": "Critical Windows : Non - Asymptotic Theory for Feature Emergence in Diffusion Models [pdf] Marvin Li , Sitan Chen ICML 2024 ."}
+{"idx": 4, "title": "(PDF) On the Feature Learning in Diffusion Models", "date": "", "ddg_snippet": "In. addition, Li & Chen ( 2024 ) theoretically verified critical windows of feature emergence during the. sampling process, provided accurate score estimation.On ... [Show full abstract ] 4 datasets, we demonstrate the effectiveness of diffusion features for representation learning.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386374040_On_the_Feature_Learning_in_Diffusion_Models", "content": "In. addition, Li & Chen ( 2024 ) theoretically verified critical windows of feature emergence during the. sampling process, provided accurate score estimation.On ... [Show full abstract ] 4 datasets, we demonstrate the effectiveness of diffusion features for representation learning."}
+{"idx": 5, "title": "A Sharp Convergence Theory for", "date": "", "ddg_snippet": "Critical windows : non - asymptotic theory for feature emergence in diffusion models . arXiv preprint arXiv:2403.01633.", "subpage_snippet": "", "source": "users.ece.cmu.edu", "link": "https://users.ece.cmu.edu/~yuejiec/papers/DiffusionODE.pdf", "content": "Critical windows : non - asymptotic theory for feature emergence in diffusion models . arXiv preprint arXiv:2403.01633."}
+{"idx": 6, "title": "Paper Digest: ICML 2024 Papers & Highlights – Resources", "date": "", "ddg_snippet": "June 12, 2024 October 11, 2024 admin. The International Conference on Machine Learning (ICML) is one of the top machine learning conferences in the world. In 2024 , it is to be held in Vienna.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2024/06/icml-2024-highlights/", "content": "June 12, 2024 October 11, 2024 admin. The International Conference on Machine Learning (ICML) is one of the top machine learning conferences in the world. In 2024 , it is to be held in Vienna."}
+{"idx": 7, "title": "AI conference paper analysis 2024", "date": "", "ddg_snippet": "Critical windows : non - asymptotic theory for feature emergence in diffusion models .( Abstract ). PDF: link. LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens.", "subpage_snippet": "", "source": "arxiv-cat.pages.dev", "link": "https://arxiv-cat.pages.dev/icml_embeddings", "content": "Critical windows : non - asymptotic theory for feature emergence in diffusion models .( Abstract ). PDF: link. LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens."}
+{"idx": 8, "title": "Critical windows : non - asymptotic theory for feature emergence in...", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=NbnBmi5k1UQ", "content": ""}
+{"idx": 9, "title": "Hi, my name is Marvin. - Marvin Li", "date": "", "ddg_snippet": "Critical Windows : Non - Asymptotic Theory for Feature Emergence in Diffusion Models Marvin Li , Sitan Chen .", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/", "content": "Critical Windows : Non - Asymptotic Theory for Feature Emergence in Diffusion Models Marvin Li , Sitan Chen ."}
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+{"idx": 3, "title": "vue.js - How to minify internal css in NuxtJs? - Stack Overflow", "date": "", "ddg_snippet": "... find answers and collaborate at work with Stack ... Site design / logo © 2024 Stack Exchange Inc; user contributions licensed under CC BY-SA .", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/65871269/how-to-minify-internal-css-in-nuxtjs", "content": "... find answers and collaborate at work with Stack ... Site design / logo © 2024 Stack Exchange Inc; user contributions licensed under CC BY-SA ."}
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+{"idx": 5, "title": "Azure Cosmos Db, select after row? - Stack Overflow", "date": "", "ddg_snippet": "... find answers and collaborate at work with Stack ... Site design / logo © 2024 Stack Exchange Inc; user contributions licensed under CC BY-SA .", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/51254389/azure-cosmos-db-select-after-row", "content": "... find answers and collaborate at work with Stack ... Site design / logo © 2024 Stack Exchange Inc; user contributions licensed under CC BY-SA ."}
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diff --git a/data/sampled_jsons/Cronbach_Meehl_1955_construct_validity_psychological_tests_year_1955.jsonl b/data/sampled_jsons/Cronbach_Meehl_1955_construct_validity_psychological_tests_year_1955.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..787836525bcc0205d641c8e035da418f949d4f34
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+{"idx": 0, "title": "Construct validity - Wikipedia", "date": "", "ddg_snippet": "Lee Cronbach and Paul Meehl ( 1955 )[1] proposed that the development of a nomological net was essential to the measurement of a test 's construct validity . A nomological network defines a construct by illustrating its relation to other constructs and behaviors.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Construct_validity", "content": "Lee Cronbach and Paul Meehl ( 1955 )[1] proposed that the development of a nomological net was essential to the measurement of a test 's construct validity . A nomological network defines a construct by illustrating its relation to other constructs and behaviors."}
+{"idx": 1, "title": "Classics in the History of Psychology -- Cronbach & Meehl ( 1955 )", "date": "", "ddg_snippet": "Construct validity in psychological tests . Lee J. Cronbach and Paul E. Meehl ( 1955 )[1]. First published in Psychological Bulletin, 52, 281-302. Validation of psychological tests has not yet been adequately conceptualized, as the APA Committee on Psychological ...", "subpage_snippet": "", "source": "psychclassics.yorku.ca", "link": "https://psychclassics.yorku.ca/Cronbach/construct.htm", "content": "Construct validity in psychological tests . Lee J. Cronbach and Paul E. Meehl ( 1955 )[1]. First published in Psychological Bulletin, 52, 281-302. Validation of psychological tests has not yet been adequately conceptualized, as the APA Committee on Psychological ..."}
+{"idx": 2, "title": "construct validity", "date": "", "ddg_snippet": "Construct validity in psychological tests . Lee J. Cronbach and Paul E. Meehl ( 1955 ). Construct validity is ordinarily studied when the tester has no definite criterion measure of the quality with which he is concerned, and must use indirect measures.", "subpage_snippet": "", "source": "www3.nd.edu", "link": "https://www3.nd.edu/~ghaeffel/Cronbach&Meehl(1955).pdf", "content": "Construct validity in psychological tests . Lee J. Cronbach and Paul E. Meehl ( 1955 ). Construct validity is ordinarily studied when the tester has no definite criterion measure of the quality with which he is concerned, and must use indirect measures."}
+{"idx": 3, "title": "Psychological", "date": "", "ddg_snippet": "Construct validity in psychological tests .at places in the testing literature. 286 lee j. cronbach and paul e. meehl . Thus Anastasi (2) makes many state- ultimately be judged to have greater. ments of the latter character: \"It is construct validity than the criterion.", "subpage_snippet": "", "source": "www.sfu.ca", "link": "https://www.sfu.ca/~palys/Cronbach&Meehl-1955-ConstructValidityInPsychologicalTests.pdf", "content": "Construct validity in psychological tests .at places in the testing literature. 286 lee j. cronbach and paul e. meehl . Thus Anastasi (2) makes many state- ultimately be judged to have greater. ments of the latter character: \"It is construct validity than the criterion."}
+{"idx": 4, "title": "Introduction to the special section on construct validity of...", "date": "", "ddg_snippet": "The year 2005 marks the 50th anniversary of Cronbach and Meehl 's ( 1955 ) article \" Construct Validity in Psychological Tests .\" A submission last year by Smith (2005b) struck me as an excellent time to honor the signal contributions of these psychologists of the last century through...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/7378545_Introduction_to_the_special_section_on_construct_validity_of_psychological_tests_50_Years_after_Cronbach_and_Meehl_1955", "content": "The year 2005 marks the 50th anniversary of Cronbach and Meehl 's ( 1955 ) article \" Construct Validity in Psychological Tests .\" A submission last year by Smith (2005b) struck me as an excellent time to honor the signal contributions of these psychologists of the last century through..."}
+{"idx": 5, "title": "Validity In Psychology Research: Types & Examples", "date": "", "ddg_snippet": "Construct validity was invented by Cronbach and Meehl ( 1955 ).If the prediction is born out, then the test has predictive validity . References. Cronbach , L. J., and Meehl , P. E. ( 1955 ) Construct validity in psychological tests .", "subpage_snippet": "", "source": "www.simplypsychology.org", "link": "https://www.simplypsychology.org/validity.html", "content": "Construct validity was invented by Cronbach and Meehl ( 1955 ).If the prediction is born out, then the test has predictive validity . References. Cronbach , L. J., and Meehl , P. E. ( 1955 ) Construct validity in psychological tests ."}
+{"idx": 6, "title": "Glottolog 5.2 - Cronbach , Lee J. and Meehl , Paul E. 1955", "date": "", "ddg_snippet": "Cronbach , Lee J. & Paul E. Meehl . 1955 . Construct validity in psychological tests . Psychological Bulletin 52. 281-302.", "subpage_snippet": "", "source": "glottolog.org", "link": "https://glottolog.org/resource/reference/id/554768", "content": "Cronbach , Lee J. & Paul E. Meehl . 1955 . Construct validity in psychological tests . Psychological Bulletin 52. 281-302."}
+{"idx": 7, "title": "Cronbach , L. J., & Meehl , P. E. ( 1955 ). Construct validity in...", "date": "", "ddg_snippet": "Science and Education An Open Access and Academic Publisher. Article citationsMore >>. Cronbach , L. J., & Meehl , P. E. ( 1955 ). Construct validity in psychological tests . Psychological bulletin, 52(4), 281.", "subpage_snippet": "", "source": "www.sciepub.com", "link": "https://www.sciepub.com/reference/449543", "content": "Science and Education An Open Access and Academic Publisher. Article citationsMore >>. Cronbach , L. J., & Meehl , P. E. ( 1955 ). Construct validity in psychological tests . Psychological bulletin, 52(4), 281."}
+{"idx": 8, "title": "Construct validity in psychological tests .", "date": "", "ddg_snippet": "Psychological Bulletin. 1955 .“…Simultaneous tests of the proposed pattern of effects against observation permit confirmation or rejection of the theory, referred to as nomological validity (Bagozzi, 1981; Cronbach and Meehl , 1955 ).", "subpage_snippet": "", "source": "scite.ai", "link": "https://scite.ai/reports/construct-validity-in-psychological-tests-wNk34y", "content": "Psychological Bulletin. 1955 .“…Simultaneous tests of the proposed pattern of effects against observation permit confirmation or rejection of the theory, referred to as nomological validity (Bagozzi, 1981; Cronbach and Meehl , 1955 )."}
+{"idx": 9, "title": "Validity : Construct -Related Evidence Flashcards | Quizlet", "date": "", "ddg_snippet": "Cronbach & Meehl ( 1955 ). wrote a paper in 1955 ( construct validity in psychological tests ) in which they outlined a conceptual framework of construct validity based on a nomological network. they did not present a way to evaluate construct validity in a strictly statistical manner.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/ca/57793438/validity-construct-related-evidence-flash-cards/", "content": "Cronbach & Meehl ( 1955 ). wrote a paper in 1955 ( construct validity in psychological tests ) in which they outlined a conceptual framework of construct validity based on a nomological network. they did not present a way to evaluate construct validity in a strictly statistical manner."}
diff --git a/data/sampled_jsons/Cui_et_al_2023_empirical_study_license_conflict_free_open_source_software_year_2023.jsonl b/data/sampled_jsons/Cui_et_al_2023_empirical_study_license_conflict_free_open_source_software_year_2023.jsonl
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+{"idx": 1, "title": "Detecting Brittle Decisions for Free : Leveraging Margin Consistency in...", "date": "", "ddg_snippet": "Previous research studies have explored input margins of deep neural networks during training, focusing on their temporal evolution (Mickisch et al ., 2020; Xu et al ., 2023 ), and their exploitation in improving adversarial robustness through instance-reweighting with approximations...", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04768240v1/document", "content": "Previous research studies have explored input margins of deep neural networks during training, focusing on their temporal evolution (Mickisch et al ., 2020; Xu et al ., 2023 ), and their exploitation in improving adversarial robustness through instance-reweighting with approximations..."}
+{"idx": 2, "title": "(PDF) An empirical Study of Determinants of E-commerce Adoption.pdf", "date": "", "ddg_snippet": "Of course the Cui et al study differs in that it is drawn from 14 industries and firms that were on balance much larger than the firms in our own sample. Moreover, Shanghai is one of China’s most vibrant and well-developed regions in terms of Internet penetration (Pick et al , 2011).", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/36334221/An_empirical_Study_of_Determinants_of_E_commerce_Adoption_pdf", "content": "Of course the Cui et al study differs in that it is drawn from 14 industries and firms that were on balance much larger than the firms in our own sample. Moreover, Shanghai is one of China’s most vibrant and well-developed regions in terms of Internet penetration (Pick et al , 2011)."}
+{"idx": 3, "title": "A Systematic Review on the Effectiveness of Interventions for...", "date": "", "ddg_snippet": "In contrast, the quantitative study by Lätth et al . (2022), involving the randomized controlled trial evaluation of Prevent It, received a lower quality rating based on several methodological limitations identified through the MMAT criteria.", "subpage_snippet": "", "source": "journals.copmadrid.org", "link": "https://journals.copmadrid.org/pi/art/pi2025a16", "content": "In contrast, the quantitative study by Lätth et al . (2022), involving the randomized controlled trial evaluation of Prevent It, received a lower quality rating based on several methodological limitations identified through the MMAT criteria."}
+{"idx": 4, "title": "Interactive Map: Russia's Invasion of Ukraine", "date": "", "ddg_snippet": "Download the Mozilla Firefox web browser and open this map in Mozilla Firefox to view this map.", "subpage_snippet": "", "source": "storymaps.arcgis.com", "link": "https://storymaps.arcgis.com/stories/36a7f6a6f5a9448496de641cf64bd375", "content": "Download the Mozilla Firefox web browser and open this map in Mozilla Firefox to view this map."}
+{"idx": 5, "title": "Tackling the AI-Creativity paradox: How educational innovation and...", "date": "", "ddg_snippet": "Vartiainen et al . ( 2023 ) empirically demonstrate such democratisation within middle school classrooms; here, students used AI to co-construct original digital artifacts, thus highlighting emergent, collaborative creativity.", "subpage_snippet": "", "source": "www.elsevier.es", "link": "https://www.elsevier.es/es-revista-journal-innovation-knowledge-376-articulo-tackling-ai-creativity-paradox-how-educational-S2444569X25001611", "content": "Vartiainen et al . ( 2023 ) empirically demonstrate such democratisation within middle school classrooms; here, students used AI to co-construct original digital artifacts, thus highlighting emergent, collaborative creativity."}
+{"idx": 6, "title": "Journal of Medical Internet Research - Evaluating the Utility of...", "date": "", "ddg_snippet": "2023 ;72:1-9. [CrossRef]64 ] addressed the staged diagnosis of PD. One study [ Sigcha L, Pavón I, Costa N, Costa S, Gago M, Arezes P, et al . Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks.", "subpage_snippet": "", "source": "www.jmir.org", "link": "https://www.jmir.org/2025/1/e69422/", "content": "2023 ;72:1-9. [CrossRef]64 ] addressed the staged diagnosis of PD. One study [ Sigcha L, Pavón I, Costa N, Costa S, Gago M, Arezes P, et al . Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks."}
+{"idx": 7, "title": "A Survey on Human-AI Collaboration with Large Foundation Models", "date": "", "ddg_snippet": "Hemmer et al ., 2023 ; Braun et al ., 2021) . These factors are integral to creating an ethical and responsible environment for AI’s integration into human-centric workflows. Section 5 explores the broad spectrum of Human-AI collaboration applications across various sectors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.04931v3", "content": "Hemmer et al ., 2023 ; Braun et al ., 2021) . These factors are integral to creating an ethical and responsible environment for AI’s integration into human-centric workflows. Section 5 explores the broad spectrum of Human-AI collaboration applications across various sectors."}
+{"idx": 8, "title": "MELBA – Prompting Medical Large Vision-Language Models to...", "date": "", "ddg_snippet": "We have no conflicts of interest. Data Availability.Wang et al . ( 2023 ) Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, and Huan Sun. Towards understanding chain-of-thought prompting: An empirical study of what matters.", "subpage_snippet": "", "source": "www.melba-journal.org", "link": "https://www.melba-journal.org/papers/2025:004.html", "content": "We have no conflicts of interest. Data Availability.Wang et al . ( 2023 ) Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, and Huan Sun. Towards understanding chain-of-thought prompting: An empirical study of what matters."}
+{"idx": 9, "title": "Determinants of Wholly Owned Foreign Direct Investments in...", "date": "", "ddg_snippet": "International online experience refers to ECFs’ degree of general nonlocation-bound knowledge regarding virtual operations in foreign markets, which is acquired via local domains over time (as an empirically established proxy; e.g., Wan et al ., 2023 ). Empirical studies have often...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11575-025-00575-7", "content": "International online experience refers to ECFs’ degree of general nonlocation-bound knowledge regarding virtual operations in foreign markets, which is acquired via local domains over time (as an empirically established proxy; e.g., Wan et al ., 2023 ). Empirical studies have often..."}
diff --git a/data/sampled_jsons/Cybench_Zhang_2024_benchmark_cybersecurity_AI_agents_abstract_year_2024.jsonl b/data/sampled_jsons/Cybench_Zhang_2024_benchmark_cybersecurity_AI_agents_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..514d4cd2ae728fbb3133d97e0b8095a1556853d9
--- /dev/null
+++ b/data/sampled_jsons/Cybench_Zhang_2024_benchmark_cybersecurity_AI_agents_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cybench: A Framework for Evaluating Cybersecurity ...", "date": "", "ddg_snippet": "by AK Zhang · 2024 · Cited by 72 — We introduce Cybench , a framework for specifying cybersecurity tasks and evaluating agents on those tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.08926", "content": "by AK Zhang · 2024 · Cited by 72 — We introduce Cybench , a framework for specifying cybersecurity tasks and evaluating agents on those tasks."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — Our findings indicate that existing LLM agents designed for cybersecurity , such as the agent developed in Cybench ( Zhang et al., 2024a ),.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "by Y Zhu · 2025 · Cited by 12 — Our findings indicate that existing LLM agents designed for cybersecurity , such as the agent developed in Cybench ( Zhang et al., 2024a ),."}
+{"idx": 2, "title": "Cybench: A Framework for Evaluating Cybersecurity ...", "date": "", "ddg_snippet": "by AK Zhang · Cited by 72 — Cybench is a cybersecurity agent benchmark with 40 professional-level Capture the Flag tasks that are recent, meaningful, and difficult with subtasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tc90LV0yRL", "content": "by AK Zhang · Cited by 72 — Cybench is a cybersecurity agent benchmark with 40 professional-level Capture the Flag tasks that are recent, meaningful, and difficult with subtasks."}
+{"idx": 3, "title": "CyAgent: LM Agent for Multi-Turn Cybersecurity Tasks", "date": "", "ddg_snippet": "by K Li — Cybench ( Zhang et al., 2025), a benchmark about solving cybersecurity tasks from Capture the Flag (CTF) competitions, has emerged as one of the top agent .", "subpage_snippet": "", "source": "cs191.stanford.edu", "link": "https://cs191.stanford.edu/projects/Spring2025/Katherine___Li_.pdf", "content": "by K Li — Cybench ( Zhang et al., 2025), a benchmark about solving cybersecurity tasks from Capture the Flag (CTF) competitions, has emerged as one of the top agent ."}
+{"idx": 4, "title": "Andy Zhang, Riya Dulepet Emails", "date": "", "ddg_snippet": "AI agents have the potential to significantly alter the cybersecurity landscape. To help us understand this change, we introduce the first framework to capture ...", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/CS224R_Final_Report+(1)1.pdf", "content": "AI agents have the potential to significantly alter the cybersecurity landscape. To help us understand this change, we introduce the first framework to capture ..."}
+{"idx": 5, "title": "When LLMs meet cybersecurity: a systematic literature review", "date": "", "ddg_snippet": "by J Zhang · 2025 · Cited by 120 — This study aims to shed light on the extensive potential of LLMs in enhancing cybersecurity practices and serve as a valuable resource for applying LLMs in ...", "subpage_snippet": "", "source": "cybersecurity.springeropen.com", "link": "https://cybersecurity.springeropen.com/articles/10.1186/s42400-025-00361-w", "content": "by J Zhang · 2025 · Cited by 120 — This study aims to shed light on the extensive potential of LLMs in enhancing cybersecurity practices and serve as a valuable resource for applying LLMs in ..."}
+{"idx": 6, "title": "EnIGMA: Interactive Tools Substantially Assist LM Agents ...", "date": "", "ddg_snippet": "We compare each benchmark result with the respective previous best—NYU agent (Shao et al., 2024b ), CyBench agent ( Zhang et al., 2024 ) and Google DeepMind Agent ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45428", "content": "We compare each benchmark result with the respective previous best—NYU agent (Shao et al., 2024b ), CyBench agent ( Zhang et al., 2024 ) and Google DeepMind Agent ..."}
+{"idx": 7, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real- ...", "date": "", "ddg_snippet": "The benchmark's modular design enables expansion to additional vulnerability types and attack scenarios, supporting longitudinal studies of AI capability ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17332", "content": "The benchmark's modular design enables expansion to additional vulnerability types and attack scenarios, supporting longitudinal studies of AI capability ..."}
+{"idx": 8, "title": "Evaluation and Benchmarking of LLM Agents: A Survey", "date": "", "ddg_snippet": "3 Aug 2025 — This survey provides an in-depth overview of the emerging field of LLM agent evaluation, introducing a two-dimensional taxonomy that organizes ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3711896.3736570", "content": "3 Aug 2025 — This survey provides an in-depth overview of the emerging field of LLM agent evaluation, introducing a two-dimensional taxonomy that organizes ..."}
+{"idx": 9, "title": "HCAST: Human-Calibrated Autonomy Software Tasks", "date": "", "ddg_snippet": "by D Rein · Cited by 1 — The. NYU CTF dataset (Shao et al., 2024 ) and CyBench ( Zhang et al., 2024 ) evaluate the cyber- security capabilities of AI agents using difficult Capture the ...", "subpage_snippet": "", "source": "metr.org", "link": "https://metr.org/hcast.pdf", "content": "by D Rein · Cited by 1 — The. NYU CTF dataset (Shao et al., 2024 ) and CyBench ( Zhang et al., 2024 ) evaluate the cyber- security capabilities of AI agents using difficult Capture the ..."}
diff --git a/data/sampled_jsons/D1gs8QT74m_MultiPDENet-_PDE-embedded_Learning_with_Multi-time-stepping_for_Accelerated_Flow_Simulati.jsonl b/data/sampled_jsons/D1gs8QT74m_MultiPDENet-_PDE-embedded_Learning_with_Multi-time-stepping_for_Accelerated_Flow_Simulati.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7a5d2b64213c334f68771cd660176802d340cfce
--- /dev/null
+++ b/data/sampled_jsons/D1gs8QT74m_MultiPDENet-_PDE-embedded_Learning_with_Multi-time-stepping_for_Accelerated_Flow_Simulati.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping ...", "date": "", "ddg_snippet": "Solving partial differential equations ( PDEs ) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required. Machine learning can accelerate this process, but struggle with weak generalizability...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "Solving partial differential equations ( PDEs ) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required. Machine learning can accelerate this process, but struggle with weak generalizability..."}
+{"idx": 1, "title": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping ...", "date": "", "ddg_snippet": "The paper titled \" MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation \" presents several significant contributions to the field of computational fluid dynamics and machine learning .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-multipdenet-pde-embedded-learning-with-cm6gypmal2nr407s36ekweuc5", "content": "The paper titled \" MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation \" presents several significant contributions to the field of computational fluid dynamics and machine learning ."}
+{"idx": 2, "title": "Awesome AI4 PDE", "date": "", "ddg_snippet": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .NINO integrates Newton’s method with operator learning to efficiently solve nonlinear PDEs with multiple solutions, significantly reducing computational costs.", "subpage_snippet": "", "source": "ai4pde.notion.site", "link": "https://ai4pde.notion.site/", "content": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .NINO integrates Newton’s method with operator learning to efficiently solve nonlinear PDEs with multiple solutions, significantly reducing computational costs."}
+{"idx": 3, "title": "Papers by Yi Zhang with links to code and results.", "date": "", "ddg_snippet": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .Test-time Distribution Learning Adapter for Cross-modal Visual Reasoning.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Yi+Zhang", "content": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .Test-time Distribution Learning Adapter for Cross-modal Visual Reasoning."}
+{"idx": 4, "title": "MultiPDENet : встроенное в PDE обучение с несколькими...", "date": "", "ddg_snippet": "С этой целью мы предлагаем встроенную в PDE сеть с многомасштабным временным шагом ( MultiPDENet ), которая объединяет численные методы и машинное обучение для ускоренного моделирования потоков.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/MultiPDENet:-PDE-embedded-Learning-with-Multi-time-stepping-for-Accelerated-Flow-Simulation-d706dd88-bdd4-4d82-98f6-c8e2b25382fe/ru", "content": "С этой целью мы предлагаем встроенную в PDE сеть с многомасштабным временным шагом ( MultiPDENet ), которая объединяет численные методы и машинное обучение для ускоренного моделирования потоков."}
+{"idx": 5, "title": "文继荣 - School of Information, Renmin University of China - AMiner", "date": "", "ddg_snippet": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/profile/ji-rong-wen/53f4b5bfdabfaedd74eba680", "content": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation ."}
+{"idx": 6, "title": "(PDF) physics-informed neural networks: a key to speed up flow ...", "date": "", "ddg_snippet": "Physics-Informed Neural Networks (PINNs) have emerged as a powerful paradigm for accelerating numerical simulations in fluid dynamics, particularly in flowline systems where traditional computational methods face scalability challenges. This study.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/144091661/PHYSICS_INFORMED_NEURAL_NETWORKS_A_KEY_TO_SPEED_UP_FLOW_SIMULATIONS", "content": "Physics-Informed Neural Networks (PINNs) have emerged as a powerful paradigm for accelerating numerical simulations in fluid dynamics, particularly in flowline systems where traditional computational methods face scalability challenges. This study."}
+{"idx": 7, "title": "Qwen-Image ComfyUI Native Workflow Example - ComfyUI", "date": "", "ddg_snippet": "8- step accelerated version: Qwen-Image original model fp8_e4m3fn with lightx2v 8- step LoRA.Upload input image. This subgraph uses the Lotus Depth model. You can find it in the templates or edit the subgraph to learn more, make sure all the models are loaded correctly.", "subpage_snippet": "", "source": "docs.comfy.org", "link": "https://docs.comfy.org/tutorials/image/qwen/qwen-image", "content": "8- step accelerated version: Qwen-Image original model fp8_e4m3fn with lightx2v 8- step LoRA.Upload input image. This subgraph uses the Lotus Depth model. You can find it in the templates or edit the subgraph to learn more, make sure all the models are loaded correctly."}
+{"idx": 8, "title": "Hongsheng LIU | Researcher | Doctor of Philosophy", "date": "", "ddg_snippet": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .The results of PDEformer-1 (1D model) and PDEformer-2 (2D model) are included.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Hongsheng-Liu-7", "content": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .The results of PDEformer-1 (1D model) and PDEformer-2 (2D model) are included."}
+{"idx": 9, "title": "GitHub Gist: instantly share code, notes, and snippets.", "date": "", "ddg_snippet": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .Active Learning with Selective Time - Step Acquisition for PDEs. Yegon Kim. Physics-Informed Learning (PINNs).", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/sinapordanesh/f7de4d124d287db61088a3a653a0c1f1", "content": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .Active Learning with Selective Time - Step Acquisition for PDEs. Yegon Kim. Physics-Informed Learning (PINNs)."}
diff --git a/data/sampled_jsons/DAGGER_Dataset_Aggregation_imitation_learning_Ross_2011.jsonl b/data/sampled_jsons/DAGGER_Dataset_Aggregation_imitation_learning_Ross_2011.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..905efaed74ce935b49532bcb14a0e13aae8b329c
--- /dev/null
+++ b/data/sampled_jsons/DAGGER_Dataset_Aggregation_imitation_learning_Ross_2011.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Imitation learning - Wikipedia", "date": "", "ddg_snippet": "Dagger ( D ataset Aggr egation) [ 13 ] improves on behavior cloning by iteratively training on a dataset of expert demonstrations.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Imitation_learning", "content": "Dagger ( D ataset Aggr egation) [ 13 ] improves on behavior cloning by iteratively training on a dataset of expert demonstrations."}
+{"idx": 1, "title": "ASKDAGGER: Active Skill-level Data Aggregation for Interactive", "date": "", "ddg_snippet": "... imitation learning , the data is not independent and identically distributed because past predictions can influence future states ( Ross et al., 2011 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05310v1", "content": "... imitation learning , the data is not independent and identically distributed because past predictions can influence future states ( Ross et al., 2011 ..."}
+{"idx": 2, "title": "‘imitation learning’ directory · Gwern.net", "date": "", "ddg_snippet": "Imitating, Fast and Slow: Robust Learning from Demonstrations via Decision-Time Planning”, Qi et al 2022 ... Imitate Immediately (DOME): Learning ...", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/imitation-learning/index", "content": "Imitating, Fast and Slow: Robust Learning from Demonstrations via Decision-Time Planning”, Qi et al 2022 ... Imitate Immediately (DOME): Learning ..."}
+{"idx": 3, "title": "‘preference learning’ directory · Gwern.net", "date": "", "ddg_snippet": "Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-Oriented Dialogue Systems ”, Feng et al 2023", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/preference-learning/index", "content": "Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-Oriented Dialogue Systems ”, Feng et al 2023"}
+{"idx": 4, "title": "DART: Noise injection for robust imitation learning - Robohub", "date": "", "ddg_snippet": "The key idea of DAgger is to collect data from the current robot policy and update the model on the aggregate dataset .", "subpage_snippet": "", "source": "robohub.org", "link": "https://robohub.org/dart-noise-injection-for-robust-imitation-learning/", "content": "The key idea of DAgger is to collect data from the current robot policy and update the model on the aggregate dataset ."}
+{"idx": 5, "title": "(PDF) A Comparison of Imitation Learning Algorithms for", "date": "", "ddg_snippet": "Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383090428_A_Comparison_of_Imitation_Learning_Algorithms_for_Bimanual_Manipulation", "content": "Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training ..."}
+{"idx": 6, "title": "Lisbon Machine Learning Summer School Highlights", "date": "", "ddg_snippet": "The Lisbon Machine Learning School (LxMLS) is an annual event that brings together researchers and graduate students in the fields of NLP and ...", "subpage_snippet": "", "source": "aylien.com", "link": "https://aylien.com/blog/lisbon-machine-learning-summer-school-highlights", "content": "The Lisbon Machine Learning School (LxMLS) is an annual event that brings together researchers and graduate students in the fields of NLP and ..."}
+{"idx": 7, "title": "Knowledge- and ambiguity-aware robot learning from corrective", "date": "", "ddg_snippet": "... a policy model is trained in order to imitate that dataset either with behavioral cloning (BC) [ 7 , 8 ] or with inverse reinforcement learning (IRL ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00521-022-08118-z", "content": "... a policy model is trained in order to imitate that dataset either with behavioral cloning (BC) [ 7 , 8 ] or with inverse reinforcement learning (IRL ..."}
+{"idx": 8, "title": "Distilling Realizable Students from Unrealizable Teachers", "date": "", "ddg_snippet": "Instead, imitation learning (IL) algorithms such as DAgger [ 13 ] provide a promising alternative. ... imitation learning methods like DAgger ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.09546v1", "content": "Instead, imitation learning (IL) algorithms such as DAgger [ 13 ] provide a promising alternative. ... imitation learning methods like DAgger ..."}
+{"idx": 9, "title": "How a big shift in training LLMs led to a capability explosion", "date": "", "ddg_snippet": "In a landmark 2011 paper , Ross and his advisor, Drew Bagnell, explained why imitation learning is prone to this kind of error.", "subpage_snippet": "", "source": "arstechnica.com", "link": "https://arstechnica.com/ai/2025/07/how-a-big-shift-in-training-llms-led-to-a-capability-explosion/", "content": "In a landmark 2011 paper , Ross and his advisor, Drew Bagnell, explained why imitation learning is prone to this kind of error."}
diff --git a/data/sampled_jsons/DART_CVPR_2025_Park_methodology_equation_5_lambda_m.jsonl b/data/sampled_jsons/DART_CVPR_2025_Park_methodology_equation_5_lambda_m.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8dfb37f1533f9e845b53c2fde4150115b2175c41
--- /dev/null
+++ b/data/sampled_jsons/DART_CVPR_2025_Park_methodology_equation_5_lambda_m.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multimodal Generative AI with Autoregressive LLMs for ...", "date": "", "ddg_snippet": "31 May 2025 — This paper presents an in-depth survey on the use of multimodal Generative Artificial Intelligence (GenAI) and autoregressive Large Language ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03191v1", "content": "31 May 2025 — This paper presents an in-depth survey on the use of multimodal Generative Artificial Intelligence (GenAI) and autoregressive Large Language ..."}
+{"idx": 1, "title": "Towards fair decentralized benchmarking of healthcare AI ...", "date": "", "ddg_snippet": "by M Zenk · 2025 — In Task 2, the objective was to develop methods that enhance the robustness of segmentation algorithms when faced with realistic dataset shifts.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-025-60466-1", "content": "by M Zenk · 2025 — In Task 2, the objective was to develop methods that enhance the robustness of segmentation algorithms when faced with realistic dataset shifts."}
+{"idx": 2, "title": "A Comprehensive Review on Autonomous Navigation", "date": "", "ddg_snippet": "by S Nahavandi · 2025 · Cited by 49 — This article tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3727642", "content": "by S Nahavandi · 2025 · Cited by 49 — This article tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, ..."}
+{"idx": 3, "title": "Domain Adaptation and Representation Transfer, and ...", "date": "", "ddg_snippet": "The third MICCAI workshop on Domain Adaptation and Representation Transfer. ( DART 2021) aimed at creating a discussion forum to compare, evaluate, and discuss.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-87722-4.pdf", "content": "The third MICCAI workshop on Domain Adaptation and Representation Transfer. ( DART 2021) aimed at creating a discussion forum to compare, evaluate, and discuss."}
+{"idx": 4, "title": "Advances in Knowledge Discovery and Data Mining", "date": "", "ddg_snippet": "10 Jun 2025 — The main conference of PAKDD 2025 had three tracks, Main Track, Survey Track, and Special Track on LLMs for Data Science. This year we also ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-981-96-8180-8.pdf", "content": "10 Jun 2025 — The main conference of PAKDD 2025 had three tracks, Main Track, Survey Track, and Special Track on LLMs for Data Science. This year we also ..."}
+{"idx": 5, "title": "Recovering Parametric Scenes from Very Few Time-of- ...", "date": "", "ddg_snippet": "4 days ago — We aim to recover the geometry of 3D parametric scenes using very few depth measurements from low-cost, commercially available ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16132v1", "content": "4 days ago — We aim to recover the geometry of 3D parametric scenes using very few depth measurements from low-cost, commercially available ..."}
+{"idx": 6, "title": "BARK: A Fully Bayesian Tree Kernel for Black-box Optimization", "date": "", "ddg_snippet": "Abstract. We perform Bayesian optimization using a Gaus- sian process perspective on Bayesian Additive. Regression Trees (BART). Our BART Kernel.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DYeVXcPsN6", "content": "Abstract. We perform Bayesian optimization using a Gaus- sian process perspective on Bayesian Additive. Regression Trees (BART). Our BART Kernel."}
+{"idx": 7, "title": "Skilful nowcasting of extreme precipitation with NowcastNet", "date": "", "ddg_snippet": "by Y Zhang · 2023 · Cited by 398 — We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-023-06184-4", "content": "by Y Zhang · 2023 · Cited by 398 — We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods."}
+{"idx": 8, "title": "opencv - Calculate Intrinsics for a Thermal Camera?", "date": "", "ddg_snippet": "The standard chessboard method for geometric calibration, correction of lens distortion, and alignment of the cameras relies on colour difference.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/5799784/calculate-intrinsics-for-a-thermal-camera", "content": "The standard chessboard method for geometric calibration, correction of lens distortion, and alignment of the cameras relies on colour difference."}
+{"idx": 9, "title": "The 10th International Electronic Conference on Sensors ...", "date": "", "ddg_snippet": "Stefano Mariani. Dr. Stefano Mariani received an M.S. degree (cum laude) in civil engineering in 1995 and a. Ph.D. degree in structural engineering in 1999; ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/proceedings/9664/The_10th_International_Electronic_Conference_on_Sensors_and_Applications.pdf?v=1754874546", "content": "Stefano Mariani. Dr. Stefano Mariani received an M.S. degree (cum laude) in civil engineering in 1995 and a. Ph.D. degree in structural engineering in 1999; ..."}
diff --git "a/data/sampled_jsons/DART_CVPR_2025_methodology_equation_5_lambda_m_OR_\316\273m_1.jsonl" "b/data/sampled_jsons/DART_CVPR_2025_methodology_equation_5_lambda_m_OR_\316\273m_1.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..ebf894beaefc8ae512541f3c2fad58e59ed393e5
--- /dev/null
+++ "b/data/sampled_jsons/DART_CVPR_2025_methodology_equation_5_lambda_m_OR_\316\273m_1.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How Much To Guide: Revisiting Adaptive Guidance in", "date": "", "ddg_snippet": "Stochastic Differential Equations (SDE) and Ordinary Differential Equations (ODE) are essential frameworks for understanding diffusion models (Song ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08351v1", "content": "Stochastic Differential Equations (SDE) and Ordinary Differential Equations (ODE) are essential frameworks for understanding diffusion models (Song ..."}
+{"idx": 1, "title": "A combined Machine Learning and Finite Element Modelling tool", "date": "", "ddg_snippet": "The proposed methodology involves creating personalised synthetic skulls based on three-dimensional (3D) photographs, incorporating population ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03202v1", "content": "The proposed methodology involves creating personalised synthetic skulls based on three-dimensional (3D) photographs, incorporating population ..."}
+{"idx": 2, "title": "Multimodal Generative AI with Autoregressive LLMs for ...", "date": "", "ddg_snippet": "31 May 2025 — This paper presents an in-depth survey on the use of multimodal Generative Artificial Intelligence (GenAI) and autoregressive Large Language ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03191v1", "content": "31 May 2025 — This paper presents an in-depth survey on the use of multimodal Generative Artificial Intelligence (GenAI) and autoregressive Large Language ..."}
+{"idx": 3, "title": "Towards fair decentralized benchmarking of healthcare AI ...", "date": "", "ddg_snippet": "by M Zenk · 2025 — The FeTS challenge is an international competition to benchmark brain tumor segmentation algorithms, involving data contributors, participants, ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-025-60466-1", "content": "by M Zenk · 2025 — The FeTS challenge is an international competition to benchmark brain tumor segmentation algorithms, involving data contributors, participants, ..."}
+{"idx": 4, "title": "A Comprehensive Review on Autonomous Navigation", "date": "", "ddg_snippet": "by S Nahavandi · 2025 · Cited by 49 — This article tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3727642", "content": "by S Nahavandi · 2025 · Cited by 49 — This article tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, ..."}
+{"idx": 5, "title": "Domain Adaptation and Representation Transfer, and ...", "date": "", "ddg_snippet": "The third MICCAI workshop on Domain Adaptation and Representation Transfer. ( DART 2021) aimed at creating a discussion forum to compare, evaluate, and discuss.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-87722-4.pdf", "content": "The third MICCAI workshop on Domain Adaptation and Representation Transfer. ( DART 2021) aimed at creating a discussion forum to compare, evaluate, and discuss."}
+{"idx": 6, "title": "andrew/ultimate-awesome", "date": "", "ddg_snippet": "awesome- dart - A curated list of awesome Dart frameworks, libraries, and software. ... equations , deep learning, dynamical systems, control and numerical methods .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/andrew/ultimate-awesome", "content": "awesome- dart - A curated list of awesome Dart frameworks, libraries, and software. ... equations , deep learning, dynamical systems, control and numerical methods ."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 8, "title": "Sequential Policy Gradient for Adaptive Hyperparameter", "date": "", "ddg_snippet": "Two predominant methodologies aim to resolve this issue: (1) Truncated Trajectories [ 51 , 52 ] that impose a fixed-length segment per episode ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15051v1", "content": "Two predominant methodologies aim to resolve this issue: (1) Truncated Trajectories [ 51 , 52 ] that impose a fixed-length segment per episode ..."}
+{"idx": 9, "title": "Disproving the Feasibility of Learned Confidence Calibration", "date": "", "ddg_snippet": "... symmetric loss functions, and post-hoc calibration methods, we demonstrate this is an information-theoretic constraint, not a methodological failure.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14386v1", "content": "... symmetric loss functions, and post-hoc calibration methods, we demonstrate this is an information-theoretic constraint, not a methodological failure."}
diff --git a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Equation_5_lambda_m_weighting_coefficient.jsonl b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Equation_5_lambda_m_weighting_coefficient.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..583d838cf5849aee687f3f200d3de4e321ad70ca
--- /dev/null
+++ b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Equation_5_lambda_m_weighting_coefficient.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetry-aware recursive image similarity exploration for ...", "date": "", "ddg_snippet": "by TNM Nguyen · 2021 · Cited by 12 — This tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41524-021-00637-y", "content": "by TNM Nguyen · 2021 · Cited by 12 — This tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales."}
+{"idx": 1, "title": "Text-to-Image Synthesis: A Decade Survey", "date": "", "ddg_snippet": "25 Nov 2024 — In T2I, the generation process is also conditioned on the text prompt, which can be represented by Equation ( 5 ): where T 𝑇 T italic_T is the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.16164v1", "content": "25 Nov 2024 — In T2I, the generation process is also conditioned on the text prompt, which can be represented by Equation ( 5 ): where T 𝑇 T italic_T is the ..."}
+{"idx": 2, "title": "High Precision Word Alignment Algorithm in Python", "date": "", "ddg_snippet": "I am working on a project for building a high precision word alignment between sentences and their translations in other languages, for measuring translation ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/59615759/high-precision-word-alignment-algorithm-in-python", "content": "I am working on a project for building a high precision word alignment between sentences and their translations in other languages, for measuring translation ..."}
+{"idx": 3, "title": "Neural Methods for Data-to-text Generation", "date": "", "ddg_snippet": "This survey offers a consolidated view into the neural D2T paradigm with a structured examination of the approaches, benchmark datasets, and evaluation ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3660639", "content": "This survey offers a consolidated view into the neural D2T paradigm with a structured examination of the approaches, benchmark datasets, and evaluation ..."}
+{"idx": 4, "title": "Novel nonlinear reconstruction method with grey-level ...", "date": "", "ddg_snippet": "by N Baba · 2020 · Cited by 5 — A unique mathematical operator named the lambda reconstruction operator demonstrates that streak artefacts caused by the Fourier integral ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-020-77156-1", "content": "by N Baba · 2020 · Cited by 5 — A unique mathematical operator named the lambda reconstruction operator demonstrates that streak artefacts caused by the Fourier integral ..."}
+{"idx": 5, "title": "Lightweight hybrid transformers-based dyslexia detection ...", "date": "", "ddg_snippet": "by ARW Sait · 2025 — We propose an innovative model for DD using magnetic resonance imaging (MRI), electroencephalography (EEG), and handwriting images.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12084289/", "content": "by ARW Sait · 2025 — We propose an innovative model for DD using magnetic resonance imaging (MRI), electroencephalography (EEG), and handwriting images."}
+{"idx": 6, "title": "Parameter-efficient fine-tuning in large language models", "date": "", "ddg_snippet": "by L Wang · 2025 · Cited by 15 — A comprehensive evaluation framework is proposed, using metrics for concept fidelity, text - image alignment , diversity, and style preservation.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11236-4", "content": "by L Wang · 2025 · Cited by 15 — A comprehensive evaluation framework is proposed, using metrics for concept fidelity, text - image alignment , diversity, and style preservation."}
+{"idx": 7, "title": "AI Interview Mastery Series Day 0 — Building the Bedrock", "date": "", "ddg_snippet": "This formula means the error of layer $l$ is computed by taking the error from the next layer ($\\delta^{l+1}$), multiplying by the transpose of ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@adnanmasood/ai-interview-mastery-series-day-0-building-the-bedrock-core-mathematics-ml-theory-for-bb83340441f8", "content": "This formula means the error of layer $l$ is computed by taking the error from the next layer ($\\delta^{l+1}$), multiplying by the transpose of ..."}
+{"idx": 8, "title": "Enhancing Training Data Attribution with Representational ...", "date": "", "ddg_snippet": "24 May 2025 — We optimize AirRep on the dataset with a weighted pairwise ranking objective so that the learned representations and aggregation accurately ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18513v1", "content": "24 May 2025 — We optimize AirRep on the dataset with a weighted pairwise ranking objective so that the learned representations and aggregation accurately ..."}
+{"idx": 9, "title": "Domain Adaptation and Representation Transfer, and ...", "date": "", "ddg_snippet": "Computer vision and medical imaging have been revolutionized by the introduction of advanced machine learning and deep learning methodologies.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-87722-4.pdf", "content": "Computer vision and medical imaging have been revolutionized by the introduction of advanced machine learning and deep learning methodologies."}
diff --git a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Self-correcting_Re-alignment_Radiology_Report_Generation.jsonl b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Self-correcting_Re-alignment_Radiology_Report_Generation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..21942d9e50d2c94d5613880af27986963abf8a2c
--- /dev/null
+++ b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Self-correcting_Re-alignment_Radiology_Report_Generation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2504.11786] DART : Disease - aware Image - Text Alignment and...", "date": "", "ddg_snippet": "View a PDF of the paper titled DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation , by Sang-Jun Park and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "View a PDF of the paper titled DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation , by Sang-Jun Park and 5 other authors."}
+{"idx": 1, "title": "(PDF) DART : Disease - aware Image - Text Alignment and...", "date": "", "ddg_snippet": "In this study, we propose a Disease - aware image - text Alignment and self - correcting Re - alignment for Trustworthy radiology report generation ( DART ) framework.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390845711_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_Report_Generation", "content": "In this study, we propose a Disease - aware image - text Alignment and self - correcting Re - alignment for Trustworthy radiology report generation ( DART ) framework."}
+{"idx": 2, "title": "DART : Disease - aware Image - Text Alignment and Self - correcting ...", "date": "", "ddg_snippet": "In this study, we propose a Disease - aware image - text Alignment and self - correcting Re - alignment for Trustworthy radiology report generation ( DART ) framework.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "In this study, we propose a Disease - aware image - text Alignment and self - correcting Re - alignment for Trustworthy radiology report generation ( DART ) framework."}
+{"idx": 3, "title": "Dart", "date": "", "ddg_snippet": "In this study, we propose a Disease - aware image - text Alignment and self - correcting Re - alignment for Trustworthy radiology report generation ( DART ) framework.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/s/Dart", "content": "In this study, we propose a Disease - aware image - text Alignment and self - correcting Re - alignment for Trustworthy radiology report generation ( DART ) framework."}
+{"idx": 4, "title": "[PDF] Generating Radiology Reports via... | Semantic Scholar", "date": "", "ddg_snippet": "A Disease - aware image - text Alignment and self - correcting Re - alignment for Trustworthy radiology report generation ( DART ) framework achieves state-of-the-art results on two widely used benchmarks, surpassing previous approaches in both report generation and clinical...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Generating-Radiology-Reports-via-Memory-driven-Chen-Song/19adf1af8daa9551328226fc6c0140e955bf5689", "content": "A Disease - aware image - text Alignment and self - correcting Re - alignment for Trustworthy radiology report generation ( DART ) framework achieves state-of-the-art results on two widely used benchmarks, surpassing previous approaches in both report generation and clinical..."}
+{"idx": 5, "title": "Automating Radiology Report Generation ... | Preprints.org", "date": "", "ddg_snippet": "Automating radiology report generation can help alleviate radiologists ' workload, reduce reporting inconsistencies, and enhance diagnostic accuracy.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202503.2218/v1", "content": "Automating radiology report generation can help alleviate radiologists ' workload, reduce reporting inconsistencies, and enhance diagnostic accuracy."}
+{"idx": 6, "title": "Online Iterative Self - Alignment for Radiology Report Generation ...", "date": "", "ddg_snippet": "Radiology Report Generation (RRG) is an important research topic forrelieving radiologist ' heavy workload. Existing RRG models mainly rely onsupervised fine-tuning (SFT) based on different model architectures using datapairs of radiological images and corresponding...", "subpage_snippet": "", "source": "trendtoknow.com", "link": "https://trendtoknow.com/arxiv/30700/online-iterative-self-alignment-for-radiology-report-generation", "content": "Radiology Report Generation (RRG) is an important research topic forrelieving radiologist ' heavy workload. Existing RRG models mainly rely onsupervised fine-tuning (SFT) based on different model architectures using datapairs of radiological images and corresponding..."}
+{"idx": 7, "title": "TRRG: Towards Truthful Radiology Report Generation With...", "date": "", "ddg_snippet": "Radiology report generation aims to generate reports for medical images . It can reduce clinician workload and misdiagnosis.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/trrg-towards-truthful-radiology-report-generation-with-cross-modal-disease-clue-enhanced-large-language-model/1033990250038296633-108597", "content": "Radiology report generation aims to generate reports for medical images . It can reduce clinician workload and misdiagnosis."}
+{"idx": 8, "title": "Articles by Jae-Hyuk Oh | Synthical", "date": "", "ddg_snippet": "High Energy Physics. DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/d020f4a8-4269-4de9-b83d-12a7f9e33801/articles", "content": "High Energy Physics. DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation ."}
+{"idx": 9, "title": "Heo Keunsoo - Google Akademik", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation .", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=UJJGePQAAAAJ&hl=tr", "content": "DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation ."}
diff --git "a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_paper_Equation_(5)_\316\273m_disease-matching_constraint_year_2023.jsonl" "b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_paper_Equation_(5)_\316\273m_disease-matching_constraint_year_2023.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..18033c7595cc3d89115250644ae1af5acda2c3dc
--- /dev/null
+++ "b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_paper_Equation_(5)_\316\273m_disease-matching_constraint_year_2023.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fast Pattern Matching in Strings | SIAM Journal on Computing", "date": "", "ddg_snippet": "... is low enough to make this algorithm of practical use, and the procedure can also be extended to deal with some more general pattern- matching ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/0206024?cookieSet=1", "content": "... is low enough to make this algorithm of practical use, and the procedure can also be extended to deal with some more general pattern- matching ..."}
+{"idx": 1, "title": "Fast Pattern Matching in Strings | SIAM Journal on Computing", "date": "", "ddg_snippet": "... is low enough to make this algorithm of practical use, and the procedure can also be extended to deal with some more general pattern- matching ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/0206024", "content": "... is low enough to make this algorithm of practical use, and the procedure can also be extended to deal with some more general pattern- matching ..."}
+{"idx": 2, "title": "Vacuum hose diagram question | Dodge Dart Forum", "date": "", "ddg_snippet": "Jun 7, 2025 · Having issues with my 2013 dodge dart 1.4l. Keep getting underboost code and a sligh shudder when turbo kicks in. New to this type of vehicle and not at all familiar with turbos in general. Someone suggested I replace the boost selenoid and they took it off. Got a new one but now have no idea...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/vacuum-hose-diagram-question.71323/", "content": "Jun 7, 2025 · Having issues with my 2013 dodge dart 1.4l. Keep getting underboost code and a sligh shudder when turbo kicks in. New to this type of vehicle and not at all familiar with turbos in general. Someone suggested I replace the boost selenoid and they took it off. Got a new one but now have no idea..."}
+{"idx": 3, "title": "Recall Shifter Bushing Replacement - 2013 Dart", "date": "", "ddg_snippet": "Jul 20, 2024 · 2013 1.4 dart manual, 87k miles, and accidentally discovered that my bushing was holding on by a thread while investigating oil on top of my transmission (looks like my vaccuum pump gasket is rotting away).", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/recall-shifter-bushing-replacement-2013-dart.70634/", "content": "Jul 20, 2024 · 2013 1.4 dart manual, 87k miles, and accidentally discovered that my bushing was holding on by a thread while investigating oil on top of my transmission (looks like my vaccuum pump gasket is rotting away)."}
+{"idx": 4, "title": "2.4 multiair variable valve timing actuator - Dodge Dart Forum", "date": "", "ddg_snippet": "Nov 9, 2019 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/2-4-multiair-variable-valve-timing-actuator.65832/", "content": "Nov 9, 2019 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!"}
+{"idx": 5, "title": "No Crank, No Start - Dodge Dart Forum", "date": "", "ddg_snippet": "Feb 16, 2023 · Y'all helped me spectacularly last time I had issues with my Dart I figured I'd come back for round 2. So since the last time I asked for help my Dart has started refusing to start. No crank, a singular click, and nothing more. The Dashboard comes to life, the AC works fine, and the Radio works...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/no-crank-no-start.69518/", "content": "Feb 16, 2023 · Y'all helped me spectacularly last time I had issues with my Dart I figured I'd come back for round 2. So since the last time I asked for help my Dart has started refusing to start. No crank, a singular click, and nothing more. The Dashboard comes to life, the AC works fine, and the Radio works..."}
+{"idx": 6, "title": "UConnect Bluetooth Issue | Dodge Dart Forum", "date": "", "ddg_snippet": "Jan 2, 2025 · I have attached some images of the messages I've been getting on my phone when trying to connect, these messages appear just after I tap uconnect on my phone. I have tried many things already such as the temperature button soft reset procedure and the corner of the screen soft reset procedure...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/uconnect-bluetooth-issue.71014/", "content": "Jan 2, 2025 · I have attached some images of the messages I've been getting on my phone when trying to connect, these messages appear just after I tap uconnect on my phone. I have tried many things already such as the temperature button soft reset procedure and the corner of the screen soft reset procedure..."}
+{"idx": 7, "title": "Coolant hose connector - Dodge Dart Forum", "date": "", "ddg_snippet": "May 10, 2024 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/coolant-hose-connector.70491/", "content": "May 10, 2024 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!"}
+{"idx": 8, "title": "Transmission Shudder - Dodge Dart Forum", "date": "", "ddg_snippet": "Aug 15, 2018 · 2016 dart gt blacktop. 10k miles. First owner. This has been going on for almost a year now. At about 2000rpm (coasting) if I lightly press the throttle the entire car will jerk violently, the feeling can be compared to almost stalling out a manual transmission. Most the time it's when it...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/transmission-shudder.62830/", "content": "Aug 15, 2018 · 2016 dart gt blacktop. 10k miles. First owner. This has been going on for almost a year now. At about 2000rpm (coasting) if I lightly press the throttle the entire car will jerk violently, the feeling can be compared to almost stalling out a manual transmission. Most the time it's when it..."}
+{"idx": 9, "title": "Dart Wiring Diagrams | Page 5 | Dodge Dart Forum", "date": "", "ddg_snippet": "Jan 28, 2024 · Search Wiring Diagrams Use the following link to search for wiring diagrams for the dart .", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/dart-wiring-diagrams.50274/page-5", "content": "Jan 28, 2024 · Search Wiring Diagrams Use the following link to search for wiring diagrams for the dart ."}
diff --git a/data/sampled_jsons/DART_correction_loss_L_cor_equation_7_sitearxiv.org.jsonl b/data/sampled_jsons/DART_correction_loss_L_cor_equation_7_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b19ec7814be71329d28078f55e0ce0e6846baa15
--- /dev/null
+++ b/data/sampled_jsons/DART_correction_loss_L_cor_equation_7_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Modeling of Core Loss Based on Machine Learning and Deep Learning", "date": "", "ddg_snippet": "Feb 8, 2025 · The main purpose of this correction equation is to predict all magnetic core loss without considering waveforms, but the results show a significant decrease in prediction accuracy, so the correction equation cannot fit all samples well.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05487v1", "content": "Feb 8, 2025 · The main purpose of this correction equation is to predict all magnetic core loss without considering waveforms, but the results show a significant decrease in prediction accuracy, so the correction equation cannot fit all samples well."}
+{"idx": 1, "title": "[2307.02694] Loss Functions and Metrics in Deep Learning Making Deep Neural Networks Robust to Label Noise: a Loss ... The Limits of Error Correction with l Decoding - arXiv.org Modeling of Core Loss Based on Machine Learning and Deep Learning Modeling of Core Loss Based on Machine Learning and Deep Learning DART : Denoising Autoregressive Transformer for Scalable Text-to-Imag… Modeling of Core Loss Based on Machine Learning and Deep Learning DART : Denoising Autoregressive Transformer for Scalable Text-to-Imag… DART : Denoising Autoregressive Transformer for Scalable Text-to-Imag… DART: Denoising Autoregressive Transformer for Scalable Text ...", "date": "", "ddg_snippet": "Jul 5, 2023 · This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights across diverse application areas. We begin by outlining fundamental considerations in classic tasks such as regression and classification, then extend our analysis to specialized domains like computer vision and natural language processing ... Sep 13, 2016 · We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. They simply amount to at most a matrix inversion and multiplication, provided that we know the probability of each class being ... . Our main contribution is a threshold ρ∗(p) for all p ≤ 1 such that for ρ < ρ∗(p), if m Cn for some constant C and the entries of A ≥ are i.i.d. Gaussian, then lp-minimization can recover f with overwhelming probability. We provide two thresholds: one (ρ∗) is for the case when e is an arbitrary unknown vector, and the other (ρ∗ w) assumes that e has fixed support and fixed ... Can a deep learning model predict core loss? The trained model can not only predict the core loss at these four temperature points, but also predict the entire temperature range. This demonstrates the powerful generalization ability of machine learning and deep learning models. Can a modified equation predict a magnetic core loss? They all predict core loss under more complex operating modes by introducing more factors. Although these modified equations compensate for many shortcomings of SE and provide better predictive ability under different conditions, they still cannot fully predict the magnetic core loss under all different conditions . Does Dart unify autoregressive and diffusion within a non-Markovian framework? In this paper, we propose DART, a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework . DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models. What is the prediction model of magnetic core loss? At present, the prediction model of magnetic core loss mainly relies on empirical equations such as Steinmetz Equation (SE) . The expression for the SE is How does Dart work? DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models. DART does not rely on image quantization, which enables more effective image modeling while maintaining flexibility. Furthermore, DART seamlessly trains with both text and image data in a unified model. What is a non-Markovian formulation in Dart? The non-Markovian formulation in DART enables the model to leverage the full generative trajectory during training and inference , while retaining the progressive modeling benefits of diffusion models, resulting in more efficient and flexible generation compared to traditional diffusion and autoregressive approaches. In this paper, we propose DART , a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework. DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.02694", "content": "Jul 5, 2023 · This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights across diverse application areas. We begin by outlining fundamental considerations in classic tasks such as regression and classification, then extend our analysis to specialized domains like computer vision and natural language processing ... Sep 13, 2016 · We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. They simply amount to at most a matrix inversion and multiplication, provided that we know the probability of each class being ... . Our main contribution is a threshold ρ∗(p) for all p ≤ 1 such that for ρ < ρ∗(p), if m Cn for some constant C and the entries of A ≥ are i.i.d. Gaussian, then lp-minimization can recover f with overwhelming probability. We provide two thresholds: one (ρ∗) is for the case when e is an arbitrary unknown vector, and the other (ρ∗ w) assumes that e has fixed support and fixed ... Can a deep learning model predict core loss? The trained model can not only predict the core loss at these four temperature points, but also predict the entire temperature range. This demonstrates the powerful generalization ability of machine learning and deep learning models. Can a modified equation predict a magnetic core loss? They all predict core loss under more complex operating modes by introducing more factors. Although these modified equations compensate for many shortcomings of SE and provide better predictive ability under different conditions, they still cannot fully predict the magnetic core loss under all different conditions . Does Dart unify autoregressive and diffusion within a non-Markovian framework? In this paper, we propose DART, a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework . DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models. What is the prediction model of magnetic core loss? At present, the prediction model of magnetic core loss mainly relies on empirical equations such as Steinmetz Equation (SE) . The expression for the SE is How does Dart work? DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models. DART does not rely on image quantization, which enables more effective image modeling while maintaining flexibility. Furthermore, DART seamlessly trains with both text and image data in a unified model. What is a non-Markovian formulation in Dart? The non-Markovian formulation in DART enables the model to leverage the full generative trajectory during training and inference , while retaining the progressive modeling benefits of diffusion models, resulting in more efficient and flexible generation compared to traditional diffusion and autoregressive approaches. In this paper, we propose DART , a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework. DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models."}
+{"idx": 2, "title": "Making Deep Neural Networks Robust to Label Noise: a Loss ... The Limits of Error Correction with l Decoding - arXiv.org Modeling of Core Loss Based on Machine Learning and Deep Learning Modeling of Core Loss Based on Machine Learning and Deep Learning DART : Denoising Autoregressive Transformer for Scalable Text-to-Imag… Modeling of Core Loss Based on Machine Learning and Deep Learning DART : Denoising Autoregressive Transformer for Scalable Text-to-Imag… DART : Denoising Autoregressive Transformer for Scalable Text-to-Imag… DART: Denoising Autoregressive Transformer for Scalable Text ...", "date": "", "ddg_snippet": "Sep 13, 2016 · We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. They simply amount to at most a matrix inversion and multiplication, provided that we know the probability of each class being ... . Our main contribution is a threshold ρ∗(p) for all p ≤ 1 such that for ρ < ρ∗(p), if m Cn for some constant C and the entries of A ≥ are i.i.d. Gaussian, then lp-minimization can recover f with overwhelming probability. We provide two thresholds: one (ρ∗) is for the case when e is an arbitrary unknown vector, and the other (ρ∗ w) assumes that e has fixed support and fixed ... Can a deep learning model predict core loss? The trained model can not only predict the core loss at these four temperature points, but also predict the entire temperature range. This demonstrates the powerful generalization ability of machine learning and deep learning models. Can a modified equation predict a magnetic core loss? They all predict core loss under more complex operating modes by introducing more factors. Although these modified equations compensate for many shortcomings of SE and provide better predictive ability under different conditions, they still cannot fully predict the magnetic core loss under all different conditions . Does Dart unify autoregressive and diffusion within a non-Markovian framework? In this paper, we propose DART, a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework . DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models. What is the prediction model of magnetic core loss? At present, the prediction model of magnetic core loss mainly relies on empirical equations such as Steinmetz Equation (SE) . The expression for the SE is How does Dart work? DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models. DART does not rely on image quantization, which enables more effective image modeling while maintaining flexibility. Furthermore, DART seamlessly trains with both text and image data in a unified model. What is a non-Markovian formulation in Dart? The non-Markovian formulation in DART enables the model to leverage the full generative trajectory during training and inference , while retaining the progressive modeling benefits of diffusion models, resulting in more efficient and flexible generation compared to traditional diffusion and autoregressive approaches. In this paper, we propose DART , a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework. DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1609.03683", "content": "Sep 13, 2016 · We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. They simply amount to at most a matrix inversion and multiplication, provided that we know the probability of each class being ... . Our main contribution is a threshold ρ∗(p) for all p ≤ 1 such that for ρ < ρ∗(p), if m Cn for some constant C and the entries of A ≥ are i.i.d. Gaussian, then lp-minimization can recover f with overwhelming probability. We provide two thresholds: one (ρ∗) is for the case when e is an arbitrary unknown vector, and the other (ρ∗ w) assumes that e has fixed support and fixed ... Can a deep learning model predict core loss? The trained model can not only predict the core loss at these four temperature points, but also predict the entire temperature range. This demonstrates the powerful generalization ability of machine learning and deep learning models. Can a modified equation predict a magnetic core loss? They all predict core loss under more complex operating modes by introducing more factors. Although these modified equations compensate for many shortcomings of SE and provide better predictive ability under different conditions, they still cannot fully predict the magnetic core loss under all different conditions . Does Dart unify autoregressive and diffusion within a non-Markovian framework? In this paper, we propose DART, a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework . DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models. What is the prediction model of magnetic core loss? At present, the prediction model of magnetic core loss mainly relies on empirical equations such as Steinmetz Equation (SE) . The expression for the SE is How does Dart work? DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models. DART does not rely on image quantization, which enables more effective image modeling while maintaining flexibility. Furthermore, DART seamlessly trains with both text and image data in a unified model. What is a non-Markovian formulation in Dart? The non-Markovian formulation in DART enables the model to leverage the full generative trajectory during training and inference , while retaining the progressive modeling benefits of diffusion models, resulting in more efficient and flexible generation compared to traditional diffusion and autoregressive approaches. In this paper, we propose DART , a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework. DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models."}
+{"idx": 3, "title": "DART: Denoising Autoregressive Transformer for Scalable Text ...", "date": "", "ddg_snippet": "In this paper, we propose DART , a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework. DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.08159v1", "content": "In this paper, we propose DART , a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework. DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models."}
+{"idx": 4, "title": "DART : Disease-aware Image-Text Alignment and Self- correcting ...", "date": "", "ddg_snippet": "Sang-Jun Park111 Equal contribution.The correction loss . ℒcorsubscriptℒ𝑐𝑜𝑟\\mathcal{ L }_{ cor }caligraphic_L start_POSTSUBSCRIPT italic_c italic_o italic_r end_POSTSUBSCRIPT. is defined as", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "Sang-Jun Park111 Equal contribution.The correction loss . ℒcorsubscriptℒ𝑐𝑜𝑟\\mathcal{ L }_{ cor }caligraphic_L start_POSTSUBSCRIPT italic_c italic_o italic_r end_POSTSUBSCRIPT. is defined as"}
+{"idx": 5, "title": "ReLoop: A Self- Correction Continual Learning Loop for Recommender...", "date": "", "ddg_snippet": "where 𝛼 ∈ [0, 1] is the hyper-parameter to adjust the importance of self- correction loss and cross-entropy loss . Thanks to the design for reflecting on past errors, our proposed ReLoop framework enables the CTR prediction task to be optimized in a self- correction manner.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2204.11165", "content": "where 𝛼 ∈ [0, 1] is the hyper-parameter to adjust the importance of self- correction loss and cross-entropy loss . Thanks to the design for reflecting on past errors, our proposed ReLoop framework enables the CTR prediction task to be optimized in a self- correction manner."}
+{"idx": 6, "title": "Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale...", "date": "", "ddg_snippet": "2, the predicted correction ow map can also be converted to the segmentation mask. Hence the loss function can be constructed between portraits correction and segmentation, making it feasible to introduce unlabeled data for our semi", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.08024", "content": "2, the predicted correction ow map can also be converted to the segmentation mask. Hence the loss function can be constructed between portraits correction and segmentation, making it feasible to introduce unlabeled data for our semi"}
+{"idx": 7, "title": "Beam dynamics corrections to the Run-1 measurement of the muon...", "date": "", "ddg_snippet": "VII . MUON LOSS CORRECTION Cml. Several driving mechanisms can lead to loss of muons during storage.The correction to ωam from this eect, Cml, is given in Table VII . The muon loss -induced phase change ar-ticially increases the measured value of ωam, so Cml is negative.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2104.03240", "content": "VII . MUON LOSS CORRECTION Cml. Several driving mechanisms can lead to loss of muons during storage.The correction to ωam from this eect, Cml, is given in Table VII . The muon loss -induced phase change ar-ticially increases the measured value of ωam, so Cml is negative."}
+{"idx": 8, "title": "The Limits of Error Correction with l Decoding - arXiv.org", "date": "", "ddg_snippet": ". Our main contribution is a threshold ρ∗(p) for all p ≤ 1 such that for ρ < ρ∗(p), if m Cn for some constant C and the entries of A ≥ are i.i.d. Gaussian, then lp-minimization can recover f with overwhelming probability. We provide two thresholds: one (ρ∗) is for the case when e is an arbitrary unknown vector, and the other (ρ∗ w) assumes that e has fixed support and fixed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1006.0277.pdf", "content": ". Our main contribution is a threshold ρ∗(p) for all p ≤ 1 such that for ρ < ρ∗(p), if m Cn for some constant C and the entries of A ≥ are i.i.d. Gaussian, then lp-minimization can recover f with overwhelming probability. We provide two thresholds: one (ρ∗) is for the case when e is an arbitrary unknown vector, and the other (ρ∗ w) assumes that e has fixed support and fixed ..."}
+{"idx": 9, "title": "wedm2401, gjrmstn1440, dongheeshin, yhson135, meeeo , kamte ...", "date": "", "ddg_snippet": "To optimize the self- correction module, we introduce a correction loss that measures the similarity, specifically co- sine similarity, between the self-corrected text features and the image features, encouraging the model to minimize any errors or omissions in the generated report.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.11786", "content": "To optimize the self- correction module, we introduce a correction loss that measures the similarity, specifically co- sine similarity, between the self-corrected text features and the image features, encouraging the model to minimize any errors or omissions in the generated report."}
diff --git a/data/sampled_jsons/DART_radiology_report_generation_correction_loss_L_cor_equation.jsonl b/data/sampled_jsons/DART_radiology_report_generation_correction_loss_L_cor_equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a934012a2382186ae02c70b2ca426b432743169
--- /dev/null
+++ b/data/sampled_jsons/DART_radiology_report_generation_correction_loss_L_cor_equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ASNR23 ABSTRACT PROCEEDINGS", "date": "", "ddg_snippet": "Purpose. To determine the etiology, MRI findings, and outcomes in fetal brain intraparenchymal hemorrhage (IPH). Materials and Methods. We searched reports ... 594 pages", "subpage_snippet": "", "source": "www.asnr.org", "link": "https://www.asnr.org/wp-content/uploads/2023/10/ASNR23_Proceedings10.16.23.pdf", "content": "Purpose. To determine the etiology, MRI findings, and outcomes in fetal brain intraparenchymal hemorrhage (IPH). Materials and Methods. We searched reports ... 594 pages"}
+{"idx": 1, "title": "V1-20210107 AWARD NUMBER: W81XWH-16-2-0023 TITLE", "date": "", "ddg_snippet": "by T Pape · 2021 — The views, opinions and/or findings contained in this report are those of the author(s) and should not be construed as an official Department of ...", "subpage_snippet": "", "source": "apps.dtic.mil", "link": "https://apps.dtic.mil/sti/trecms/pdf/AD1213581.pdf", "content": "by T Pape · 2021 — The views, opinions and/or findings contained in this report are those of the author(s) and should not be construed as an official Department of ..."}
+{"idx": 2, "title": "PRICAI 2024: Trends in Artificial Intelligence", "date": "", "ddg_snippet": "18 Nov 2024 — ... calculation , these scores are normalized to a range from 0 to 1. The loss function used for training is L1 loss , and the optimizer is Adam ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-981-96-0116-5.pdf", "content": "18 Nov 2024 — ... calculation , these scores are normalized to a range from 0 to 1. The loss function used for training is L1 loss , and the optimizer is Adam ..."}
+{"idx": 3, "title": "GE - 2021423-503H - MLAB - CLAB - 14 CCCCC", "date": "", "ddg_snippet": "Click a blank cell to add an X, which generates the report . d. Click Generate to generate the selected report . e. Highlight the report created and click View.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/626328606/GE-2021423-503H-MLAB-CLAB-14-ccccc", "content": "Click a blank cell to add an X, which generates the report . d. Click Generate to generate the selected report . e. Highlight the report created and click View."}
+{"idx": 4, "title": "Study on Genotypes and Phenotypes of ...", "date": "", "ddg_snippet": "... loss are reported in up to 50% of patients, and it can take up to two years to recover completely [61]. The genetic analysis for LCDR did not unveil ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/10136/Study_on_Genotypes_and_Phenotypes_of_Neurodegenerative_Diseases.pdf?v=1742436634", "content": "... loss are reported in up to 50% of patients, and it can take up to two years to recover completely [61]. The genetic analysis for LCDR did not unveil ..."}
+{"idx": 5, "title": "Download book PDF", "date": "", "ddg_snippet": "Technical Report TR2004-515, Department of Computer Science, Dart - mouth College, 2004. Page 213. Automatic Merging of 3D Attribute Meshes. Krzysztof Skabek ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-540-75175-5.pdf", "content": "Technical Report TR2004-515, Department of Computer Science, Dart - mouth College, 2004. Page 213. Automatic Merging of 3D Attribute Meshes. Krzysztof Skabek ..."}
+{"idx": 6, "title": "Volume Information", "date": "", "ddg_snippet": "Radiology , Squires Fundamentals of Radiology (Novelline, ed). {Medicine and books). 316:1029 (V). Radiotherapy ? Breast cancer and, women lose case (C Dyer). 38 pages", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/pdf/25179624.pdf", "content": "Radiology , Squires Fundamentals of Radiology (Novelline, ed). {Medicine and books). 316:1029 (V). Radiotherapy ? Breast cancer and, women lose case (C Dyer). 38 pages"}
+{"idx": 7, "title": "Analytical Chemistry Vol. 86 No. 10 - ACS Publications", "date": "", "ddg_snippet": "Read research published in the Analytical Chemistry Vol. 86 Issue 10 on ACS Publications, a trusted source for peer-reviewed journals.", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/toc/ancham/86/10", "content": "Read research published in the Analytical Chemistry Vol. 86 Issue 10 on ACS Publications, a trusted source for peer-reviewed journals."}
+{"idx": 8, "title": "Equilibrium Approaches to Modern Deep Learning", "date": "", "ddg_snippet": "Deep learning (DL) has become one of the most successful and widely-adopted methods in modern artificial intelligence. Accompanying these successes are also. 155 pages", "subpage_snippet": "", "source": "ml.cmu.edu", "link": "https://ml.cmu.edu/research/phd-dissertation-pdfs/phd_thesis_shaojie_bai.pdf", "content": "Deep learning (DL) has become one of the most successful and widely-adopted methods in modern artificial intelligence. Accompanying these successes are also. 155 pages"}
+{"idx": 9, "title": "\\' .,, .\\ --/ 4 \\", "date": "", "ddg_snippet": "Section IV. Biological Effects of Gravity. General Principles and Methods of Animal. Experim€nts Flown on Cosmos Biosatefiiles. E. I. Illn.", "subpage_snippet": "", "source": "www.physiology.org", "link": "https://www.physiology.org/docs/default-source/archive_tphys/the-physiologist-newsletter-1983-december-supplement.pdf?sfvrsn=a2630541_2", "content": "Section IV. Biological Effects of Gravity. General Principles and Methods of Animal. Experim€nts Flown on Cosmos Biosatefiiles. E. I. Illn."}
diff --git a/data/sampled_jsons/DART_radiology_report_generation_disease-aware_image-text_alignment.jsonl b/data/sampled_jsons/DART_radiology_report_generation_disease-aware_image-text_alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8dfd900f64bad4af26e85e280d2d5f35602e220a
--- /dev/null
+++ b/data/sampled_jsons/DART_radiology_report_generation_disease-aware_image-text_alignment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Vacuum hose diagram question | Dodge Dart Forum", "date": "", "ddg_snippet": "Jun 7, 2025 · Having issues with my 2013 dodge dart 1.4l. Keep getting underboost code and a sligh shudder when turbo kicks in. New to this type of vehicle and not at all familiar with turbos in general. Someone suggested I replace the boost selenoid and they took it off. Got a new one but now have no idea...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/vacuum-hose-diagram-question.71323/", "content": "Jun 7, 2025 · Having issues with my 2013 dodge dart 1.4l. Keep getting underboost code and a sligh shudder when turbo kicks in. New to this type of vehicle and not at all familiar with turbos in general. Someone suggested I replace the boost selenoid and they took it off. Got a new one but now have no idea..."}
+{"idx": 1, "title": "Recall Shifter Bushing Replacement - 2013 Dart", "date": "", "ddg_snippet": "Jul 20, 2024 · 2013 1.4 dart manual, 87k miles, and accidentally discovered that my bushing was holding on by a thread while investigating oil on top of my transmission (looks like my vaccuum pump gasket is rotting away).", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/recall-shifter-bushing-replacement-2013-dart.70634/", "content": "Jul 20, 2024 · 2013 1.4 dart manual, 87k miles, and accidentally discovered that my bushing was holding on by a thread while investigating oil on top of my transmission (looks like my vaccuum pump gasket is rotting away)."}
+{"idx": 2, "title": "2.4 multiair variable valve timing actuator - Dodge Dart Forum", "date": "", "ddg_snippet": "Nov 9, 2019 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/2-4-multiair-variable-valve-timing-actuator.65832/", "content": "Nov 9, 2019 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!"}
+{"idx": 3, "title": "No Crank, No Start - Dodge Dart Forum", "date": "", "ddg_snippet": "Feb 16, 2023 · Y'all helped me spectacularly last time I had issues with my Dart I figured I'd come back for round 2. So since the last time I asked for help my Dart has started refusing to start. No crank, a singular click, and nothing more. The Dashboard comes to life, the AC works fine, and the Radio works...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/no-crank-no-start.69518/", "content": "Feb 16, 2023 · Y'all helped me spectacularly last time I had issues with my Dart I figured I'd come back for round 2. So since the last time I asked for help my Dart has started refusing to start. No crank, a singular click, and nothing more. The Dashboard comes to life, the AC works fine, and the Radio works..."}
+{"idx": 4, "title": "UConnect Bluetooth Issue | Dodge Dart Forum", "date": "", "ddg_snippet": "Jan 2, 2025 · I have attached some images of the messages I've been getting on my phone when trying to connect, these messages appear just after I tap uconnect on my phone. I have tried many things already such as the temperature button soft reset procedure and the corner of the screen soft reset procedure...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/uconnect-bluetooth-issue.71014/", "content": "Jan 2, 2025 · I have attached some images of the messages I've been getting on my phone when trying to connect, these messages appear just after I tap uconnect on my phone. I have tried many things already such as the temperature button soft reset procedure and the corner of the screen soft reset procedure..."}
+{"idx": 5, "title": "Coolant hose connector - Dodge Dart Forum", "date": "", "ddg_snippet": "May 10, 2024 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/coolant-hose-connector.70491/", "content": "May 10, 2024 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!"}
+{"idx": 6, "title": "Transmission Shudder - Dodge Dart Forum", "date": "", "ddg_snippet": "Aug 15, 2018 · 2016 dart gt blacktop. 10k miles. First owner. This has been going on for almost a year now. At about 2000rpm (coasting) if I lightly press the throttle the entire car will jerk violently, the feeling can be compared to almost stalling out a manual transmission. Most the time it's when it...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/transmission-shudder.62830/", "content": "Aug 15, 2018 · 2016 dart gt blacktop. 10k miles. First owner. This has been going on for almost a year now. At about 2000rpm (coasting) if I lightly press the throttle the entire car will jerk violently, the feeling can be compared to almost stalling out a manual transmission. Most the time it's when it..."}
+{"idx": 7, "title": "Dart Wiring Diagrams | Page 5 | Dodge Dart Forum", "date": "", "ddg_snippet": "Jan 28, 2024 · Search Wiring Diagrams Use the following link to search for wiring diagrams for the dart .", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/dart-wiring-diagrams.50274/page-5", "content": "Jan 28, 2024 · Search Wiring Diagrams Use the following link to search for wiring diagrams for the dart ."}
+{"idx": 8, "title": "Front strut replacement - Dodge Dart Forum", "date": "", "ddg_snippet": "May 24, 2020 · Front struts are gone, will replace with KYB's struts part #'s 334982 and 334981. Rockauto.com has them for $83 each rightnow compared to the next cheapest autozone $127 so definitely gonna pull the trigger, however I need to know, do I need to replace the strut mounts with KYB's also (sm5811...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/front-strut-replacement.66659/", "content": "May 24, 2020 · Front struts are gone, will replace with KYB's struts part #'s 334982 and 334981. Rockauto.com has them for $83 each rightnow compared to the next cheapest autozone $127 so definitely gonna pull the trigger, however I need to know, do I need to replace the strut mounts with KYB's also (sm5811..."}
+{"idx": 9, "title": "Recirculation Door Actuator Location - Dodge Dart Forum", "date": "", "ddg_snippet": "Jun 10, 2024 · The recirculation door actuator (1) is a reversible 12 volt Direct Current (DC) servo motor. The recirculation door actuator is located on the bottom of the HVAC air inlet housing, behind the instrument panel. The recirculation door actuator is contained within a black molded plastic housing with an integral wire connector receptacle (4). Three mounting tabs (3) allow the actuator to be ...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/recirculation-door-actuator-location.70551/", "content": "Jun 10, 2024 · The recirculation door actuator (1) is a reversible 12 volt Direct Current (DC) servo motor. The recirculation door actuator is located on the bottom of the HVAC air inlet housing, behind the instrument panel. The recirculation door actuator is contained within a black molded plastic housing with an integral wire connector receptacle (4). Three mounting tabs (3) allow the actuator to be ..."}
diff --git a/data/sampled_jsons/DC28Fpk76s_Intervention_Conditioning_Causal_Bayesian_Networks_Section_2_formal_definition_causal_mod.jsonl b/data/sampled_jsons/DC28Fpk76s_Intervention_Conditioning_Causal_Bayesian_Networks_Section_2_formal_definition_causal_mod.jsonl
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index 0000000000000000000000000000000000000000..5e595a70a34f6d4fb8aaacabe2f9483831616e55
--- /dev/null
+++ b/data/sampled_jsons/DC28Fpk76s_Intervention_Conditioning_Causal_Bayesian_Networks_Section_2_formal_definition_causal_mod.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal model - Wikipedia", "date": "", "ddg_snippet": "Comparison of two competing causal models used for interpretation of fMRI images. In metaphysics, a causal model is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation may be used in the development o...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Causal_model", "content": "Comparison of two competing causal models used for interpretation of fMRI images. In metaphysics, a causal model is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation may be used in the development o..."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "These examples underscore the versatility and utility of causal models for providing a formal representation of system variables. Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal mechanisms.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DC28Fpk76s", "content": "These examples underscore the versatility and utility of causal models for providing a formal representation of system variables. Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal mechanisms."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian Networks Causal Inference with Bayesian Networks. Main Concepts and ... Learning causal Bayesian networks based on causality analysis ... Bayesian Causal Inference: A Tutorial - Ohio State University Intervention and Conditioning in Causal Bayesian Networks Learning causal Bayesian networks based on causality analysis for Causal Inference with Bayesian Networks . Main Concepts and Meth… Causal Inference with Bayesian Networks . Main Concepts and Meth… Learning causal Bayesian networks based on causality analysis for Optimizing Causal Interventions in Hybrid Bayesian Networks", "date": "", "ddg_snippet": "May 23, 2024 · Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range ... Potential outcomes framework (Rubin causal model ), propensity score matching and structural causal models are, arguably, the most popular frameworks for observational causal inference. Here, we focus on the structural causal models and one particular type, Bayesian Networks . Interested users can find more details in the references below. Sep 1, 2022 · In this paper, two assertions, called causal dependence and log-likelihood equivalence, are introduced to learn Bayesian network classifiers (BNCs) to represent causal relationships. Information-theoretic metrics based on point-wise log-likelihood function and entropy function are proposed to identify and verify the rationality of causality between attribute values. The resulting algorithm ... Bayesian causal inference: Summary “Any complication that creates problems for one form of inference creates problems for all forms of inference, just in different ways\" – Don Rubin (2014, interview) Sep 26, 2024 · This paper significantly advances causal inference by uniquely estimating probabilities in Causal Bayesian Networks (CBNs), enabling analysis using observational data, and simplifying calculations for crucial counterfactual probabilities. This addresses a critical limitation of CBNs and opens avenues for practical applications in various fields. Why is causal interpretation important for BNS? Causal interpretation is important for BNs (Spirtes et al., 2000) because it allows to predict the effects of interventions in a domain —something that cannot be done without a causal interpretation. However, Bayesian methods for learning causal networks are not fairly well developed. Does Bayes' rule require a conditional distribution? Even though we are interested in the joint distribution of the variables in the graph, Bayes’ rule requires us to only specify the conditional distributions of each variable given its parents. The links between variables in Bayesian Networks encode dependency but not necessarily causality. Does correlation imply causation? Often, in the absence of randomised control trials, there is a need for causal inference purely from observational data. However, in this case the commonly known fact that correlation does not imply causation comes to life. Therefore, it is crucial to distinguish between events that cause specific outcomes and those that merely correlate. Why is causal knowledge important in machine learning? Discovering, modeling and understanding causal mechanisms behind natural phenomena are fundamental tasks in numerous scientific disciplines (Zhu et al., 2021). Causality expresses a kind of a “law” necessity, and causal knowledge can facilitate various machine learning tasks , including semi-supervised learning and domain adaptation. In the case of causal Bayesian networks , the edges in BNs convey a causal meaning, and the do-operator can be used to model causal interventions. The behavior of the do-operator within a causal BN leads to a truncated factorization of the distribution [45].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.14728", "content": "May 23, 2024 · Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range ... Potential outcomes framework (Rubin causal model ), propensity score matching and structural causal models are, arguably, the most popular frameworks for observational causal inference. Here, we focus on the structural causal models and one particular type, Bayesian Networks . Interested users can find more details in the references below. Sep 1, 2022 · In this paper, two assertions, called causal dependence and log-likelihood equivalence, are introduced to learn Bayesian network classifiers (BNCs) to represent causal relationships. Information-theoretic metrics based on point-wise log-likelihood function and entropy function are proposed to identify and verify the rationality of causality between attribute values. The resulting algorithm ... Bayesian causal inference: Summary “Any complication that creates problems for one form of inference creates problems for all forms of inference, just in different ways\" – Don Rubin (2014, interview) Sep 26, 2024 · This paper significantly advances causal inference by uniquely estimating probabilities in Causal Bayesian Networks (CBNs), enabling analysis using observational data, and simplifying calculations for crucial counterfactual probabilities. This addresses a critical limitation of CBNs and opens avenues for practical applications in various fields. Why is causal interpretation important for BNS? Causal interpretation is important for BNs (Spirtes et al., 2000) because it allows to predict the effects of interventions in a domain —something that cannot be done without a causal interpretation. However, Bayesian methods for learning causal networks are not fairly well developed. Does Bayes' rule require a conditional distribution? Even though we are interested in the joint distribution of the variables in the graph, Bayes’ rule requires us to only specify the conditional distributions of each variable given its parents. The links between variables in Bayesian Networks encode dependency but not necessarily causality. Does correlation imply causation? Often, in the absence of randomised control trials, there is a need for causal inference purely from observational data. However, in this case the commonly known fact that correlation does not imply causation comes to life. Therefore, it is crucial to distinguish between events that cause specific outcomes and those that merely correlate. Why is causal knowledge important in machine learning? Discovering, modeling and understanding causal mechanisms behind natural phenomena are fundamental tasks in numerous scientific disciplines (Zhu et al., 2021). Causality expresses a kind of a “law” necessity, and causal knowledge can facilitate various machine learning tasks , including semi-supervised learning and domain adaptation. In the case of causal Bayesian networks , the edges in BNs convey a causal meaning, and the do-operator can be used to model causal interventions. The behavior of the do-operator within a causal BN leads to a truncated factorization of the distribution [45]."}
+{"idx": 3, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Sep 26, 2024 · This paper significantly advances causal inference by uniquely estimating probabilities in Causal Bayesian Networks (CBNs), enabling analysis using observational data, and simplifying calculations for crucial counterfactual probabilities. This addresses a critical limitation of CBNs and opens avenues for practical applications in various fields.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/dc28fpk76s/", "content": "Sep 26, 2024 · This paper significantly advances causal inference by uniquely estimating probabilities in Causal Bayesian Networks (CBNs), enabling analysis using observational data, and simplifying calculations for crucial counterfactual probabilities. This addresses a critical limitation of CBNs and opens avenues for practical applications in various fields."}
+{"idx": 4, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by J Halpern — Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DC28Fpk76s", "content": "by J Halpern — Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models ..."}
+{"idx": 5, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by J Halpern — Section 2 reviews the formalism of causal models . Section 3 gives semantics to formulas in Causal Bayesian Networks (CBNs) and Section 4 shows that any CBN ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "by J Halpern — Section 2 reviews the formalism of causal models . Section 3 gives semantics to formulas in Causal Bayesian Networks (CBNs) and Section 4 shows that any CBN ..."}
+{"idx": 6, "title": "Causal Inference with Bayesian Networks. Main Concepts and ...", "date": "", "ddg_snippet": "Potential outcomes framework (Rubin causal model ), propensity score matching and structural causal models are, arguably, the most popular frameworks for observational causal inference. Here, we focus on the structural causal models and one particular type, Bayesian Networks . Interested users can find more details in the references below.", "subpage_snippet": "", "source": "causalnex.readthedocs.io", "link": "https://causalnex.readthedocs.io/en/latest/04_user_guide/04_user_guide.html", "content": "Potential outcomes framework (Rubin causal model ), propensity score matching and structural causal models are, arguably, the most popular frameworks for observational causal inference. Here, we focus on the structural causal models and one particular type, Bayesian Networks . Interested users can find more details in the references below."}
+{"idx": 7, "title": "Learning causal Bayesian networks based on causality analysis ...", "date": "", "ddg_snippet": "Sep 1, 2022 · In this paper, two assertions, called causal dependence and log-likelihood equivalence, are introduced to learn Bayesian network classifiers (BNCs) to represent causal relationships. Information-theoretic metrics based on point-wise log-likelihood function and entropy function are proposed to identify and verify the rationality of causality between attribute values. The resulting algorithm ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197622002962", "content": "Sep 1, 2022 · In this paper, two assertions, called causal dependence and log-likelihood equivalence, are introduced to learn Bayesian network classifiers (BNCs) to represent causal relationships. Information-theoretic metrics based on point-wise log-likelihood function and entropy function are proposed to identify and verify the rationality of causality between attribute values. The resulting algorithm ..."}
+{"idx": 8, "title": "Bayesian Causal Inference: A Tutorial - Ohio State University", "date": "", "ddg_snippet": "Bayesian causal inference: Summary “Any complication that creates problems for one form of inference creates problems for all forms of inference, just in different ways\" – Don Rubin (2014, interview)", "subpage_snippet": "", "source": "mbi.osu.edu", "link": "https://mbi.osu.edu/sites/default/files/2019-07/bayesian_causal_tutorial_ohiostate_june2019.pdf", "content": "Bayesian causal inference: Summary “Any complication that creates problems for one form of inference creates problems for all forms of inference, just in different ways\" – Don Rubin (2014, interview)"}
+{"idx": 9, "title": "Optimizing Causal Interventions in Hybrid Bayesian Networks", "date": "", "ddg_snippet": "In the case of causal Bayesian networks , the edges in BNs convey a causal meaning, and the do-operator can be used to model causal interventions. The behavior of the do-operator within a causal BN leads to a truncated factorization of the distribution [45].", "subpage_snippet": "", "source": "hcss.nl", "link": "https://hcss.nl/wp-content/uploads/2024/07/Optimizing-Causal-Interventions-in-Hybrid-Bayesian-Networks-HCSS-2024.pdf", "content": "In the case of causal Bayesian networks , the edges in BNs convey a causal meaning, and the do-operator can be used to model causal interventions. The behavior of the do-operator within a causal BN leads to a truncated factorization of the distribution [45]."}
diff --git a/data/sampled_jsons/DCBM_'Grounding_Dino'_promptable_concept_proposal_methods_Section_4.1.jsonl b/data/sampled_jsons/DCBM_'Grounding_Dino'_promptable_concept_proposal_methods_Section_4.1.jsonl
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index 0000000000000000000000000000000000000000..ba03a63d5eb001bbf18811b7e0c31752bb5b597d
--- /dev/null
+++ b/data/sampled_jsons/DCBM_'Grounding_Dino'_promptable_concept_proposal_methods_Section_4.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "concept_extraction/: Scripts and modules for extracting concepts . dcbm_training/: Code for training the DCBM model. data/: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. utils/: Helper scripts for running experiments.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "concept_extraction/: Scripts and modules for extracting concepts . dcbm_training/: Code for training the DCBM model. data/: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. utils/: Helper scripts for running experiments."}
+{"idx": 1, "title": "Fine-Tuning Grounding DINO: Open-Vocabulary Object Detection", "date": "", "ddg_snippet": "Object detection has traditionally been a closed-set problem: you train on a fixed list of classes and cannot recognize new ones. Grounding DINO breaks this mold, becoming an open-set, language-conditioned detector that can localize any user-specified phrase, zero-shot. Grounding DINO shatters this limitation by weaving language understanding directly into a transformer-based detector. It can ...", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/fine-tuning-grounding-dino/", "content": "Object detection has traditionally been a closed-set problem: you train on a fixed list of classes and cannot recognize new ones. Grounding DINO breaks this mold, becoming an open-set, language-conditioned detector that can localize any user-specified phrase, zero-shot. Grounding DINO shatters this limitation by weaving language understanding directly into a transformer-based detector. It can ..."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Moreover, concept proposal steering using the promptable Grounding Dino does not result in performance differences compared to steering-free methods such as SAM2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576v2", "content": "Moreover, concept proposal steering using the promptable Grounding Dino does not result in performance differences compared to steering-free methods such as SAM2."}
+{"idx": 3, "title": "Grounding Dino — object detection with prompt - Medium", "date": "", "ddg_snippet": "Grounding DINO is a cutting-edge zero-shot object detection model that marries the powerful DINO architecture with grounded pre-training.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@elvenkim1/grounding-dino-object-detection-with-prompt-1e65545feced", "content": "Grounding DINO is a cutting-edge zero-shot object detection model that marries the powerful DINO architecture with grounded pre-training."}
+{"idx": 4, "title": "Grounding DINO - GitHub", "date": "", "ddg_snippet": "🔥 Grounded SAM 2 is released now, which combines Grounding DINO with SAM 2 for any object tracking in open-world scenarios. 🔥 Grounding DINO 1.5 is released now, which is IDEA Research's Most Capable Open-World Object Detection Model!", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IDEA-Research/GroundingDINO", "content": "🔥 Grounded SAM 2 is released now, which combines Grounding DINO with SAM 2 for any object tracking in open-world scenarios. 🔥 Grounding DINO 1.5 is released now, which is IDEA Research's Most Capable Open-World Object Detection Model!"}
+{"idx": 5, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We extensively evaluate the DCBM framework across concept proposal generation methods and datasets. To ensure its usefulness for real-life applications, we as-sess the transferability of DCBMs to out-of-domain (OOD) settings and verify the localization of the im-portant concepts .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576v3", "content": "We extensively evaluate the DCBM framework across concept proposal generation methods and datasets. To ensure its usefulness for real-life applications, we as-sess the transferability of DCBMs to out-of-domain (OOD) settings and verify the localization of the im-portant concepts ."}
+{"idx": 6, "title": "VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal Video ...", "date": "", "ddg_snippet": "We aim to leverage the strong generalization capabilities of such foundation models to achieve strong open-set spatio-temporal video grounding performance. Our proposed spatio-temporal video grounding method uses DETR-like [1] design, with temporal aggregation and adaptation modules for learning video-specific representations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00901v2", "content": "We aim to leverage the strong generalization capabilities of such foundation models to achieve strong open-set spatio-temporal video grounding performance. Our proposed spatio-temporal video grounding method uses DETR-like [1] design, with temporal aggregation and adaptation modules for learning video-specific representations."}
+{"idx": 7, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "• We extensively evaluate the DCBM framework across concept proposal generation methods and datasets. To ensure its usefulness for real-life applications, we assess the transferability of DCBMs to out-of-domain (OOD) settings and verify the localization of the important concepts . Report issue for preceding element", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "• We extensively evaluate the DCBM framework across concept proposal generation methods and datasets. To ensure its usefulness for real-life applications, we assess the transferability of DCBMs to out-of-domain (OOD) settings and verify the localization of the important concepts . Report issue for preceding element"}
+{"idx": 8, "title": "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set ...", "date": "", "ddg_snippet": "In pursuit of this goal, we design the strong open-set object detector Grounding DINO by following the two principles: tight modality fusion based on DINO [57] and large-scale grounded pre-train for concept generalization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2303.05499?_immersive_translate_auto_translate=1", "content": "In pursuit of this goal, we design the strong open-set object detector Grounding DINO by following the two principles: tight modality fusion based on DINO [57] and large-scale grounded pre-train for concept generalization."}
+{"idx": 9, "title": "Releases: IDEA-Research/GroundingDINO - GitHub", "date": "", "ddg_snippet": "👍 39 rentainhe, Marinto-Richee, xingsaifei, subhankar-trisetra, abdulghani91, usama-axcelerate, kjerk, omerbenamram, MauriceChen210-cc, rfan-debug, and 29 more reacted with thumbs up emoji 🎉 1 ️ 1 🚀 1", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IDEA-Research/GroundingDINO/releases", "content": "👍 39 rentainhe, Marinto-Richee, xingsaifei, subhankar-trisetra, abdulghani91, usama-axcelerate, kjerk, omerbenamram, MauriceChen210-cc, rfan-debug, and 29 more reacted with thumbs up emoji 🎉 1 ️ 1 🚀 1"}
diff --git a/data/sampled_jsons/DCBM_arXiv_Algorithm_1_pseudocode_area_minimum_maximum_threshold_variables_year_2024.jsonl b/data/sampled_jsons/DCBM_arXiv_Algorithm_1_pseudocode_area_minimum_maximum_threshold_variables_year_2024.jsonl
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index 0000000000000000000000000000000000000000..887a8779d6f863cc65f983f823aa98bb7c30d710
--- /dev/null
+++ b/data/sampled_jsons/DCBM_arXiv_Algorithm_1_pseudocode_area_minimum_maximum_threshold_variables_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What is Pseudocode : Use Cases and Examples - PseudoEditor", "date": "", "ddg_snippet": "Use Cases for Pseudocode . Algorithm design: When designing an algorithm , pseudocode helps in outlining the steps clearly. This makes it easier to test the logic before implementing it in a programming language.", "subpage_snippet": "", "source": "pseudoeditor.com", "link": "https://pseudoeditor.com/guides/what-is-pseudocode", "content": "Use Cases for Pseudocode . Algorithm design: When designing an algorithm , pseudocode helps in outlining the steps clearly. This makes it easier to test the logic before implementing it in a programming language."}
+{"idx": 1, "title": "Pseudocode in Software Development: A Practical... - DEV Community", "date": "", "ddg_snippet": "This is where pseudocode comes in. Pseudocode is a way of describing the structure and logic of a program using plain, natural language—like English or Spanish. It helps developers effectively explain their ideas, fix logic mistakes, and talk to their peers before they start writing code .", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/mzunairtariq/pseudocode-in-software-development-a-practical-guide-to-planning-and-problem-solving-4k52", "content": "This is where pseudocode comes in. Pseudocode is a way of describing the structure and logic of a program using plain, natural language—like English or Spanish. It helps developers effectively explain their ideas, fix logic mistakes, and talk to their peers before they start writing code ."}
+{"idx": 2, "title": "Maximum Subarray Sum - Kadane's Algorithm - GeeksforGeeks", "date": "", "ddg_snippet": "[Expected Approach] Using Kadane's Algorithm - O(n) Time and O( 1 ) Space. The idea of Kadane's algorithm is to traverse over the array from left to right and for each element, find the maximum sum among all subarrays ending at that element.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/largest-sum-contiguous-subarray/", "content": "[Expected Approach] Using Kadane's Algorithm - O(n) Time and O( 1 ) Space. The idea of Kadane's algorithm is to traverse over the array from left to right and for each element, find the maximum sum among all subarrays ending at that element."}
+{"idx": 3, "title": "Dynamic Programming Algorithm to Compute the Max Dot Product of...", "date": "", "ddg_snippet": "We can use DFS (Depth First Search) to enumerate the possible subsequences combination of both, but the complexity is exponetial. The key to solve this problem is to re-use the intermediate results, via Dynamic Programming algorithm .", "subpage_snippet": "", "source": "helloacm.com", "link": "https://helloacm.com/dynamic-programming-algorithm-to-compute-the-max-dot-product-of-two-subsequences/", "content": "We can use DFS (Depth First Search) to enumerate the possible subsequences combination of both, but the complexity is exponetial. The key to solve this problem is to re-use the intermediate results, via Dynamic Programming algorithm ."}
+{"idx": 4, "title": "Find minimum and maximum values in an array in C++ | Techie Delight", "date": "", "ddg_snippet": "The recommended solution is to use the std::minmax_element to find the smallest and largest array elements. It returns a pair of iterators with the first value pointing to the minimum element and the second value pointing to the maximum element.", "subpage_snippet": "", "source": "www.techiedelight.com", "link": "https://www.techiedelight.com/find-minimum-maximum-value-array-cpp/", "content": "The recommended solution is to use the std::minmax_element to find the smallest and largest array elements. It returns a pair of iterators with the first value pointing to the minimum element and the second value pointing to the maximum element."}
+{"idx": 5, "title": "Near-optimal Linear Sketches and Fully-Dynamic Algorithms for", "date": "", "ddg_snippet": "Since our algorithm is conceptually fairly simple, we present its full pseudocode in Section 2. 1 . Our framework immediately yields a novel and simple nearly-linear time algorithm for computing hypergraph spectral sparsifiers of nearly-linear size (see Section 4.3).", "subpage_snippet": "", "source": "www.cis.upenn.edu", "link": "https://www.cis.upenn.edu/~sanjeev/papers/stoc25_hypergraph_spectral.pdf", "content": "Since our algorithm is conceptually fairly simple, we present its full pseudocode in Section 2. 1 . Our framework immediately yields a novel and simple nearly-linear time algorithm for computing hypergraph spectral sparsifiers of nearly-linear size (see Section 4.3)."}
+{"idx": 6, "title": "What is Sliding Window Algorithm ? Examples? - Stack Overflow", "date": "", "ddg_snippet": "The art of sliding window technical is keep previous calculation to avoid recalculate again, and this will help reduce complexity of code from O(n^2) to O(n). For example, find the maximum of k consecutive numbers in array", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/8269916/what-is-sliding-window-algorithm-examples", "content": "The art of sliding window technical is keep previous calculation to avoid recalculate again, and this will help reduce complexity of code from O(n^2) to O(n). For example, find the maximum of k consecutive numbers in array"}
+{"idx": 7, "title": "Gammon Forum : MUSHclient : General : Comparing 2 variables in an...", "date": "", "ddg_snippet": "Message. Hey all, Is it possible to have 2 variables in an if statement? Here's what I 'm trying to create. if @steak and @chicken == \"0\" then Send(\"cook fish\") end -- if Trying to create an auto cooking script. It looks at my inventory, sets the amount of each type of food in their own variables .", "subpage_snippet": "", "source": "gammon.com.au", "link": "https://gammon.com.au/forum/bbshowpost.php?bbsubject_id=15021", "content": "Message. Hey all, Is it possible to have 2 variables in an if statement? Here's what I 'm trying to create. if @steak and @chicken == \"0\" then Send(\"cook fish\") end -- if Trying to create an auto cooking script. It looks at my inventory, sets the amount of each type of food in their own variables ."}
+{"idx": 8, "title": "Protecting participants or population? Comparison of k-anonymous...", "date": "", "ddg_snippet": "Require: OD matrix , threshold k, max generalization levels L, suppression budget β. max _supp then 14: Suppress all problematic rows 15: else 16: Sort problematic rows by increasing count 17: Suppress the first max _supp rows. return OD matrix with valid rows only.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.12950", "content": "Require: OD matrix , threshold k, max generalization levels L, suppression budget β. max _supp then 14: Suppress all problematic rows 15: else 16: Sort problematic rows by increasing count 17: Suppress the first max _supp rows. return OD matrix with valid rows only."}
+{"idx": 9, "title": "Маг крови через Кровавую Жатву. Билд на Ведьму", "date": "", "ddg_snippet": "3 Акт - 25% повышение порога оглушения25% increased Stun Threshold . 4 Акт - 30% увеличение восстановления здоровья от флаконов30% increased Life Recovery from Flasks. 4 Акт - Татуировки: 5% сопротивления Огню, Холоду и Молнии.", "subpage_snippet": "", "source": "guides.lootkeeper.com", "link": "https://guides.lootkeeper.com/poe2/bloodmage-krovavaya-zhatva", "content": "3 Акт - 25% повышение порога оглушения25% increased Stun Threshold . 4 Акт - 30% увеличение восстановления здоровья от флаконов30% increased Life Recovery from Flasks. 4 Акт - Татуировки: 5% сопротивления Огню, Холоду и Молнии."}
diff --git a/data/sampled_jsons/DCBM_dependency_on_segmentation_models_OR_dependency_on_detection_models_limitation.jsonl b/data/sampled_jsons/DCBM_dependency_on_segmentation_models_OR_dependency_on_detection_models_limitation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7220577fb662970378d9f9a971bef62d6ab4fb06
--- /dev/null
+++ b/data/sampled_jsons/DCBM_dependency_on_segmentation_models_OR_dependency_on_detection_models_limitation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2304.09427] Boosting Semantic Segmentation with Semantic ... DeepFake video detection: Insights into model generalisation ... A comprehensive survey of deep learning for time series ... Healthcare Fraud Data Mining Methods: A Look Back and Look ... Genetics and Pathogenesis of Diffuse Large B Cell Lymphoma Patient and Caregiver Outcomes of Health System, Community ...", "date": "", "ddg_snippet": "Apr 19, 2023 · In this paper, we present the Semantic Boundary Conditioned Backbone (SBCB) framework, a simple yet effective training framework that is model-agnostic and boosts segmentation performance, especially around the boundaries. Motivated by the recent development in improving semantic segmentation by incorporating boundaries as auxiliary tasks, we propose a multi-task framework that uses semantic ... Mar 28, 2025 · This limitation is especially significant in cases where real-time detection or response is necessary, making large-scale deployment difficult at present. The challenge of generalising DeepFake detection models remains significant and requires further research. Apr 23, 2025 · MTST (Zhang et al 2024e) addresses the limitation of PatchTST in learning patterns across different scales present in time-series data. By adopting a multi-scale approach, MTST proposes an effective model utilizing both shorter and longer patches for locality and long-term trend analysis. The inherent limitation with such rule-based detection is that once the fraudster becomes aware of the rules—either due to unpaid/rejected/held out claims, or due to a retrospective inspection or audit of adjudicated claims—their fraudulent patterns could change, and these rule-based detection programs cannot quickly adapt to the fraud ... METHODS We studied 574 DLBCL biopsies using exome and transcriptome sequencing, array-based DNA copy number analysis and targeted amplicon resequencing of 372 genes to identify genes with recurrent aberrations. We developed and implemented an algorithm to discover genetic subtypes based on co-occurrence of genetic alterations. Sep 2, 2025 · This randomized clinical trial compares the effectiveness of health system dementia care, community-based dementia care, and usual care on person living with dementia and caregiver outcomes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.09427", "content": "Apr 19, 2023 · In this paper, we present the Semantic Boundary Conditioned Backbone (SBCB) framework, a simple yet effective training framework that is model-agnostic and boosts segmentation performance, especially around the boundaries. Motivated by the recent development in improving semantic segmentation by incorporating boundaries as auxiliary tasks, we propose a multi-task framework that uses semantic ... Mar 28, 2025 · This limitation is especially significant in cases where real-time detection or response is necessary, making large-scale deployment difficult at present. The challenge of generalising DeepFake detection models remains significant and requires further research. Apr 23, 2025 · MTST (Zhang et al 2024e) addresses the limitation of PatchTST in learning patterns across different scales present in time-series data. By adopting a multi-scale approach, MTST proposes an effective model utilizing both shorter and longer patches for locality and long-term trend analysis. The inherent limitation with such rule-based detection is that once the fraudster becomes aware of the rules—either due to unpaid/rejected/held out claims, or due to a retrospective inspection or audit of adjudicated claims—their fraudulent patterns could change, and these rule-based detection programs cannot quickly adapt to the fraud ... METHODS We studied 574 DLBCL biopsies using exome and transcriptome sequencing, array-based DNA copy number analysis and targeted amplicon resequencing of 372 genes to identify genes with recurrent aberrations. We developed and implemented an algorithm to discover genetic subtypes based on co-occurrence of genetic alterations. Sep 2, 2025 · This randomized clinical trial compares the effectiveness of health system dementia care, community-based dementia care, and usual care on person living with dementia and caregiver outcomes."}
+{"idx": 1, "title": "DeepFake video detection: Insights into model generalisation ...", "date": "", "ddg_snippet": "Mar 28, 2025 · This limitation is especially significant in cases where real-time detection or response is necessary, making large-scale deployment difficult at present. The challenge of generalising DeepFake detection models remains significant and requires further research.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2543925125000075", "content": "Mar 28, 2025 · This limitation is especially significant in cases where real-time detection or response is necessary, making large-scale deployment difficult at present. The challenge of generalising DeepFake detection models remains significant and requires further research."}
+{"idx": 2, "title": "Network Goodness-of-Fit for the Block-Model Family", "date": "", "ddg_snippet": "Oct 4, 2023 · Abstract The block-model family has four popular network models (SBM, DCBM , MMSBM, and DCMM). A fundamental problem is, how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with two main ideas. The first is to use cycle count statistics as a general recipe for GoF. The second is a novel ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/01621459.2025.2479242", "content": "Oct 4, 2023 · Abstract The block-model family has four popular network models (SBM, DCBM , MMSBM, and DCMM). A fundamental problem is, how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with two main ideas. The first is to use cycle count statistics as a general recipe for GoF. The second is a novel ..."}
+{"idx": 3, "title": "A comprehensive survey of deep learning for time series ...", "date": "", "ddg_snippet": "Apr 23, 2025 · MTST (Zhang et al 2024e) addresses the limitation of PatchTST in learning patterns across different scales present in time-series data. By adopting a multi-scale approach, MTST proposes an effective model utilizing both shorter and longer patches for locality and long-term trend analysis.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11223-9", "content": "Apr 23, 2025 · MTST (Zhang et al 2024e) addresses the limitation of PatchTST in learning patterns across different scales present in time-series data. By adopting a multi-scale approach, MTST proposes an effective model utilizing both shorter and longer patches for locality and long-term trend analysis."}
+{"idx": 4, "title": "Healthcare Fraud Data Mining Methods: A Look Back and Look ...", "date": "", "ddg_snippet": "The inherent limitation with such rule-based detection is that once the fraudster becomes aware of the rules—either due to unpaid/rejected/held out claims, or due to a retrospective inspection or audit of adjudicated claims—their fraudulent patterns could change, and these rule-based detection programs cannot quickly adapt to the fraud ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9013219/", "content": "The inherent limitation with such rule-based detection is that once the fraudster becomes aware of the rules—either due to unpaid/rejected/held out claims, or due to a retrospective inspection or audit of adjudicated claims—their fraudulent patterns could change, and these rule-based detection programs cannot quickly adapt to the fraud ..."}
+{"idx": 5, "title": "Genetics and Pathogenesis of Diffuse Large B Cell Lymphoma", "date": "", "ddg_snippet": "METHODS We studied 574 DLBCL biopsies using exome and transcriptome sequencing, array-based DNA copy number analysis and targeted amplicon resequencing of 372 genes to identify genes with recurrent aberrations. We developed and implemented an algorithm to discover genetic subtypes based on co-occurrence of genetic alterations.", "subpage_snippet": "", "source": "gdc.cancer.gov", "link": "https://gdc.cancer.gov/about-data/publications/DLBCL-2018", "content": "METHODS We studied 574 DLBCL biopsies using exome and transcriptome sequencing, array-based DNA copy number analysis and targeted amplicon resequencing of 372 genes to identify genes with recurrent aberrations. We developed and implemented an algorithm to discover genetic subtypes based on co-occurrence of genetic alterations."}
+{"idx": 6, "title": "Patient and Caregiver Outcomes of Health System, Community ...", "date": "", "ddg_snippet": "Sep 2, 2025 · This randomized clinical trial compares the effectiveness of health system dementia care, community-based dementia care, and usual care on person living with dementia and caregiver outcomes.", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2838336", "content": "Sep 2, 2025 · This randomized clinical trial compares the effectiveness of health system dementia care, community-based dementia care, and usual care on person living with dementia and caregiver outcomes."}
+{"idx": 7, "title": "Publications - Max Planck Institut für Informatik", "date": "", "ddg_snippet": "FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training,” in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV ...", "subpage_snippet": "", "source": "www.mpi-inf.mpg.de", "link": "https://www.mpi-inf.mpg.de/de/departments/computer-vision-and-machine-learning/publications", "content": "FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training,” in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV ..."}
+{"idx": 8, "title": "US20060064583A1 - Programming interface for configuring a", "date": "", "ddg_snippet": "H04L9/32 — Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20060064583A1/en", "content": "H04L9/32 — Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including ..."}
+{"idx": 9, "title": "US9424192B1 - Private memory table for reduced memory coherence", "date": "", "ddg_snippet": "G06F12/0802 — Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US9424192B1/en", "content": "G06F12/0802 — Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g."}
diff --git a/data/sampled_jsons/DINO_self-supervised_vision_transformer_computer_vision_paper.jsonl b/data/sampled_jsons/DINO_self-supervised_vision_transformer_computer_vision_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b959a235ac934cf0882af648626c856bd22219f
--- /dev/null
+++ b/data/sampled_jsons/DINO_self-supervised_vision_transformer_computer_vision_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Emerging Properties in Self-Supervised Vision Transformers", "date": "", "ddg_snippet": "Apr 29, 2021 · In this paper , we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the following observations: first, self-supervised ViT features contain explicit information about the semantic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2104.14294", "content": "Apr 29, 2021 · In this paper , we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the following observations: first, self-supervised ViT features contain explicit information about the semantic ..."}
+{"idx": 1, "title": "Self-Supervised Vision Transformers with DINO - GitHub DINO: Self-Supervised Vision Transformers and Their ... - Medium Paper explained: DINO – Emerging Properties in Self ... DINO Explained: How Self-Supervised Learning Unlocked Vision ... DINO : Self - Supervised Vision Transformers and Their Emerging ... - M… Emerging Properties in Self - Supervised Vision Transformers ( DINO ) DINO : Self - Supervised Vision Transformers and Their Emerging ... - M… DINO : Self - Supervised Vision Transformers and Their Emerging ... - M… DINO : Self - Supervised Vision Transformers and Their Emerging ... - M… Paper explained: DINO - Emerging Properties in Self - Supervised Vision Emerging Properties in Self-Supervised Vision Transformers (DINO)", "date": "", "ddg_snippet": "🆕 Please check out our more recent DINOv2 effort in the same line of work. See full list on github.com PyTorch implementation and pretrained models for DINO . For details, see Emerging Properties in Self - Supervised Vision Transformers. [blogpost] [arXiv] [Yannic Kilcher's video] See full list on github.com Documentation Please install PyTorch and download the ImageNet dataset. This codebase has been developed with python version 3.6, PyTorch version 1.7.1, CUDA 11.0 and torchvision 0.8.2. The exact arguments to reproduce the models presented in our paper can be found in the args column of the pretrained models section. For a glimpse at the full documentation of DINO training please run: Vanilla DINO training 🦕 Run DINO with ViT-small network on a single node with 8 GPUs for 100 epochs with the following command. Training time is 1.75 day and the resulting checkpoint should reach 69.3% on k-NN eval and 74.0% on linear eval. We provide training and linear evaluation logs (with batch size 256 at evaluation time) for this run to help reproducibility. Multi-node training We use Slurm and submitit (pip install submitit). To train on 2 nodes with 8 GPUs each (total 16 GPUs): See full list on github.com You can look at the self -attention of the [CLS] token on the different heads of the last layer by running: See full list on github.com example.mp4 Extract frames from input video and generate attention video:Use folder of frames already extracted and generate attention video:Only generate video from folder of attention maps images: See full list on github.com To evaluate a simple k-NN classifier with a single GPU on a pre-trained model, run: If you choose not to specify --pretrained_weights, then DINO reference weights are used by default. If you want instead to evaluate checkpoints from a run of your own, you can run for example: See full list on github.com To train a supervised linear classifier on frozen weights on a single node with 8 gpus, run: We release the logs and weights from evaluating the different models: See full list on github.com Please verify that you're using pytorch version 1.7.1 since we are not able to reproduce the results with most recent pytorch 1.8.1 at the moment. Step 1: Prepare DAVIS 2017 data Step 2: Video object segmentation Step 3: Evaluate the obtained segmentation See full list on github.com Step 1: Prepare revisited Oxford and Paris by following this repo. Step 2: Image retrieval (if you do not specify weights with --pretrained_weights then by default DINO weights pretrained on Google Landmark v2 dataset will be used). Paris: Oxford: See full list on github.com Step 1: Prepare Copydays dataset. Step 2 (opt): Prepare a set of image distractors and a set of images on which to learn the whitening operator. In our paper , we use 10k random images from YFCC100M as distractors and 20k random images from YFCC100M (different from the distractors) for computing the whitening operation. Step 3: Run copy detection: We report result on the strong subset. For example in the stdout from the command above we get: eval on strong mAP=0.858. See full list on github.com Apr 9, 2025 · This article is a summary of the groundbreaking paper “Emerging Properties in Self-Supervised Vision Transformers ” by Caron et al. Dec 1, 2021 · Advancing Vision Transformers for Self-Supervised Learning In this story, I would love to give you a a good idea of how the DINO paper works and what makes it great. Aug 4, 2025 · DINO builds largely off of BYOL's momentum encoder approach but identifies the key modifications necessary to make self-supervised learning work effectively with vision transformers . Why the name DINO ? DINO stands for Self -DIstillation with NO labels, which is a nod to how two different fields knowledge distillation and self training come together. Why is Dino a breakthrough in self-supervised learning for vision? Exploring new applications of the emerging properties DINO represents a significant breakthrough in self-supervised learning for vision, revealing unique properties of Vision Transformers and achieving state-of-the-art performance . Does self-supervised learning provide new properties to vision transformer (vit)? In this paper , we question if self - supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). self - supervised ViT features contain explicit information about the semantic segmentation of an image, which does not emerge as clearly with supervised ViTs, nor with convnets. Are self-supervised vision Transformers a viable alternative to convolutional neural networks? This article is a summary of the groundbreaking paper “Emerging Properties in Self-Supervised Vision Transformers” by Caron et al. Vision Transformers (ViTs) have emerged as a powerful alternative to convolutional neural networks (convnets) for visual recognition. What is Dino & how does it work? DINO represents a significant breakthrough in self-supervised learning for vision , revealing unique properties of Vision Transformers and achieving state-of-the-art performance. Its ability to learn rich visual representations without labels opens up new possibilities for computer vision research and applications. Are self-supervised vision Transformers effective in natural language processing? However, their success has been somewhat muted compared to their impact in natural language processing. The paper “Emerging Properties in Self-Supervised Vision Transformers” introduces DINO, a novel self-supervised learning approach that reveals unique properties of Vision Transformers and achieves state-of-the-art performance. What is self-supervised learning in vision Transformers? Self-supervised learning allows it to train models without any labels . So, in the case of computer vision tasks, only images are fed to the model and the network itself learns to understand the visual world around it. It turns out, applying self-supervision to Vision Transformers leads to the following desirable properties: In this paper , we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/dino", "content": "🆕 Please check out our more recent DINOv2 effort in the same line of work. See full list on github.com PyTorch implementation and pretrained models for DINO . For details, see Emerging Properties in Self - Supervised Vision Transformers. [blogpost] [arXiv] [Yannic Kilcher's video] See full list on github.com Documentation Please install PyTorch and download the ImageNet dataset. This codebase has been developed with python version 3.6, PyTorch version 1.7.1, CUDA 11.0 and torchvision 0.8.2. The exact arguments to reproduce the models presented in our paper can be found in the args column of the pretrained models section. For a glimpse at the full documentation of DINO training please run: Vanilla DINO training 🦕 Run DINO with ViT-small network on a single node with 8 GPUs for 100 epochs with the following command. Training time is 1.75 day and the resulting checkpoint should reach 69.3% on k-NN eval and 74.0% on linear eval. We provide training and linear evaluation logs (with batch size 256 at evaluation time) for this run to help reproducibility. Multi-node training We use Slurm and submitit (pip install submitit). To train on 2 nodes with 8 GPUs each (total 16 GPUs): See full list on github.com You can look at the self -attention of the [CLS] token on the different heads of the last layer by running: See full list on github.com example.mp4 Extract frames from input video and generate attention video:Use folder of frames already extracted and generate attention video:Only generate video from folder of attention maps images: See full list on github.com To evaluate a simple k-NN classifier with a single GPU on a pre-trained model, run: If you choose not to specify --pretrained_weights, then DINO reference weights are used by default. If you want instead to evaluate checkpoints from a run of your own, you can run for example: See full list on github.com To train a supervised linear classifier on frozen weights on a single node with 8 gpus, run: We release the logs and weights from evaluating the different models: See full list on github.com Please verify that you're using pytorch version 1.7.1 since we are not able to reproduce the results with most recent pytorch 1.8.1 at the moment. Step 1: Prepare DAVIS 2017 data Step 2: Video object segmentation Step 3: Evaluate the obtained segmentation See full list on github.com Step 1: Prepare revisited Oxford and Paris by following this repo. Step 2: Image retrieval (if you do not specify weights with --pretrained_weights then by default DINO weights pretrained on Google Landmark v2 dataset will be used). Paris: Oxford: See full list on github.com Step 1: Prepare Copydays dataset. Step 2 (opt): Prepare a set of image distractors and a set of images on which to learn the whitening operator. In our paper , we use 10k random images from YFCC100M as distractors and 20k random images from YFCC100M (different from the distractors) for computing the whitening operation. Step 3: Run copy detection: We report result on the strong subset. For example in the stdout from the command above we get: eval on strong mAP=0.858. See full list on github.com Apr 9, 2025 · This article is a summary of the groundbreaking paper “Emerging Properties in Self-Supervised Vision Transformers ” by Caron et al. Dec 1, 2021 · Advancing Vision Transformers for Self-Supervised Learning In this story, I would love to give you a a good idea of how the DINO paper works and what makes it great. Aug 4, 2025 · DINO builds largely off of BYOL's momentum encoder approach but identifies the key modifications necessary to make self-supervised learning work effectively with vision transformers . Why the name DINO ? DINO stands for Self -DIstillation with NO labels, which is a nod to how two different fields knowledge distillation and self training come together. Why is Dino a breakthrough in self-supervised learning for vision? Exploring new applications of the emerging properties DINO represents a significant breakthrough in self-supervised learning for vision, revealing unique properties of Vision Transformers and achieving state-of-the-art performance . Does self-supervised learning provide new properties to vision transformer (vit)? In this paper , we question if self - supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). self - supervised ViT features contain explicit information about the semantic segmentation of an image, which does not emerge as clearly with supervised ViTs, nor with convnets. Are self-supervised vision Transformers a viable alternative to convolutional neural networks? This article is a summary of the groundbreaking paper “Emerging Properties in Self-Supervised Vision Transformers” by Caron et al. Vision Transformers (ViTs) have emerged as a powerful alternative to convolutional neural networks (convnets) for visual recognition. What is Dino & how does it work? DINO represents a significant breakthrough in self-supervised learning for vision , revealing unique properties of Vision Transformers and achieving state-of-the-art performance. Its ability to learn rich visual representations without labels opens up new possibilities for computer vision research and applications. Are self-supervised vision Transformers effective in natural language processing? However, their success has been somewhat muted compared to their impact in natural language processing. The paper “Emerging Properties in Self-Supervised Vision Transformers” introduces DINO, a novel self-supervised learning approach that reveals unique properties of Vision Transformers and achieves state-of-the-art performance. What is self-supervised learning in vision Transformers? Self-supervised learning allows it to train models without any labels . So, in the case of computer vision tasks, only images are fed to the model and the network itself learns to understand the visual world around it. It turns out, applying self-supervision to Vision Transformers leads to the following desirable properties: In this paper , we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets)."}
+{"idx": 2, "title": "DINO : Self - Supervised Vision Transformers and Their... | Medium", "date": "", "ddg_snippet": "The paper “Emerging Properties in Self - Supervised Vision Transformers ” introduces DINO , a novel self - supervised learning approach that reveals unique properties of Vision Transformers and achieves state-of-the-art performance.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jimcanary/dino-self-supervised-vision-transformers-and-their-emerging-properties-7f9e5f4adac4", "content": "The paper “Emerging Properties in Self - Supervised Vision Transformers ” introduces DINO , a novel self - supervised learning approach that reveals unique properties of Vision Transformers and achieves state-of-the-art performance."}
+{"idx": 3, "title": "Paper explained: DINO - Emerging Properties in Self-Supervised Vision ...", "date": "", "ddg_snippet": "Advancing Vision Transformers for Self-Supervised Learning In this story, I would love to give you a a good idea of how the DINO paper works and what makes it great. I've tried to keep the article simple so that even readers with little prior knowledge can follow along. The attention of DINO visualized for an image of a monkey on a tree. Source: [2] Traditionally, Vision Transformers (ViT ...", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/paper-explained-dino-emerging-properties-in-self-supervised-vision-transformers-f9386df266f1/", "content": "Advancing Vision Transformers for Self-Supervised Learning In this story, I would love to give you a a good idea of how the DINO paper works and what makes it great. I've tried to keep the article simple so that even readers with little prior knowledge can follow along. The attention of DINO visualized for an image of a monkey on a tree. Source: [2] Traditionally, Vision Transformers (ViT ..."}
+{"idx": 4, "title": "Paper Walkthrough: DINO - Erik Storrs", "date": "", "ddg_snippet": "Paper Walkthrough: DINO In this post, I'll cover Emerging Properties in Self-Supervised Vision Transformers by Caron et al., which introduces a new self-supervised training framework for vision transformers .", "subpage_snippet": "", "source": "storrs.io", "link": "https://storrs.io/dino/", "content": "Paper Walkthrough: DINO In this post, I'll cover Emerging Properties in Self-Supervised Vision Transformers by Caron et al., which introduces a new self-supervised training framework for vision transformers ."}
+{"idx": 5, "title": "Emerging Properties in Self-Supervised Vision Transformers (DINO)", "date": "", "ddg_snippet": "In this paper , we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).", "subpage_snippet": "", "source": "timtimchang.github.io", "link": "https://timtimchang.github.io/yctimchang_note/Paper+Explore/DINO/", "content": "In this paper , we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets)."}
+{"idx": 6, "title": "PDF Emerging Properties in Self-Supervised Vision Transformers", "date": "", "ddg_snippet": "In this paper , we question if self-supervised learning pro- vides new properties to Vision Transformer (ViT) [16] that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the follow- ing observations: ・〉st, self-supervised ViT features contain explicit information about the ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2021/papers/Caron_Emerging_Properties_in_Self-Supervised_Vision_Transformers_ICCV_2021_paper.pdf", "content": "In this paper , we question if self-supervised learning pro- vides new properties to Vision Transformer (ViT) [16] that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the follow- ing observations: ・〉st, self-supervised ViT features contain explicit information about the ..."}
+{"idx": 7, "title": "Talking to DINO: Bridging Self-Supervised Vision Backbones with ...", "date": "", "ddg_snippet": "Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision -language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers , they face challenges in spatial localization due to their global alignment of image and text features. Conversely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.19331", "content": "Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision -language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers , they face challenges in spatial localization due to their global alignment of image and text features. Conversely ..."}
+{"idx": 8, "title": "DINO: Emerging Properties in Self-Supervised Vision Transformers", "date": "", "ddg_snippet": "This study questions if Self Supervised Learning provides new properties to Vision Transformers [1] that standout from convolutional networks and underlines the importance of momentum encoder [2], multiu001d-u001dcu001drop training [3] and the use of small patches. Although the emergence of segmentation masks seem to be a property shared across self-supervised methods.", "subpage_snippet": "", "source": "wandb.ai", "link": "https://wandb.ai/self-supervised-learning/dino/reports/DINO-Emerging-Properties-in-Self-Supervised-Vision-Transformers--VmlldzoxMzM2MTAz", "content": "This study questions if Self Supervised Learning provides new properties to Vision Transformers [1] that standout from convolutional networks and underlines the importance of momentum encoder [2], multiu001d-u001dcu001drop training [3] and the use of small patches. Although the emergence of segmentation masks seem to be a property shared across self-supervised methods."}
+{"idx": 9, "title": "DINO: Emerging Properties in Self-Supervised Vision Transformers ...", "date": "", "ddg_snippet": "An in-depth summary of Facebook's amazing Vision Transformer DINO DINO , a new self supervised system by Facebook AI, is able to learn incredible representations from unlabeled data. Below is a video visualising it's attention maps and we see the model was able to automatically learn class-specific features leading to accurate unsupervised object segmentation. It was […]", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/dino-emerging-properties-in-self-supervised-vision-transformers-summary-ab91df82cc3c/", "content": "An in-depth summary of Facebook's amazing Vision Transformer DINO DINO , a new self supervised system by Facebook AI, is able to learn incredible representations from unlabeled data. Below is a video visualising it's attention maps and we see the model was able to automatically learn class-specific features leading to accurate unsupervised object segmentation. It was […]"}
diff --git a/data/sampled_jsons/DISC_method_35.34%_78.60%_Socialized_Coevolution_paper.jsonl b/data/sampled_jsons/DISC_method_35.34%_78.60%_Socialized_Coevolution_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0ce1a764ccd963894d98bfc6d2e53aeb00f69d67
--- /dev/null
+++ b/data/sampled_jsons/DISC_method_35.34%_78.60%_Socialized_Coevolution_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DISC assessment - Wikipedia", "date": "", "ddg_snippet": "A DISC assessment is a pseudoscientific personality testing tool based on psychologist William Moulton Marston 's DISC emotional and behavioral theory, first published in 1928. [1]", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/DISC_assessment", "content": "A DISC assessment is a pseudoscientific personality testing tool based on psychologist William Moulton Marston 's DISC emotional and behavioral theory, first published in 1928. [1]"}
+{"idx": 1, "title": "Socialized Coevolution: Advancing a Better World through Cross-Task ...", "date": "", "ddg_snippet": "This paper introduces a practical learning paradigm of Socialized Coevolution (SC), to overcome the limitations of methods that rely heavily on data-driven, single-model approaches and often fall short in leveraging inter-model knowledge transfer.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0WQJ6DFSKp", "content": "This paper introduces a practical learning paradigm of Socialized Coevolution (SC), to overcome the limitations of methods that rely heavily on data-driven, single-model approaches and often fall short in leveraging inter-model knowledge transfer."}
+{"idx": 2, "title": "What is the DiSC assessment? - DiSC Profile", "date": "", "ddg_snippet": "DiSC ® is a scientifically validated personality assessment tool that identifies behavioral insights to improve communication and productivity in the workplace.", "subpage_snippet": "", "source": "www.discprofile.com", "link": "https://www.discprofile.com/what-is-disc", "content": "DiSC ® is a scientifically validated personality assessment tool that identifies behavioral insights to improve communication and productivity in the workplace."}
+{"idx": 3, "title": "Using the DISC behavioral instrument to guide leadership and ...", "date": "", "ddg_snippet": "THIS ARTICLE DESCRIBES the benefits of using the DISC behavioral evaluation method to better understand and work with team members and gives role-play scenarios for dealing with each personality.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/16355940/", "content": "THIS ARTICLE DESCRIBES the benefits of using the DISC behavioral evaluation method to better understand and work with team members and gives role-play scenarios for dealing with each personality."}
+{"idx": 4, "title": "The DISC Personality Assessment: Worthwhile Investment Or Con?", "date": "", "ddg_snippet": "Can I rely on the DISC personality assessment? Read this article for the essential information on this tool.", "subpage_snippet": "", "source": "thepracticalpsych.com", "link": "https://thepracticalpsych.com/blog/disc-personality-types", "content": "Can I rely on the DISC personality assessment? Read this article for the essential information on this tool."}
+{"idx": 5, "title": "Science behind DiSC® - DiSC Profile", "date": "", "ddg_snippet": "The DISC model While the original theory behind DISC began with the work of William Moulton Marston, Wiley has been researching and analyzing DISC for over 40 years. In fact, our DiSC ® Classic paper profile was the first DISC assessment ever. The foundation of DISC was first described by William Moulton Marston in his 1928 book, Emotions of Normal People. Marston identified what he called ...", "subpage_snippet": "", "source": "www.discprofile.com", "link": "https://www.discprofile.com/what-is-disc/research-reliability-and-validity", "content": "The DISC model While the original theory behind DISC began with the work of William Moulton Marston, Wiley has been researching and analyzing DISC for over 40 years. In fact, our DiSC ® Classic paper profile was the first DISC assessment ever. The foundation of DISC was first described by William Moulton Marston in his 1928 book, Emotions of Normal People. Marston identified what he called ..."}
+{"idx": 6, "title": "PDF About Everything DiSC: Theory and Research", "date": "", "ddg_snippet": "About Everything DiSC : Theory and Research Everything DiSC ® is a personal development learning experience that measures an individual's preferences and tendencies based on the DiSC ® model. But, what is the DiSC model?", "subpage_snippet": "", "source": "www.everythingdisc.com", "link": "https://www.everythingdisc.com/EverythingDiSC/media/SiteFiles/Assets/History/Everything-DiSC-resources-aboutdisc.pdf", "content": "About Everything DiSC : Theory and Research Everything DiSC ® is a personal development learning experience that measures an individual's preferences and tendencies based on the DiSC ® model. But, what is the DiSC model?"}
+{"idx": 7, "title": "History of DiSC® - DiSC Profile", "date": "", "ddg_snippet": "From Marston's model to today's Everything DiSC suite of applications, research and innovation have improved the assessment and profiles.", "subpage_snippet": "", "source": "www.discprofile.com", "link": "https://www.discprofile.com/what-is-disc/history-of-disc", "content": "From Marston's model to today's Everything DiSC suite of applications, research and innovation have improved the assessment and profiles."}
+{"idx": 8, "title": "DISC Theory | Understanding the DISC Assessment", "date": "", "ddg_snippet": "DISC is a model used to describe human behavior, based on four personality traits: Dominance (D), Influence (I), Steadiness (S), and Conscientiousness (C). This theory was developed by Dr. William Marston, a psychologist who believed that people have unique, observable ways of thinking, feeling, and behaving.", "subpage_snippet": "", "source": "discinsights.com", "link": "https://discinsights.com/pages/disc-theory", "content": "DISC is a model used to describe human behavior, based on four personality traits: Dominance (D), Influence (I), Steadiness (S), and Conscientiousness (C). This theory was developed by Dr. William Marston, a psychologist who believed that people have unique, observable ways of thinking, feeling, and behaving."}
+{"idx": 9, "title": "The History and Evolution of the DISC Personality Test", "date": "", "ddg_snippet": "The DISC personality test is a widely recognized tool used to assess individual behavior and communication styles.", "subpage_snippet": "", "source": "www.businessapac.com", "link": "https://www.businessapac.com/disc-personality-test/", "content": "The DISC personality test is a widely recognized tool used to assess individual behavior and communication styles."}
diff --git a/data/sampled_jsons/DISEF_Flowers_16-shot_original_paper_accuracy.jsonl b/data/sampled_jsons/DISEF_Flowers_16-shot_original_paper_accuracy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05eb9232a277d2f9cda9e492bb3caa61ed551042
--- /dev/null
+++ b/data/sampled_jsons/DISEF_Flowers_16-shot_original_paper_accuracy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The 16 + year-old original Stalker games have Nvidia... | PC Gamer", "date": "", "ddg_snippet": "On September 18, GSC Game World released a patch for its Legends of the Zone remaster of the original Stalker games that adds Nvidia DLSS support, a seemingly absurd addition to the ancient shooters that makes more sense when you dig into the specifics of the remaster.", "subpage_snippet": "", "source": "www.pcgamer.com", "link": "https://www.pcgamer.com/games/fps/the-16-year-old-original-stalker-games-have-nvidia-dlss-support-now-which-isnt-as-crazy-as-it-sounds/", "content": "On September 18, GSC Game World released a patch for its Legends of the Zone remaster of the original Stalker games that adds Nvidia DLSS support, a seemingly absurd addition to the ancient shooters that makes more sense when you dig into the specifics of the remaster."}
+{"idx": 1, "title": "তাপগতিবিদ্যা One Shot CQ | Thermodynamics | Physics 2nd Paper", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=zDTBoezeXME", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
+{"idx": 2, "title": "DeepSeek-R1-0528: How to Run Locally | Unsloth Documentation", "date": "", "ddg_snippet": "All uploads use Unsloth Dynamic 2.0 for SOTA 5- shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized DeepSeek LLMs with minimal accuracy loss.", "subpage_snippet": "", "source": "docs.unsloth.ai", "link": "https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/deepseek-r1-0528-how-to-run-locally", "content": "All uploads use Unsloth Dynamic 2.0 for SOTA 5- shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized DeepSeek LLMs with minimal accuracy loss."}
+{"idx": 3, "title": "The Illusion of Thinking: Understanding the Strengths and Limitations of...", "date": "", "ddg_snippet": "Current evaluations primarily focus on established mathematical and coding benchmarks, emphasizing final answer accuracy . However, this evaluation paradigm often suffers from data contamination and does not provide insights into the reasoning traces’ structure and quality.", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/illusion-of-thinking", "content": "Current evaluations primarily focus on established mathematical and coding benchmarks, emphasizing final answer accuracy . However, this evaluation paradigm often suffers from data contamination and does not provide insights into the reasoning traces’ structure and quality."}
+{"idx": 4, "title": "50+ Prompts Nano Banana AI (Google Gemini) Photo or Image Editing...", "date": "", "ddg_snippet": "Nano Banana uses Google’s Gemini model for fast results. The AI maintains realism, color accuracy as well as high resolution. You can apply multiple prompts for unique creations and adjust lighting, perspective as well as textures.", "subpage_snippet": "", "source": "unplix.com", "link": "https://unplix.com/nano-banana-ai-prompts/", "content": "Nano Banana uses Google’s Gemini model for fast results. The AI maintains realism, color accuracy as well as high resolution. You can apply multiple prompts for unique creations and adjust lighting, perspective as well as textures."}
+{"idx": 5, "title": "Read Tears on a Withered Flower - Chapter 70 | MangaBuddy", "date": "", "ddg_snippet": "One shot . Police. Psychological.Romantic Revenge. 5.0. Chapter 16 . 21 minutes ago.", "subpage_snippet": "", "source": "mangabuddy.com", "link": "https://mangabuddy.com/tears-on-a-withered-flower/chapter-70", "content": "One shot . Police. Psychological.Romantic Revenge. 5.0. Chapter 16 . 21 minutes ago."}
+{"idx": 6, "title": "[2404.19756] KAN: Kolmogorov-Arnold Networks", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2404.19756: KAN: Kolmogorov-Arnold Networks.View a PDF of the paper titled KAN: Kolmogorov-Arnold Networks, by Ziming Liu and 6 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.19756", "content": "Abstract page for arXiv paper 2404.19756: KAN: Kolmogorov-Arnold Networks.View a PDF of the paper titled KAN: Kolmogorov-Arnold Networks, by Ziming Liu and 6 other authors."}
+{"idx": 7, "title": "Roswell footage uploaded to National Archives... | Daily Mail Online", "date": "", "ddg_snippet": "The local paper 's front page story reported that the Roswell Army field recovered a flying saucer on a New Mexico Ranch after metallic-looking, light but strong material was scattered across the land.", "subpage_snippet": "", "source": "www.dailymail.co.uk", "link": "https://www.dailymail.co.uk/sciencetech/article-15111191/Roswell-footage-National-Archives-UFO-alien.html", "content": "The local paper 's front page story reported that the Roswell Army field recovered a flying saucer on a New Mexico Ranch after metallic-looking, light but strong material was scattered across the land."}
+{"idx": 8, "title": "Borderlands 4: Specialization, Explained", "date": "", "ddg_snippet": "Sharpshooter - Increases your reload speed and gun accuracy .You gain increased gun damage and critical hit damage while aiming. This effect stacks and is lost when you don't aim for a few seconds. One Shot , One Kill.", "subpage_snippet": "", "source": "www.dualshockers.com", "link": "https://www.dualshockers.com/borderlands-4-specialization-explained/", "content": "Sharpshooter - Increases your reload speed and gun accuracy .You gain increased gun damage and critical hit damage while aiming. This effect stacks and is lost when you don't aim for a few seconds. One Shot , One Kill."}
+{"idx": 9, "title": "Download FRAG Pro Shooter 4.13.0 [Free shoping]... - Androeed.Store", "date": "", "ddg_snippet": "Original PvP First.Free in-app purchases Rewards for advertising without watching it. {%alt%}. FRAG Pro Shooter [Free shoping]. First-person real-time combat.", "subpage_snippet": "", "source": "androeed.store", "link": "https://androeed.store/files/frag-pro-shooter.html", "content": "Original PvP First.Free in-app purchases Rewards for advertising without watching it. {%alt%}. FRAG Pro Shooter [Free shoping]. First-person real-time combat."}
diff --git a/data/sampled_jsons/DISEF_Turrisi_da_Costa_2023_Flowers_dataset_16-shot_accuracy.jsonl b/data/sampled_jsons/DISEF_Turrisi_da_Costa_2023_Flowers_dataset_16-shot_accuracy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1e8a24e09a8eed15164c0ca7b8b06e082f8e26e7
--- /dev/null
+++ b/data/sampled_jsons/DISEF_Turrisi_da_Costa_2023_Flowers_dataset_16-shot_accuracy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diogo Costa - Wikipedia", "date": "", "ddg_snippet": "Diogo Meireles da Costa ComM is a professional footballer who plays as a goalkeeper for Primeira Liga club Porto, which he captains. Born in Switzerland, he plays for the Portugal national team. He is considered one of the best goalkeepers in the wor...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Diogo_Costa", "content": "Diogo Meireles da Costa ComM is a professional footballer who plays as a goalkeeper for Primeira Liga club Porto, which he captains. Born in Switzerland, he plays for the Portugal national team. He is considered one of the best goalkeepers in the wor..."}
+{"idx": 1, "title": "GitHub - vturrisi/ disef : Pytorch implementation of \"Diversified in-domain.....", "date": "", "ddg_snippet": "author={Victor G. Turrisi da Costa and Nicola Dall'Asen and Yiming Wang and Nicu Sebe and Elisa Ricci}", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vturrisi/disef", "content": "author={Victor G. Turrisi da Costa and Nicola Dall'Asen and Yiming Wang and Nicu Sebe and Elisa Ricci}"}
+{"idx": 2, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 3, "title": "Retrieval-enriched zero- shot image classification", "date": "", "ddg_snippet": "Victor G Turrisi da Costa , Nicola Dall’Asen, Yiming Wang, Nicu Sebe, and Elisa Ricci. 2023 . Diversified in-domain synthesis with efficient fine-tuning for few- shot classification. arXiv preprint arXiv:2312.03046.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.1186.pdf", "content": "Victor G Turrisi da Costa , Nicola Dall’Asen, Yiming Wang, Nicu Sebe, and Elisa Ricci. 2023 . Diversified in-domain synthesis with efficient fine-tuning for few- shot classification. arXiv preprint arXiv:2312.03046."}
+{"idx": 4, "title": "UCI Machine Learning Repository | Discover datasets around the world!", "date": "", "ddg_snippet": "Donated on 6/30/1988. A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods.", "subpage_snippet": "", "source": "archive.ics.uci.edu", "link": "https://archive.ics.uci.edu/dataset/53/iris", "content": "Donated on 6/30/1988. A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods."}
+{"idx": 5, "title": "[2312.03046] Diversified in-domain synthesis with efficient fine-tuning...", "date": "", "ddg_snippet": "Authors:Victor G. Turrisi da Costa , Nicola Dall'Asen, Yiming Wang, Nicu Sebe, Elisa Ricci. View a PDF of the paper titled Diversified in-domain synthesis with efficient fine-tuning for few- shot classification, by Victor G. Turrisi da Costa and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "Authors:Victor G. Turrisi da Costa , Nicola Dall'Asen, Yiming Wang, Nicu Sebe, Elisa Ricci. View a PDF of the paper titled Diversified in-domain synthesis with efficient fine-tuning for few- shot classification, by Victor G. Turrisi da Costa and 4 other authors."}
+{"idx": 6, "title": "2312.03046 - Diversified in-domain synthesis with efficient fine-tuning...", "date": "", "ddg_snippet": "Published Dec 5, 2023 in cs.CV by Victor G. Turrisi da Costa , Nicola Dall'Asen, Yiming Wang, Nicu Sebe, and Elisa Ricci. Abstract: “Few- shot image classification aims to learn an image classifier using only a small set of labeled examples per class.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2312.03046", "content": "Published Dec 5, 2023 in cs.CV by Victor G. Turrisi da Costa , Nicola Dall'Asen, Yiming Wang, Nicu Sebe, and Elisa Ricci. Abstract: “Few- shot image classification aims to learn an image classifier using only a small set of labeled examples per class."}
+{"idx": 7, "title": "Злокачественное новообразование бронхов и легкого...", "date": "", "ddg_snippet": "Кодирование по Международной статистической классификации болезней и проблем, связанных со здоровьем: C34. Год утверждения (частота пересмотра): 2021. Год окончания действия: 2023 .", "subpage_snippet": "", "source": "diseases.medelement.com", "link": "https://diseases.medelement.com/disease/злокачественное-новообразование-бронхов-и-легкого-кп-рф-2021/16976", "content": "Кодирование по Международной статистической классификации болезней и проблем, связанных со здоровьем: C34. Год утверждения (частота пересмотра): 2021. Год окончания действия: 2023 ."}
+{"idx": 8, "title": "Bayesian Prompt Learning for Image-Language Model Generalization", "date": "", "ddg_snippet": "Victor Guilherme Turrisi Da Costa .Moreover, with LLM contextual data mapped within the learned prompts, it enables zero- shot transfer of prompts to new classes and datasets potentially cutting the LLM prompt engineering cost.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/377418793_Bayesian_Prompt_Learning_for_Image-Language_Model_Generalization", "content": "Victor Guilherme Turrisi Da Costa .Moreover, with LLM contextual data mapped within the learned prompts, it enables zero- shot transfer of prompts to new classes and datasets potentially cutting the LLM prompt engineering cost."}
+{"idx": 9, "title": "Supplementary Material for “ImagineFSL: Self-Supervised Pretraining...", "date": "", "ddg_snippet": "Although DISEF already leverages synthetic images to complement its few- shot data , our approach still provides an additional 0.6%/0.5% improvement.[S-4] Victor G Turrisi da Costa , Nicola Dall’Asen, Yiming Wang, Nicu Sebe, and Elisa Ricci.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "Although DISEF already leverages synthetic images to complement its few- shot data , our approach still provides an additional 0.6%/0.5% improvement.[S-4] Victor G Turrisi da Costa , Nicola Dall’Asen, Yiming Wang, Nicu Sebe, and Elisa Ricci."}
diff --git a/data/sampled_jsons/DPO_GRPO_IPO_RLOO_PPO_alternative_RLHF_loss_function_2023_2024_year_2024.jsonl b/data/sampled_jsons/DPO_GRPO_IPO_RLOO_PPO_alternative_RLHF_loss_function_2023_2024_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DPO Meets PPO: Reinforced Token Optimization for RLHF", "date": "", "ddg_snippet": "Apr 29, 2024 · For its practical implementation, \\texttt {RTO} innovatively integrates Direct Preference Optimization ( DPO ) and PPO . DPO , originally derived from sparse sentence rewards, surprisingly provides us with a token-wise characterization of response quality, which is seamlessly incorporated into our subsequent PPO training stage.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.18922", "content": "Apr 29, 2024 · For its practical implementation, \\texttt {RTO} innovatively integrates Direct Preference Optimization ( DPO ) and PPO . DPO , originally derived from sparse sentence rewards, surprisingly provides us with a token-wise characterization of response quality, which is seamlessly incorporated into our subsequent PPO training stage."}
+{"idx": 1, "title": "The LLM Training Journey: From SFT to PPO, DPO & GRPO ...", "date": "", "ddg_snippet": "Aug 14, 2025 · Learn how RLHF , preference tuning, and techniques like PPO , DPO , and GRPO shape Large Language Models into helpful, human-aligned systems.", "subpage_snippet": "", "source": "blog.gopenai.com", "link": "https://blog.gopenai.com/the-llm-training-journey-from-sft-to-ppo-dpo-grpo-explained-4fe65b8711fd", "content": "Aug 14, 2025 · Learn how RLHF , preference tuning, and techniques like PPO , DPO , and GRPO shape Large Language Models into helpful, human-aligned systems."}
+{"idx": 2, "title": "Fine Tuning Beyond SFT - On PPO, DPO and RLHF", "date": "", "ddg_snippet": "Jan 5, 2025 · PPO is used to finetune the baseline LLM based on rewards by the reward model. PPO is designed to be more stable and efficient than traditional policy gradient methods. DPO is an alternative to use PPO within RLHF . It simplifies the fine-tuning process by directly optimizing the LLM based on the human ratings, without training a reward model.", "subpage_snippet": "", "source": "heinzermch.github.io", "link": "https://heinzermch.github.io/posts/on-rlhf-dpo-and-ppo/", "content": "Jan 5, 2025 · PPO is used to finetune the baseline LLM based on rewards by the reward model. PPO is designed to be more stable and efficient than traditional policy gradient methods. DPO is an alternative to use PPO within RLHF . It simplifies the fine-tuning process by directly optimizing the LLM based on the human ratings, without training a reward model."}
+{"idx": 3, "title": "LLM Alignment Techniques: A Summary | by Kaige | Medium Mastering LLM Fine-Tuning: GRPO, PPO, and DPO Compared Direct Alignment Algorithms - CS234 Lecture Fine Tuning Beyond SFT - On PPO , DPO and RLHF Fine Tuning Beyond SFT - On PPO , DPO and RLHF Mastering LLM Fine-Tuning: GRPO , PPO , and DPO Compared Fine Tuning Beyond SFT - On PPO , DPO and RLHF Mastering LLM Fine-Tuning: GRPO , PPO , and DPO Compared Direct Alignment Algorithms - CS234 Lecture RLHF, PPO, and DPO: How AI Models Learn from Human Preferences", "date": "", "ddg_snippet": "Oct 20, 2024 · This article summaries LLM alignment techniques: RLHF / DPO / GRPO / IPO / RLOO /RoCoTO/OAIF The Alignment Problem Align the output of LLMs with human expectations and values. Jun 13, 2025 · Compare and contrast PG, TRPO, PPO , DPO , and GRPO to identify the most suitable methods for RL-based fine-tuning of LLMs. Gain practical experience by implementing policy optimization algorithms in Python. Does using a weaker optimizer, such as PPO provide a better solution (regularization). DPO fits an implicit reward function : Is the DPO implicit reward as good as the explicit one? What is the difference between PPO and DPO in rlhf? PPO is used to finetune the baseline LLM based on rewards by the reward model. PPO is designed to be more stable and efficient than traditional policy gradient methods. DPO is an alternative to use PPO within RLHF . It simplifies the fine-tuning process by directly optimizing the LLM based on the human ratings, without training a reward model. What is DPO in rlhf? DPO is an alternative to use PPO within RLHF. It simplifies the fine-tuning process by directly optimizing the LLM based on the human ratings, without training a reward model. This works by reformulating the RL problem into a simpler classification problem, simplifying the training process. What is DPO & grpo? Dive into advanced approaches like Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO), which enable preference-based training without relying on explicit reward models. Compare and contrast PG, TRPO, PPO, DPO, and GRPO to identify the most suitable methods for RL-based fine-tuning of LLMs. What is the process of rlhf optimization? The process of RLHF optimization is shown on the left. PPO is used to finetune the baseline LLM based on rewards by the reward model. PPO is designed to be more stable and efficient than traditional policy gradient methods. DPO is an alternative to use PPO within RLHF . How does DPO work? Unlike traditional RL-based approaches like PPO, which depend on building and training a separate reward model, DPO operates directly on human-labeled preference pairs , where one model response is preferred over another. This simplifies the fine-tuning process and helps models generate more human-aligned outputs. Does DPO fit an implicit reward function? DPO fits an implicit reward function : Is the DPO implicit reward as good as the explicit one? Does using a weaker optimizer, such as PPO provide a better solution (regularization). Is the DPO implicit reward as good as the explicit one? Does using a weaker optimizer, such as PPO provide a better solution (regularization). Jan 27, 2025 · RLHF , PPO , and DPO help AI models learn from human preferences to improve performance and decision-making in real-world applications.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@kaige.yang0110/llm-alignment-techniques-a-summary-842622621407", "content": "Oct 20, 2024 · This article summaries LLM alignment techniques: RLHF / DPO / GRPO / IPO / RLOO /RoCoTO/OAIF The Alignment Problem Align the output of LLMs with human expectations and values. Jun 13, 2025 · Compare and contrast PG, TRPO, PPO , DPO , and GRPO to identify the most suitable methods for RL-based fine-tuning of LLMs. Gain practical experience by implementing policy optimization algorithms in Python. Does using a weaker optimizer, such as PPO provide a better solution (regularization). DPO fits an implicit reward function : Is the DPO implicit reward as good as the explicit one? What is the difference between PPO and DPO in rlhf? PPO is used to finetune the baseline LLM based on rewards by the reward model. PPO is designed to be more stable and efficient than traditional policy gradient methods. DPO is an alternative to use PPO within RLHF . It simplifies the fine-tuning process by directly optimizing the LLM based on the human ratings, without training a reward model. What is DPO in rlhf? DPO is an alternative to use PPO within RLHF. It simplifies the fine-tuning process by directly optimizing the LLM based on the human ratings, without training a reward model. This works by reformulating the RL problem into a simpler classification problem, simplifying the training process. What is DPO & grpo? Dive into advanced approaches like Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO), which enable preference-based training without relying on explicit reward models. Compare and contrast PG, TRPO, PPO, DPO, and GRPO to identify the most suitable methods for RL-based fine-tuning of LLMs. What is the process of rlhf optimization? The process of RLHF optimization is shown on the left. PPO is used to finetune the baseline LLM based on rewards by the reward model. PPO is designed to be more stable and efficient than traditional policy gradient methods. DPO is an alternative to use PPO within RLHF . How does DPO work? Unlike traditional RL-based approaches like PPO, which depend on building and training a separate reward model, DPO operates directly on human-labeled preference pairs , where one model response is preferred over another. This simplifies the fine-tuning process and helps models generate more human-aligned outputs. Does DPO fit an implicit reward function? DPO fits an implicit reward function : Is the DPO implicit reward as good as the explicit one? Does using a weaker optimizer, such as PPO provide a better solution (regularization). Is the DPO implicit reward as good as the explicit one? Does using a weaker optimizer, such as PPO provide a better solution (regularization). Jan 27, 2025 · RLHF , PPO , and DPO help AI models learn from human preferences to improve performance and decision-making in real-world applications."}
+{"idx": 4, "title": "Mastering LLM Fine-Tuning: GRPO, PPO, and DPO Compared", "date": "", "ddg_snippet": "Jun 13, 2025 · Compare and contrast PG, TRPO, PPO , DPO , and GRPO to identify the most suitable methods for RL-based fine-tuning of LLMs. Gain practical experience by implementing policy optimization algorithms in Python.", "subpage_snippet": "", "source": "pub.towardsai.net", "link": "https://pub.towardsai.net/mastering-llm-fine-tuning-grpo-ppo-and-dpo-compared-e362257d4036", "content": "Jun 13, 2025 · Compare and contrast PG, TRPO, PPO , DPO , and GRPO to identify the most suitable methods for RL-based fine-tuning of LLMs. Gain practical experience by implementing policy optimization algorithms in Python."}
+{"idx": 5, "title": "RLHF, PPO, and DPO: How AI Models Learn from Human Preferences", "date": "", "ddg_snippet": "Jan 27, 2025 · RLHF , PPO , and DPO help AI models learn from human preferences to improve performance and decision-making in real-world applications.", "subpage_snippet": "", "source": "www.ve3.global", "link": "https://www.ve3.global/rlhf-ppo-and-dpo-how-ai-models-learn-from-human-preferences/", "content": "Jan 27, 2025 · RLHF , PPO , and DPO help AI models learn from human preferences to improve performance and decision-making in real-world applications."}
+{"idx": 6, "title": "Direct Alignment Algorithms - CS234 Lecture", "date": "", "ddg_snippet": "Does using a weaker optimizer, such as PPO provide a better solution (regularization). DPO fits an implicit reward function : Is the DPO implicit reward as good as the explicit one?", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs234/CS234Spr2024/slides/dpo_slides.pdf", "content": "Does using a weaker optimizer, such as PPO provide a better solution (regularization). DPO fits an implicit reward function : Is the DPO implicit reward as good as the explicit one?"}
+{"idx": 7, "title": "Reinforcement Learning (i.e. Policy Gradient Algorithms) |", "date": "", "ddg_snippet": "When RLHF came onto the scene with ChatGPT, it was largely known that they used a variant of PPO , and many initial efforts were built upon that.", "subpage_snippet": "", "source": "rlhfbook.com", "link": "https://rlhfbook.com/c/11-policy-gradients.html", "content": "When RLHF came onto the scene with ChatGPT, it was largely known that they used a variant of PPO , and many initial efforts were built upon that."}
+{"idx": 8, "title": "A Survey of Direct Preference Optimization", "date": "", "ddg_snippet": "In this context, Direct Preference Optimization ( DPO ) has recently gained prominence as a streamlined alternative that directly optimizes LLMs using ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.11701v1", "content": "In this context, Direct Preference Optimization ( DPO ) has recently gained prominence as a streamlined alternative that directly optimizes LLMs using ..."}
+{"idx": 9, "title": "A Survey of Frontiers in LLM Reasoning: Inference Scaling,", "date": "", "ddg_snippet": "Additionally, we cover a broad spectrum of learning algorithms, from supervised fine-tuning to reinforcement learning such as PPO and GRPO , and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09037v3", "content": "Additionally, we cover a broad spectrum of learning algorithms, from supervised fine-tuning to reinforcement learning such as PPO and GRPO , and the ..."}
diff --git a/data/sampled_jsons/DPO_GRPO_IPO_alternative_PPO_RLHF_loss_functions_2022_2023_2024.jsonl b/data/sampled_jsons/DPO_GRPO_IPO_alternative_PPO_RLHF_loss_functions_2022_2023_2024.jsonl
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+++ b/data/sampled_jsons/DPO_GRPO_IPO_alternative_PPO_RLHF_loss_functions_2022_2023_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Group Robust Preference Optimization in Reward-free RLHF", "date": "", "ddg_snippet": "by SS Ramesh · Cited by 55 — The DPO loss (logistic log loss ) in Equation (7) can be replaced with alternatives like hinge or squared loss (see [47]). We label this objective GR- DPO when ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=PRAsjrmXXK", "content": "by SS Ramesh · Cited by 55 — The DPO loss (logistic log loss ) in Equation (7) can be replaced with alternatives like hinge or squared loss (see [47]). We label this objective GR- DPO when ..."}
+{"idx": 1, "title": "Preference Tuning LLMs: PPO, DPO, GRPO — A Simple Guide", "date": "", "ddg_snippet": "This blog post is your friendly guide to three key algorithms in RLHF : PPO , DPO , and GRPO . We'll unpack them step-by-step, making it clear even if you're just ...", "subpage_snippet": "", "source": "anukriti-ranjan.medium.com", "link": "https://anukriti-ranjan.medium.com/preference-tuning-llms-ppo-dpo-grpo-a-simple-guide-135765c87090", "content": "This blog post is your friendly guide to three key algorithms in RLHF : PPO , DPO , and GRPO . We'll unpack them step-by-step, making it clear even if you're just ..."}
+{"idx": 2, "title": "A UNIFIED APPROACH TO ONLINE AND OFFLINE RLHF", "date": "", "ddg_snippet": "by S Cen · Cited by 50 — We evaluate pessimistic VPO and compare its performance to several offline RLHF baselines. ( DPO (Rafailov et al., 2023 ) and IPO (Azar et al., 2024 )) on several ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=SQnitDuow6", "content": "by S Cen · Cited by 50 — We evaluate pessimistic VPO and compare its performance to several offline RLHF baselines. ( DPO (Rafailov et al., 2023 ) and IPO (Azar et al., 2024 )) on several ..."}
+{"idx": 3, "title": "A Technical Survey of Reinforcement Learning Techniques ...", "date": "", "ddg_snippet": "5 Jul 2025 — We cover foundational methods such as RLHF and RLAIF, as well as advanced approaches like Direct Preference Optimization ( DPO ) and Group ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.04136v1", "content": "5 Jul 2025 — We cover foundational methods such as RLHF and RLAIF, as well as advanced approaches like Direct Preference Optimization ( DPO ) and Group ..."}
+{"idx": 4, "title": "Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "6 Jun 2025 — Reinforcement learning from human feedback ( RLHF ) is a technique used to incorporate human information into AI systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.12501v2", "content": "6 Jun 2025 — Reinforcement learning from human feedback ( RLHF ) is a technique used to incorporate human information into AI systems."}
+{"idx": 5, "title": "Online versus Offline RL for LLMs - Deep (Learning) Focus", "date": "", "ddg_snippet": "Because DPO was so much simpler to use relative to PPO -based RLHF , this technique quickly became popular within LLM research. As a result, many ...", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/online-rl", "content": "Because DPO was so much simpler to use relative to PPO -based RLHF , this technique quickly became popular within LLM research. As a result, many ..."}
+{"idx": 6, "title": "Extended Abstract - CS 224R Deep Reinforcement Learning", "date": "", "ddg_snippet": "by E Hellman — Group Relative Policy Optimization ( GRPO ) Complementary to DPO , GRPO has been proposed as a group-wise alternative to standard policy gradient methods. Shao et ...", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/CS_224R_Final_Paper_2.pdf", "content": "by E Hellman — Group Relative Policy Optimization ( GRPO ) Complementary to DPO , GRPO has been proposed as a group-wise alternative to standard policy gradient methods. Shao et ..."}
+{"idx": 7, "title": "Aman's AI Journal • Preference Optimization", "date": "", "ddg_snippet": "Group Relative Policy Optimization ( GRPO ): A PPO variant that removes the critic model and estimates the baseline from group scores, improving memory efficiency ...", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/preference-optimization/", "content": "Group Relative Policy Optimization ( GRPO ): A PPO variant that removes the critic model and estimates the baseline from group scores, improving memory efficiency ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Unlike supervised KD approaches, GKD also offers the flexibility to employ alternative loss functions between the student and teacher, which can be useful when ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=RLHF+policy", "content": "Unlike supervised KD approaches, GKD also offers the flexibility to employ alternative loss functions between the student and teacher, which can be useful when ..."}
+{"idx": 9, "title": "Preference Tuning with Human Feedback on Language, ...", "date": "", "ddg_snippet": "by GI Winata · 2025 · Cited by 22 — There are many variants of. PPO for RLHF , e.g. P3O (Wu et al., 2023c ), or RLHF -V (Yu et al., 2024 ) for multi-modal models. Pairwise Proximal Policy Optimization ...", "subpage_snippet": "", "source": "www.columbia.edu", "link": "http://www.columbia.edu/~wt2319/Preference_survey.pdf", "content": "by GI Winata · 2025 · Cited by 22 — There are many variants of. PPO for RLHF , e.g. P3O (Wu et al., 2023c ), or RLHF -V (Yu et al., 2024 ) for multi-modal models. Pairwise Proximal Policy Optimization ..."}
diff --git a/data/sampled_jsons/DSDFM_Algorithm_1_pseudo_code_drift_diffusion_human_motion.jsonl b/data/sampled_jsons/DSDFM_Algorithm_1_pseudo_code_drift_diffusion_human_motion.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MDM: Human Motion Diffusion Model - GitHub PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "The official PyTorch implementation of the paper \" Human Motion Diffusion Model\". Please visit our webpage for more details. Bibtex If you find this code useful in your research, please cite: See full list on github.com 🐉 SinMDM - Learns single motion motifs - even for non-humanoid characters. 👯 PriorMDM - Uses MDM as a generative prior, enabling new generation tasks with few examples or even no data at all. See full list on github.com 📢 25/Jan/24 - Fixed bug in evalutation code (#182) - Please use the fixed results when citing MDM. 📢 1 /Jun/23 - Fixed generation issue (#104) - Please pull to improve generation results. 📢 23/Nov/22 - Fixed evaluation issue (#42) - Please pull and run bash prepare/download_t2m_evaluators.sh from the top of the repo to adapt. 📢 4/Nov/22 - Added sampling, training and evaluation of unconstrained tasks. Note slight env changes adapting to the new code . If you already have an installed environment, run bash prepare/download_unconstrained_assets.sh; conda install -y -c anaconda scikit-learn to adapt. 📢 3/Nov/22 - Added in-between and upper-body editing. 📢 31/Oct/22 - Added sampling, training and evaluation of action-to- motion tasks. See full list on github.com 1 . Setup environment Install ffmpeg (if not already installed):For windows use this instead.Setup conda env:Download dependencies: Text to Motion There are two paths to get the data:(a) Go the easy way if you just want to generate text-to- motion (excluding editing which does require motion capture data)(b) Get full data to train and evaluate the model. Action to Motion UESTC, HumanAct12 See full list on github.com See full list on github.com Unconditioned editing You may also define:•--num_samples (default is 10) / --num_repetitions (default is 3).•--device id.•--seed to sample different prompts.•--edit_mode upper_body For upper body editing (lower body is fixed).The output will look like this (blue frames are from the input motion ; orange were generated by the model):•As in Motion Synthesis, you may follow the Render SMPL mesh section to obtain meshes for your edited motions. Text conditioned editing Just add the text conditioning using --text_condition. For example:The output will look like this (blue joints are from the input motion ; orange were generated by the model): See full list on github.com Text to Motion HumanML3DKIT Unconstrained •Use --device to define GPU id.•Use --arch to choose one of the architectures reported in the paper {trans_enc, trans_dec, gru} (trans_enc is default).•Add --train_platform_type {ClearmlPlatform, TensorboardPlatform} to track results with either ClearML or Tensorboard.•Add --eval_during_training to run a short (90 minutes) evaluation for each saved checkpoint. This will slow down training but will give you better monitoring. See full list on github.com Text to Motion •Takes about 20 hours (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.HumanML3DKIT Action to Motion •Takes about 7 hours for UESTC and 2 hours for HumanAct12 (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.where path-to-model-ckpt can be a path to any of the pretrained action-to- motion models listed above, or to a checkpoint trained by the user. Unconstrained •Takes about 3 hours (on a single GPU)Precision and recall are not computed to save computing time. If you wish to compute them, edit the file eval/a2m/gru_eval.py and change the string fast=True to fast=False. See full list on github.com This code is standing on the shoulders of giants. We want to thank the following contributors that our code is based on: guided- diffusion , MotionCLIP, text-to- motion , actor, joints2smpl, MoDi. See full list on github.com This code is distributed under an MIT LICENSE. Note that our code depends on other libraries, including CLIP, SMPL, SMPL-X, PyTorch3D, and uses datasets that each have their own respective licenses that must also be followed. See full list on github.com Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and ... What is dsdfm method for Human Motion Synthesis? However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. What does dsdfm stand for? In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. Do denoising diffusion models produce realistic human motions? year={2023} Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. What is physics-guided motion diffusion model? To address this issue, we present a novel physics-guided motion diffusion model ( PhysDiff ), which incorporates physical constraints into the diffusion process. How does motion diffusion affect real-world applications? However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and limits their real-world application . How is projected motion used in the denoising diffusion process? The projected motion is further used in the next diffusion step to guide the denoising diffusion process . Intuitively, the use of physics in our model iteratively pulls the motion toward a physically-plausible space, which cannot be achieved by simple post-processing. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments, DSDFM achieves state-of-the-art results surpassing the latest methods, validating its superiority in human motion synthesis. Live content is unavailable.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GuyTevet/motion-diffusion-model", "content": "The official PyTorch implementation of the paper \" Human Motion Diffusion Model\". Please visit our webpage for more details. Bibtex If you find this code useful in your research, please cite: See full list on github.com 🐉 SinMDM - Learns single motion motifs - even for non-humanoid characters. 👯 PriorMDM - Uses MDM as a generative prior, enabling new generation tasks with few examples or even no data at all. See full list on github.com 📢 25/Jan/24 - Fixed bug in evalutation code (#182) - Please use the fixed results when citing MDM. 📢 1 /Jun/23 - Fixed generation issue (#104) - Please pull to improve generation results. 📢 23/Nov/22 - Fixed evaluation issue (#42) - Please pull and run bash prepare/download_t2m_evaluators.sh from the top of the repo to adapt. 📢 4/Nov/22 - Added sampling, training and evaluation of unconstrained tasks. Note slight env changes adapting to the new code . If you already have an installed environment, run bash prepare/download_unconstrained_assets.sh; conda install -y -c anaconda scikit-learn to adapt. 📢 3/Nov/22 - Added in-between and upper-body editing. 📢 31/Oct/22 - Added sampling, training and evaluation of action-to- motion tasks. See full list on github.com 1 . Setup environment Install ffmpeg (if not already installed):For windows use this instead.Setup conda env:Download dependencies: Text to Motion There are two paths to get the data:(a) Go the easy way if you just want to generate text-to- motion (excluding editing which does require motion capture data)(b) Get full data to train and evaluate the model. Action to Motion UESTC, HumanAct12 See full list on github.com See full list on github.com Unconditioned editing You may also define:•--num_samples (default is 10) / --num_repetitions (default is 3).•--device id.•--seed to sample different prompts.•--edit_mode upper_body For upper body editing (lower body is fixed).The output will look like this (blue frames are from the input motion ; orange were generated by the model):•As in Motion Synthesis, you may follow the Render SMPL mesh section to obtain meshes for your edited motions. Text conditioned editing Just add the text conditioning using --text_condition. For example:The output will look like this (blue joints are from the input motion ; orange were generated by the model): See full list on github.com Text to Motion HumanML3DKIT Unconstrained •Use --device to define GPU id.•Use --arch to choose one of the architectures reported in the paper {trans_enc, trans_dec, gru} (trans_enc is default).•Add --train_platform_type {ClearmlPlatform, TensorboardPlatform} to track results with either ClearML or Tensorboard.•Add --eval_during_training to run a short (90 minutes) evaluation for each saved checkpoint. This will slow down training but will give you better monitoring. See full list on github.com Text to Motion •Takes about 20 hours (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.HumanML3DKIT Action to Motion •Takes about 7 hours for UESTC and 2 hours for HumanAct12 (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.where path-to-model-ckpt can be a path to any of the pretrained action-to- motion models listed above, or to a checkpoint trained by the user. Unconstrained •Takes about 3 hours (on a single GPU)Precision and recall are not computed to save computing time. If you wish to compute them, edit the file eval/a2m/gru_eval.py and change the string fast=True to fast=False. See full list on github.com This code is standing on the shoulders of giants. We want to thank the following contributors that our code is based on: guided- diffusion , MotionCLIP, text-to- motion , actor, joints2smpl, MoDi. See full list on github.com This code is distributed under an MIT LICENSE. Note that our code depends on other libraries, including CLIP, SMPL, SMPL-X, PyTorch3D, and uses datasets that each have their own respective licenses that must also be followed. See full list on github.com Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and ... What is dsdfm method for Human Motion Synthesis? However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. What does dsdfm stand for? In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. Do denoising diffusion models produce realistic human motions? year={2023} Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. What is physics-guided motion diffusion model? To address this issue, we present a novel physics-guided motion diffusion model ( PhysDiff ), which incorporates physical constraints into the diffusion process. How does motion diffusion affect real-world applications? However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and limits their real-world application . How is projected motion used in the denoising diffusion process? The projected motion is further used in the next diffusion step to guide the denoising diffusion process . Intuitively, the use of physics in our model iteratively pulls the motion toward a physically-plausible space, which cannot be achieved by simple post-processing. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments, DSDFM achieves state-of-the-art results surpassing the latest methods, validating its superiority in human motion synthesis. Live content is unavailable."}
+{"idx": 1, "title": "[2505.00998] Deterministic-to-Stochastic Diverse Latent ... Deterministic-to-Stochastic Diverse Latent Feature Mapping ... arXiv:2505.00998v1 [cs.CV] 2 May 2025 CVPR 2025 Open Access Repository MDM: Human Motion Diffusion Model - GitHub PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "May 2, 2025 · In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human mo-tion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. To synthesize diverse and accurate human motions, we propose a novel method called DSDFM for human motion synthesis. The proposed method has straight trajectories and is easy to train compared to previous SGMs methods, while guaranteeing the diversity and accuracy of the generated hu-man motions. The proposed DSDFM consists of two stages. In the first stage, a human motion reconstruction ... DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments, DSDFM achieves state-of-the-art results surpassing the latest methods, validating its superiority in human motion synthesis. The official PyTorch implementation of the paper \" Human Motion Diffusion Model\". Please visit our webpage for more details. Bibtex If you find this code useful in your research, please cite: See full list on github.com 🐉 SinMDM - Learns single motion motifs - even for non-humanoid characters. 👯 PriorMDM - Uses MDM as a generative prior, enabling new generation tasks with few examples or even no data at all. See full list on github.com 📢 25/Jan/24 - Fixed bug in evalutation code (#182) - Please use the fixed results when citing MDM. 📢 1 /Jun/23 - Fixed generation issue (#104) - Please pull to improve generation results. 📢 23/Nov/22 - Fixed evaluation issue (#42) - Please pull and run bash prepare/download_t2m_evaluators.sh from the top of the repo to adapt. 📢 4/Nov/22 - Added sampling, training and evaluation of unconstrained tasks. Note slight env changes adapting to the new code . If you already have an installed environment, run bash prepare/download_unconstrained_assets.sh; conda install -y -c anaconda scikit-learn to adapt. 📢 3/Nov/22 - Added in-between and upper-body editing. 📢 31/Oct/22 - Added sampling, training and evaluation of action-to- motion tasks. See full list on github.com 1 . Setup environment Install ffmpeg (if not already installed):For windows use this instead.Setup conda env:Download dependencies: Text to Motion There are two paths to get the data:(a) Go the easy way if you just want to generate text-to- motion (excluding editing which does require motion capture data)(b) Get full data to train and evaluate the model. Action to Motion UESTC, HumanAct12 See full list on github.com See full list on github.com Unconditioned editing You may also define:•--num_samples (default is 10) / --num_repetitions (default is 3).•--device id.•--seed to sample different prompts.•--edit_mode upper_body For upper body editing (lower body is fixed).The output will look like this (blue frames are from the input motion ; orange were generated by the model):•As in Motion Synthesis, you may follow the Render SMPL mesh section to obtain meshes for your edited motions. Text conditioned editing Just add the text conditioning using --text_condition. For example:The output will look like this (blue joints are from the input motion ; orange were generated by the model): See full list on github.com Text to Motion HumanML3DKIT Unconstrained •Use --device to define GPU id.•Use --arch to choose one of the architectures reported in the paper {trans_enc, trans_dec, gru} (trans_enc is default).•Add --train_platform_type {ClearmlPlatform, TensorboardPlatform} to track results with either ClearML or Tensorboard.•Add --eval_during_training to run a short (90 minutes) evaluation for each saved checkpoint. This will slow down training but will give you better monitoring. See full list on github.com Text to Motion •Takes about 20 hours (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.HumanML3DKIT Action to Motion •Takes about 7 hours for UESTC and 2 hours for HumanAct12 (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.where path-to-model-ckpt can be a path to any of the pretrained action-to- motion models listed above, or to a checkpoint trained by the user. Unconstrained •Takes about 3 hours (on a single GPU)Precision and recall are not computed to save computing time. If you wish to compute them, edit the file eval/a2m/gru_eval.py and change the string fast=True to fast=False. See full list on github.com This code is standing on the shoulders of giants. We want to thank the following contributors that our code is based on: guided- diffusion , MotionCLIP, text-to- motion , actor, joints2smpl, MoDi. See full list on github.com This code is distributed under an MIT LICENSE. Note that our code depends on other libraries, including CLIP, SMPL, SMPL-X, PyTorch3D, and uses datasets that each have their own respective licenses that must also be followed. See full list on github.com Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and ... What is dsdfm method for Human Motion Synthesis? However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. What does dsdfm stand for? In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. Do denoising diffusion models produce realistic human motions? year={2023} Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. What is physics-guided motion diffusion model? To address this issue, we present a novel physics-guided motion diffusion model ( PhysDiff ), which incorporates physical constraints into the diffusion process. How does motion diffusion affect real-world applications? However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and limits their real-world application . How is projected motion used in the denoising diffusion process? The projected motion is further used in the next diffusion step to guide the denoising diffusion process . Intuitively, the use of physics in our model iteratively pulls the motion toward a physically-plausible space, which cannot be achieved by simple post-processing. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments, DSDFM achieves state-of-the-art results surpassing the latest methods, validating its superiority in human motion synthesis. Live content is unavailable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "May 2, 2025 · In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human mo-tion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. To synthesize diverse and accurate human motions, we propose a novel method called DSDFM for human motion synthesis. The proposed method has straight trajectories and is easy to train compared to previous SGMs methods, while guaranteeing the diversity and accuracy of the generated hu-man motions. The proposed DSDFM consists of two stages. In the first stage, a human motion reconstruction ... DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments, DSDFM achieves state-of-the-art results surpassing the latest methods, validating its superiority in human motion synthesis. The official PyTorch implementation of the paper \" Human Motion Diffusion Model\". Please visit our webpage for more details. Bibtex If you find this code useful in your research, please cite: See full list on github.com 🐉 SinMDM - Learns single motion motifs - even for non-humanoid characters. 👯 PriorMDM - Uses MDM as a generative prior, enabling new generation tasks with few examples or even no data at all. See full list on github.com 📢 25/Jan/24 - Fixed bug in evalutation code (#182) - Please use the fixed results when citing MDM. 📢 1 /Jun/23 - Fixed generation issue (#104) - Please pull to improve generation results. 📢 23/Nov/22 - Fixed evaluation issue (#42) - Please pull and run bash prepare/download_t2m_evaluators.sh from the top of the repo to adapt. 📢 4/Nov/22 - Added sampling, training and evaluation of unconstrained tasks. Note slight env changes adapting to the new code . If you already have an installed environment, run bash prepare/download_unconstrained_assets.sh; conda install -y -c anaconda scikit-learn to adapt. 📢 3/Nov/22 - Added in-between and upper-body editing. 📢 31/Oct/22 - Added sampling, training and evaluation of action-to- motion tasks. See full list on github.com 1 . Setup environment Install ffmpeg (if not already installed):For windows use this instead.Setup conda env:Download dependencies: Text to Motion There are two paths to get the data:(a) Go the easy way if you just want to generate text-to- motion (excluding editing which does require motion capture data)(b) Get full data to train and evaluate the model. Action to Motion UESTC, HumanAct12 See full list on github.com See full list on github.com Unconditioned editing You may also define:•--num_samples (default is 10) / --num_repetitions (default is 3).•--device id.•--seed to sample different prompts.•--edit_mode upper_body For upper body editing (lower body is fixed).The output will look like this (blue frames are from the input motion ; orange were generated by the model):•As in Motion Synthesis, you may follow the Render SMPL mesh section to obtain meshes for your edited motions. Text conditioned editing Just add the text conditioning using --text_condition. For example:The output will look like this (blue joints are from the input motion ; orange were generated by the model): See full list on github.com Text to Motion HumanML3DKIT Unconstrained •Use --device to define GPU id.•Use --arch to choose one of the architectures reported in the paper {trans_enc, trans_dec, gru} (trans_enc is default).•Add --train_platform_type {ClearmlPlatform, TensorboardPlatform} to track results with either ClearML or Tensorboard.•Add --eval_during_training to run a short (90 minutes) evaluation for each saved checkpoint. This will slow down training but will give you better monitoring. See full list on github.com Text to Motion •Takes about 20 hours (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.HumanML3DKIT Action to Motion •Takes about 7 hours for UESTC and 2 hours for HumanAct12 (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.where path-to-model-ckpt can be a path to any of the pretrained action-to- motion models listed above, or to a checkpoint trained by the user. Unconstrained •Takes about 3 hours (on a single GPU)Precision and recall are not computed to save computing time. If you wish to compute them, edit the file eval/a2m/gru_eval.py and change the string fast=True to fast=False. See full list on github.com This code is standing on the shoulders of giants. We want to thank the following contributors that our code is based on: guided- diffusion , MotionCLIP, text-to- motion , actor, joints2smpl, MoDi. See full list on github.com This code is distributed under an MIT LICENSE. Note that our code depends on other libraries, including CLIP, SMPL, SMPL-X, PyTorch3D, and uses datasets that each have their own respective licenses that must also be followed. See full list on github.com Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and ... What is dsdfm method for Human Motion Synthesis? However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. What does dsdfm stand for? In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. Do denoising diffusion models produce realistic human motions? year={2023} Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. What is physics-guided motion diffusion model? To address this issue, we present a novel physics-guided motion diffusion model ( PhysDiff ), which incorporates physical constraints into the diffusion process. How does motion diffusion affect real-world applications? However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and limits their real-world application . How is projected motion used in the denoising diffusion process? The projected motion is further used in the next diffusion step to guide the denoising diffusion process . Intuitively, the use of physics in our model iteratively pulls the motion toward a physically-plausible space, which cannot be achieved by simple post-processing. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments, DSDFM achieves state-of-the-art results surpassing the latest methods, validating its superiority in human motion synthesis. Live content is unavailable."}
+{"idx": 2, "title": "PhysDiff: Physics-Guided Human Motion Diffusion Model - GitHub", "date": "", "ddg_snippet": "Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and ...", "subpage_snippet": "", "source": "nvlabs.github.io", "link": "https://nvlabs.github.io/PhysDiff/", "content": "Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and ..."}
+{"idx": 3, "title": "DivDiff: A Conditional Diffusion Model for Diverse Human Motion ...", "date": "", "ddg_snippet": "DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387413105_DivDiff_A_Conditional_Diffusion_Model_for_Diverse_Human_Motion_Prediction", "content": "DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 4, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 5, "title": "How to write a Pseudo Code ? - GeeksforGeeks", "date": "", "ddg_snippet": "Pseudo code : It's simply an implementation of an algorithm in the form of annotations and informative text written in plain English. It has no syntax like any of the programming language and thus can't be compiled or interpreted by the computer.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/how-to-write-a-pseudo-code/", "content": "Pseudo code : It's simply an implementation of an algorithm in the form of annotations and informative text written in plain English. It has no syntax like any of the programming language and thus can't be compiled or interpreted by the computer."}
+{"idx": 6, "title": "Pseudo code of the algorithm for calculating the motion trajectory ...", "date": "", "ddg_snippet": "Algorithm 1 Pseudocode Of Tracking Human Motion Download Scientific. Source: researchgate.net. Pseudo Code Of Motion Cue Based Proposal Generation Inputs Position.", "subpage_snippet": "", "source": "www.tpsearchtool.com", "link": "https://www.tpsearchtool.com/images/pseudo-code-of-the-algorithm-for-calculating-the-motion-trajectory", "content": "Algorithm 1 Pseudocode Of Tracking Human Motion Download Scientific. Source: researchgate.net. Pseudo Code Of Motion Cue Based Proposal Generation Inputs Position."}
+{"idx": 7, "title": "InterControl: Zero-shot Human Interaction", "date": "", "ddg_snippet": "2. 1 Human Motion Generation. Synthesizing human motions is a long-standing topic.We first formulate interaction generation in Sec. 3. 1 , and then introduce control modules for a single-person motion diffusion model in Sec.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/be41269a9fe258f1ecba663b0b402322-Paper-Conference.pdf", "content": "2. 1 Human Motion Generation. Synthesizing human motions is a long-standing topic.We first formulate interaction generation in Sec. 3. 1 , and then introduce control modules for a single-person motion diffusion model in Sec."}
+{"idx": 8, "title": "How to format a pseudocode algorithm - TeX - LaTeX Stack Exchange", "date": "", "ddg_snippet": "I 'm new to writing pseudocode algorithms with Latex, but i suspect the style and formatting i 'm looking for is in the package algorithm 2e . Can someone show me how to achieve the following result", "subpage_snippet": "", "source": "tex.stackexchange.com", "link": "https://tex.stackexchange.com/questions/204592/how-to-format-a-pseudocode-algorithm", "content": "I 'm new to writing pseudocode algorithms with Latex, but i suspect the style and formatting i 'm looking for is in the package algorithm 2e . Can someone show me how to achieve the following result"}
+{"idx": 9, "title": "(3) define pseudocode Algorithm flowchart | StudyX", "date": "", "ddg_snippet": "(3) define pseudocode , Algorithm , flowchart and write the pseudocode . Pseudocode : A high-level description of an algorithm that uses the structural conventions of programming languages but is intended for human reading rather than machine reading.", "subpage_snippet": "", "source": "studyx.ai", "link": "https://studyx.ai/homework/109513745-3-define-pseudocode-algorithm-flowchart-and-write-the-pseudocode-algorithm-flowcharat-to", "content": "(3) define pseudocode , Algorithm , flowchart and write the pseudocode . Pseudocode : A high-level description of an algorithm that uses the structural conventions of programming languages but is intended for human reading rather than machine reading."}
diff --git a/data/sampled_jsons/DSDFM_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_DivSDE_diversity_mechanism_year_2024.jsonl b/data/sampled_jsons/DSDFM_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_DivSDE_diversity_mechanism_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d683bf7e8e7fddd162a37794a97ddaff22dcb7a5
--- /dev/null
+++ b/data/sampled_jsons/DSDFM_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_DivSDE_diversity_mechanism_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stochastic - Wikipedia", "date": "", "ddg_snippet": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic", "content": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve..."}
+{"idx": 1, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00998v1", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis."}
+{"idx": 2, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis. DSDFM consists of two stages.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis. DSDFM consists of two stages."}
+{"idx": 3, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "The second diverse motion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of human motions, thereby enhancing the diversity and accuracy of the generated human motions. This stage is achieved by the designed...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "The second diverse motion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of human motions, thereby enhancing the diversity and accuracy of the generated human motions. This stage is achieved by the designed..."}
+{"idx": 4, "title": "DivDiff: A Conditional Diffusion Model for Diverse Human Motion...", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387413105_DivDiff_A_Conditional_Diffusion_Model_for_Diverse_Human_Motion_Prediction", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis."}
+{"idx": 5, "title": "From Stochastic to Deterministic : The Reproducibility... | LinkedIn", "date": "", "ddg_snippet": "The Unseen Challenge of LLMs: From Stochastic to Deterministic We all admire the creative power of LLMs. But beneath the surface lies a critical engineering hurdle: reproducibility.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/sridhar-nomula-ds_ai-machinelearning-llm-activity-7373774931387596800-nuh1", "content": "The Unseen Challenge of LLMs: From Stochastic to Deterministic We all admire the creative power of LLMs. But beneath the surface lies a critical engineering hurdle: reproducibility."}
+{"idx": 6, "title": "10 Papers Accepted at CVPR 2025", "date": "", "ddg_snippet": "This paper proposes a Deterministic - to - Stochastic Diverse Latent Feature Mapping approach for human motion synthesis.", "subpage_snippet": "", "source": "www.a-star.edu.sg", "link": "https://www.a-star.edu.sg/cfar/news/news/features/10-papers-accepted-at-cvpr-2025", "content": "This paper proposes a Deterministic - to - Stochastic Diverse Latent Feature Mapping approach for human motion synthesis."}
+{"idx": 7, "title": "GitHub - Zilize/awesome-text-to-motion: Text-driven human motion...", "date": "", "ddg_snippet": "DSDFM : \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis\".SALAD: \"SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Zilize/awesome-text-to-motion", "content": "DSDFM : \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis\".SALAD: \"SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing\"."}
+{"idx": 8, "title": "Hua Yu - Google Akademik", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=DVQ_F3IAAAAJ&hl=tr", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis."}
+{"idx": 9, "title": "UNIF: United Neural Implicit Functions for Clothed Human...", "date": "", "ddg_snippet": "[4] Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/unif-united-neural-implicit-functions-for-clothed-human-reconstruction-and-animation/867766725599297675-108597", "content": "[4] Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis."}
diff --git a/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_arXiv.jsonl b/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d890cbcbbbfc74c59344085e9937b05908ff4c4f
--- /dev/null
+++ b/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DVI: A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR , then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Xnqm4f71y", "content": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR , then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information."}
+{"idx": 1, "title": "ICML Poster DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI:A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI:A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo"}
+{"idx": 2, "title": "DVI:A Derivative-based Vision Network for INR - AMiner", "date": "", "ddg_snippet": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/6853e848163c01c8502f0416/dvi-a-derivative-based-vision-network-for-inr", "content": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre"}
+{"idx": 3, "title": "[2411.03688] Where Do We Stand with Implicit Neural ... Implicit Neural Representation for Vision Implicit Neural Representation in Medical Imaging: A ... DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "Nov 6, 2024 · This survey provides a comprehensive review of state-of-the-art INR methods, introducing a clear taxonomy that categorises them into four key areas: activation functions, position encoding, combined strategies, and network structure optimisation. Downstream tasks based on INRs: Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data. A. further discussion The field of medical imaging has witnessed a remarkable upsurge in the utilization of implicit neural representation ( INR ) techniques, as evident from the exponential growth in research papers dedicated to this domain (Figure 1). This surge of interest in INR within the medical imaging com-munity has resulted in a multitude of applications across di-verse medical imaging ... DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.03688", "content": "Nov 6, 2024 · This survey provides a comprehensive review of state-of-the-art INR methods, introducing a clear taxonomy that categorises them into four key areas: activation functions, position encoding, combined strategies, and network structure optimisation. Downstream tasks based on INRs: Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data. A. further discussion The field of medical imaging has witnessed a remarkable upsurge in the utilization of implicit neural representation ( INR ) techniques, as evident from the exponential growth in research papers dedicated to this domain (Figure 1). This surge of interest in INR within the medical imaging com-munity has resulted in a multitude of applications across di-verse medical imaging ... DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 4, "title": "Implicit Neural Representation for Vision", "date": "", "ddg_snippet": "Downstream tasks based on INRs: Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data.", "subpage_snippet": "", "source": "inrv.github.io", "link": "https://inrv.github.io/", "content": "Downstream tasks based on INRs: Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data."}
+{"idx": 5, "title": "DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/167863", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 6, "title": "DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "by R Yang — This paper proposes DVI, a derivative-based vision framework that bridges implicit neural representations (INRs) and raster-based vision networks. By extracting ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Xnqm4f71y", "content": "by R Yang — This paper proposes DVI, a derivative-based vision framework that bridges implicit neural representations (INRs) and raster-based vision networks. By extracting ..."}
+{"idx": 7, "title": "DVI: A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "Abstract. Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) be- coming a dominant representation form for data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/8af4896dadf2d937662bf44030f56cae6f0e40e4.pdf", "content": "Abstract. Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) be- coming a dominant representation form for data."}
+{"idx": 8, "title": "Biological patterning in networks of interacting cells", "date": "", "ddg_snippet": "18 May 2020 — In this thesis, I develop methods for understanding and implementing multicellular pattern- ing through the lens of networked dynamical systems, ...", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-51.pdf", "content": "18 May 2020 — In this thesis, I develop methods for understanding and implementing multicellular pattern- ing through the lens of networked dynamical systems, ..."}
+{"idx": 9, "title": "Implicit Neural Representation in Medical Imaging: A ...", "date": "", "ddg_snippet": "A. further discussion The field of medical imaging has witnessed a remarkable upsurge in the utilization of implicit neural representation ( INR ) techniques, as evident from the exponential growth in research papers dedicated to this domain (Figure 1). This surge of interest in INR within the medical imaging com-munity has resulted in a multitude of applications across di-verse medical imaging ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023W/CVAMD/supplemental/Molaei_Implicit_Neural_Representation_ICCVW_2023_supplemental.pdf", "content": "A. further discussion The field of medical imaging has witnessed a remarkable upsurge in the utilization of implicit neural representation ( INR ) techniques, as evident from the exponential growth in research papers dedicated to this domain (Figure 1). This surge of interest in INR within the medical imaging com-munity has resulted in a multitude of applications across di-verse medical imaging ..."}
diff --git a/data/sampled_jsons/Dasgupta_et_al._2020_GumbelBox_abstract_year_2020.jsonl b/data/sampled_jsons/Dasgupta_et_al._2020_GumbelBox_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..56ab53f52d00770d4d6e3f613ec9bb8c709cd892
--- /dev/null
+++ b/data/sampled_jsons/Dasgupta_et_al._2020_GumbelBox_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Capturing Set-Theoretic Semantics of Words using Box ...", "date": "", "ddg_snippet": "by S Dasgupta · 2022 · Cited by 24 — Specifically, we use a variant of box embeddings known as Gumbel boxes, introduced in ( Dasgupta et al ., 2020 ). Our objective (both for training ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.acl-long.161.pdf", "content": "by S Dasgupta · 2022 · Cited by 24 — Specifically, we use a variant of box embeddings known as Gumbel boxes, introduced in ( Dasgupta et al ., 2020 ). Our objective (both for training ..."}
+{"idx": 1, "title": "Box-To-Box Transformations for Modeling Joint Hierarchies", "date": "", "ddg_snippet": "by SS Dasgupta · 2021 · Cited by 7 — However using. GumbelBox formulation (Dasgupta et al., 2020), we observe significant performance boost as. GumbelBox improves the local ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.repl4nlp-1.28.pdf", "content": "by SS Dasgupta · 2021 · Cited by 7 — However using. GumbelBox formulation (Dasgupta et al., 2020), we observe significant performance boost as. GumbelBox improves the local ..."}
+{"idx": 2, "title": "Event-Event Relation Extraction using Probabilistic ... - Jay-Yoon Lee", "date": "", "ddg_snippet": "In this work, we show that it is possible to induce co- herence in a much stronger manner by representing each event using a box ( Dasgupta et al ., 2020 ). We ...", "subpage_snippet": "", "source": "leejayyoon.github.io", "link": "https://leejayyoon.github.io/paper/ACL22_box_eventrelation.pdf", "content": "In this work, we show that it is possible to induce co- herence in a much stronger manner by representing each event using a box ( Dasgupta et al ., 2020 ). We ..."}
+{"idx": 3, "title": "A Geometric Approach to Personalized Recommendation ...", "date": "", "ddg_snippet": "by S Dasgupta · 2025 — We utilize the data curated by Dasgupta et al . (2023) to construct DA for the Movielens data. This dataset employs. Wikidata (Vrandecic & ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10875", "content": "by S Dasgupta · 2025 — We utilize the data curated by Dasgupta et al . (2023) to construct DA for the Movielens data. This dataset employs. Wikidata (Vrandecic & ..."}
+{"idx": 4, "title": "MODELING LABEL SPACE INTERACTIONS IN MULTI", "date": "", "ddg_snippet": "by D Patel · Cited by 42 — Definition 3 (Approximate Bessel Volume ( Dasgupta et al ., 2020 )). For a gumbel box B = Qd i=1[b− i ,b+ i ] we define the approximate Bessel volume λ : Id ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tyTH9kOxcvh", "content": "by D Patel · Cited by 42 — Definition 3 (Approximate Bessel Volume ( Dasgupta et al ., 2020 )). For a gumbel box B = Qd i=1[b− i ,b+ i ] we define the approximate Bessel volume λ : Id ..."}
+{"idx": 5, "title": "Concept2Box: Joint Geometric Embeddings for Learning ...", "date": "", "ddg_snippet": "Dasgupta et al . ( 2020 ) Shib Dasgupta , Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, and Andrew McCallum. 2020 . Improving local identifiability in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.01933v1", "content": "Dasgupta et al . ( 2020 ) Shib Dasgupta , Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, and Andrew McCallum. 2020 . Improving local identifiability in ..."}
+{"idx": 6, "title": "Min/Max Stability and Box Distributions", "date": "", "ddg_snippet": "by M Boratko · 2021 · Cited by 8 — Dasgupta et al . [ 2020 ] introduce the Gumbel dis- tribution to mitigate this issue. Example 3. The Gumbel max distribution provides a source.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v161/boratko21a/boratko21a.pdf", "content": "by M Boratko · 2021 · Cited by 8 — Dasgupta et al . [ 2020 ] introduce the Gumbel dis- tribution to mitigate this issue. Example 3. The Gumbel max distribution provides a source."}
+{"idx": 7, "title": "Improving Local Identifiability in Probabilistic Box ...", "date": "", "ddg_snippet": "by SS Dasgupta · Cited by 76 — In this work we model the box parameters with min and max. Gumbel distributions, which were chosen such that the space is still closed under the operation of ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2020/file/01c9d2c5b3ff5cbba349ec39a570b5e3-Paper.pdf", "content": "by SS Dasgupta · Cited by 76 — In this work we model the box parameters with min and max. Gumbel distributions, which were chosen such that the space is still closed under the operation of ..."}
+{"idx": 8, "title": "Measure-Theoretic Set Representation Learning", "date": "", "ddg_snippet": "by M Boratko · 2022 · Cited by 1 — The use of. Gumbel variables, as proposed in Dasgupta et al. [2020], provides a solution due to their min/max stability properties . 6.1 ... 22 pages", "subpage_snippet": "", "source": "www.mboratko.com", "link": "https://www.mboratko.com/mtsrl.pdf", "content": "by M Boratko · 2022 · Cited by 1 — The use of. Gumbel variables, as proposed in Dasgupta et al. [2020], provides a solution due to their min/max stability properties . 6.1 ... 22 pages"}
+{"idx": 9, "title": "Modeling Fine-Grained Entity Types with Box Embeddings", "date": "", "ddg_snippet": "We follow the more recent approach of Dasgupta et al . ( 2020 ), who further im- prove training of box embeddings using max and min Gumbel distributions (i.e., ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/modeling-fine-grained-entity-types-with-box-embeddings-3m162uo02e.pdf", "content": "We follow the more recent approach of Dasgupta et al . ( 2020 ), who further im- prove training of box embeddings using max and min Gumbel distributions (i.e., ..."}
diff --git a/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_limitations_Section_5_first_factor.jsonl b/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_limitations_Section_5_first_factor.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5db4ab758b36c4a2845c7f058ce8a80e22ea85cd
--- /dev/null
+++ b/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_limitations_Section_5_first_factor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Graph Information Bottleneck for Multi-Behavior", "date": "", "ddg_snippet": "In this paper, we propose a novel model -agnostic Hierarchical Graph Information Bottleneck (HGIB) framework for multi-behavior recommendation to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15395v1", "content": "In this paper, we propose a novel model -agnostic Hierarchical Graph Information Bottleneck (HGIB) framework for multi-behavior recommendation to ..."}
+{"idx": 1, "title": "Training Large Language Models to Reason in a Continuous Latent", "date": "", "ddg_snippet": "Large language models (LLMs) are restricted to reason in the “language space”, where they typically express the reasoning process with a chain-of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06769v2", "content": "Large language models (LLMs) are restricted to reason in the “language space”, where they typically express the reasoning process with a chain-of ..."}
+{"idx": 2, "title": "US20230246969A1 - Bottleneck structures for capacity planning -", "date": "", "ddg_snippet": "US20230246969A1 - Bottleneck structures for capacity planning ... H04L45/00 — Routing or path finding of packets in data switching networks", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20230246969A1/en", "content": "US20230246969A1 - Bottleneck structures for capacity planning ... H04L45/00 — Routing or path finding of packets in data switching networks"}
+{"idx": 3, "title": "The role of mobility apps in memorable tourism experiences of ...", "date": "", "ddg_snippet": "After that, we present the results of the empirical analysis in Section 5 . Finally, the implications and limitations are pre-sented, as well as the conclusions of this study, in Section 6.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Chulmo_Koo/publication/356173957_The_role_of_mobility_apps_in_memorable_tourism_experiences_of_Korean_tourists_Stress-coping_theory_perspective/links/6880aa0bb54d247a3955edd6/The-role-of-mobility-apps-in-memorable-tourism-experiences-of-Korean-tourists-Stress-coping-theory-perspective.pdf", "content": "After that, we present the results of the empirical analysis in Section 5 . Finally, the implications and limitations are pre-sented, as well as the conclusions of this study, in Section 6."}
+{"idx": 4, "title": "Topic Identification in LLM Input-Output Pairs through the Lens", "date": "", "ddg_snippet": "... Information Bottleneck (DIB) method for geometric clustering [ 7 , 8 ] into a practical and highly efficient algorithm for high-dimensional data .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03533v1", "content": "... Information Bottleneck (DIB) method for geometric clustering [ 7 , 8 ] into a practical and highly efficient algorithm for high-dimensional data ."}
+{"idx": 5, "title": "BlueGlass: A Framework for Composite AI Safety", "date": "", "ddg_snippet": "... building and composing of safety tools that operate over model internals, outputs, and evaluation metrics, all within a common execution and data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10106v1", "content": "... building and composing of safety tools that operate over model internals, outputs, and evaluation metrics, all within a common execution and data ..."}
+{"idx": 6, "title": "FedSA-GCL: A Semi-Asynchronous Federated Graph Learning", "date": "", "ddg_snippet": "However, with growing concerns about data security and user privacy, this centralized model is often impractical in real-world scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.18219v1", "content": "However, with growing concerns about data security and user privacy, this centralized model is often impractical in real-world scenarios."}
+{"idx": 7, "title": "Towards the Operationalization of Philosophy & Wisdom -", "date": "", "ddg_snippet": "... study into a set of higher-level concepts , which characterize the domain in a way that is compact, comprehensive, and could be used to produce models ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/LbfWAq4F8tHaQsTyT/towards-the-operationalization-of-philosophy-and-wisdom", "content": "... study into a set of higher-level concepts , which characterize the domain in a way that is compact, comprehensive, and could be used to produce models ..."}
+{"idx": 8, "title": "The Future of Marketing Jobs in 2025 | Storyblok", "date": "", "ddg_snippet": "It s about mastering complex ecosystems – understanding APIs, data structures, content models , and how it all fits together to drive business ...", "subpage_snippet": "", "source": "www.storyblok.com", "link": "https://www.storyblok.com/mp/the-future-of-marketing-jobs", "content": "It s about mastering complex ecosystems – understanding APIs, data structures, content models , and how it all fits together to drive business ..."}
+{"idx": 9, "title": "What is a Touch Screen Laptop? (Features & Benefits", "date": "", "ddg_snippet": "... become more conscious of their environmental footprint, manufacturers are under pressure to create devices that are not only powerful and efficient ...", "subpage_snippet": "", "source": "laptopjudge.com", "link": "https://laptopjudge.com/what-is-a-touch-screen-laptop-2", "content": "... become more conscious of their environmental footprint, manufacturers are under pressure to create devices that are not only powerful and efficient ..."}
diff --git a/data/sampled_jsons/Davies_Nature_2021_'Advancing_mathematics_by_guiding_human_intuition_with_AI'_knot_theory_representa_year_2021.jsonl b/data/sampled_jsons/Davies_Nature_2021_'Advancing_mathematics_by_guiding_human_intuition_with_AI'_knot_theory_representa_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..347f79ef87d0da05b984ec1495dfb5679297f55b
--- /dev/null
+++ b/data/sampled_jsons/Davies_Nature_2021_'Advancing_mathematics_by_guiding_human_intuition_with_AI'_knot_theory_representa_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Advancing mathematics by guiding human intuition with AI", "date": "", "ddg_snippet": "Dec 1, 2021 · Here we provide examples of new fundamental results in pure mathematics that have been discovered with the assistance of machine learning—demonstrating a method by which machine learning can aid ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/356700733_Advancing_mathematics_by_guiding_human_intuition_with_AI", "content": "Dec 1, 2021 · Here we provide examples of new fundamental results in pure mathematics that have been discovered with the assistance of machine learning—demonstrating a method by which machine learning can aid ..."}
+{"idx": 1, "title": "Advancing mathematics by guiding human intuition with AI", "date": "", "ddg_snippet": "Dec 1, 2021 · A framework through which machine learning can guide mathematicians in discovering new conjectures and theorems is presented and shown to yield mathematical insight on important open problems in different areas of pure mathematics . The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems. Since the 1960s, mathematicians ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Advancing-mathematics-by-guiding-human-intuition-AI-Davies-Velickovic/c0cd4b4844c31a27a7900a754d0a91b160b00e55", "content": "Dec 1, 2021 · A framework through which machine learning can guide mathematicians in discovering new conjectures and theorems is presented and shown to yield mathematical insight on important open problems in different areas of pure mathematics . The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems. Since the 1960s, mathematicians ..."}
+{"idx": 2, "title": "Advancing mathematics by guiding human intuition with AI - Nature", "date": "", "ddg_snippet": "Dec 1, 2021 · A framework through which machine learning can guide mathematicians in discovering new conjectures and theorems is presented and shown to yield mathematical insight on important open problems in ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-021-04086-x", "content": "Dec 1, 2021 · A framework through which machine learning can guide mathematicians in discovering new conjectures and theorems is presented and shown to yield mathematical insight on important open problems in ..."}
+{"idx": 3, "title": "Advancing mathematics by guiding human intuition with AI", "date": "", "ddg_snippet": "Here we provide examples of new fundamental results in pure mathematics that have been discovered with the assistance of machine learning-demonstrating a method by which machine learning can aid mathematicians in discovering new conjectures and theorems.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/34853458/", "content": "Here we provide examples of new fundamental results in pure mathematics that have been discovered with the assistance of machine learning-demonstrating a method by which machine learning can aid mathematicians in discovering new conjectures and theorems."}
+{"idx": 4, "title": "Advancing mathematics by guiding human intuition with AI Advancing mathematics by guiding human intuition with AI Advancing mathematics by guiding human intuition with AI Advancing mathematics by guiding human intuition with AI - Natu… Advancing mathematics by guiding human intuition with AI Advancing mathematics by guiding human intuition with AI - Natu… Advancing mathematics by guiding human intuition with AI Advancing mathematics by guiding human intuition with AI Advancing mathematics by guiding human intuition with AI Advancing mathematics by guiding human intuition with AI - Nature", "date": "", "ddg_snippet": "Here we provide examples of new fundamental results in pure mathematics that have been discovered with the assistance of machine learning—demonstrating a method by which machine learning can aid mathematicians in discovering new conjectures and theorems. Dec 1, 2021 · A framework through which machine learning can guide mathematicians in discovering new conjectures and theorems is presented and shown to yield mathematical insight on important open problems in different areas of pure mathematics . The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems. Since the 1960s, mathematicians ... The example of knots theory demonstrate how a foundational connection in a well-studied and mathematically interesting area can go unnoticed, and how the framework allows mathematicians to better understand the behaviour of objects that are too large for them to otherwise observe patterns in. Who invented knot theory & representation theory? Author contributions A.D., D.H. and P.K. conceived of the project. A.D., A.J. and M.L. discovered the knot theory results, with D.Z. and N.T. running additional experiments. A.D., P.V. and G.W. discovered the representation theory results, with P.V. designing the model, L.B. running additional experiments, and C.B. providing advice and ideas. Can artificial intelligence be a model for collaboration between mathematicians and Ai? Our work may serve as a model for collaboration between the fields of mathematics and artificial intelligence (AI) that can achieve surprising results by leveraging the respective strengths of mathematicians and machine learning. How can AI help mathematicians find patterns? that AI can also be used to assist in the discovery of theorems and con-jectures at the forefront of mathematical research. This extends work using supervised learning to find patterns20–24 by focusing on enabling mathematicians to understand the learned functions and derive useful mathematical insight. Who funded the knot theory research? This research was funded by DeepMind . A.D., D.H. and P.K. conceived of the project. A.D., A.J. and M.L. discovered the knot theory results, with D.Z. and N.T. running additional experiments. Why is intuition important in mathematics? A mathematician’s intuition plays an enormously important role in mathematical discovery —“It is only with a combination of both rigorous formalism and good intuition that one can tackle complex mathematical problems” 25. Can deep artificial neural networks prove mathematical theorems? The theoretical prospects of deep artificial neural networks in proving mathematical theorems and whether such AI systems could, or should, become accepted as active agents in mathematical communities are analyzed. The process helps guide a mathematician’s intuition about a hypothesized function f, by training a machine learning model to estimate that function over a particular distribution of data PZ.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2021Natur.600...70D/abstract", "content": "Here we provide examples of new fundamental results in pure mathematics that have been discovered with the assistance of machine learning—demonstrating a method by which machine learning can aid mathematicians in discovering new conjectures and theorems. Dec 1, 2021 · A framework through which machine learning can guide mathematicians in discovering new conjectures and theorems is presented and shown to yield mathematical insight on important open problems in different areas of pure mathematics . The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems. Since the 1960s, mathematicians ... The example of knots theory demonstrate how a foundational connection in a well-studied and mathematically interesting area can go unnoticed, and how the framework allows mathematicians to better understand the behaviour of objects that are too large for them to otherwise observe patterns in. Who invented knot theory & representation theory? Author contributions A.D., D.H. and P.K. conceived of the project. A.D., A.J. and M.L. discovered the knot theory results, with D.Z. and N.T. running additional experiments. A.D., P.V. and G.W. discovered the representation theory results, with P.V. designing the model, L.B. running additional experiments, and C.B. providing advice and ideas. Can artificial intelligence be a model for collaboration between mathematicians and Ai? Our work may serve as a model for collaboration between the fields of mathematics and artificial intelligence (AI) that can achieve surprising results by leveraging the respective strengths of mathematicians and machine learning. How can AI help mathematicians find patterns? that AI can also be used to assist in the discovery of theorems and con-jectures at the forefront of mathematical research. This extends work using supervised learning to find patterns20–24 by focusing on enabling mathematicians to understand the learned functions and derive useful mathematical insight. Who funded the knot theory research? This research was funded by DeepMind . A.D., D.H. and P.K. conceived of the project. A.D., A.J. and M.L. discovered the knot theory results, with D.Z. and N.T. running additional experiments. Why is intuition important in mathematics? A mathematician’s intuition plays an enormously important role in mathematical discovery —“It is only with a combination of both rigorous formalism and good intuition that one can tackle complex mathematical problems” 25. Can deep artificial neural networks prove mathematical theorems? The theoretical prospects of deep artificial neural networks in proving mathematical theorems and whether such AI systems could, or should, become accepted as active agents in mathematical communities are analyzed. The process helps guide a mathematician’s intuition about a hypothesized function f, by training a machine learning model to estimate that function over a particular distribution of data PZ."}
+{"idx": 5, "title": "Advancing mathematics by guiding human intuition with AI", "date": "", "ddg_snippet": "The example of knots theory demonstrate how a foundational connection in a well-studied and mathematically interesting area can go unnoticed, and how the framework allows mathematicians to better understand the behaviour of objects that are too large for them to otherwise observe patterns in.", "subpage_snippet": "", "source": "stevenyuan666.github.io", "link": "https://stevenyuan666.github.io/presentation_slides/advance_math_AI.pdf", "content": "The example of knots theory demonstrate how a foundational connection in a well-studied and mathematically interesting area can go unnoticed, and how the framework allows mathematicians to better understand the behaviour of objects that are too large for them to otherwise observe patterns in."}
+{"idx": 6, "title": "Advancing mathematics by guiding human intuition with AI - Nature", "date": "", "ddg_snippet": "The process helps guide a mathematician’s intuition about a hypothesized function f, by training a machine learning model to estimate that function over a particular distribution of data PZ.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-021-04086-x.pdf", "content": "The process helps guide a mathematician’s intuition about a hypothesized function f, by training a machine learning model to estimate that function over a particular distribution of data PZ."}
+{"idx": 7, "title": "Advancing mathematics by guiding human intuition with AI | Nature", "date": "", "ddg_snippet": "The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems . Since the 1960s, mathematicians have used computers to assist in the discovery of patterns and formulation of conjectures1...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-021-04086-x?error=cookies_not_supported", "content": "The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems . Since the 1960s, mathematicians have used computers to assist in the discovery of patterns and formulation of conjectures1..."}
+{"idx": 8, "title": "(Open Access) Advancing mathematics by guiding human intuition ...", "date": "", "ddg_snippet": "(DOI: 10.1038/S41586-021-04086-X) The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/advancing-mathematics-by-guiding-human-intuition-with-ai-315a0sd9vb", "content": "(DOI: 10.1038/S41586-021-04086-X) The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems ."}
+{"idx": 9, "title": "Advancing mathematics by guiding human intuition with AI - ORA...", "date": "", "ddg_snippet": "The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems . Since the 1960s, mathematicians have used computers to assist in the discovery of patterns and formulation of conjectures1...", "subpage_snippet": "", "source": "ora.ox.ac.uk", "link": "https://ora.ox.ac.uk/objects/uuid:f1e61139-46a9-46f7-ba7f-e85d115ed779", "content": "The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems . Since the 1960s, mathematicians have used computers to assist in the discovery of patterns and formulation of conjectures1..."}
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+{"idx": 0, "title": "Achieving Collective Welfare in Multi-Agent Reinforcement", "date": "", "ddg_snippet": "As artificial agents increasingly serve as autonomous proxies for humans, we propose using multi - agent reinforcement learning (MARL) to address this ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.12326v1", "content": "As artificial agents increasingly serve as autonomous proxies for humans, we propose using multi - agent reinforcement learning (MARL) to address this ..."}
+{"idx": 1, "title": "On Agent Incentives to Manipulate Human Feedback in Multi-Agent", "date": "", "ddg_snippet": "In settings without well-defined goals, methods for reward learning allow reinforcement learning agents to infer the goal from human feedback.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/6TxmJRDGzDbwcLE3w/on-agent-incentives-to-manipulate-human-feedback-in-multi", "content": "In settings without well-defined goals, methods for reward learning allow reinforcement learning agents to infer the goal from human feedback."}
+{"idx": 2, "title": "AAMAS 2019 - Accepted Extended Abstracts", "date": "", "ddg_snippet": "... Multiagent Systems Industrial Applications ... Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas", "subpage_snippet": "", "source": "aamas2019.encs.concordia.ca", "link": "http://aamas2019.encs.concordia.ca/accea.html", "content": "... Multiagent Systems Industrial Applications ... Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas"}
+{"idx": 3, "title": "AAMAS 2019 - Accepted Extended Abstracts", "date": "", "ddg_snippet": "... Multiagent Systems Industrial Applications ... Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas", "subpage_snippet": "", "source": "www.ifaamas.org", "link": "https://www.ifaamas.org/AAMAS/aamas2019/accea.html", "content": "... Multiagent Systems Industrial Applications ... Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas"}
+{"idx": 4, "title": "Multi-Agent Reinforcement Learning Made Simple & 9 Tools", "date": "", "ddg_snippet": "Multi - Agent Reinforcement Learning (MARL) is a subfield of reinforcement learning in which multiple agents interact within a shared environment, each ...", "subpage_snippet": "", "source": "spotintelligence.com", "link": "https://spotintelligence.com/2025/06/16/multi-agent-reinforcement-learning-marl/", "content": "Multi - Agent Reinforcement Learning (MARL) is a subfield of reinforcement learning in which multiple agents interact within a shared environment, each ..."}
+{"idx": 5, "title": "Optimal Deception Asset Deployment in Cybersecurity: A Nash", "date": "", "ddg_snippet": "The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/14/1/357", "content": "The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal."}
+{"idx": 6, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "... Affects Memory before In -context Learning ... Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi -Armed Bandits", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "... Affects Memory before In -context Learning ... Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi -Armed Bandits"}
+{"idx": 7, "title": "Thirty-Second AAAI Conference on Artificial Intelligence 2018", "date": "", "ddg_snippet": "... Social Advertising Meets Viral ... Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/proceeding/01-thirty-second-aaai-conference-on-artificial-intelligence-2018/", "content": "... Social Advertising Meets Viral ... Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning"}
+{"idx": 8, "title": "AAMAS 2019 - EA Poster Schedule", "date": "", "ddg_snippet": "... A New Approach for Aggregating ... Collaborative Reinforcement Learning Model for Sustainability of Cooperation in Sequential Social Dilemmas", "subpage_snippet": "", "source": "www.ifaamas.org", "link": "https://www.ifaamas.org/AAMAS/aamas2019/eaposters.html", "content": "... A New Approach for Aggregating ... Collaborative Reinforcement Learning Model for Sustainability of Cooperation in Sequential Social Dilemmas"}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "A Generalized Algorithm for Multi -Objective ... A Structured Prediction Approach for Generalization in Cooperative Multi - Agent Reinforcement Learning", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "A Generalized Algorithm for Multi -Objective ... A Structured Prediction Approach for Generalization in Cooperative Multi - Agent Reinforcement Learning"}
diff --git a/data/sampled_jsons/Deception_in_Social_Learning_Multi-Agent_Reinforcement_Learning_2021_arxiv.jsonl b/data/sampled_jsons/Deception_in_Social_Learning_Multi-Agent_Reinforcement_Learning_2021_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7df7fbb47c808d7ec6061dc02c2a1a132fec329f
--- /dev/null
+++ b/data/sampled_jsons/Deception_in_Social_Learning_Multi-Agent_Reinforcement_Learning_2021_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deception in Social Learning: A Multi-Agent Reinforcement ...", "date": "", "ddg_snippet": "Abstract Within the framework of Multi-Agent Reinforcement Learning , Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of promoting cooperation and achieving higher global rewards in mixed-motive games. However, this new modification allows agents unprecedented access to each other’s learning process, which can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2106.05402", "content": "Abstract Within the framework of Multi-Agent Reinforcement Learning , Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of promoting cooperation and achieving higher global rewards in mixed-motive games. However, this new modification allows agents unprecedented access to each other’s learning process, which can ..."}
+{"idx": 1, "title": "Deception in Social Learning: A Multi-Agent Reinforcement ...", "date": "", "ddg_snippet": "Jun 9, 2021 · Within the framework of Multi-Agent Reinforcement Learning , Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of promoting cooperation and achieving higher global rewards in mixed-motive games. However, this new modification allows agents unprecedented access to each other's learning process, which can drastically ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2106.05402", "content": "Jun 9, 2021 · Within the framework of Multi-Agent Reinforcement Learning , Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of promoting cooperation and achieving higher global rewards in mixed-motive games. However, this new modification allows agents unprecedented access to each other's learning process, which can drastically ..."}
+{"idx": 2, "title": "Deception in Social Learning: A Multi-Agent Reinforcement ...", "date": "", "ddg_snippet": "Abstract Within the framework of Multi-Agent Reinforcement Learning , Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of promoting cooperation and achieving higher global rewards in mixed-motive games.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2106.05402", "content": "Abstract Within the framework of Multi-Agent Reinforcement Learning , Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of promoting cooperation and achieving higher global rewards in mixed-motive games."}
+{"idx": 3, "title": "Learning to Deceive in Multi-Agent Hidden Role Games Emergent Social Learning via Multi-agent Reinforcement Learning Training Language Models for Social Deduction with Multi ... Title: Emergent Social Learning via Multi - agent Reinforcement Title: Emergent Social Learning via Multi - agent Reinforcement [2209.01551] Learning to Deceive in Multi - Agent Hidden Role Games - a… dblp: Deception in Social Learning: A Multi-Agent ...", "date": "", "ddg_snippet": "Sep 4, 2022 · Deception is prevalent in human social settings. However, studies into the effect of deception on reinforcement learning algorithms have been limited to simplistic settings, restricting their applicability to complex real-world problems. This paper addresses this by introducing a new mixed competitive-cooperative multi-agent reinforcement learning (MARL) environment inspired by popular role ... Oct 1, 2020 · Social learning is a key component of human and animal intelligence. By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi-agent environment can learn to use social learning to improve their performance ... Specifically, we improve a model’s listening skills by training them to predict information about the environment based on discussions, and we simultaneously improve a model’s speaking skills with multi-agent reinforcement learning by rewarding messages based on their influence on other agents . Can independent reinforcement learning agents learn to use social learning? This paper investigates whether independent reinforcement learning (RL) agents in a multi-agent environment can learn to use social learning to improve their performance. We find that in most circumstances, vanilla model-free RL agents do not use social learning . Can RL agents learn to use social learning to improve performance? By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi - agent environment can learn to use social learning to improve their performance. Does deception affect reinforcement learning algorithms? Deception is prevalent in human social settings. However, studies into the effect of deception on reinforcement learning algorithms have been limited to simplistic settings, restricting their applicability to complex real-world problems. Jun 15, 2021 · Bibliographic details on Deception in Social Learning : A Multi-Agent Reinforcement Learning Perspective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2209.01551", "content": "Sep 4, 2022 · Deception is prevalent in human social settings. However, studies into the effect of deception on reinforcement learning algorithms have been limited to simplistic settings, restricting their applicability to complex real-world problems. This paper addresses this by introducing a new mixed competitive-cooperative multi-agent reinforcement learning (MARL) environment inspired by popular role ... Oct 1, 2020 · Social learning is a key component of human and animal intelligence. By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi-agent environment can learn to use social learning to improve their performance ... Specifically, we improve a model’s listening skills by training them to predict information about the environment based on discussions, and we simultaneously improve a model’s speaking skills with multi-agent reinforcement learning by rewarding messages based on their influence on other agents . Can independent reinforcement learning agents learn to use social learning? This paper investigates whether independent reinforcement learning (RL) agents in a multi-agent environment can learn to use social learning to improve their performance. We find that in most circumstances, vanilla model-free RL agents do not use social learning . Can RL agents learn to use social learning to improve performance? By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi - agent environment can learn to use social learning to improve their performance. Does deception affect reinforcement learning algorithms? Deception is prevalent in human social settings. However, studies into the effect of deception on reinforcement learning algorithms have been limited to simplistic settings, restricting their applicability to complex real-world problems. Jun 15, 2021 · Bibliographic details on Deception in Social Learning : A Multi-Agent Reinforcement Learning Perspective."}
+{"idx": 4, "title": "Emergent Social Learning via Multi-agent Reinforcement Learning", "date": "", "ddg_snippet": "Oct 1, 2020 · Social learning is a key component of human and animal intelligence. By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi-agent environment can learn to use social learning to improve their performance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.00581", "content": "Oct 1, 2020 · Social learning is a key component of human and animal intelligence. By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi-agent environment can learn to use social learning to improve their performance ..."}
+{"idx": 5, "title": "Training Language Models for Social Deduction with Multi ...", "date": "", "ddg_snippet": "Specifically, we improve a model’s listening skills by training them to predict information about the environment based on discussions, and we simultaneously improve a model’s speaking skills with multi-agent reinforcement learning by rewarding messages based on their influence on other agents .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.06060v1", "content": "Specifically, we improve a model’s listening skills by training them to predict information about the environment based on discussions, and we simultaneously improve a model’s speaking skills with multi-agent reinforcement learning by rewarding messages based on their influence on other agents ."}
+{"idx": 6, "title": "dblp: Deception in Social Learning: A Multi-Agent ...", "date": "", "ddg_snippet": "Jun 15, 2021 · Bibliographic details on Deception in Social Learning : A Multi-Agent Reinforcement Learning Perspective.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2106-05402", "content": "Jun 15, 2021 · Bibliographic details on Deception in Social Learning : A Multi-Agent Reinforcement Learning Perspective."}
+{"idx": 7, "title": "(PDF) Deception in Social Learning : A Multi - Agent Reinforcement ...", "date": "", "ddg_snippet": "Multi - agent . reinforcement learning in sequential social dilemmas. arXiv preprint arXiv :1702.03037, 2017. Chunmao Li, Xuanguang Wei, Yinliang Zhao, and Xupeng Geng. An effective maximum entropy.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/352308945_Deception_in_Social_Learning_A_Multi-Agent_Reinforcement_Learning_Perspective", "content": "Multi - agent . reinforcement learning in sequential social dilemmas. arXiv preprint arXiv :1702.03037, 2017. Chunmao Li, Xuanguang Wei, Yinliang Zhao, and Xupeng Geng. An effective maximum entropy."}
+{"idx": 8, "title": "chrisyrniu/Recent-Advances-in- Multi - Agent - Reinforcement - Learning ...", "date": "", "ddg_snippet": "An Overview of Multi - Agent Reinforcement Learning from Game Theoretical Perspective. arXiv 2021 . [paper].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chrisyrniu/Recent-Advances-in-Multi-Agent-Reinforcement-Learning", "content": "An Overview of Multi - Agent Reinforcement Learning from Game Theoretical Perspective. arXiv 2021 . [paper]."}
+{"idx": 9, "title": "(Open Access) Resilient Consensus-based Multi - agent ...", "date": "", "ddg_snippet": "Resilient Consensus-based Multi - agent Reinforcement Learning . Martin Figura, Yixuan Lin, Ji Liu, Vijay Gupta +3 more. - 12 Nov 2021 .Abstract: Adversarial attacks during training can strongly influence the performance of multi - agent reinforcement learning algorithms.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/resilient-consensus-based-multi-agent-reinforcement-learning-940p6mnmyp", "content": "Resilient Consensus-based Multi - agent Reinforcement Learning . Martin Figura, Yixuan Lin, Ji Liu, Vijay Gupta +3 more. - 12 Nov 2021 .Abstract: Adversarial attacks during training can strongly influence the performance of multi - agent reinforcement learning algorithms."}
diff --git a/data/sampled_jsons/Dehghani_et_al._2019_Universal_Transformers_abstract.jsonl b/data/sampled_jsons/Dehghani_et_al._2019_Universal_Transformers_abstract.jsonl
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index 0000000000000000000000000000000000000000..f2fed0a45c25b0e613df17c52ed2b52542c7f9cb
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+++ b/data/sampled_jsons/Dehghani_et_al._2019_Universal_Transformers_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Abstract page for arXiv paper 1807.03819: Universal Transformers", "date": "", "ddg_snippet": "Title: Universal Transformers . Authors:Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser. View a PDF of the paper titled Universal Transformers , by Mostafa Dehghani and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1807.03819", "content": "Title: Universal Transformers . Authors:Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser. View a PDF of the paper titled Universal Transformers , by Mostafa Dehghani and 4 other authors."}
+{"idx": 1, "title": "Universal Transformers | DeepAI", "date": "", "ddg_snippet": "Universal Transformers . 07/10/2018. ∙. by Mostafa Dehghani , et al .In this paper we propose the Universal Transformer which addresses these practical and theoretical shortcomings and we show that it leads to improved performance on several tasks.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/universal-transformers", "content": "Universal Transformers . 07/10/2018. ∙. by Mostafa Dehghani , et al .In this paper we propose the Universal Transformer which addresses these practical and theoretical shortcomings and we show that it leads to improved performance on several tasks."}
+{"idx": 2, "title": "Universal Transformers - Iterative Refinement for Better... | Medium", "date": "", "ddg_snippet": "Universal Transformers : Recursion Over Representations. The Universal Transformer changes one crucial detail: instead of stacking a fixed number of different layers, it reuses the same layer recurrently. At each iteration: Tokens exchange information via self-attention.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jain.sm/universal-transformers-iterative-refinement-for-better-reasoning-a1fca43fea56", "content": "Universal Transformers : Recursion Over Representations. The Universal Transformer changes one crucial detail: instead of stacking a fixed number of different layers, it reuses the same layer recurrently. At each iteration: Tokens exchange information via self-attention."}
+{"idx": 3, "title": "Poster: Universal Transformers by Mostafa Dehghani et al .", "date": "", "ddg_snippet": "(ICLR 2019 ). Universal Transformers . Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser. ICLR 2019 . (Poster PDF).", "subpage_snippet": "", "source": "postersession.ai", "link": "https://postersession.ai/poster/universal-transformers/", "content": "(ICLR 2019 ). Universal Transformers . Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser. ICLR 2019 . (Poster PDF)."}
+{"idx": 4, "title": "Lessons on Parameter Sharing across Layers in Transformers", "date": "", "ddg_snippet": "... For parameter sharing, the Universal Transformer ( Dehghani et al ., 2019 ) proposed a model where all layers are shared (i.e., in effect it reduced the model to a single shared layer).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372919326_Lessons_on_Parameter_Sharing_across_Layers_in_Transformers", "content": "... For parameter sharing, the Universal Transformer ( Dehghani et al ., 2019 ) proposed a model where all layers are shared (i.e., in effect it reduced the model to a single shared layer)."}
+{"idx": 5, "title": "Designing Robust Transformers using", "date": "", "ddg_snippet": "Nguyen et al . (2022c) has linked the self-attention mechanism with a non-parametric regression perspective, which offers enhanced interpretability of Transformers . Dehghani , M., Gouws, S., Vinyals, O., Uszkoreit, J., and Kaiser, L. Universal transformers .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BqTv1Mtuhu", "content": "Nguyen et al . (2022c) has linked the self-attention mechanism with a non-parametric regression perspective, which offers enhanced interpretability of Transformers . Dehghani , M., Gouws, S., Vinyals, O., Uszkoreit, J., and Kaiser, L. Universal transformers ."}
+{"idx": 6, "title": "Learning to Reason with Transformers via Search Inductive Biases...", "date": "", "ddg_snippet": "Our approach can be regarded as an extension of Univer - sal Transformers ( Dehghani et al . 2019 ). However, instead of recursively processing each token, we propose to incorpo-rate a stronger inductive bias for learning a search procedure.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04873693/document", "content": "Our approach can be regarded as an extension of Univer - sal Transformers ( Dehghani et al . 2019 ). However, instead of recursively processing each token, we propose to incorpo-rate a stronger inductive bias for learning a search procedure."}
+{"idx": 7, "title": "Transformers are RNNs: Fast Autoregressive Transformers with...", "date": "", "ddg_snippet": "Transformer models were originally introduced by Vaswani et al .Towards this end, Child et al . ( 2019 ) introduced sparse factorizations of the attentio√n matrix to reduce the self-attention complexity to O N N . Kitaev et al .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/katharopoulos20a/katharopoulos20a.pdf", "content": "Transformer models were originally introduced by Vaswani et al .Towards this end, Child et al . ( 2019 ) introduced sparse factorizations of the attentio√n matrix to reduce the self-attention complexity to O N N . Kitaev et al ."}
+{"idx": 8, "title": "The Transformer Family | Lil'Log", "date": "", "ddg_snippet": "The Universal Transformer ( Dehghani , et al . 2019 ) combines self-attention in Transformer with the recurrent mechanism in RNN, aiming to benefit from both a long-term global receptive field of Transformer and learned inductive biases of RNN.", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2020-04-07-the-transformer-family/", "content": "The Universal Transformer ( Dehghani , et al . 2019 ) combines self-attention in Transformer with the recurrent mechanism in RNN, aiming to benefit from both a long-term global receptive field of Transformer and learned inductive biases of RNN."}
+{"idx": 9, "title": "Google & DeepMind Study the Interactions Between Scaling Laws and...", "date": "", "ddg_snippet": "Funnel Transformers (Dai et al ., 2020).In a new paper Learning Universal Predictors, a Google DeepMind research team proposes the utilization of Universal Turing Machines (UTMs) for generating training data, thereby enhancing meta-learning and enabling trained neural networks...", "subpage_snippet": "", "source": "syncedreview.com", "link": "https://syncedreview.com/2022/07/27/google-deepmind-study-the-interactions-between-scaling-laws-and-neural-network-architectures/", "content": "Funnel Transformers (Dai et al ., 2020).In a new paper Learning Universal Predictors, a Google DeepMind research team proposes the utilization of Universal Turing Machines (UTMs) for generating training data, thereby enhancing meta-learning and enabling trained neural networks..."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_FAST_tracking_translation_motion_robustness.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_FAST_tracking_translation_motion_robustness.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90ce82a003126f2025e28b5517534bcd51a0b261
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_FAST_tracking_translation_motion_robustness.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enhancing robustness in asynchronous feature tracking for ...", "date": "", "ddg_snippet": "by H Xu · 2024 · Cited by 3 — We aim to increase feature age and improve tracking robustness by using FAST-based feature patch initialization, quality assessment for patch ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40747-024-01513-0", "content": "by H Xu · 2024 · Cited by 3 — We aim to increase feature age and improve tracking robustness by using FAST-based feature patch initialization, quality assessment for patch ..."}
+{"idx": 1, "title": "A robust framework for tracking simultaneously rigid and ...", "date": "", "ddg_snippet": "by NT Tran · 2015 · Cited by 3 — This paper presents a robust framework for simultaneously tracking rigid pose and non-rigid animation of a single face with a monocular camera.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167865515002007", "content": "by NT Tran · 2015 · Cited by 3 — This paper presents a robust framework for simultaneously tracking rigid pose and non-rigid animation of a single face with a monocular camera."}
+{"idx": 2, "title": "DCT-Based Local Descriptor for Robust Matching and Feature ...", "date": "", "ddg_snippet": "by K Gao · Cited by 22 — A robust feature descriptor is expected to be invariant to a range of image transformations, including translation, scale, illumination, ...", "subpage_snippet": "", "source": "cell.missouri.edu", "link": "https://cell.missouri.edu/api/media/KeGao_DCTF_IEEE-GRSL2020.pdf", "content": "by K Gao · Cited by 22 — A robust feature descriptor is expected to be invariant to a range of image transformations, including translation, scale, illumination, ..."}
+{"idx": 3, "title": "A Robust Scale and Motion Adaptive Algorithm for Tracking ...", "date": "", "ddg_snippet": "26 Oct 2024 — This approach mitigates UAV motion by applying adjustments to the bounding boxes used for object tracking while preserving their aspect ratios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.20079v1", "content": "26 Oct 2024 — This approach mitigates UAV motion by applying adjustments to the bounding boxes used for object tracking while preserving their aspect ratios."}
+{"idx": 4, "title": "GMS: Grid-based Motion Statistics for Fast, Ultra-Robust ...", "date": "", "ddg_snippet": "by JW Bian · 2017 · Cited by 891 — This paper proposes. GMS (Grid-based Motion Statistics), a simple means of en- capsulating motion smoothness as the statistical likelihood of a certain number ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_cvpr_2017/papers/Bian_GMS_Grid-based_Motion_CVPR_2017_paper.pdf", "content": "by JW Bian · 2017 · Cited by 891 — This paper proposes. GMS (Grid-based Motion Statistics), a simple means of en- capsulating motion smoothness as the statistical likelihood of a certain number ... 10 pages"}
+{"idx": 5, "title": "Fast and Robust Monocular Visua-Inertial Odometry Using ...", "date": "", "ddg_snippet": "by N Zhang · 2019 · Cited by 8 — In challenging environments such as lighting changes, motion blur, and fast motion , camera tracking robustness can be improved. ... Linear System Model and Robust ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6832589/", "content": "by N Zhang · 2019 · Cited by 8 — In challenging environments such as lighting changes, motion blur, and fast motion , camera tracking robustness can be improved. ... Linear System Model and Robust ..."}
+{"idx": 6, "title": "IPDDF: an improved precision dense descriptor based flow ...", "date": "", "ddg_snippet": "by W Eng · 2020 · Cited by 6 — Descriptor -based approaches are robust to geometric variation, however they have inherent localisation precision limitation due to histogram ...", "subpage_snippet": "", "source": "ietresearch.onlinelibrary.wiley.com", "link": "https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/trit.2019.0052", "content": "by W Eng · 2020 · Cited by 6 — Descriptor -based approaches are robust to geometric variation, however they have inherent localisation precision limitation due to histogram ..."}
+{"idx": 7, "title": "Robust 3D Tracking with Descriptor Fields", "date": "", "ddg_snippet": "by A Crivellaro · 2014 · Cited by 89 — We introduce a method that can register challenging im- ages from specular and poorly textured 3D environments, on which previous approaches fail.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_cvpr_2014/papers/Crivellaro_Robust_3D_Tracking_2014_CVPR_paper.pdf", "content": "by A Crivellaro · 2014 · Cited by 89 — We introduce a method that can register challenging im- ages from specular and poorly textured 3D environments, on which previous approaches fail."}
+{"idx": 8, "title": "FPC-Net: Revisiting SuperPoint with Descriptor-Free ...", "date": "", "ddg_snippet": "14 Jul 2025 — These applications rely heavily on accurately identifying salient points across different views and establishing reliable correspondences. The ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10770v1", "content": "14 Jul 2025 — These applications rely heavily on accurately identifying salient points across different views and establishing reliable correspondences. The ..."}
+{"idx": 9, "title": "Fast normalized cross correlation for motion tracking using ...", "date": "", "ddg_snippet": "by AJH Hii · 2006 · Cited by 125 — Normalized cross correlation (NCC) is the most robust correlation measure for determining similarity between points in two or more images providing an accurate ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0169260706000459", "content": "by AJH Hii · 2006 · Cited by 125 — Normalized cross correlation (NCC) is the most robust correlation measure for determining similarity between points in two or more images providing an accurate ..."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_Pixel_Processor_Arrays_equation_2_response_R.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_Pixel_Processor_Arrays_equation_2_response_R.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f511eb090da7654889ac0b94dfa15cadc1847b9
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_Pixel_Processor_Arrays_equation_2_response_R.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Descriptor-In-Pixel : Point-Feature Tracking For Pixel ...", "date": "", "ddg_snippet": "The PPA’s architecture enables the response of every processor ’s descriptor , upon the current image, to be computed in parallel. This produces a“ descriptor response map” which, by generating the correct layout of descrip-tors across the pixel - processors , can be used for both point-feature detection and tracking .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "The PPA’s architecture enables the response of every processor ’s descriptor , upon the current image, to be computed in parallel. This produces a“ descriptor response map” which, by generating the correct layout of descrip-tors across the pixel - processors , can be used for both point-feature detection and tracking ."}
+{"idx": 1, "title": "Descriptor-In-Pixel: Point-Feature Tracking for Pixel ...", "date": "", "ddg_snippet": "Point-Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power. All computation is performed inside the sensor itself, upon thousands of \" Pixel -Procesors\".", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Point-Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power. All computation is performed inside the sensor itself, upon thousands of \" Pixel -Procesors\"."}
+{"idx": 2, "title": "Bose Descriptor-In-Pixel Point-Feature Tracking For Pixel ...", "date": "", "ddg_snippet": "This paper introduces a novel method for point-feature detection and tracking using Pixel Processor Array (PPA) vision sensors, which enables in- pixel computation and significantly reduces data transfer requirements. The proposed Descriptor-In-Pixel paradigm allows for efficient processing at over 3000 FPS, making it suitable for high-speed applications while maintaining low latency. By ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/890539401/Bose-Descriptor-In-Pixel-Point-Feature-Tracking-for-Pixel-Processor-Arrays-CVPR-2025-Paper", "content": "This paper introduces a novel method for point-feature detection and tracking using Pixel Processor Array (PPA) vision sensors, which enables in- pixel computation and significantly reduces data transfer requirements. The proposed Descriptor-In-Pixel paradigm allows for efficient processing at over 3000 FPS, making it suitable for high-speed applications while maintaining low latency. By ..."}
+{"idx": 3, "title": "DEMO : Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "Jun 12, 2025 · PPAs consist of thousands of “ pixel - processors ”, enabling massive parallel computation at the point of light capture. Our approach performs all computation for feature detection & tracking within these pixel - processors , allowing sensor output to be reduced to just sparse event like feature locations & descriptors , entirely avoiding the need ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11147459", "content": "Jun 12, 2025 · PPAs consist of thousands of “ pixel - processors ”, enabling massive parallel computation at the point of light capture. Our approach performs all computation for feature detection & tracking within these pixel - processors , allowing sensor output to be reduced to just sparse event like feature locations & descriptors , entirely avoiding the need ..."}
+{"idx": 4, "title": "Descriptor In Pixel : Point Feature Tracking for Pixel ... CVPR Poster Descriptor-In-Pixel : Point-Feature Tracking For ... Descriptor-In-Pixel : Point-Feature Tracking for Pixel ...", "date": "", "ddg_snippet": "This \" response map\" is utilized for both detection and tracking of point - features across the pixel - processor array . The PPA's architecture enables us to compute the response of every stored descriptor in parallel. This \" response map\" is utilized for both detection and tracking of point - features across the pixel - processor array . This paper presents a novel approach for joint point-feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=HIdQtf6mFSs", "content": "This \" response map\" is utilized for both detection and tracking of point - features across the pixel - processor array . The PPA's architecture enables us to compute the response of every stored descriptor in parallel. This \" response map\" is utilized for both detection and tracking of point - features across the pixel - processor array . This paper presents a novel approach for joint point-feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of"}
+{"idx": 5, "title": "CVPR Poster Descriptor-In-Pixel : Point-Feature Tracking For ...", "date": "", "ddg_snippet": "The PPA's architecture enables us to compute the response of every stored descriptor in parallel. This \" response map\" is utilized for both detection and tracking of point - features across the pixel - processor array .", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "The PPA's architecture enables us to compute the response of every stored descriptor in parallel. This \" response map\" is utilized for both detection and tracking of point - features across the pixel - processor array ."}
+{"idx": 6, "title": "Descriptor-In-Pixel : Point-Feature Tracking for Pixel ...", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point-feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2025/436400f392/299a1Zz9yow", "content": "This paper presents a novel approach for joint point-feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of"}
+{"idx": 7, "title": "US20130343606A1 - Systems and methods for tracking human hands", "date": "", "ddg_snippet": "... includes a processor , a reference camera configured to capture sequences of frames of video data, where each frame of video data comprises intensity ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20130343606A1/en", "content": "... includes a processor , a reference camera configured to capture sequences of frames of video data, where each frame of video data comprises intensity ..."}
+{"idx": 8, "title": "US9111135B2 - Systems and methods for tracking human hands", "date": "", "ddg_snippet": "Local 2D- descriptor based approaches typically apply interest point detectors to detect salient points in an image, which are then characterized by a ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US9111135B2/en", "content": "Local 2D- descriptor based approaches typically apply interest point detectors to detect salient points in an image, which are then characterized by a ..."}
+{"idx": 9, "title": "US8363973B2 - Descriptor for image corresponding point matching", "date": "", "ddg_snippet": "... invention generally relates to the field of computer vision and, more particularly, to detection and description of local features in images using ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US8363973B2/en", "content": "... invention generally relates to the field of computer vision and, more particularly, to detection and description of local features in images using ..."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_SCAMP-7_Computation_Time_Breakdown_Table_1_year_2023.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_SCAMP-7_Computation_Time_Breakdown_Table_1_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..062624b4907c48be6b9e1d6918c9f0b8400f3456
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_SCAMP-7_Computation_Time_Breakdown_Table_1_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "... Descriptor -In- Pixel . Table 1 . SCAMP - 7 Computation Time Breakdown . image. Figure 8. Comparison of feature lifetime histograms, our approach vs tracking ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "... Descriptor -In- Pixel . Table 1 . SCAMP - 7 Computation Time Breakdown . image. Figure 8. Comparison of feature lifetime histograms, our approach vs tracking ..."}
+{"idx": 1, "title": "Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "1 . Descriptor -In- Pixel : Point - Feature Tracking For Pixel Processor Arrays ... Our implementation upon the SCAMP - 7 PPA prototype runs at over 3000 FPS ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/154940", "content": "1 . Descriptor -In- Pixel : Point - Feature Tracking For Pixel Processor Arrays ... Our implementation upon the SCAMP - 7 PPA prototype runs at over 3000 FPS ..."}
+{"idx": 2, "title": "Findings of the Association for Computational Linguistics", "date": "", "ddg_snippet": "by W Che · 2025 — ... ( 1 ) grammars in linear time , ensuring efficient processing via deterministic pushdown automata. ... scam records, and prior fraud datasets. pdf bib abs. Mitigating ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2025.findings-acl/", "content": "by W Che · 2025 — ... ( 1 ) grammars in linear time , ensuring efficient processing via deterministic pushdown automata. ... scam records, and prior fraud datasets. pdf bib abs. Mitigating ..."}
+{"idx": 3, "title": "PixelMod: Improving Soft Moderation of Visual Misleading ...", "date": "", "ddg_snippet": "by P Paudel · 2024 · Cited by 2 — We use three different datasets to evaluate PIXELMOD (sum- marized in Table 1 ). ... sites [47], scam websites [46, 56], fraudulent services [60], and to ... 19 pages", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/usenixsecurity24-paudel-pixelmod.pdf", "content": "by P Paudel · 2024 · Cited by 2 — We use three different datasets to evaluate PIXELMOD (sum- marized in Table 1 ). ... sites [47], scam websites [46, 56], fraudulent services [60], and to ... 19 pages"}
+{"idx": 4, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor...", "date": "", "ddg_snippet": "Each tracked point - feature ’s descriptor is spread into a local “patch” of PEs surrounding its location (i.e. around the PE the feature currently resides in). Table 1 . SCAMP - 7 Computation Time Breakdown . Task Descriptor Response Map.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "Each tracked point - feature ’s descriptor is spread into a local “patch” of PEs surrounding its location (i.e. around the PE the feature currently resides in). Table 1 . SCAMP - 7 Computation Time Breakdown . Task Descriptor Response Map."}
+{"idx": 5, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor...", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA).", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays@CVPR2025@CVF", "content": "This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA)."}
+{"idx": 6, "title": "SCAMP Vision Sensor", "date": "", "ddg_snippet": "On-sensor feature extraction, keypoint selection, and tracking . Applications in motion estimation, visual odometry, SLAM. Up to 3000 fps (when returning only keypoint coordinates / descriptors ).", "subpage_snippet": "", "source": "personalpages.manchester.ac.uk", "link": "https://personalpages.manchester.ac.uk/staff/p.dudek/scamp/", "content": "On-sensor feature extraction, keypoint selection, and tracking . Applications in motion estimation, visual odometry, SLAM. Up to 3000 fps (when returning only keypoint coordinates / descriptors )."}
+{"idx": 7, "title": "Acquisition of know-how related to SCAMP -5 and... - GOV.UK", "date": "", "ddg_snippet": "The Secretary of State for Business, Energy and Industrial Strategy made a final order under the National Security and Investment Act 2021 on 20 July 2022, in respect of the acquisition of know-how related to SCAMP-5 and SCAMP - 7 vision sensing technology by Beijing Infinite Vision...", "subpage_snippet": "", "source": "www.gov.uk", "link": "https://www.gov.uk/government/publications/acquisition-of-know-how-related-to-scamp-5-and-scamp-7-vision-sensing-technology-notice-of-final-order", "content": "The Secretary of State for Business, Energy and Industrial Strategy made a final order under the National Security and Investment Act 2021 on 20 July 2022, in respect of the acquisition of know-how related to SCAMP-5 and SCAMP - 7 vision sensing technology by Beijing Infinite Vision..."}
+{"idx": 8, "title": "Стоит ли покупать смартфоны Google Pixel ... - AndroidInsider.ru", "date": "", "ddg_snippet": "Google Pixel - классные смартфоны с большим потенциалом, но в России их владельцев ждут серьёзные ограничения. Разбираемся, о каких ограничениях идёт речь и стоят ли эти неудобства тех преимуществ, за которые люди вообще покупают пиксели.", "subpage_snippet": "", "source": "AndroidInsider.ru", "link": "https://AndroidInsider.ru/smartfony/stoit-li-pokupat-smartfony-google-pixel-v-rossii-tut-vsyo-chto-nuzhno-znat.html", "content": "Google Pixel - классные смартфоны с большим потенциалом, но в России их владельцев ждут серьёзные ограничения. Разбираемся, о каких ограничениях идёт речь и стоят ли эти неудобства тех преимуществ, за которые люди вообще покупают пиксели."}
+{"idx": 9, "title": "Descriptor - In _ Pixel", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays. Point - Feature detection and tracking at thousands of frames-per-second, using ~ 1 Watt of power.", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays. Point - Feature detection and tracking at thousands of frames-per-second, using ~ 1 Watt of power."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_Computation_Time_Breakdown_Table_1_Descriptor_Response_Map_percentage.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_Computation_Time_Breakdown_Table_1_Descriptor_Response_Map_percentage.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..54ab7b0e3746f470c72a9a9662bcd88bf6dd5797
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_Computation_Time_Breakdown_Table_1_Descriptor_Response_Map_percentage.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "Table 1 . SCAMP - 7 Computation Time Breakdown . Task Descriptor Response Map . Feature Initialization Feature-Wise NMS Misc/Other Computation .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "Table 1 . SCAMP - 7 Computation Time Breakdown . Task Descriptor Response Map . Feature Initialization Feature-Wise NMS Misc/Other Computation ."}
+{"idx": 1, "title": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "The PPA's architecture enables us to compute the response of every stored descriptor in parallel. This \" response map \" is utilized for both detection and tracking of point-features across the pixel -processor array.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays@CVPR2025@CVF", "content": "The PPA's architecture enables us to compute the response of every stored descriptor in parallel. This \" response map \" is utilized for both detection and tracking of point-features across the pixel -processor array."}
+{"idx": 2, "title": "VRChat - Set Up Avatar Descriptor In SDK 3 - YouTube", "date": "", "ddg_snippet": "A quick guide on how to set up your avatar descriptor for VRChat using SDK3. Only covering the bare minimum to use most of 2.0 features.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=60GVmPZb1EY", "content": "A quick guide on how to set up your avatar descriptor for VRChat using SDK3. Only covering the bare minimum to use most of 2.0 features."}
+{"idx": 3, "title": "Acquisition of know-how related to SCAMP -5 and... - GOV.UK", "date": "", "ddg_snippet": "The Secretary of State for Business, Energy and Industrial Strategy made a final order under the National Security and Investment Act 2021 on 20 July 2022, in respect of the acquisition of know-how related to SCAMP-5 and SCAMP - 7 vision sensing technology by Beijing Infinite Vision...", "subpage_snippet": "", "source": "www.gov.uk", "link": "https://www.gov.uk/government/publications/acquisition-of-know-how-related-to-scamp-5-and-scamp-7-vision-sensing-technology-notice-of-final-order", "content": "The Secretary of State for Business, Energy and Industrial Strategy made a final order under the National Security and Investment Act 2021 on 20 July 2022, in respect of the acquisition of know-how related to SCAMP-5 and SCAMP - 7 vision sensing technology by Beijing Infinite Vision..."}
+{"idx": 4, "title": "Resize Image Pixel Online", "date": "", "ddg_snippet": "Pi 7 Image Tool can resize the image pixel . Here, you can resize JPEG and PNG images. The tool also provides lossless compression to images.", "subpage_snippet": "", "source": "image.pi7.org", "link": "https://image.pi7.org/resize-image-pixel", "content": "Pi 7 Image Tool can resize the image pixel . Here, you can resize JPEG and PNG images. The tool also provides lossless compression to images."}
+{"idx": 5, "title": "Descriptor Buffer :: Vulkan Documentation Project", "date": "", "ddg_snippet": "Mapping Data to Shaders. Descriptor Buffer.64 in this example size_t descriptor _size = VkPhysicalDeviceDescriptorBufferPropertiesEXT::storageBufferDescriptorSize", "subpage_snippet": "", "source": "docs.vulkan.org", "link": "https://docs.vulkan.org/guide/latest/descriptor_buffer.html", "content": "Mapping Data to Shaders. Descriptor Buffer.64 in this example size_t descriptor _size = VkPhysicalDeviceDescriptorBufferPropertiesEXT::storageBufferDescriptorSize"}
+{"idx": 6, "title": "Markdown Tables - GeeksforGeeks", "date": "", "ddg_snippet": "In this guide, we will break down how to create and format tables in Markdown step by step. Basic Table Structure. Markdown tables are created using pipes (|) to separate columns and hyphens (-) to define the header row.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/html/markdown-tables/", "content": "In this guide, we will break down how to create and format tables in Markdown step by step. Basic Table Structure. Markdown tables are created using pipes (|) to separate columns and hyphens (-) to define the header row."}
+{"idx": 7, "title": "Why am I getting a Fortran runtime error when trying to print more...", "date": "", "ddg_snippet": "The Fortran runtime error encountered is due to a mismatch between the number of output items in the WRITE statement and the specified format descriptors .", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/why-am-i-getting-a-fortran-runtime-error-when-trying-to-print-more-values.780863/", "content": "The Fortran runtime error encountered is due to a mismatch between the number of output items in the WRITE statement and the specified format descriptors ."}
+{"idx": 8, "title": "How Python’s Descriptor Protocol Forced Me to Rethink...", "date": "", "ddg_snippet": "When to Use Descriptors in Practice. Descriptors aren’t for everything — but they shine in certain cases: Validation: Enforcing constraints like positive numbers or non-empty strings. Computed attributes: Caching, lazy evaluation, or transformations.", "subpage_snippet": "", "source": "python.plainenglish.io", "link": "https://python.plainenglish.io/how-pythons-descriptor-protocol-forced-me-to-rethink-object-oriented-design-0d7487bc8497", "content": "When to Use Descriptors in Practice. Descriptors aren’t for everything — but they shine in certain cases: Validation: Enforcing constraints like positive numbers or non-empty strings. Computed attributes: Caching, lazy evaluation, or transformations."}
+{"idx": 9, "title": "The __set_name__ method for descriptors - IT Arch", "date": "", "ddg_snippet": "assert MetaManaged. descriptor .name == ' descriptor '. One detail is that the __init__ of the descriptor accepts the name to be nullable so this works.", "subpage_snippet": "", "source": "rmariano.eu", "link": "https://rmariano.eu/posts/the-__set_name__-method-for-descriptors/", "content": "assert MetaManaged. descriptor .name == ' descriptor '. One detail is that the __init__ of the descriptor accepts the name to be nullable so this works."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_Computation_Time_Breakdown_percentage_values.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_Computation_Time_Breakdown_percentage_values.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eea1c5cf395fc076c0f89f6f3a0ea26f89191576
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_Computation_Time_Breakdown_percentage_values.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "This descriptor computation can be performed for every PE (i.e. every pixel location) simultaneously in parallel.Table 1. SCAMP - 7 Computation Time Breakdown . Task Descriptor Response Map. Feature Initialization Feature-Wise NMS Misc/Other Computation .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "This descriptor computation can be performed for every PE (i.e. every pixel location) simultaneously in parallel.Table 1. SCAMP - 7 Computation Time Breakdown . Task Descriptor Response Map. Feature Initialization Feature-Wise NMS Misc/Other Computation ."}
+{"idx": 1, "title": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "The PPA's architecture enables us to compute the response of every stored descriptor in parallel. This \"response map\" is utilized for both detection and tracking of point-features across the pixel -processor array.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays@CVPR2025@CVF", "content": "The PPA's architecture enables us to compute the response of every stored descriptor in parallel. This \"response map\" is utilized for both detection and tracking of point-features across the pixel -processor array."}
+{"idx": 2, "title": "GitHub - lauriebose/ Scamp 7 -Image-Transformations: Algorithms and...", "date": "", "ddg_snippet": "Algorithms and examples of performing focal plane image transformations on SCAMP vision system.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lauriebose/Scamp7-Image-Transformations", "content": "Algorithms and examples of performing focal plane image transformations on SCAMP vision system."}
+{"idx": 3, "title": "Garden Tower Defense Value List - Vaulted Values X", "date": "", "ddg_snippet": "At Garden Tower Defense Values , we believe that accuracy is key when it comes to providing value lists for the popular Roblox game, Garden Tower Defense (GTD).", "subpage_snippet": "", "source": "vaultedvaluesx.com", "link": "https://vaultedvaluesx.com/garden-tower-defense", "content": "At Garden Tower Defense Values , we believe that accuracy is key when it comes to providing value lists for the popular Roblox game, Garden Tower Defense (GTD)."}
+{"idx": 4, "title": "Acquisition of know-how related to SCAMP -5 and... - GOV.UK", "date": "", "ddg_snippet": "The Secretary of State for Business, Energy and Industrial Strategy made a final order under the National Security and Investment Act 2021 on 20 July 2022, in respect of the acquisition of know-how related to SCAMP-5 and SCAMP - 7 vision sensing technology by Beijing Infinite Vision...", "subpage_snippet": "", "source": "www.gov.uk", "link": "https://www.gov.uk/government/publications/acquisition-of-know-how-related-to-scamp-5-and-scamp-7-vision-sensing-technology-notice-of-final-order", "content": "The Secretary of State for Business, Energy and Industrial Strategy made a final order under the National Security and Investment Act 2021 on 20 July 2022, in respect of the acquisition of know-how related to SCAMP-5 and SCAMP - 7 vision sensing technology by Beijing Infinite Vision..."}
+{"idx": 5, "title": "Percentage Calculator", "date": "", "ddg_snippet": "This free percentage calculator computes a number of values involving percentages , including the percentage difference between two given values .", "subpage_snippet": "", "source": "www.calculator.net", "link": "https://www.calculator.net/percent-calculator.html", "content": "This free percentage calculator computes a number of values involving percentages , including the percentage difference between two given values ."}
+{"idx": 6, "title": "Wplace Pixel Art Converter | 64-Color Converter & Paint Tool", "date": "", "ddg_snippet": "Every Wplace Pixel in your art matches the platform perfectly. Smart Free/Premium Color Detection. Automatically identifies free and premium colors in your Wplace Pixel Art, helping optimize costs for wplace.live placement.", "subpage_snippet": "", "source": "wplacepixel.org", "link": "https://wplacepixel.org/", "content": "Every Wplace Pixel in your art matches the platform perfectly. Smart Free/Premium Color Detection. Automatically identifies free and premium colors in your Wplace Pixel Art, helping optimize costs for wplace.live placement."}
+{"idx": 7, "title": "Image Describer: 100% Free & Unlimited Generator (No Login)", "date": "", "ddg_snippet": "Use AI to describe any image or picture! Get instant object recognition, detailed visual analysis, and question answering. Enhance your photo analysis capabilities with real- time AI insights.", "subpage_snippet": "", "source": "describeimage.ai", "link": "https://describeimage.ai/", "content": "Use AI to describe any image or picture! Get instant object recognition, detailed visual analysis, and question answering. Enhance your photo analysis capabilities with real- time AI insights."}
+{"idx": 8, "title": "P.Dudek - publications", "date": "", "ddg_snippet": "H.M.So, J.N.P.Martel, P.Dudek, G.Wetzstein, \"MantissaCam: Learning Snapshot High-dynamic-range Imaging with Perceptually-based In - pixel Irradiance Encoding\", International Conference on Computational Photography, ICCP 2022, Pasadena, CA, 1-3 August 2022...", "subpage_snippet": "", "source": "personalpages.manchester.ac.uk", "link": "https://personalpages.manchester.ac.uk/staff/p.Dudek/papers/", "content": "H.M.So, J.N.P.Martel, P.Dudek, G.Wetzstein, \"MantissaCam: Learning Snapshot High-dynamic-range Imaging with Perceptually-based In - pixel Irradiance Encoding\", International Conference on Computational Photography, ICCP 2022, Pasadena, CA, 1-3 August 2022..."}
+{"idx": 9, "title": "(PDF) In-Sensor Visual Perception and Inference", "date": "", "ddg_snippet": "Therefore, the in-sensor computing paradigm may hold the key to realizing extremely efficient and low power visual signal processing by integrating sensing, storage, and computation onto focal planes using either novel circuit designs or new materials.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372670088_In-Sensor_Visual_Perception_and_Inference", "content": "Therefore, the in-sensor computing paradigm may hold the key to realizing extremely efficient and low power visual signal processing by integrating sensing, storage, and computation onto focal planes using either novel circuit designs or new materials."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel__Point-Feature_Tracking_for_Pixel_Processor_Arrays_equation_2.jsonl b/data/sampled_jsons/Descriptor-In-Pixel__Point-Feature_Tracking_for_Pixel_Processor_Arrays_equation_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7fcddf73d1e9458c38f326edf1d8c38703fdcc82
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel__Point-Feature_Tracking_for_Pixel_Processor_Arrays_equation_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "Our in - pixel approach for point - feature detection and tracking is designed specifically for the PPA’s architec-ture, providing high pixel - processor compute resource util-isation, and minimizing data transfer between sensor and external processing.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "Our in - pixel approach for point - feature detection and tracking is designed specifically for the PPA’s architec-ture, providing high pixel - processor compute resource util-isation, and minimizing data transfer between sensor and external processing."}
+{"idx": 1, "title": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor ...", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point - feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11092646/", "content": "This paper presents a novel approach for joint point - feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors."}
+{"idx": 2, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "Bose_ Descriptor - In - Pixel __ Point - Feature _ Tracking _ For _ Pixel _ Processor _ Arrays @CVPR2025@CVF. Total: 1.This paper presents a novel approach for joint point - feature detection and tracking, specifically designed for Pixel Processor Array sensors (PPA).", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays@CVPR2025@CVF", "content": "Bose_ Descriptor - In - Pixel __ Point - Feature _ Tracking _ For _ Pixel _ Processor _ Arrays @CVPR2025@CVF. Total: 1.This paper presents a novel approach for joint point - feature detection and tracking, specifically designed for Pixel Processor Array sensors (PPA)."}
+{"idx": 3, "title": "GitHub - wangxiao5791509/Single_Object_ Tracking _Paper_List...", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays Laurie Bose · Piotr Dudek · Jianing Chen.PG-Net: Pixel to Global Matching Network for Visual Tracking [Paper].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wangxiao5791509/Single_Object_Tracking_Paper_List", "content": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays Laurie Bose · Piotr Dudek · Jianing Chen.PG-Net: Pixel to Global Matching Network for Visual Tracking [Paper]."}
+{"idx": 4, "title": "(PDF) BRISK: Binary Robust invariant scalable keypoints", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays .In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221110715_BRISK_Binary_Robust_invariant_scalable_keypoints", "content": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays .In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features)."}
+{"idx": 5, "title": "Descriptor - In _ Pixel", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays .All computation is performed inside the sensor itself, upon thousands of \"Pixel-Procesors\".", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays .All computation is performed inside the sensor itself, upon thousands of \"Pixel-Procesors\"."}
+{"idx": 6, "title": "Descriptor список видео на ютуб. Скачать Descriptor ... - ClipSaver.ru", "date": "", "ddg_snippet": "Список видео по теме Descriptor для скачивания с ютуба. Здесь вы можете бесплатно скачать видео Descriptor или музыку с youtube по ссылке, в 4K качестве на пк и телефон.", "subpage_snippet": "", "source": "clipsaver.ru", "link": "https://clipsaver.ru/search/Descriptor", "content": "Список видео по теме Descriptor для скачивания с ютуба. Здесь вы можете бесплатно скачать видео Descriptor или музыку с youtube по ссылке, в 4K качестве на пк и телефон."}
+{"idx": 7, "title": "Descriptor In Pixel : Point Feature Tracking for Pixel Processor ...", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=QDucNhl8ir8", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
+{"idx": 8, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "If you are attendinding #cvpr2025 and interested in next-generation visual perception, check out the award-nominated latest paper by Laurie Bose Jianing Chen and Piotr Dudek.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/walterio-mayol-cuevas_descriptor-in-pixel-point-feature-tracking-activity-7337315353846235137-Uo0o", "content": "If you are attendinding #cvpr2025 and interested in next-generation visual perception, check out the award-nominated latest paper by Laurie Bose Jianing Chen and Piotr Dudek."}
+{"idx": 9, "title": "P.Dudek - publications", "date": "", "ddg_snippet": "L.Bose, J.Chen and P.Dudek, \" Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays \", IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025, pp.5392-5400, June 2025. [website].", "subpage_snippet": "", "source": "personalpages.manchester.ac.uk", "link": "https://personalpages.manchester.ac.uk/staff/p.Dudek/papers/", "content": "L.Bose, J.Chen and P.Dudek, \" Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays \", IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025, pp.5392-5400, June 2025. [website]."}
diff --git a/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_DerODE_DivSDE_algorithm_mathematical_form.jsonl b/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_DerODE_DivSDE_algorithm_mathematical_form.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb769f174dc37ab101b02f8e51d82143faab1824
--- /dev/null
+++ b/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_DerODE_DivSDE_algorithm_mathematical_form.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions."}
+{"idx": 1, "title": "PDF Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE . DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE . DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters."}
+{"idx": 2, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic-to-Stochastic ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094650", "content": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic-to-Stochastic ..."}
+{"idx": 3, "title": "\"Deterministic-to-Stochastic Diverse Latent Feature Mapping for ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/cvpr/00060XHOZ25", "content": "Bibliographic details on Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis."}
+{"idx": 4, "title": "Harmonizing Stochasticity and Determinism: Scene-responsive Diverse ...", "date": "", "ddg_snippet": "On top of that, DiMoP3D identifies deterministic factors in the scene and integrates them into the stochastic modeling, making the diverse HMP in realistic scenes become a controllable stochastic generation process.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=NQCkNM6TES", "content": "On top of that, DiMoP3D identifies deterministic factors in the scene and integrates them into the stochastic modeling, making the diverse HMP in realistic scenes become a controllable stochastic generation process."}
+{"idx": 5, "title": "[2505.00998] Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE .DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments ...", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2505.00998", "content": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE .DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments ..."}
+{"idx": 6, "title": "CVPR 2025 Sunday 06/15", "date": "", "ddg_snippet": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE .", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/day/6/15", "content": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE ."}
+{"idx": 7, "title": "Track: Poster Session 5 - CVPR", "date": "", "ddg_snippet": "15 Jun 2025 — ... deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/session/35269", "content": "15 Jun 2025 — ... deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train ..."}
+{"idx": 8, "title": "论文阅读--dsdfm--用于人体运动合成的确定性到随机多种潜在特征映射 - 知乎", "date": "", "ddg_snippet": "确定性特征映射过程（ Deterministic Feature Mapping Procedure）：使用确定性常微分方程（DerODE）操作，通过最优传输理论建立连接。 随机多样输出生成过程（ Stochastic Diverse Output Generation Procedure）：使用多样随机微分方程（DivSDE）在采样过程中引入随机性，增强多样性。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1911144623440625860", "content": "确定性特征映射过程（ Deterministic Feature Mapping Procedure）：使用确定性常微分方程（DerODE）操作，通过最优传输理论建立连接。 随机多样输出生成过程（ Stochastic Diverse Output Generation Procedure）：使用多样随机微分方程（DivSDE）在采样过程中引入随机性，增强多样性。"}
+{"idx": 9, "title": "Foruck/Awesome-Human-Motion - GitHub", "date": "", "ddg_snippet": "(CVPR 2025) DSDFM: Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis, Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. (CVPR 2025) UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing, Li et al.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Foruck/Awesome-Human-Motion", "content": "(CVPR 2025) DSDFM: Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis, Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. (CVPR 2025) UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing, Li et al."}
diff --git a/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis.jsonl b/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..22499e2b04168d186d4534e7744aee9e19854c12
--- /dev/null
+++ b/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generative modelling in latent space – Sander Dieleman", "date": "", "ddg_snippet": "The former maps an input signal to its corresponding latent representation (encoding). ... to this as the bottleneck loss , because the latent ...", "subpage_snippet": "", "source": "sander.ai", "link": "https://sander.ai/2025/04/15/latents.html", "content": "The former maps an input signal to its corresponding latent representation (encoding). ... to this as the bottleneck loss , because the latent ..."}
+{"idx": 1, "title": "CVPR 2025 Papers", "date": "", "ddg_snippet": "Text Embedding is Not All You Need: Attention Control for Text- to -Image Semantic Alignment with Text Self-Attention Maps", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/papers.html", "content": "Text Embedding is Not All You Need: Attention Control for Text- to -Image Semantic Alignment with Text Self-Attention Maps"}
+{"idx": 2, "title": "CVPR 2023 Papers", "date": "", "ddg_snippet": "Learning Common Rationale To Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems ... Token Learning With Implicit ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/papers.html", "content": "Learning Common Rationale To Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems ... Token Learning With Implicit ..."}
+{"idx": 3, "title": "CVPR 2025 Schedule", "date": "", "ddg_snippet": "Cognitive AI for the Future: Agentic Multimodal Models and RAG for Vision Language Applications, from Training to Deployment", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/calendar", "content": "Cognitive AI for the Future: Agentic Multimodal Models and RAG for Vision Language Applications, from Training to Deployment"}
+{"idx": 4, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks ... Stochastic Zeroth-Order Frank-Wolfe ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks ... Stochastic Zeroth-Order Frank-Wolfe ..."}
+{"idx": 5, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks ... Stochastic Zeroth-Order Frank-Wolfe ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks ... Stochastic Zeroth-Order Frank-Wolfe ..."}
+{"idx": 6, "title": "CVPR 2023 Papers", "date": "", "ddg_snippet": "Learning Common Rationale To Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems ... Token Learning With Implicit ...", "subpage_snippet": "", "source": "cvpr2023.thecvf.com", "link": "https://cvpr2023.thecvf.com/virtual/2023/papers.html", "content": "Learning Common Rationale To Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems ... Token Learning With Implicit ..."}
+{"idx": 7, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models ... Towards Theory and Design of Robust ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models ... Towards Theory and Design of Robust ..."}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2018", "content": "Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization"}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "... Towards Crafting Adversarial ... Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many- to -one Maps", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "... Towards Crafting Adversarial ... Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many- to -one Maps"}
diff --git a/data/sampled_jsons/Digital_Disparities_median_website_size_developed_developing_countries_WWW_2025_year_2025.jsonl b/data/sampled_jsons/Digital_Disparities_median_website_size_developed_developing_countries_WWW_2025_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..29c4ee3d5cf2c4f4569b50bb76b68d14559205c9
--- /dev/null
+++ b/data/sampled_jsons/Digital_Disparities_median_website_size_developed_developing_countries_WWW_2025_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Most populated U.S. cities: median household income 2022|", "date": "", "ddg_snippet": "In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household ...", "subpage_snippet": "", "source": "www.statista.com", "link": "https://www.statista.com/statistics/205609/median-household-income-in-the-top-20-most-populated-cities-in-the-us/", "content": "In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household ..."}
+{"idx": 1, "title": "Changes in Racial and Ethnic Disparities in Access to Care and", "date": "", "ddg_snippet": "For each outcome, regression discontinuity estimates the adjusted disparities for individuals age 65 years right before Medicare eligibility (in ...", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2782345", "content": "For each outcome, regression discontinuity estimates the adjusted disparities for individuals age 65 years right before Medicare eligibility (in ..."}
+{"idx": 2, "title": "United Arab Emirates (UAE) Population Statistics 2025 | GMI", "date": "", "ddg_snippet": "With an array of development plans in the pipeline, ranging from infrastructure developments to renewable and nuclear energy generation, the country ...", "subpage_snippet": "", "source": "www.globalmediainsight.com", "link": "https://www.globalmediainsight.com/blog/uae-population-statistics/", "content": "With an array of development plans in the pipeline, ranging from infrastructure developments to renewable and nuclear energy generation, the country ..."}
+{"idx": 3, "title": "Nasonex drug prices in U.S. and abroad 2017| Statista", "date": "", "ddg_snippet": "This statistic displays price disparities for Merck's pharmaceutical product Nasonex as of Q2 2017. ... Price disparities for pharmaceutical product ...", "subpage_snippet": "", "source": "www.statista.com", "link": "https://www.statista.com/statistics/818902/prices-for-nasonex-in-the-us-and-abroad/", "content": "This statistic displays price disparities for Merck's pharmaceutical product Nasonex as of Q2 2017. ... Price disparities for pharmaceutical product ..."}
+{"idx": 4, "title": "Demographic disparity in Wikipedia coverage: a global", "date": "", "ddg_snippet": "... from Wikipedia over 10 years across the 12 largest language editions of Wikipedia to study coverage disparities in gender, geography, and development ...", "subpage_snippet": "", "source": "epjdatascience.springeropen.com", "link": "https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-025-00530-4", "content": "... from Wikipedia over 10 years across the 12 largest language editions of Wikipedia to study coverage disparities in gender, geography, and development ..."}
+{"idx": 5, "title": "A Reality Check on the Progress toward a Gigabit Society |", "date": "", "ddg_snippet": "Median Download Speeds and Infrastructure Development: Several European countries are making substantial progress in offering high-speed broadband.", "subpage_snippet": "", "source": "www.ookla.com", "link": "https://www.ookla.com/articles/europe-fiber-gigabit-society-q3-2023", "content": "Median Download Speeds and Infrastructure Development: Several European countries are making substantial progress in offering high-speed broadband."}
+{"idx": 6, "title": "**Option 1 (Concise & Informative):** Coronavirus Deaths in", "date": "", "ddg_snippet": "Content marketing has emerged as a cornerstone of successful digital strategies, allowing organizations to connect with their target audiences in ...", "subpage_snippet": "", "source": "www.archynewsy.com", "link": "https://www.archynewsy.com/coronavirus-deaths-southern-africa-country-breakdown/", "content": "Content marketing has emerged as a cornerstone of successful digital strategies, allowing organizations to connect with their target audiences in ..."}
+{"idx": 7, "title": "Wealth Redistribution to Extend Longevity in the US | Health", "date": "", "ddg_snippet": "Association between racial wealth inequities and racial disparities in longevity among US adults and role of reparations payments, 1992 to 2018 ...", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2814487", "content": "Association between racial wealth inequities and racial disparities in longevity among US adults and role of reparations payments, 1992 to 2018 ..."}
+{"idx": 8, "title": "Framework for Monitoring the Spatiotemporal Distribution and", "date": "", "ddg_snippet": "Sustainable Evolution of China ’ s Provincial New Quality Productivity Based on Three Dimensions of Multi-Period Development and Combination ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2071-1050/16/24/11258", "content": "Sustainable Evolution of China ’ s Provincial New Quality Productivity Based on Three Dimensions of Multi-Period Development and Combination ..."}
+{"idx": 9, "title": "The development of an Android platform to undertake a discrete", "date": "", "ddg_snippet": "... were provided to the team to develop an offline Android-based application using Java programming with Android studio as an integrated development ...", "subpage_snippet": "", "source": "archpublichealth.biomedcentral.com", "link": "https://archpublichealth.biomedcentral.com/articles/10.1186/s13690-019-0346-0", "content": "... were provided to the team to develop an offline Android-based application using Java programming with Android studio as an integrated development ..."}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2023_abstract_year_2023.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2023_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..65f84a0199bb133e713974c41f7a83d47b41798a
--- /dev/null
+++ b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2023_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.18290] Direct Preference Optimization : Your Language Model...", "date": "", "ddg_snippet": "[Submitted on 29 May 2023 (v1), last revised 29 Jul 2024 (this version, v3)].View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model, by Rafael Rafailov and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "[Submitted on 29 May 2023 (v1), last revised 29 Jul 2024 (this version, v3)].View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model, by Rafael Rafailov and 5 other authors."}
+{"idx": 1, "title": "Direct Preference Optimization", "date": "", "ddg_snippet": "Direct Preference Optimization . Theoretical Analysis of DPO. Direct Preference Optimization : Your Language Model is Secretly a Reward Model. Rafael Rafailov ⇤†.", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/also/Rafailov-2023.pdf", "content": "Direct Preference Optimization . Theoretical Analysis of DPO. Direct Preference Optimization : Your Language Model is Secretly a Reward Model. Rafael Rafailov ⇤†."}
+{"idx": 2, "title": "NeurIPS Poster Direct Preference Optimization : Your Language...", "date": "", "ddg_snippet": "2023 Oral Poster. Abstract Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences , often with reinforcement learning from human feedback (RLHF).", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/poster/72164", "content": "2023 Oral Poster. Abstract Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences , often with reinforcement learning from human feedback (RLHF)."}
+{"idx": 3, "title": "GitHub - eric-mitchell/ direct - preference - optimization : Reference...", "date": "", "ddg_snippet": "DPO: Direct Preference Optimization . New: in addition to the original DPO algorithm, this repo now supports 'conservative' DPO and IPO.See this article for more information about optimizing FSDP.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/eric-mitchell/direct-preference-optimization", "content": "DPO: Direct Preference Optimization . New: in addition to the original DPO algorithm, this repo now supports 'conservative' DPO and IPO.See this article for more information about optimizing FSDP."}
+{"idx": 4, "title": "(PDF) As Simple as Fine-tuning: LLM Alignment via Bidirectional...", "date": "", "ddg_snippet": "Abstract : Direct Preference Optimization (DPO) has emerged as a more computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) with Proximal Policy Optimization (PPO), eliminating the need for reward models and online sampling.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/as-simple-as-fine-tuning-llm-alignment-via-bidirectional-4kmnsd6tpkju", "content": "Abstract : Direct Preference Optimization (DPO) has emerged as a more computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) with Proximal Policy Optimization (PPO), eliminating the need for reward models and online sampling."}
+{"idx": 5, "title": "Year in Review: Deep Learning Papers in 2023 | Hippocampus's Garden", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model. Direct Preference Optimization [ Rafailov +, 2023 , NeurIPS] DPO allows LLM to follow instructions without RLHF, replacing the reward model with classification loss.", "subpage_snippet": "", "source": "hippocampus-garden.com", "link": "https://hippocampus-garden.com/deep_learning_2023/", "content": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model. Direct Preference Optimization [ Rafailov +, 2023 , NeurIPS] DPO allows LLM to follow instructions without RLHF, replacing the reward model with classification loss."}
+{"idx": 6, "title": "(PDF) Correcting the Mythos of KL-Regularization: Direct Alignment...", "date": "", "ddg_snippet": "direct preference optimization with diverse divergence constraints. arXiv:2309.16240, 2023 a. Lequn Wang, Akshay Krishnamurthy, and Alex Slivkins.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382363225_Correcting_the_Mythos_of_KL-Regularization_Direct_Alignment_without_Overparameterization_via_Chi-squared_Preference_Optimization", "content": "direct preference optimization with diverse divergence constraints. arXiv:2309.16240, 2023 a. Lequn Wang, Akshay Krishnamurthy, and Alex Slivkins."}
+{"idx": 7, "title": "ORPO : Monolithic Preference Optimization without Reference Model", "date": "", "ddg_snippet": "( 2023 ) introduce direct pref - erence optimization (DPO), which removes the re-ward modeling stage.We adopt the odds to model the preference given the likelihood of binary outcomes, preferred and dispreferred responses ( Rafailov et al., 2023 ).", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.626.pdf", "content": "( 2023 ) introduce direct pref - erence optimization (DPO), which removes the re-ward modeling stage.We adopt the odds to model the preference given the likelihood of binary outcomes, preferred and dispreferred responses ( Rafailov et al., 2023 )."}
+{"idx": 8, "title": "NumByNum :: ORPO — Monolithic Optimization without... | Medium", "date": "", "ddg_snippet": "This review of “ORPO — Monolithic Optimization without Reference Model (Hong et al., 2024) Reviewed” begins at Number 1 and concludes at…43. Since RLHF can directly solve the problem with simple classification loss, it’s called DPO, or ‘ Direct ’ Preference Optimization .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@AriaLeeNotAriel/numbynum-orpo-monolithic-optimization-without-reference-model-hong-et-al-2024-reviewed-262d0778e08c", "content": "This review of “ORPO — Monolithic Optimization without Reference Model (Hong et al., 2024) Reviewed” begins at Number 1 and concludes at…43. Since RLHF can directly solve the problem with simple classification loss, it’s called DPO, or ‘ Direct ’ Preference Optimization ."}
+{"idx": 9, "title": "Diffusion Model Alignment Using Direct Preference Optimization", "date": "", "ddg_snippet": "We propose Diffusion-DPO, a method to align diffusion models to human preferences by directly optimizing on human comparison data. Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO)...", "subpage_snippet": "", "source": "www.senthilpurushwalkam.com", "link": "https://www.senthilpurushwalkam.com/publication/diffdpo/", "content": "We propose Diffusion-DPO, a method to align diffusion models to human preferences by directly optimizing on human comparison data. Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO)..."}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2024_abstract_arXiv_year_2024.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2024_abstract_arXiv_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d3c6eb358a8ced1fe5640e605002d622a8b77b4
--- /dev/null
+++ b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_2024_abstract_arXiv_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.18290] Direct Preference Optimization: Your Language", "date": "", "ddg_snippet": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model, by Rafael Rafailov and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model, by Rafael Rafailov and 5 other authors"}
+{"idx": 1, "title": "A Survey of Direct Preference Optimization", "date": "", "ddg_snippet": "In this context, Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative that directly optimizes LLMs using ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.11701v1", "content": "In this context, Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative that directly optimizes LLMs using ..."}
+{"idx": 2, "title": "Evaluating the Effectiveness of Direct Preference Optimization", "date": "", "ddg_snippet": "... SFT) approach for adapting LLM-based ATS models by leveraging a computationally efficient LLM alignment technique— direct preference optimization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01479v1", "content": "... SFT) approach for adapting LLM-based ATS models by leveraging a computationally efficient LLM alignment technique— direct preference optimization ..."}
+{"idx": 3, "title": "Right Now, Wrong Then: Non-Stationary Direct Preference", "date": "", "ddg_snippet": "However, standard preference optimization approaches (e.g., DPO and IPO ( Rafailov et al. ... 2024 ) propose Direct Preference Optimization (DPO ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.18676v2", "content": "However, standard preference optimization approaches (e.g., DPO and IPO ( Rafailov et al. ... 2024 ) propose Direct Preference Optimization (DPO ..."}
+{"idx": 4, "title": "A Comprehensive Survey of Direct Preference Optimization:", "date": "", "ddg_snippet": "On the other hand, staring from the KL-constrained reward maximization objective in RL, Direct Preference Optimization (DPO; ( Rafailov et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.15595v2", "content": "On the other hand, staring from the KL-constrained reward maximization objective in RL, Direct Preference Optimization (DPO; ( Rafailov et al ..."}
+{"idx": 5, "title": "Direct Preference Optimization: Your Language Model is Secretly", "date": "", "ddg_snippet": "It innovatively employs a straightforward classification loss to directly optimize the model s policy to meet these preferences efficiently.", "subpage_snippet": "", "source": "blog.athina.ai", "link": "https://blog.athina.ai/direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "content": "It innovatively employs a straightforward classification loss to directly optimize the model s policy to meet these preferences efficiently."}
+{"idx": 6, "title": "Frontiers in Artificial Intelligence Algorithm Optimization: A", "date": "", "ddg_snippet": "... optimizer innovations, quantization-aware ... Direct preference optimization : Your language model is secretly a reward model [Paper presentation].", "subpage_snippet": "", "source": "journals.zeuspress.org", "link": "https://journals.zeuspress.org/index.php/CAI/article/view/322", "content": "... optimizer innovations, quantization-aware ... Direct preference optimization : Your language model is secretly a reward model [Paper presentation]."}
+{"idx": 7, "title": "ReXPref-Prior: A MIMIC-CXR Preference Dataset for Reducing", "date": "", "ddg_snippet": "... Prior will be useful for training models that hallucinate prior exams less frequently, through techniques such as direct preference optimization ...", "subpage_snippet": "", "source": "physionet.org", "link": "https://physionet.org/content/rexpref-prior/1.0.0/", "content": "... Prior will be useful for training models that hallucinate prior exams less frequently, through techniques such as direct preference optimization ..."}
+{"idx": 8, "title": "Most Influential ArXiv (Machine Learning) Papers (2025-03", "date": "", "ddg_snippet": "... Optimization IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight : Using a Kahneman-Tversky ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/03/most-influential-arxiv-machine-learning-papers-2025-03-version/", "content": "... Optimization IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight : Using a Kahneman-Tversky ..."}
+{"idx": 9, "title": "Zhaorun Chen, University of Chicago", "date": "", "ddg_snippet": "... Explanations}, author={Chen, Zhaorun and Pinto, Francesco and Pan, Minzhou and Li, Bo}, journal={ arXiv preprint arXiv :2412.06878}, year= { 2024 ...", "subpage_snippet": "", "source": "billchan226.github.io", "link": "https://billchan226.github.io/", "content": "... Explanations}, author={Chen, Zhaorun and Pinto, Francesco and Pan, Minzhou and Li, Bo}, journal={ arXiv preprint arXiv :2412.06878}, year= { 2024 ..."}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model_Rafael_Rafailov_abstra.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model_Rafael_Rafailov_abstra.jsonl
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+{"idx": 0, "title": "[2305.18290] Direct Preference Optimization: Your Language ...", "date": "", "ddg_snippet": "May 29, 2023 · View a PDF of the paper titled Direct Preference Optimization: Your Language Model is Secretly a Reward Model , by Rafael Rafailov and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "May 29, 2023 · View a PDF of the paper titled Direct Preference Optimization: Your Language Model is Secretly a Reward Model , by Rafael Rafailov and 5 other authors"}
+{"idx": 1, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "Authors Rafael Rafailov , Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, Chelsea Finn Abstract While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html", "content": "Authors Rafael Rafailov , Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, Chelsea Finn Abstract While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect ..."}
+{"idx": 2, "title": "Direct preference optimization | Proceedings of the 37th ...", "date": "", "ddg_snippet": "Dec 10, 2023 · research-article Direct preference optimization: your language model is secretly a reward model AUTHORs: Rafael Rafailov , Archit Sharma , Eric Mitchell", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3666122.3668460", "content": "Dec 10, 2023 · research-article Direct preference optimization: your language model is secretly a reward model AUTHORs: Rafael Rafailov , Archit Sharma , Eric Mitchell"}
+{"idx": 3, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "Sep 21, 2023 · Direct Preference Optimization: Your Language Model is Secretly a Reward Model Rafael Rafailov , Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, Chelsea Finn Published: 21 Sept 2023, Last Modified: 02 Nov 2023 NeurIPS 2023 oral Everyone Revisions BibTeX", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=HPuSIXJaa9", "content": "Sep 21, 2023 · Direct Preference Optimization: Your Language Model is Secretly a Reward Model Rafael Rafailov , Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, Chelsea Finn Published: 21 Sept 2023, Last Modified: 02 Nov 2023 NeurIPS 2023 oral Everyone Revisions BibTeX"}
+{"idx": 4, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "Rafael Rafailov , Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, Chelsea Finn While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training.", "subpage_snippet": "", "source": "www.deepnlp.org", "link": "https://www.deepnlp.org/content/articles/direct-preference-optimization:-your-language-model-is-secretly-a-reward-model", "content": "Rafael Rafailov , Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, Chelsea Finn While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training."}
+{"idx": 5, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model Rafael Rafailov ∗† Archit Sharma∗† Eric Mitchell∗† Stefano Ermon†‡", "subpage_snippet": "", "source": "public.agent-matrix.com", "link": "https://public.agent-matrix.com/publish/shared/Paper/DPO.pdf", "content": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model Rafael Rafailov ∗† Archit Sharma∗† Eric Mitchell∗† Stefano Ermon†‡"}
+{"idx": 6, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "Direct Preference Optimization: Your Language Model is Secretly a Reward Model Rafael Rafailov 2 &Archit Sharma1 2 &Eric Mitchell1 2 Stefano Ermon2 3 &Christopher D. Manning2 &Chelsea Finn2 2 Stanford University 3 CZ Biohub { rafailov ,architsh,eric.mitchell}@cs.stanford.edu Equal contribution; more junior authors listed earlier.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v3", "content": "Direct Preference Optimization: Your Language Model is Secretly a Reward Model Rafael Rafailov 2 &Archit Sharma1 2 &Eric Mitchell1 2 Stefano Ermon2 3 &Christopher D. Manning2 &Chelsea Finn2 2 Stanford University 3 CZ Biohub { rafailov ,architsh,eric.mitchell}@cs.stanford.edu Equal contribution; more junior authors listed earlier."}
+{"idx": 7, "title": "Direct Preference Optimization", "date": "", "ddg_snippet": "Your Language Model Is Secretly a Reward Model . Instability of Actor-Critic Algorithms.Human study details. Direct Preference Optimization : Your Language Model is Secretly a Reward Model . arXiv:2305.18290v3 [cs.LG] 29 Jul 2024. Rafael Rafailov ∗†.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.18290", "content": "Your Language Model Is Secretly a Reward Model . Instability of Actor-Critic Algorithms.Human study details. Direct Preference Optimization : Your Language Model is Secretly a Reward Model . arXiv:2305.18290v3 [cs.LG] 29 Jul 2024. Rafael Rafailov ∗†."}
+{"idx": 8, "title": "Direct Preference Optimization : Your Language Model is Secretly ...", "date": "", "ddg_snippet": "Rafailov et al. (2023) propose Direct Preference Optimization (DPO) as an alternative, directly optimizing language models to align with human preferences without relying on reinforcement learning or separate reward models.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@EleventhHourEnthusiast/direct-preference-optimization-your-language-model-is-secretly-a-reward-model-5b8f44cb9b9b", "content": "Rafailov et al. (2023) propose Direct Preference Optimization (DPO) as an alternative, directly optimizing language models to align with human preferences without relying on reinforcement learning or separate reward models."}
+{"idx": 9, "title": "(PDF) Direct Preference Optimization : Your Language Model is ...", "date": "", "ddg_snippet": "Your Language Model is Secretly a Reward Model .4 Direct Preference Optimization . Motivated by the challenges of applying reinforcement learning algorithms on large-scale problems. such as fine-tuning language models, our goal is to derive a simple approach for policy optimization.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371136760_Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model", "content": "Your Language Model is Secretly a Reward Model .4 Direct Preference Optimization . Motivated by the challenges of applying reinforcement learning algorithms on large-scale problems. such as fine-tuning language models, our goal is to derive a simple approach for policy optimization."}
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+{"idx": 0, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model , by Rafael Rafailov and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model , by Rafael Rafailov and 5 other authors"}
+{"idx": 1, "title": "NeurIPS 2023 Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/oral/73865", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning."}
+{"idx": 2, "title": "PDF Direct Preference Optimization(DPO)", "date": "", "ddg_snippet": "R. Rafailov , K. Lee, J. Ba, and M. Zhao, \" Direct Preference Optimization : Your Language Model is Secretly a Reward Model ,\" arXiv preprint arXiv:2305.18290, 2023.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~cmaddis/courses/csc2541_w25/presentations/mu_cao_dpo.pdf", "content": "R. Rafailov , K. Lee, J. Ba, and M. Zhao, \" Direct Preference Optimization : Your Language Model is Secretly a Reward Model ,\" arXiv preprint arXiv:2305.18290, 2023."}
+{"idx": 3, "title": "Direct preference optimization | Proceedings of the 37th International ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3666122.3668460", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning."}
+{"idx": 4, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant and computationally lightweight, eliminating the need for fitting a reward model , sampling from ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371136760_Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant and computationally lightweight, eliminating the need for fitting a reward model , sampling from ..."}
+{"idx": 5, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences , and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model .", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2023arXiv230518290R/abstract", "content": "However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences , and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model ."}
+{"idx": 6, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "Figure 1: DPO optimizes for human preferences while avoiding reinforcement learning. Existing methods for fine-tuning language models with human feedback first fit a reward model to a dataset of prompts and human preferences over pairs of responses, and then use RL to find a policy that maximizes the learned reward . In contrast, DPO directly optimizes for the policy best satisfying the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v3", "content": "Figure 1: DPO optimizes for human preferences while avoiding reinforcement learning. Existing methods for fine-tuning language models with human feedback first fit a reward model to a dataset of prompts and human preferences over pairs of responses, and then use RL to find a policy that maximizes the learned reward . In contrast, DPO directly optimizes for the policy best satisfying the ..."}
+{"idx": 7, "title": "PDF Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "5.1 Your Language Model Is Secretly a Reward Model DPO 能够绕过既要拟合一个显式奖励又执行RL 来学习单一最大似然目标下的策略。 注意优化目标方程π∗", "subpage_snippet": "", "source": "public.agent-matrix.com", "link": "https://public.agent-matrix.com/publish/shared/Paper/DPO.pdf", "content": "5.1 Your Language Model Is Secretly a Reward Model DPO 能够绕过既要拟合一个显式奖励又执行RL 来学习单一最大似然目标下的策略。 注意优化目标方程π∗"}
+{"idx": 8, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "A new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form is introduced, allowing us to solve the standard RLHF problem with only a simple classification loss. While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Direct-Preference-Optimization:-Your-Language-Model-Rafailov-Sharma/0d1c76d45afa012ded7ab741194baf142117c495", "content": "A new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form is introduced, allowing us to solve the standard RLHF problem with only a simple classification loss. While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due ..."}
+{"idx": 9, "title": "[Article] Direct preference optimization: Your language model is ...", "date": "", "ddg_snippet": "Direct preference optimization : Your language model is secretly a reward model Summary: This paper proposes a method to directly align language models using human preference data without reinforcement learning. It simplifies the optimization process by utilizing a binary classification loss based on human preferences , eliminating the need for explicit reward model training.", "subpage_snippet": "", "source": "bspl-ku.github.io", "link": "https://bspl-ku.github.io/seminar_papers/2025_01_14_rafailov_et_al_advances_in_neural_information_processing_systems_gulcehre_et_al_arxiv_yang_et_al_arxiv/", "content": "Direct preference optimization : Your language model is secretly a reward model Summary: This paper proposes a method to directly align language models using human preference data without reinforcement learning. It simplifies the optimization process by utilizing a binary classification loss based on human preferences , eliminating the need for explicit reward model training."}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model_abstract_arxiv_year_2023.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model_abstract_arxiv_year_2023.jsonl
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+{"idx": 0, "title": "Direct Preference Optimization: Your Language Model is Secretly", "date": "", "ddg_snippet": "In this paper, we show how to directly optimize a language model to adhere to human preferences , without explicit reward modeling or reinforcement ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v3", "content": "In this paper, we show how to directly optimize a language model to adhere to human preferences , without explicit reward modeling or reinforcement ..."}
+{"idx": 1, "title": "[2305.18290] Direct Preference Optimization: Your Language", "date": "", "ddg_snippet": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model , by Rafael Rafailov and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model , by Rafael Rafailov and 5 other authors"}
+{"idx": 2, "title": "Why is Your Language Model a Poor Implicit Reward Model?", "date": "", "ddg_snippet": "Language model post-training and inference pipelines often rely on reward models to assess the quality of generated responses (Cobbe et al., 2021 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07981v1", "content": "Language model post-training and inference pipelines often rely on reward models to assess the quality of generated responses (Cobbe et al., 2021 ..."}
+{"idx": 3, "title": "Fine-Tuning Language Models with Reward Learning on Policy", "date": "", "ddg_snippet": "... optimization shifts the language model ’s data distribution during the RL phase, the (fixed) reward model will be inaccurate off-distribution which ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.19279v1", "content": "... optimization shifts the language model ’s data distribution during the RL phase, the (fixed) reward model will be inaccurate off-distribution which ..."}
+{"idx": 4, "title": "On Diversified Preferences of Large Language Model Alignment", "date": "", "ddg_snippet": "Hence, a more comprehensive understanding of the impact of diversified human preference datasets on the reward model becomes crucial, yet it has not ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.07401v5", "content": "Hence, a more comprehensive understanding of the impact of diversified human preference datasets on the reward model becomes crucial, yet it has not ..."}
+{"idx": 5, "title": "Direct Preference Optimization: Your Language Model is Secretly", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language ... https://blog.athina.ai/ direct - preference - optimization - your - language - model - is - secretly - a - reward - model", "subpage_snippet": "", "source": "blog.athina.ai", "link": "https://blog.athina.ai/direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "content": "Direct Preference Optimization : Your Language ... https://blog.athina.ai/ direct - preference - optimization - your - language - model - is - secretly - a - reward - model"}
+{"idx": 6, "title": "Direct Preference Optimization: Your Language Model is Secretly", "date": "", "ddg_snippet": "In this paper, we show how to directly optimize a language model to adhere to human preferences , without explicit reward modeling or reinforcement ...", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "content": "In this paper, we show how to directly optimize a language model to adhere to human preferences , without explicit reward modeling or reinforcement ..."}
+{"idx": 7, "title": "Direct Preference Optimization 논문리뷰", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model is Secretly a ... ... algorithm, which we call Direct Preference Optimization (DPO) , is stable, ...", "subpage_snippet": "", "source": "junbuml.ee", "link": "https://junbuml.ee/dpo", "content": "Direct Preference Optimization : Your Language Model is Secretly a ... ... algorithm, which we call Direct Preference Optimization (DPO) , is stable, ..."}
+{"idx": 8, "title": "Frontiers in Artificial Intelligence Algorithm Optimization: A", "date": "", "ddg_snippet": "... model fusion via preference optimization in ... Direct preference optimization : Your language model is secretly a reward model [Paper presentation].", "subpage_snippet": "", "source": "journals.zeuspress.org", "link": "https://journals.zeuspress.org/index.php/CAI/article/view/322", "content": "... model fusion via preference optimization in ... Direct preference optimization : Your language model is secretly a reward model [Paper presentation]."}
+{"idx": 9, "title": "GitHub - RLHFlow/Directional-Preference-Alignment: Directional", "date": "", "ddg_snippet": "Code : The multi-objective reward model training code is provided at https://github.com/RLHFlow/RLHF- Reward - Modeling /tree/main/armo-rm", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/RLHFlow/Directional-Preference-Alignment", "content": "Code : The multi-objective reward model training code is provided at https://github.com/RLHFlow/RLHF- Reward - Modeling /tree/main/armo-rm"}
diff --git a/data/sampled_jsons/Directionality_Score_Definition_3.2_gpizm0I3lp_The_underlying_structures_of_self-attention-_symmetry.jsonl b/data/sampled_jsons/Directionality_Score_Definition_3.2_gpizm0I3lp_The_underlying_structures_of_self-attention-_symmetry.jsonl
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diff --git a/data/sampled_jsons/Disjunctive_counterfactuals_using_causal_models_a_critical_examination_Beckers.jsonl b/data/sampled_jsons/Disjunctive_counterfactuals_using_causal_models_a_critical_examination_Beckers.jsonl
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+{"idx": 0, "title": "Causal Modeling Semantics for Counterfactuals with ...", "date": "", "ddg_snippet": "Abstract Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counter-factuals whose antecedent is a conjunction of atomic formulas. We ex-tend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.14817.pdf", "content": "Abstract Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counter-factuals whose antecedent is a conjunction of atomic formulas. We ex-tend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean ..."}
+{"idx": 1, "title": "Causal modeling semantics for counterfactuals with ...", "date": "", "ddg_snippet": "Oct 1, 2024 · Causal Modeling Semantics (CMS, e.g., [6], [22], [12]) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0168007223000933", "content": "Oct 1, 2024 · Causal Modeling Semantics (CMS, e.g., [6], [22], [12]) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to ..."}
+{"idx": 2, "title": "Counterfactuals and causal models: introduction to the ...", "date": "", "ddg_snippet": "Abstract Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation of something false or nonexistent ...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/23927017/", "content": "Abstract Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation of something false or nonexistent ..."}
+{"idx": 3, "title": "(PDF) Causal modeling semantics for counterfactuals with ...", "date": "", "ddg_snippet": "Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/111776171/Causal_modeling_semantics_for_counterfactuals_with_disjunctive_antecedents", "content": "Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of"}
+{"idx": 4, "title": "Explainable AI and Causal Understanding: Counterfactual ...", "date": "", "ddg_snippet": "The work of Beckers (2022) is much closer to the present study, insofar as he is interested in applying (and, indeed, refining) notions of causation. However, Beckers (2022) focuses pri-marily on applying notions of causation to target systems: real-world systems that machine learning models represent.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s11023-023-09637-x.pdf", "content": "The work of Beckers (2022) is much closer to the present study, insofar as he is interested in applying (and, indeed, refining) notions of causation. However, Beckers (2022) focuses pri-marily on applying notions of causation to target systems: real-world systems that machine learning models represent."}
+{"idx": 5, "title": "Backtracking Counterfactuals - PMLR", "date": "", "ddg_snippet": "Aug 10, 2023 · @InProceedings{pmlr-v213-kugelgen23a, title = {Backtracking Counterfactuals}, author = {K\\\"ugelgen, Julius Von and Mohamed, Abdirisak and Beckers , Sander}, booktitle = {Proceedings of the Second Conference on Causal Learning and Reasoning}, pages = {177--196}, year = {2023}, editor = {van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik}, volume = {213}, series = {Proceedings of ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v213/kugelgen23a.html", "content": "Aug 10, 2023 · @InProceedings{pmlr-v213-kugelgen23a, title = {Backtracking Counterfactuals}, author = {K\\\"ugelgen, Julius Von and Mohamed, Abdirisak and Beckers , Sander}, booktitle = {Proceedings of the Second Conference on Causal Learning and Reasoning}, pages = {177--196}, year = {2023}, editor = {van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik}, volume = {213}, series = {Proceedings of ..."}
+{"idx": 6, "title": "(PDF) Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Disjunctive counterfactuals using causal models : a critical examination . Un-. published manuscript. Galhotra, S., R. Pradhan, and B. Salimi (2021). Explaining black-box algorithms using proba-. bilistic contrastive counterfactuals.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380847642_Intervention_and_Conditioning_in_Causal_Bayesian_Networks", "content": "Disjunctive counterfactuals using causal models : a critical examination . Un-. published manuscript. Galhotra, S., R. Pradhan, and B. Salimi (2021). Explaining black-box algorithms using proba-. bilistic contrastive counterfactuals."}
+{"idx": 7, "title": "Intervention and Conditioning in Causal Bayesian", "date": "", "ddg_snippet": "Beckers , S. (2023). Disjunctive counterfactuals using causal models : a critical examination .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DC28Fpk76s", "content": "Beckers , S. (2023). Disjunctive counterfactuals using causal models : a critical examination ."}
+{"idx": 8, "title": "Causal Modeling - Bibliography - PhilPapers", "date": "", "ddg_snippet": "... interest, I develop a dialectic connecting the Rubin causal model to the Lewis-Stalnaker debate on a logical principle of counterfactuals ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/causal-modeling", "content": "... interest, I develop a dialectic connecting the Rubin causal model to the Lewis-Stalnaker debate on a logical principle of counterfactuals ..."}
+{"idx": 9, "title": "Actual Causality", "date": "", "ddg_snippet": "I thank Sander Beckers for extended email discussions on causality, Robert Maxton for his careful proofreading, and Chris Hitchcock, David Lagnado ...", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/home/halpern/papers/causalitybook-ch1-3.html", "content": "I thank Sander Beckers for extended email discussions on causality, Robert Maxton for his careful proofreading, and Chris Hitchcock, David Lagnado ..."}
diff --git a/data/sampled_jsons/Disjunctive_counterfactuals_using_causal_models_a_critical_examination_Beckers_2023_year_2023.jsonl b/data/sampled_jsons/Disjunctive_counterfactuals_using_causal_models_a_critical_examination_Beckers_2023_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c98d168f232fa348bd0737db7b6a50043de857e
--- /dev/null
+++ b/data/sampled_jsons/Disjunctive_counterfactuals_using_causal_models_a_critical_examination_Beckers_2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling Semantics for Counterfactuals with ...", "date": "", "ddg_snippet": "The present paper extends Causal Modeling Semantics to the evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to any counterfactuals whose antecedents are truth-functional com-pounds of atomic sentences.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.14817.pdf", "content": "The present paper extends Causal Modeling Semantics to the evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to any counterfactuals whose antecedents are truth-functional com-pounds of atomic sentences."}
+{"idx": 1, "title": "(PDF) Causal Modeling Semantics for Counterfactuals with ...", "date": "", "ddg_snippet": "Apr 28, 2023 · In this paper, we explore the possibility of using graphical causal models to resolve counterfactual queries about causal structure by introducing a notion of similarity between causal ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370417686_Causal_Modeling_Semantics_for_Counterfactuals_with_Disjunctive_Antecedents", "content": "Apr 28, 2023 · In this paper, we explore the possibility of using graphical causal models to resolve counterfactual queries about causal structure by introducing a notion of similarity between causal ..."}
+{"idx": 2, "title": "Causal modeling semantics for counterfactuals with ...", "date": "", "ddg_snippet": "Oct 1, 2024 · The present paper extends Causal Modeling Semantics to the evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to any counterfactuals whose antecedents are truth-functional compounds of atomic sentences.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0168007223000933", "content": "Oct 1, 2024 · The present paper extends Causal Modeling Semantics to the evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to any counterfactuals whose antecedents are truth-functional compounds of atomic sentences."}
+{"idx": 3, "title": "An event algebra for causal counterfactuals | Philosophical ... Causal Models and the Logic of Counterfactuals [2304.14817] Causal Modeling Semantics for Counterfactuals ... [PDF] Causal Modeling Semantics for Counterfactuals with ...", "date": "", "ddg_snippet": "Oct 26, 2023 · I apply this algebra to counterfactuals expressed using underdeterministic causal models - models that encode non-probabilistic causal indeterminacies. Specifically, I develop semaphore interventions, which represent how the target system may be modified from without in a coordinated fashion. In this paper, I o er a causal model for counterfactuals which applies to a wide class of counterfactuals , including those with antecedents of arbitrary log-ical complexity and backtracking counterfactuals , and develop the connection with similarity-based approaches and counterfactual logic. Apr 28, 2023 · We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Apart from solving a major problem in the epistemology of counterfactuals , our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11098-023-02015-4", "content": "Oct 26, 2023 · I apply this algebra to counterfactuals expressed using underdeterministic causal models - models that encode non-probabilistic causal indeterminacies. Specifically, I develop semaphore interventions, which represent how the target system may be modified from without in a coordinated fashion. In this paper, I o er a causal model for counterfactuals which applies to a wide class of counterfactuals , including those with antecedents of arbitrary log-ical complexity and backtracking counterfactuals , and develop the connection with similarity-based approaches and counterfactual logic. Apr 28, 2023 · We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Apart from solving a major problem in the epistemology of counterfactuals , our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined."}
+{"idx": 4, "title": "Causal Models and the Logic of Counterfactuals", "date": "", "ddg_snippet": "In this paper, I o er a causal model for counterfactuals which applies to a wide class of counterfactuals , including those with antecedents of arbitrary log-ical complexity and backtracking counterfactuals , and develop the connection with similarity-based approaches and counterfactual logic.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/328766349.pdf", "content": "In this paper, I o er a causal model for counterfactuals which applies to a wide class of counterfactuals , including those with antecedents of arbitrary log-ical complexity and backtracking counterfactuals , and develop the connection with similarity-based approaches and counterfactual logic."}
+{"idx": 5, "title": "[2304.14817] Causal Modeling Semantics for Counterfactuals ...", "date": "", "ddg_snippet": "Apr 28, 2023 · We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.14817", "content": "Apr 28, 2023 · We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas."}
+{"idx": 6, "title": "[PDF] Causal Modeling Semantics for Counterfactuals with ...", "date": "", "ddg_snippet": "Apart from solving a major problem in the epistemology of counterfactuals , our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Causal-Modeling-Semantics-for-Counterfactuals-with-Rosella-Sprenger/8e5ff31aad07e3f7fd2cc5bdc55b545e453861eb", "content": "Apart from solving a major problem in the epistemology of counterfactuals , our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined."}
+{"idx": 7, "title": "Causal Modeling - Bibliography - PhilPapers", "date": "", "ddg_snippet": "... interest, I develop a dialectic connecting the Rubin causal model to the Lewis-Stalnaker debate on a logical principle of counterfactuals ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/causal-modeling", "content": "... interest, I develop a dialectic connecting the Rubin causal model to the Lewis-Stalnaker debate on a logical principle of counterfactuals ..."}
+{"idx": 8, "title": "Long-Term Future Fund: April 2023 grant recommendations — EA", "date": "", "ddg_snippet": "Continue to develop features at phenomenal speeds, lots of EAs and others in adjacent communities use the product, team is still producing fast and ...", "subpage_snippet": "", "source": "forum.effectivealtruism.org", "link": "https://forum.effectivealtruism.org/posts/zZ2vq7YEckpunrQS4/long-term-future-fund-april-2023-grant-recommendations", "content": "Continue to develop features at phenomenal speeds, lots of EAs and others in adjacent communities use the product, team is still producing fast and ..."}
+{"idx": 9, "title": "Counterfactual Theories of Causation - Bibliography - PhilPapers", "date": "", "ddg_snippet": "... acyclic deterministic structural causal models to the nondeterministic case and argue that this offers an improved semantics for counterfactuals .", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/counterfactual-theories-of-causation", "content": "... acyclic deterministic structural causal models to the nondeterministic case and argue that this offers an improved semantics for counterfactuals ."}
diff --git a/data/sampled_jsons/Distill_Olah_et_al._2020_circuits_abstract_mechanistic_interpretability_year_2020.jsonl b/data/sampled_jsons/Distill_Olah_et_al._2020_circuits_abstract_mechanistic_interpretability_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2d46c23a76f425a5f99ebc9f94a3c4539dce22eb
--- /dev/null
+++ b/data/sampled_jsons/Distill_Olah_et_al._2020_circuits_abstract_mechanistic_interpretability_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mechanistic Interpretability Needs Philosophy", "date": "", "ddg_snippet": "to those associated with Distill .pub’ s Circuits thread [ Olah et al ., 2020 ] and Anthropic’ s Transformer Circuits thread [Elhage et al ., 2022 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.18852v1", "content": "to those associated with Distill .pub’ s Circuits thread [ Olah et al ., 2020 ] and Anthropic’ s Transformer Circuits thread [Elhage et al ., 2022 ..."}
+{"idx": 1, "title": "Open Problems in Mechanistic Interpretability", "date": "", "ddg_snippet": "Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks’ capabilities in order to accomplish ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.16496v1", "content": "Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks’ capabilities in order to accomplish ..."}
+{"idx": 2, "title": "Interpretability in Action: Exploratory Analysis of VPT, a", "date": "", "ddg_snippet": "Most mechanistic interpretability work has focused on reverse engineering circuits in LLMs (Wang et al ., 2022 ; Lieberum et al ., 2023 ; Conmy et ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.12161v1", "content": "Most mechanistic interpretability work has focused on reverse engineering circuits in LLMs (Wang et al ., 2022 ; Lieberum et al ., 2023 ; Conmy et ..."}
+{"idx": 3, "title": "Mechanistic?", "date": "", "ddg_snippet": "... mechanistic interpretability was coined by Chris Olah and first publicly used in the Distill .pub Circuits thread , a series of blogposts by OpenAI ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09087v1", "content": "... mechanistic interpretability was coined by Chris Olah and first publicly used in the Distill .pub Circuits thread , a series of blogposts by OpenAI ..."}
+{"idx": 4, "title": "EIS VI: Critiques of Mechanistic Interpretability Work in AI", "date": "", "ddg_snippet": "... here is to highlight some problems with cherrypicking but not to claim that the methods from papers like ( Olah et al ., 2017) and ( Olah et al ., 2020 ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/wt7HXaCWzuKQipqz3/eis-vi-critiques-of-mechanistic-interpretability-work-in-ai", "content": "... here is to highlight some problems with cherrypicking but not to claim that the methods from papers like ( Olah et al ., 2017) and ( Olah et al ., 2020 ..."}
+{"idx": 5, "title": "Sparsify: A mechanistic interpretability research agenda -", "date": "", "ddg_snippet": "... and work is, of course, very heavily inspired by the work of Chris Olah , other Anthropic researchers, and other early mechanistic interpretability ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/64MizJXzyvrYpeKqm/sparsify-a-mechanistic-interpretability-research-agenda", "content": "... and work is, of course, very heavily inspired by the work of Chris Olah , other Anthropic researchers, and other early mechanistic interpretability ..."}
+{"idx": 6, "title": "EIS VI: Critiques of Mechanistic Interpretability Work in AI", "date": "", "ddg_snippet": "... is to highlight some problems with cherrypicking but not to claim that the methods from papers like ( Olah et al ., 2017) and ( Olah et al ., 2020 ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/wt7HXaCWzuKQipqz3/eis-vi-critiques-of-mechanistic-interpretability-work-in-ai", "content": "... is to highlight some problems with cherrypicking but not to claim that the methods from papers like ( Olah et al ., 2017) and ( Olah et al ., 2020 ..."}
+{"idx": 7, "title": "EIS VI: Critiques of Mechanistic Interpretability Work in AI", "date": "", "ddg_snippet": "... is to highlight some problems with cherrypicking but not to claim that the methods from papers like ( Olah et al ., 2017) and ( Olah et al ., 2020 ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/wt7HXaCWzuKQipqz3/eis-vi-critiques-of-mechanistic-interpretability-work-in-ai", "content": "... is to highlight some problems with cherrypicking but not to claim that the methods from papers like ( Olah et al ., 2017) and ( Olah et al ., 2020 ..."}
+{"idx": 8, "title": "Explaining AI through mechanistic interpretability | European", "date": "", "ddg_snippet": "While research to this end is currently gaining momentum among researchers working in industry ( e .g., Cammarata et al ., 2020 ; Olah et al ., 2018 ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s13194-024-00614-4", "content": "While research to this end is currently gaining momentum among researchers working in industry ( e .g., Cammarata et al ., 2020 ; Olah et al ., 2018 ..."}
+{"idx": 9, "title": "InterpBench: Semi-Synthetic Transformers for Evaluating", "date": "", "ddg_snippet": "The field of mechanistic interpretability (MI) aims to reverse-engineer the algorithm implemented by a neural network [ 14 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.14494v2", "content": "The field of mechanistic interpretability (MI) aims to reverse-engineer the algorithm implemented by a neural network [ 14 ] ."}
diff --git a/data/sampled_jsons/DnCNN_MATLAB_code_m_head_m_body_layers.jsonl b/data/sampled_jsons/DnCNN_MATLAB_code_m_head_m_body_layers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cdd5f749300615431925ada4445abf06a7113b1f
--- /dev/null
+++ b/data/sampled_jsons/DnCNN_MATLAB_code_m_head_m_body_layers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - cszn/ DnCNN : Beyond a Gaussian Denoiser: Residual...", "date": "", "ddg_snippet": "Testing (MatConvNet or Matlab ). [demos] Demo_test_ DnCNN -. m . [models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN", "content": "Testing (MatConvNet or Matlab ). [demos] Demo_test_ DnCNN -. m . [models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking."}
+{"idx": 1, "title": "Review: DnCNN — Residual Learning of Deep CNN for... | Medium", "date": "", "ddg_snippet": "Residual learning, originated in ResNet, and batch normalization, originated in Inception-v2, is used. With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This is a paper in 2017 TIP with over 1700 citations...", "subpage_snippet": "", "source": "sh-tsang.medium.com", "link": "https://sh-tsang.medium.com/review-dncnn-residual-learning-of-deep-cnn-image-denoising-super-resolution-jpeg-deblocking-cbf464b03130", "content": "Residual learning, originated in ResNet, and batch normalization, originated in Inception-v2, is used. With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This is a paper in 2017 TIP with over 1700 citations..."}
+{"idx": 2, "title": "model.architectures.keras. dncnn — OpenDenoising 0.1 documentation", "date": "", "ddg_snippet": "Source code for model.architectures.keras. dncnn .The fact that you are presently reading this means that you have had # knowledge of the CeCILL-C license and that you accept its terms. import tensorflow as tf from keras import layers , models.", "subpage_snippet": "", "source": "opendenoising-docs.readthedocs.io", "link": "https://opendenoising-docs.readthedocs.io/en/latest/_modules/model/architectures/keras/dncnn.html", "content": "Source code for model.architectures.keras. dncnn .The fact that you are presently reading this means that you have had # knowledge of the CeCILL-C license and that you accept its terms. import tensorflow as tf from keras import layers , models."}
+{"idx": 3, "title": "KAIR/models/network_ dncnn .py · lambdalabs/LambdaSuperRes at main", "date": "", "ddg_snippet": "self.model = B.sequential( m _ head , * m _ body , m_tail). def forward(self, x)nc: channel number. nb: total number of conv layers . act_mode: batch norm + activation function; 'BR' means BN+ReLU.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/lambdalabs/LambdaSuperRes/blob/main/KAIR/models/network_dncnn.py", "content": "self.model = B.sequential( m _ head , * m _ body , m_tail). def forward(self, x)nc: channel number. nb: total number of conv layers . act_mode: batch norm + activation function; 'BR' means BN+ReLU."}
+{"idx": 4, "title": "How to apply Matlab CNN code on an input image with 6 channels", "date": "", "ddg_snippet": "However, the same training data with 3 channels or 1 channels I can run the CNN code without any error message. It will be a great help if anyone can suggest how to use image data with more than 3 channels in Matlab for CNN classification.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/matlabcentral/answers/350106-how-to-apply-matlab-cnn-code-on-an-input-image-with-6-channels", "content": "However, the same training data with 3 channels or 1 channels I can run the CNN code without any error message. It will be a great help if anyone can suggest how to use image data with more than 3 channels in Matlab for CNN classification."}
+{"idx": 5, "title": "Single Image DnCNN Visibility Improvement (SImDnCNNVI)", "date": "", "ddg_snippet": "Thus, the DnCNN model is used efficiently to remove SSIR, JPEG artifacts and to clean hidden layers . e. Tending to General Image Denoising: The Gaussian denoising model is best suitable for a fixed noise level.", "subpage_snippet": "", "source": "sv-journal.org", "link": "https://sv-journal.org/2022-3/07/", "content": "Thus, the DnCNN model is used efficiently to remove SSIR, JPEG artifacts and to clean hidden layers . e. Tending to General Image Denoising: The Gaussian denoising model is best suitable for a fixed noise level."}
+{"idx": 6, "title": "DnCNN Denoiser - Programmer Sought", "date": "", "ddg_snippet": "DnCNN Code learning -main_train.py. DnCNN reading notes. DnCNN paper reading notes [ MATLAB ]. Denoiser is checked but the Arnold RGB_denoiser channel does not appear.", "subpage_snippet": "", "source": "programmersought.com", "link": "https://programmersought.com/article/14387321141/", "content": "DnCNN Code learning -main_train.py. DnCNN reading notes. DnCNN paper reading notes [ MATLAB ]. Denoiser is checked but the Arnold RGB_denoiser channel does not appear."}
+{"idx": 7, "title": "DnCNN | Ecosystem Directory | market.dev", "date": "", "ddg_snippet": "DnCNN . Compare To View Code on GitHub.Testing (MatConvNet or Matlab ). [demos] Demo_test_ DnCNN -. m . [models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking.", "subpage_snippet": "", "source": "explore.market.dev", "link": "https://explore.market.dev/ecosystems/matlab/projects/dncnn", "content": "DnCNN . Compare To View Code on GitHub.Testing (MatConvNet or Matlab ). [demos] Demo_test_ DnCNN -. m . [models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking."}
+{"idx": 8, "title": "MatLab : что это за программа - характеристики и возможности", "date": "", "ddg_snippet": "Программа MatLab . Для чего используется, ее основные характеристики и возможности.", "subpage_snippet": "", "source": "blog.skillfactory.ru", "link": "https://blog.skillfactory.ru/glossary/matlab/", "content": "Программа MatLab . Для чего используется, ее основные характеристики и возможности."}
+{"idx": 9, "title": "Network architecture of the DnCNN -based channel estimator.", "date": "", "ddg_snippet": "Copy caption. Embed figure. Network architecture of the DnCNN -based channel estimator.The DnCNN network architecture is shown in Fig. 4. The input tensor is processed by a number of convolutional layers to produce the output.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Network-architecture-of-the-DnCNN-based-channel-estimator_fig4_345971380", "content": "Copy caption. Embed figure. Network architecture of the DnCNN -based channel estimator.The DnCNN network architecture is shown in Fig. 4. The input tensor is processed by a number of convolutional layers to produce the output."}
diff --git a/data/sampled_jsons/DnCNN_PyTorch_implementation_code_m_head_m_body_layers.jsonl b/data/sampled_jsons/DnCNN_PyTorch_implementation_code_m_head_m_body_layers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e376ba6ba6dd3f26150efa34a96e51e690d260ba
--- /dev/null
+++ b/data/sampled_jsons/DnCNN_PyTorch_implementation_code_m_head_m_body_layers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - SaoYan/DnCNN-PyTorch: PyTorch implementation of the ... DnCNN - Image noise remover 【图像去噪】论文复现：新手入门必看！DnCNN的Pytorch源码训练测试全... cszn/DnCNN | DeepWiki This is a PyTorch implementation of DNCnn (modified) for ... GitHub - AmzadHossainrafis/DncnnV GitHub - SaoYan/ DnCNN-PyTorch : PyTorch implementation of the TIP2… DnCNN - Image noise remover GitHub - SaoYan/ DnCNN-PyTorch : PyTorch implementation of the TIP2… How to implement neural networks in PyTorch ? - GeeksforGeeks How to implement neural networks in PyTorch ? - GeeksforGeeks How to implement neural networks in PyTorch? - GeeksforGeeks", "date": "", "ddg_snippet": "This is a PyTorch implementation of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. The author's MATLAB implementation is here. This code was written with PyTorch =0.4 today. Migrating the code is easy. Please refer to PyTorch 0.4.0 Migration Guide. See full list on github.com 1. Dependences • PyTorch (<0.4)•torchvision•OpenCV for Python•HDF5 for Python•tensorboardX (TensorBoard for PyTorch ) 2. Train DnCNN -S ( DnCNN with known noise level) NOTE•If you've already built the training and validation dataset (i.e. train.h5 & val.h5 files), set preprocess to be False.•According to the paper, DnCNN -S has 17 layers .•noiseL is used for training and val_noiseL is used for validation. They should be set to the same value for unbiased validation. You can set whatever noise level you need. 3. Train DnCNN -B ( DnCNN with blind noise level) NOTE•If you've already built the training and validation dataset (i.e. train.h5 & val.h5 files), set preprocess to be False.•According to the paper, DnCNN -B has 20 layers .•noiseL is ingnored when training DnCNN -B. You can set val_noiseL to whatever you need. See full list on github.com •Parameter initialization: Use kaiming_normal initialization for Conv; Pay attention to the initialization of BatchNorm •The definition of loss function Set size_average to be False when defining the loss function. When size_average=True, the pixel-wise average will be computed, but what we need is sample-wise average. The computation of loss will be like: where we divide the sum over one batch of samples by 2N, with N being # samples. See full list on github.com DnCNN - Residual Learning of Deep CNN for Image Denoising The DnCNN (Denoising Convolutional Neural Network) is a deep learning model designed for image denoising tasks, offering state-of-the-art performance by learning end-to-end mappings from noisy to clean images. It employs a deep architecture consisting of several convolutional layers without any fully connected layers , enabling it to ... Aug 12, 2024 · 【图像去噪】论文复现：新手入门必看！DnCNN的 Pytorch 源码训练测试全流程解析！为源码做详细注释！补充DnCNN-B和DnCNN-3的模型训练和测试！附各种情况下训练好的模型权重文件！ 阅读量1.3w 174 分类专栏：文章标签： Pytorch 深度学习图像去噪算法100例（论文精读+复现）专栏收录该内容 303 篇文章¥299.90¥ ... May 12, 2025 · For the PyTorch implementation , the author recommends using the KAIR repository which contains the latest PyTorch code for training and testing DnCNN models. Sources: README.md 17-29 README.md 87-95 This is a PyTorch implementation of DNCnn (modified) for image denoising and deblurring. Dncnn is a deep convolutional neural network for image denoising. It is a fully convolutional network with residual learning and batch normalization. What is dncnn in PyTorch? Dncnn is a deep convolutional neural network for image denoising . It is a fully convolutional network with residual learning and batch normalization. Cannot retrieve latest commit at this time. This is a PyTorch implementation of DNCnn for image denoising and de-blurring. Dncnn is a deep convolutional neural network for image denoising. Is there a PyTorch implementation for image denoising? Cannot retrieve latest commit at this time. This is a PyTorch implementation of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. The author's MATLAB implementation is here. This code was written with PyTorch =0.4 today. Migrating the code is easy. What is dncnn (denoising convolutional neural network)? The DnCNN (Denoising Convolutional Neural Network) is a deep learning model designed for image denoising tasks, offering state-of-the-art performance by learning end-to-end mappings from noisy to clean images. Is PyTorch a MATLAB implementation? This is a PyTorch implementation of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. The author's MATLAB implementation is here. This code was written with PyTorch =0.4 today. Migrating the code is easy. Please refer to PyTorch 0.4.0 Migration Guide. 1. How to build a neural network using PyTorch? PyTorch offers two primary methods for building neural networks: using the nn.Module class or the nn.Sequential container. Using nn.Module: To create a custom network, subclass the nn.Module class and define the __init__ and forward functions. How to classify handwritten digits from MNIST dataset using PyTorch? Let's implement a Feedforward Neural Network (FNN) for classifying handwritten digits from the MNIST dataset using PyTorch . We start by importing the necessary PyTorch libraries, which include torch, torch.nn for building the model, torch.optim for the optimizer, and torchvision for dataset handling and image transformations. Mar 1, 2025 · The __init__ method sets up the layers and parameters, while the forward function defines how input flows through the network and produces output. Using nn.Sequential: This container allows you to specify layers in a list. The layers are automatically connected in the order provided. Steps to Implement a Neural Network in PyTorch : 1.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SaoYan/DnCNN-PyTorch", "content": "This is a PyTorch implementation of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. The author's MATLAB implementation is here. This code was written with PyTorch =0.4 today. Migrating the code is easy. Please refer to PyTorch 0.4.0 Migration Guide. See full list on github.com 1. Dependences • PyTorch (<0.4)•torchvision•OpenCV for Python•HDF5 for Python•tensorboardX (TensorBoard for PyTorch ) 2. Train DnCNN -S ( DnCNN with known noise level) NOTE•If you've already built the training and validation dataset (i.e. train.h5 & val.h5 files), set preprocess to be False.•According to the paper, DnCNN -S has 17 layers .•noiseL is used for training and val_noiseL is used for validation. They should be set to the same value for unbiased validation. You can set whatever noise level you need. 3. Train DnCNN -B ( DnCNN with blind noise level) NOTE•If you've already built the training and validation dataset (i.e. train.h5 & val.h5 files), set preprocess to be False.•According to the paper, DnCNN -B has 20 layers .•noiseL is ingnored when training DnCNN -B. You can set val_noiseL to whatever you need. See full list on github.com •Parameter initialization: Use kaiming_normal initialization for Conv; Pay attention to the initialization of BatchNorm •The definition of loss function Set size_average to be False when defining the loss function. When size_average=True, the pixel-wise average will be computed, but what we need is sample-wise average. The computation of loss will be like: where we divide the sum over one batch of samples by 2N, with N being # samples. See full list on github.com DnCNN - Residual Learning of Deep CNN for Image Denoising The DnCNN (Denoising Convolutional Neural Network) is a deep learning model designed for image denoising tasks, offering state-of-the-art performance by learning end-to-end mappings from noisy to clean images. It employs a deep architecture consisting of several convolutional layers without any fully connected layers , enabling it to ... Aug 12, 2024 · 【图像去噪】论文复现：新手入门必看！DnCNN的 Pytorch 源码训练测试全流程解析！为源码做详细注释！补充DnCNN-B和DnCNN-3的模型训练和测试！附各种情况下训练好的模型权重文件！ 阅读量1.3w 174 分类专栏：文章标签： Pytorch 深度学习图像去噪算法100例（论文精读+复现）专栏收录该内容 303 篇文章¥299.90¥ ... May 12, 2025 · For the PyTorch implementation , the author recommends using the KAIR repository which contains the latest PyTorch code for training and testing DnCNN models. Sources: README.md 17-29 README.md 87-95 This is a PyTorch implementation of DNCnn (modified) for image denoising and deblurring. Dncnn is a deep convolutional neural network for image denoising. It is a fully convolutional network with residual learning and batch normalization. What is dncnn in PyTorch? Dncnn is a deep convolutional neural network for image denoising . It is a fully convolutional network with residual learning and batch normalization. Cannot retrieve latest commit at this time. This is a PyTorch implementation of DNCnn for image denoising and de-blurring. Dncnn is a deep convolutional neural network for image denoising. Is there a PyTorch implementation for image denoising? Cannot retrieve latest commit at this time. This is a PyTorch implementation of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. The author's MATLAB implementation is here. This code was written with PyTorch =0.4 today. Migrating the code is easy. What is dncnn (denoising convolutional neural network)? The DnCNN (Denoising Convolutional Neural Network) is a deep learning model designed for image denoising tasks, offering state-of-the-art performance by learning end-to-end mappings from noisy to clean images. Is PyTorch a MATLAB implementation? This is a PyTorch implementation of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. The author's MATLAB implementation is here. This code was written with PyTorch =0.4 today. Migrating the code is easy. Please refer to PyTorch 0.4.0 Migration Guide. 1. How to build a neural network using PyTorch? PyTorch offers two primary methods for building neural networks: using the nn.Module class or the nn.Sequential container. Using nn.Module: To create a custom network, subclass the nn.Module class and define the __init__ and forward functions. How to classify handwritten digits from MNIST dataset using PyTorch? Let's implement a Feedforward Neural Network (FNN) for classifying handwritten digits from the MNIST dataset using PyTorch . We start by importing the necessary PyTorch libraries, which include torch, torch.nn for building the model, torch.optim for the optimizer, and torchvision for dataset handling and image transformations. Mar 1, 2025 · The __init__ method sets up the layers and parameters, while the forward function defines how input flows through the network and produces output. Using nn.Sequential: This container allows you to specify layers in a list. The layers are automatically connected in the order provided. Steps to Implement a Neural Network in PyTorch : 1."}
+{"idx": 1, "title": "【图像去噪】论文复现：新手入门必看！DnCNN的Pytorch源码训练测试全...", "date": "", "ddg_snippet": "Aug 12, 2024 · 【图像去噪】论文复现：新手入门必看！DnCNN的 Pytorch 源码训练测试全流程解析！为源码做详细注释！补充DnCNN-B和DnCNN-3的模型训练和测试！附各种情况下训练好的模型权重文件！ 阅读量1.3w 174 分类专栏：文章标签： Pytorch 深度学习图像去噪算法100例（论文精读+复现）专栏收录该内容 303 篇文章¥299.90¥ ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_36584673/article/details/139743314", "content": "Aug 12, 2024 · 【图像去噪】论文复现：新手入门必看！DnCNN的 Pytorch 源码训练测试全流程解析！为源码做详细注释！补充DnCNN-B和DnCNN-3的模型训练和测试！附各种情况下训练好的模型权重文件！ 阅读量1.3w 174 分类专栏：文章标签： Pytorch 深度学习图像去噪算法100例（论文精读+复现）专栏收录该内容 303 篇文章¥299.90¥ ..."}
+{"idx": 2, "title": "PyTorch Implementation | cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · PyTorch Implementation Relevant source files This document describes the PyTorch implementation of DnCNN (Denoising Convolutional Neural Network), detailing the model structure, data handling, training process, and testing procedures.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN/3-pytorch-implementation", "content": "May 12, 2025 · PyTorch Implementation Relevant source files This document describes the PyTorch implementation of DnCNN (Denoising Convolutional Neural Network), detailing the model structure, data handling, training process, and testing procedures."}
+{"idx": 3, "title": "DnCNN - Image noise remover", "date": "", "ddg_snippet": "DnCNN - Residual Learning of Deep CNN for Image Denoising The DnCNN (Denoising Convolutional Neural Network) is a deep learning model designed for image denoising tasks, offering state-of-the-art performance by learning end-to-end mappings from noisy to clean images. It employs a deep architecture consisting of several convolutional layers without any fully connected layers , enabling it to ...", "subpage_snippet": "", "source": "atikul-islam-sajib.github.io", "link": "https://atikul-islam-sajib.github.io/DnCNN-deploy/", "content": "DnCNN - Residual Learning of Deep CNN for Image Denoising The DnCNN (Denoising Convolutional Neural Network) is a deep learning model designed for image denoising tasks, offering state-of-the-art performance by learning end-to-end mappings from noisy to clean images. It employs a deep architecture consisting of several convolutional layers without any fully connected layers , enabling it to ..."}
+{"idx": 4, "title": "cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · For the PyTorch implementation , the author recommends using the KAIR repository which contains the latest PyTorch code for training and testing DnCNN models. Sources: README.md 17-29 README.md 87-95", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN", "content": "May 12, 2025 · For the PyTorch implementation , the author recommends using the KAIR repository which contains the latest PyTorch code for training and testing DnCNN models. Sources: README.md 17-29 README.md 87-95"}
+{"idx": 5, "title": "This is a PyTorch implementation of DNCnn (modified) for ...", "date": "", "ddg_snippet": "This is a PyTorch implementation of DNCnn (modified) for image denoising and deblurring. Dncnn is a deep convolutional neural network for image denoising. It is a fully convolutional network with residual learning and batch normalization.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AmzadHossainrafis/DncnnV", "content": "This is a PyTorch implementation of DNCnn (modified) for image denoising and deblurring. Dncnn is a deep convolutional neural network for image denoising. It is a fully convolutional network with residual learning and batch normalization."}
+{"idx": 6, "title": "How to implement neural networks in PyTorch? - GeeksforGeeks", "date": "", "ddg_snippet": "Mar 1, 2025 · The __init__ method sets up the layers and parameters, while the forward function defines how input flows through the network and produces output. Using nn.Sequential: This container allows you to specify layers in a list. The layers are automatically connected in the order provided. Steps to Implement a Neural Network in PyTorch : 1.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/deep-learning/how-to-implement-neural-networks-in-pytorch/", "content": "Mar 1, 2025 · The __init__ method sets up the layers and parameters, while the forward function defines how input flows through the network and produces output. Using nn.Sequential: This container allows you to specify layers in a list. The layers are automatically connected in the order provided. Steps to Implement a Neural Network in PyTorch : 1."}
+{"idx": 7, "title": "model.architectures. pytorch . dncnn — OpenDenoising...", "date": "", "ddg_snippet": "Pytorch implementation of DnCNN . Implementation followed the original paper [1]_. Authors original code can be. found on `their Github Page <.depth : int. Number of fully convolutional layers in dncnn .", "subpage_snippet": "", "source": "opendenoising-docs.readthedocs.io", "link": "https://opendenoising-docs.readthedocs.io/en/latest/_modules/model/architectures/pytorch/dncnn.html", "content": "Pytorch implementation of DnCNN . Implementation followed the original paper [1]_. Authors original code can be. found on `their Github Page <.depth : int. Number of fully convolutional layers in dncnn ."}
+{"idx": 8, "title": "Alternatives and detailed information of Dncnn Pytorch - GitPlanet", "date": "", "ddg_snippet": "PyTorch implementation of the TIP2017 paper \"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising\".According to the paper, DnCNN -B has 20 layers . noiseL is ingnored when training DnCNN -B. You can set val_noiseL to whatever you need. 4. Test.", "subpage_snippet": "", "source": "gitplanet.com", "link": "https://gitplanet.com/project/dncnn-pytorch", "content": "PyTorch implementation of the TIP2017 paper \"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising\".According to the paper, DnCNN -B has 20 layers . noiseL is ingnored when training DnCNN -B. You can set val_noiseL to whatever you need. 4. Test."}
+{"idx": 9, "title": "Removing Noise from Images using a CNN model in Pytorch ... | Medium", "date": "", "ddg_snippet": "DnCNN Model Architecture (source: Beyond a Guassian Denoiser paper). In conclusion, for our model, we’ll be passing a noisy image y at the input and get the residual image R(y) at the output.In the next post, we’ll be seeing a Pytorch implementation of the paper.", "subpage_snippet": "", "source": "olaleyeayoola.medium.com", "link": "https://olaleyeayoola.medium.com/removing-noise-from-images-using-a-cnn-model-in-pytorch-part-1-45e119c03f52", "content": "DnCNN Model Architecture (source: Beyond a Guassian Denoiser paper). In conclusion, for our model, we’ll be passing a noisy image y at the input and get the residual image R(y) at the output.In the next post, we’ll be seeing a Pytorch implementation of the paper."}
diff --git a/data/sampled_jsons/DnCNN_Zhang_2017_architecture_layers_year_2017.jsonl b/data/sampled_jsons/DnCNN_Zhang_2017_architecture_layers_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1fa106bb97b1918991c62ec7ba3a72346a7fcbca
--- /dev/null
+++ b/data/sampled_jsons/DnCNN_Zhang_2017_architecture_layers_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Experiments Details 1. DnCNN (Zhang et al., ...", "date": "", "ddg_snippet": "DnCNN -Wide: a wide version of the DnCNN architecture ; specifically convolutional layers with a width of 128 instead of 64. Check the code for more details. 3.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/f9fd2624beefbc7808e4e405d73f57ab-Supplemental.pdf", "content": "DnCNN -Wide: a wide version of the DnCNN architecture ; specifically convolutional layers with a width of 128 instead of 64. Check the code for more details. 3."}
+{"idx": 1, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep ...", "date": "", "ddg_snippet": "by K Zhang · 2017 · Cited by 10018 — In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs)", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/7839189", "content": "by K Zhang · 2017 · Cited by 10018 — In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs)"}
+{"idx": 2, "title": "Image denoising using deep CNN with batch renormalization", "date": "", "ddg_snippet": "by C Tian · 2020 · Cited by 614 — For example, a 17-layer DnCNN (Zhang, Zuo, Chen et al., 2017) has been proposed as a CNN-based method of predicting noise. This baseline improves the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0893608019302394", "content": "by C Tian · 2020 · Cited by 614 — For example, a 17-layer DnCNN (Zhang, Zuo, Chen et al., 2017) has been proposed as a CNN-based method of predicting noise. This baseline improves the ..."}
+{"idx": 3, "title": "Toward a Fast and Flexible Solution for CNN based Image ...", "date": "", "ddg_snippet": "by K Zhang · 2017 · Cited by 3066 — Benefitted from the advances in deep CNN, Zhang et al. [20] proposed a plain denoising CNN. ( DnCNN ) method which achieves state-of-the-art denoising performance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1710.04026", "content": "by K Zhang · 2017 · Cited by 3066 — Benefitted from the advances in deep CNN, Zhang et al. [20] proposed a plain denoising CNN. ( DnCNN ) method which achieves state-of-the-art denoising performance ..."}
+{"idx": 4, "title": "Methods for image denoising using convolutional neural ...", "date": "", "ddg_snippet": "by AE Ilesanmi · 2021 · Cited by 389 — Zhang et al. [40] used the denoising CNN ( DnCNN ) for image denoising, super-resolution, and JPEG image blocking. The network consists of ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40747-021-00428-4", "content": "by AE Ilesanmi · 2021 · Cited by 389 — Zhang et al. [40] used the denoising CNN ( DnCNN ) for image denoising, super-resolution, and JPEG image blocking. The network consists of ..."}
+{"idx": 5, "title": "When Image Denoising Meets High-Level Vision Tasks", "date": "", "ddg_snippet": "We use a similar architecture as the feature encoding module except that the number of kernels in the four convolutional layers are 256,. 64, 64 and 256. Its ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2018/0117.pdf", "content": "We use a similar architecture as the feature encoding module except that the number of kernels in the four convolutional layers are 256,. 64, 64 and 256. Its ..."}
+{"idx": 6, "title": "Denoising Phase-Unwrapped Images in Laser Imaging via ...", "date": "", "ddg_snippet": "by Y Xie · 2024 · Cited by 1 — Schematic diagram of DnCNN architecture . Each convolutional layer is accompanied by a ReLU activation function, which introduces nonlinearity to ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11596192/", "content": "by Y Xie · 2024 · Cited by 1 — Schematic diagram of DnCNN architecture . Each convolutional layer is accompanied by a ReLU activation function, which introduces nonlinearity to ..."}
+{"idx": 7, "title": "A parallel and serial denoising network", "date": "", "ddg_snippet": "by Q Zhang · 2023 · Cited by 16 — In this paper, we propose a parallel and serial denoising network (PSDNet) for image denoising to preserve image texture.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0957417423011302", "content": "by Q Zhang · 2023 · Cited by 16 — In this paper, we propose a parallel and serial denoising network (PSDNet) for image denoising to preserve image texture."}
+{"idx": 8, "title": "denoising via bias-free convolutional neural networks", "date": "", "ddg_snippet": "by S Mohan · Cited by 170 — DnCNN (Zhang et al., 2017): A feedforward CNN with 20 convolutional layers , each consisting of 3 × 3 filters, 64 channels, batch normalization (Ioffe & Szegedy, ...", "subpage_snippet": "", "source": "www.cns.nyu.edu", "link": "https://www.cns.nyu.edu/pub/lcv/mohanKadkhodaie19b.pdf", "content": "by S Mohan · Cited by 170 — DnCNN (Zhang et al., 2017): A feedforward CNN with 20 convolutional layers , each consisting of 3 × 3 filters, 64 channels, batch normalization (Ioffe & Szegedy, ..."}
+{"idx": 9, "title": "IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL ...", "date": "", "ddg_snippet": "by D Valsesia · Cited by 73 — Image denoising is a fundamental task in signal processing. Recent works have shown that data-driven approaches employing convolutional neural networks can.", "subpage_snippet": "", "source": "rlgm.github.io", "link": "https://rlgm.github.io/papers/16.pdf", "content": "by D Valsesia · Cited by 73 — Image denoising is a fundamental task in signal processing. Recent works have shown that data-driven approaches employing convolutional neural networks can."}
diff --git a/data/sampled_jsons/Do_Not_Trust_What_They_Tell_equation_8_conn(a,b)_=_2__co-occur__(occur_a_+_occur_b).jsonl b/data/sampled_jsons/Do_Not_Trust_What_They_Tell_equation_8_conn(a,b)_=_2__co-occur__(occur_a_+_occur_b).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..187643c7798c70b972e891fb759e6dbe2590cf6f
--- /dev/null
+++ b/data/sampled_jsons/Do_Not_Trust_What_They_Tell_equation_8_conn(a,b)_=_2__co-occur__(occur_a_+_occur_b).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International recognition of Palestine - Wikipedia", "date": "", "ddg_snippet": "The following day, Ban told reporters: \"I support ... the statehood of Palestinians; an independent, sovereign state of Palestine. It has been long overdue\", but he also stated that \"recognition of a state is something to be determined by the member states.\"[85].", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/International_recognition_of_Palestine", "content": "The following day, Ban told reporters: \"I support ... the statehood of Palestinians; an independent, sovereign state of Palestine. It has been long overdue\", but he also stated that \"recognition of a state is something to be determined by the member states.\"[85]."}
+{"idx": 1, "title": "[FREE] Write balanced net ionic equations for the reactions that occur ...", "date": "", "ddg_snippet": "Net ionic equations represent the actual chemical change occurring in a reaction, excluding spectator ions.Reaction: Two chromium ions will react with carbonate ions to form insoluble chromium(III) carbonate ( Cr 2 ( CO 3 )3 ) and soluble ammonium sulfate.", "subpage_snippet": "", "source": "brainly.com", "link": "https://brainly.com/question/14325831", "content": "Net ionic equations represent the actual chemical change occurring in a reaction, excluding spectator ions.Reaction: Two chromium ions will react with carbonate ions to form insoluble chromium(III) carbonate ( Cr 2 ( CO 3 )3 ) and soluble ammonium sulfate."}
+{"idx": 2, "title": "Aspect | How to tell if ovulation is over? 7 telltale signs", "date": "", "ddg_snippet": "Ovulation occurs when a mature egg is released from the ovary into the fallopian tube, where it may be fertilized by sperm.", "subpage_snippet": "", "source": "www.aspect-health.com", "link": "https://www.aspect-health.com/blog/how-to-tell-if-ovulation-is-over-7-telltale-signs", "content": "Ovulation occurs when a mature egg is released from the ovary into the fallopian tube, where it may be fertilized by sperm."}
+{"idx": 3, "title": "What stage of sleep does bedwetting occur ?", "date": "", "ddg_snippet": "Because enuresis rarely occurs in REM sleep (17), the transition from intermittent NREM and REM sleep to prolonged nonarousable NREM sleep may cause enuresis to appear in a previously night dry child; this is known as secondary enuresis.", "subpage_snippet": "", "source": "www.calendar-uk.co.uk", "link": "https://www.calendar-uk.co.uk/frequently-asked-questions/what-stage-of-sleep-does-bedwetting-occur", "content": "Because enuresis rarely occurs in REM sleep (17), the transition from intermittent NREM and REM sleep to prolonged nonarousable NREM sleep may cause enuresis to appear in a previously night dry child; this is known as secondary enuresis."}
+{"idx": 4, "title": "How did the 2004 tsunami occur ? - Answers", "date": "", "ddg_snippet": "The 2004 Tsunami occurred due to a massive earthquake that happened in the Indian Ocean.", "subpage_snippet": "", "source": "www.Answers.com", "link": "https://www.Answers.com/natural-sciences/How_did_the_2004_tsunami_occur", "content": "The 2004 Tsunami occurred due to a massive earthquake that happened in the Indian Ocean."}
+{"idx": 5, "title": "E = mc ² | Equation , Explanation, & Proof | Britannica", "date": "", "ddg_snippet": "E = mc 2 , equation in German-born physicist Albert Einstein’s theory of special relativity that expresses the fact that mass and energy are the same physical entity and can be changed into each other.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/science/E-mc2-equation", "content": "E = mc 2 , equation in German-born physicist Albert Einstein’s theory of special relativity that expresses the fact that mass and energy are the same physical entity and can be changed into each other."}
+{"idx": 6, "title": "why do breakdowns in communication occur", "date": "", "ddg_snippet": "There are many reasons why breakdowns in communication can occur . Here are some of the most common: Lack of attention: If one or more of the people involved in the communication are not paying attention, they may miss important information or misunderstand what is being said.", "subpage_snippet": "", "source": "lesoutrali.com", "link": "https://lesoutrali.com/us/ads/why-do-breakdowns-in-communication-occur/", "content": "There are many reasons why breakdowns in communication can occur . Here are some of the most common: Lack of attention: If one or more of the people involved in the communication are not paying attention, they may miss important information or misunderstand what is being said."}
+{"idx": 7, "title": "Does Interference Between Entangled Photons Ever Occur ? | Medium", "date": "", "ddg_snippet": "“Interference between two different photons never occurs .” Paul Dirac [1]. Each time when we think we measure one of two entangled photons we actually measure a component of their joint entangled system that can not be an individual photon.", "subpage_snippet": "", "source": "yuribarzov.medium.com", "link": "https://yuribarzov.medium.com/does-interference-between-entangled-photons-ever-occur-f23551b4a342", "content": "“Interference between two different photons never occurs .” Paul Dirac [1]. Each time when we think we measure one of two entangled photons we actually measure a component of their joint entangled system that can not be an individual photon."}
+{"idx": 8, "title": "Solved Complete and balance each equation . If no | Chegg.com", "date": "", "ddg_snippet": "If no reaction occurs , write NOREACTION. Express your answer as a chemical equation . PART A LiI(aq)+BaS(aq)→ PART B KCl(aq)+Pb(C 2 H3O 2 ) 2 (aq)→ PART C CrBr 2 (aq)+Na 2 CO 3(aq)→ PART.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/complete-balance-equation-reaction-occurs-write-noreaction-express-answer-chemical-equatio-q14105779", "content": "If no reaction occurs , write NOREACTION. Express your answer as a chemical equation . PART A LiI(aq)+BaS(aq)→ PART B KCl(aq)+Pb(C 2 H3O 2 ) 2 (aq)→ PART C CrBr 2 (aq)+Na 2 CO 3(aq)→ PART."}
+{"idx": 9, "title": "algebra precalculus - Why does this pattern occur ? - Mathematics...", "date": "", "ddg_snippet": ". My question is, why does this pattern occur ? Maybe I'm missing something obvious, but there are so many variables that I don't know where to start.In the original post, x= a − b . and was changed to x= a − b 2 . . This answer addresses both formulae.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1676170/why-does-this-pattern-occur", "content": ". My question is, why does this pattern occur ? Maybe I'm missing something obvious, but there are so many variables that I don't know where to start.In the original post, x= a − b . and was changed to x= a − b 2 . . This answer addresses both formulae."}
diff --git a/data/sampled_jsons/Donhauser_Ulicna_control_data_PCA_whitening_dictionary_learning_biological_concepts_year_2024.jsonl b/data/sampled_jsons/Donhauser_Ulicna_control_data_PCA_whitening_dictionary_learning_biological_concepts_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..df3c080832dbba05917fa9f816f3df732669c9f4
--- /dev/null
+++ b/data/sampled_jsons/Donhauser_Ulicna_control_data_PCA_whitening_dictionary_learning_biological_concepts_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[PDF] Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Dec 20, 2024 · This work proposes a novel combination of a sparse DL algorithm, Iterative Codebook Feature Learning (ICFL), with a PCA whitening pre-processing step derived from control data , and demonstrates how this combined approach successfully retrieve biologically meaningful concepts , such as cell types and genetic perturbations. Sparse dictionary learning (DL) has emerged as a powerful approach to ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Towards-scientific-discovery-with-dictionary-from-Donhauser-Ulicna/05236df2a670cfdd0132fe13de868dc15ed0876a", "content": "Dec 20, 2024 · This work proposes a novel combination of a sparse DL algorithm, Iterative Codebook Feature Learning (ICFL), with a PCA whitening pre-processing step derived from control data , and demonstrates how this combined approach successfully retrieve biologically meaningful concepts , such as cell types and genetic perturbations. Sparse dictionary learning (DL) has emerged as a powerful approach to ..."}
+{"idx": 1, "title": "Towards scientific discovery with dictionary learning", "date": "", "ddg_snippet": "Two key differences with the dictionary learning approach¨ are: (i) disentanglement/CRL methods consider low-dimensional representations to capture the factors of variation in data, whereas overcomplete dictionary learning seeks a higher-dimensional representation to capture a large set of sparsely-firing concepts ; and (ii) disentanglement/CRL ...", "subpage_snippet": "", "source": "pure.manchester.ac.uk", "link": "https://pure.manchester.ac.uk/ws/portalfiles/portal/352977690/2412.16247v1.pdf", "content": "Two key differences with the dictionary learning approach¨ are: (i) disentanglement/CRL methods consider low-dimensional representations to capture the factors of variation in data, whereas overcomplete dictionary learning seeks a higher-dimensional representation to capture a large set of sparsely-firing concepts ; and (ii) disentanglement/CRL ..."}
+{"idx": 2, "title": "Unsupervised Feature Learning and Deep Learning Tutorial Towards scientific discovery with dictionary learning ... Towards scientific discovery with dictionary learning ... Unsupervised Feature Learning and Deep Learning Tutorial Unsupervised Feature Learning and Deep Learning Tutorial Unsupervised Feature Learning and Deep Learning Tutorial Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Principal Components Analysis ( PCA ) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening , which is an important pre-processing step for many algorithms. Suppose you are training your algorithm o... See full list on ufldl.stanford.edu For our running example, we will use a dataset {x(1),x(2),…,x(m)} with n=2 dimensional inputs, so that x(i)∈ℜ2. Suppose we want to reduce the data from 2 dimensions to 1. (In practice, we might want to reduce data from 256 to 50 dimensions, say; but using lower dimensional data in our example allows us to visualize the algorithms better.) Here is o... See full list on ufldl.stanford.edu Thus, we can represent x in the (u1,u2)-basis by computing (The subscript “rot” comes from the observation that this corresponds to a rotation (and possibly reflection) of the original data .) Lets take the entire training set, and compute x(i)rot=UTx(i) for every i. Plotting this transformed data xrot, we get: This is the training set rotated into ... See full list on ufldl.stanford.edu We see that the principal direction of variation of the data is the first dimension xrot,1of this rotated data . Thus, if we want to reduce this data to one dimension, we can set More generally, if x∈ℜn and we want to reduce it to a k dimensional representation ˜x∈ℜk (where k < n), we would take the first k components of xrot, which correspond to th... See full list on ufldl.stanford.edu Now, ˜x∈ℜk is a lower-dimensional, “compressed” representation of the original x∈ℜn. Given ˜x, how can we recover an approximation ˆx to the original value of x? From an earlier section, we know that x=Uxrot. Further, we can think of ˜x as an approximation to xrot, where we have set the last n−k components to zeros. Thus, given ˜x∈ℜk, we can pad it... See full list on ufldl.stanford.edu How do we set k; i.e., how many PCA components should we retain? In our simple 2 dimensional example, it seemed natural to retain 1 out of the 2 components, but for higher dimensional data , this decision is less trivial. If k is too large, then we won’t be compressing the data much; in the limit of k=n, then we’re just using the original data (but ... See full list on ufldl.stanford.edu For PCA to work, usually we want each of the features x1,x2,…,xn to have a similar range of values to the others (and to have a mean close to zero). If you’ve used PCA on other applications before, you may therefore have separately pre-processed each feature to have zero mean and unit variance, by separately estimating the mean and variance of each... See full list on ufldl.stanford.edu We have used PCA to reduce the dimension of the data. There is a closely related preprocessing step called whitening (or, in some other literatures, sphering) which is needed for some algorithms. If we are training on images, the raw input is redundant, since adjacent pixel values are highly correlated. The goal of whitening is to make the input le... See full list on ufldl.stanford.edu We will first describe whitening using our previous 2D example. We will then describe how this can be combined with smoothing, and finally how to combine this with PCA . How can we make our input features uncorrelated with each other? We had already done this when computing x(i)rot=UTx(i). Repeating our previous figure, our plot for xrotwas: The cov... See full list on ufldl.stanford.edu Finally, it turns out that this way of getting the data to have covariance identity I isn’t unique. Concretely, if R is any orthogonal matrix, so that it satisfies RRT=RTR=I (less formally, if R is a rotation/reflection matrix), then RxPCAwhitewill also have identity covariance. In ZCA whitening , we choose R=U. We define Plotting xZCAwhite, we get:... See full list on ufldl.stanford.edu Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models Konstantin Donhauser * † 1Kristina Ulicna * 2 3Gemma Elyse Moran4Aditya Ravuri† 5Kian Kenyon-Dean3 Cian Eastwood2 3Jason Hartford2 3 6 Abstract Dec 19, 2024 · Abstract Dictionary learning is used to discover biologically meaningful concepts from scientific data, like cell images, with a new algorithm (ICFL) and PCA whitening improving feature selectivity. What is principal components analysis (PCA)? Principal Components Analysis (PCA) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening, which is an important pre-processing step for many algorithms. Why is PCA important? More importantly, understanding PCA will enable us to later implement whitening , which is an important pre-processing step for many algorithms. Suppose you are training your algorithm on images. Then the input will be somewhat redundant, because the values of adjacent pixels in an image are highly correlated. What is the goal of whitening? The goal of whitening is to make the input less redundant ; more formally, our desiderata are that our learning algorithms sees a training input where (i) the features are less correlated with each other, and (ii) the features all have the same variance. We will first describe whitening using our previous 2D example. Dec 20, 2024 · We propose a novel combination of a sparse DL algorithm, Iterative Codebook Feature Learning (ICFL), with a PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful concepts , such as cell types and genetic perturbations.", "subpage_snippet": "", "source": "ufldl.stanford.edu", "link": "http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/", "content": "Principal Components Analysis ( PCA ) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening , which is an important pre-processing step for many algorithms. Suppose you are training your algorithm o... See full list on ufldl.stanford.edu For our running example, we will use a dataset {x(1),x(2),…,x(m)} with n=2 dimensional inputs, so that x(i)∈ℜ2. Suppose we want to reduce the data from 2 dimensions to 1. (In practice, we might want to reduce data from 256 to 50 dimensions, say; but using lower dimensional data in our example allows us to visualize the algorithms better.) Here is o... See full list on ufldl.stanford.edu Thus, we can represent x in the (u1,u2)-basis by computing (The subscript “rot” comes from the observation that this corresponds to a rotation (and possibly reflection) of the original data .) Lets take the entire training set, and compute x(i)rot=UTx(i) for every i. Plotting this transformed data xrot, we get: This is the training set rotated into ... See full list on ufldl.stanford.edu We see that the principal direction of variation of the data is the first dimension xrot,1of this rotated data . Thus, if we want to reduce this data to one dimension, we can set More generally, if x∈ℜn and we want to reduce it to a k dimensional representation ˜x∈ℜk (where k < n), we would take the first k components of xrot, which correspond to th... See full list on ufldl.stanford.edu Now, ˜x∈ℜk is a lower-dimensional, “compressed” representation of the original x∈ℜn. Given ˜x, how can we recover an approximation ˆx to the original value of x? From an earlier section, we know that x=Uxrot. Further, we can think of ˜x as an approximation to xrot, where we have set the last n−k components to zeros. Thus, given ˜x∈ℜk, we can pad it... See full list on ufldl.stanford.edu How do we set k; i.e., how many PCA components should we retain? In our simple 2 dimensional example, it seemed natural to retain 1 out of the 2 components, but for higher dimensional data , this decision is less trivial. If k is too large, then we won’t be compressing the data much; in the limit of k=n, then we’re just using the original data (but ... See full list on ufldl.stanford.edu For PCA to work, usually we want each of the features x1,x2,…,xn to have a similar range of values to the others (and to have a mean close to zero). If you’ve used PCA on other applications before, you may therefore have separately pre-processed each feature to have zero mean and unit variance, by separately estimating the mean and variance of each... See full list on ufldl.stanford.edu We have used PCA to reduce the dimension of the data. There is a closely related preprocessing step called whitening (or, in some other literatures, sphering) which is needed for some algorithms. If we are training on images, the raw input is redundant, since adjacent pixel values are highly correlated. The goal of whitening is to make the input le... See full list on ufldl.stanford.edu We will first describe whitening using our previous 2D example. We will then describe how this can be combined with smoothing, and finally how to combine this with PCA . How can we make our input features uncorrelated with each other? We had already done this when computing x(i)rot=UTx(i). Repeating our previous figure, our plot for xrotwas: The cov... See full list on ufldl.stanford.edu Finally, it turns out that this way of getting the data to have covariance identity I isn’t unique. Concretely, if R is any orthogonal matrix, so that it satisfies RRT=RTR=I (less formally, if R is a rotation/reflection matrix), then RxPCAwhitewill also have identity covariance. In ZCA whitening , we choose R=U. We define Plotting xZCAwhite, we get:... See full list on ufldl.stanford.edu Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models Konstantin Donhauser * † 1Kristina Ulicna * 2 3Gemma Elyse Moran4Aditya Ravuri† 5Kian Kenyon-Dean3 Cian Eastwood2 3Jason Hartford2 3 6 Abstract Dec 19, 2024 · Abstract Dictionary learning is used to discover biologically meaningful concepts from scientific data, like cell images, with a new algorithm (ICFL) and PCA whitening improving feature selectivity. What is principal components analysis (PCA)? Principal Components Analysis (PCA) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening, which is an important pre-processing step for many algorithms. Why is PCA important? More importantly, understanding PCA will enable us to later implement whitening , which is an important pre-processing step for many algorithms. Suppose you are training your algorithm on images. Then the input will be somewhat redundant, because the values of adjacent pixels in an image are highly correlated. What is the goal of whitening? The goal of whitening is to make the input less redundant ; more formally, our desiderata are that our learning algorithms sees a training input where (i) the features are less correlated with each other, and (ii) the features all have the same variance. We will first describe whitening using our previous 2D example. Dec 20, 2024 · We propose a novel combination of a sparse DL algorithm, Iterative Codebook Feature Learning (ICFL), with a PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful concepts , such as cell types and genetic perturbations."}
+{"idx": 3, "title": "Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models Konstantin Donhauser * † 1Kristina Ulicna * 2 3Gemma Elyse Moran4Aditya Ravuri† 5Kian Kenyon-Dean3 Cian Eastwood2 3Jason Hartford2 3 6 Abstract", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fBn6om49Ur", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models Konstantin Donhauser * † 1Kristina Ulicna * 2 3Gemma Elyse Moran4Aditya Ravuri† 5Kian Kenyon-Dean3 Cian Eastwood2 3Jason Hartford2 3 6 Abstract"}
+{"idx": 4, "title": "Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Dec 19, 2024 · Abstract Dictionary learning is used to discover biologically meaningful concepts from scientific data, like cell images, with a new algorithm (ICFL) and PCA whitening improving feature selectivity.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2412.16247", "content": "Dec 19, 2024 · Abstract Dictionary learning is used to discover biologically meaningful concepts from scientific data, like cell images, with a new algorithm (ICFL) and PCA whitening improving feature selectivity."}
+{"idx": 5, "title": "Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Dec 20, 2024 · We propose a novel combination of a sparse DL algorithm, Iterative Codebook Feature Learning (ICFL), with a PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful concepts , such as cell types and genetic perturbations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.16247v3", "content": "Dec 20, 2024 · We propose a novel combination of a sparse DL algorithm, Iterative Codebook Feature Learning (ICFL), with a PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful concepts , such as cell types and genetic perturbations."}
+{"idx": 6, "title": "Towards scientific discovery with dictionary learning", "date": "", "ddg_snippet": "by K Donhauser · 2024 · Cited by 1 — ... PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.16247", "content": "by K Donhauser · 2024 · Cited by 1 — ... PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful ..."}
+{"idx": 7, "title": "Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "... PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful concepts , such ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fBn6om49Ur&noteId=A66YEyGgUV", "content": "... PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful concepts , such ..."}
+{"idx": 8, "title": "Towards scientific discovery with dictionary learning", "date": "", "ddg_snippet": "... PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful concepts , such ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44540", "content": "... PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful concepts , such ..."}
+{"idx": 9, "title": "Towards scientific discovery with dictionary learning", "date": "", "ddg_snippet": "... PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful concepts , such ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165789", "content": "... PCA whitening pre-processing step derived from control data . Using this combined approach, we successfully retrieve biologically meaningful concepts , such ..."}
diff --git a/data/sampled_jsons/Dynamic_Hierarchical_Collaboration_Equation_19_scaling_factor.jsonl b/data/sampled_jsons/Dynamic_Hierarchical_Collaboration_Equation_19_scaling_factor.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3049522bffdbf3aee7c84d0e8b2562099b2c6c8
--- /dev/null
+++ b/data/sampled_jsons/Dynamic_Hierarchical_Collaboration_Equation_19_scaling_factor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dynamic hierarchical intrusion detection system for internet of ...", "date": "", "ddg_snippet": "17 Nov 2024 — As a multiplier, the scaling factor affects the magnitude of the values of the response time. ... Including an exponential decay factor , which ...", "subpage_snippet": "", "source": "ietresearch.onlinelibrary.wiley.com", "link": "https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/cmu2.12865", "content": "17 Nov 2024 — As a multiplier, the scaling factor affects the magnitude of the values of the response time. ... Including an exponential decay factor , which ..."}
+{"idx": 1, "title": "A Hierarchical Feature Fusion and Dynamic Collaboration ...", "date": "", "ddg_snippet": "by X Yan · 2025 · Cited by 7 — The hierarchical feature fusion algorithm enhances the feature expression capability of small targets by combining shallow detail information ... 12 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/11005582.pdf", "content": "by X Yan · 2025 · Cited by 7 — The hierarchical feature fusion algorithm enhances the feature expression capability of small targets by combining shallow detail information ... 12 pages"}
+{"idx": 2, "title": "Large-scale dynamic surgical scheduling under uncertainty ...", "date": "", "ddg_snippet": "We propose a two-level dynamic scheduling framework based on hierarchical reinforcement learning to solve dynamic surgical scheduling problems.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/00207543.2024.2361449?af=R", "content": "We propose a two-level dynamic scheduling framework based on hierarchical reinforcement learning to solve dynamic surgical scheduling problems."}
+{"idx": 3, "title": "HED-FL: A hierarchical, energy efficient, and dynamic ...", "date": "", "ddg_snippet": "by F De Rango · 2023 · Cited by 35 — This paper proposes a novel, energy-efficient, and dynamic FL-based approach considering a hierarchical edge FL architecture called HED-FL.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1574119223000627", "content": "by F De Rango · 2023 · Cited by 35 — This paper proposes a novel, energy-efficient, and dynamic FL-based approach considering a hierarchical edge FL architecture called HED-FL."}
+{"idx": 4, "title": "A SCALING METHODOLOGY FOR DYNAMIC SYSTEMS", "date": "", "ddg_snippet": "by B Kittirungsi · 2008 · Cited by 12 — Substituting the definition of a scale factor into Equation 3.3 and complying with the similitude principle yields the following scaling laws for the ...", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "https://deepblue.lib.umich.edu/bitstream/handle/2027.42/58383/burit_1.pdf?sequence=1", "content": "by B Kittirungsi · 2008 · Cited by 12 — Substituting the definition of a scale factor into Equation 3.3 and complying with the similitude principle yields the following scaling laws for the ..."}
+{"idx": 5, "title": "Hierarchical Intention Tracking with Switching Trees for ...", "date": "", "ddg_snippet": "8 Jun 2025 — We propose a Hierarchical Intention Tracking (HIT) algorithm for collaborative robots to track dynamic and hierarchical human intentions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07004v1", "content": "8 Jun 2025 — We propose a Hierarchical Intention Tracking (HIT) algorithm for collaborative robots to track dynamic and hierarchical human intentions ..."}
+{"idx": 6, "title": "A Hierarchical Behavioral Dynamic Approach for Naturally ...", "date": "", "ddg_snippet": "9 Jun 2019 — Note that the constant scaling factors , σs and δs, operate to normalize the task specific αs with respect to the current task space. Details ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1155/2019/5964632", "content": "9 Jun 2019 — Note that the constant scaling factors , σs and δs, operate to normalize the task specific αs with respect to the current task space. Details ..."}
+{"idx": 7, "title": "Dual-branch dynamic hierarchical U-Net with multi-layer ...", "date": "", "ddg_snippet": "by Z Wang · 2025 · Cited by 5 — D2HU-Net enables the most advanced segmentation capabilities on different medical image datasets, which can help doctors diagnose and treat diseases.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11894187/", "content": "by Z Wang · 2025 · Cited by 5 — D2HU-Net enables the most advanced segmentation capabilities on different medical image datasets, which can help doctors diagnose and treat diseases."}
+{"idx": 8, "title": "Dynamic Hierarchical Reinforcement Learning Framework ...", "date": "", "ddg_snippet": "by D Xu · 2025 — In the lower-layer, we combine attention mechanism with multi-agent RL and graph convolutional networks to design a scalable algorithm that maximizes local ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tm/5555/01/10947617/25AFjrl9g3u", "content": "by D Xu · 2025 — In the lower-layer, we combine attention mechanism with multi-agent RL and graph convolutional networks to design a scalable algorithm that maximizes local ..."}
+{"idx": 9, "title": "Dynamic Hierarchical Mimicking Towards Consistent ...", "date": "", "ddg_snippet": "by D Li · 2020 · Cited by 32 — We go one step further to promote multi-level inter- actions among different branches through an optimization formula with probabilistic prediction matching ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Dynamic_Hierarchical_Mimicking_Towards_Consistent_Optimization_Objectives_CVPR_2020_paper.pdf", "content": "by D Li · 2020 · Cited by 32 — We go one step further to promote multi-level inter- actions among different branches through an optimization formula with probabilistic prediction matching ... 10 pages"}
diff --git a/data/sampled_jsons/E91gjsccP1_HtmlRAG-_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl b/data/sampled_jsons/E91gjsccP1_HtmlRAG-_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl
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index 0000000000000000000000000000000000000000..d02abb34f6c28418ac641c7281406133aa605a4e
--- /dev/null
+++ b/data/sampled_jsons/E91gjsccP1_HtmlRAG-_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Retrieved Knowledge in RAG Systems . HTML of HTML cleaning is suitable for RAG systems equipped 328. missing in plain text . We emphasize that HTML is a popular data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "Retrieved Knowledge in RAG Systems . HTML of HTML cleaning is suitable for RAG systems equipped 328. missing in plain text . We emphasize that HTML is a popular data."}
+{"idx": 1, "title": "[2411.02959] HtmlRAG : HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
+{"idx": 2, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG ."}
+{"idx": 3, "title": "Paper tables with annotated results for HtmlRAG : HTML is Better ...", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems . Retrieval -Augmented Generation ( RAG ) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/htmlrag-html-is-better-than-plain-text-for/review/", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems . Retrieval -Augmented Generation ( RAG ) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs."}
+{"idx": 4, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/HtmlRAG:-HTML-is-Better-Than-Plain-Text-for-Modeling-Retrieved-Knowledge-in-RAG-Systems-31e6dffd-f34d-48e0-8884-b4b1d1deeb4a", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
+{"idx": 5, "title": "plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 6, "title": "HtmlRAG HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Traditional RAG systems convert HTML documents to plain text , which leads to the loss of structural and semantic information. The authors propose using HTML directly to preserve this information, arguing that large language models (LLMs)...", "subpage_snippet": "", "source": "aithemes.net", "link": "https://aithemes.net/en/posts/HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_tags", "content": "Traditional RAG systems convert HTML documents to plain text , which leads to the loss of structural and semantic information. The authors propose using HTML directly to preserve this information, arguing that large language models (LLMs)..."}
+{"idx": 7, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "View 1 comments: The comparison in Table 1 is not fair. HtmlRAG has a pruner, while other methods compared do not.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2411.02959v1", "content": "View 1 comments: The comparison in Table 1 is not fair. HtmlRAG has a pruner, while other methods compared do not."}
+{"idx": 8, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Traditional RAG systems often convert HTML to plain text , resulting in a significant loss of structural and semantic information. This loss can negatively impact the LLM’s ability to accurately comprehend and generate responses based on the retrieved knowledge .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2411.02959/", "content": "Traditional RAG systems often convert HTML to plain text , resulting in a significant loss of structural and semantic information. This loss can negatively impact the LLM’s ability to accurately comprehend and generate responses based on the retrieved knowledge ."}
+{"idx": 9, "title": "Implementing HtmlRAG : Enhancing Retrieval -Augmented Generation...", "date": "", "ddg_snippet": "Traditional RAG systems typically use plain text extracted from HTML documents as the format for retrieved knowledge . However, this approach often leads to the loss of structural and semantic information inherent in HTML , such as headings, lists, and tables.", "subpage_snippet": "", "source": "blog.devgenius.io", "link": "https://blog.devgenius.io/implementing-htmlrag-enhancing-retrieval-augmented-generation-with-html-knowledge-91cdd6278e23", "content": "Traditional RAG systems typically use plain text extracted from HTML documents as the format for retrieved knowledge . However, this approach often leads to the loss of structural and semantic information inherent in HTML , such as headings, lists, and tables."}
diff --git a/data/sampled_jsons/E91gjsccP1_HtmlRAG-_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_year_2023.jsonl b/data/sampled_jsons/E91gjsccP1_HtmlRAG-_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0de8ef6b0e270a9e36d3f32d5e1d2f7b5832f540
--- /dev/null
+++ b/data/sampled_jsons/E91gjsccP1_HtmlRAG-_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG ."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... Understanding HtmlRAG: HTML is Better Than Plain Text for ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG - a zstanjj Collection - Hugging Face (PDF) HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "Nov 5, 2024 · To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy. Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. Nov 5, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71 Nov 5, 2024 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. Dec 31, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "Nov 5, 2024 · To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy. Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. Nov 5, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71 Nov 5, 2024 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. Dec 31, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen"}
+{"idx": 2, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 3, "title": "Understanding HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy.", "subpage_snippet": "", "source": "techchilli.com", "link": "https://techchilli.com/artificial-intelligence/understanding-htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledge-in-rag-systems/", "content": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy."}
+{"idx": 4, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3696410.3714546", "content": "Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system."}
+{"idx": 5, "title": "HtmlRAG - a zstanjj Collection - Hugging Face", "date": "", "ddg_snippet": "Nov 5, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/zstanjj/htmlrag-671f03af5c3da2e7b5371aa4", "content": "Nov 5, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71"}
+{"idx": 6, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "Dec 31, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=leCEFyMyxg", "content": "Dec 31, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen"}
+{"idx": 7, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Retrieved Knowledge in RAG Systems . HTML of HTML cleaning is suitable for RAG systems equipped 328. missing in plain text . We emphasize that HTML is a popular data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "Retrieved Knowledge in RAG Systems . HTML of HTML cleaning is suitable for RAG systems equipped 328. missing in plain text . We emphasize that HTML is a popular data."}
+{"idx": 8, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
+{"idx": 9, "title": "Paper page - HtmlRAG : HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "Abstract. HtmlRAG enhances Retrieval -Augmented Generation ( RAG ) systems by using HTML instead of plain text , improving knowledge modeling and reducing information loss through HTML cleaning, compression, and pruning.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "Abstract. HtmlRAG enhances Retrieval -Augmented Generation ( RAG ) systems by using HTML instead of plain text , improving knowledge modeling and reducing information loss through HTML cleaning, compression, and pruning."}
diff --git a/data/sampled_jsons/E91gjsccP1_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl b/data/sampled_jsons/E91gjsccP1_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d190fde14c29a1c4a0736c0088379125fd3d499
--- /dev/null
+++ b/data/sampled_jsons/E91gjsccP1_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 1, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG ."}
+{"idx": 2, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... Understanding HtmlRAG: HTML is Better Than Plain Text for ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG - a zstanjj Collection - Hugging Face (PDF) HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "Nov 5, 2024 · To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy. Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. Nov 5, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71 Nov 5, 2024 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. Dec 31, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "Nov 5, 2024 · To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy. Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. Nov 5, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71 Nov 5, 2024 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. Dec 31, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen"}
+{"idx": 3, "title": "Understanding HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy.", "subpage_snippet": "", "source": "techchilli.com", "link": "https://techchilli.com/artificial-intelligence/understanding-htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledge-in-rag-systems/", "content": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy."}
+{"idx": 4, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3696410.3714546", "content": "Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system."}
+{"idx": 5, "title": "HtmlRAG - a zstanjj Collection - Hugging Face", "date": "", "ddg_snippet": "Nov 5, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/zstanjj/htmlrag-671f03af5c3da2e7b5371aa4", "content": "Nov 5, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71"}
+{"idx": 6, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "Dec 31, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=leCEFyMyxg", "content": "Dec 31, 2024 · HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen"}
+{"idx": 7, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Retrieved Knowledge in RAG Systems . HTML of HTML cleaning is suitable for RAG systems equipped 328. missing in plain text . We emphasize that HTML is a popular data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "Retrieved Knowledge in RAG Systems . HTML of HTML cleaning is suitable for RAG systems equipped 328. missing in plain text . We emphasize that HTML is a popular data."}
+{"idx": 8, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
+{"idx": 9, "title": "Paper page - HtmlRAG : HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "Abstract. HtmlRAG enhances Retrieval -Augmented Generation ( RAG ) systems by using HTML instead of plain text , improving knowledge modeling and reducing information loss through HTML cleaning, compression, and pruning.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "Abstract. HtmlRAG enhances Retrieval -Augmented Generation ( RAG ) systems by using HTML instead of plain text , improving knowledge modeling and reducing information loss through HTML cleaning, compression, and pruning."}
diff --git a/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_paper_year_2024.jsonl b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_paper_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9fd2264c390586feebd2495f9aa6b0f9b578d0f7
--- /dev/null
+++ b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_paper_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "by W Lee · 2025 — The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04757", "content": "by W Lee · 2025 — The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts."}
+{"idx": 1, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The paper introduces ELITE, a new safety benchmark designed to evaluate the toxicity and risks associated with Vision-Language Models (VLMs). Current ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=583klsIjNx&noteId=lkuNd7fIAG", "content": "The paper introduces ELITE, a new safety benchmark designed to evaluate the toxicity and risks associated with Vision-Language Models (VLMs). Current ..."}
+{"idx": 2, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for ...", "date": "", "ddg_snippet": "by W Lee · 2025 — Our experiments demonstrate that the ELITE evaluator achieves superior align- ment with human evaluations compared to prior automated methods, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "by W Lee · 2025 — Our experiments demonstrate that the ELITE evaluator achieves superior align- ment with human evaluations compared to prior automated methods, ..."}
+{"idx": 3, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "These steps and results illustrate the comprehensive evaluation conducted in the paper to assess the safety of Vision Language Models using the ELITE benchmark.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/167387", "content": "These steps and results illustrate the comprehensive evaluation conducted in the paper to assess the safety of Vision Language Models using the ELITE benchmark."}
+{"idx": 4, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "7 Feb 2025 — The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety \" presents a novel framework designed to evaluate the safety of ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/es/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "7 Feb 2025 — The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety \" presents a novel framework designed to evaluate the safety of ..."}
+{"idx": 5, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "This paper introduces a new tool called the ELITE evaluator to better assess the safety of Vision Language Models (VLMs), which can sometimes produce ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46445/paper", "content": "This paper introduces a new tool called the ELITE evaluator to better assess the safety of Vision Language Models (VLMs), which can sometimes produce ..."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "5 days ago — ELITE: Enhanced Language-Image Toxicity Evaluation for Safety ... Current Vision Language Models (VLMs) remain vulnerable to malicious prompts ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=safety+assessment+benchmark", "content": "5 days ago — ELITE: Enhanced Language-Image Toxicity Evaluation for Safety ... Current Vision Language Models (VLMs) remain vulnerable to malicious prompts ..."}
+{"idx": 7, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for ...", "date": "", "ddg_snippet": "We believe ELITE represents a meaningful step toward establishing higher standards in VLM safety assessment . Paper : https://lnkd.in/dwctW22g.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/aim-intelligence-co_elite-enhanced-language-image-toxicity-evaluation-activity-7300928673929465857-ra1m", "content": "We believe ELITE represents a meaningful step toward establishing higher standards in VLM safety assessment . Paper : https://lnkd.in/dwctW22g."}
+{"idx": 8, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITEevaluator explicitly incorporates a toxicity score to accurately assessharmfulness in multimodal contexts, where VLMs often provide specific,convincing ...", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/574698/elite:-enhanced-language-image-toxicity-evaluation-for-safety", "content": "The ELITEevaluator explicitly incorporates a toxicity score to accurately assessharmfulness in multimodal contexts, where VLMs often provide specific,convincing ..."}
+{"idx": 9, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "TLDR: This paper introduces a rubric-based safety evaluation method and a high-quality benchmark to address inaccuracies in previous safety evaluations of ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/zh-CN/chatpaper/paper/167387", "content": "TLDR: This paper introduces a rubric-based safety evaluation method and a high-quality benchmark to address inaccuracies in previous safety evaluations of ..."}
diff --git a/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_methodology_StrongREJECT_comparison.jsonl b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_methodology_StrongREJECT_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4262a11e3df336e70ec5904de22d2580c94ba8f8
--- /dev/null
+++ b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_methodology_StrongREJECT_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "For a fair comparison , both the ELITE and StrongREJECT evaluators are evaluated using the GPT-4o on the human evaluation dataset consisting of 963 image -text pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "For a fair comparison , both the ELITE and StrongREJECT evaluators are evaluated using the GPT-4o on the human evaluation dataset consisting of 963 image -text pairs."}
+{"idx": 1, "title": "How ELITE Reveals Dangerous Weaknesses in Vision-Language AI", "date": "", "ddg_snippet": "2. ELITE Evaluator: Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aim-intelligence/how-elite-reveals-dangerous-weaknesses-in-vision-language-ai-ffa208b7546c", "content": "2. ELITE Evaluator: Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ..."}
+{"idx": 2, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "New York, New York--(Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety ...", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/aim-intelligences-elite-collaborative-paper-161800502.html", "content": "New York, New York--(Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety ..."}
+{"idx": 3, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "www.newsfilecorp.com", "link": "https://www.newsfilecorp.com/release/252268/AIM-Intelligences-ELITE-Collaborative-Paper-Accepted-by-the-ICML", "content": "The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 4, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04757", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator."}
+{"idx": 5, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "New York, New York-- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety\", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University. AIM Intelligence CI The paper proposes ...", "subpage_snippet": "", "source": "index.businessinsurance.com", "link": "https://index.businessinsurance.com/businessinsurance/article/newsfile-2025-5-15-aim-intelligences-elite-collaborative-paper-accepted-by-the-icml", "content": "New York, New York-- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety\", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University. AIM Intelligence CI The paper proposes ..."}
+{"idx": 6, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ELITE:-Enhanced-Language-Image-Toxicity-Evaluation-Lee-Lee/2bf4206276d5f574bbb2e13a56b29b4522fea675", "content": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ..."}
+{"idx": 7, "title": "Our paper on ELITE, a safety benchmark for VLMs, accepted at ICML 2025", "date": "", "ddg_snippet": "Our paper \"ELITE: Enhanced Language-Image Toxicity Evaluation for Safety\" has been accepted at ICML 2025 in Vancouver! We propose the ELITE benchmark, a high-quality safety evaluation ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/wonjun-lee-7566b7259_our-paper-elite-enhanced-language-image-activity-7324072840427642880-XG3o", "content": "Our paper \"ELITE: Enhanced Language-Image Toxicity Evaluation for Safety\" has been accepted at ICML 2025 in Vancouver! We propose the ELITE benchmark, a high-quality safety evaluation ..."}
+{"idx": 8, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "New York, New York- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety\", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University.", "subpage_snippet": "", "source": "matribhumisamachar.com", "link": "https://matribhumisamachar.com/en/2025/05/15/aim-intelligences-elite-collaborative-paper-accepted-by-the-icml/", "content": "New York, New York- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety\", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University."}
+{"idx": 9, "title": "cvlab.yonsei.ac.kr", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator.", "subpage_snippet": "", "source": "cvlab.yonsei.ac.kr", "link": "https://cvlab.yonsei.ac.kr/projects/ELITE/", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator."}
diff --git a/data/sampled_jsons/ELITE_benchmark_generated_higher_E-ASR_VLGuard_reason_explanation_why_substantially_year_2024.jsonl b/data/sampled_jsons/ELITE_benchmark_generated_higher_E-ASR_VLGuard_reason_explanation_why_substantially_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..091de1365f7354fbd3a5f8d6323d9ade19a37f28
--- /dev/null
+++ b/data/sampled_jsons/ELITE_benchmark_generated_higher_E-ASR_VLGuard_reason_explanation_why_substantially_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Jul 24, 2025 · Furthermore, the ELITE benchmark ( generated ) demonstrates a substantial increase in E-ASR through effective filtering. These experimental results indicate that the low E-ASR observed in existing benchmarks suggests a substantial number of image-text pairs that fail to elicit harmful responses from VLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "Jul 24, 2025 · Furthermore, the ELITE benchmark ( generated ) demonstrates a substantial increase in E-ASR through effective filtering. These experimental results indicate that the low E-ASR observed in existing benchmarks suggests a substantial number of image-text pairs that fail to elicit harmful responses from VLMs."}
+{"idx": 1, "title": "How ELITE Reveals Dangerous Weaknesses in Vision-Language AI", "date": "", "ddg_snippet": "The ELITE benchmark consists of 4,587 image-text pairs drawn from both existing benchmarks (like VLGuard , MM-SafetyBench) and 1,054 newly generated examples using four distinct attack methods:", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aim-intelligence/how-elite-reveals-dangerous-weaknesses-in-vision-language-ai-ffa208b7546c", "content": "The ELITE benchmark consists of 4,587 image-text pairs drawn from both existing benchmarks (like VLGuard , MM-SafetyBench) and 1,054 newly generated examples using four distinct attack methods:"}
+{"idx": 2, "title": "GitHub - ys-zong/VLGuard: [ICML 2024] Safety Fine-Tuning at ...", "date": "", "ddg_snippet": "Feb 6, 2024 · [2024/06/19] We released the fine-tuned model weights that we used for experiments. [2024/05/01] VLGuard is accepted to ICML 2024! [2024/02/06] We released arXiv and data for VLGuard . With our safety fine-tuning, the we substantially improve the safety of vision large language models while maintaining the helpfulness.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ys-zong/VLGuard", "content": "Feb 6, 2024 · [2024/06/19] We released the fine-tuned model weights that we used for experiments. [2024/05/01] VLGuard is accepted to ICML 2024! [2024/02/06] We released arXiv and data for VLGuard . With our safety fine-tuning, the we substantially improve the safety of vision large language models while maintaining the helpfulness."}
+{"idx": 3, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "achieves significantly higher E - ASR across all models. Fur- thermore, the ELITE benchmark ( generated ) demonstrates a substantial increase in E - ASR through ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.04757v1", "content": "achieves significantly higher E - ASR across all models. Fur- thermore, the ELITE benchmark ( generated ) demonstrates a substantial increase in E - ASR through ..."}
+{"idx": 4, "title": "Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision ...", "date": "", "ddg_snippet": "We analyze existing VLLMs and underpinning LLMs and show how popular VLM instruction-following protocols make VLLMs substantially more susceptible to jailbreak attacks than the corresponding LLMs. To the best of our knowledge, we build the first safety fine-tuning dataset VLGuard for VLLMs. VLGuard also comes with a test suite for evaluation.", "subpage_snippet": "", "source": "ys-zong.github.io", "link": "https://ys-zong.github.io/VLGuard/", "content": "We analyze existing VLLMs and underpinning LLMs and show how popular VLM instruction-following protocols make VLLMs substantially more susceptible to jailbreak attacks than the corresponding LLMs. To the best of our knowledge, we build the first safety fine-tuning dataset VLGuard for VLLMs. VLGuard also comes with a test suite for evaluation."}
+{"idx": 5, "title": "[2402.02207] Safety Fine-Tuning at (Almost) No Cost: A ... velpegor.github.io Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision ...", "date": "", "ddg_snippet": "Feb 3, 2024 · To address this issue, we first curate a vision-language safe instruction-following dataset VLGuard covering various harmful categories. Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs. Contributions of ELITE . (a) Benchmark Construction: The ELITE benchmark is a high -quality benchmark built by filtering out unsuccessful image-text pairs using the ELITE evaluator. (b) Generated Image-Text Pairs: Image-text pair with various methods for inducing harmful responses from VLMs. (c) Evaluation Method: The ELITE evaluator is a more precise rubric-based safety evaluation method ... May 1, 2024 · To address this issue, we first curate a vision-language safe instruction-following dataset VLGuard covering various harmful categories. Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.02207", "content": "Feb 3, 2024 · To address this issue, we first curate a vision-language safe instruction-following dataset VLGuard covering various harmful categories. Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs. Contributions of ELITE . (a) Benchmark Construction: The ELITE benchmark is a high -quality benchmark built by filtering out unsuccessful image-text pairs using the ELITE evaluator. (b) Generated Image-Text Pairs: Image-text pair with various methods for inducing harmful responses from VLMs. (c) Evaluation Method: The ELITE evaluator is a more precise rubric-based safety evaluation method ... May 1, 2024 · To address this issue, we first curate a vision-language safe instruction-following dataset VLGuard covering various harmful categories. Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs."}
+{"idx": 6, "title": "velpegor.github.io", "date": "", "ddg_snippet": "Contributions of ELITE . (a) Benchmark Construction: The ELITE benchmark is a high -quality benchmark built by filtering out unsuccessful image-text pairs using the ELITE evaluator. (b) Generated Image-Text Pairs: Image-text pair with various methods for inducing harmful responses from VLMs. (c) Evaluation Method: The ELITE evaluator is a more precise rubric-based safety evaluation method ...", "subpage_snippet": "", "source": "velpegor.github.io", "link": "https://velpegor.github.io/ELITE/", "content": "Contributions of ELITE . (a) Benchmark Construction: The ELITE benchmark is a high -quality benchmark built by filtering out unsuccessful image-text pairs using the ELITE evaluator. (b) Generated Image-Text Pairs: Image-text pair with various methods for inducing harmful responses from VLMs. (c) Evaluation Method: The ELITE evaluator is a more precise rubric-based safety evaluation method ..."}
+{"idx": 7, "title": "Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision ...", "date": "", "ddg_snippet": "May 1, 2024 · To address this issue, we first curate a vision-language safe instruction-following dataset VLGuard covering various harmful categories. Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=bWZKvF0g7G", "content": "May 1, 2024 · To address this issue, we first curate a vision-language safe instruction-following dataset VLGuard covering various harmful categories. Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs."}
+{"idx": 8, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "18 Jun 2025 — To address these issues, we introduce the ELITE benchmark , a new safety benchmark designed to more accurately evaluate harmful responses in VLMs ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=583klsIjNx&noteId=lkuNd7fIAG", "content": "18 Jun 2025 — To address these issues, we introduce the ELITE benchmark , a new safety benchmark designed to more accurately evaluate harmful responses in VLMs ..."}
+{"idx": 9, "title": "Do We Really Need Curated Malicious Data for Safety ...", "date": "", "ddg_snippet": "by Y Wang · 2025 · Cited by 2 — In the training phase, VLGuard [55] collects a safety dataset for supervised finetuning (SFT), achieving satisfying results in the defense of typographic ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Do_We_Really_Need_Curated_Malicious_Data_for_Safety_Alignment_CVPR_2025_paper.pdf", "content": "by Y Wang · 2025 · Cited by 2 — In the training phase, VLGuard [55] collects a safety dataset for supervised finetuning (SFT), achieving satisfying results in the defense of typographic ... 11 pages"}
diff --git a/data/sampled_jsons/ELITE_benchmark_higher_E-ASR_VLGuard_reason_explanation.jsonl b/data/sampled_jsons/ELITE_benchmark_higher_E-ASR_VLGuard_reason_explanation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..de337480bc9d9e80a43478d7de080fb19eaf5cd3
--- /dev/null
+++ b/data/sampled_jsons/ELITE_benchmark_higher_E-ASR_VLGuard_reason_explanation.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for ...", "date": "", "ddg_snippet": "by W Lee · 2025 — In this section, we demonstrate the superiority of both the. ELITE benchmark and the ELITE benchmark (generated). ... VLGuard (Zong et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "by W Lee · 2025 — In this section, we demonstrate the superiority of both the. ELITE benchmark and the ELITE benchmark (generated). ... VLGuard (Zong et al."}
+{"idx": 1, "title": "How ELITE Reveals Dangerous Weaknesses in Vision ...", "date": "", "ddg_snippet": "... VLGuard , MM-SafetyBench) and 1,054 newly generated examples ... ELITE benchmark yields ~2–3x higher E-ASR compared to previous benchmarks ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aim-intelligence/how-elite-reveals-dangerous-weaknesses-in-vision-language-ai-ffa208b7546c", "content": "... VLGuard , MM-SafetyBench) and 1,054 newly generated examples ... ELITE benchmark yields ~2–3x higher E-ASR compared to previous benchmarks ..."}
+{"idx": 2, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "24 Jul 2025 — VLGuard (Zong et al., 2024) introduces a fine-grained evaluation ... ELITE benchmark , and the ELITE benchmark (generated). Note that we ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "24 Jul 2025 — VLGuard (Zong et al., 2024) introduces a fine-grained evaluation ... ELITE benchmark , and the ELITE benchmark (generated). Note that we ..."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/ELITE_equation_2_toxicity_formula_score_calculation_year_2024.jsonl b/data/sampled_jsons/ELITE_equation_2_toxicity_formula_score_calculation_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0bdbcf4f5b8274c534b197b27a7250bbdca5adff
--- /dev/null
+++ b/data/sampled_jsons/ELITE_equation_2_toxicity_formula_score_calculation_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Acute Toxicity Classification for Mixture (Acute Toxicity ...", "date": "", "ddg_snippet": "Feb 9, 2019 · There are 2 equations to choose depending on whether the total concentration of ingredients with unknown toxicity is less than 10% or not. How to Get Acute Toxicity Estimate (ATE) for Ingredient To calculate the ATE of a mixture, you must get the ATE of all ingredients with known toxicity and the concentration of each ingredient first.", "subpage_snippet": "", "source": "www.chemsafetypro.com", "link": "https://www.chemsafetypro.com/Topics/GHS/Examples_of_Acute_Toxicity_Classification_for_Mixture.html", "content": "Feb 9, 2019 · There are 2 equations to choose depending on whether the total concentration of ingredients with unknown toxicity is less than 10% or not. How to Get Acute Toxicity Estimate (ATE) for Ingredient To calculate the ATE of a mixture, you must get the ATE of all ingredients with known toxicity and the concentration of each ingredient first."}
+{"idx": 1, "title": "ATE Calculations | Help Center | ICC Compliance Center", "date": "", "ddg_snippet": "Apr 25, 2022 · ATE or Acute Toxicity Estimate is a calculation needed when authoring a Safety Data Sheet (SDS) for an untested mixture. Let’s face it, most mixtures are untested.", "subpage_snippet": "", "source": "www.thecompliancecenter.com", "link": "https://www.thecompliancecenter.com/ate-calculations/", "content": "Apr 25, 2022 · ATE or Acute Toxicity Estimate is a calculation needed when authoring a Safety Data Sheet (SDS) for an untested mixture. Let’s face it, most mixtures are untested."}
+{"idx": 2, "title": "Calculating the toxicity of plant protection products", "date": "", "ddg_snippet": "Systemic Toxicity - Additivity Formula Accounts for the contribution of each component to the toxicity of the mixture All ingredients treated equally, doesn’t give more weightage to active ingredient(s) Does not consider the type of solvent (dosing vehicle). Assumes the use of same solvent for all co-formulants.", "subpage_snippet": "", "source": "ntp.niehs.nih.gov", "link": "https://ntp.niehs.nih.gov/sites/default/files/iccvam/meetings/at-wksp-2019/ppt/04-settivari-508.pdf", "content": "Systemic Toxicity - Additivity Formula Accounts for the contribution of each component to the toxicity of the mixture All ingredients treated equally, doesn’t give more weightage to active ingredient(s) Does not consider the type of solvent (dosing vehicle). Assumes the use of same solvent for all co-formulants."}
+{"idx": 3, "title": "Acute Toxicity Estimate (ATE) Calculation and Hazard ...", "date": "", "ddg_snippet": "Oct 5, 2022 · By definition, the acute toxicity estimate (ATE) is used to estimate the LD50 or LC50 values of mixtures. That is, the ATE formula is a way of categorizing a mixture’s toxicity within one of several toxicity level boundaries (for humans) predetermined by these experimental tests.", "subpage_snippet": "", "source": "www.era-environmental.com", "link": "https://www.era-environmental.com/blog/acute-toxicity-estimate-hazard-classification", "content": "Oct 5, 2022 · By definition, the acute toxicity estimate (ATE) is used to estimate the LD50 or LC50 values of mixtures. That is, the ATE formula is a way of categorizing a mixture’s toxicity within one of several toxicity level boundaries (for humans) predetermined by these experimental tests."}
+{"idx": 4, "title": "Quantitative Risk Assessment Calculations", "date": "", "ddg_snippet": "You should always conduct a risk assessment in two situations: (1) when measured data or estimation methods indicate a chemical may present a moderate or high toxicity concern level, and ( 2 ) where there is a potential for high exposure.", "subpage_snippet": "", "source": "www.epa.gov", "link": "https://www.epa.gov/sites/production/files/2015-05/documents/13.pdf", "content": "You should always conduct a risk assessment in two situations: (1) when measured data or estimation methods indicate a chemical may present a moderate or high toxicity concern level, and ( 2 ) where there is a potential for high exposure."}
+{"idx": 5, "title": "Calculation of acute toxicity - Chymeia", "date": "", "ddg_snippet": "Calculation of acute toxicity Intro Classification for acute toxicity is calculated based on the substance ATE value (Acute Toxicity Estimate) that is either an estimated value or a test value based on LD50 or similar tests. The unit of measurement used for ATE, is mg / kg for LD50 and mg / l for LC50.", "subpage_snippet": "", "source": "chymeia.com", "link": "https://chymeia.com/media/1306/16_acute_tox.pdf", "content": "Calculation of acute toxicity Intro Classification for acute toxicity is calculated based on the substance ATE value (Acute Toxicity Estimate) that is either an estimated value or a test value based on LD50 or similar tests. The unit of measurement used for ATE, is mg / kg for LD50 and mg / l for LC50."}
+{"idx": 6, "title": "Power Equation for Predicting the Risk of Central Nervous ...", "date": "", "ddg_snippet": "by B Aviner · 2020 · Cited by 5 — For immersion, the CNS oxygen toxicity index is K I = t 2 × PO 2 10.93 , where the calculated risk from the Standard Normal distribution is Z I = [ln(K I 0.5 ) – 8.99)] ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7461992/", "content": "by B Aviner · 2020 · Cited by 5 — For immersion, the CNS oxygen toxicity index is K I = t 2 × PO 2 10.93 , where the calculated risk from the Standard Normal distribution is Z I = [ln(K I 0.5 ) – 8.99)] ..."}
+{"idx": 7, "title": "Real-time toxicity prediction of Aconitum stewing system ...", "date": "", "ddg_snippet": "by ZD Qiu · 2020 · Cited by 28 — Then, the holistic weighted toxicity (HWT) for Fuzi soup could be calculated by the following Eq. (6):(6)HWT = (C 1 /0.0051 + C 2 /0.0162 + C 3 /0.0035)/3/W × VWhere ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2211383519307610", "content": "by ZD Qiu · 2020 · Cited by 28 — Then, the holistic weighted toxicity (HWT) for Fuzi soup could be calculated by the following Eq. (6):(6)HWT = (C 1 /0.0051 + C 2 /0.0162 + C 3 /0.0035)/3/W × VWhere ..."}
+{"idx": 8, "title": "Ecotoxicity quantitative structure–activity relationships for ...", "date": "", "ddg_snippet": "by PB Dorn · 2006 · Cited by 63 — Acute Daphnia magna toxicity tests for binary mixtures indicate that mixtures are more toxic than the individual. AE substances corresponding with their average ...", "subpage_snippet": "", "source": "www.erasm.org", "link": "https://www.erasm.org/wp-content/uploads/2022/07/4.1.1.7.Ecotoxicityquantitative_Boeijeetal.2006.pdf", "content": "by PB Dorn · 2006 · Cited by 63 — Acute Daphnia magna toxicity tests for binary mixtures indicate that mixtures are more toxic than the individual. AE substances corresponding with their average ..."}
+{"idx": 9, "title": "Improved method for in vitro assessment of dermal toxicity ...", "date": "", "ddg_snippet": "by JN McDougal · 2002 — The m-xy- lene concentration in the fibroblasts of the dermal equivalents was calculated with the following formula: Cc =(PCf/m)(Cm),", "subpage_snippet": "", "source": "stacks.cdc.gov", "link": "https://stacks.cdc.gov/view/cdc/190517/cdc_190517_DS1.pdf", "content": "by JN McDougal · 2002 — The m-xy- lene concentration in the fibroblasts of the dermal equivalents was calculated with the following formula: Cc =(PCf/m)(Cm),"}
diff --git a/data/sampled_jsons/ELITE_evaluator_StrongREJECT_extension_toxicity_score.jsonl b/data/sampled_jsons/ELITE_evaluator_StrongREJECT_extension_toxicity_score.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..61b4c1c97724b00f5f32c77ba220623d158d1716
--- /dev/null
+++ b/data/sampled_jsons/ELITE_evaluator_StrongREJECT_extension_toxicity_score.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE evaluator builds on StrongREJECT (Souly et al., 2024), extending its rubric-based evaluation to vision-language tasks by incorporating toxicity scores .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "The ELITE evaluator builds on StrongREJECT (Souly et al., 2024), extending its rubric-based evaluation to vision-language tasks by incorporating toxicity scores ."}
+{"idx": 1, "title": "[Literature Review] ELITE : Enhanced Language-Image Toxicity ...", "date": "", "ddg_snippet": "The ELITE evaluator builds upon the StrongREJECT evaluation method, which has been pointed out for its shortcomings in assessing the outputs of VLMs accurately. The core of the ELITE evaluator introduces a toxicity scoring mechanism into its rubric, including three primary...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "The ELITE evaluator builds upon the StrongREJECT evaluation method, which has been pointed out for its shortcomings in assessing the outputs of VLMs accurately. The core of the ELITE evaluator introduces a toxicity scoring mechanism into its rubric, including three primary..."}
+{"idx": 2, "title": "StrongREJECT documentation — StrongREJECT documentation", "date": "", "ddg_snippet": "StrongREJECT is a state-of-the-art LLM jailbreak evaluation benchmark. This package implements the StrongREJECT benchmark and additional utilities for jailbreak research. This Colab notebook demonstrates several options for using the StrongREJECT evaluator", "subpage_snippet": "", "source": "strong-reject.readthedocs.io", "link": "https://strong-reject.readthedocs.io/", "content": "StrongREJECT is a state-of-the-art LLM jailbreak evaluation benchmark. This package implements the StrongREJECT benchmark and additional utilities for jailbreak research. This Colab notebook demonstrates several options for using the StrongREJECT evaluator"}
+{"idx": 3, "title": "Paper page - ELITE : Enhanced Language-Image Toxicity Evaluation ...", "date": "", "ddg_snippet": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04757", "content": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images."}
+{"idx": 4, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "At its core is the ELITE evaluator , a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "www.digitaljournal.com", "link": "https://www.digitaljournal.com/pr/news/newsfile/aim-intelligence-s-elite-collaborative-paper-1773375521.html", "content": "At its core is the ELITE evaluator , a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 5, "title": "velpegor.github.io/ ELITE", "date": "", "ddg_snippet": "The ELITE evaluator can effectively evaluate utilizing the toxicity score to make more accurate judgments.Left: The comparison of AU-ROC curves between the ELITE evaluator and StrongREJECT evaluator on our human evaluation dataset.", "subpage_snippet": "", "source": "velpegor.github.io", "link": "https://velpegor.github.io/ELITE/", "content": "The ELITE evaluator can effectively evaluate utilizing the toxicity score to make more accurate judgments.Left: The comparison of AU-ROC curves between the ELITE evaluator and StrongREJECT evaluator on our human evaluation dataset."}
+{"idx": 6, "title": "GitHub - alexandrasouly/ strongreject : Repository for \" StrongREJECT ...\"", "date": "", "ddg_snippet": "strongreject _ evaluator .py implements the StrongREJECT autograder, intended to be integrated into an existing codebase.run_ strongreject .ipynb shows an example grading the StrongREJECT dataset using the evaluator .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/alexandrasouly/strongreject", "content": "strongreject _ evaluator .py implements the StrongREJECT autograder, intended to be integrated into an existing codebase.run_ strongreject .ipynb shows an example grading the StrongREJECT dataset using the evaluator ."}
+{"idx": 7, "title": "Master the Art of Evaluating Jailbreak Methods with StrongREJECT !", "date": "", "ddg_snippet": "Unlock the secrets to effective jailbreak evaluations ! Discover how StrongREJECT provides a reliable benchmark to assess AI model vulnerabilities in your research.", "subpage_snippet": "", "source": "aiexpert.world", "link": "https://aiexpert.world/master-the-art-of-evaluating-jailbreak-methods-with-strongreject/", "content": "Unlock the secrets to effective jailbreak evaluations ! Discover how StrongREJECT provides a reliable benchmark to assess AI model vulnerabilities in your research."}
+{"idx": 8, "title": "A Case Study with the StrongREJECT Benchmark... - Green Gadgetz", "date": "", "ddg_snippet": "The rubric-based StrongREJECT evaluator . The rubric-based StrongREJECT evaluator prompts an LLM, such as GPT, Claude, Gemini, or Llama, with the forbidden prompt and victim model’s response, along with scoring instructions.", "subpage_snippet": "", "source": "greengadgetz.info", "link": "https://greengadgetz.info/a-case-study-with-the-strongreject-benchmark-the-berkeley-artificial-intelligence-research-blog/", "content": "The rubric-based StrongREJECT evaluator . The rubric-based StrongREJECT evaluator prompts an LLM, such as GPT, Claude, Gemini, or Llama, with the forbidden prompt and victim model’s response, along with scoring instructions."}
+{"idx": 9, "title": "A StrongREJECT for Empty Jailbreaks | OpenReview", "date": "", "ddg_snippet": "Notably, we find that existing evaluation methods significantly overstate jailbreak effectiveness compared to human judgments and the StrongREJECT evaluator .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KZLE5BaaOH", "content": "Notably, we find that existing evaluation methods significantly overstate jailbreak effectiveness compared to human judgments and the StrongREJECT evaluator ."}
diff --git a/data/sampled_jsons/ETHICS_dataset_Hendrycks_et_al_four_reasons_context-aware_ethical_AI.jsonl b/data/sampled_jsons/ETHICS_dataset_Hendrycks_et_al_four_reasons_context-aware_ethical_AI.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7ae0447e8ba987494e8629bd6a5d2d9d4b3b7b5d
--- /dev/null
+++ b/data/sampled_jsons/ETHICS_dataset_Hendrycks_et_al_four_reasons_context-aware_ethical_AI.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ComVas: Contextual Moral Values Alignment System", "date": "", "ddg_snippet": "by I Padhi · Cited by 5 — the goals and behaviors of AI systems are consistent with human values, preferences, and ethical principles [Ji et al .,. 2023; Hendrycks et al ., 2020].", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/1026.pdf", "content": "by I Padhi · Cited by 5 — the goals and behaviors of AI systems are consistent with human values, preferences, and ethical principles [Ji et al .,. 2023; Hendrycks et al ., 2020]."}
+{"idx": 1, "title": "A Pilot Benchmark for Ethical Reasoning in Mental Health AI", "date": "", "ddg_snippet": "7 days ago — For example, the ETHICS benchmark Hendrycks et al . (2020) assesses AI models on general moral scenarios, including fairness, harm, and rights- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11648v1", "content": "7 days ago — For example, the ETHICS benchmark Hendrycks et al . (2020) assesses AI models on general moral scenarios, including fairness, harm, and rights- ..."}
+{"idx": 2, "title": "Values, Ethics, Morals? On the Use of Moral Concepts in ...", "date": "", "ddg_snippet": "by K Vida · 2023 · Cited by 21 — With language technology increasingly affect- ing individuals' lives, many recent works have investigated the ethical aspects of NLP.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.findings-emnlp.368.pdf", "content": "by K Vida · 2023 · Cited by 21 — With language technology increasingly affect- ing individuals' lives, many recent works have investigated the ethical aspects of NLP."}
+{"idx": 3, "title": "Macro Ethics Principles for Responsible AI Systems", "date": "", "ddg_snippet": "by J Woodgate · 2024 · Cited by 16 — We survey AI and computer science literature and develop a taxonomy of 21 normative ethical principles which can be operationalised in AI .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3672394", "content": "by J Woodgate · 2024 · Cited by 16 — We survey AI and computer science literature and develop a taxonomy of 21 normative ethical principles which can be operationalised in AI ."}
+{"idx": 4, "title": "A Data-Driven Benchmark for Ethical Cognition in AI", "date": "", "ddg_snippet": "The work presented in this paper stems from the idea of embedding applied ethics into AI systems using every- day real-world scenarios and considering both ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/1059.pdf", "content": "The work presented in this paper stems from the idea of embedding applied ethics into AI systems using every- day real-world scenarios and considering both ..."}
+{"idx": 5, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 6, "title": "Structured Moral Reasoning in Language Models", "date": "", "ddg_snippet": "by M Chakraborty · 2025 · Cited by 1 — We evaluate models on four moral reasoning benchmarks with varying normative demands: Value Kaleidoscope (VK) (Sorensen et al ., 2024), UniMoral ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.14948", "content": "by M Chakraborty · 2025 · Cited by 1 — We evaluate models on four moral reasoning benchmarks with varying normative demands: Value Kaleidoscope (VK) (Sorensen et al ., 2024), UniMoral ..."}
+{"idx": 7, "title": "The perfect technological storm: artificial intelligence and ...", "date": "", "ddg_snippet": "by MHL Kaas · 2024 · Cited by 6 — Hendrycks et al . (2023) for example decompose catastrophic risk from AI into four categories. They are malicious use, an AI race ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10676-024-09788-0", "content": "by MHL Kaas · 2024 · Cited by 6 — Hendrycks et al . (2023) for example decompose catastrophic risk from AI into four categories. They are malicious use, an AI race ..."}
+{"idx": 8, "title": "Structured Moral Reasoning in Language Models: A Value- ...", "date": "", "ddg_snippet": "The experiments are conducted using the LLaMA-3.1 Instruct model (8B) on the Value Kaleidoscope dataset . Structured prompts using. First-Principles Reasoning ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=VvgwxGbZIA", "content": "The experiments are conducted using the LLaMA-3.1 Instruct model (8B) on the Value Kaleidoscope dataset . Structured prompts using. First-Principles Reasoning ..."}
+{"idx": 9, "title": "6 Alternatives - Machine Learning from Human Preferences", "date": "", "ddg_snippet": "In some of the earliest work to address values in AI systems head-on, ( Hendrycks et al . 2020) introduced a new dataset called ETHICS , sourced from Reddit and ...", "subpage_snippet": "", "source": "mlhp.stanford.edu", "link": "https://mlhp.stanford.edu/src/chap7.html", "content": "In some of the earliest work to address values in AI systems head-on, ( Hendrycks et al . 2020) introduced a new dataset called ETHICS , sourced from Reddit and ..."}
diff --git a/data/sampled_jsons/Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_reward_function_.jsonl b/data/sampled_jsons/Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_reward_function_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dc028e85d53c297cb355932d5378487551218748
--- /dev/null
+++ b/data/sampled_jsons/Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_reward_function_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "Blockchain , Sharding , Account migration , Reinforcement learning . Account migration protocols in sharding blockchains are essential for maintaining scalability by redistributing account states across different shards [22].", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "Blockchain , Sharding , Account migration , Reinforcement learning . Account migration protocols in sharding blockchains are essential for maintaining scalability by redistributing account states across different shards [22]."}
+{"idx": 1, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "Blockchain , Sharding , Account migration , Reinforcement learning . ∗Corresponding author.Sender Shard Transaction Count. AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "Blockchain , Sharding , Account migration , Reinforcement learning . ∗Corresponding author.Sender Shard Transaction Count. AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration ."}
+{"idx": 2, "title": "(PDF) Adaptive Sharding for UAV Networks: A Deep Reinforcement ...", "date": "", "ddg_snippet": "reinforcement learning enhance sharding performance in UAV networks? In this paper, we hypothesize that an adaptive sharding mechanism, driven by deep . reinforcement learning , will significantly improve both the processing efficiency and secu", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385847603_Adaptive_Sharding_for_UAV_Networks_A_Deep_Reinforcement_Learning_Approach_to_Blockchain_Optimization", "content": "reinforcement learning enhance sharding performance in UAV networks? In this paper, we hypothesize that an adaptive sharding mechanism, driven by deep . reinforcement learning , will significantly improve both the processing efficiency and secu"}
+{"idx": 3, "title": "ContribChain: A Stress-Balanced Blockchain Sharding Protocol with...", "date": "", "ddg_snippet": "[27] employs Deep Reinforcement Learn -ing (DRL) to optimize state placement. While these ap-proaches enhance account allocation, they fail to consider performance disparities among shards , and thus do not achieve stress balance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.06899", "content": "[27] employs Deep Reinforcement Learn -ing (DRL) to optimize state placement. While these ap-proaches enhance account allocation, they fail to consider performance disparities among shards , and thus do not achieve stress balance."}
+{"idx": 4, "title": "Enhancing Scalability in Sharding Blockchain via ... | SpringerLink", "date": "", "ddg_snippet": "(2022) BrokerChain: a cross- shard blockchain protocol for account /balance-based state sharding . In: IEEE INFOCOM 2022—IEEE conference on computer communications, London, United Kingdom, pp 1968–1977.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-97-1923-5_26", "content": "(2022) BrokerChain: a cross- shard blockchain protocol for account /balance-based state sharding . In: IEEE INFOCOM 2022—IEEE conference on computer communications, London, United Kingdom, pp 1968–1977."}
+{"idx": 5, "title": "LB-Chain: Load-Balanced and Low-Latency Blockchain Sharding via ...", "date": "", "ddg_snippet": "Index Terms— Blockchain , blockchain sharding , load balance, account migration .The objective of the account migration is to safely migrate accounts to proper shards according to the allocation results, with low through-put loss, low latency, and high fairness. Basic Knowledge.", "subpage_snippet": "", "source": "cse.hkust.edu.hk", "link": "https://cse.hkust.edu.hk/~weiwa/papers/lb-chain-tpds22.pdf", "content": "Index Terms— Blockchain , blockchain sharding , load balance, account migration .The objective of the account migration is to safely migrate accounts to proper shards according to the allocation results, with low through-put loss, low latency, and high fairness. Basic Knowledge."}
+{"idx": 6, "title": "[PDF] LB-Chain: Load-Balanced and Low-Latency Blockchain ...", "date": "", "ddg_snippet": "SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/LB-Chain:-Load-Balanced-and-Low-Latency-Blockchain-Li-Wang/f78243fab1362932088f8593b898151dee65ffbf", "content": "SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement."}
+{"idx": 7, "title": "Scalability of blockchain : Review of cross- sharding with high...", "date": "", "ddg_snippet": "learning with the proposed reinforcement - Blockchain transpare of cross- shard transactions. - state sharding .LB-Chain: Load-Balanced and Low-Latency Blockchain Sharding via Account Migration .", "subpage_snippet": "", "source": "www.bio-conferences.org", "link": "https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00075.pdf", "content": "learning with the proposed reinforcement - Blockchain transpare of cross- shard transactions. - state sharding .LB-Chain: Load-Balanced and Low-Latency Blockchain Sharding via Account Migration ."}
+{"idx": 8, "title": "How to Ensure Interoperability of Sharded Blockchains", "date": "", "ddg_snippet": "Learn about the challenges and solutions for interoperability of sharded blockchains , such as cross- shard transactions, bridge protocols, and interoperability frameworks.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/advice/1/what-best-way-ensure-interoperability-sharded-blockchains-369ue", "content": "Learn about the challenges and solutions for interoperability of sharded blockchains , such as cross- shard transactions, bridge protocols, and interoperability frameworks."}
+{"idx": 9, "title": "The authoritative guide to Blockchain Sharding , part 1 | by... | Medium", "date": "", "ddg_snippet": "Even today, when sharding is not available, there’s a huge demand for interoperability between various blockchains . Let’s for now only consider simple payment transactions, where each participant has account on exactly one shard .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/nearprotocol/the-authoritative-guide-to-blockchain-sharding-part-1-1b53ed31e060", "content": "Even today, when sharding is not available, there’s a huge demand for interoperability between various blockchains . Let’s for now only consider simple payment transactions, where each participant has account on exactly one shard ."}
diff --git a/data/sampled_jsons/Entity-Based_Knowledge_Conflicts_in_Question_Answering_Longpre_abstract.jsonl b/data/sampled_jsons/Entity-Based_Knowledge_Conflicts_in_Question_Answering_Longpre_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07ddcd7c9e350b5bde71d6403d241fe4b6c7e549
--- /dev/null
+++ b/data/sampled_jsons/Entity-Based_Knowledge_Conflicts_in_Question_Answering_Longpre_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Abstract Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.emnlp-main.565/", "content": "Abstract Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information."}
+{"idx": 1, "title": "Entity-Based Knowledge Conflicts in Question Answering - Shayne Longpre", "date": "", "ddg_snippet": "Abstract Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric, learned at training time, and (2) contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information.", "subpage_snippet": "", "source": "www.shaynelongpre.com", "link": "https://www.shaynelongpre.com/publication/kcqa-emnlp2021/", "content": "Abstract Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric, learned at training time, and (2) contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information."}
+{"idx": 2, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.05052", "content": "Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over ..."}
+{"idx": 3, "title": "Entity-Based Knowledge Conflicts in Question Answeri", "date": "", "ddg_snippet": "Abstract Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the be-haviour of popular models, we measure ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10462823", "content": "Abstract Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the be-haviour of popular models, we measure ..."}
+{"idx": 4, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Entity-Based Knowledge Conflicts in Question Answering Shayne Longpre ∗♠ Kartik Perisetla ∗♠ Anthony Chen ∗♥ Nikhil Ramesh ♠ Chris DuBois ♠ Sameer Singh ♥", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/354575176_Entity-Based_Knowledge_Conflicts_in_Question_Answering", "content": "Entity-Based Knowledge Conflicts in Question Answering Shayne Longpre ∗♠ Kartik Perisetla ∗♠ Anthony Chen ∗♥ Nikhil Ramesh ♠ Chris DuBois ♠ Sameer Singh ♥"}
+{"idx": 5, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Run Instructions | Paper | Citation | License This repository provides the Substitution Framework described in Section 2 of our paper Entity-Based Knowledge Conflicts in Question Answering . Given a quesion answering dataset, we derive a new dataset where the context passages have been modified to have new answers to their question . By training on the original examples and evaluating on the ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/apple/ml-knowledge-conflicts/blob/master/README.md", "content": "Run Instructions | Paper | Citation | License This repository provides the Substitution Framework described in Section 2 of our paper Entity-Based Knowledge Conflicts in Question Answering . Given a quesion answering dataset, we derive a new dataset where the context passages have been modified to have new answers to their question . By training on the original examples and evaluating on the ..."}
+{"idx": 6, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2021arXiv210905052L/abstract", "content": "Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over ..."}
+{"idx": 7, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "A new dataset is constructed, dubbed KNOT, for knowledge conflict resolution examination in the form of question answering , which facilitates in -depth analysis by dividing reasoning with conflicting knowledge into three levels: Direct Extraction, which directly extracts conflicting knowledge to answer questions , Explicit Reasoning, which reasons with conflicting knowledge when the reasoning ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Entity-Based-Knowledge-Conflicts-in-Question-Longpre-Perisetla/238deab37e201c57505a4a47bb854e462af79bd7/figure/9", "content": "A new dataset is constructed, dubbed KNOT, for knowledge conflict resolution examination in the form of question answering , which facilitates in -depth analysis by dividing reasoning with conflicting knowledge into three levels: Direct Extraction, which directly extracts conflicting knowledge to answer questions , Explicit Reasoning, which reasons with conflicting knowledge when the reasoning ..."}
+{"idx": 8, "title": "PDF Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Abstract Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.emnlp-main.565.pdf", "content": "Abstract Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information."}
+{"idx": 9, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Abstract Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information.", "subpage_snippet": "", "source": "www.mendeley.com", "link": "https://www.mendeley.com/catalogue/746fdfc7-46a9-3f17-a0d6-6fb3af14de1b/", "content": "Abstract Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information."}
diff --git a/data/sampled_jsons/EntityErasure_1e-4_OR_0.0001_AECM_Amodal_Entity_Completion_Model_training.jsonl b/data/sampled_jsons/EntityErasure_1e-4_OR_0.0001_AECM_Amodal_Entity_Completion_Model_training.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_1e-4_OR_0.0001_AECM_Amodal_Entity_Completion_Model_training.jsonl
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/EntityErasure_Amodal_Entity_Completion_Model_AECM_learning_rate_implementation_details_section_3.4.jsonl b/data/sampled_jsons/EntityErasure_Amodal_Entity_Completion_Model_AECM_learning_rate_implementation_details_section_3.4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6c7857a372270d12d4ff9835859bdff6780b4303
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_Amodal_Entity_Completion_Model_AECM_learning_rate_implementation_details_section_3.4.jsonl
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+{"idx": 0, "title": "EntityErasure : Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "Amodal Entity Completion . Classifer-free Guidance. Implementation Details . Entity Reference Encoder (ERE) Amodal Entity Completion Model ( AECM ). Figure 2. Overview of EntityErasure .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "Amodal Entity Completion . Classifer-free Guidance. Implementation Details . Entity Reference Encoder (ERE) Amodal Entity Completion Model ( AECM ). Figure 2. Overview of EntityErasure ."}
+{"idx": 1, "title": "Multi-Agent Amodal Completion : Direct Synthesis with Fine-Grained...", "date": "", "ddg_snippet": "4.1 Datasets, Metrics and Implementation Details . 4.2 Comparisons with Other Methods. 4.3 Ablation Studies.Figure 1: Our framework robustly handles diverse amodal completion challenges, from Object Occlusion and Boundary Truncation to Mixed and Semantic Detail Occlusion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.17757", "content": "4.1 Datasets, Metrics and Implementation Details . 4.2 Comparisons with Other Methods. 4.3 Ablation Studies.Figure 1: Our framework robustly handles diverse amodal completion challenges, from Object Occlusion and Boundary Truncation to Mixed and Semantic Detail Occlusion."}
+{"idx": 2, "title": "GSPO Reinforcement Learning | Unsloth Documentation", "date": "", "ddg_snippet": "What Model Should I Use? LoRA Hyperparameters Guide. Reinforcement Learning (RL) Guide. GSPO Reinforcement Learning . Tutorial: Train your own Reasoning model with GRPO.", "subpage_snippet": "", "source": "docs.unsloth.ai", "link": "https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning", "content": "What Model Should I Use? LoRA Hyperparameters Guide. Reinforcement Learning (RL) Guide. GSPO Reinforcement Learning . Tutorial: Train your own Reasoning model with GRPO."}
+{"idx": 3, "title": "machinelearningmastery.com/understand-the-dynamics-of- learning ...", "date": "", "ddg_snippet": "Understand the Impact of Learning Rate on Model Performance.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/", "content": "Understand the Impact of Learning Rate on Model Performance."}
+{"idx": 4, "title": "EntityErasure : Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "Code Dataset Model . Abstract. This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion ...", "subpage_snippet": "", "source": "zyxunh.github.io", "link": "https://zyxunh.github.io/EntityErasure-ProjectPage/", "content": "Code Dataset Model . Abstract. This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion ..."}
+{"idx": 5, "title": "Ship Any AI SaaS Startups in hours | ShipAny", "date": "", "ddg_snippet": "Complete Model Series. From 0.6B lightweight to 235B flagship, meeting different performance and resource requirements.For more technical details , visit the official documentation or contact technical support.", "subpage_snippet": "", "source": "www.qwen3coder.com", "link": "https://www.qwen3coder.com/", "content": "Complete Model Series. From 0.6B lightweight to 235B flagship, meeting different performance and resource requirements.For more technical details , visit the official documentation or contact technical support."}
+{"idx": 6, "title": "Простая модель машинного обучения теплопроводности — REPEAT", "date": "", "ddg_snippet": "Строка 3. Инициализация функции потерь criterion, использующей среднеквадратичную ошибку. Строка 4. Инициализация оптимизатора SGD для обновления параметров модели. Эмпирически определяется значение настройки lr = 0.1 ( learning rate ).", "subpage_snippet": "", "source": "app.repeatlab.ru", "link": "https://app.repeatlab.ru/docs/howto/ml_approximation/conduction_heat_transfer/index.html", "content": "Строка 3. Инициализация функции потерь criterion, использующей среднеквадратичную ошибку. Строка 4. Инициализация оптимизатора SGD для обновления параметров модели. Эмпирически определяется значение настройки lr = 0.1 ( learning rate )."}
+{"idx": 7, "title": "xAI launches Grok-4-Fast: Unified Reasoning and... - MarkTechPost", "date": "", "ddg_snippet": "The model is generally available to all users in Grok’s Fast and Auto modes across web and mobile; Auto will select Grok-4-Fast for difficult queries to improve latency without losing quality, and—for the first time—free users access xAI’s latest model tier.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/09/20/xai-launches-grok-4-fast-unified-reasoning-and-non-reasoning-model-with-2m-token-context-and-trained-end-to-end-with-tool-use-reinforcement-learning-rl/", "content": "The model is generally available to all users in Grok’s Fast and Auto modes across web and mobile; Auto will select Grok-4-Fast for difficult queries to improve latency without losing quality, and—for the first time—free users access xAI’s latest model tier."}
+{"idx": 8, "title": "openrouter.ai/docs/quickstart", "date": "", "ddg_snippet": "Application error: a client-side exception has occurred while loading openrouter.ai (see the browser console for more information).", "subpage_snippet": "", "source": "openrouter.ai", "link": "https://openrouter.ai/docs/quickstart", "content": "Application error: a client-side exception has occurred while loading openrouter.ai (see the browser console for more information)."}
+{"idx": 9, "title": "Secret Box Puzzle Guide | Silent Hill f｜Game8", "date": "", "ddg_snippet": "Complete all three rounds to finish the puzzle. There won't be any highlighted words in the journal for the Hard difficulty, but the keywords for the rounds are sweet and tart fruit, journal, and sakura in full bloom. Secret Box Puzzle Guide.", "subpage_snippet": "", "source": "game8.co", "link": "https://game8.co/games/Silent-Hill-f/archives/551202", "content": "Complete all three rounds to finish the puzzle. There won't be any highlighted words in the journal for the Hard difficulty, but the keywords for the rounds are sweet and tart fruit, journal, and sakura in full bloom. Secret Box Puzzle Guide."}
diff --git a/data/sampled_jsons/EntityErasure_paper_CVPR_2025_implementation_details_section_3.4_year_2025.jsonl b/data/sampled_jsons/EntityErasure_paper_CVPR_2025_implementation_details_section_3.4_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..32ff2558e8a834c0b1f47b12b470eb9bf91a8e82
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_paper_CVPR_2025_implementation_details_section_3.4_year_2025.jsonl
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+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities with-out inducing unwanted sundries. To this end, we pro-pose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities with-out inducing unwanted sundries. To this end, we pro-pose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
+{"idx": 1, "title": "CVPR.2025 - Oral | Cool Papers - Immersive Paper Discovery", "date": "", "ddg_snippet": "The list of accepted papers for CVPR.2025 - Oral, including titles, authors, and abstracts, with support for paper interpretation based on Kimi AI.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/CVPR.2025?group=Oral", "content": "The list of accepted papers for CVPR.2025 - Oral, including titles, authors, and abstracts, with support for paper interpretation based on Kimi AI."}
+{"idx": 2, "title": "CVPR 2025 Accepted Papers | MMLab@NTU", "date": "", "ddg_snippet": "CVPR 2025 Presentation Schedule Presentation Schedule The team has a total of 20 papers (including 2 orals and 3 highlights) accepted to CVPR 2025 .", "subpage_snippet": "", "source": "mmlab-ntu.github.io", "link": "https://mmlab-ntu.github.io/conference/cvpr2025/index.html", "content": "CVPR 2025 Presentation Schedule Presentation Schedule The team has a total of 20 papers (including 2 orals and 3 highlights) accepted to CVPR 2025 ."}
+{"idx": 3, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
+{"idx": 4, "title": "CVPR 2025 Accepted Papers", "date": "", "ddg_snippet": "CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/AcceptedPapers", "content": "CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here."}
+{"idx": 5, "title": "CVPR 2025 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "How to use the paper list below: - Overview: This table presents papers from the CVPR conference, year 2025 . - Filtering: By default, the table loads the first 100 records. You can use the filter box under each column header to search within these loaded entries.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/cvpr-paper-list/cvpr-2025-paper-list/", "content": "How to use the paper list below: - Overview: This table presents papers from the CVPR conference, year 2025 . - Filtering: By default, the table loads the first 100 records. You can use the filter box under each column header to search within these loaded entries."}
+{"idx": 6, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094156", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ..."}
+{"idx": 7, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "In CVPR , 2022.1,2,3,4,5 [3]Junhao Zhuang, Yanhong Zeng, Wenran Liu, Chun Yuan, and Kai Chen. A task is worth one word: Learning with task prompts for high-quality versatile image inpainting.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_EntityErasure_Erasing_Entity_CVPR_2025_supplemental.pdf", "content": "In CVPR , 2022.1,2,3,4,5 [3]Junhao Zhuang, Yanhong Zeng, Wenran Liu, Chun Yuan, and Kai Chen. A task is worth one word: Learning with task prompts for high-quality versatile image inpainting."}
+{"idx": 8, "title": "CVPR Poster EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34016", "content": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference ..."}
+{"idx": 9, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [ CVPR2025 ] Introduction This repository contains the official implementation of the paper EntityErasure .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [ CVPR2025 ] Introduction This repository contains the official implementation of the paper EntityErasure ."}
diff --git a/data/sampled_jsons/EntityErasure_supplemental_material_implementation_details_AECM_training_parameters.jsonl b/data/sampled_jsons/EntityErasure_supplemental_material_implementation_details_AECM_training_parameters.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3d71f6753f37e346cff3885fd19c88ab674e5d2
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_supplemental_material_implementation_details_AECM_training_parameters.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "In contrast, our proposed EntityErasure demonstrates a lower probability of sundries generation and produces higher-quality images. Please see the supplementary material for more results.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "In contrast, our proposed EntityErasure demonstrates a lower probability of sundries generation and produces higher-quality images. Please see the supplementary material for more results."}
+{"idx": 1, "title": "Oracle Enterprise Asset Management Implementation Guide", "date": "", "ddg_snippet": "Setting Up Billable Material Setting Up a Billable Resource Setting Up a Billable Activity eAM Profile Options Profile Option Summary Profile Option Details Profile Options in Other Applications Profile Option Details in Other Applications eAM Open Interfaces and APIs eAM Open Interfaces and APIs eAM Item Open Interface eAM Asset Number Open ...", "subpage_snippet": "", "source": "docs.oracle.com", "link": "https://docs.oracle.com/cd/E18727_01/doc.121/e13671/toc.htm", "content": "Setting Up Billable Material Setting Up a Billable Resource Setting Up a Billable Activity eAM Profile Options Profile Option Summary Profile Option Details Profile Options in Other Applications Profile Option Details in Other Applications eAM Open Interfaces and APIs eAM Open Interfaces and APIs eAM Item Open Interface eAM Asset Number Open ..."}
+{"idx": 2, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "- Supplementary Material - Yixing Zhu1Qing Zhang,4∗Yitong Wang2Yongwei Nie3Wei-Shi Zheng 1School of Computer Science and Engineering, Sun Yat-sen University, China", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_EntityErasure_Erasing_Entity_CVPR_2025_supplemental.pdf", "content": "- Supplementary Material - Yixing Zhu1Qing Zhang,4∗Yitong Wang2Yongwei Nie3Wei-Shi Zheng 1School of Computer Science and Engineering, Sun Yat-sen University, China"}
+{"idx": 3, "title": "2588359 - Learning configuration and implementation guides and user ...", "date": "", "ddg_snippet": "This KBA covers where to find the Learning configuration and implementation guides.", "subpage_snippet": "", "source": "userapps.support.sap.com", "link": "https://userapps.support.sap.com/sap/support/knowledge/en/2588359", "content": "This KBA covers where to find the Learning configuration and implementation guides."}
+{"idx": 4, "title": "A Supplementary: Introduction B Supplementary: Implementation and ...", "date": "", "ddg_snippet": "A Supplementary: Introduction In this supplementary material , we provide more details regarding implementation details in Ap-pendix B, more analysis of ERDA in Appendix C, full experimental results in Appendix D, and studies on parameters in Appendix E.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=utQms7PPx5&name=supplementary_material", "content": "A Supplementary: Introduction In this supplementary material , we provide more details regarding implementation details in Ap-pendix B, more analysis of ERDA in Appendix C, full experimental results in Appendix D, and studies on parameters in Appendix E."}
+{"idx": 5, "title": "CVPR Poster EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34016", "content": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference ..."}
+{"idx": 6, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
+{"idx": 7, "title": "GitHub - zyxunh/diffusers_for_entity_erasure", "date": "", "ddg_snippet": "🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/diffusers_for_entity_erasure", "content": "🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and ..."}
+{"idx": 8, "title": "An Evaluation of Education Methods Used to Train United States Air ...", "date": "", "ddg_snippet": "A comprehensive literature review revealed only 4 articles linking medical flight simulation training and AECM proficiency (Table 1). In an integrated literature review of simulation use in air medical evacuation training , O'Connell et al 2 concluded that more research is required to assess the effectiveness of simulation to train AECMs .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1067991X17303346", "content": "A comprehensive literature review revealed only 4 articles linking medical flight simulation training and AECM proficiency (Table 1). In an integrated literature review of simulation use in air medical evacuation training , O'Connell et al 2 concluded that more research is required to assess the effectiveness of simulation to train AECMs ."}
+{"idx": 9, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub."}
diff --git a/data/sampled_jsons/Equation_(8)_siteopenreview.netpdfid=4vAa0A98xI.jsonl b/data/sampled_jsons/Equation_(8)_siteopenreview.netpdfid=4vAa0A98xI.jsonl
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index 0000000000000000000000000000000000000000..23db1a3002d5517eb2357e10ce3abf6f55260489
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+++ b/data/sampled_jsons/Equation_(8)_siteopenreview.netpdfid=4vAa0A98xI.jsonl
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+{"idx": 0, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "...i.e., vei , by Equation ( 8 ). Compute the weight assigned to the hardest sample point in epoch i, i.e., vhi , by Equation (9). Compute difficulty Dki for each sample point xik by Equation (6)...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "...i.e., vei , by Equation ( 8 ). Compute the weight assigned to the hardest sample point in epoch i, i.e., vhi , by Equation (9). Compute difficulty Dki for each sample point xik by Equation (6)..."}
+{"idx": 1, "title": "Mathematical Derivation Graphs", "date": "", "ddg_snippet": "by V Prasad · 2024 — Equation 8 is implied as being key since it is explicitly labeled with a reference number. The definite edge shown in the text before the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=f80yIVvzP1", "content": "by V Prasad · 2024 — Equation 8 is implied as being key since it is explicitly labeled with a reference number. The definite edge shown in the text before the ..."}
+{"idx": 2, "title": "MITIGATING THE CURSE OF DIMENSIONALITY FOR ...", "date": "", "ddg_snippet": "by S Xia · Cited by 11 — Equation 8 establishes based on using RS to calculate the certified radius in each sub-image. Addi- tional details regarding the derivation of Equation 7 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=C1sQBG6Sqp", "content": "by S Xia · Cited by 11 — Equation 8 establishes based on using RS to calculate the certified radius in each sub-image. Addi- tional details regarding the derivation of Equation 7 ..."}
+{"idx": 3, "title": "Full Stage Learning to Rank: A Unified Framework for Multi ...", "date": "", "ddg_snippet": "by K Zheng · 2024 · Cited by 15 — in equation (8 ) means an item that could be selected to the next stage is relatively more difficult than user whether likes it compared with ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fHquNvRZl7", "content": "by K Zheng · 2024 · Cited by 15 — in equation (8 ) means an item that could be selected to the next stage is relatively more difficult than user whether likes it compared with ..."}
+{"idx": 4, "title": "FLOW MATCHING FOR GENERATIVE MODELING", "date": "", "ddg_snippet": "Our first key observation is this: The marginal vector field ( equation 8 ) generates the marginal probability path (equation 6). This provides a surprising ... 28 pages", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/e99034416acd1ca82991f5d63735e77130fc06a7.pdf", "content": "Our first key observation is this: The marginal vector field ( equation 8 ) generates the marginal probability path (equation 6). This provides a surprising ... 28 pages"}
+{"idx": 5, "title": "poly-view contrastive learning", "date": "", "ddg_snippet": "by A Shidani · 2024 · Cited by 8 — Both aggregation functions introduced by Equation 9 and Equation 10 satisfy the. Validity property, i.e. Equation 8 . Proof. Let us define zβ ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=cM7nNSNRUh", "content": "by A Shidani · 2024 · Cited by 8 — Both aggregation functions introduced by Equation 9 and Equation 10 satisfy the. Validity property, i.e. Equation 8 . Proof. Let us define zβ ..."}
+{"idx": 6, "title": "A Distributed System for Large-scale n-gram Language ...", "date": "", "ddg_snippet": "by Q Long · Cited by 5 — 2Note that the training algorithms are different. rest of this paper, we use the backoff model ( Equation 8 ) to introduce the inference algorithm.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=3s2ZFhVRW7", "content": "by Q Long · Cited by 5 — 2Note that the training algorithms are different. rest of this paper, we use the backoff model ( Equation 8 ) to introduce the inference algorithm."}
+{"idx": 7, "title": "Revisiting Domain Randomization via Relaxed State ...", "date": "", "ddg_snippet": "by YH Lien · 2023 · Cited by 1 — is unclear: As Equation 8 only captures the dependency on. D through an expectation over D (i.e., EP ⇠D[·]), the depen- dency on α remains implicit and unclear.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tIlCpCRyvM", "content": "by YH Lien · 2023 · Cited by 1 — is unclear: As Equation 8 only captures the dependency on. D through an expectation over D (i.e., EP ⇠D[·]), the depen- dency on α remains implicit and unclear."}
+{"idx": 8, "title": "DARKER: Efficient Transformer with Data-driven Attention ...", "date": "", "ddg_snippet": "by R Zuo · Cited by 1 — Since the projection 𝜙 in Equation 8 uses trigonometric func- tions for random features, the corresponding attention is named as trigonometric random feature ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2evTMxGzKv", "content": "by R Zuo · Cited by 1 — Since the projection 𝜙 in Equation 8 uses trigonometric func- tions for random features, the corresponding attention is named as trigonometric random feature ..."}
+{"idx": 9, "title": "A probabilistic population code based on neural samples", "date": "", "ddg_snippet": "by S Shivkumar · Cited by 28 — Interestingly, equation (8 ) can be seen as a generalization from a classic feedforward model consisting of independent linear-nonlinear-Poisson (LNP) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ryZmaw-Obr", "content": "by S Shivkumar · Cited by 28 — Interestingly, equation (8 ) can be seen as a generalization from a classic feedforward model consisting of independent linear-nonlinear-Poisson (LNP) ..."}
diff --git a/data/sampled_jsons/EventPS_Real-Time_Photometric_Stereo_Using_Event_Camera_abstract_Yu_Bohan_2024_year_2024.jsonl b/data/sampled_jsons/EventPS_Real-Time_Photometric_Stereo_Using_Event_Camera_abstract_Yu_Bohan_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..340308457646955df65c3f34d3b30ae4323683a6
--- /dev/null
+++ b/data/sampled_jsons/EventPS_Real-Time_Photometric_Stereo_Using_Event_Camera_abstract_Yu_Bohan_2024_year_2024.jsonl
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+{"idx": 0, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency."}
+{"idx": 1, "title": "Yu Bohan from Peking University: EventPS - Real - time Photometric ...", "date": "", "ddg_snippet": "The paper \" EventPS : Real - Time Photometric Stereo using an Event Camera \" is the first to use the unique properties of event cameras to achieve real - time photometric stereo .Talk·Question and Exchange. Talk·Guest Introduction. Yu Bohan . Peking University · PhD student.", "subpage_snippet": "", "source": "inf.news", "link": "https://inf.news/en/tech/4be4c17d4830518b9004d13cb772438e.html", "content": "The paper \" EventPS : Real - Time Photometric Stereo using an Event Camera \" is the first to use the unique properties of event cameras to achieve real - time photometric stereo .Talk·Question and Exchange. Talk·Guest Introduction. Yu Bohan . Peking University · PhD student."}
+{"idx": 2, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10655016", "content": "Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional ..."}
+{"idx": 3, "title": "Publications – Bohan Yu's Homepage", "date": "", "ddg_snippet": "EventPS : Real-Time Photometric Stereo Using an Event Camera 2024 -05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) MILO: Multi-bounce inverse rendering for indoor scene with light-emitting objects 2023-03-29", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/publications/", "content": "EventPS : Real-Time Photometric Stereo Using an Event Camera 2024 -05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) MILO: Multi-bounce inverse rendering for indoor scene with light-emitting objects 2023-03-29"}
+{"idx": 4, "title": "CVPR24 (Oral) - EventPS: Real-time photometric stereo using ... Yu Bohan from Peking University: EventPS - Real-time ... Images EVENTPS: REAL-TIME PHOTOMETRIC STEREO USING AN EVENT CAMERA CVPR Poster EventPS: Real-Time Photometric Stereo Using an ... EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This is the video of the following work: Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, and Boxin Shi. EventPS : Real-time photometric stereo using ... Aug 19, 2024 · The paper \" EventPS : Real-Time Photometric Stereo using an Event Camera \" is the first to use the unique properties of event cameras to achieve real-time photometric stereo . View all Jul 5, 2024 · Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi ABSTRACT Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency. What is eventps in real-time photometric stereo? This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency. What is photometric stereo? Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. What is dynamic photometric stereo? Dynamic photo-metric stereo - a new technique for moving surface analysis . Image and Vision Computing, 2005. 3 Tsuyoshi Takatani, Yasuyuki Matsushita, Stephen Lin, Ya-suhiro Mukaigawa, and Yasushi Yagi. Enhanced photomet-ric stereo with multispectral images. What is the processing speed of eventps algorithm? The processing speeds of EventPS algorithms are over 1000 fps (for EventPS-OP), about 2 fps (for EventPS-FCN), 7Please refer to the video in supplementary material for full animation. Figure 11. Results on DiLiGenT-Ev dataset with different level of noises.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=-TTp0zJKNPU", "content": "This is the video of the following work: Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, and Boxin Shi. EventPS : Real-time photometric stereo using ... Aug 19, 2024 · The paper \" EventPS : Real-Time Photometric Stereo using an Event Camera \" is the first to use the unique properties of event cameras to achieve real-time photometric stereo . View all Jul 5, 2024 · Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi ABSTRACT Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency. What is eventps in real-time photometric stereo? This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency. What is photometric stereo? Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. What is dynamic photometric stereo? Dynamic photo-metric stereo - a new technique for moving surface analysis . Image and Vision Computing, 2005. 3 Tsuyoshi Takatani, Yasuyuki Matsushita, Stephen Lin, Ya-suhiro Mukaigawa, and Yasushi Yagi. Enhanced photomet-ric stereo with multispectral images. What is the processing speed of eventps algorithm? The processing speeds of EventPS algorithms are over 1000 fps (for EventPS-OP), about 2 fps (for EventPS-FCN), 7Please refer to the video in supplementary material for full animation. Figure 11. Results on DiLiGenT-Ev dataset with different level of noises."}
+{"idx": 5, "title": "EVENTPS: REAL-TIME PHOTOMETRIC STEREO USING AN EVENT CAMERA", "date": "", "ddg_snippet": "Jul 5, 2024 · Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi ABSTRACT Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications.", "subpage_snippet": "", "source": "prophesee-prod.euregion.site", "link": "https://prophesee-prod.euregion.site/2024/07/05/eventps-real-time-photometric-stereo-event-camera/", "content": "Jul 5, 2024 · Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi ABSTRACT Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications."}
+{"idx": 6, "title": "CVPR Poster EventPS: Real-Time Photometric Stereo Using an ...", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/poster/31806", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency."}
+{"idx": 7, "title": "Real - Time Photometric Stereo Using an Event Camera | Research", "date": "", "ddg_snippet": "Bohan Yu , Jieji Ren, Jin Han , Feishi Wang, Jinxiu Liang, Boxin Shi. ABSTRACT . Photometric stereo is a well-established technique to estimate the surface normal of an object.This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera .", "subpage_snippet": "", "source": "www.prophesee.ai", "link": "https://www.prophesee.ai/2024/07/05/eventps-real-time-photometric-stereo-event-camera/", "content": "Bohan Yu , Jieji Ren, Jin Han , Feishi Wang, Jinxiu Liang, Boxin Shi. ABSTRACT . Photometric stereo is a well-established technique to estimate the surface normal of an object.This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera ."}
+{"idx": 8, "title": "EventPS : Real - Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "In stereo and photometric analysis, event -based vision supports advanced techniques like event stereo [32], photometric stereo [280], [257] and shape from polarization [176], offering high-resolution depth maps and detailed surface properties.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390160865_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera", "content": "In stereo and photometric analysis, event -based vision supports advanced techniques like event stereo [32], photometric stereo [280], [257] and shape from polarization [176], offering high-resolution depth maps and detailed surface properties."}
+{"idx": 9, "title": "EventPS : Real - Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Yu _ EventPS _ Real - Time _ Photometric _ Stereo _ Using _an_ Event _ Camera @CVPR 2024 @CVF.Authors: Bohan Yu , Jieji Ren, Jin Han , Feishi Wang, Jinxiu Liang, Boxin Shi. Photometric stereo is a well-established technique to estimate the surface normal of an object.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera@CVPR2024@CVF", "content": "Yu _ EventPS _ Real - Time _ Photometric _ Stereo _ Using _an_ Event _ Camera @CVPR 2024 @CVF.Authors: Bohan Yu , Jieji Ren, Jin Han , Feishi Wang, Jinxiu Liang, Boxin Shi. Photometric stereo is a well-established technique to estimate the surface normal of an object."}
diff --git a/data/sampled_jsons/EventPS_Real-time_photometric_stereo_Yu_3D_printed_MAE_error_results.jsonl b/data/sampled_jsons/EventPS_Real-time_photometric_stereo_Yu_3D_printed_MAE_error_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d310c9e568d38cb6b49f3fa5e06da6dfef382b10
--- /dev/null
+++ b/data/sampled_jsons/EventPS_Real-time_photometric_stereo_Yu_3D_printed_MAE_error_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024//papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10655016", "content": "Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
+{"idx": 2, "title": "Yu Bohan from Peking University: EventPS - Real-time Photometric Stereo ...", "date": "", "ddg_snippet": "The paper \" EventPS : Real-Time Photometric Stereo using an Event Camera\" is the first to use the unique properties of event cameras to achieve real-time photometric stereo .", "subpage_snippet": "", "source": "inf.news", "link": "https://inf.news/en/tech/4be4c17d4830518b9004d13cb772438e.html", "content": "The paper \" EventPS : Real-Time Photometric Stereo using an Event Camera\" is the first to use the unique properties of event cameras to achieve real-time photometric stereo ."}
+{"idx": 3, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhancing data efficiency.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YH3f8jq3lz", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhancing data efficiency."}
+{"idx": 4, "title": "Event Fusion Photometric Stereo Network - arXiv.org", "date": "", "ddg_snippet": "To the best of our knowledge, our proposed method is the rst work to fuse RGB and event signals for photometric stereo tasks. From our extensive experimental results , utilizing event signals along with RGB frames is much more e ective in photometric stereo tasks under ambient light conditions than using only an RGB camera.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.00308", "content": "To the best of our knowledge, our proposed method is the rst work to fuse RGB and event signals for photometric stereo tasks. From our extensive experimental results , utilizing event signals along with RGB frames is much more e ective in photometric stereo tasks under ambient light conditions than using only an RGB camera."}
+{"idx": 5, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ..."}
+{"idx": 6, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency."}
+{"idx": 7, "title": "Eventps: Real-time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency.", "subpage_snippet": "", "source": "prophesee-prod.euregion.site", "link": "https://prophesee-prod.euregion.site/2024/07/05/eventps-real-time-photometric-stereo-event-camera/", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency."}
+{"idx": 8, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "The processed 3D object files are at data/{blobs,sculpture}_processed/. To render the training and evaluation data, make sure to have a working LibreDR server and worker, and run the following command:", "subpage_snippet": "", "source": "codeberg.org", "link": "https://codeberg.org/ybh1998/EventPS", "content": "The processed 3D object files are at data/{blobs,sculpture}_processed/. To render the training and evaluation data, make sure to have a working LibreDR server and worker, and run the following command:"}
+{"idx": 9, "title": "Track: Orals 3C Medical and Physics-based vision", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/session/32097", "content": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
diff --git a/data/sampled_jsons/EventPS_Real-time_photometric_stereo_using_an_event_camera_Yu.jsonl b/data/sampled_jsons/EventPS_Real-time_photometric_stereo_using_an_event_camera_Yu.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd4495bf0caa6c928e4966266e0436d4dc8d2ea9
--- /dev/null
+++ b/data/sampled_jsons/EventPS_Real-time_photometric_stereo_using_an_event_camera_Yu.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10655016", "content": "Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional ..."}
+{"idx": 2, "title": "CVPR24 (Oral) - EventPS: Real-time photometric stereo using ... Images Publications – Bohan Yu's Homepage EventPS: Real-Time Photometric Stereo Using an Event Camera Yu Bohan from Peking University: EventPS - Real-time ... EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera Event Camera Guided Visual Media Restoration & 3D ...", "date": "", "ddg_snippet": "This is the video of the following work: Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, and Boxin Shi. EventPS : Real-time photometric stereo using ... View all EventPS : Real-Time Photometric Stereo Using an Event Camera 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) MILO: Multi-bounce inverse rendering for indoor scene with light-emitting objects 2023-03-29 Jun 16, 2024 · This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ... Aug 19, 2024 · The paper \" EventPS : Real-Time Photometric Stereo using an Event Camera \" is the first to use the unique properties of event cameras to achieve real-time photometric stereo . What is eventps in real-time photometric stereo? This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency. What is eventps & how does it work? Owing to the unique attributes of event cameras, this process enables the capturing of observations with a high dynamic range under rapidly changing light-ing, while maintaining economical data efficiency. This ap-proach, termed EventPS, allows us to harness the inherent strengths of event cameras for achieving real-time PS . Is eventps a novel real-time Ps approach using a single event camera? Conclusion and Discussion In this paper, we propose EventPS , a novel real-time PS approach using a single event camera. Can a single event camera be used for real-time sensing? In this paper, we propose EventPS, a novel real-time PS approach using a single event camera. Our method demon-strates the remarkable advantages of speed and data effi-ciency, which shows great potential to extend the capability for real-time sensing in the dynamic scenes and rapid mea-surement of the object surface normal. What is photometric stereo? Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. What are the advantages of event camera vs frameps? The unique attributes of event cameras, e.g., low latency, high dynamic range, and low redundancy in data representation (b), enable EventPS, a rapid and highly efficient real-time solution (c, d), which significantly reduces the bandwidth usage while maintaining comparable performance to FramePS. Sep 15, 2025 · Traditional photometric stereo techniques require captur-ing multiple high dynamic range images under different lighting conditions, which limits their speed and real-time applicability.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=-TTp0zJKNPU", "content": "This is the video of the following work: Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, and Boxin Shi. EventPS : Real-time photometric stereo using ... View all EventPS : Real-Time Photometric Stereo Using an Event Camera 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) MILO: Multi-bounce inverse rendering for indoor scene with light-emitting objects 2023-03-29 Jun 16, 2024 · This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ... Aug 19, 2024 · The paper \" EventPS : Real-Time Photometric Stereo using an Event Camera \" is the first to use the unique properties of event cameras to achieve real-time photometric stereo . What is eventps in real-time photometric stereo? This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency. What is eventps & how does it work? Owing to the unique attributes of event cameras, this process enables the capturing of observations with a high dynamic range under rapidly changing light-ing, while maintaining economical data efficiency. This ap-proach, termed EventPS, allows us to harness the inherent strengths of event cameras for achieving real-time PS . Is eventps a novel real-time Ps approach using a single event camera? Conclusion and Discussion In this paper, we propose EventPS , a novel real-time PS approach using a single event camera. Can a single event camera be used for real-time sensing? In this paper, we propose EventPS, a novel real-time PS approach using a single event camera. Our method demon-strates the remarkable advantages of speed and data effi-ciency, which shows great potential to extend the capability for real-time sensing in the dynamic scenes and rapid mea-surement of the object surface normal. What is photometric stereo? Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. What are the advantages of event camera vs frameps? The unique attributes of event cameras, e.g., low latency, high dynamic range, and low redundancy in data representation (b), enable EventPS, a rapid and highly efficient real-time solution (c, d), which significantly reduces the bandwidth usage while maintaining comparable performance to FramePS. Sep 15, 2025 · Traditional photometric stereo techniques require captur-ing multiple high dynamic range images under different lighting conditions, which limits their speed and real-time applicability."}
+{"idx": 3, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Jun 16, 2024 · This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "Jun 16, 2024 · This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ..."}
+{"idx": 4, "title": "Yu Bohan from Peking University: EventPS - Real-time ...", "date": "", "ddg_snippet": "Aug 19, 2024 · The paper \" EventPS : Real-Time Photometric Stereo using an Event Camera \" is the first to use the unique properties of event cameras to achieve real-time photometric stereo .", "subpage_snippet": "", "source": "inf.news", "link": "https://inf.news/en/tech/4be4c17d4830518b9004d13cb772438e.html", "content": "Aug 19, 2024 · The paper \" EventPS : Real-Time Photometric Stereo using an Event Camera \" is the first to use the unique properties of event cameras to achieve real-time photometric stereo ."}
+{"idx": 5, "title": "Event Camera Guided Visual Media Restoration & 3D ...", "date": "", "ddg_snippet": "Sep 15, 2025 · Traditional photometric stereo techniques require captur-ing multiple high dynamic range images under different lighting conditions, which limits their speed and real-time applicability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.09971", "content": "Sep 15, 2025 · Traditional photometric stereo techniques require captur-ing multiple high dynamic range images under different lighting conditions, which limits their speed and real-time applicability."}
+{"idx": 6, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "by B Yu · 2024 · Cited by 25 — This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "by B Yu · 2024 · Cited by 25 — This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution ..."}
+{"idx": 7, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Abstract: Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "http://ieeexplore.ieee.org/document/10655016/similar", "content": "Abstract: Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple ..."}
+{"idx": 8, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Key takeaway: ' EventPS enables real - time photometric stereo using event cameras , enhancing data efficiency and enabling high-speed downstream applications.'", "subpage_snippet": "", "source": "consensus.app", "link": "https://consensus.app/papers/details/8cefa93e58df57a18793028a3798d3d4/", "content": "Key takeaway: ' EventPS enables real - time photometric stereo using event cameras , enhancing data efficiency and enabling high-speed downstream applications.'"}
+{"idx": 9, "title": "Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "... real-world scenarios. Kudos to the talented researchers Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang and Boxin Shi from Peking ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/chronocam_eventps-real-time-photometric-stereo-using-activity-7209602086332739585-CNFS", "content": "... real-world scenarios. Kudos to the talented researchers Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang and Boxin Shi from Peking ..."}
diff --git a/data/sampled_jsons/EventPS_Yu_et_al_3D_printed_objects_MAE_results.jsonl b/data/sampled_jsons/EventPS_Yu_et_al_3D_printed_objects_MAE_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..018b6da8d304bea22d60648a185b1ec404cab098
--- /dev/null
+++ b/data/sampled_jsons/EventPS_Yu_et_al_3D_printed_objects_MAE_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency."}
+{"idx": 1, "title": "3D printed parts and mechanical properties: Influencing parameters ...", "date": "", "ddg_snippet": "The review deals with the mechanical and the tribological properties of 3D printed parts, hence the emphasis has been put on developing a systematic literature survey of effect on mechanical properties of 3-D printed parts.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S254250482200001X", "content": "The review deals with the mechanical and the tribological properties of 3D printed parts, hence the emphasis has been put on developing a systematic literature survey of effect on mechanical properties of 3-D printed parts."}
+{"idx": 2, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of ..."}
+{"idx": 3, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10655016", "content": "Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
+{"idx": 4, "title": "3D printed electronics: Processes, materials and future trends", "date": "", "ddg_snippet": "This review offers an in-depth overview for the latest 3D electronic printing techniques and developments in innovative practical technologies used to fabricate 3D printed electronics devices.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0079642522000263", "content": "This review offers an in-depth overview for the latest 3D electronic printing techniques and developments in innovative practical technologies used to fabricate 3D printed electronics devices."}
+{"idx": 5, "title": "3D-Printed Objects for Multipurpose Applications - PMC", "date": "", "ddg_snippet": "3D printing is a popular nonconventional manufacturing technique used to print 3D objects by using conventional and nonconventional materials. The application and uses of 3D printing are rapidly increasing in each dimension of the engineering and medical sectors. This article overviews the multipurpose applications of 3D printing based on current research. In the beginning, various popular ...", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7996717/", "content": "3D printing is a popular nonconventional manufacturing technique used to print 3D objects by using conventional and nonconventional materials. The application and uses of 3D printing are rapidly increasing in each dimension of the engineering and medical sectors. This article overviews the multipurpose applications of 3D printing based on current research. In the beginning, various popular ..."}
+{"idx": 6, "title": "Deep learning for the rare-event rational design of 3D printed multi ...", "date": "", "ddg_snippet": "Multi-material 3D printing techniques are now enabling the rational design of metamaterials with both complex geometries and multiple materials compositions. Here, deep-learning methods are used ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s43246-022-00270-2", "content": "Multi-material 3D printing techniques are now enabling the rational design of metamaterials with both complex geometries and multiple materials compositions. Here, deep-learning methods are used ..."}
+{"idx": 7, "title": "Publications - Bohan Yu's Homepage", "date": "", "ddg_snippet": "EventPS : Real-Time Photometric Stereo Using an Event Camera 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) MILO: Multi-bounce inverse rendering for indoor scene with light-emitting objects 2023-03-29", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/publications/", "content": "EventPS : Real-Time Photometric Stereo Using an Event Camera 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) MILO: Multi-bounce inverse rendering for indoor scene with light-emitting objects 2023-03-29"}
+{"idx": 8, "title": "PDF PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "Experiments using real event data from 3D-printed objects demonstrate that PS-EIP significantly improves ro-bustness to outliers compared to EventPS's deep-learning variant, EventPS -FCN, without relying on deep learning.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "Experiments using real event data from 3D-printed objects demonstrate that PS-EIP significantly improves ro-bustness to outliers compared to EventPS's deep-learning variant, EventPS -FCN, without relying on deep learning."}
+{"idx": 9, "title": "PDF Projection micro stereolithography based 3D printing and its applications", "date": "", "ddg_snippet": "Projection micro stereolithography (PμSL) is a high-resolution (up to 0.6 μm) 3D printing technology based on area projection triggered photopolymerization, and capable of fabricating complex 3D architectures covering multiple scales and with multiple materials. This paper reviews the recent development of the PμSL based 3D printing technologies, together with the related applications. It ...", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/138749/Ge_2020_Int._J._Extrem._Manuf._2_022004.pdf?sequence=2", "content": "Projection micro stereolithography (PμSL) is a high-resolution (up to 0.6 μm) 3D printing technology based on area projection triggered photopolymerization, and capable of fabricating complex 3D architectures covering multiple scales and with multiple materials. This paper reviews the recent development of the PμSL based 3D printing technologies, together with the related applications. It ..."}
diff --git a/data/sampled_jsons/EventPS_abstract_Photometric_stereo_is_a_well-established_technique_to_estimate_the_surface_normal_Y_year_2024.jsonl b/data/sampled_jsons/EventPS_abstract_Photometric_stereo_is_a_well-established_technique_to_estimate_the_surface_normal_Y_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f554cdf3a2e2b34383a974408d4cd43824f3c1f2
--- /dev/null
+++ b/data/sampled_jsons/EventPS_abstract_Photometric_stereo_is_a_well-established_technique_to_estimate_the_surface_normal_Y_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Abstract : Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications.", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/eventps-real-time-photometric-stereo-using-an-event-camera/", "content": "Abstract : Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal ..."}
+{"idx": 2, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera | Cool ...", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera@CVPR2024@CVF", "content": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
+{"idx": 3, "title": "CVPR Poster EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/poster/31806", "content": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
+{"idx": 4, "title": "Eventps: Real-time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi ABSTRACT Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications.", "subpage_snippet": "", "source": "prophesee-prod.euregion.site", "link": "https://prophesee-prod.euregion.site/2024/07/05/eventps-real-time-photometric-stereo-event-camera/", "content": "Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi ABSTRACT Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications."}
+{"idx": 5, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "摘要: Photometric stereo is a well-established technique to estimate the surface normal of an object. However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "scholar.pku.edu.cn", "link": "http://scholar.pku.edu.cn/cssherryliang/publications/eventps-real-time-photometric-stereo-using-event-camera", "content": "摘要: Photometric stereo is a well-established technique to estimate the surface normal of an object. However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
+{"idx": 6, "title": "EventPS : Real-Time Photometric Stereo Using... | Papers With Code", "date": "", "ddg_snippet": "Photometric stereo is a well - established technique to estimate the surface normal of an object. However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/eventps-real-time-photometric-stereo-using-an", "content": "Photometric stereo is a well - established technique to estimate the surface normal of an object. However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications."}
+{"idx": 7, "title": "Track: Orals 3C Medical and Physics-based vision", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/session/32097", "content": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
+{"idx": 8, "title": "Deming Chen's Post", "date": "", "ddg_snippet": "... Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/demingchen_200-people-attended-the-first-international-activity-7213261886799196161-0EY0", "content": "... Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic ..."}
+{"idx": 9, "title": "Track: Poster Session 3 & Exhibit Hall - CVPR", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/session/32085", "content": "Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic ..."}
diff --git a/data/sampled_jsons/EventPS_quantitative_results_MAE_mean_angular_error_3D_printed_objects.jsonl b/data/sampled_jsons/EventPS_quantitative_results_MAE_mean_angular_error_3D_printed_objects.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2b766fdf6d92ebf7426d0a2ce5a30303d92ddcd2
--- /dev/null
+++ b/data/sampled_jsons/EventPS_quantitative_results_MAE_mean_angular_error_3D_printed_objects.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "The Mean Angular Error ( MAE ) and data rate com- parison are shown in Tab. 1. On average , EventPS reduces the required data rate to around 25.9% (for EventPS -OP) ...", "subpage_snippet": "", "source": "downloads.ctfassets.net", "link": "https://downloads.ctfassets.net/yreyglvi5sud/7xx2vePh8HxPPWwBbz5ikd/23e483fbf3dc9763bd7811cbee301f29/Yu_CVPR24.pdf", "content": "The Mean Angular Error ( MAE ) and data rate com- parison are shown in Tab. 1. On average , EventPS reduces the required data rate to around 25.9% (for EventPS -OP) ..."}
+{"idx": 1, "title": "Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "... quantitative evaluation using 21 3D printed objects with various shapes, achieving a mean angular error of 8.12◦ in the presence of non-Lambertian effects ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33500", "content": "... quantitative evaluation using 21 3D printed objects with various shapes, achieving a mean angular error of 8.12◦ in the presence of non-Lambertian effects ..."}
+{"idx": 2, "title": "Event Fusion Photometric Stereo Network", "date": "", "ddg_snippet": "Mean Angular Error (MAE ). K=1. K=5. K=10. Figure 10: Mean Angular Error (MAE) scores for each model with different rotational pseudo-invariances. Table 2: To ...", "subpage_snippet": "", "source": "web3.arxiv.org", "link": "https://web3.arxiv.org/pdf/2303.00308v1", "content": "Mean Angular Error (MAE ). K=1. K=5. K=10. Figure 10: Mean Angular Error (MAE) scores for each model with different rotational pseudo-invariances. Table 2: To ..."}
+{"idx": 3, "title": "Application of machine learning in polymer additive ...", "date": "", "ddg_snippet": "by T Nasrin · 2024 · Cited by 56 — Root mean squared error (RMSE), mean absolute error ( MAE ), coefficient of determination (R 2 ), and relative error (RE) are used to evaluate ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/pol.20230649", "content": "by T Nasrin · 2024 · Cited by 56 — Root mean squared error (RMSE), mean absolute error ( MAE ), coefficient of determination (R 2 ), and relative error (RE) are used to evaluate ..."}
+{"idx": 4, "title": "Evaluation of 3D Markerless Motion Capture Accuracy ...", "date": "", "ddg_snippet": "by N Nakano · 2020 · Cited by 432 — Mean absolute error ( MAE ) of the two time-series data during the analysis period durations was used as the indicator of the difference as ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2020.00050/full", "content": "by N Nakano · 2020 · Cited by 432 — Mean absolute error ( MAE ) of the two time-series data during the analysis period durations was used as the indicator of the difference as ..."}
+{"idx": 5, "title": "High-Speed Event Vision-Based Tactile Roller Sensor for ...", "date": "", "ddg_snippet": "26 Jul 2025 — The evaluations focus on: (1) 3D reconstruction accuracy, primarily quantified using Mean Absolute Error ( MAE ) against ground truth geometries, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.19914v1", "content": "26 Jul 2025 — The evaluations focus on: (1) 3D reconstruction accuracy, primarily quantified using Mean Absolute Error ( MAE ) against ground truth geometries, ..."}
+{"idx": 6, "title": "A novel vision-based multi-functional sensor for normality ...", "date": "", "ddg_snippet": "by M Halwani · 2024 · Cited by 20 — The study achieved a Mean Absolute Error ( MAE ) of 0.3° for roll and pitch measurements. ... Quantitative results show an average normality error of 0.13°, and ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0141635924000515", "content": "by M Halwani · 2024 · Cited by 20 — The study achieved a Mean Absolute Error ( MAE ) of 0.3° for roll and pitch measurements. ... Quantitative results show an average normality error of 0.13°, and ..."}
+{"idx": 7, "title": "3D Touch Force Estimation from Capacitive Images", "date": "", "ddg_snippet": "by J Yu · 2025 — The evaluation metrics used are mean absolute error ( MAE ), root mean squared error (RMSE), and standard deviation (SD). 5.1 Baseline Methods.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3708359.3712123", "content": "by J Yu · 2025 — The evaluation metrics used are mean absolute error ( MAE ), root mean squared error (RMSE), and standard deviation (SD). 5.1 Baseline Methods."}
+{"idx": 8, "title": "Machine learning-based prediction and optimisation ...", "date": "", "ddg_snippet": "by C Zhang · 2024 · Cited by 18 — This study developed a quantitative framework integrating numerical simulation and machine learning. Support vector regression and simulation were utilised.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/17452759.2024.2400330", "content": "by C Zhang · 2024 · Cited by 18 — This study developed a quantitative framework integrating numerical simulation and machine learning. Support vector regression and simulation were utilised."}
+{"idx": 9, "title": "Revisiting Supervised Learning-Based Photometric Stereo ...", "date": "", "ddg_snippet": "by X Wei · 2025 · Cited by 2 — The mean angular error ( MAE ) between estimated and ground truth normals is used as the evaluation metric. A. Validation of the Effectiveness on Shading ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/08/10948383/25BYR9bjtq8", "content": "by X Wei · 2025 · Cited by 2 — The mean angular error ( MAE ) between estimated and ground truth normals is used as the evaluation metric. A. Validation of the Effectiveness on Shading ..."}
diff --git "a/data/sampled_jsons/Example_3.3_PrM\342\200\240_probability_causal_bayesian_networks_intervention_conditioning.jsonl" "b/data/sampled_jsons/Example_3.3_PrM\342\200\240_probability_causal_bayesian_networks_intervention_conditioning.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..5854f3a9513f9ffd254abc6ed381207c4e8a3d39
--- /dev/null
+++ "b/data/sampled_jsons/Example_3.3_PrM\342\200\240_probability_causal_bayesian_networks_intervention_conditioning.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.14728", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities ."}
+{"idx": 1, "title": "Differential Semantics of Intervention in Bayesian Networks", "date": "", "ddg_snippet": "the intervention in causal Bayesian networks . Bayesian networks , their differential semantics and interven-. tions in Section 2. We introduce a new approach to represent. intervention in Section 3 and discover its differential seman", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/Proceedings/15/Papers/106.pdf", "content": "the intervention in causal Bayesian networks . Bayesian networks , their differential semantics and interven-. tions in Section 2. We introduce a new approach to represent. intervention in Section 3 and discover its differential seman"}
+{"idx": 2, "title": "Intervention , determinism, and the causal minimality condition", "date": "", "ddg_snippet": "Causal Bayesian network . Causation . Determinism.keywords = \" Causal Bayesian network , Causation , Determinism, Intervention , Markov condition , Probability \", author = \"Jiji ZHANG and Peter SPIRTES\", year = \"2011\"", "subpage_snippet": "", "source": "scholars.ln.edu.hk", "link": "https://scholars.ln.edu.hk/en/publications/intervention-determinism-and-the-causal-minimality-condition", "content": "Causal Bayesian network . Causation . Determinism.keywords = \" Causal Bayesian network , Causation , Determinism, Intervention , Markov condition , Probability \", author = \"Jiji ZHANG and Peter SPIRTES\", year = \"2011\""}
+{"idx": 3, "title": "Intervention and causality in a dynamic Bayesian network", "date": "", "ddg_snippet": "Dynamic Bayesian network effectively represent the causal relationship between genes and gene and protein. Modern approaches employ single multivariate gene expression data set to estimate time varying dynamic Bayesian network .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228393282_Intervention_and_causality_in_a_dynamic_Bayesian_network", "content": "Dynamic Bayesian network effectively represent the causal relationship between genes and gene and protein. Modern approaches employ single multivariate gene expression data set to estimate time varying dynamic Bayesian network ."}
+{"idx": 4, "title": "A Step-by-Step Guide in detecting causal relationships using...", "date": "", "ddg_snippet": "Bayesian network is a happy marriage between probability and graph theory.Figure 3: Example of the best DAG for the Sprinkler system. It encodes the following logic: the probability that the grass is wet is dependent on Sprinkler and Rain.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/a-step-by-step-guide-in-detecting-causal-relationships-using-bayesian-structure-learning-in-python-c20c6b31cee5", "content": "Bayesian network is a happy marriage between probability and graph theory.Figure 3: Example of the best DAG for the Sprinkler system. It encodes the following logic: the probability that the grass is wet is dependent on Sprinkler and Rain."}
+{"idx": 5, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/intervention-conditioning-causal-bayesian-networks", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities ."}
+{"idx": 6, "title": "(PDF) Dis-entangling Mixture of Interventions on a Causal Bayesian ...", "date": "", "ddg_snippet": "A causal Bayesian network is a pair consisting of a directed acyclic graph (called a causal graph) that represents causal relationships and a set of probability tables, that together with the graph specify the joint probability of the variables represented as nodes in the graph.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/67748624/Dis_entangling_Mixture_of_Interventions_on_a_Causal_Bayesian_Network_Using_Aggregate_Observations", "content": "A causal Bayesian network is a pair consisting of a directed acyclic graph (called a causal graph) that represents causal relationships and a set of probability tables, that together with the graph specify the joint probability of the variables represented as nodes in the graph."}
+{"idx": 7, "title": "Towards causal inference", "date": "", "ddg_snippet": "• Reminder: • Last lecture: general Bayesian networks • Now: axiomatic approaches to causal inference (~ causality ). • Limits of conditional predictive machine learning • From associations to direct dependencies • The ultimate limit of observational learning • Causal inference • Learning...", "subpage_snippet": "", "source": "www.mit.bme.hu", "link": "https://www.mit.bme.hu/system/files/oktatas/targyak/9892/IDA_FromAssociationsToCausations_v2020Presented.pdf", "content": "• Reminder: • Last lecture: general Bayesian networks • Now: axiomatic approaches to causal inference (~ causality ). • Limits of conditional predictive machine learning • From associations to direct dependencies • The ultimate limit of observational learning • Causal inference • Learning..."}
+{"idx": 8, "title": "Causality , Probability and Strategies for Avoiding Illness", "date": "", "ddg_snippet": "“ Bayes networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify the existence of direct causal influences between the linked propositions, and the strengths of these influences are quantified by conditional probabilities .”", "subpage_snippet": "", "source": "philarchive.org", "link": "https://philarchive.org/archive/GILSCM", "content": "“ Bayes networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify the existence of direct causal influences between the linked propositions, and the strengths of these influences are quantified by conditional probabilities .”"}
+{"idx": 9, "title": "A Simple Model: From Causality to Bayesian Networks", "date": "", "ddg_snippet": "Bayesian Networks . Introduction to Conditional Probability . Bayesian networks are powerful probabilistic graphical models that capture the dependencies between random variables and their conditional probabilities .", "subpage_snippet": "", "source": "www.educative.io", "link": "https://www.educative.io/courses/designing-causal-bayesian-networks-in-python/a-simple-model-from-causality-to-bayesian-networks", "content": "Bayesian Networks . Introduction to Conditional Probability . Bayesian networks are powerful probabilistic graphical models that capture the dependencies between random variables and their conditional probabilities ."}
diff --git a/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al_2023.jsonl b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al_2023.jsonl
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index 0000000000000000000000000000000000000000..fd1cd001ebe7e23ee2b3bd5ad7257494812a31d5
--- /dev/null
+++ b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Our proposed Motion Latent -based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.04048", "content": "Our proposed Motion Latent -based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages."}
+{"idx": 1, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "CVPR 2023 . Xin Chen 1.Our proposed Motion Latent Diffusion model (MLD) could produce vivid motion sequences (left) conforming to the given conditional inputs and substantially reduce the computational overhead (right) in both the training and inference stages.", "subpage_snippet": "", "source": "chenxin.tech", "link": "https://chenxin.tech/mld/", "content": "CVPR 2023 . Xin Chen 1.Our proposed Motion Latent Diffusion model (MLD) could produce vivid motion sequences (left) conforming to the given conditional inputs and substantially reduce the computational overhead (right) in both the training and inference stages."}
+{"idx": 2, "title": "Executing Your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "We propose a motion latent -based diffusion model to generate plausible human motion sequences conforming to the action classes or natural language descriptions.[6] Xuan Cao, Zhang Chen , Anpei Chen , Xin Chen , Shiying Li, and Jingyi Yu.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf", "content": "We propose a motion latent -based diffusion model to generate plausible human motion sequences conforming to the action classes or natural language descriptions.[6] Xuan Cao, Zhang Chen , Anpei Chen , Xin Chen , Shiying Li, and Jingyi Yu."}
+{"idx": 3, "title": "ChenFengYe/ motion - latent - diffusion : [CVPR 2023 ] Executing your ...", "date": "", "ddg_snippet": "[CVPR 2023 ] Executing your Commands via Motion Diffusion in Latent Space , a fast and high-quality motion diffusion model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion", "content": "[CVPR 2023 ] Executing your Commands via Motion Diffusion in Latent Space , a fast and high-quality motion diffusion model."}
+{"idx": 4, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Our proposed Motion Latent -based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages.CVPR 2023 PDF.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/executing-your-commands-via-motion-diffusion?ref=taskswithcode.ghost.io", "content": "Our proposed Motion Latent -based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages.CVPR 2023 PDF."}
+{"idx": 5, "title": "EMDM: Efficient Motion Diffusion Model for Fast and... | SpringerLink", "date": "", "ddg_snippet": "Executing your commands via motion diffusion in latent space . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18000–18010 ( 2023 ).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-72627-9_2", "content": "Executing your commands via motion diffusion in latent space . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18000–18010 ( 2023 )."}
+{"idx": 6, "title": "Priority-Centric Human Motion Generation in Discrete Latent Space", "date": "", "ddg_snippet": "Executing your Commands via Motion Diffusion in Latent Space 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/iccv/2023/071800o4760/1TJdbBOshMY", "content": "Executing your Commands via Motion Diffusion in Latent Space 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."}
+{"idx": 7, "title": "Robot Motion Diffusion Model: Motion Generation for Robotic...", "date": "", "ddg_snippet": "2023 . Executing your Commands via Motion Diffusion in Latent Space . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18000–18010.", "subpage_snippet": "", "source": "la.disneyresearch.com", "link": "https://la.disneyresearch.com/wp-content/uploads/RobotMDM_2.pdf", "content": "2023 . Executing your Commands via Motion Diffusion in Latent Space . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18000–18010."}
+{"idx": 8, "title": "Project Home - awesome-3d- diffusion :A collection of papers... - GitCode", "date": "", "ddg_snippet": "Executing your Commands via Motion Diffusion in Latent Space , Jiang et al ., CVPR 2023 .", "subpage_snippet": "", "source": "gitcode.com", "link": "https://gitcode.com/gh_mirrors/aw/awesome-3d-diffusion", "content": "Executing your Commands via Motion Diffusion in Latent Space , Jiang et al ., CVPR 2023 ."}
+{"idx": 9, "title": "Wen Liu - Google Akademik | DeepSeek AI - 5.911 tarafından alıntılandı", "date": "", "ddg_snippet": "2019. Executing your Commands via Motion Diffusion in Latent Space . 2023 . MotionGPT: Human Motion as a Foreign Language.", "subpage_snippet": "", "source": "scholar.google.nl", "link": "https://scholar.google.nl/citations?user=A6K6bkoAAAAJ&hl=tr", "content": "2019. Executing your Commands via Motion Diffusion in Latent Space . 2023 . MotionGPT: Human Motion as a Foreign Language."}
diff --git a/data/sampled_jsons/Exploration_by_Optimisation_in_Partial_Monitoring_Lattimore_Szepesvari_abstract_optimism.jsonl b/data/sampled_jsons/Exploration_by_Optimisation_in_Partial_Monitoring_Lattimore_Szepesvari_abstract_optimism.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2d70b6dcdd7d70ba76c94fde4f7cc9ced22849d3
--- /dev/null
+++ b/data/sampled_jsons/Exploration_by_Optimisation_in_Partial_Monitoring_Lattimore_Szepesvari_abstract_optimism.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Abstract We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for the non-degenerate locally observable games, the n-round minimax regret is bounded by 6m k^ (3/2) sqrt (n log (k)), where m is the number of signals.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v125/lattimore20a.html", "content": "Abstract We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for the non-degenerate locally observable games, the n-round minimax regret is bounded by 6m k^ (3/2) sqrt (n log (k)), where m is the number of signals."}
+{"idx": 1, "title": "[1907.05772] Exploration by Optimisation in Partial Monitoring COLT 2020: Exploration by Optimisation in Partial Monitoring Exploration by Optimisation in Partial Monitoring - NASA/ADS Exploration by Optimisation in Partial Monitoring | Request PDF Exploration by Optimisation in Partial Monitoring \"Exploration by Optimisation in Partial Monitoring.\" - dblp", "date": "", "ddg_snippet": "Jul 12, 2019 · View a PDF of the paper titled Exploration by Optimisation in Partial Monitoring , by Tor Lattimore and Csaba Szepesvari Exploration by Optimisation in Partial Monitoring Tor Lattimore , Csaba Szepesvari [Proceedings link] [PDF] Subject areas: Bandit problems, Online learning Presented in: Session 2A, Session 2E [Zoom link for poster in Session 2A], [Zoom link for poster in Session 2E] Abstract Computer Science - Machine Learning; Mathematics - Optimization and Control; Statistics - Machine Learning E-Print: high probability bounds, experiments and simplified algorithms/analysis full text sources arXiv | Jul 12, 2019 · Recently, this condition has been shown by (Bartok, Pal, and Szepesvari , 2011) to imply the O (\\sqrt {T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured ... Figure 2: An exploration distribution p derived from q for the game in Eq. (7). The expected loss when playing p is smaller than playing q and simultaneously more information is gained because the third action is revealing. - \"Exploration by Optimisation in Partial Monitoring \" Bibliographic details on Exploration by Optimisation in Partial Monitoring .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1907.05772", "content": "Jul 12, 2019 · View a PDF of the paper titled Exploration by Optimisation in Partial Monitoring , by Tor Lattimore and Csaba Szepesvari Exploration by Optimisation in Partial Monitoring Tor Lattimore , Csaba Szepesvari [Proceedings link] [PDF] Subject areas: Bandit problems, Online learning Presented in: Session 2A, Session 2E [Zoom link for poster in Session 2A], [Zoom link for poster in Session 2E] Abstract Computer Science - Machine Learning; Mathematics - Optimization and Control; Statistics - Machine Learning E-Print: high probability bounds, experiments and simplified algorithms/analysis full text sources arXiv | Jul 12, 2019 · Recently, this condition has been shown by (Bartok, Pal, and Szepesvari , 2011) to imply the O (\\sqrt {T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured ... Figure 2: An exploration distribution p derived from q for the game in Eq. (7). The expected loss when playing p is smaller than playing q and simultaneously more information is gained because the third action is revealing. - \"Exploration by Optimisation in Partial Monitoring \" Bibliographic details on Exploration by Optimisation in Partial Monitoring ."}
+{"idx": 2, "title": "COLT 2020: Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Exploration by Optimisation in Partial Monitoring Tor Lattimore , Csaba Szepesvari [Proceedings link] [PDF] Subject areas: Bandit problems, Online learning Presented in: Session 2A, Session 2E [Zoom link for poster in Session 2A], [Zoom link for poster in Session 2E] Abstract", "subpage_snippet": "", "source": "www.learningtheory.org", "link": "https://www.learningtheory.org/colt2020/virtual/papers/paper_66.html", "content": "Exploration by Optimisation in Partial Monitoring Tor Lattimore , Csaba Szepesvari [Proceedings link] [PDF] Subject areas: Bandit problems, Online learning Presented in: Session 2A, Session 2E [Zoom link for poster in Session 2A], [Zoom link for poster in Session 2E] Abstract"}
+{"idx": 3, "title": "Exploration by Optimisation in Partial Monitoring | Request PDF", "date": "", "ddg_snippet": "Jul 12, 2019 · Recently, this condition has been shown by (Bartok, Pal, and Szepesvari , 2011) to imply the O (\\sqrt {T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/334457375_Exploration_by_Optimisation_in_Partial_Monitoring", "content": "Jul 12, 2019 · Recently, this condition has been shown by (Bartok, Pal, and Szepesvari , 2011) to imply the O (\\sqrt {T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured ..."}
+{"idx": 4, "title": "Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Figure 2: An exploration distribution p derived from q for the game in Eq. (7). The expected loss when playing p is smaller than playing q and simultaneously more information is gained because the third action is revealing. - \"Exploration by Optimisation in Partial Monitoring \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Exploration-by-Optimisation-in-Partial-Monitoring-Lattimore-Szepesvari/c527571cea74c9a689d4d2b9a9e1608046f7ff95/figure/2", "content": "Figure 2: An exploration distribution p derived from q for the game in Eq. (7). The expected loss when playing p is smaller than playing q and simultaneously more information is gained because the third action is revealing. - \"Exploration by Optimisation in Partial Monitoring \""}
+{"idx": 5, "title": "\"Exploration by Optimisation in Partial Monitoring.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Exploration by Optimisation in Partial Monitoring .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-1907-05772", "content": "Bibliographic details on Exploration by Optimisation in Partial Monitoring ."}
+{"idx": 6, "title": "Exploration by Optimisation in Partial Monitoring - NASA/ADS", "date": "", "ddg_snippet": "Computer Science - Machine Learning; Mathematics - Optimization and Control; Statistics - Machine Learning E-Print: high probability bounds, experiments and simplified algorithms/analysis full text sources arXiv |", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2019arXiv190705772L/abstract", "content": "Computer Science - Machine Learning; Mathematics - Optimization and Control; Statistics - Machine Learning E-Print: high probability bounds, experiments and simplified algorithms/analysis full text sources arXiv |"}
+{"idx": 7, "title": "Csaba Szepesvári", "date": "", "ddg_snippet": "Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making. ... Optimistic Natural Policy Gradient: a Simple ...", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/csaba-szepesvari/", "content": "Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making. ... Optimistic Natural Policy Gradient: a Simple ..."}
+{"idx": 8, "title": "No-Regret M♮-Concave Function Maximization: Stochastic Bandit", "date": "", "ddg_snippet": "For instance, the minimax regret of hopeless games in partial monitoring is Ω ( T ) \\Omega(T) [ 27 , Section 37.2] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.12439v2", "content": "For instance, the minimax regret of hopeless games in partial monitoring is Ω ( T ) \\Omega(T) [ 27 , Section 37.2] ."}
+{"idx": 9, "title": "Journal of Machine Learning Research", "date": "", "ddg_snippet": "... of Washington Stochastic optimization, distributional shift, differentiable programming, federated learning, high-dimensional statistical inference ...", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/editorial-board.html", "content": "... of Washington Stochastic optimization, distributional shift, differentiable programming, federated learning, high-dimensional statistical inference ..."}
diff --git a/data/sampled_jsons/FBox_score_intersection_volume_normalized_box_embeddings.jsonl b/data/sampled_jsons/FBox_score_intersection_volume_normalized_box_embeddings.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e8664ae20444bc7f171e710ec10309cc83e5197
--- /dev/null
+++ b/data/sampled_jsons/FBox_score_intersection_volume_normalized_box_embeddings.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - nianticlabs/image-box-overlap: [ECCV 2020] Training neural ...", "date": "", "ddg_snippet": "The ratio of intersection over volume can be used to approximate normalized surface overlap. So, box representation allows us to model non-symmetric (non-metric) relations between pairs of images. The result is that with box embeddings we can quickly identify, for example, which test image is a close-up version of another.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nianticlabs/image-box-overlap", "content": "The ratio of intersection over volume can be used to approximate normalized surface overlap. So, box representation allows us to model non-symmetric (non-metric) relations between pairs of images. The result is that with box embeddings we can quickly identify, for example, which test image is a close-up version of another."}
+{"idx": 1, "title": "PDF Improving Local Identifiability in Probabilistic Box Embeddin", "date": "", "ddg_snippet": "Abstract Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings , where objects are represented by n-dimensional hyperrectangles, are a particularly promising example of such an embedding as they are closed under intersection and their volume can be calculated easily, allowing them to naturally ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/01c9d2c5b3ff5cbba349ec39a570b5e3-Paper.pdf", "content": "Abstract Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings , where objects are represented by n-dimensional hyperrectangles, are a particularly promising example of such an embedding as they are closed under intersection and their volume can be calculated easily, allowing them to naturally ..."}
+{"idx": 2, "title": "Smoothing the Geometry of Probabilistic Box Embeddings", "date": "", "ddg_snippet": "Box embeddings get a very similar score on this task, so we only include that result, since the aim of the experiment is to compare the softbox and hard box models and not to demonstrate a new state of the art.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=H1xSNiRcF7", "content": "Box embeddings get a very similar score on this task, so we only include that result, since the aim of the experiment is to compare the softbox and hard box models and not to demonstrate a new state of the art."}
+{"idx": 3, "title": "Improving Local Identifiability in Probabilistic Box Embeddings", "date": "", "ddg_snippet": "Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings , where objects are represented by n-dimensional hyperrectangles, are a particularly promising example of such an embedding as they are closed under intersection and their volume can be calculated easily, allowing them to naturally ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.04831", "content": "Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings , where objects are represented by n-dimensional hyperrectangles, are a particularly promising example of such an embedding as they are closed under intersection and their volume can be calculated easily, allowing them to naturally ..."}
+{"idx": 4, "title": "Box Embeddings: Paper Overviews - Tamanna Hossain-Kay", "date": "", "ddg_snippet": "Gumbel Intersection + Bessel Approx. Volume : Improving Local Identifiability in Probabilistic Box Embeddings (Dasgupta et al., 2020) Read on for brief overviews of each method.", "subpage_snippet": "", "source": "www.tamanna-hossain-kay.com", "link": "https://www.tamanna-hossain-kay.com/post/2021/11/09/box-embeddings-paper-overviews/", "content": "Gumbel Intersection + Bessel Approx. Volume : Improving Local Identifiability in Probabilistic Box Embeddings (Dasgupta et al., 2020) Read on for brief overviews of each method."}
+{"idx": 5, "title": "FBox Line Intersection - Epic Developer Community Forums", "date": "", "ddg_snippet": "I'm testing a line to the origin of a transformed FBox and want to figure out where on the box the line meets using a FMath::LineExtentBoxIntersection(). Everything works perfectly until I add rotation which simply increases or decreases the extent of the box and doesn't provide an accurate hit result. FVector GetTransformedMeshExtents(UStaticMesh* ISMMesh, const FTransform& MeshTransform ...", "subpage_snippet": "", "source": "forums.unrealengine.com", "link": "https://forums.unrealengine.com/t/fbox-line-intersection/466635", "content": "I'm testing a line to the origin of a transformed FBox and want to figure out where on the box the line meets using a FMath::LineExtentBoxIntersection(). Everything works perfectly until I add rotation which simply increases or decreases the extent of the box and doesn't provide an accurate hit result. FVector GetTransformedMeshExtents(UStaticMesh* ISMMesh, const FTransform& MeshTransform ..."}
+{"idx": 6, "title": "Optimizing Probabilistic Box Embeddings with Distance Measures", "date": "", "ddg_snippet": "Recently, geometric-inspired embedding methods draw research interests for their superior ability in representing transitive and asymmetric relations. A typical example, box embeddings , in which objects are parameterized as axis-aligned hyper-rectangles (i.e. boxes), can effectively model the partial orders and similarities between objects with the inclusion and overlapping relations of the ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10598148", "content": "Recently, geometric-inspired embedding methods draw research interests for their superior ability in representing transitive and asymmetric relations. A typical example, box embeddings , in which objects are parameterized as axis-aligned hyper-rectangles (i.e. boxes), can effectively model the partial orders and similarities between objects with the inclusion and overlapping relations of the ..."}
+{"idx": 7, "title": "Improving Local Identifiability in Probabilistic Box Embeddings", "date": "", "ddg_snippet": "Box embeddings , where objects are represented by n-dimensional hyperrectangles, are a particularly promising example of such an embedding as they are closed under intersection and their volume can be calculated easily, allowing them to naturally represent calibrated probability distributions.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2020/public/poster_01c9d2c5b3ff5cbba349ec39a570b5e3.html", "content": "Box embeddings , where objects are represented by n-dimensional hyperrectangles, are a particularly promising example of such an embedding as they are closed under intersection and their volume can be calculated easily, allowing them to naturally represent calibrated probability distributions."}
+{"idx": 8, "title": "GitHub - ralphabb/BoxE: Implementation of the BoxE model from the ...", "date": "", "ddg_snippet": "This repository contains the source code for the BoxE model, presented at NeurIPS 2020 in the paper \"BoxE: A Box Embedding Model for Knowledge Base Completion\". The repository includes all evaluation datasets, code for training and testing BoxE on these datasets to reproduce results presented in the paper, and a D3.JS visualization tool, BoxEViz, to show the evolution of box embeddings over ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ralphabb/BoxE", "content": "This repository contains the source code for the BoxE model, presented at NeurIPS 2020 in the paper \"BoxE: A Box Embedding Model for Knowledge Base Completion\". The repository includes all evaluation datasets, code for training and testing BoxE on these datasets to reproduce results presented in the paper, and a D3.JS visualization tool, BoxEViz, to show the evolution of box embeddings over ..."}
+{"idx": 9, "title": "Geometric Approach to Personalized Recommendation with Set-Theoretic ...", "date": "", "ddg_snippet": "We also evaluate score multiplication and threshold-based prediction for both vector and box embedding models, and find that performing set operations directly on the box embeddings performs best, solidifying our claim that the inductive bias of box embeddings provides the necessary generalization capabilities to address set-theoretic queries.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10875", "content": "We also evaluate score multiplication and threshold-based prediction for both vector and box embedding models, and find that performing set operations directly on the box embeddings performs best, solidifying our claim that the inductive bias of box embeddings provides the necessary generalization capabilities to address set-theoretic queries."}
diff --git a/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl b/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/FLAIROx_ah2ac2_production_README.md_3079.jsonl b/data/sampled_jsons/FLAIROx_ah2ac2_production_README.md_3079.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0908bb5d54fe783f1d027f5efc451b91490821ab
--- /dev/null
+++ b/data/sampled_jsons/FLAIROx_ah2ac2_production_README.md_3079.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ah2ac2/README.md at production · FLAIROx/ah2ac2 · GitHub", "date": "", "ddg_snippet": "Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ). Contribute to FLAIROx / ah2ac2 development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2/blob/production/README.md", "content": "Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ). Contribute to FLAIROx / ah2ac2 development by creating an account on GitHub."}
+{"idx": 1, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "The AH2AC2 Challenge The Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) provides a standardized environment for evaluating AI agents on their ability to coordinate with human-like counterparts in Hanabi.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/", "content": "The AH2AC2 Challenge The Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) provides a standardized environment for evaluating AI agents on their ability to coordinate with human-like counterparts in Hanabi."}
+{"idx": 2, "title": "AH2AC2", "date": "", "ddg_snippet": "🎉 Official website for the AH2AC2 research paper accepted to ICML 2025 for a spotlight poster presentation!", "subpage_snippet": "", "source": "ah2ac2.com", "link": "https://ah2ac2.com/", "content": "🎉 Official website for the AH2AC2 research paper accepted to ICML 2025 for a spotlight poster presentation!"}
+{"idx": 3, "title": "Flairox | LinkedIn", "date": "", "ddg_snippet": "Flairox is your end-to-end Amazon growth partner that provides a complete e-commerce services suite, protecting your brand as our own and finding the right solutions to scale it on Amazon and beyond.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/company/flairox/", "content": "Flairox is your end-to-end Amazon growth partner that provides a complete e-commerce services suite, protecting your brand as our own and finding the right solutions to scale it on Amazon and beyond."}
+{"idx": 4, "title": "GitHub - FLAIROx /jaxirl: Contains JAX implementation of algorithms...", "date": "", "ddg_snippet": "IRL is commonly framed as a two -player zero-sum game between a policy player and a reward function player. Intuitively, the reward function player tries to pick out differences between the current learner policy and the expert...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/jaxirl", "content": "IRL is commonly framed as a two -player zero-sum game between a policy player and a reward function player. Intuitively, the reward function player tries to pick out differences between the current learner policy and the expert..."}
+{"idx": 5, "title": "Our Blogs - Flairox", "date": "", "ddg_snippet": "41 min read . How To Launch A Sponsored Brands Ad Campaign on Amazon (Click-by-Click).", "subpage_snippet": "", "source": "www.flairox.com", "link": "https://www.flairox.com/our-blogs/", "content": "41 min read . How To Launch A Sponsored Brands Ad Campaign on Amazon (Click-by-Click)."}
+{"idx": 6, "title": "Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop human proxy agents on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21490", "content": "In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop human proxy agents on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 ."}
+{"idx": 7, "title": "Ad-Hoc Human-AI Coordination Challenge | OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · We develop \\textit {human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 . To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games, deliberately limiting the amount of available human gameplay data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FuGps5Zyia", "content": "May 1, 2025 · We develop \\textit {human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 . To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games, deliberately limiting the amount of available human gameplay data."}
+{"idx": 8, "title": "Tin Dizdarević", "date": "", "ddg_snippet": "Jun 26, 2025 · We develop \\textit {human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 . To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games, deliberately limiting the amount of available human gameplay data.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Tin+Dizdarević", "content": "Jun 26, 2025 · We develop \\textit {human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 . To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games, deliberately limiting the amount of available human gameplay data."}
+{"idx": 9, "title": "Ad-Hoc Human-AI Coordination Challenge - Semantic Scholar", "date": "", "ddg_snippet": "Jun 26, 2025 · This work develops human proxy agents on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 , and open-source a dataset of 3,079 games to encourage the development of data-efficient methods. Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Ad-Hoc-Human-AI-Coordination-Challenge-Dizdarevic-Hammond/76e21098d2925daeb769a647c9af4886d00b05fd", "content": "Jun 26, 2025 · This work develops human proxy agents on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 , and open-source a dataset of 3,079 games to encourage the development of data-efficient methods. Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open ..."}
diff --git a/data/sampled_jsons/Face_X-Ray_Li_et_al._2020_sitearxiv.org_year_2020.jsonl b/data/sampled_jsons/Face_X-Ray_Li_et_al._2020_sitearxiv.org_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f5f51dcea6b0e794b062763b849a0a9fe795e616
--- /dev/null
+++ b/data/sampled_jsons/Face_X-Ray_Li_et_al._2020_sitearxiv.org_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "When Deepfakes Look Real: Detecting AI-Generated Faces with", "date": "", "ddg_snippet": "This allows the model to better handle different types of deepfake faces while leveraging textual information. ... leveraging unlabeled data in face ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.09022v1", "content": "This allows the model to better handle different types of deepfake faces while leveraging textual information. ... leveraging unlabeled data in face ..."}
+{"idx": 1, "title": "X-ray Halos of Early-Type Galaxies with AGN Feedback and", "date": "", "ddg_snippet": "The model X - ray emission and absorption are integrated along the line of sight, to obtain maps of the surface brightness Σ X \\Sigma_{\\rm X } and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03536v1", "content": "The model X - ray emission and absorption are integrated along the line of sight, to obtain maps of the surface brightness Σ X \\Sigma_{\\rm X } and ..."}
+{"idx": 2, "title": "DevFD: Developmental Face Forgery Detection by Learning Shared", "date": "", "ddg_snippet": "Tianshuo Zhang 1,2 Li Gao 3 Siran Peng 1,2 Xiangyu Zhu 1,2 Zhen Lei 1,2,4,5 1 School of Artificial Intelligence, University of Chinese ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.19230v1", "content": "Tianshuo Zhang 1,2 Li Gao 3 Siran Peng 1,2 Xiangyu Zhu 1,2 Zhen Lei 1,2,4,5 1 School of Artificial Intelligence, University of Chinese ..."}
+{"idx": 3, "title": "Combating Biomedical Misinformation through Multi-modal Claim", "date": "", "ddg_snippet": "... demonstrate that CER achieves state-of-the-art performance on benchmarks such as HealthFC (Vladika et al ., 2024 ) , BioASQ-7 (Nentidis et al ., 2020 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13888v1", "content": "... demonstrate that CER achieves state-of-the-art performance on benchmarks such as HealthFC (Vladika et al ., 2024 ) , BioASQ-7 (Nentidis et al ., 2020 ..."}
+{"idx": 4, "title": "The Tug-of-War Between Deepfake Generation and Detection", "date": "", "ddg_snippet": "Initial detection tools focused primarily on visual artifacts such as blended face edges ( Li et al ., 2020a ) and image forgery techniques that have ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06174v4", "content": "Initial detection tools focused primarily on visual artifacts such as blended face edges ( Li et al ., 2020a ) and image forgery techniques that have ..."}
+{"idx": 5, "title": "Robust Deepfake Detection for Electronic Know Your Customer", "date": "", "ddg_snippet": "... face swapping and face reenactment, while also being robust to ... Face X - ray [ 29 ] represents the boundary between the face and background images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22601v1", "content": "... face swapping and face reenactment, while also being robust to ... Face X - ray [ 29 ] represents the boundary between the face and background images."}
+{"idx": 6, "title": "Towards Trustworthy AI: Secure Deepfake Detection using CNNs", "date": "", "ddg_snippet": "Building upon this foundation, authors in [ 11 ] proposed Face X - Ray , which identifies blending artifacts that are specific to facial image ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.17010v1", "content": "Building upon this foundation, authors in [ 11 ] proposed Face X - Ray , which identifies blending artifacts that are specific to facial image ..."}
+{"idx": 7, "title": "Veritas: Generalizable Deepfake Detection via Pattern-Aware", "date": "", "ddg_snippet": "However, directly applying deep reasoning faces a critical challenge: current MLLMs are extremely short for deepfake detection Ren et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21048v1", "content": "However, directly applying deep reasoning faces a critical challenge: current MLLMs are extremely short for deepfake detection Ren et al ."}
+{"idx": 8, "title": "Learning Counterfactually Decoupled Attention for Open-World", "date": "", "ddg_snippet": "Although deepfake detectors can effectively distinguish real from synthetic content [ 32 , 69 , 35 , 42 , 54 , 62 ] , such capability alone does ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.23074v1", "content": "Although deepfake detectors can effectively distinguish real from synthetic content [ 32 , 69 , 35 , 42 , 54 , 62 ] , such capability alone does ..."}
+{"idx": 9, "title": "Mitigating Hallucinations in Multimodal LLMs via Object-aware", "date": "", "ddg_snippet": "Experiments on multiple hallucination benchmarks, such as AMBER [ Wang et al .(2023)Wang, Wang, Xu, Zhang, Gu, Jia, Wang, Xu, Yan, Zhang, et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20181v1", "content": "Experiments on multiple hallucination benchmarks, such as AMBER [ Wang et al .(2023)Wang, Wang, Xu, Zhang, Gu, Jia, Wang, Xu, Yan, Zhang, et al ."}
diff --git a/data/sampled_jsons/Face_X-ray_Li_Chang_Lyu_CVPR_2020_abstract_cross-blended_images_year_2020.jsonl b/data/sampled_jsons/Face_X-ray_Li_Chang_Lyu_CVPR_2020_abstract_cross-blended_images_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..294ae92bb15908def35b60e484e281480f39269f
--- /dev/null
+++ b/data/sampled_jsons/Face_X-ray_Li_Chang_Lyu_CVPR_2020_abstract_cross-blended_images_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Face X-Ray for More General Face Forgery Detection", "date": "", "ddg_snippet": "by L Li · 2020 · Cited by 1241 — In this paper we propose a novel image representation called face X - ray for detecting forgery in face images . The face X - ray of an input face image is a ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Face_X-Ray_for_More_General_Face_Forgery_Detection_CVPR_2020_paper.pdf", "content": "by L Li · 2020 · Cited by 1241 — In this paper we propose a novel image representation called face X - ray for detecting forgery in face images . The face X - ray of an input face image is a ..."}
+{"idx": 1, "title": "Detecting Deepfakes With Self-Blended Images", "date": "", "ddg_snippet": "by K Shiohara · 2022 · Cited by 496 — In this paper, we present novel synthetic training data called self- blended images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Shiohara_Detecting_Deepfakes_With_Self-Blended_Images_CVPR_2022_paper.pdf", "content": "by K Shiohara · 2022 · Cited by 496 — In this paper, we present novel synthetic training data called self- blended images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source ... 10 pages"}
+{"idx": 2, "title": "Multi-Task Self-Blended Images for Face Forgery Detection", "date": "", "ddg_snippet": "1 Jan 2024 — 2020 . Retinaface: Single-shot multi-level face localisation in the wild. IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3595916.3626426", "content": "1 Jan 2024 — 2020 . Retinaface: Single-shot multi-level face localisation in the wild. IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR )."}
+{"idx": 3, "title": "Exploring Bi-Level Inconsistency via Blended Images for ...", "date": "", "ddg_snippet": "Abstract —The challenge of generalization in face forgery detection has become increasingly prominent as manipulation techniques continue to evolve.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/10206/4358835/10565921.pdf", "content": "Abstract —The challenge of generalization in face forgery detection has become increasingly prominent as manipulation techniques continue to evolve."}
+{"idx": 4, "title": "Face selection not selecting the faces that I want", "date": "", "ddg_snippet": "Apr 25, 2019 · I'm totally new to this and can't find a solution anywhere about this problem. I'm trying to select faces however it will not select the faces that I want.", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/maya-modeling-forum/face-selection-not-selecting-the-faces-that-i-want/td-p/8751937", "content": "Apr 25, 2019 · I'm totally new to this and can't find a solution anywhere about this problem. I'm trying to select faces however it will not select the faces that I want."}
+{"idx": 5, "title": "[Question] How to create a face from vertices? (Very beginner...", "date": "", "ddg_snippet": "Jul 11, 2022 · I'm new to 3ds max as of today. I need to connect one side of this mesh to the other. How can I select vertices and create faces from them? Like this picture... Thanks for any and all help!", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/3ds-max-modeling-forum/question-how-to-create-a-face-from-vertices-very-beginner/td-p/6455580", "content": "Jul 11, 2022 · I'm new to 3ds max as of today. I need to connect one side of this mesh to the other. How can I select vertices and create faces from them? Like this picture... Thanks for any and all help!"}
+{"idx": 6, "title": "Solved: Change Family Host Type - Autodesk Community", "date": "", "ddg_snippet": "Apr 11, 2014 · Therefore, Families that are hosted to a Face are necessary. Any of these element-specific Families can be converted to Face -Based with the following procedure: 1. Create a new Project and draw a Wall, Floor, or Ceiling - which ever element is an appropriate host. 2. Load in the desired Family and place one instance of each Type on the host ...", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-architecture-forum/change-family-host-type/td-p/4950442", "content": "Apr 11, 2014 · Therefore, Families that are hosted to a Face are necessary. Any of these element-specific Families can be converted to Face -Based with the following procedure: 1. Create a new Project and draw a Wall, Floor, or Ceiling - which ever element is an appropriate host. 2. Load in the desired Family and place one instance of each Type on the host ..."}
+{"idx": 7, "title": "How to align an object to a face of another object?", "date": "", "ddg_snippet": "Mar 24, 2016 · Hello! I have the following problem: I would like to align an object (the hemisphere in the figure) on top of a face of another object (the selected face in the figure). I tried with Align and Snap Tools but I could not reach my goal. Thanks so much!", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/maya-modeling-forum/how-to-align-an-object-to-a-face-of-another-object/td-p/6233028", "content": "Mar 24, 2016 · Hello! I have the following problem: I would like to align an object (the hemisphere in the figure) on top of a face of another object (the selected face in the figure). I tried with Align and Snap Tools but I could not reach my goal. Thanks so much!"}
+{"idx": 8, "title": "change hosted family to non hosted family - Autodesk Community", "date": "", "ddg_snippet": "Jun 1, 2017 · Select the elements from the face based families (geometry, reference planes, parametric dimensions), CRTL+C, and CTRL+V align to view on the non-host family. Re-constrain and add whatever is missing.", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-architecture-forum/change-hosted-family-to-non-hosted-family/td-p/7124047", "content": "Jun 1, 2017 · Select the elements from the face based families (geometry, reference planes, parametric dimensions), CRTL+C, and CTRL+V align to view on the non-host family. Re-constrain and add whatever is missing."}
+{"idx": 9, "title": "Solved: Face Based Family won't host - Autodesk Community", "date": "", "ddg_snippet": "Feb 1, 2012 · Question: After placing your hosted Air Terminals on the face of the linked ceiling, what does \"Host\" read under its Properties? I cannot get a face -based fixture to host to a linked ceiling in this project, period. I can go into a Section and designate the ceiling face as the current Work Plane and then place the fixtures. And I made a fake test project with a linked ceiling, and it worked ...", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-mep-forum/face-based-family-won-t-host/td-p/3314501", "content": "Feb 1, 2012 · Question: After placing your hosted Air Terminals on the face of the linked ceiling, what does \"Host\" read under its Properties? I cannot get a face -based fixture to host to a linked ceiling in this project, period. I can go into a Section and designate the ceiling face as the current Work Plane and then place the fixtures. And I made a fake test project with a linked ceiling, and it worked ..."}
diff --git a/data/sampled_jsons/Face_x-ray_Li_et_al._2020_year_2020.jsonl b/data/sampled_jsons/Face_x-ray_Li_et_al._2020_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b19b19d92022bb61cfd3990c479c7e1ae7c15a38
--- /dev/null
+++ b/data/sampled_jsons/Face_x-ray_Li_et_al._2020_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Face selection not selecting the faces that I want", "date": "", "ddg_snippet": "Apr 25, 2019 · I'm totally new to this and can't find a solution anywhere about this problem. I'm trying to select faces however it will not select the faces that I want.", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/maya-modeling-forum/face-selection-not-selecting-the-faces-that-i-want/td-p/8751937", "content": "Apr 25, 2019 · I'm totally new to this and can't find a solution anywhere about this problem. I'm trying to select faces however it will not select the faces that I want."}
+{"idx": 1, "title": "[Question] How to create a face from vertices? (Very beginner...", "date": "", "ddg_snippet": "Jul 11, 2022 · I'm new to 3ds max as of today. I need to connect one side of this mesh to the other. How can I select vertices and create faces from them? Like this picture... Thanks for any and all help!", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/3ds-max-modeling-forum/question-how-to-create-a-face-from-vertices-very-beginner/td-p/6455580", "content": "Jul 11, 2022 · I'm new to 3ds max as of today. I need to connect one side of this mesh to the other. How can I select vertices and create faces from them? Like this picture... Thanks for any and all help!"}
+{"idx": 2, "title": "How to align an object to a face of another object?", "date": "", "ddg_snippet": "Mar 24, 2016 · Hello! I have the following problem: I would like to align an object (the hemisphere in the figure) on top of a face of another object (the selected face in the figure). I tried with Align and Snap Tools but I could not reach my goal. Thanks so much!", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/maya-modeling-forum/how-to-align-an-object-to-a-face-of-another-object/td-p/6233028", "content": "Mar 24, 2016 · Hello! I have the following problem: I would like to align an object (the hemisphere in the figure) on top of a face of another object (the selected face in the figure). I tried with Align and Snap Tools but I could not reach my goal. Thanks so much!"}
+{"idx": 3, "title": "Solved: Face Based Family won't host - Autodesk Community", "date": "", "ddg_snippet": "Feb 1, 2012 · Question: After placing your hosted Air Terminals on the face of the linked ceiling, what does \"Host\" read under its Properties? I cannot get a face -based fixture to host to a linked ceiling in this project, period. I can go into a Section and designate the ceiling face as the current Work Plane and then place the fixtures. And I made a fake test project with a linked ceiling, and it worked ...", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-mep-forum/face-based-family-won-t-host/td-p/3314501", "content": "Feb 1, 2012 · Question: After placing your hosted Air Terminals on the face of the linked ceiling, what does \"Host\" read under its Properties? I cannot get a face -based fixture to host to a linked ceiling in this project, period. I can go into a Section and designate the ceiling face as the current Work Plane and then place the fixtures. And I made a fake test project with a linked ceiling, and it worked ..."}
+{"idx": 4, "title": "Solved: wall by face flip orientation - Autodesk Community", "date": "", "ddg_snippet": "Aug 29, 2016 · can someone help me out; I've created a mass surface, and I've applied a \"wall by face \" to it. Unfortunately, the wall is displayed as inside out, and I'd like to know how to flip it (the flip arrows dont appear, and space does nothing). also, is there a way to flip the mass normal?", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-architecture-forum/wall-by-face-flip-orientation/td-p/6528270", "content": "Aug 29, 2016 · can someone help me out; I've created a mass surface, and I've applied a \"wall by face \" to it. Unfortunately, the wall is displayed as inside out, and I'd like to know how to flip it (the flip arrows dont appear, and space does nothing). also, is there a way to flip the mass normal?"}
+{"idx": 5, "title": "How to get the host face of an instance if the host face is from...", "date": "", "ddg_snippet": "Jun 7, 2024 · see if this explanation helps, it is also aligned to what @jeremy_tammik mentioned In short: t o get the host face of a family instance that is hosted to a face from a linked document, you can follow these steps: 1. Retrieve the Host Face Reference: Access the `HostFace` property of the family instance to get the reference to the host face . 2.", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-api-forum/how-to-get-the-host-face-of-an-instance-if-the-host-face-is-from/td-p/12825655", "content": "Jun 7, 2024 · see if this explanation helps, it is also aligned to what @jeremy_tammik mentioned In short: t o get the host face of a family instance that is hosted to a face from a linked document, you can follow these steps: 1. Retrieve the Host Face Reference: Access the `HostFace` property of the family instance to get the reference to the host face . 2."}
+{"idx": 6, "title": "Is there an easy way to find the centre/centroid of a face?", "date": "", "ddg_snippet": "May 11, 2023 · Hi all, I find myself wanting to find the centre of faces that are irregular polygons or have a mixture of curved and straight sides, and I am wondering if there is a better/easier way to find the centre of these faces rather than drawing a bunch of lines and doing lots of maths. It has been sugg...", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/fusion-design-validate-document/is-there-an-easy-way-to-find-the-centre-centroid-of-a-face/td-p/9275747", "content": "May 11, 2023 · Hi all, I find myself wanting to find the centre of faces that are irregular polygons or have a mixture of curved and straight sides, and I am wondering if there is a better/easier way to find the centre of these faces rather than drawing a bunch of lines and doing lots of maths. It has been sugg..."}
+{"idx": 7, "title": "Face turning contour issue - Autodesk Community", "date": "", "ddg_snippet": "May 18, 2025 · hi i am trying to perform a simple finish turning profile on my part but fusion360 does not like it i guess . maybe i am doing something wrong. can some one have a look and explain to me what my mistake is? this is the profile i am trying to cut: fusion360 file included! thanks in advance...", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/fusion-manufacture-forum/face-turning-contour-issue/td-p/13635959", "content": "May 18, 2025 · hi i am trying to perform a simple finish turning profile on my part but fusion360 does not like it i guess . maybe i am doing something wrong. can some one have a look and explain to me what my mistake is? this is the profile i am trying to cut: fusion360 file included! thanks in advance..."}
+{"idx": 8, "title": "change hosted family to non hosted family - Autodesk Community", "date": "", "ddg_snippet": "Jun 1, 2017 · Select the elements from the face based families (geometry, reference planes, parametric dimensions), CRTL+C, and CTRL+V align to view on the non-host family. Re-constrain and add whatever is missing.", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-architecture-forum/change-hosted-family-to-non-hosted-family/td-p/7124047", "content": "Jun 1, 2017 · Select the elements from the face based families (geometry, reference planes, parametric dimensions), CRTL+C, and CTRL+V align to view on the non-host family. Re-constrain and add whatever is missing."}
+{"idx": 9, "title": "Solved: Change Family Host Type - Autodesk Community", "date": "", "ddg_snippet": "Apr 11, 2014 · Therefore, Families that are hosted to a Face are necessary. Any of these element-specific Families can be converted to Face -Based with the following procedure: 1. Create a new Project and draw a Wall, Floor, or Ceiling - which ever element is an appropriate host. 2. Load in the desired Family and place one instance of each Type on the host ...", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-architecture-forum/change-family-host-type/td-p/4950442", "content": "Apr 11, 2014 · Therefore, Families that are hosted to a Face are necessary. Any of these element-specific Families can be converted to Face -Based with the following procedure: 1. Create a new Project and draw a Wall, Floor, or Ceiling - which ever element is an appropriate host. 2. Load in the desired Family and place one instance of each Type on the host ..."}
diff --git a/data/sampled_jsons/Face_x-ray_Li_et_al_2020_deepfake_detection_abstract.jsonl b/data/sampled_jsons/Face_x-ray_Li_et_al_2020_deepfake_detection_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e82a2a8c58f82bc95ade5308ba6bbcac7985f909
--- /dev/null
+++ b/data/sampled_jsons/Face_x-ray_Li_et_al_2020_deepfake_detection_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FaceShifter: Towards High Fidelity And Occlusion Aware Face ...", "date": "", "ddg_snippet": "Experiments show that existing deepfake detection algorithm performs poorly with FaceShifter, since it achieves advantageous quality over all existing benchmarks. However, our newly developed Face X-Ray [ Li et al . CVPR 2020 ] method can reliably detect forged images created by FaceShifter. Dataset", "subpage_snippet": "", "source": "lingzhili.com", "link": "https://lingzhili.com/FaceShifterPage/", "content": "Experiments show that existing deepfake detection algorithm performs poorly with FaceShifter, since it achieves advantageous quality over all existing benchmarks. However, our newly developed Face X-Ray [ Li et al . CVPR 2020 ] method can reliably detect forged images created by FaceShifter. Dataset"}
+{"idx": 1, "title": "Face X-ray for More General Face Forgery Detection Face X-Ray for More General Face Forgery Detection - IEEE Xplore Deep learning model for deep fake face recognition and detection FaceShifter: Towards High Fidelity And Occlusion Aware Face ... DeepFake Detection in the AIGC Era: A Survey, Benchmarks, and ... Enhancing Deepfake Detection via Adversarial Generative Learning Deep learning model for deep fake face recognition and detection Deep learning model for deep fake face recognition and detection Deep learning model for deep fake face recognition and detection Face X - ray for More General Face Forgery Detection Deep learning model for deep fake face recognition and detection Face X - ray for More General Face Forgery Detection Real Appearance Modeling for More General Deepfake Detection", "date": "", "ddg_snippet": "Dec 31, 2019 · Extensive experiments show that face X-ray remains effective when applied to forgery generated by unseen face manipulation techniques, while most existing face forgery detection or deepfake detection algorithms experience a significant performance drop. In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We ... The process include identifying the forgery of face X-ray by blending the boundary forged image in the CNN model and classifying the loss in the detection of face X-ray ( Li et al ., 2020 ; Li & Lyu, 2018; Afchar et al ., 2018; Dang et al ., 2020 ). In the media, articles use the biometric technology for the detection of deep fakes. Experiments show that existing deepfake detection algorithm performs poorly with FaceShifter, since it achieves advantageous quality over all existing benchmarks. However, our newly developed Face X-Ray [ Li et al . CVPR 2020 ] method can reliably detect forged images created by FaceShifter. Dataset Sep 16, 2025 · A series of work [27], [145], [146] presented by Qiao et al . open a way to fully unsupervised DeepFake detection , but these methods still face challenges in computational efficiency, interpretability, generalization, robustness, etc. These approaches focus on improving the detec-tion performance by leveraging various techniques such as locality-aware autoen-coders [12], face X-ray methods [19], and two-branch recurrent networks [26]. However, how to effectively enhance the generalization of Deepfake detection remains an open and hot problem. How to detect fake face X-ray? The process include identifying the forgery of face X-ray by blending the boundary forged image in the CNN model and classifying the loss in the detection of face X-ray (Li et al., 2020; Li & Lyu, 2018; Afchar et al., 2018; Dang et al., 2020). In the media, articles use the biometric technology for the detection of deep fakes. How to detect fake and real image using deepfake detection method? 1. A new hybrid high-performance deep fake face detection method is used based on the analysis of the Fisher face algorithm (LBHH) with dimensional reduction in features of the face image. 2. To detect the fake and real image using deepfake detection classifier based on DBN with the RBM technique. How to detect deep fake face image analysis using deep learning technique? In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. What is a face X ray? The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. Can generative adversarial neural networks detect deep fake image content? By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation . The main issues in the existing techniques are inaccurate, consumption time is high. Can face X-ray detect forgery in face images? In this paper we propose a novel image representation called face X-ray for detecting forgery in face images . The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. Nov 29, 2024 · Li , L., et al .: Face X-ray for more general face forgery detection . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5001–5010 ( 2020 )", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1912.13458", "content": "Dec 31, 2019 · Extensive experiments show that face X-ray remains effective when applied to forgery generated by unseen face manipulation techniques, while most existing face forgery detection or deepfake detection algorithms experience a significant performance drop. In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We ... The process include identifying the forgery of face X-ray by blending the boundary forged image in the CNN model and classifying the loss in the detection of face X-ray ( Li et al ., 2020 ; Li & Lyu, 2018; Afchar et al ., 2018; Dang et al ., 2020 ). In the media, articles use the biometric technology for the detection of deep fakes. Experiments show that existing deepfake detection algorithm performs poorly with FaceShifter, since it achieves advantageous quality over all existing benchmarks. However, our newly developed Face X-Ray [ Li et al . CVPR 2020 ] method can reliably detect forged images created by FaceShifter. Dataset Sep 16, 2025 · A series of work [27], [145], [146] presented by Qiao et al . open a way to fully unsupervised DeepFake detection , but these methods still face challenges in computational efficiency, interpretability, generalization, robustness, etc. These approaches focus on improving the detec-tion performance by leveraging various techniques such as locality-aware autoen-coders [12], face X-ray methods [19], and two-branch recurrent networks [26]. However, how to effectively enhance the generalization of Deepfake detection remains an open and hot problem. How to detect fake face X-ray? The process include identifying the forgery of face X-ray by blending the boundary forged image in the CNN model and classifying the loss in the detection of face X-ray (Li et al., 2020; Li & Lyu, 2018; Afchar et al., 2018; Dang et al., 2020). In the media, articles use the biometric technology for the detection of deep fakes. How to detect fake and real image using deepfake detection method? 1. A new hybrid high-performance deep fake face detection method is used based on the analysis of the Fisher face algorithm (LBHH) with dimensional reduction in features of the face image. 2. To detect the fake and real image using deepfake detection classifier based on DBN with the RBM technique. How to detect deep fake face image analysis using deep learning technique? In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. What is a face X ray? The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. Can generative adversarial neural networks detect deep fake image content? By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation . The main issues in the existing techniques are inaccurate, consumption time is high. Can face X-ray detect forgery in face images? In this paper we propose a novel image representation called face X-ray for detecting forgery in face images . The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. Nov 29, 2024 · Li , L., et al .: Face X-ray for more general face forgery detection . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5001–5010 ( 2020 )"}
+{"idx": 2, "title": "Face X-Ray for More General Face Forgery Detection - IEEE Xplore", "date": "", "ddg_snippet": "In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9157215", "content": "In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We ..."}
+{"idx": 3, "title": "Deep learning model for deep fake face recognition and detection", "date": "", "ddg_snippet": "The process include identifying the forgery of face X-ray by blending the boundary forged image in the CNN model and classifying the loss in the detection of face X-ray ( Li et al ., 2020 ; Li & Lyu, 2018; Afchar et al ., 2018; Dang et al ., 2020 ). In the media, articles use the biometric technology for the detection of deep fakes.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9044351/", "content": "The process include identifying the forgery of face X-ray by blending the boundary forged image in the CNN model and classifying the loss in the detection of face X-ray ( Li et al ., 2020 ; Li & Lyu, 2018; Afchar et al ., 2018; Dang et al ., 2020 ). In the media, articles use the biometric technology for the detection of deep fakes."}
+{"idx": 4, "title": "DeepFake Detection in the AIGC Era: A Survey, Benchmarks, and ...", "date": "", "ddg_snippet": "Sep 16, 2025 · A series of work [27], [145], [146] presented by Qiao et al . open a way to fully unsupervised DeepFake detection , but these methods still face challenges in computational efficiency, interpretability, generalization, robustness, etc.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253525008024", "content": "Sep 16, 2025 · A series of work [27], [145], [146] presented by Qiao et al . open a way to fully unsupervised DeepFake detection , but these methods still face challenges in computational efficiency, interpretability, generalization, robustness, etc."}
+{"idx": 5, "title": "Enhancing Deepfake Detection via Adversarial Generative Learning", "date": "", "ddg_snippet": "These approaches focus on improving the detec-tion performance by leveraging various techniques such as locality-aware autoen-coders [12], face X-ray methods [19], and two-branch recurrent networks [26]. However, how to effectively enhance the generalization of Deepfake detection remains an open and hot problem.", "subpage_snippet": "", "source": "rd.springer.com", "link": "https://rd.springer.com/content/pdf/10.1007/978-981-96-1068-6_22.pdf?pdf=inline+link", "content": "These approaches focus on improving the detec-tion performance by leveraging various techniques such as locality-aware autoen-coders [12], face X-ray methods [19], and two-branch recurrent networks [26]. However, how to effectively enhance the generalization of Deepfake detection remains an open and hot problem."}
+{"idx": 6, "title": "Real Appearance Modeling for More General Deepfake Detection", "date": "", "ddg_snippet": "Nov 29, 2024 · Li , L., et al .: Face X-ray for more general face forgery detection . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5001–5010 ( 2020 )", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/978-3-031-72943-0_23", "content": "Nov 29, 2024 · Li , L., et al .: Face X-ray for more general face forgery detection . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5001–5010 ( 2020 )"}
+{"idx": 7, "title": "Testing human ability to detect ‘deepfake’ images of human", "date": "", "ddg_snippet": "Prior literature has suggested that deepfakes fall into three major categories: head puppetry, face swapping, and lip syncing [ 13 ], but the ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/cybersecurity/article/9/1/tyad011/7205694", "content": "Prior literature has suggested that deepfakes fall into three major categories: head puppetry, face swapping, and lip syncing [ 13 ], but the ..."}
+{"idx": 8, "title": "Robust Deepfake Detection for Electronic Know Your Customer", "date": "", "ddg_snippet": "... deepfake attacks, it is essential to develop a robust deepfake detector capable of identifying both face swapping and face reenactment, while also ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22601v1", "content": "... deepfake attacks, it is essential to develop a robust deepfake detector capable of identifying both face swapping and face reenactment, while also ..."}
+{"idx": 9, "title": "The Tug-of-War Between Deepfake Generation and Detection", "date": "", "ddg_snippet": "... detection tools focused primarily on visual artifacts such as blended face edges ( Li et al ., 2020a ) and image forgery techniques that have preceded ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06174v4", "content": "... detection tools focused primarily on visual artifacts such as blended face edges ( Li et al ., 2020a ) and image forgery techniques that have preceded ..."}
diff --git a/data/sampled_jsons/Fan_et_al_2024_scaling_laws_synthetic_images_static_dynamic_data_generation_year_2024.jsonl b/data/sampled_jsons/Fan_et_al_2024_scaling_laws_synthetic_images_static_dynamic_data_generation_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..99c59885ae98db339b625a3c1d2a77f1c44ae3f0
--- /dev/null
+++ b/data/sampled_jsons/Fan_et_al_2024_scaling_laws_synthetic_images_static_dynamic_data_generation_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SpatialVID: A Large-Scale Video Dataset with Spatial Annotations", "date": "", "ddg_snippet": "... scale neural models have recently emerged: the LRM series (Hong et al ., 2023 ; Zhang et al ., 2024b ; Wei et al ., 2024 ) learns to reconstruct ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09676v1", "content": "... scale neural models have recently emerged: the LRM series (Hong et al ., 2023 ; Zhang et al ., 2024b ; Wei et al ., 2024 ) learns to reconstruct ..."}
+{"idx": 1, "title": "‘ML dataset’ directory · Gwern.net", "date": "", "ddg_snippet": "... Generating Verifiable and Diverse ... Newswire: A Large- Scale Structured Database of a Century of Historical News ”, Silcock et al 2024", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/dataset/index", "content": "... Generating Verifiable and Diverse ... Newswire: A Large- Scale Structured Database of a Century of Historical News ”, Silcock et al 2024"}
+{"idx": 2, "title": "Is Data Really a Barrier to Entry? Rethinking Competition", "date": "", "ddg_snippet": "Generative AI foundation models (FMs) use machine learning software algorithms to ascertain and predict statistical relationships between data point ...", "subpage_snippet": "", "source": "www.mercatus.org", "link": "https://www.mercatus.org/research/working-papers/data-really-barrier-entry-rethinking-competition-regulation-generative-ai", "content": "Generative AI foundation models (FMs) use machine learning software algorithms to ascertain and predict statistical relationships between data point ..."}
+{"idx": 3, "title": "From Web Search towards Agentic Deep Research: Incentivizing", "date": "", "ddg_snippet": "... test-time scaling (TTS) has emerged as a potent paradigm for boosting the reasoning and agentic capabilities of LLMs (Snell et al .,, 2024 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.18959v3", "content": "... test-time scaling (TTS) has emerged as a potent paradigm for boosting the reasoning and agentic capabilities of LLMs (Snell et al .,, 2024 ) ."}
+{"idx": 4, "title": "WorldWeaver: Generating Long-Horizon Video Worlds via Rich", "date": "", "ddg_snippet": "... color and texture over structural and dynamical attributes, such as motion trajectories, object geometry, and scale relationships Chefer et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15720v1", "content": "... color and texture over structural and dynamical attributes, such as motion trajectories, object geometry, and scale relationships Chefer et al ..."}
+{"idx": 5, "title": "From Virtual Games to Real-World Play", "date": "", "ddg_snippet": "Existing video generation models are typically designed for one-shot image -to-video generation , which does not align with our use case where video ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.18901v1", "content": "Existing video generation models are typically designed for one-shot image -to-video generation , which does not align with our use case where video ..."}
+{"idx": 6, "title": "Debias your Large Multi-Modal Model at Test-Time via", "date": "", "ddg_snippet": "Instead of crafting a dataset of contrasting prompts, we only need to leverage the information already available to the LMM, the input image .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.12590v3", "content": "Instead of crafting a dataset of contrasting prompts, we only need to leverage the information already available to the LMM, the input image ."}
+{"idx": 7, "title": "‘RNN’ directory · Gwern.net", "date": "", "ddg_snippet": "... Scaling Diffusion Mamba With Bidirectional SSMs for ... Data Scaling Laws in NMT: The Effect of Noise and Architecture ”, Bansal et al 2022", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/rnn/index", "content": "... Scaling Diffusion Mamba With Bidirectional SSMs for ... Data Scaling Laws in NMT: The Effect of Noise and Architecture ”, Bansal et al 2022"}
+{"idx": 8, "title": "Most Influential CVPR Papers (2025-03 Version) –", "date": "", "ddg_snippet": "... However most benchmarks predominantly assess spatial understanding in the static image tasks while overlooking temporal understanding in the dynamic ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/03/most-influential-cvpr-papers-2025-03-version/", "content": "... However most benchmarks predominantly assess spatial understanding in the static image tasks while overlooking temporal understanding in the dynamic ..."}
+{"idx": 9, "title": "Proceedings Posts - IMAVS.ORG", "date": "", "ddg_snippet": "Richardson}, title = {Robust Heading Estimation from Polarization Images by Deep Neural Networks}, year = { 2024 } , month = {Sep}, day = {16-20 ...", "subpage_snippet": "", "source": "www.imavs.org", "link": "https://www.imavs.org/category/proceedings/", "content": "Richardson}, title = {Robust Heading Estimation from Polarization Images by Deep Neural Networks}, year = { 2024 } , month = {Sep}, day = {16-20 ..."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_arxiv.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44ad1a26a09a182ed0e5fa92d504a7874a010948
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies : Formalization , Implementation ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/064ae24cdbb3eaacc801ee7f4fe0e4f2-Abstract-Conference.html", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Feint Formalization in Multi-Player Games", "date": "", "ddg_snippet": "The author introduces the formalization , implementation , and evaluation of Feint in Multi-Player Games to enhance game experiences and diversity.", "subpage_snippet": "", "source": "linnk.ai", "link": "https://linnk.ai/topic/feint-formalization-in-multi-player-games/", "content": "The author introduces the formalization , implementation , and evaluation of Feint in Multi-Player Games to enhance game experiences and diversity."}
+{"idx": 2, "title": "Feint Behaviors and Strategies : Formalization , Implementation ...", "date": "", "ddg_snippet": "Feint Formalization . Dual- Behavior Model.It offers a unified implementation scheme usable across various MARL frameworks, opening new avenues for research in deception and strategy in multi-agent systems.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "Feint Formalization . Dual- Behavior Model.It offers a unified implementation scheme usable across various MARL frameworks, opening new avenues for research in deception and strategy in multi-agent systems."}
+{"idx": 3, "title": "Evaluation Strategies", "date": "", "ddg_snippet": "Inductively sequential TRSs were initially characterized through the concept of a denitional tree Antoy (1992). Denitional trees have become the tool of choice for the formalization and implementation of narrowing strategies for several subclasses of the constructor-based TRSs.", "subpage_snippet": "", "source": "web.cecs.pdx.edu", "link": "https://web.cecs.pdx.edu/~antoy/homepage/publications/jsc/paper.pdf", "content": "Inductively sequential TRSs were initially characterized through the concept of a denitional tree Antoy (1992). Denitional trees have become the tool of choice for the formalization and implementation of narrowing strategies for several subclasses of the constructor-based TRSs."}
+{"idx": 4, "title": "Computer Science and Game Theory Mar 2024", "date": "", "ddg_snippet": "Title: Application of Nash equilibrium for developing an optimal forest harvesting strategy in Toruń Forest District.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.GT/2024-03", "content": "Title: Application of Nash equilibrium for developing an optimal forest harvesting strategy in Toruń Forest District."}
+{"idx": 5, "title": "Artificial Generals Intelligence: Mastering Generals.io with", "date": "", "ddg_snippet": "... al., 2017 ; Brown & Sandholm, 2018 ) and Stratego (Perolat et al., 2022 ) , which require reasoning under uncertainty and strategic deception.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.06825v2", "content": "... al., 2017 ; Brown & Sandholm, 2018 ) and Stratego (Perolat et al., 2022 ) , which require reasoning under uncertainty and strategic deception."}
+{"idx": 6, "title": "When LLMs Copy to Think: Uncovering Copy-Guided Attacks in", "date": "", "ddg_snippet": "Models such as DeepSeek-R1 [ 8 ] and o4-mini [ 14 ] are explicitly optimized for such behavior and have achieved state-of-the-art performance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16773v1", "content": "Models such as DeepSeek-R1 [ 8 ] and o4-mini [ 14 ] are explicitly optimized for such behavior and have achieved state-of-the-art performance ..."}
+{"idx": 7, "title": "Mechanistic Interpretability Needs Philosophy", "date": "", "ddg_snippet": "AI Interpretability [Ghosh and Kandasamy, 2020 , Sathyan et al., 2022 , Shin et al., 2022 , Calderon and Reichart, 2024 ] and Explainable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.18852v1", "content": "AI Interpretability [Ghosh and Kandasamy, 2020 , Sathyan et al., 2022 , Shin et al., 2022 , Calderon and Reichart, 2024 ] and Explainable ..."}
+{"idx": 8, "title": "Unleashing GHOST: An LLM-Powered Framework for Automated", "date": "", "ddg_snippet": "We evaluate three state-of-the-art LLMs (GPT-4, Gemini-1.5-pro, and LLaMA3) in generating and inserting HTs across different hardware designs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.02816v1", "content": "We evaluate three state-of-the-art LLMs (GPT-4, Gemini-1.5-pro, and LLaMA3) in generating and inserting HTs across different hardware designs ..."}
+{"idx": 9, "title": "Junyu LIU - Google Scholar", "date": "", "ddg_snippet": "Feint behaviors and strategies : formalization , implementation and evaluation .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=EiSrD1UAAAAJ&hl=en", "content": "Feint behaviors and strategies : formalization , implementation and evaluation ."}
diff --git "a/data/sampled_jsons/Feint_Behaviors_and_Strategies_paper_Section_4.2.2_implementation_scheduler_\316\273_short_\316\273_long_0.5_0.8.jsonl" "b/data/sampled_jsons/Feint_Behaviors_and_Strategies_paper_Section_4.2.2_implementation_scheduler_\316\273_short_\316\273_long_0.5_0.8.jsonl"
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index 0000000000000000000000000000000000000000..841e5fffd3e732daf10e825aa13be5b65d5a4d9e
--- /dev/null
+++ "b/data/sampled_jsons/Feint_Behaviors_and_Strategies_paper_Section_4.2.2_implementation_scheduler_\316\273_short_\316\273_long_0.5_0.8.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v2", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "However, existing literature does not provide a comprehensive ( and /or concrete) formalization for Feint behaviors , and their implications on game strategies . In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/064ae24cdbb3eaacc801ee7f4fe0e4f2-Abstract-Conference.html", "content": "However, existing literature does not provide a comprehensive ( and /or concrete) formalization for Feint behaviors , and their implications on game strategies . In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation ..."}
+{"idx": 2, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation, and ...", "date": "", "ddg_snippet": "However, existing literature do not provide comprehensive or concrete formalization for Feint behaviors , and their implications on game strategies . In this paper , we introduce the first comprehensive formalization of Feint behaviors at action-level and strategy-level, and provide concreteimplementationandquantitativeevaluationinMulti-Playergames.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/Feint-preprint.pdf", "content": "However, existing literature do not provide comprehensive or concrete formalization for Feint behaviors , and their implications on game strategies . In this paper , we introduce the first comprehensive formalization of Feint behaviors at action-level and strategy-level, and provide concreteimplementationandquantitativeevaluationinMulti-Playergames."}
+{"idx": 3, "title": "Formalizing Feint Actions, and Example Studies in Two-Player Games", "date": "", "ddg_snippet": "Feint actions, as an important feature in Two-player Games, have received a limited amount of attention and lack detailed studies. Feint actions is first mentioned in . 2010 as a proof-of-concept, to construct animations for nuanced game strategies with enhanced unpredictability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07931v1", "content": "Feint actions, as an important feature in Two-player Games, have received a limited amount of attention and lack detailed studies. Feint actions is first mentioned in . 2010 as a proof-of-concept, to construct animations for nuanced game strategies with enhanced unpredictability."}
+{"idx": 4, "title": "PDF Reinforcementlearning Andstochasticoptimization", "date": "", "ddg_snippet": "1.1 Why a universal formulation? 5 1.2 Some sample problems 7 1.3 Dimensions of a stochastic optimization problem 10 1.3.1 State variables 10 1.3.2 Types of decisions 11 1.3.3 Types of uncertainty 12 1.3.4 Models of system dynamics 12 1.3.5 Objectives 13 1.3.6 Staging of information and decisions 13 1.4 Formulating a stochastic optimization problem 14 1.4.1 A deterministic inventory problem 14 ...", "subpage_snippet": "", "source": "castle.princeton.edu", "link": "https://castle.princeton.edu/wp-content/uploads/2019/10/Powell-Reinforcement-Learning-and-Stochastic-Optimization.pdf", "content": "1.1 Why a universal formulation? 5 1.2 Some sample problems 7 1.3 Dimensions of a stochastic optimization problem 10 1.3.1 State variables 10 1.3.2 Types of decisions 11 1.3.3 Types of uncertainty 12 1.3.4 Models of system dynamics 12 1.3.5 Objectives 13 1.3.6 Staging of information and decisions 13 1.4 Formulating a stochastic optimization problem 14 1.4.1 A deterministic inventory problem 14 ..."}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Why does it matter? This paper is important because it provides the first comprehensive formalization of feint behaviors in game AI, significantly improving game rewards and diversity. It offers a unified implementation scheme usable across various MARL frameworks, opening new avenues for research in deception and strategy in multi-agent systems.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "Why does it matter? This paper is important because it provides the first comprehensive formalization of feint behaviors in game AI, significantly improving game rewards and diversity. It offers a unified implementation scheme usable across various MARL frameworks, opening new avenues for research in deception and strategy in multi-agent systems."}
+{"idx": 6, "title": "(PDF) FEINT: Automated Framework for Efficient INsertion of Templates ...", "date": "", "ddg_snippet": "The method employs short -term aging effects in FinFET transistors and circuit overclocking to induce bit errors at the circuit outputs in conjunction with Machine Learning (ML) tools learning ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382091580_FEINT_Automated_Framework_for_Efficient_INsertion_of_TemplatesTrojans_into_FPGAs", "content": "The method employs short -term aging effects in FinFET transistors and circuit overclocking to induce bit errors at the circuit outputs in conjunction with Machine Learning (ML) tools learning ..."}
+{"idx": 7, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Behavioral analysis showed agents developing sophisticated deceptive strategies through self-play and multi-agent training. The system demonstrated strong performance across different game environments while maintaining interpretability.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/feint-behaviors-strategies-formalization-implementation-evaluation", "content": "Behavioral analysis showed agents developing sophisticated deceptive strategies through self-play and multi-agent training. The system demonstrated strong performance across different game environments while maintaining interpretability."}
+{"idx": 8, "title": "PDF Enhancing energy efficiency in cloud - Springer", "date": "", "ddg_snippet": "Enhancing energy efficiency in cloud computing through task scheduling with hybrid cuckoo search and transformer models", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10791-025-09716-w.pdf", "content": "Enhancing energy efficiency in cloud computing through task scheduling with hybrid cuckoo search and transformer models"}
+{"idx": 9, "title": "Result page 18: Smart Solutions - Higher Education questions - Filo", "date": "", "ddg_snippet": "Result page 18: Step by Step Solutions for Smart Solutions - Higher Education questions from expert tutors over 1:1 instant tutoring sessions. Get solutions, concepts, examples or practice problems.", "subpage_snippet": "", "source": "askfilo.com", "link": "https://askfilo.com/user-question-answers/higher-education/smart-solutions/2025-09/14?page=18", "content": "Result page 18: Step by Step Solutions for Smart Solutions - Higher Education questions from expert tutors over 1:1 instant tutoring sessions. Get solutions, concepts, examples or practice problems."}
diff --git a/data/sampled_jsons/Figure_2_SFT_RFT_Dsyn_performance_relationship.jsonl b/data/sampled_jsons/Figure_2_SFT_RFT_Dsyn_performance_relationship.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d11cd76702a4b85d6dda085b61b778aefa56edaa
--- /dev/null
+++ b/data/sampled_jsons/Figure_2_SFT_RFT_Dsyn_performance_relationship.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 67 — ... Dsyn , and compare them with only positive training ( SFT ) on Dsyn . ... of the 2× sample efficiency gains we observe for RFT in Figure 2 (a,b) can be attributed to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "by A Setlur · Cited by 67 — ... Dsyn , and compare them with only positive training ( SFT ) on Dsyn . ... of the 2× sample efficiency gains we observe for RFT in Figure 2 (a,b) can be attributed to ..."}
+{"idx": 1, "title": "Offline RL on Sub-optimal Rollouts Scales Synthetic Data ...", "date": "", "ddg_snippet": "by A Setlur — Figure 2 : Positive data scaling laws: On GSM8K (a) and MATH (b), we evaluate SFT trained on Dsyn and RFT that uses SFT policy generated positives (D+ πsft ) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=v2PV1yCFJk", "content": "by A Setlur — Figure 2 : Positive data scaling laws: On GSM8K (a) and MATH (b), we evaluate SFT trained on Dsyn and RFT that uses SFT policy generated positives (D+ πsft ) ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of", "date": "", "ddg_snippet": "Figure 2 : Positive data scaling laws: On GSM8K (a) and MATH (b), we evaluate SFT trained on Dsyn and RFT that uses SFT policy generated positives ( D+πsft ) ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96295/paper", "content": "Figure 2 : Positive data scaling laws: On GSM8K (a) and MATH (b), we evaluate SFT trained on Dsyn and RFT that uses SFT policy generated positives ( D+πsft ) ..."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 67 — Figure 2 : Positive data scaling laws: On GSM8K (a) and MATH (b), we evaluate SFT trained on Dsyn and RFT that uses SFT policy generated ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 67 — Figure 2 : Positive data scaling laws: On GSM8K (a) and MATH (b), we evaluate SFT trained on Dsyn and RFT that uses SFT policy generated ..."}
+{"idx": 4, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM...", "date": "", "ddg_snippet": "rejection finetuning ( RFT ; positive self-generated synthetic data from the SFT m odel) and step-level RL (via per-step DPO) algorithms.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "rejection finetuning ( RFT ; positive self-generated synthetic data from the SFT m odel) and step-level RL (via per-step DPO) algorithms."}
+{"idx": 5, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Figure_2_focal_consolidation_sitehttpsarxiv.orghtml2504.11786v1_year_2024.jsonl b/data/sampled_jsons/Figure_2_focal_consolidation_sitehttpsarxiv.orghtml2504.11786v1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d121bd38d5bb17674f7b96d0fe8a2ff71b3e18f8
--- /dev/null
+++ b/data/sampled_jsons/Figure_2_focal_consolidation_sitehttpsarxiv.orghtml2504.11786v1_year_2024.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "Figure 1: An overview of our proposed framework, which consists of two stages: (1) report generation based on disease-aware image-text alignment and ( 2 ) self-correcting re-alignment of generated reports.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "Figure 1: An overview of our proposed framework, which consists of two stages: (1) report generation based on disease-aware image-text alignment and ( 2 ) self-correcting re-alignment of generated reports."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_arXiv_year_2023.jsonl b/data/sampled_jsons/Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_arXiv_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..74403f6c10e551fd983304260f93e0fe657b9ca9
--- /dev/null
+++ b/data/sampled_jsons/Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_arXiv_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v2", "content": "Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective."}
+{"idx": 2, "title": "(PDF) SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Find , read and cite all the research you need on ResearchGate.J. Adaptive sharpness-aware pruning for robust sparse . networks. arXiv , 2023. Beck, A. and Teboulle, M. A fast iterative shrinkage", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392531034_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning", "content": "Find , read and cite all the research you need on ResearchGate.J. Adaptive sharpness-aware pruning for robust sparse . networks. arXiv , 2023. Beck, A. and Teboulle, M. A fast iterative shrinkage"}
+{"idx": 3, "title": "Frontiers | Dynamic spatio-temporal pruning for efficient spiking neural...", "date": "", "ddg_snippet": "1. We propose a spatio-temporal pruning algorithm for SNNs, which dynamically reduces both spatial and temporal redundancy. The method integrates adaptive temporal pruning and LAMPS-based layer-wise balanced spatial pruning to achieve high sparsity with minimal performance loss.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1545583/full", "content": "1. We propose a spatio-temporal pruning algorithm for SNNs, which dynamically reduces both spatial and temporal redundancy. The method integrates adaptive temporal pruning and LAMPS-based layer-wise balanced spatial pruning to achieve high sparsity with minimal performance loss."}
+{"idx": 4, "title": "A Deep-Dive into Nueral Network Pruning AI Models", "date": "", "ddg_snippet": "Unstructured pruning can be understood as finding and removing the less salient connection in the model wherever they are. Technically speaking, structured pruning prunes weights in groups (remove entire neurons, filters, or channels of convolution neural networks).", "subpage_snippet": "", "source": "www.clarifai.com", "link": "https://www.clarifai.com/blog/neural-network-pruning-for-compression-understanding", "content": "Unstructured pruning can be understood as finding and removing the less salient connection in the model wherever they are. Technically speaking, structured pruning prunes weights in groups (remove entire neurons, filters, or channels of convolution neural networks)."}
+{"idx": 5, "title": "Neural Network Pruning 101 | by Hugo Tessier - Freedium", "date": "", "ddg_snippet": "The lottery ticket hypothesis: Finding sparse , trainable neural networks. arXiv preprint arXiv :1803.03635, 2018.", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/af816aaea61", "content": "The lottery ticket hypothesis: Finding sparse , trainable neural networks. arXiv preprint arXiv :1803.03635, 2018."}
+{"idx": 6, "title": "The Generalization-Stability Tradeoff In Neural Network Pruning", "date": "", "ddg_snippet": "DSD (dense- sparse -dense training), retraining a model after pruning then returning the pruned weights.neural networks. arXiv preprint arXiv :1701.05369, 2017. [27] Jonathan Frankle and Michael Carbin. The lottery ticket hypothesis: Finding sparse , trainable.", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/the-generalization-stability-tradeoff-in-neural-network-7569419", "content": "DSD (dense- sparse -dense training), retraining a model after pruning then returning the pruned weights.neural networks. arXiv preprint arXiv :1701.05369, 2017. [27] Jonathan Frankle and Michael Carbin. The lottery ticket hypothesis: Finding sparse , trainable."}
+{"idx": 7, "title": "Sub-networks and Spectral", "date": "", "ddg_snippet": "Flat minima are less susceptible to parameter perturbations, making the model more resilient to noise and better equipped to maintain stable performance across varying inputs.Sharpness-Aware Minimization for Efficiently Improving Generalization. arXiv preprint, arXiv :2010.01412.", "subpage_snippet": "", "source": "odr.chalmers.se", "link": "https://odr.chalmers.se/server/api/core/bitstreams/199295ba-a1f6-4421-99d4-1f1534ea8945/content", "content": "Flat minima are less susceptible to parameter perturbations, making the model more resilient to noise and better equipped to maintain stable performance across varying inputs.Sharpness-Aware Minimization for Efficiently Improving Generalization. arXiv preprint, arXiv :2010.01412."}
+{"idx": 8, "title": "The status of research in sparsity / pruning of deep neural networks...", "date": "", "ddg_snippet": "in computing operations. Both models [5, 7] required no special software/hardware accelerators for execution while unstructured pruning leads to irregular sparsity in pruned network, and requires sparse conv libraries and special hardware.", "subpage_snippet": "", "source": "dspace.library.uvic.ca", "link": "https://dspace.library.uvic.ca/server/api/core/bitstreams/58b789d7-a608-42af-8bd9-0eebd6a57a93/content", "content": "in computing operations. Both models [5, 7] required no special software/hardware accelerators for execution while unstructured pruning leads to irregular sparsity in pruned network, and requires sparse conv libraries and special hardware."}
+{"idx": 9, "title": "Sparse Double Descent: Where Network Pruning Aggravates Overfitting", "date": "", "ddg_snippet": "Thus, measuring the minima flatness of pruned networks and comparing them across different sparsities possibly leads to unfair comparison.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/128407693/Sparse_Double_Descent_Where_Network_Pruning_Aggravates_Overfitting", "content": "Thus, measuring the minima flatness of pruned networks and comparing them across different sparsities possibly leads to unfair comparison."}
diff --git a/data/sampled_jsons/Fisher_information_I(p)_integral_gradient_log_p_squared_probability_density_function_definition.jsonl b/data/sampled_jsons/Fisher_information_I(p)_integral_gradient_log_p_squared_probability_density_function_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..649f90b6e3f08ba23922294c35312fdc1cc18f46
--- /dev/null
+++ b/data/sampled_jsons/Fisher_information_I(p)_integral_gradient_log_p_squared_probability_density_function_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Exponential distribution - Wikipedia", "date": "", "ddg_snippet": "Exponential. Probability density function .The Fisher information , denoted.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Exponential_distribution", "content": "Exponential. Probability density function .The Fisher information , denoted."}
+{"idx": 1, "title": "(PDF) Fisher information and the complex nature of the Schr dinger...", "date": "", "ddg_snippet": "We show that the minimum Fisher i ~formation (MFI) approach to estimating the probability law p (x) on particle position x, over the class of all two-component taws p (x), yields the complex Sehr6dinger wave equation.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/129740550/Fisher_information_and_the_complex_nature_of_the_Schr_dinger_wave_equation", "content": "We show that the minimum Fisher i ~formation (MFI) approach to estimating the probability law p (x) on particle position x, over the class of all two-component taws p (x), yields the complex Sehr6dinger wave equation."}
+{"idx": 2, "title": "Fisher Information Flow in Artificial Neural Networks", "date": "", "ddg_snippet": "10. Fisher information flow through an ANN trained on the Gaussian dataset. The curves show the (normalized) Fisher information as a function of the estimated parameter θ. Each curve corresponds to a certain layer of the ANN, which is indicated by the color of the curve.", "subpage_snippet": "", "source": "journals.aps.org", "link": "https://journals.aps.org/prx/pdf/10.1103/kn3z-rmm8", "content": "10. Fisher information flow through an ANN trained on the Gaussian dataset. The curves show the (normalized) Fisher information as a function of the estimated parameter θ. Each curve corresponds to a certain layer of the ANN, which is indicated by the color of the curve."}
+{"idx": 3, "title": "PPT - Natural Gradient Works Efficiently in Learning S Amari...", "date": "", "ddg_snippet": "Abstract • The ordinary gradient of a function does not represent its steepest direction, but the natural gradient does. • The dynamical behavior of natural gradient online learning is analyzed and is proved to be Fisher efficient. •", "subpage_snippet": "", "source": "www.slideserve.com", "link": "https://www.slideserve.com/luna/natural-gradient-works-efficiently-in-learning-s-amari", "content": "Abstract • The ordinary gradient of a function does not represent its steepest direction, but the natural gradient does. • The dynamical behavior of natural gradient online learning is analyzed and is proved to be Fisher efficient. •"}
+{"idx": 4, "title": "Notes on probability distribution functions in Python using SciPy", "date": "", "ddg_snippet": "Probability density function . cdf. Cumulative distribution function . sf.Random samples. Functions such as pdf and cdf are defined over the entire real line.", "subpage_snippet": "", "source": "www.johndcook.com", "link": "https://www.johndcook.com/blog/distributions_scipy/", "content": "Probability density function . cdf. Cumulative distribution function . sf.Random samples. Functions such as pdf and cdf are defined over the entire real line."}
+{"idx": 5, "title": "Probability Density Function (rus) — Indicator by... — TradingView", "date": "", "ddg_snippet": "KZ Indicator ( Probability Density Function ) KZ estimates price move probabilities using a normal distribution model. It automatically detects trend direction, volatility level, and strength, then visualizes the likelihood of reaching an upside or downside target.", "subpage_snippet": "", "source": "www.tradingview.com", "link": "https://www.tradingview.com/script/5DsJuYR3-probability-density-function-rus/", "content": "KZ Indicator ( Probability Density Function ) KZ estimates price move probabilities using a normal distribution model. It automatically detects trend direction, volatility level, and strength, then visualizes the likelihood of reaching an upside or downside target."}
+{"idx": 6, "title": "Hidden Markov Model: Simple Definition & Overview - Statistics How To", "date": "", "ddg_snippet": "Fisher Information / Expected Information : Definition . Mixed Derivative (Partial, Iterated).", "subpage_snippet": "", "source": "www.statisticshowto.com", "link": "https://www.statisticshowto.com/hidden-markov-model/", "content": "Fisher Information / Expected Information : Definition . Mixed Derivative (Partial, Iterated)."}
+{"idx": 7, "title": "Can a Dirac delta function be a probability density function of...", "date": "", "ddg_snippet": "As explained in Gortaur's answer a delta function cannot be the probability density function of a real random variable. Nevertheless sums of delta functions can be viewed as the \"missing link\" between discrete and continuous random variables / probability distributions, in the following way", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/54197/can-a-dirac-delta-function-be-a-probability-density-function-of-a-random-variabl)", "content": "As explained in Gortaur's answer a delta function cannot be the probability density function of a real random variable. Nevertheless sums of delta functions can be viewed as the \"missing link\" between discrete and continuous random variables / probability distributions, in the following way"}
+{"idx": 8, "title": "Integral Calculator • With Steps!", "date": "", "ddg_snippet": "Solve definite and indefinite integrals (antiderivatives) using this free online calculator. Step-by-step solution and graphs included!", "subpage_snippet": "", "source": "www.integral-calculator.com", "link": "https://www.integral-calculator.com/", "content": "Solve definite and indefinite integrals (antiderivatives) using this free online calculator. Step-by-step solution and graphs included!"}
+{"idx": 9, "title": "What is box plot ? - Jingwen Zheng", "date": "", "ddg_snippet": "This blog talks about what is box plot, understanding box plot with help of probability density function (pdf), how to make a box plot by python module matplotlib and how to interprete a boxplot.", "subpage_snippet": "", "source": "jingwen-z.github.io", "link": "https://jingwen-z.github.io/what-is-box-plot/", "content": "This blog talks about what is box plot, understanding box plot with help of probability density function (pdf), how to make a box plot by python module matplotlib and how to interprete a boxplot."}
diff --git a/data/sampled_jsons/FlowDec-_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality_paper.jsonl b/data/sampled_jsons/FlowDec-_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a553a31ca4048dd5ba36a105dc736e707ad1c481
--- /dev/null
+++ b/data/sampled_jsons/FlowDec-_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 1, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio codec for ...", "date": "", "ddg_snippet": "FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 2, "title": "AUDIO CODEC WITH HIGH PERCEPTUAL QUALITY - OpenReview", "date": "", "ddg_snippet": "ABSTRACT We propose FlowDec , a neural full-band audio codec for general audio sampled at 48kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=uxDFlPGRLX", "content": "ABSTRACT We propose FlowDec , a neural full-band audio codec for general audio sampled at 48kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 3, "title": "FlowDec | A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "FlowDec : A flow-based full-band general audio codec with high perceptual quality Simon Welker, Matthew Le, Ricky T. Q. Chen, Wei-Ning Hsu, Timo Gerkmann, Alexander Richard, Yi-Chiao Wu ICLR 2025 Abstract We propose FlowDec [1], a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel ...", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "FlowDec : A flow-based full-band general audio codec with high perceptual quality Simon Welker, Matthew Le, Ricky T. Q. Chen, Wei-Ning Hsu, Timo Gerkmann, Alexander Richard, Yi-Chiao Wu ICLR 2025 Abstract We propose FlowDec [1], a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel ..."}
+{"idx": 4, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "Join the discussion on this paper pageFlowDec: A flow-based full-band general audio codec with high perceptual quality", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.01485", "content": "Join the discussion on this paper pageFlowDec: A flow-based full-band general audio codec with high perceptual quality"}
+{"idx": 5, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs , achieving FAD scores better than those of the established GAN- based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music. We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 ...", "subpage_snippet": "", "source": "semanticscholar.org", "link": "https://semanticscholar.org/paper/FlowDec:-A-flow-based-full-band-general-audio-codec-Welker-Le/ec40a4902b277f0f9e3704c1b23634cad4fe0dcf", "content": "FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs , achieving FAD scores better than those of the established GAN- based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music. We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 ..."}
+{"idx": 6, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "The paper introduces FlowDec , a neural audio codec that employs a two-stage approach: (1) an autoencoder with residual vector quantization, trained without adversarial loss; and (2) a postfilter that mitigates coding artifacts and enhances perceptual quality . FlowDec leverages conditional flow matching for signal enhancement, achieving notable improvements over previous score- based and flow ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "The paper introduces FlowDec , a neural audio codec that employs a two-stage approach: (1) an autoencoder with residual vector quantization, trained without adversarial loss; and (2) a postfilter that mitigates coding artifacts and enhances perceptual quality . FlowDec leverages conditional flow matching for signal enhancement, achieving notable improvements over previous score- based and flow ..."}
+{"idx": 7, "title": "FlowDec/README.md at main · facebookresearch/FlowDec · GitHub", "date": "", "ddg_snippet": "FlowDec FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec/blob/main/README.md", "content": "FlowDec FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 8, "title": "FlowDec/index.html at main · sp-uhh/FlowDec · GitHub", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sp-uhh/FlowDec/blob/main/index.html", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 9, "title": "What are the best Bluetooth codecs? aptX, AAC, LDAC and more explained", "date": "", "ddg_snippet": "The higher a codec's bitrate, the more 'bandwidth' it has, meaning the more efficiently it can carry higher- quality audio without losing information. Think of a codec like a tube, and the music file as something that needs to pass through it - the bigger the tube (bandwidth), the more music information can fit and more easily flow through it.", "subpage_snippet": "", "source": "www.whathifi.com", "link": "https://www.whathifi.com/advice/what-are-the-best-bluetooth-codecs-aptx-aac-ldac-and-more-explained", "content": "The higher a codec's bitrate, the more 'bandwidth' it has, meaning the more efficiently it can carry higher- quality audio without losing information. Think of a codec like a tube, and the music file as something that needs to pass through it - the bigger the tube (bandwidth), the more music information can fit and more easily flow through it."}
diff --git a/data/sampled_jsons/FlowDec_ScoreDec_ICLR_2025_paper_technical_differences.jsonl b/data/sampled_jsons/FlowDec_ScoreDec_ICLR_2025_paper_technical_differences.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..de8873769b40b45b9652d3c046b4f3fc1588d21b
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_ScoreDec_ICLR_2025_paper_technical_differences.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FLOWDEC: A FLOW-BASED FULL-BAND GENERAL", "date": "", "ddg_snippet": "Compared to the prior work. ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24kbit/s to as low as 4kbit/s, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/46581252e0bf80bf4efd8fcbc4002f8627f498bb.pdf", "content": "Compared to the prior work. ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24kbit/s to as low as 4kbit/s, ..."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "3 Mar 2025 — Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "3 Mar 2025 — Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as ..."}
+{"idx": 2, "title": "FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "by S Welker · Cited by 8 — This paper proposes FlowDec , a 48 kHz general audio codec with a flow-matching diffusion post-filter. FlowDec modifies the DAC audio codec with different loss ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "by S Welker · Cited by 8 — This paper proposes FlowDec , a 48 kHz general audio codec with a flow-matching diffusion post-filter. FlowDec modifies the DAC audio codec with different loss ..."}
+{"idx": 3, "title": "FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "by S Welker · 2025 · Cited by 8 — Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "by S Welker · 2025 · Cited by 8 — Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as ..."}
+{"idx": 4, "title": "FlowDec | A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, ...", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, ..."}
+{"idx": 5, "title": "ICLR 2025 Thursday 04/24", "date": "", "ddg_snippet": "In research, the papers generated by the CycleResearcher model achieved a score of 5.36 in simulated peer reviews, showing some competitiveness in terms of ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/day/4/24", "content": "In research, the papers generated by the CycleResearcher model achieved a score of 5.36 in simulated peer reviews, showing some competitiveness in terms of ..."}
+{"idx": 6, "title": "A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "3 Mar 2025 — The contributors claim that FlowDec provides substantial improvements over prior work (notably ScoreDec , which focuses primarily on speech).", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/flowdec-a-flow-based-full-band-general-audio-codec-with-high-perceptual-quality", "content": "3 Mar 2025 — The contributors claim that FlowDec provides substantial improvements over prior work (notably ScoreDec , which focuses primarily on speech)."}
+{"idx": 7, "title": "Yi-Chiao Wu", "date": "", "ddg_snippet": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Yi-Chiao+Wu", "content": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, ..."}
+{"idx": 8, "title": "Wei-Ning Hsu", "date": "", "ddg_snippet": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Wei-Ning+Hsu", "content": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, ..."}
+{"idx": 9, "title": "Alexander Richard - Research Scientist - Meta Reality Labs ...", "date": "", "ddg_snippet": "We provide theoretical insights and geometric intuitions for our approach in comparison to ScoreDec ... International Conference on Learning Representations ( ICLR ) ...", "subpage_snippet": "", "source": "alexanderrichard.github.io", "link": "https://alexanderrichard.github.io/", "content": "We provide theoretical insights and geometric intuitions for our approach in comparison to ScoreDec ... International Conference on Learning Representations ( ICLR ) ..."}
diff --git a/data/sampled_jsons/FlowDec_ScoreDec_NFE_score_matching_flow_matching_Section_5.1_unusable_results_year_2024.jsonl b/data/sampled_jsons/FlowDec_ScoreDec_NFE_score_matching_flow_matching_Section_5.1_unusable_results_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f9c927fdf805b1c813aca0169c3e403ceccfd955
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_ScoreDec_NFE_score_matching_flow_matching_Section_5.1_unusable_results_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec : A flow -based full-band general audio codec... | OpenReview", "date": "", "ddg_snippet": "Keywords: audio, audio codec, generative models, flow matching , postfilter, signal enhancement. TL;DR: FlowDec is a flow -based postfilter codec for general audio without adversarial training, and a competitive alternative to current GAN-based SOTA codecs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "Keywords: audio, audio codec, generative models, flow matching , postfilter, signal enhancement. TL;DR: FlowDec is a flow -based postfilter codec for general audio without adversarial training, and a competitive alternative to current GAN-based SOTA codecs."}
+{"idx": 1, "title": "GitHub - facebookresearch/ FlowDec : An neural full-band audio codec...", "date": "", "ddg_snippet": "FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Demo.(or whatever matches your local CUDA version).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Demo.(or whatever matches your local CUDA version)."}
+{"idx": 2, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "conditional flow matching . GAN. generative adversarial network.setting used for FlowDec . As the metrics show, FlowDec works significantly better at NFE =6 where ScoreDec and FlowAVSE fail to produce acceptable results , and also generally outperforms ScoreDec at NFE =50.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "conditional flow matching . GAN. generative adversarial network.setting used for FlowDec . As the metrics show, FlowDec works significantly better at NFE =6 where ScoreDec and FlowAVSE fail to produce acceptable results , and also generally outperforms ScoreDec at NFE =50."}
+{"idx": 3, "title": "FLOWDEC: A FLOW-BASED FULL-BAND GENERAL", "date": "", "ddg_snippet": "We can see that for NFE = 6, FlowDec is a clear improvement over ScoreDec which produces unusable results at this NFE and also performs significantly better ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/46581252e0bf80bf4efd8fcbc4002f8627f498bb.pdf", "content": "We can see that for NFE = 6, FlowDec is a clear improvement over ScoreDec which produces unusable results at this NFE and also performs significantly better ..."}
+{"idx": 4, "title": "KL-Divergence, Score Matching, and Flow Matching", "date": "", "ddg_snippet": "Jul 15, 2023 · KL-divergence minimization is typically used to train normalizing flows , score matching for diffusion models and flow matching for continuous normalizing flows. In this post, we'll go over how these three methods are related and when to use each one.", "subpage_snippet": "", "source": "eddiecunningham.github.io", "link": "https://eddiecunningham.github.io/kl-div-and-matching.html", "content": "Jul 15, 2023 · KL-divergence minimization is typically used to train normalizing flows , score matching for diffusion models and flow matching for continuous normalizing flows. In this post, we'll go over how these three methods are related and when to use each one."}
+{"idx": 5, "title": "[2401.12160] ScoreDec: A Phase-preserving High-Fidelity Audio ... 得分匹配 Score Matching - 知乎 GitHub - facebookresearch/FlowDec: An neural full-band audio ... FlowDec : A flow -based full-band general audio codec with high GitHub - facebookresearch/ FlowDec : An neural full-band audio code… FlowDec : A flow -based full-band general audio codec with high FlowDec : A flow -based full-band general audio codec with high GitHub - facebookresearch/ FlowDec : An neural full-band audio code… FlowDec : A flow -based full-band general audio codec with high An Investigation of Noise Robustness for Flow-Matching-Based ...", "date": "", "ddg_snippet": "Jan 22, 2024 · Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information. 最近，以宋博士为代表的研究者提出基于得分的概率生成模型在图像生成等领域取得佳绩，打破了一些主流生成模型（如，对抗生成模型-GAN，变分自编码器 -VAE，基于流的生成模型-Flow-based Genarative Model等）在生成领域的制霸权，引领了一波生成模型的研究热潮 (详情请参考宋博士博客Generative Modeling by Estimating Gradients of the Data Distribution)。 基于得分的概率生成模型最主要的思想是估计得分，而估计得分的主流方法得分匹配 (Score Matching) 早在2005年已经有研究者提出。因此，了解得分匹配的原理能够帮助理解和学习主流的生成模型。 See full list on zhuanlan.zhihu.com 概率模型通常是通过一系列数学公式推导出数据的概率分布情况，考虑概率模型： \\begin{array}{l} p(x;\\theta ) = \\frac{ 1 }{{Z(\\theta )}}q(x;\\theta )\\\\ Z(\\theta ) = \\int\\limits_{x \\in {\\mathbb{R}^n}} {q(x;\\theta )dx} \\end{array} ( 1 )， 其中， q(x;\\theta) 为未归一化的概率密度函数， Z(\\theta ) 是一个常数（保证概率密度函数的积分为 1 ）。通常情况，我们知道 q 的函数形式是一个解析表达式或者神经网络，而然由这样的一个积分计算常数 Z(\\theta ) 并不容易。因此，引出下面要定义的得分 (Score)。 定义得分 Score 为相对于数据的log密度的梯度 (the gradient of the log-density with respect to the data vector)。这样翻译可能有些拗口，下面直接看表达式： s(x;\\theta)=\\left( {\\begin{array}{*{20}{c}} {\\frac{{\\partial \\log p(x;\\theta )}}{{\\partial {x_1}}}}\\\\ {...}\\\\ {\\frac{{\\partial \\log p(x;\\theta )}}{{\\partial {x_n}}}} \\end{array}} \\right) = \\left( {\\begin{array}{*{20}{c}} {{s_1}(x;\\theta )}\\\\ {...}\\\\ {{s_n}(x;\\theta )} \\end{array}} \\right) = {\\nabla _x}\\log p(x;\\theta ) (2), 以上表达式即为得分函数 score function。因为数据 x 通常是多维度的，所以在公式中会对每个维度求偏导，即score与数据 x 相同维度。将(1)式中概率表达代入(2)中，可得 {\\nabla _x}\\log p(x;\\theta )= {\\nabla _x}[\\log q(x;\\theta )-logZ(\\theta)] ，可以观察到后面一项为0（对常数求导）。因此，定义这样一个score就可以通过估计数据概率密度函数的梯度来了解数据的分布，可以不用考虑常数项 Z(\\theta ) 。 See full list on zhuanlan.zhihu.com 得分匹配score matching的目的实际上是估计得分函数score function。考虑一个得分函数模型 s(x; \\theta) ，我们的目标是使得该模型输出能够尽量的逼近真实的得分 {\\nabla _x}\\log p(x) 。因此，目标函数可以表示为： \\begin{array}{l} J(\\theta ) = \\frac{ 1 }{2}\\int\\limits_{x \\in {\\mathbb{R}^n}} {p(x){{\\left\\| {s(x;\\theta ) - {\\nabla _x}\\log p(x)} \\right\\|}^2}dx} \\\\ {\\theta ^*} = \\arg \\mathop {\\min }\\limits_\\theta J(\\theta ) \\end{array} (3), See full list on zhuanlan.zhihu.com Score matching 本质上是估计数据log-密度的梯度的一种方法，本文介绍的是较早的一个版本，但至今仍在广泛的使用。作为一种估计数据密度的方法， score matching 的使用场景并不局限于score-based generative model，例如在2011年有研究者分析了denoising autoencoder和 score matching 的联系，并证明了在某些条件下的两者等价关系。 See full list on zhuanlan.zhihu.com Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. What is the difference between scoredec and flowdec? FlowDec is an improved version of ScoreDec (uses a score -based generative model as a postfilter), by switching the objective to flow matching . It further proposes a joint flow matching objective tailored for the postfiltering task (e.g. mean-shifted noise with frequency-dependent diagonal covariance). What is flowdec (ICLR 2025)? FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. See our demo page here. Create a new virtual environment (we recommend Python 3.10) and run (or whatever matches your local CUDA version). How does flowdec work? FlowDec adapts conditional flow matching for signal enhancement, achieving improvements over previous score- and flow-based models. Both listening tests and objective metrics show that FlowDec provides perceptual quality competitive to state-of-the-art GAN-based codecs. How does flowdec improve audio quality decoded from discrete audio tokens? The authors introduce FlowDEC , a flow matching -based post-filtering method designed to enhance the audio quality decoded from discrete audio tokens. This method has demonstrated strong objective and subjective results . Soundness: 2: fair Presentation: 2: fair Is flowdec CC-BY-NC? The majority of FlowDec is licensed under CC-BY-NC , however portions of the project are available under separate license terms: conditional-flow-matching, sgmse, BioinfoMachineLearning, audiotools, and descript-audio-code are licensed MIT; NCSN++ is licensed Apache 2.0. Can flowdec be used for high-frequency reconstruction? FlowDec's ability for high-frequency modeling is interesting ; to the best of my knowledge, practitioners have commonly encountered challenges in high-frequency reconstruction when using diffusion or flow-based method on audio waveform. In this paper, we aim to develop a zero-shot TTS system that can generate high-quality clean speech from any speaker, regardless of the existence of background noise in the audio prompt. We refer to this property as the noise robustness of zero-shot TTS.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.12160", "content": "Jan 22, 2024 · Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information. 最近，以宋博士为代表的研究者提出基于得分的概率生成模型在图像生成等领域取得佳绩，打破了一些主流生成模型（如，对抗生成模型-GAN，变分自编码器 -VAE，基于流的生成模型-Flow-based Genarative Model等）在生成领域的制霸权，引领了一波生成模型的研究热潮 (详情请参考宋博士博客Generative Modeling by Estimating Gradients of the Data Distribution)。 基于得分的概率生成模型最主要的思想是估计得分，而估计得分的主流方法得分匹配 (Score Matching) 早在2005年已经有研究者提出。因此，了解得分匹配的原理能够帮助理解和学习主流的生成模型。 See full list on zhuanlan.zhihu.com 概率模型通常是通过一系列数学公式推导出数据的概率分布情况，考虑概率模型： \\begin{array}{l} p(x;\\theta ) = \\frac{ 1 }{{Z(\\theta )}}q(x;\\theta )\\\\ Z(\\theta ) = \\int\\limits_{x \\in {\\mathbb{R}^n}} {q(x;\\theta )dx} \\end{array} ( 1 )， 其中， q(x;\\theta) 为未归一化的概率密度函数， Z(\\theta ) 是一个常数（保证概率密度函数的积分为 1 ）。通常情况，我们知道 q 的函数形式是一个解析表达式或者神经网络，而然由这样的一个积分计算常数 Z(\\theta ) 并不容易。因此，引出下面要定义的得分 (Score)。 定义得分 Score 为相对于数据的log密度的梯度 (the gradient of the log-density with respect to the data vector)。这样翻译可能有些拗口，下面直接看表达式： s(x;\\theta)=\\left( {\\begin{array}{*{20}{c}} {\\frac{{\\partial \\log p(x;\\theta )}}{{\\partial {x_1}}}}\\\\ {...}\\\\ {\\frac{{\\partial \\log p(x;\\theta )}}{{\\partial {x_n}}}} \\end{array}} \\right) = \\left( {\\begin{array}{*{20}{c}} {{s_1}(x;\\theta )}\\\\ {...}\\\\ {{s_n}(x;\\theta )} \\end{array}} \\right) = {\\nabla _x}\\log p(x;\\theta ) (2), 以上表达式即为得分函数 score function。因为数据 x 通常是多维度的，所以在公式中会对每个维度求偏导，即score与数据 x 相同维度。将(1)式中概率表达代入(2)中，可得 {\\nabla _x}\\log p(x;\\theta )= {\\nabla _x}[\\log q(x;\\theta )-logZ(\\theta)] ，可以观察到后面一项为0（对常数求导）。因此，定义这样一个score就可以通过估计数据概率密度函数的梯度来了解数据的分布，可以不用考虑常数项 Z(\\theta ) 。 See full list on zhuanlan.zhihu.com 得分匹配score matching的目的实际上是估计得分函数score function。考虑一个得分函数模型 s(x; \\theta) ，我们的目标是使得该模型输出能够尽量的逼近真实的得分 {\\nabla _x}\\log p(x) 。因此，目标函数可以表示为： \\begin{array}{l} J(\\theta ) = \\frac{ 1 }{2}\\int\\limits_{x \\in {\\mathbb{R}^n}} {p(x){{\\left\\| {s(x;\\theta ) - {\\nabla _x}\\log p(x)} \\right\\|}^2}dx} \\\\ {\\theta ^*} = \\arg \\mathop {\\min }\\limits_\\theta J(\\theta ) \\end{array} (3), See full list on zhuanlan.zhihu.com Score matching 本质上是估计数据log-密度的梯度的一种方法，本文介绍的是较早的一个版本，但至今仍在广泛的使用。作为一种估计数据密度的方法， score matching 的使用场景并不局限于score-based generative model，例如在2011年有研究者分析了denoising autoencoder和 score matching 的联系，并证明了在某些条件下的两者等价关系。 See full list on zhuanlan.zhihu.com Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. What is the difference between scoredec and flowdec? FlowDec is an improved version of ScoreDec (uses a score -based generative model as a postfilter), by switching the objective to flow matching . It further proposes a joint flow matching objective tailored for the postfiltering task (e.g. mean-shifted noise with frequency-dependent diagonal covariance). What is flowdec (ICLR 2025)? FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. See our demo page here. Create a new virtual environment (we recommend Python 3.10) and run (or whatever matches your local CUDA version). How does flowdec work? FlowDec adapts conditional flow matching for signal enhancement, achieving improvements over previous score- and flow-based models. Both listening tests and objective metrics show that FlowDec provides perceptual quality competitive to state-of-the-art GAN-based codecs. How does flowdec improve audio quality decoded from discrete audio tokens? The authors introduce FlowDEC , a flow matching -based post-filtering method designed to enhance the audio quality decoded from discrete audio tokens. This method has demonstrated strong objective and subjective results . Soundness: 2: fair Presentation: 2: fair Is flowdec CC-BY-NC? The majority of FlowDec is licensed under CC-BY-NC , however portions of the project are available under separate license terms: conditional-flow-matching, sgmse, BioinfoMachineLearning, audiotools, and descript-audio-code are licensed MIT; NCSN++ is licensed Apache 2.0. Can flowdec be used for high-frequency reconstruction? FlowDec's ability for high-frequency modeling is interesting ; to the best of my knowledge, practitioners have commonly encountered challenges in high-frequency reconstruction when using diffusion or flow-based method on audio waveform. In this paper, we aim to develop a zero-shot TTS system that can generate high-quality clean speech from any speaker, regardless of the existence of background noise in the audio prompt. We refer to this property as the noise robustness of zero-shot TTS."}
+{"idx": 6, "title": "得分匹配 Score Matching - 知乎 GitHub - facebookresearch/FlowDec: An neural full-band audio ... FlowDec : A flow -based full-band general audio codec with high GitHub - facebookresearch/ FlowDec : An neural full-band audio code… FlowDec : A flow -based full-band general audio codec with high FlowDec : A flow -based full-band general audio codec with high GitHub - facebookresearch/ FlowDec : An neural full-band audio code… FlowDec : A flow -based full-band general audio codec with high An Investigation of Noise Robustness for Flow-Matching-Based ...", "date": "", "ddg_snippet": "最近，以宋博士为代表的研究者提出基于得分的概率生成模型在图像生成等领域取得佳绩，打破了一些主流生成模型（如，对抗生成模型-GAN，变分自编码器 -VAE，基于流的生成模型-Flow-based Genarative Model等）在生成领域的制霸权，引领了一波生成模型的研究热潮 (详情请参考宋博士博客Generative Modeling by Estimating Gradients of the Data Distribution)。 基于得分的概率生成模型最主要的思想是估计得分，而估计得分的主流方法得分匹配 (Score Matching) 早在2005年已经有研究者提出。因此，了解得分匹配的原理能够帮助理解和学习主流的生成模型。 See full list on zhuanlan.zhihu.com 概率模型通常是通过一系列数学公式推导出数据的概率分布情况，考虑概率模型： \\begin{array}{l} p(x;\\theta ) = \\frac{ 1 }{{Z(\\theta )}}q(x;\\theta )\\\\ Z(\\theta ) = \\int\\limits_{x \\in {\\mathbb{R}^n}} {q(x;\\theta )dx} \\end{array} ( 1 )， 其中， q(x;\\theta) 为未归一化的概率密度函数， Z(\\theta ) 是一个常数（保证概率密度函数的积分为 1 ）。通常情况，我们知道 q 的函数形式是一个解析表达式或者神经网络，而然由这样的一个积分计算常数 Z(\\theta ) 并不容易。因此，引出下面要定义的得分 (Score)。 定义得分 Score 为相对于数据的log密度的梯度 (the gradient of the log-density with respect to the data vector)。这样翻译可能有些拗口，下面直接看表达式： s(x;\\theta)=\\left( {\\begin{array}{*{20}{c}} {\\frac{{\\partial \\log p(x;\\theta )}}{{\\partial {x_1}}}}\\\\ {...}\\\\ {\\frac{{\\partial \\log p(x;\\theta )}}{{\\partial {x_n}}}} \\end{array}} \\right) = \\left( {\\begin{array}{*{20}{c}} {{s_1}(x;\\theta )}\\\\ {...}\\\\ {{s_n}(x;\\theta )} \\end{array}} \\right) = {\\nabla _x}\\log p(x;\\theta ) (2), 以上表达式即为得分函数 score function。因为数据 x 通常是多维度的，所以在公式中会对每个维度求偏导，即score与数据 x 相同维度。将(1)式中概率表达代入(2)中，可得 {\\nabla _x}\\log p(x;\\theta )= {\\nabla _x}[\\log q(x;\\theta )-logZ(\\theta)] ，可以观察到后面一项为0（对常数求导）。因此，定义这样一个score就可以通过估计数据概率密度函数的梯度来了解数据的分布，可以不用考虑常数项 Z(\\theta ) 。 See full list on zhuanlan.zhihu.com 得分匹配score matching的目的实际上是估计得分函数score function。考虑一个得分函数模型 s(x; \\theta) ，我们的目标是使得该模型输出能够尽量的逼近真实的得分 {\\nabla _x}\\log p(x) 。因此，目标函数可以表示为： \\begin{array}{l} J(\\theta ) = \\frac{ 1 }{2}\\int\\limits_{x \\in {\\mathbb{R}^n}} {p(x){{\\left\\| {s(x;\\theta ) - {\\nabla _x}\\log p(x)} \\right\\|}^2}dx} \\\\ {\\theta ^*} = \\arg \\mathop {\\min }\\limits_\\theta J(\\theta ) \\end{array} (3), See full list on zhuanlan.zhihu.com Score matching 本质上是估计数据log-密度的梯度的一种方法，本文介绍的是较早的一个版本，但至今仍在广泛的使用。作为一种估计数据密度的方法， score matching 的使用场景并不局限于score-based generative model，例如在2011年有研究者分析了denoising autoencoder和 score matching 的联系，并证明了在某些条件下的两者等价关系。 See full list on zhuanlan.zhihu.com Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. What is the difference between scoredec and flowdec? FlowDec is an improved version of ScoreDec (uses a score -based generative model as a postfilter), by switching the objective to flow matching . It further proposes a joint flow matching objective tailored for the postfiltering task (e.g. mean-shifted noise with frequency-dependent diagonal covariance). What is flowdec (ICLR 2025)? FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. See our demo page here. Create a new virtual environment (we recommend Python 3.10) and run (or whatever matches your local CUDA version). How does flowdec work? FlowDec adapts conditional flow matching for signal enhancement, achieving improvements over previous score- and flow-based models. Both listening tests and objective metrics show that FlowDec provides perceptual quality competitive to state-of-the-art GAN-based codecs. How does flowdec improve audio quality decoded from discrete audio tokens? The authors introduce FlowDEC , a flow matching -based post-filtering method designed to enhance the audio quality decoded from discrete audio tokens. This method has demonstrated strong objective and subjective results . Soundness: 2: fair Presentation: 2: fair Is flowdec CC-BY-NC? The majority of FlowDec is licensed under CC-BY-NC , however portions of the project are available under separate license terms: conditional-flow-matching, sgmse, BioinfoMachineLearning, audiotools, and descript-audio-code are licensed MIT; NCSN++ is licensed Apache 2.0. Can flowdec be used for high-frequency reconstruction? FlowDec's ability for high-frequency modeling is interesting ; to the best of my knowledge, practitioners have commonly encountered challenges in high-frequency reconstruction when using diffusion or flow-based method on audio waveform. In this paper, we aim to develop a zero-shot TTS system that can generate high-quality clean speech from any speaker, regardless of the existence of background noise in the audio prompt. We refer to this property as the noise robustness of zero-shot TTS.", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/556175230", "content": "最近，以宋博士为代表的研究者提出基于得分的概率生成模型在图像生成等领域取得佳绩，打破了一些主流生成模型（如，对抗生成模型-GAN，变分自编码器 -VAE，基于流的生成模型-Flow-based Genarative Model等）在生成领域的制霸权，引领了一波生成模型的研究热潮 (详情请参考宋博士博客Generative Modeling by Estimating Gradients of the Data Distribution)。 基于得分的概率生成模型最主要的思想是估计得分，而估计得分的主流方法得分匹配 (Score Matching) 早在2005年已经有研究者提出。因此，了解得分匹配的原理能够帮助理解和学习主流的生成模型。 See full list on zhuanlan.zhihu.com 概率模型通常是通过一系列数学公式推导出数据的概率分布情况，考虑概率模型： \\begin{array}{l} p(x;\\theta ) = \\frac{ 1 }{{Z(\\theta )}}q(x;\\theta )\\\\ Z(\\theta ) = \\int\\limits_{x \\in {\\mathbb{R}^n}} {q(x;\\theta )dx} \\end{array} ( 1 )， 其中， q(x;\\theta) 为未归一化的概率密度函数， Z(\\theta ) 是一个常数（保证概率密度函数的积分为 1 ）。通常情况，我们知道 q 的函数形式是一个解析表达式或者神经网络，而然由这样的一个积分计算常数 Z(\\theta ) 并不容易。因此，引出下面要定义的得分 (Score)。 定义得分 Score 为相对于数据的log密度的梯度 (the gradient of the log-density with respect to the data vector)。这样翻译可能有些拗口，下面直接看表达式： s(x;\\theta)=\\left( {\\begin{array}{*{20}{c}} {\\frac{{\\partial \\log p(x;\\theta )}}{{\\partial {x_1}}}}\\\\ {...}\\\\ {\\frac{{\\partial \\log p(x;\\theta )}}{{\\partial {x_n}}}} \\end{array}} \\right) = \\left( {\\begin{array}{*{20}{c}} {{s_1}(x;\\theta )}\\\\ {...}\\\\ {{s_n}(x;\\theta )} \\end{array}} \\right) = {\\nabla _x}\\log p(x;\\theta ) (2), 以上表达式即为得分函数 score function。因为数据 x 通常是多维度的，所以在公式中会对每个维度求偏导，即score与数据 x 相同维度。将(1)式中概率表达代入(2)中，可得 {\\nabla _x}\\log p(x;\\theta )= {\\nabla _x}[\\log q(x;\\theta )-logZ(\\theta)] ，可以观察到后面一项为0（对常数求导）。因此，定义这样一个score就可以通过估计数据概率密度函数的梯度来了解数据的分布，可以不用考虑常数项 Z(\\theta ) 。 See full list on zhuanlan.zhihu.com 得分匹配score matching的目的实际上是估计得分函数score function。考虑一个得分函数模型 s(x; \\theta) ，我们的目标是使得该模型输出能够尽量的逼近真实的得分 {\\nabla _x}\\log p(x) 。因此，目标函数可以表示为： \\begin{array}{l} J(\\theta ) = \\frac{ 1 }{2}\\int\\limits_{x \\in {\\mathbb{R}^n}} {p(x){{\\left\\| {s(x;\\theta ) - {\\nabla _x}\\log p(x)} \\right\\|}^2}dx} \\\\ {\\theta ^*} = \\arg \\mathop {\\min }\\limits_\\theta J(\\theta ) \\end{array} (3), See full list on zhuanlan.zhihu.com Score matching 本质上是估计数据log-密度的梯度的一种方法，本文介绍的是较早的一个版本，但至今仍在广泛的使用。作为一种估计数据密度的方法， score matching 的使用场景并不局限于score-based generative model，例如在2011年有研究者分析了denoising autoencoder和 score matching 的联系，并证明了在某些条件下的两者等价关系。 See full list on zhuanlan.zhihu.com Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. What is the difference between scoredec and flowdec? FlowDec is an improved version of ScoreDec (uses a score -based generative model as a postfilter), by switching the objective to flow matching . It further proposes a joint flow matching objective tailored for the postfiltering task (e.g. mean-shifted noise with frequency-dependent diagonal covariance). What is flowdec (ICLR 2025)? FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. See our demo page here. Create a new virtual environment (we recommend Python 3.10) and run (or whatever matches your local CUDA version). How does flowdec work? FlowDec adapts conditional flow matching for signal enhancement, achieving improvements over previous score- and flow-based models. Both listening tests and objective metrics show that FlowDec provides perceptual quality competitive to state-of-the-art GAN-based codecs. How does flowdec improve audio quality decoded from discrete audio tokens? The authors introduce FlowDEC , a flow matching -based post-filtering method designed to enhance the audio quality decoded from discrete audio tokens. This method has demonstrated strong objective and subjective results . Soundness: 2: fair Presentation: 2: fair Is flowdec CC-BY-NC? The majority of FlowDec is licensed under CC-BY-NC , however portions of the project are available under separate license terms: conditional-flow-matching, sgmse, BioinfoMachineLearning, audiotools, and descript-audio-code are licensed MIT; NCSN++ is licensed Apache 2.0. Can flowdec be used for high-frequency reconstruction? FlowDec's ability for high-frequency modeling is interesting ; to the best of my knowledge, practitioners have commonly encountered challenges in high-frequency reconstruction when using diffusion or flow-based method on audio waveform. In this paper, we aim to develop a zero-shot TTS system that can generate high-quality clean speech from any speaker, regardless of the existence of background noise in the audio prompt. We refer to this property as the noise robustness of zero-shot TTS."}
+{"idx": 7, "title": "FlowDec | A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "Compared to the prior work ScoreDec which is based on score matching , we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output quality and reducing the required postfilter DNN evaluations from 60 to 6 without any...", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "Compared to the prior work ScoreDec which is based on score matching , we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output quality and reducing the required postfilter DNN evaluations from 60 to 6 without any..."}
+{"idx": 8, "title": "flowdec · PyPI", "date": "", "ddg_snippet": "Built Distribution. flowdec -1.1.0-py3-none-any.whl (4.4 MB view details).", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/flowdec/", "content": "Built Distribution. flowdec -1.1.0-py3-none-any.whl (4.4 MB view details)."}
+{"idx": 9, "title": "808Ball - Live Scores , Match Schedule & Results , Football and...", "date": "", "ddg_snippet": "Users can watch matches for free in real time, with access to the fastest live scores , detailed match schedules, and up-to-date football results . Originating from the renowned streaming site Score 808.com, 808Ball is your go-to platform for nonstop sports action.", "subpage_snippet": "", "source": "www.808ball2.com", "link": "https://www.808ball2.com/", "content": "Users can watch matches for free in real time, with access to the fastest live scores , detailed match schedules, and up-to-date football results . Originating from the renowned streaming site Score 808.com, 808Ball is your go-to platform for nonstop sports action."}
diff --git a/data/sampled_jsons/FlowDec_Table_2_nc_demb_NDAC-75_NDAC-25.jsonl b/data/sampled_jsons/FlowDec_Table_2_nc_demb_NDAC-75_NDAC-25.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f4cf86c63f5f2fc8bdc0016213bda888e0e4800
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_Table_2_nc_demb_NDAC-75_NDAC-25.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "FLOWDEC: A FLOW-BASED FULL-BAND GENERAL", "date": "", "ddg_snippet": "Bitrates are in kbit/s. Name. Bitrates fs. H nc demb . DAC. 0.86–7.75 44.1 512 ... As postfilters, we train the following variants based on NDAC-75 and NDAC-25 :.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/46581252e0bf80bf4efd8fcbc4002f8627f498bb.pdf", "content": "Bitrates are in kbit/s. Name. Bitrates fs. H nc demb . DAC. 0.86–7.75 44.1 512 ... As postfilters, we train the following variants based on NDAC-75 and NDAC-25 :."}
+{"idx": 1, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "As baselines, we train DAC- 75 and DAC- 25 , equivalent versions of NDAC-75 and NDAC-25 with the original adversarial losses. To show that the differences between FlowDec and DAC are not just caused by the extra parameters from the postfilter, we also train baselines2xDAC- 75 and 2xDAC- 25 for which we double the channels of all decoder convolution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "As baselines, we train DAC- 75 and DAC- 25 , equivalent versions of NDAC-75 and NDAC-25 with the original adversarial losses. To show that the differences between FlowDec and DAC are not just caused by the extra parameters from the postfilter, we also train baselines2xDAC- 75 and 2xDAC- 25 for which we double the channels of all decoder convolution ..."}
+{"idx": 2, "title": "AUDIO CODEC WITH HIGH PERCEPTUAL QUALITY - OpenReview", "date": "", "ddg_snippet": "NDAC-75 is targeted at 48kHz audio with a whole-number fea- ture rate (75Hz) and whole-number bi- trates. NDAC-25 is a variant tailored for downstream generative audio tasks, with a lower feature rate (25Hz) and feature dimension which are advantageous for audio generation due to more efficient memory usage and decreased modeling difficulties.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=uxDFlPGRLX", "content": "NDAC-75 is targeted at 48kHz audio with a whole-number fea- ture rate (75Hz) and whole-number bi- trates. NDAC-25 is a variant tailored for downstream generative audio tasks, with a lower feature rate (25Hz) and feature dimension which are advantageous for audio generation due to more efficient memory usage and decreased modeling difficulties."}
+{"idx": 3, "title": "PDF Audio Codec With High Perceptual Quality", "date": "", "ddg_snippet": "As baselines, we train DAC- 75 and DAC- 25 , equivalent versions of NDAC-75 and NDAC-25 with the original adversarial losses. To show that the differences between FlowDec and DAC are not just caused by the extra parameters from the postfilter, we also train baselines2xDAC- 75 and 2xDAC- 25 for which we double the channels of all decoder convolution ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/3d937d5e40a883d5da9fd4aeeeb372b8-Paper-Conference.pdf", "content": "As baselines, we train DAC- 75 and DAC- 25 , equivalent versions of NDAC-75 and NDAC-25 with the original adversarial losses. To show that the differences between FlowDec and DAC are not just caused by the extra parameters from the postfilter, we also train baselines2xDAC- 75 and 2xDAC- 25 for which we double the channels of all decoder convolution ..."}
diff --git a/data/sampled_jsons/FlowDec_Table_8_4.5_kbits_SIGMOS_scores_exact_values_3.83_3.65.jsonl b/data/sampled_jsons/FlowDec_Table_8_4.5_kbits_SIGMOS_scores_exact_values_3.83_3.65.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0713875c4c4bf20963084638ff1c18958ce3281a
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_Table_8_4.5_kbits_SIGMOS_scores_exact_values_3.83_3.65.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "FlowDec -75m: 75 Hz, multi-bitrate. Trained based on NDAC-75 with bitrates {7.5, 6.0, 4 . 5 , 3.0} kbit / s , by setting the number of codebooks at inference to {10, 8 , 6, 4}. We include only this set of bitrates for ease and speed of training...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "FlowDec -75m: 75 Hz, multi-bitrate. Trained based on NDAC-75 with bitrates {7.5, 6.0, 4 . 5 , 3.0} kbit / s , by setting the number of codebooks at inference to {10, 8 , 6, 4}. We include only this set of bitrates for ease and speed of training..."}
+{"idx": 1, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio ...", "date": "", "ddg_snippet": "Mar 3 , 2025 · An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "Mar 3 , 2025 · An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - facebookresearch/ FlowDec"}
+{"idx": 2, "title": "FlowDec | A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 3, "title": "[2503.01485] FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "Mar 3 , 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "Mar 3 , 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving output ..."}
+{"idx": 4, "title": "FlowDec:An neural full-band audio codec for general audio ...", "date": "", "ddg_snippet": "FlowDec FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "gitcode.com", "link": "https://gitcode.com/gh_mirrors/fl/FlowDec/overview", "content": "FlowDec FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 5, "title": "GitHub - splinter21/FlowDec: An neural full-band audio codec ...", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - splinter21/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/splinter21/FlowDec", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - splinter21/ FlowDec"}
+{"idx": 6, "title": "FlowDec/index.html at main · sp-uhh/FlowDec · GitHub", "date": "", "ddg_snippet": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving output quality and reducing the required postfilter DNN evaluations from 60 to 6 without any fine-tuning or distillation techniques.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sp-uhh/FlowDec/blob/main/index.html", "content": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving output quality and reducing the required postfilter DNN evaluations from 60 to 6 without any fine-tuning or distillation techniques."}
+{"idx": 7, "title": "Determining exact value of sin(4pi/3) - YouTube", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=6_MRzQLwIt4", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
+{"idx": 8, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "We showed that FlowDec achieves state-of-the-art FAD scores for the coding task and, in a listening test, performs on par with the current state-of-the-art GAN-based codec DAC (Kumar et al., 2024) at bitrates between 4.5 and 7.5kbit/ s .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "We showed that FlowDec achieves state-of-the-art FAD scores for the coding task and, in a listening test, performs on par with the current state-of-the-art GAN-based codec DAC (Kumar et al., 2024) at bitrates between 4.5 and 7.5kbit/ s ."}
+{"idx": 9, "title": "Операция Z: Военкоры Русской Весны – Telegram", "date": "", "ddg_snippet": "Операция Z: Военкоры Русской Весны. @RVvoenkor. 1.65M subscribers. 40K photos. 42.2K videos.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/RVvoenkor", "content": "Операция Z: Военкоры Русской Весны. @RVvoenkor. 1.65M subscribers. 40K photos. 42.2K videos."}
diff --git a/data/sampled_jsons/FlowDec_Table_8_SIGMOS_DAC-75_4.5_kbits_values_sitearxiv.org.jsonl b/data/sampled_jsons/FlowDec_Table_8_SIGMOS_DAC-75_4.5_kbits_values_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a856b21b0c2cb6b484b47291d317bc298fd74fcc
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_Table_8_SIGMOS_DAC-75_4.5_kbits_values_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "These results suggest that FlowDec may perform better on speech than DAC , particularly for FlowDec -75m versus DAC-75 at 4.5 kbit/s and that DAC may perform slightly better than FlowDec on sound files; score distributions for music are very similar.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "These results suggest that FlowDec may perform better on speech than DAC , particularly for FlowDec -75m versus DAC-75 at 4.5 kbit/s and that DAC may perform slightly better than FlowDec on sound files; score distributions for music are very similar."}
+{"idx": 1, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "In Fig. 4, we show the objective metric results of FlowDec -75m and FlowDec - 75s compared to EnCodec (48kHz), DAC-75 , 2xDAC- 75 and the official DAC 44.1kHz checkpoint, and also include 8", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "In Fig. 4, we show the objective metric results of FlowDec -75m and FlowDec - 75s compared to EnCodec (48kHz), DAC-75 , 2xDAC- 75 and the official DAC 44.1kHz checkpoint, and also include 8"}
+{"idx": 2, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving output ..."}
+{"idx": 3, "title": "Continuous Audio Language Models - arXiv.org", "date": "", "ddg_snippet": "Abstract Audio Language Models (ALM) have emerged as the dominant paradigm for speech and music generation by representing audio as sequences of discrete tokens. Yet, unlike text tokens, which are invertible, audio tokens are extracted from lossy codecs with a limited bitrate. As a consequence, increasing audio quality requires generating more tokens, which imposes a trade-off between fidelity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06926v2", "content": "Abstract Audio Language Models (ALM) have emerged as the dominant paradigm for speech and music generation by representing audio as sequences of discrete tokens. Yet, unlike text tokens, which are invertible, audio tokens are extracted from lossy codecs with a limited bitrate. As a consequence, increasing audio quality requires generating more tokens, which imposes a trade-off between fidelity ..."}
+{"idx": 4, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "The paper discusses the design and implementation of a CMOS integrated circuit using 180nm technology, focusing on the process from design to tape-out.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1908.10674", "content": "The paper discusses the design and implementation of a CMOS integrated circuit using 180nm technology, focusing on the process from design to tape-out."}
+{"idx": 5, "title": "METRIC TO EVALUATE NOISE SUPPRESSORS - arXiv.org", "date": "", "ddg_snippet": "METRIC TO EVALUATE NOISE SUPPRESSORS DNSMOS P.835: A NON-INTRUSIVE PERCEPTUAL OBJECTIVE SPEECH QUALITY METRIC TO EVALUATE NOISE SUPPRESSORS", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2110.01763", "content": "METRIC TO EVALUATE NOISE SUPPRESSORS DNSMOS P.835: A NON-INTRUSIVE PERCEPTUAL OBJECTIVE SPEECH QUALITY METRIC TO EVALUATE NOISE SUPPRESSORS"}
+{"idx": 6, "title": "How to wire a 1000-qubit trapped ion quantum computer", "date": "", "ddg_snippet": "Figure 1: Illustration of the standard approach to the electrical wiring of trapped-ion quantum computers. Qubit transport in a N -qubit ion trap is achieved by using ∼ 10 N electrodes. Each electrode is wired through an individual filter to an individual DAC . The DAC output waveforms are set through a digital interface, with typical data flow rates ∼ 50 Mbit/s per DAC .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2305.12773", "content": "Figure 1: Illustration of the standard approach to the electrical wiring of trapped-ion quantum computers. Qubit transport in a N -qubit ion trap is achieved by using ∼ 10 N electrodes. Each electrode is wired through an individual filter to an individual DAC . The DAC output waveforms are set through a digital interface, with typical data flow rates ∼ 50 Mbit/s per DAC ."}
+{"idx": 7, "title": "FlowMAC: Conditional Flow Matching for Audio Coding at Low Bit Rates", "date": "", "ddg_snippet": "This paper introduces FlowMAC, a novel neural audio codec for high-quality general audio compression at low bit rates based on conditional flow matching (CFM). FlowMAC jointly learns a mel spectrogram encoder, quantizer and decoder. At inference time the decoder integrates a continuous normalizing flow via an ODE solver to generate a high-quality mel spectrogram. This is the first time that a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.17635", "content": "This paper introduces FlowMAC, a novel neural audio codec for high-quality general audio compression at low bit rates based on conditional flow matching (CFM). FlowMAC jointly learns a mel spectrogram encoder, quantizer and decoder. At inference time the decoder integrates a continuous normalizing flow via an ODE solver to generate a high-quality mel spectrogram. This is the first time that a ..."}
+{"idx": 8, "title": "DeCodec: Rethinking Audio Codecs as Universal Disentangled ...", "date": "", "ddg_snippet": "The Encoder of the DeCodec cascades of 4 encoder blocks [13], downsampling the input audio waveform at rates [2, 4 , 5, 8 ]. The C, D, k of the corresponding 1D convolution are 32, 1024, 7, respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09201v1", "content": "The Encoder of the DeCodec cascades of 4 encoder blocks [13], downsampling the input audio waveform at rates [2, 4 , 5, 8 ]. The C, D, k of the corresponding 1D convolution are 32, 1024, 7, respectively."}
+{"idx": 9, "title": "Transitive Array: An Efficient GEMM Accelerator with Result Reuse", "date": "", "ddg_snippet": "Additionally, with an extremely streamlined PE design, TransArray with 8-bit weights achieves 2.47 ×, 3.75 ×, and 1.99 × speedups over ANT, Olive, and BitVert, respectively, while maintaining similar energy consumption.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.16339v1", "content": "Additionally, with an extremely streamlined PE design, TransArray with 8-bit weights achieves 2.47 ×, 3.75 ×, and 1.99 × speedups over ANT, Olive, and BitVert, respectively, while maintaining similar energy consumption."}
diff --git a/data/sampled_jsons/FlowDec_conclusion_streaming_noncausal_architecture.jsonl b/data/sampled_jsons/FlowDec_conclusion_streaming_noncausal_architecture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4be306af8c10dcd4e0fc3cf1d4445a7f319fb32e
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_conclusion_streaming_noncausal_architecture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "While FlowDec , like DAC, is currently not streaming -capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a), which would pave the way for real-time communication and audio streaming applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "While FlowDec , like DAC, is currently not streaming -capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a), which would pave the way for real-time communication and audio streaming applications."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "Jan 22, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "Jan 22, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 2, "title": "FLOWDEC: A FLOW-BASED FULL-BAND GENERAL", "date": "", "ddg_snippet": "While FlowDec, like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/46581252e0bf80bf4efd8fcbc4002f8627f498bb.pdf", "content": "While FlowDec, like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for ..."}
+{"idx": 3, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 4, "title": "facebookresearch/FlowDec | DeepWiki", "date": "", "ddg_snippet": "FlowDec integrates with established audio processing and machine learning frameworks, making it suitable for both research and production deployment. The modular architecture allows for independent development of the codec and enhancement components, while the configuration system supports systematic experimentation and hyperparameter optimization.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/facebookresearch/FlowDec", "content": "FlowDec integrates with established audio processing and machine learning frameworks, making it suitable for both research and production deployment. The modular architecture allows for independent development of the codec and enhancement components, while the configuration system supports systematic experimentation and hyperparameter optimization."}
+{"idx": 5, "title": "FlowDec | A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 6, "title": "[2503.01485] FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ..."}
+{"idx": 7, "title": "Topic: [OPEN SOURCE] FlowDec (by Meta Research)", "date": "", "ddg_snippet": "Mar 20, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "hydrogenaudio.org", "link": "https://hydrogenaudio.org/index.php/topic,127623.0.html", "content": "Mar 20, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 8, "title": "REAL-TIME STREAMING MEL VOCODING WITH GENERATIVE FLOW MATCHING", "date": "", "ddg_snippet": "A few works have previously investigated streamable vocoding [ 4 , 5 ] , but to our knowledge none have explored the use of diffusion-based models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15085v1", "content": "A few works have previously investigated streamable vocoding [ 4 , 5 ] , but to our knowledge none have explored the use of diffusion-based models ..."}
+{"idx": 9, "title": "BinauralFlow: A Causal and Streamable Approach for High ...", "date": "", "ddg_snippet": "28 May 2025 — We propose a flow matching based streaming binaural speech synthesis framework called BinauralFlow. We consider binaural rendering to be a generation problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22865v1", "content": "28 May 2025 — We propose a flow matching based streaming binaural speech synthesis framework called BinauralFlow. We consider binaural rendering to be a generation problem."}
diff --git a/data/sampled_jsons/FourCastNet_2202.11214_evaluation_metrics_RMSE_ACC_NLL.jsonl b/data/sampled_jsons/FourCastNet_2202.11214_evaluation_metrics_RMSE_ACC_NLL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ae3bd4c6527eb54a525732efea925c332854af3
--- /dev/null
+++ b/data/sampled_jsons/FourCastNet_2202.11214_evaluation_metrics_RMSE_ACC_NLL.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2202 . 11214 ] FourCastNet : A Global Data-driven High-resolution...", "date": "", "ddg_snippet": "FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.Physics > Atmospheric and Oceanic Physics. arXiv: 2202 . 11214 (physics). [Submitted on 22 Feb 2022].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.11214", "content": "FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.Physics > Atmospheric and Oceanic Physics. arXiv: 2202 . 11214 (physics). [Submitted on 22 Feb 2022]."}
+{"idx": 1, "title": "GitHub - NVlabs/ FourCastNet : Initial public release of code, data, and...", "date": "", "ddg_snippet": "This is so that you can analyze the skill of FourCastNet by comparing with the ERA5 ground truth via the RMSE and ACC metrics .arXiv preprint arXiv: 2202 . 11214 }, year={2022} }. About. Initial public release of code, data, and model weights for FourCastNet .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVlabs/FourCastNet", "content": "This is so that you can analyze the skill of FourCastNet by comparing with the ERA5 ground truth via the RMSE and ACC metrics .arXiv preprint arXiv: 2202 . 11214 }, year={2022} }. About. Initial public release of code, data, and model weights for FourCastNet ."}
+{"idx": 2, "title": "FourCastNet - NVIDIA Docs", "date": "", "ddg_snippet": "In this tutorial, we will show you how to define, train and evaluate FourCastNet in Modulus.With the trained model fourcastnet /inferencer.py is used to calculate the latitude weighted Root Mean Squared Error ( RMSE ) and the latitude weighted Anomaly Correlation Coefficient ( ACC ) values.", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/deeplearning/modulus/modulus-v2209/user_guide/neural_operators/fourcastnet.html", "content": "In this tutorial, we will show you how to define, train and evaluate FourCastNet in Modulus.With the trained model fourcastnet /inferencer.py is used to calculate the latitude weighted Root Mean Squared Error ( RMSE ) and the latitude weighted Anomaly Correlation Coefficient ( ACC ) values."}
+{"idx": 3, "title": "[Paper] FourCastNet : A Global Data-driven... - Githubissues", "date": "", "ddg_snippet": "45 forks source link. [Paper] FourCastNet : A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators #17.Yes there are the ACC and RMSE graphs but those are generalised means - they fail to show localised effects.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/openclimatefix/graph_weather/17", "content": "45 forks source link. [Paper] FourCastNet : A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators #17.Yes there are the ACC and RMSE graphs but those are generalised means - they fail to show localised effects."}
+{"idx": 4, "title": "Paper page - FourCastNet : A Global Data-driven High-resolution...", "date": "", "ddg_snippet": "arxiv: 2202 . 11214 . FourCastNet : A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. Published on Feb 22, 2022.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2202.11214", "content": "arxiv: 2202 . 11214 . FourCastNet : A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. Published on Feb 22, 2022."}
+{"idx": 5, "title": "FourCastNet : A Global Data-driven High-resolution... | BibSonomy", "date": "", "ddg_snippet": "- 2022 - FourCastNet A Global Data-driven High-resolution .pdf:application/pdf;arXiv.org Snapshot:/Users/pascal/Zotero/storage/KTIF8CTD/2202.html:text/html. DOI. 10.48550/arXiv. 2202 . 11214 . urldate. 2023-07-10. url.", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/bibtex/19c23ece78ff16fa3f6c14e3f5275cd0e", "content": "- 2022 - FourCastNet A Global Data-driven High-resolution .pdf:application/pdf;arXiv.org Snapshot:/Users/pascal/Zotero/storage/KTIF8CTD/2202.html:text/html. DOI. 10.48550/arXiv. 2202 . 11214 . urldate. 2023-07-10. url."}
+{"idx": 6, "title": "FourCastNet : A Global Data-driven High-resolution Weather Model...", "date": "", "ddg_snippet": "DOI:10.48550/arXiv. 2202 . 11214 . Authors: Jaideep Pathak. FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/358814796_FourCastNet_A_Global_Data-driven_High-resolution_Weather_Model_using_Adaptive_Fourier_Neural_Operators", "content": "DOI:10.48550/arXiv. 2202 . 11214 . Authors: Jaideep Pathak. FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at."}
+{"idx": 7, "title": "Meet FourCastNet : A Global Data-Driven Weather... - MarkTechPost", "date": "", "ddg_snippet": "3. At lead periods of up to three days, FourCastNet ’s predictions are equivalent to those of the IFS model in terms of metrics such as Root Mean Squared Error ( RMSE ) and Anomaly Correlation Coefficient ( ACC ).", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2023/10/27/meet-fourcastnet-a-global-data-driven-weather-forecasting-model-revolutionizing-weather-predictions-with-fast-and-accurate-deep-learning-approach/", "content": "3. At lead periods of up to three days, FourCastNet ’s predictions are equivalent to those of the IFS model in terms of metrics such as Root Mean Squared Error ( RMSE ) and Anomaly Correlation Coefficient ( ACC )."}
+{"idx": 8, "title": "AI and weather forecasting: the first wave of AI-based weather models", "date": "", "ddg_snippet": "This metric penalizes large errors , meaning that extreme errors are visible by means of higher error rates. Fourcastnet : A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv: 2202 . 11214 .", "subpage_snippet": "", "source": "www.infoplaza.com", "link": "https://www.infoplaza.com/en/blog/ai-weather-forecasting-first-wave-ai-based-weather-models", "content": "This metric penalizes large errors , meaning that extreme errors are visible by means of higher error rates. Fourcastnet : A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv: 2202 . 11214 ."}
+{"idx": 9, "title": "Frontiers | Optimizing data-driven arctic marine forecasting...", "date": "", "ddg_snippet": "(2022). Fourcastnet : A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint. doi: 10.48550/ARXIV. 2202 . 11214 .", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2024.1456480/full", "content": "(2022). Fourcastnet : A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint. doi: 10.48550/ARXIV. 2202 . 11214 ."}
diff --git a/data/sampled_jsons/FourCastNet_Pathak_et_al._2022_PDF_content_evaluation_metrics_section.jsonl b/data/sampled_jsons/FourCastNet_Pathak_et_al._2022_PDF_content_evaluation_metrics_section.jsonl
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+{"idx": 0, "title": "FourCastNet: A practical introduction to a state-of-the-art deep ...", "date": "", "ddg_snippet": "We refer the reader to Pathak el al ., 2022 for FourCastNet benchmark comparisons with state-of-the-art NWP models, i.e., the IFS model of the ECMWF (Figure 6) and prior benchmarks of deep learning data-driven weather models (Figure 10).", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/climatechange-ai-tutorials/fourcastnet/blob/main/FourCastNet_A_practical_introduction_to_a_state_of_the_art_deep_learning_global_weather_emulator.ipynb", "content": "We refer the reader to Pathak el al ., 2022 for FourCastNet benchmark comparisons with state-of-the-art NWP models, i.e., the IFS model of the ECMWF (Figure 6) and prior benchmarks of deep learning data-driven weather models (Figure 10)."}
+{"idx": 1, "title": "GitHub - NVlabs/FourCastNet: Initial public release of code, data, and ...", "date": "", "ddg_snippet": "We discuss how data-driven deep learning models such as FourCastNet are a valuable addition to the meteorology toolkit to aid and augment NWP models. FourCastNet is based on the vision transformer architecture with Adaptive Fourier Neural Operator (AFNO) attention proposed in Guibas-Mardani et al. [paper], [code].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVlabs/FourCastNet", "content": "We discuss how data-driven deep learning models such as FourCastNet are a valuable addition to the meteorology toolkit to aid and augment NWP models. FourCastNet is based on the vision transformer architecture with Adaptive Fourier Neural Operator (AFNO) attention proposed in Guibas-Mardani et al. [paper], [code]."}
+{"idx": 2, "title": "[PDF] FourCastNet: A Global Data-driven High-resolution Weather Model ...", "date": "", "ddg_snippet": "How data-driven deep learning models such as FourCastNet are a valuable addition to the meteorology toolkit to aid and augment NWP models is discussed. FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.25^{\\\\circ}$ resolution. FourCastNet accurately forecasts high ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/FourCastNet:-A-Global-Data-driven-High-resolution-Pathak-Subramanian/10194e9d1d6b8ca8870445c990d4933c1dac1125", "content": "How data-driven deep learning models such as FourCastNet are a valuable addition to the meteorology toolkit to aid and augment NWP models is discussed. FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.25^{\\\\circ}$ resolution. FourCastNet accurately forecasts high ..."}
+{"idx": 3, "title": "FourCastNet: A Data-driven Model for High-resolution Weather ... - ICLR", "date": "", "ddg_snippet": "FourCastNet : A Data-driven Model for High-resolution Weather Forecasts using Adaptive Fourier Neural Operators Jaideep Pathak · Shashank Subramanian · Peter Harrington · Sanjeev Raja · Ashesh Chattopadhyay · Morteza Mardani · Thorsten Kurth · David M. Hall · Zongyi Li · Kamyar Azizzadenesheli · Pedram Hassanzadeh · Karthik Kashinath ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2022/7656", "content": "FourCastNet : A Data-driven Model for High-resolution Weather Forecasts using Adaptive Fourier Neural Operators Jaideep Pathak · Shashank Subramanian · Peter Harrington · Sanjeev Raja · Ashesh Chattopadhyay · Morteza Mardani · Thorsten Kurth · David M. Hall · Zongyi Li · Kamyar Azizzadenesheli · Pedram Hassanzadeh · Karthik Kashinath ..."}
+{"idx": 4, "title": "Accelerating Community-Wide Evaluation of AI Models for Global Weather ...", "date": "", "ddg_snippet": "This has culminated in the release of AIWP models such as FourCastNet ( Pathak et al. 2022 ) and FourCastNet v2-small (Bonev et al. 2023), Pangu-Weather (Bi et al. 2023), and GraphCast (Lam et al. 2023).", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/bams/106/1/BAMS-D-24-0057.1.xml", "content": "This has culminated in the release of AIWP models such as FourCastNet ( Pathak et al. 2022 ) and FourCastNet v2-small (Bonev et al. 2023), Pangu-Weather (Bi et al. 2023), and GraphCast (Lam et al. 2023)."}
+{"idx": 5, "title": "FourCastNeXt: Improving FourCastNet Training with Limited Compute", "date": "", "ddg_snippet": "1. Introduction Recently, the FourCastNet Neural Earth System Model (NESM) by ( Pathak et al ., 2022 ) has shown impressive results on predicting various atmospheric variables, trained on the ERA5 reanalysis dataset. While FourCastNet enjoys quasi-linear time and memory complexity in sequence length compared to quadratic complexity in vanilla transformers, training FourCastNet on ERA5 from ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.05584v1", "content": "1. Introduction Recently, the FourCastNet Neural Earth System Model (NESM) by ( Pathak et al ., 2022 ) has shown impressive results on predicting various atmospheric variables, trained on the ERA5 reanalysis dataset. While FourCastNet enjoys quasi-linear time and memory complexity in sequence length compared to quadratic complexity in vanilla transformers, training FourCastNet on ERA5 from ..."}
+{"idx": 6, "title": "(PDF) Data Assimilation with Machine Learning Surrogate Models: A Case ...", "date": "", "ddg_snippet": "FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at 0.25∘ resolution.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380821429_Data_Assimilation_with_Machine_Learning_Surrogate_Models_A_Case_Study_with_FourCastNet", "content": "FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at 0.25∘ resolution."}
+{"idx": 7, "title": "FourCastNet: Accelerating Global High-Resolution Weather Forecasting ...", "date": "", "ddg_snippet": "Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy and resolution due to high computational cost and strict time-to-solution limits.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3592979.3593412", "content": "Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy and resolution due to high computational cost and strict time-to-solution limits."}
+{"idx": 8, "title": "PDF Fourc N : a Data Driven Model for High Resolution Weather Forecasts ...", "date": "", "ddg_snippet": "Most data-driven weather models, however, use low-resolution data for training, usually at the 5.625 resolution as in Rasp & Thuerey (2021b) or 2 as in Weyn et al. (2020). However, the coarsening procedure leads to the loss of crucial, fine-scale physical information. For data-driven models to be truly impactful, it is essential that they generate forecasts at resolutions equal to or greater ...", "subpage_snippet": "", "source": "ai4earthscience.github.io", "link": "https://ai4earthscience.github.io/iclr-2022-workshop/camera_ready/iclr_2022_ai4ess_25.pdf", "content": "Most data-driven weather models, however, use low-resolution data for training, usually at the 5.625 resolution as in Rasp & Thuerey (2021b) or 2 as in Weyn et al. (2020). However, the coarsening procedure leads to the loss of crucial, fine-scale physical information. For data-driven models to be truly impactful, it is essential that they generate forecasts at resolutions equal to or greater ..."}
+{"idx": 9, "title": "High-resolution-global-weather-model-afno - arXiv.org", "date": "", "ddg_snippet": "FourCastNet uses a Fourier transform-based token-mixing scheme [Guibas et al ., 2022 ] with a vision transformer (ViT) backbone [Dosovitskiy et al ., 2021]. This approach is based on the recent Fourier neural operator that learns in a resolution-invariant manner and has shown success in modeling challenging partial differential equations (PDE ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.11214", "content": "FourCastNet uses a Fourier transform-based token-mixing scheme [Guibas et al ., 2022 ] with a vision transformer (ViT) backbone [Dosovitskiy et al ., 2021]. This approach is based on the recent Fourier neural operator that learns in a resolution-invariant manner and has shown success in modeling challenging partial differential equations (PDE ..."}
diff --git a/data/sampled_jsons/FourCastNet_Pathak_et_al_2022_arxiv_evaluation_metrics.jsonl b/data/sampled_jsons/FourCastNet_Pathak_et_al_2022_arxiv_evaluation_metrics.jsonl
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--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FourCastNet 3: A geometric approach to probabilistic ...", "date": "", "ddg_snippet": "16 Jul 2025 — FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12144v1", "content": "16 Jul 2025 — FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting."}
+{"idx": 1, "title": "[2202.11214] FourCastNet: A Global Data-driven High ... - ar5iv", "date": "", "ddg_snippet": "FourCastNet is a novel global data-driven DL-based weather forecasting model based on the FNO and AFNO (Li et al., 2021a; Guibas et al., 2022) . FourCastNet's ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2202.11214", "content": "FourCastNet is a novel global data-driven DL-based weather forecasting model based on the FNO and AFNO (Li et al., 2021a; Guibas et al., 2022) . FourCastNet's ..."}
+{"idx": 2, "title": "Advancing Data-driven Weather Forecasting: Time-Sliding ...", "date": "", "ddg_snippet": "13 Feb 2024 — Pathak et al. Four castnet : A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv: ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.08185v1", "content": "13 Feb 2024 — Pathak et al. Four castnet : A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv: ..."}
+{"idx": 3, "title": "Accurate medium-range global weather forecasting with ...", "date": "", "ddg_snippet": "by K Bi · 2023 · Cited by 1573 — Pathak, J. et al. FourCastNet: a global data-driven high-resolution weather model using adaptive Fourier neural operators. Preprint at https ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-023-06185-3", "content": "by K Bi · 2023 · Cited by 1573 — Pathak, J. et al. FourCastNet: a global data-driven high-resolution weather model using adaptive Fourier neural operators. Preprint at https ..."}
+{"idx": 4, "title": "Optimizing data-driven arctic marine forecasting", "date": "", "ddg_snippet": "by AV Buinyi · 2024 — According to ( Pathak et al ., 2022 ), the FourCastNet uses such metrics as Root Mean Squared Error (RMSE), Anomaly Correlation Coefficient ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2024.1456480/full", "content": "by AV Buinyi · 2024 — According to ( Pathak et al ., 2022 ), the FourCastNet uses such metrics as Root Mean Squared Error (RMSE), Anomaly Correlation Coefficient ..."}
+{"idx": 5, "title": "Spatiotemporal inhomogeneity of accuracy degradation in ...", "date": "", "ddg_snippet": "by J Ding · 2025 · Cited by 3 — AI models such as FourCastNet ( Pathak et al ., 2022 ), Pangu-Weather (Bi et al ... 11), all evaluation metrics exhibit no significant correlation with longitude.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1569843225001207", "content": "by J Ding · 2025 · Cited by 3 — AI models such as FourCastNet ( Pathak et al ., 2022 ), Pangu-Weather (Bi et al ... 11), all evaluation metrics exhibit no significant correlation with longitude."}
+{"idx": 6, "title": "Development of a Data-driven weather forecasting system ...", "date": "", "ddg_snippet": "17 Mar 2025 — This architecture significantly outperforms IFS and FourCastNet ( Pathak et al ., , 2022 ) . ... These evaluation metrics were selected to understand ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.12956v1", "content": "17 Mar 2025 — This architecture significantly outperforms IFS and FourCastNet ( Pathak et al ., , 2022 ) . ... These evaluation metrics were selected to understand ..."}
+{"idx": 7, "title": "CoDiCast: Conditional Diffusion Model for Global Weather ...", "date": "", "ddg_snippet": "FourCastNet [Pathak et al., 2022 ] applied Vision Transformer (ViT) and Adaptive. Fourier Neural Operators (AFNO), while ClimaX [Nguyen et al., 2023] also uses a ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/1095.pdf", "content": "FourCastNet [Pathak et al., 2022 ] applied Vision Transformer (ViT) and Adaptive. Fourier Neural Operators (AFNO), while ClimaX [Nguyen et al., 2023] also uses a ..."}
+{"idx": 8, "title": "Data Assimilation with Machine Learning Surrogate Models", "date": "", "ddg_snippet": "by M Adrian · 2025 · Cited by 12 — 2022), evaluating FourCastNet is substantially faster than simulating physics-based weather models , allowing for extremely quick predictions and cheap ...", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/aies/4/3/AIES-D-24-0050.1.xml", "content": "by M Adrian · 2025 · Cited by 12 — 2022), evaluating FourCastNet is substantially faster than simulating physics-based weather models , allowing for extremely quick predictions and cheap ..."}
+{"idx": 9, "title": "An artificial intelligence-based limited area model for ...", "date": "", "ddg_snippet": "by P Xu · 2025 · Cited by 2 — FourCastNet : a global data-driven high-resolution weather model using adaptive Fourier neural operators. Preprint at https:// arxiv .org/abs/ ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s43247-025-02347-5", "content": "by P Xu · 2025 · Cited by 2 — FourCastNet : a global data-driven high-resolution weather model using adaptive Fourier neural operators. Preprint at https:// arxiv .org/abs/ ..."}
diff --git a/data/sampled_jsons/Franklin_Lorenz_1989_Sinkhorn_algorithm_linear_convergence_metric.jsonl b/data/sampled_jsons/Franklin_Lorenz_1989_Sinkhorn_algorithm_linear_convergence_metric.jsonl
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index 0000000000000000000000000000000000000000..d0ef0578e90e98ddaedd805b6466dcbac644f73a
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates.", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/Sinkhorn_rate.pdf", "content": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates."}
+{"idx": 1, "title": "On the Linear Convergence of the Multimarginal Sinkhorn Algorithm", "date": "", "ddg_snippet": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/21M1410634", "content": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ..."}
+{"idx": 2, "title": "Onthelinearconvergenceofthe multi-marginalSinkhornalgorithm", "date": "", "ddg_snippet": "The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel) method of ...", "subpage_snippet": "", "source": "www.mathtube.org", "link": "https://www.mathtube.org/sites/default/files/lecture-extra-files/linear-sinkhorn.pdf", "content": "The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel) method of ..."}
+{"idx": 3, "title": "On the linear convergence of the multi-marginal Sinkhorn ...", "date": "", "ddg_snippet": "The linear convergence of the Sinhkorn algorithm for two marginals is well-known. A very elegant proof consists in using a celebrated theorem of Birkhoff to show that the Sinkhorn algorithm consists in iterating a contrac-tion for the Hilbert projective metric , see Franklin and Lorenz [10], and more recently, Chen, Georgiou and Pavon, [4].", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/8f3b/63cdaad4f03307b7f7e2b5f307a513686322.pdf", "content": "The linear convergence of the Sinhkorn algorithm for two marginals is well-known. A very elegant proof consists in using a celebrated theorem of Birkhoff to show that the Sinkhorn algorithm consists in iterating a contrac-tion for the Hilbert projective metric , see Franklin and Lorenz [10], and more recently, Chen, Georgiou and Pavon, [4]."}
+{"idx": 4, "title": "Linear Convergence of Sinkhorn’s Algorithm for Generalized ...", "date": "", "ddg_snippet": "Franklin and Lorenz ( Franklin & Lorenz , 1989 ) established linear convergence (i.e., exponential decay of the error) in the Hilbert projective metric , and Rüschendorf (Ruschendorf, 1995) extended the analysis to continuous measures via an information projection viewpoint.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "Franklin and Lorenz ( Franklin & Lorenz , 1989 ) established linear convergence (i.e., exponential decay of the error) in the Hilbert projective metric , and Rüschendorf (Ruschendorf, 1995) extended the analysis to continuous measures via an information projection viewpoint."}
+{"idx": 5, "title": "A review of matrix scaling and Sinkhorn's normal form for ...", "date": "", "ddg_snippet": "by M Idel · 2016 · Cited by 164 — Later, Franklin and Lorenz 1989 showed that the convergence is also linear in Hilbert's projective metric, while Soules 1991 showed linear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1609.06349", "content": "by M Idel · 2016 · Cited by 164 — Later, Franklin and Lorenz 1989 showed that the convergence is also linear in Hilbert's projective metric, while Soules 1991 showed linear ..."}
+{"idx": 6, "title": "Designing Algorithms for Entropic Optimal Transport from ...", "date": "", "ddg_snippet": "by V Srinivasan · 2025 — known non-asymptotic rate of convergence was given by Franklin and Lorenz ( 1989 ). They do so by viewing the Sinkhorn algorithm as a matrix ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.12246", "content": "by V Srinivasan · 2025 — known non-asymptotic rate of convergence was given by Franklin and Lorenz ( 1989 ). They do so by viewing the Sinkhorn algorithm as a matrix ..."}
+{"idx": 7, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "16 Jul 2025 — We establish Kantorovich duality and linear convergence of Sinkhorn's algorithm for the generalized SSB problem under mild conditions. Our ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46671", "content": "16 Jul 2025 — We establish Kantorovich duality and linear convergence of Sinkhorn's algorithm for the generalized SSB problem under mild conditions. Our ..."}
+{"idx": 8, "title": "The Sinkhorn-Knopp Algorithm: Convergence and ...", "date": "", "ddg_snippet": "Franklin and Lorenz [10] give a bound on the rate of convergence when A > 0. They use Hilbert's projective metric for vectors x, y ∈ Rn. +, namely d(x, y) ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/the-sinkhorn-knopp-algorithm-convergence-and-applications-1hl7s9tzjx.pdf", "content": "Franklin and Lorenz [10] give a bound on the rate of convergence when A > 0. They use Hilbert's projective metric for vectors x, y ∈ Rn. +, namely d(x, y) ..."}
+{"idx": 9, "title": "Sinkhorn Distances: Lightspeed Computation of Optimal ...", "date": "", "ddg_snippet": "by M Cuturi · Cited by 5783 — Such algorithms include Sinkhorn's celebrated fixed point iteration (1967), which is known to have a linear convergence ( Franklin and Lorenz , 1989 ; Knight, 2008) ... 9 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "http://papers.neurips.cc/paper/4927-sinkhorn-distances-lightspeed-computation-of-optimal-transport.pdf", "content": "by M Cuturi · Cited by 5783 — Such algorithms include Sinkhorn's celebrated fixed point iteration (1967), which is known to have a linear convergence ( Franklin and Lorenz , 1989 ; Knight, 2008) ... 9 pages"}
diff --git a/data/sampled_jsons/From_Differential_Privacy_to_Bounds_on_Membership_Inference_Less_can_be_More_Thudi_Shumailov_Boenisc.jsonl b/data/sampled_jsons/From_Differential_Privacy_to_Bounds_on_Membership_Inference_Less_can_be_More_Thudi_Shumailov_Boenisc.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf00e5864f70d4e38cc233a34dec7f4aee38ebb7
--- /dev/null
+++ b/data/sampled_jsons/From_Differential_Privacy_to_Bounds_on_Membership_Inference_Less_can_be_More_Thudi_Shumailov_Boenisc.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "From Differential Privacy to Bounds on Membership Inference: Less can ...", "date": "", "ddg_snippet": "In this paper, we instead outline how training on less data can be beneficial when we are only interested in defending against specific attacks; we take the canonical example of defending against membership inference . To arrive at this result, we first derive (tight) bounds on the success of all membership inference attacks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=daXqjb6dVE", "content": "In this paper, we instead outline how training on less data can be beneficial when we are only interested in defending against specific attacks; we take the canonical example of defending against membership inference . To arrive at this result, we first derive (tight) bounds on the success of all membership inference attacks."}
+{"idx": 1, "title": "[2202.12232] Bounding Membership Inference - arXiv.org", "date": "", "ddg_snippet": "Bounding Membership Inference Anvith Thudi , Ilia Shumailov , Franziska Boenisch , Nicolas Papernot", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.12232", "content": "Bounding Membership Inference Anvith Thudi , Ilia Shumailov , Franziska Boenisch , Nicolas Papernot"}
+{"idx": 2, "title": "Nicolas Papernot - HomePage", "date": "", "ddg_snippet": "Jamie Hayes, Amr Khalifa, Ilia Shumailov , Nicolas Papernot , Eleni Triantafillou. Proceedings of the 3rd IEEE Conference on Secure and Trustworthy Machine Learning. conference", "subpage_snippet": "", "source": "www.papernot.fr", "link": "https://www.papernot.fr/", "content": "Jamie Hayes, Amr Khalifa, Ilia Shumailov , Nicolas Papernot , Eleni Triantafillou. Proceedings of the 3rd IEEE Conference on Secure and Trustworthy Machine Learning. conference"}
+{"idx": 3, "title": "\"From Differential Privacy to Bounds on Membership Inference: Less can ...", "date": "", "ddg_snippet": "To protect your privacy , all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/tmlr/ThudiSBP24", "content": "To protect your privacy , all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active."}
+{"idx": 4, "title": "Differentially Private Learning Does Not Bound Membership Inference", "date": "", "ddg_snippet": "This paper outlines how training on less data can be beneficial when the privacy protection the authors care about is defending against membership inference , and shows that decreasing the sampling rate when constructing the training dataset has a disparate effect on the bound when compared to strengthening the DP guarantee.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Differentially-Private-Learning-Does-Not-Bound-Humphries-Rafuse/f641688cd5cd6c0c0fa814a075c561ce5941abc9/figure/6", "content": "This paper outlines how training on less data can be beneficial when the privacy protection the authors care about is defending against membership inference , and shows that decreasing the sampling rate when constructing the training dataset has a disparate effect on the bound when compared to strengthening the DP guarantee."}
+{"idx": 5, "title": "FromDifferentialPrivacytoBoundsonMembershipInfer- ence: LesscanbeMore", "date": "", "ddg_snippet": "e efect on the bound when compared to strengthening the DP guarantee. Thus, when the privacy protection we care about is defending against membership inference , training on less data can yield more advantageous trade-ofs between preventing membership inference and utility than strengthening the DP guarante", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=daXqjb6dVE", "content": "e efect on the bound when compared to strengthening the DP guarantee. Thus, when the privacy protection we care about is defending against membership inference , training on less data can yield more advantageous trade-ofs between preventing membership inference and utility than strengthening the DP guarante"}
+{"idx": 6, "title": "Differentially Private Learning Does Not Bound Membership Inference", "date": "", "ddg_snippet": "Training machine learning models on privacy -sensitive data has become a popular practice, driving innovation in ever-expanding fields. This has opened the door to a series of new attacks, such as Membership Inference Attacks (MIAs), that exploit vulnerabilities in ML models in order to expose the privacy of individual training samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.12112v2", "content": "Training machine learning models on privacy -sensitive data has become a popular practice, driving innovation in ever-expanding fields. This has opened the door to a series of new attacks, such as Membership Inference Attacks (MIAs), that exploit vulnerabilities in ML models in order to expose the privacy of individual training samples."}
+{"idx": 7, "title": "(PDF) Bounding Membership Inference - ResearchGate", "date": "", "ddg_snippet": "Despite the empirical observation that DP reduces the vulnerability of models to existing membership inference (MI) attacks, a theoretical underpinning as to why this is the case is largely ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/358846358_Bounding_Membership_Inference", "content": "Despite the empirical observation that DP reduces the vulnerability of models to existing membership inference (MI) attacks, a theoretical underpinning as to why this is the case is largely ..."}
+{"idx": 8, "title": "Effects of Differential Privacy and Data Skewness on Membership ...", "date": "", "ddg_snippet": "Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evaluation system, MPLens, with three unique contributions. First, through MPLens, we demonstrate how membership inference attack methods can be leveraged in adversarial ML. Second, we highlight with MPLens how the ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9014384", "content": "Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evaluation system, MPLens, with three unique contributions. First, through MPLens, we demonstrate how membership inference attack methods can be leveraged in adversarial ML. Second, we highlight with MPLens how the ..."}
+{"idx": 9, "title": "CleverHans Lab - Publications", "date": "", "ddg_snippet": "From Differential Privacy to Bounds on Membership Inference : Less can be More Anvith Thudi , Ilia Shumailov , Franziska Boenisch , Nicolas Papernot", "subpage_snippet": "", "source": "cleverhans.io", "link": "https://cleverhans.io/publications.html", "content": "From Differential Privacy to Bounds on Membership Inference : Less can be More Anvith Thudi , Ilia Shumailov , Franziska Boenisch , Nicolas Papernot"}
diff --git a/data/sampled_jsons/Fusion-in-Decoder_k_passages_performance_Izacard_Grave.jsonl b/data/sampled_jsons/Fusion-in-Decoder_k_passages_performance_Izacard_Grave.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..95e1297bbd90e49989287ba72c4a50e50cbd599d
--- /dev/null
+++ b/data/sampled_jsons/Fusion-in-Decoder_k_passages_performance_Izacard_Grave.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Achieving State-of-the-Art Open-Domain QA Performance", "date": "", "ddg_snippet": "Our approach, termed Fusion - in - Decoder , retrieves informative passages and leverages them with a sequence-to-sequence model to generate answers.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/374031063_Achieving_State-of-the-Art_Open-Domain_QA_Performance_through_Fusion-in-Decoder_Method", "content": "Our approach, termed Fusion - in - Decoder , retrieves informative passages and leverages them with a sequence-to-sequence model to generate answers."}
+{"idx": 1, "title": "Yingbo Zhou | DeepAI", "date": "", "ddg_snippet": "Fusion - in - decoder (Fid) ( Izacard and Grave , 2020) is a generative questi... ... Existing KBQA approaches, despite achieving strong performance on i.i ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/yingbo-zhou", "content": "Fusion - in - decoder (Fid) ( Izacard and Grave , 2020) is a generative questi... ... Existing KBQA approaches, despite achieving strong performance on i.i ..."}
+{"idx": 2, "title": "Multi-view-guided Passage Reranking with Large Language Models", "date": "", "ddg_snippet": "... in large language models (LLMs) ... It consists of two key components under the Fusion - in - Decoder (FiD) architecture Izacard and Grave ( 2020 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07485v1", "content": "... in large language models (LLMs) ... It consists of two key components under the Fusion - in - Decoder (FiD) architecture Izacard and Grave ( 2020 ) ."}
+{"idx": 3, "title": "Representation Learning and Retrieval", "date": "", "ddg_snippet": "Gautier Izacard and Edouard Grave 's work \"Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering\" from July 2020 ...", "subpage_snippet": "", "source": "www.pragmatic.ml", "link": "https://www.pragmatic.ml/language-modeling-and-retrieval/", "content": "Gautier Izacard and Edouard Grave 's work \"Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering\" from July 2020 ..."}
+{"idx": 4, "title": "I think Google got it wrong with “Generate → Ground”", "date": "", "ddg_snippet": "2020), RAG pipes your user query through a vector index, pulls the top‑ k passages , and feeds «query + evidence» into the decoder in a single ...", "subpage_snippet": "", "source": "dejan.ai", "link": "https://dejan.ai/blog/generate-then-ground/", "content": "2020), RAG pipes your user query through a vector index, pulls the top‑ k passages , and feeds «query + evidence» into the decoder in a single ..."}
+{"idx": 5, "title": "Kazuma Hashimoto - ACL Anthology", "date": "", "ddg_snippet": "Fusion - in - decoder (Fid) ( Izacard and Grave , 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/k/kazuma-hashimoto/", "content": "Fusion - in - decoder (Fid) ( Izacard and Grave , 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained ..."}
+{"idx": 6, "title": "GitHub - princeton-nlp/DensePhrases: [ACL 2021] Learning Dense", "date": "", "ddg_snippet": "... provide more examples , which includes training a state-of-the-art open-domain question answering model called Fusion - in - Decoder by Izacard and Grave ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/princeton-nlp/DensePhrases", "content": "... provide more examples , which includes training a state-of-the-art open-domain question answering model called Fusion - in - Decoder by Izacard and Grave ..."}
+{"idx": 7, "title": "An Artificial Intelligence Driven Semantic Similarity-Based", "date": "", "ddg_snippet": "Kasanishi et al. [ 6 ] proposed SciReviewGen, a large-scale dataset and framework designed for automatic generation of literature reviews by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15292v1", "content": "Kasanishi et al. [ 6 ] proposed SciReviewGen, a large-scale dataset and framework designed for automatic generation of literature reviews by ..."}
+{"idx": 8, "title": "GitHub - facebookresearch/atlas: Code repository for supporting", "date": "", "ddg_snippet": "We perform evaluations on a wide range of tasks, including MMLU, KILT and NaturalQuestions, and study the impact of the content of the document index ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/atlas", "content": "We perform evaluations on a wide range of tasks, including MMLU, KILT and NaturalQuestions, and study the impact of the content of the document index ..."}
+{"idx": 9, "title": "Patterns for Building LLM-based Systems & Products", "date": "", "ddg_snippet": "We’ll build on academic research, industry resources, and practitioner know-how, and distill them into key ideas and practices.", "subpage_snippet": "", "source": "eugeneyan.com", "link": "https://eugeneyan.com/writing/llm-patterns/", "content": "We’ll build on academic research, industry resources, and practitioner know-how, and distill them into key ideas and practices."}
diff --git a/data/sampled_jsons/GPT-4_MedQA_accuracy_percentage.jsonl b/data/sampled_jsons/GPT-4_MedQA_accuracy_percentage.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d1c22c0095970e5344ed195b7a515e22ed52d25
--- /dev/null
+++ b/data/sampled_jsons/GPT-4_MedQA_accuracy_percentage.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Capabilities of GPT-4 on Medical Challenge Problems", "date": "", "ddg_snippet": "Implications of the findings are discussed for potential uses of GPT-4 in medical education, assessment, and clinical practice, with appropriate attention to challenges of accuracy and safety.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2023/03/GPT-4_medical_benchmarks.pdf", "content": "Implications of the findings are discussed for potential uses of GPT-4 in medical education, assessment, and clinical practice, with appropriate attention to challenges of accuracy and safety."}
+{"idx": 1, "title": "Diagnostic accuracy of GPT‐4 on common clinical scenarios and ...", "date": "", "ddg_snippet": "Large language models (LLMs) have a high diagnostic accuracy when they evaluate previously published clinical cases. We compared the accuracy of GPT‐4 's differential diagnoses for previously unpublished challenging case scenarios with the diagnostic ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11257049/", "content": "Large language models (LLMs) have a high diagnostic accuracy when they evaluate previously published clinical cases. We compared the accuracy of GPT‐4 's differential diagnoses for previously unpublished challenging case scenarios with the diagnostic ..."}
+{"idx": 2, "title": "Accuracy of a Generative Artificial Intelligence Model in a ...", "date": "", "ddg_snippet": "Jun 15, 2023 · This study assesses the diagnostic accuracy of the Generative Pre-trained Transformer 4 ( GPT-4 ) artificial intelligence (AI) model in a series of challenging cases.", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jama/fullarticle/2806457", "content": "Jun 15, 2023 · This study assesses the diagnostic accuracy of the Generative Pre-trained Transformer 4 ( GPT-4 ) artificial intelligence (AI) model in a series of challenging cases."}
+{"idx": 3, "title": "Beyond Accuracy: Investigating Error Types in GPT-4 Responses ...", "date": "", "ddg_snippet": "Recent LLMs such as Med-PaLM 2 and GPT-4 achieve passing performance in terms of accuracy with 86.5% and 86.7% respectively on the USMLE- MedQA [13] dataset. Simply evaluating accuracy on large QA datasets like USMLE is not enough to understand errors, as no insights into the types of errors can be gained and the types of errors as well as their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.13307", "content": "Recent LLMs such as Med-PaLM 2 and GPT-4 achieve passing performance in terms of accuracy with 86.5% and 86.7% respectively on the USMLE- MedQA [13] dataset. Simply evaluating accuracy on large QA datasets like USMLE is not enough to understand errors, as no insights into the types of errors can be gained and the types of errors as well as their ..."}
+{"idx": 4, "title": "MedQA benchmark improves with GPT-4 - LinkedIn", "date": "", "ddg_snippet": "\"AI performance on the MedQA benchmark has seen remarkable improvement, with the leading system, GPT-4 Medprompt, achieving an accuracy rate of 90.2%—an increase of 22.6 percentage points from ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/andrewrebhan_ai-performance-on-the-medqa-benchmark-has-activity-7185667319908511744-ISG4", "content": "\"AI performance on the MedQA benchmark has seen remarkable improvement, with the leading system, GPT-4 Medprompt, achieving an accuracy rate of 90.2%—an increase of 22.6 percentage points from ..."}
+{"idx": 5, "title": "Main results on accuracy and F1-score across MedQA and ...", "date": "", "ddg_snippet": "Main results on accuracy and F1-score across MedQA and PubMedQA datasets (all results were obtained using gpt-4 -turbo). We highlight the optimal and suboptimal methods in bold and underline ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Main-results-on-accuracy-and-F1-score-across-MedQA-and-PubMedQA-datasets-all-results_tbl1_389947634", "content": "Main results on accuracy and F1-score across MedQA and PubMedQA datasets (all results were obtained using gpt-4 -turbo). We highlight the optimal and suboptimal methods in bold and underline ..."}
+{"idx": 6, "title": "Evaluation of the performance of GPT-3.5 and GPT-4 on the ...", "date": "", "ddg_snippet": "Jun 4 , 2023 · The newest version of the GPT model outperformed GPT -3.5 with the improvement of its accuracy by over 30 percentage points [12]. In this study, GPT-4 turned out to be superior compared to its previous version and Flan-PaLM 540B model [13] in evaluation on other medical benchmarks like MedQA , PubMedQA and MedMCQA.", "subpage_snippet": "", "source": "www.medrxiv.org", "link": "https://www.medrxiv.org/content/10.1101/2023.06.04.23290939v1.full.pdf", "content": "Jun 4 , 2023 · The newest version of the GPT model outperformed GPT -3.5 with the improvement of its accuracy by over 30 percentage points [12]. In this study, GPT-4 turned out to be superior compared to its previous version and Flan-PaLM 540B model [13] in evaluation on other medical benchmarks like MedQA , PubMedQA and MedMCQA."}
+{"idx": 7, "title": "MedQA", "date": "", "ddg_snippet": "The cost- accuracy graph shows a few models that define a Pareto curve of tradeoffs: o1, Grok 2, Llama 3.1 70b, and GPT 4 o mini. Among these five models, Grok 2 stands out for its strong quality-to-price ratio.", "subpage_snippet": "", "source": "www.vals.ai", "link": "https://www.vals.ai/benchmarks/medqa-04-15-2025", "content": "The cost- accuracy graph shows a few models that define a Pareto curve of tradeoffs: o1, Grok 2, Llama 3.1 70b, and GPT 4 o mini. Among these five models, Grok 2 stands out for its strong quality-to-price ratio."}
+{"idx": 8, "title": "Google's Med-Gemini Outperforms OpenAI GPT - 4 in... | IBTimes UK", "date": "", "ddg_snippet": "Google's Med-Gemini outperforms GPT - 4 and achieves 91.1 percent accuracy in medical diagnostics.According to New Atlas, Med-Gemini's performance on the MedQA (USMLE) benchmark stands out, achieving an impressive 91.1 percent accuracy .", "subpage_snippet": "", "source": "www.ibtimes.co.uk", "link": "https://www.ibtimes.co.uk/googles-med-gemini-outperforms-openai-gpt-4-diagnostics-can-we-trust-it-1724586", "content": "Google's Med-Gemini outperforms GPT - 4 and achieves 91.1 percent accuracy in medical diagnostics.According to New Atlas, Med-Gemini's performance on the MedQA (USMLE) benchmark stands out, achieving an impressive 91.1 percent accuracy ."}
+{"idx": 9, "title": "Llama 2 7B Chat Medqa 4 bit By jesse-tong: Benchmarks, Features and...", "date": "", "ddg_snippet": "Details and insights about Llama 2 7B Chat Medqa 4bit LLM by jesse-tong: benchmarks, internals, and performance insights.nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 (\"so35\"), GPT - 4 o (\" gpt 4 o\") or GPT - 4 (\" gpt 4 \").", "subpage_snippet": "", "source": "llm-explorer.com", "link": "https://llm-explorer.com/model/jesse-tong/llama-2-7b-chat-medqa-4bit,1ZxGczjy1s8xDdJtOcEVW9", "content": "Details and insights about Llama 2 7B Chat Medqa 4bit LLM by jesse-tong: benchmarks, internals, and performance insights.nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 (\"so35\"), GPT - 4 o (\" gpt 4 o\") or GPT - 4 (\" gpt 4 \")."}
diff --git a/data/sampled_jsons/GPT-4_accuracy_0.28_real-world_data_57%_unanswered_MedQA_arXiv_2503.10694.jsonl b/data/sampled_jsons/GPT-4_accuracy_0.28_real-world_data_57%_unanswered_MedQA_arXiv_2503.10694.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d73e4b3c2124b78b2a8f6e92b781efc59bf4739f
--- /dev/null
+++ b/data/sampled_jsons/GPT-4_accuracy_0.28_real-world_data_57%_unanswered_MedQA_arXiv_2503.10694.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MedQA", "date": "", "ddg_snippet": "MedQA Benchmark. Last updated. 02/25/2025.Both Llama 3.1 Instruct Turbo (405B) and Llama 3.1 Instruct Turbo (70B) are cost-effective solutions with high cost-to- accuracy compared to the other LLMs measured, but suffer from significant performance decreases on bias-injected data .", "subpage_snippet": "", "source": "www.vals.ai", "link": "https://www.vals.ai/benchmarks/medqa-02-25-2025", "content": "MedQA Benchmark. Last updated. 02/25/2025.Both Llama 3.1 Instruct Turbo (405B) and Llama 3.1 Instruct Turbo (70B) are cost-effective solutions with high cost-to- accuracy compared to the other LLMs measured, but suffer from significant performance decreases on bias-injected data ."}
+{"idx": 1, "title": "GitHub - nomic-ai/ gpt 4 all: GPT 4 All: Run Local LLMs on Any Device.", "date": "", "ddg_snippet": "Offline build support for running old versions of the GPT 4 All Local LLM Chat Client. September 18th, 2023: Nomic Vulkan launches supporting local LLM inference on NVIDIA and AMD GPUs. July 2023: Stable support for LocalDocs, a feature that allows you to privately and locally chat with...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nomic-ai/gpt4all", "content": "Offline build support for running old versions of the GPT 4 All Local LLM Chat Client. September 18th, 2023: Nomic Vulkan launches supporting local LLM inference on NVIDIA and AMD GPUs. July 2023: Stable support for LocalDocs, a feature that allows you to privately and locally chat with..."}
+{"idx": 2, "title": "GPT - 4 | OpenAI", "date": "", "ddg_snippet": "We’ve created GPT - 4 , the latest milestone in OpenAI’s effort in scaling up deep learning.", "subpage_snippet": "", "source": "openai.com", "link": "https://openai.com/index/gpt-4-research/", "content": "We’ve created GPT - 4 , the latest milestone in OpenAI’s effort in scaling up deep learning."}
+{"idx": 3, "title": "ChatGPT- 4 : что это, как работает и как... / Skillbox Media", "date": "", "ddg_snippet": "Исследуем ChatGPT-4: что это за нейросеть, как она работает, как воспользоваться ей в России бесплатно. Подробный гайд по таинственной нейросети GPT - 4 и секретам её искусственного интеллекта.", "subpage_snippet": "", "source": "skillbox.ru", "link": "https://skillbox.ru/media/code/zakrytyy-ii-ot-openai-issleduem-neyroset-gpt4/", "content": "Исследуем ChatGPT-4: что это за нейросеть, как она работает, как воспользоваться ей в России бесплатно. Подробный гайд по таинственной нейросети GPT - 4 и секретам её искусственного интеллекта."}
+{"idx": 4, "title": "GPT - 4 Has Arrived — Here’s What You Need To Know", "date": "", "ddg_snippet": "The wait is over, GPT - 4 is finally here. With increased context length, more advanced reasoning and the capability of processing visual input, we are in for a treat. If you are looking for more content, check out my reading lists in AI, Python or Dat...", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/gpt-4-has-arrived-heres-what-you-need-to-know-398c3c72191c", "content": "The wait is over, GPT - 4 is finally here. With increased context length, more advanced reasoning and the capability of processing visual input, we are in for a treat. If you are looking for more content, check out my reading lists in AI, Python or Dat..."}
+{"idx": 5, "title": "How to fine-tune GPT - 4 o", "date": "", "ddg_snippet": "OpenAI launched the ability to fine-tune GPT - 4 o (with free tokens!).USAID adopted ChatGPT Enterprise to be more efficient.Virginia's water usage surged due to AI-driven data centers.", "subpage_snippet": "", "source": "www.theneurondaily.com", "link": "https://www.theneurondaily.com/p/teach-gpt-secret-sauce", "content": "OpenAI launched the ability to fine-tune GPT - 4 o (with free tokens!).USAID adopted ChatGPT Enterprise to be more efficient.Virginia's water usage surged due to AI-driven data centers."}
+{"idx": 6, "title": "GPT 4 o's images and lessons from native input-output multimodality", "date": "", "ddg_snippet": "GPT - 4 o and Gemini are the flagship models surrounding this question. I remember it vividly — when GPT - 4 o launched in May of 2024, many people asked, “Wait, isn’t this model worse?”", "subpage_snippet": "", "source": "www.interconnects.ai", "link": "https://www.interconnects.ai/p/gpt-4os-images-and-lessons-from-native", "content": "GPT - 4 o and Gemini are the flagship models surrounding this question. I remember it vividly — when GPT - 4 o launched in May of 2024, many people asked, “Wait, isn’t this model worse?”"}
+{"idx": 7, "title": "A Knowledge-driven Adaptive Collaboration of LLMs for Enhancing...", "date": "", "ddg_snippet": "Table 1: Main results on four common metrics across MedQA and Progn-VQA datasets, evaluated using GPT - 4 .1-mini. Bold values indicate the best performance. Here, ‘SA’ means the single-agent methods, and ‘MA’ means the multi-agent methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14998v1", "content": "Table 1: Main results on four common metrics across MedQA and Progn-VQA datasets, evaluated using GPT - 4 .1-mini. Bold values indicate the best performance. Here, ‘SA’ means the single-agent methods, and ‘MA’ means the multi-agent methods."}
+{"idx": 8, "title": "Искусственный интеллект в 2025 году: что происходит на... / Хабр", "date": "", "ddg_snippet": "5. Наука и медицина. Большие языковые модели в клинике. o1 набрала 96% на тесте MedQA (ответы на вопросы по медицинским темам, +28, 4 % с 2022).", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/902602/", "content": "5. Наука и медицина. Большие языковые модели в клинике. o1 набрала 96% на тесте MedQA (ответы на вопросы по медицинским темам, +28, 4 % с 2022)."}
+{"idx": 9, "title": "Story is AI-native infrastructure for the intellectual property economy.", "date": "", "ddg_snippet": "Story is the infrastructure layer that makes IP and real - world data programmable, enforceable, and monetizable. Data Providers. Enable permissionless licensing for datasets and DePIN infrastructure.", "subpage_snippet": "", "source": "www.story.foundation", "link": "https://www.story.foundation/", "content": "Story is the infrastructure layer that makes IP and real - world data programmable, enforceable, and monetizable. Data Providers. Enable permissionless licensing for datasets and DePIN infrastructure."}
diff --git a/data/sampled_jsons/GTA_Greedy_Task_Allocation_Section_1_page_8_total_worker_time_workers_tasks_n_B.jsonl b/data/sampled_jsons/GTA_Greedy_Task_Allocation_Section_1_page_8_total_worker_time_workers_tasks_n_B.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0c64369ea0617d73bd78d6ce2a1af21d0db4de44
--- /dev/null
+++ b/data/sampled_jsons/GTA_Greedy_Task_Allocation_Section_1_page_8_total_worker_time_workers_tasks_n_B.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Profit-driven Task Assignment in Spatial Crowdsourcing", "date": "", "ddg_snippet": "by J Xia · Cited by 63 — For the sake of efficiency, we pro- pose a Greedy Task Assignment (GTA) algorithm that tries to give priority to the tasks with the highest possible reward per ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2019/0265.pdf", "content": "by J Xia · Cited by 63 — For the sake of efficiency, we pro- pose a Greedy Task Assignment (GTA) algorithm that tries to give priority to the tasks with the highest possible reward per ..."}
+{"idx": 1, "title": "Coalition-based task assignment with priority-aware fairness ...", "date": "", "ddg_snippet": "by Y Zhao · 2023 · Cited by 23 — The greedy approach forms a set of worker coalitions greedily for performing tasks and uses an acceptance probability to identify high-value ... 22 pages", "subpage_snippet": "", "source": "zheng-kai.com", "link": "https://zheng-kai.com/paper/vldbj_2023_zhao.pdf", "content": "by Y Zhao · 2023 · Cited by 23 — The greedy approach forms a set of worker coalitions greedily for performing tasks and uses an acceptance probability to identify high-value ... 22 pages"}
+{"idx": 2, "title": "Coalition-based task assignment with priority-aware ...", "date": "", "ddg_snippet": "by Y Zhao · 2024 · Cited by 23 — Given a set of workers and a set of tasks , the objective is to assign a stable worker coalition to each task to achieve the highest total reward ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00778-023-00802-3", "content": "by Y Zhao · 2024 · Cited by 23 — Given a set of workers and a set of tasks , the objective is to assign a stable worker coalition to each task to achieve the highest total reward ..."}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "18 Jun 2025 — This paper studies how to assign tasks more intelligently when machine speeds are unknown and unpredictable. The goal is to complete the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i&noteId=hkH8Wi9zZm", "content": "18 Jun 2025 — This paper studies how to assign tasks more intelligently when machine speeds are unknown and unpredictable. The goal is to complete the ..."}
+{"idx": 4, "title": "Coalition-based Task Assignment in Spatial Crowdsourcing", "date": "", "ddg_snippet": "by Y Zhao · Cited by 65 — More specifically, the greedy algorithm is a non-reducing reward allocation strategy that incentivises workers to enlarge a coalition for more total rewards. By ... 12 pages", "subpage_snippet": "", "source": "zheng-kai.com", "link": "https://zheng-kai.com/paper/icde_2021_zhao.pdf", "content": "by Y Zhao · Cited by 65 — More specifically, the greedy algorithm is a non-reducing reward allocation strategy that incentivises workers to enlarge a coalition for more total rewards. By ... 12 pages"}
+{"idx": 5, "title": "Task allocation for maximum cooperation in complex ...", "date": "", "ddg_snippet": "by J Wang · 2024 · Cited by 1 — The execution of a business process usually involves the cooperation of many resources (actors) performing various tasks ( activities ).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0950705124006233", "content": "by J Wang · 2024 · Cited by 1 — The execution of a business process usually involves the cooperation of many resources (actors) performing various tasks ( activities )."}
+{"idx": 6, "title": "Consensus-Based Group Task Assignment with Social ...", "date": "", "ddg_snippet": "by X Li · 2020 · Cited by 45 — The aim of the group task assignment is to maximize the total task assignments by giving priority to the worker groups with higher consensus ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s41019-020-00142-0", "content": "by X Li · 2020 · Cited by 45 — The aim of the group task assignment is to maximize the total task assignments by giving priority to the worker groups with higher consensus ..."}
+{"idx": 7, "title": "Multiattribute E-CARGO Task Assignment Model Based on ...", "date": "", "ddg_snippet": "by Z Liu · Cited by 8 — The CTO requires A to organize different groups to complete these tasks and achieve the most effective performance in the shortest time . Obviously, GTA is used.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=dhQ3OKfzRu", "content": "by Z Liu · Cited by 8 — The CTO requires A to organize different groups to complete these tasks and achieve the most effective performance in the shortest time . Obviously, GTA is used."}
+{"idx": 8, "title": "cherryATA", "date": "", "ddg_snippet": "... working on some task , and stop once B tasks have been completed. In GTA , we initially ask all n workers to start working on a task , and as soon as some worker ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "... working on some task , and stop once B tasks have been completed. In GTA , we initially ask all n workers to start working on a task , and as soon as some worker ..."}
+{"idx": 9, "title": "Sequence-dependent group scheduling problem on ...", "date": "", "ddg_snippet": "by MA Bozorgirad · 2012 · Cited by 57 — GTA forces all jobs of a group to be processed contiguously, but scheduling without GTA allows the groups to be split into subgroups. Scheduling without GTA ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S095741741200276X", "content": "by MA Bozorgirad · 2012 · Cited by 57 — GTA forces all jobs of a group to be processed contiguously, but scheduling without GTA allows the groups to be split into subgroups. Scheduling without GTA ..."}
diff --git a/data/sampled_jsons/GUI_agent_textual_answers_concrete_actions_bridge_technology_2024.jsonl b/data/sampled_jsons/GUI_agent_textual_answers_concrete_actions_bridge_technology_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..87b4a0a7005180ff8b7fe0c9cf4841490b3f4859
--- /dev/null
+++ b/data/sampled_jsons/GUI_agent_textual_answers_concrete_actions_bridge_technology_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large Language Model-Brained GUI Agents: A Survey", "date": "", "ddg_snippet": "... LLM-brained” GUI agents capable of interpreting complex GUI elements and autonomously executing actions based on natural language instructions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.18279v12", "content": "... LLM-brained” GUI agents capable of interpreting complex GUI elements and autonomously executing actions based on natural language instructions."}
+{"idx": 1, "title": "VCA: Video Curious Agent for Long Video Understanding", "date": "", "ddg_snippet": "Finally, emulating human reasoning, the agent actively selects segments to explore until the correct answer is found.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.10471v2", "content": "Finally, emulating human reasoning, the agent actively selects segments to explore until the correct answer is found."}
+{"idx": 2, "title": "Large Language Model-Based Agents for Software Engineering: A", "date": "", "ddg_snippet": "The memory component records the historical thoughts, actions , and environmental observations generated during the agent execution [ 21 , 26 , 27 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.02977v1", "content": "The memory component records the historical thoughts, actions , and environmental observations generated during the agent execution [ 21 , 26 , 27 ..."}
+{"idx": 3, "title": "Magentic-UI: Towards Human-in-the-loop Agentic Systems", "date": "", "ddg_snippet": "Status indicators, background tasks, and answer verification mechanisms to help people assess whether the agent ’s goal was achieved (Challenge A5).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22358v1", "content": "Status indicators, background tasks, and answer verification mechanisms to help people assess whether the agent ’s goal was achieved (Challenge A5)."}
+{"idx": 4, "title": "UI-Hawk: Unleashing the Screen Stream Understanding for GUI", "date": "", "ddg_snippet": "Existing GUI agents merely rely on current visual observations and plain-text action history, ignoring the significance of history screens.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202408.2137/v1", "content": "Existing GUI agents merely rely on current visual observations and plain-text action history, ignoring the significance of history screens."}
+{"idx": 5, "title": "GitHub - WooooDyy/LLM-Agent-Paper-List: The paper list of the", "date": "", "ddg_snippet": "Specifically, we start by the general conceptual framework for LLM-based agents : comprising three main components: brain, perception, and action , and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/woooodyy/llm-agent-paper-list", "content": "Specifically, we start by the general conceptual framework for LLM-based agents : comprising three main components: brain, perception, and action , and ..."}
+{"idx": 6, "title": "Empowering biomedical discovery with AI agents: Cell", "date": "", "ddg_snippet": "These multi- agent systems consist of agents based on conversable large language models (LLMs) and can coordinate machine learning (ML) tools ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5", "content": "These multi- agent systems consist of agents based on conversable large language models (LLMs) and can coordinate machine learning (ML) tools ..."}
+{"idx": 7, "title": "Toward a Human-Centered Evaluation Framework for Trustworthy", "date": "", "ddg_snippet": "When integrated with assistive technologies such as screen readers and speech-to-text systems, GUI agents further enhance accessibility for users ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.17934v2", "content": "When integrated with assistive technologies such as screen readers and speech-to-text systems, GUI agents further enhance accessibility for users ..."}
+{"idx": 8, "title": "Papers by Michael Eickenberg", "date": "", "ddg_snippet": "GUI Grounding: Learning to Navigate Web Interfaces The localizer is essential for bridging visual and action spaces, enabling the agent to determine ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/author-profile/michael-eickenberg-64b34d87-d13e-4979-9898-31cbc7d13827", "content": "GUI Grounding: Learning to Navigate Web Interfaces The localizer is essential for bridging visual and action spaces, enabling the agent to determine ..."}
+{"idx": 9, "title": "US8914438B2 - Methods and systems for providing a user", "date": "", "ddg_snippet": "G06F3/0481 — Interaction techniques based on graphical user interfaces [ GUI ] based on specific properties of the displayed interaction object ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US8914438B2/en", "content": "G06F3/0481 — Interaction techniques based on graphical user interfaces [ GUI ] based on specific properties of the displayed interaction object ..."}
diff --git a/data/sampled_jsons/GUMBELBOX_Dasgupta_et_al._2020_year_2020.jsonl b/data/sampled_jsons/GUMBELBOX_Dasgupta_et_al._2020_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f30752e820bdecd1129bbd903dd97681fffbbc81
--- /dev/null
+++ b/data/sampled_jsons/GUMBELBOX_Dasgupta_et_al._2020_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF arXiv:2010.04831v2 [cs.LG] 29 Oct 2020", "date": "", "ddg_snippet": "e incorrectly represented as disjoint. Vilnis et al. [26] introduce a surrogate function which serves to minimize the volume of the smallest containing box. This approach was replaced with a more principled approach in Li et al. [13], where the authors smoothed the hard indicator functions of boxes using Gaussian convolutions and approximated ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2010.04831.pdf", "content": "e incorrectly represented as disjoint. Vilnis et al. [26] introduce a surrogate function which serves to minimize the volume of the smallest containing box. This approach was replaced with a more principled approach in Li et al. [13], where the authors smoothed the hard indicator functions of boxes using Gaussian convolutions and approximated ..."}
+{"idx": 1, "title": "arXiv:2010.04831v1 [cs.LG] 9 Oct 2020", "date": "", "ddg_snippet": "incorrectly represented as disjoint. Vilnis et al. [24] introduce a surrogate function which serves to minimize the olume of the smallest containing box. This approach was replaced with a more principled approach in Li et al. [11], where the authors smoothed the hard indicator functions of boxes using Gaussian convolutions and approximated the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2010.04831v1", "content": "incorrectly represented as disjoint. Vilnis et al. [24] introduce a surrogate function which serves to minimize the olume of the smallest containing box. This approach was replaced with a more principled approach in Li et al. [11], where the authors smoothed the hard indicator functions of boxes using Gaussian convolutions and approximated the ..."}
+{"idx": 2, "title": "Review for NeurIPS paper: Improving Local Identifiability in ...", "date": "", "ddg_snippet": "Ensembling nature of the GumbelBox model provides a global perspective on the optimized task and analytically computed expected intersection makes it an efficient method.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2020/file/01c9d2c5b3ff5cbba349ec39a570b5e3-Review.html", "content": "Ensembling nature of the GumbelBox model provides a global perspective on the optimized task and analytically computed expected intersection makes it an efficient method."}
+{"idx": 3, "title": "Box-To-Box Transformation for Modeling Joint Hierarchies", "date": "", "ddg_snippet": "Review: This paper builds upon the work of Patel et al. ( 2020 ) in modeling two hierarchies jointly within the box embedding framework. It also incorporates the GumbelBox formulation of Dasgupta et al. ( 2020 ) to resolve local identifiability issues during training. The contribution of the paper seems to only lie in the learning of a function \\phi that maps entity boxes to HasPart-* boxes. This ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=CLYe1Yke1r", "content": "Review: This paper builds upon the work of Patel et al. ( 2020 ) in modeling two hierarchies jointly within the box embedding framework. It also incorporates the GumbelBox formulation of Dasgupta et al. ( 2020 ) to resolve local identifiability issues during training. The contribution of the paper seems to only lie in the learning of a function \\phi that maps entity boxes to HasPart-* boxes. This ..."}
+{"idx": 4, "title": "Improving Local Identifiability in Probabilistic Box Embeddings", "date": "", "ddg_snippet": "arXiv:2010.04831v2 [cs.LG] 29 Oct 2020 Improving Local Identifiability in Probabilistic Box Embeddings Shib Sankar Dasgupta ∗ Department of Computer Science University of Massachusetts, Amherst ssdasgupta@cs.umass.edu Michael Boratko∗ Department of Computer Science University of Massachusetts, Amherst mboratko@cs.umass.edu Dongxu Zhang ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/75819906/Improving_Local_Identifiability_in_Probabilistic_Box_Embeddings", "content": "arXiv:2010.04831v2 [cs.LG] 29 Oct 2020 Improving Local Identifiability in Probabilistic Box Embeddings Shib Sankar Dasgupta ∗ Department of Computer Science University of Massachusetts, Amherst ssdasgupta@cs.umass.edu Michael Boratko∗ Department of Computer Science University of Massachusetts, Amherst mboratko@cs.umass.edu Dongxu Zhang ..."}
+{"idx": 5, "title": "GitHub - iesl/gumbel-box-embeddings", "date": "", "ddg_snippet": "data - Contains all the dataset used in this paper. models/box/gumbel_bce_box.py - Contains the implementation of Tasks for GumbelBox . models/box/bce_box.py - Contains the implementation of Tasks for SmoothBox and Gaussian. configs - Contains \".jsonnet\" files for each experiemnt that we report. These files contains the best hyperparameter for the corresponding task that file refers to.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/iesl/gumbel-box-embeddings", "content": "data - Contains all the dataset used in this paper. models/box/gumbel_bce_box.py - Contains the implementation of Tasks for GumbelBox . models/box/bce_box.py - Contains the implementation of Tasks for SmoothBox and Gaussian. configs - Contains \".jsonnet\" files for each experiemnt that we report. These files contains the best hyperparameter for the corresponding task that file refers to."}
+{"idx": 6, "title": "Improving Local Identifiability in Probabilistic Box Embeddings", "date": "", "ddg_snippet": "Improving Local Identifiability in Pr obabilistic Box Embeddings Shib Sankar Dasgupta Department of Computer Science University of Massachusetts, Amherst ssdasgupta@cs.umass.edu", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/344622460_Improving_Local_Identifiability_in_Probabilistic_Box_Embeddings", "content": "Improving Local Identifiability in Pr obabilistic Box Embeddings Shib Sankar Dasgupta Department of Computer Science University of Massachusetts, Amherst ssdasgupta@cs.umass.edu"}
+{"idx": 7, "title": "PDF Box-To-Box Transformations for Modeling Joint Hierarchies", "date": "", "ddg_snippet": "Various training improvement methods for box embeddings have been proposed (Li et al ., 2019; Dasgupta et al ., 2020 ), the most recent of which, GumbelBox , use a latent noise model where box parameters are repre-sented via Gumbel distributions to improve on the loss landscape by making the gradient smooth for the geometric operations involved ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.repl4nlp-1.28.pdf", "content": "Various training improvement methods for box embeddings have been proposed (Li et al ., 2019; Dasgupta et al ., 2020 ), the most recent of which, GumbelBox , use a latent noise model where box parameters are repre-sented via Gumbel distributions to improve on the loss landscape by making the gradient smooth for the geometric operations involved ..."}
+{"idx": 8, "title": "Improving Local Identifiability in Probabilistic Box Embeddings - NIPS", "date": "", "ddg_snippet": "Authors Shib Dasgupta , Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum Abstract Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings, where objects are represented by n-dimensional hyperrectangles, are a particularly promising example of such an embedding as they are ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2020/hash/01c9d2c5b3ff5cbba349ec39a570b5e3-Abstract.html", "content": "Authors Shib Dasgupta , Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum Abstract Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings, where objects are represented by n-dimensional hyperrectangles, are a particularly promising example of such an embedding as they are ..."}
+{"idx": 9, "title": "PDF Improving Local Identifiability in Probabilistic Box Embeddin", "date": "", "ddg_snippet": "In this work, we focus on the probabilistic box embedding model, which represents the probabilities of binary random variables in terms of volumes of axis-aligned hyperrectangles. While this model flexibly allows for the expression of positively and negatively correlated random variables with complex latent dependency structures, it can be difficult to train due to a lack of local ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/01c9d2c5b3ff5cbba349ec39a570b5e3-Paper.pdf", "content": "In this work, we focus on the probabilistic box embedding model, which represents the probabilities of binary random variables in terms of volumes of axis-aligned hyperrectangles. While this model flexibly allows for the expression of positively and negatively correlated random variables with complex latent dependency structures, it can be difficult to train due to a lack of local ..."}
diff --git a/data/sampled_jsons/Gaussian_Processes_preference-based_reinforcement_learning_non-linear_reward_functions.jsonl b/data/sampled_jsons/Gaussian_Processes_preference-based_reinforcement_learning_non-linear_reward_functions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8dd8980d6f34115923881c982282b9d8a33ef192
--- /dev/null
+++ b/data/sampled_jsons/Gaussian_Processes_preference-based_reinforcement_learning_non-linear_reward_functions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Active preference - based Gaussian process regression for...", "date": "", "ddg_snippet": "Reward Learning , Active Learning , Inverse Reinforcement Learning , Preference - based Learning , Human-Robot. Interaction, Gaussian Processes . Introduction.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/375481888_Active_preference-based_Gaussian_process_regression_for_reward_learning_and_optimization", "content": "Reward Learning , Active Learning , Inverse Reinforcement Learning , Preference - based Learning , Human-Robot. Interaction, Gaussian Processes . Introduction."}
+{"idx": 1, "title": "Active Preference - Based Gaussian Process", "date": "", "ddg_snippet": "Active Preference - Based Gaussian Process Regression for Reward Learning .", "subpage_snippet": "", "source": "roboticsproceedings.org", "link": "https://roboticsproceedings.org/rss16/p041.pdf", "content": "Active Preference - Based Gaussian Process Regression for Reward Learning ."}
+{"idx": 2, "title": "Preference - based Learning of Reward Function Features", "date": "", "ddg_snippet": "Preference - based learning is an iterative process between. querying the user for their preference and updating our.In this work, we developed a method for learning extra features for a linear reward function based on user responses to preference queries.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.02727", "content": "Preference - based learning is an iterative process between. querying the user for their preference and updating our.In this work, we developed a method for learning extra features for a linear reward function based on user responses to preference queries."}
+{"idx": 3, "title": "Active Preference - Based Gaussian", "date": "", "ddg_snippet": "Keywords Reward Learning , Active Learning , Inverse Reinforcement Learning , Preference - based Learning , Human-Robot Interaction, Gaussian Processes .", "subpage_snippet": "", "source": "liralab.usc.edu", "link": "https://liralab.usc.edu/pdfs/publications/biyik2024active.pdf", "content": "Keywords Reward Learning , Active Learning , Inverse Reinforcement Learning , Preference - based Learning , Human-Robot Interaction, Gaussian Processes ."}
+{"idx": 4, "title": "Active Preference - Based Gaussian Process", "date": "", "ddg_snippet": "Active Preference - Based Gaussian Process Regression for Reward Learning . Preference - based reward learning can then leverage a sequence of pairwise comparisons to accurately estimate a reward function .", "subpage_snippet": "", "source": "iliad.stanford.edu", "link": "https://iliad.stanford.edu/pdfs/publications/biyik2020active.pdf", "content": "Active Preference - Based Gaussian Process Regression for Reward Learning . Preference - based reward learning can then leverage a sequence of pairwise comparisons to accurately estimate a reward function ."}
+{"idx": 5, "title": "Preference - Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Preference - based reinforcement learning (PbRL) is a paradigm for learning from non -numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Preference - based reinforcement learning (PbRL) is a paradigm for learning from non -numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal."}
+{"idx": 6, "title": "Interpretable Preference - based Reinforcement Learning ... | DeepAI", "date": "", "ddg_snippet": "Interpretable Preference - based Reinforcement Learning with Tree-Structured Reward Functions .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/interpretable-preference-based-reinforcement-learning-with-tree-structured-reward-functions", "content": "Interpretable Preference - based Reinforcement Learning with Tree-Structured Reward Functions ."}
+{"idx": 7, "title": "Efficient Preference - based", "date": "", "ddg_snippet": "Efficient Preference - based Reinforcement Learning . Gaussian Processes for Reinforcement Learning . A basic idea for learning functions without knowing the correct function class in advance are kernel methods.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/132578339.pdf", "content": "Efficient Preference - based Reinforcement Learning . Gaussian Processes for Reinforcement Learning . A basic idea for learning functions without knowing the correct function class in advance are kernel methods."}
+{"idx": 8, "title": "Reinforcement Learning with Function Approximation: From Linear ...", "date": "", "ddg_snippet": "[1] Gaussian Based Non - linear Function Approximation for Reinforcement Learning . Reward -free reinforcement learning (RL) is a framework which is suitable for both the batch RL setting and the setting where there are many reward functions of interest.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/reinforcement-learning-with-function-approximation-from-linear-to-nonlinear/867766743022436814-108614", "content": "[1] Gaussian Based Non - linear Function Approximation for Reinforcement Learning . Reward -free reinforcement learning (RL) is a framework which is suitable for both the batch RL setting and the setting where there are many reward functions of interest."}
+{"idx": 9, "title": "Asymmetric and adaptive reward coding via normalized reinforcement ...", "date": "", "ddg_snippet": "Standard reinforcement learning models use linear value functions , despite strong empirical evidence that biological value representations are nonlinear functions of external rewards . Here, we examine the properties of a biologically- based nonlinear reinforcement learning ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9345478/", "content": "Standard reinforcement learning models use linear value functions , despite strong empirical evidence that biological value representations are nonlinear functions of external rewards . Here, we examine the properties of a biologically- based nonlinear reinforcement learning ..."}
diff --git a/data/sampled_jsons/GenAI_Arena_Elo_rating_system_equation_formula_year_2024.jsonl b/data/sampled_jsons/GenAI_Arena_Elo_rating_system_equation_formula_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c28f05f0a43b003d9e6c404caecec42514d5163b
--- /dev/null
+++ b/data/sampled_jsons/GenAI_Arena_Elo_rating_system_equation_formula_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "For example, our plotted winning fraction heatmaps reveal that while the Elo rating system is generally effective, it can be biased by imbalances ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v4", "content": "For example, our plotted winning fraction heatmaps reveal that while the Elo rating system is generally effective, it can be biased by imbalances ..."}
+{"idx": 1, "title": "GenAI-Arena arXiv:2406.04485v1 [cs.AI] 6 Jun 2024", "date": "", "ddg_snippet": "GenAI - Arena , the first open platform to rank multi-modal generative AI based on user preferences. Discussion and case studies of collected user votes, showing the reliability of GenAI - Arena . GenAI-Bench, a public benchmark for judging MLLM’s evaluation ability for generative tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04485v1", "content": "GenAI - Arena , the first open platform to rank multi-modal generative AI based on user preferences. Discussion and case studies of collected user votes, showing the reliability of GenAI - Arena . GenAI-Bench, a public benchmark for judging MLLM’s evaluation ability for generative tasks."}
+{"idx": 2, "title": "arena_elo/elo_rating/elo_analysis.py · TIGER-Lab/GenAI-Arena ...", "date": "", "ddg_snippet": "from .basic_stats import get_log_files from .clean_battle_data import clean_battle_data pd.options.display.float_format = \" {:.2f}\". format def compute_ elo ( battles, K=4, SCALE=400, BASE=10, INIT_ RATING =1000 ): rating = defaultdict ( lambda: INIT_RATING) for rd, model_a, model_b, winner in battles [ [ \"model_a\", \"model_b\", \"winner\"]", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena/blob/main/arena_elo/elo_rating/elo_analysis.py", "content": "from .basic_stats import get_log_files from .clean_battle_data import clean_battle_data pd.options.display.float_format = \" {:.2f}\". format def compute_ elo ( battles, K=4, SCALE=400, BASE=10, INIT_ RATING =1000 ): rating = defaultdict ( lambda: INIT_RATING) for rd, model_a, model_b, winner in battles [ [ \"model_a\", \"model_b\", \"winner\"]"}
+{"idx": 3, "title": "GitHub - TIGER-AI-Lab/GenAI-Bench: Code and Data for \"GenAI ...", "date": "", "ddg_snippet": "Aug 9, 2024 · GenAI -Bench is a benchmark designed to benchmark MLLMs’s ability in judging the quality of AI generative contents by comparing with human preferences collected through our 🤗 GenAI - Arnea .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TIGER-AI-Lab/GenAI-Bench", "content": "Aug 9, 2024 · GenAI -Bench is a benchmark designed to benchmark MLLMs’s ability in judging the quality of AI generative contents by comparing with human preferences collected through our 🤗 GenAI - Arnea ."}
+{"idx": 4, "title": "How to Read Elo Ratings and Arena Scores for LLMs - Statology", "date": "", "ddg_snippet": "Jul 23, 2025 · Elo ratings and Arena scores provide a dynamic, tournament-style way to rank large language models (LLMs) based on head-to-head comparisons, similar to how chess players are ranked through competitive matches. Unlike static benchmarks that test specific skills, these systems measure which models users actually prefer in conversation through millions of pairwise votes.", "subpage_snippet": "", "source": "www.statology.org", "link": "https://www.statology.org/how-to-read-elo-ratings-and-arena-scores-for-llms/", "content": "Jul 23, 2025 · Elo ratings and Arena scores provide a dynamic, tournament-style way to rank large language models (LLMs) based on head-to-head comparisons, similar to how chess players are ranked through competitive matches. Unlike static benchmarks that test specific skills, these systems measure which models users actually prefer in conversation through millions of pairwise votes."}
+{"idx": 5, "title": "[2406.04485] GenAI Arena: An Open Evaluation Platform for ...", "date": "", "ddg_snippet": "Jun 6, 2024 · GenAI - Arena has been operating for seven months, amassing over 9000 votes from the community. We describe our platform, analyze the data, and explain the statistical methods for ranking the models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04485", "content": "Jun 6, 2024 · GenAI - Arena has been operating for seven months, amassing over 9000 votes from the community. We describe our platform, analyze the data, and explain the statistical methods for ranking the models."}
+{"idx": 6, "title": "arena_elo/README.md · TIGER-Lab/GenAI-Arena at main", "date": "", "ddg_snippet": "Computing the Elo Ratings apt-get -y install pkg-config pip install -r requirements.txt", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena/blob/main/arena_elo/README.md", "content": "Computing the Elo Ratings apt-get -y install pkg-config pip install -r requirements.txt"}
+{"idx": 7, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "Specifically, we calculate the accuracy between different image/video auto-raters (i.e. MLLM judges like GPT-4o, Gemini, etc.) with user preference to understand their judging abilities.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/92249f9233286e437f808fa535d88b26-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "Specifically, we calculate the accuracy between different image/video auto-raters (i.e. MLLM judges like GPT-4o, Gemini, etc.) with user preference to understand their judging abilities."}
+{"idx": 8, "title": "Academy - ETS Asset Management Factory", "date": "", "ddg_snippet": "Fed ’ s interest rate actions, which have been a topic of much discussion recently, can be very valuable information when making investment ...", "subpage_snippet": "", "source": "www.etsfactory.com", "link": "https://www.etsfactory.com/academy", "content": "Fed ’ s interest rate actions, which have been a topic of much discussion recently, can be very valuable information when making investment ..."}
+{"idx": 9, "title": "DataPro | 33 articles | Packt Newsletter Hub", "date": "", "ddg_snippet": "... Rated 4.9/10 by global learners – this will truly make you an AI Generalist that can build, solve & work on anything with AI.In just 16 hours ...", "subpage_snippet": "", "source": "www.packtpub.com", "link": "https://www.packtpub.com/en-dk/newsletters/datapro", "content": "... Rated 4.9/10 by global learners – this will truly make you an AI Generalist that can build, solve & work on anything with AI.In just 16 hours ..."}
diff --git a/data/sampled_jsons/GenAI_Arena_Elo_rating_system_formula_Equation_1.jsonl b/data/sampled_jsons/GenAI_Arena_Elo_rating_system_formula_Equation_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3be2d68ef33b4b4c579abf6e42bb2ec16aefcac6
--- /dev/null
+++ b/data/sampled_jsons/GenAI_Arena_Elo_rating_system_formula_Equation_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "Figure 1 : GenAI Arena contains threecomponents: ( 1 ) text-to-image, text-to-video and image editing arena , which accept community voting to obtain the preference pairs. (2) The leaderboard utilizes the preference pairs to calculate elo ranking for all the evaluated models. (3) We further release GenAI -Bench to judge different multimodal LLM judges.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/92249f9233286e437f808fa535d88b26-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "Figure 1 : GenAI Arena contains threecomponents: ( 1 ) text-to-image, text-to-video and image editing arena , which accept community voting to obtain the preference pairs. (2) The leaderboard utilizes the preference pairs to calculate elo ranking for all the evaluated models. (3) We further release GenAI -Bench to judge different multimodal LLM judges."}
+{"idx": 1, "title": "GenAI-Arena arXiv:2406.04485v1 [cs.AI] 6 Jun 2024", "date": "", "ddg_snippet": "-Turbo with other models (around 10) in Figure 4. These anomalies highlight potential drawbacks of the Elo rating system : ( 1 ) a reliable and robust Elo rating requires a large amount of voting data, and (2) the estimated Elo rating may be biased by the imbalance between \"easy games\" and \"harder games", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04485v1", "content": "-Turbo with other models (around 10) in Figure 4. These anomalies highlight potential drawbacks of the Elo rating system : ( 1 ) a reliable and robust Elo rating requires a large amount of voting data, and (2) the estimated Elo rating may be biased by the imbalance between \"easy games\" and \"harder games"}
+{"idx": 2, "title": "GitHub - TIGER-AI-Lab/GenAI-Bench: Code and Data for \"GenAI Arena: An ...", "date": "", "ddg_snippet": "GenAI -Bench is a benchmark designed to benchmark MLLMs's ability in judging the quality of AI generative contents by comparing with human preferences collected through our 🤗 GenAI -Arnea. In other words, we are evaluting the capabilities of existing MLLMs as a multimodal reward model, and in ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TIGER-AI-Lab/GenAI-Bench", "content": "GenAI -Bench is a benchmark designed to benchmark MLLMs's ability in judging the quality of AI generative contents by comparing with human preferences collected through our 🤗 GenAI -Arnea. In other words, we are evaluting the capabilities of existing MLLMs as a multimodal reward model, and in ..."}
+{"idx": 3, "title": "arena_elo/elo_rating/elo_analysis.py · TIGER-Lab/GenAI-Arena at main", "date": "", "ddg_snippet": "/ GenAI-Arena like 199 Running on Zero 14 main GenAI-Arena / arena_elo / elo_rating / elo_analysis.py DongfuJiang update No virus 14.3 kB import argparse from collections import defaultdict import datetime", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena/blob/main/arena_elo/elo_rating/elo_analysis.py", "content": "/ GenAI-Arena like 199 Running on Zero 14 main GenAI-Arena / arena_elo / elo_rating / elo_analysis.py DongfuJiang update No virus 14.3 kB import argparse from collections import defaultdict import datetime"}
+{"idx": 4, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04485", "content": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance."}
+{"idx": 5, "title": "arena_elo/README.md · TIGER-Lab/GenAI-Arena at main", "date": "", "ddg_snippet": "Computing the Elo Ratings apt-get -y install pkg-config pip install -r requirements.txt", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena/blob/main/arena_elo/README.md", "content": "Computing the Elo Ratings apt-get -y install pkg-config pip install -r requirements.txt"}
+{"idx": 6, "title": "How to Write an Elo Calculator in a Spreadsheet Without Knowing How to Code", "date": "", "ddg_snippet": "The Elo rating system is named after Arpad Elo , a physics professor and chess player. It's a point-earning system for two-player games that awards some number of points to the winner and detracts the same number of points from the loser.", "subpage_snippet": "", "source": "obscuredinosaurfacts.com", "link": "https://obscuredinosaurfacts.com/blog/post/2024/08/06/elo.html", "content": "The Elo rating system is named after Arpad Elo , a physics professor and chess player. It's a point-earning system for two-player games that awards some number of points to the winner and detracts the same number of points from the loser."}
+{"idx": 7, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "Figure 1 : GenAI Arena contains three components: ( 1 ) text-to-image, text-to-video and image editing arena , which accept community voting to obtain the preference pairs. (2) The leaderboard utilizes the preference pairs to calculate elo ranking for all the evaluated models. (3) We further release GenAI -Bench to judge different multimodal LLM judges. 1 Introduction", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v1", "content": "Figure 1 : GenAI Arena contains three components: ( 1 ) text-to-image, text-to-video and image editing arena , which accept community voting to obtain the preference pairs. (2) The leaderboard utilizes the preference pairs to calculate elo ranking for all the evaluated models. (3) We further release GenAI -Bench to judge different multimodal LLM judges. 1 Introduction"}
+{"idx": 8, "title": "Chatbot Arena (聊天机器人竞技场) (含英文原文)：使用 Elo 评级对LLM进行基准测试 -- 总篇 - 知乎", "date": "", "ddg_snippet": "Chatbot Arena adopts the Elo rating system , which is a widely-used rating system in chess and other competitive games. The Elo rating system is promising to provide the desired property mentioned above. We noticed that the Anthropic LLM paper also adopted the Elo rating system .", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/635176986", "content": "Chatbot Arena adopts the Elo rating system , which is a widely-used rating system in chess and other competitive games. The Elo rating system is promising to provide the desired property mentioned above. We noticed that the Anthropic LLM paper also adopted the Elo rating system ."}
+{"idx": 9, "title": "How to Read Elo Ratings and Arena Scores for LLMs - Statology", "date": "", "ddg_snippet": "Elo ratings and Arena scores provide a dynamic, tournament-style way to rank large language models (LLMs) based on head-to-head comparisons, similar to how chess players are ranked through competitive matches. Unlike static benchmarks that test specific skills, these systems measure which models users actually prefer in conversation through millions of pairwise votes.", "subpage_snippet": "", "source": "www.statology.org", "link": "https://www.statology.org/how-to-read-elo-ratings-and-arena-scores-for-llms/", "content": "Elo ratings and Arena scores provide a dynamic, tournament-style way to rank large language models (LLMs) based on head-to-head comparisons, similar to how chess players are ranked through competitive matches. Unlike static benchmarks that test specific skills, these systems measure which models users actually prefer in conversation through millions of pairwise votes."}
diff --git a/data/sampled_jsons/GenAI_Arena_paper_Section_4.1_Text-to-Image_results_analysis_training_dataset_year_2024.jsonl b/data/sampled_jsons/GenAI_Arena_paper_Section_4.1_Text-to-Image_results_analysis_training_dataset_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..12dac7c5231aa5af1148f20d1d713d8885367873
--- /dev/null
+++ b/data/sampled_jsons/GenAI_Arena_paper_Section_4.1_Text-to-Image_results_analysis_training_dataset_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenAI Arena : An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "This paper proposes an open platform GenAI - Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v4", "content": "This paper proposes an open platform GenAI - Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models."}
+{"idx": 1, "title": "Overview Leaderboard | LMArena", "date": "", "ddg_snippet": "Leaderboard Overview. See how leading models stack up across text , image , vision, and beyond. This page gives you a snapshot of each Arena , you can explore deeper insights in their dedicated tabs.", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/leaderboard", "content": "Leaderboard Overview. See how leading models stack up across text , image , vision, and beyond. This page gives you a snapshot of each Arena , you can explore deeper insights in their dedicated tabs."}
+{"idx": 2, "title": "GenAI - Arena : An Open Platform for... - MarkTechPost", "date": "", "ddg_snippet": "GenAI - Arena supports text - to - image generation, image editing, and text - to -video generation tasks with features like anonymous side-by-side voting, battle playground, direct generation tab, and leaderboards.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/06/12/genai-arena-an-open-platform-for-community-based-evaluation-of-generative-ai-models/", "content": "GenAI - Arena supports text - to - image generation, image editing, and text - to -video generation tasks with features like anonymous side-by-side voting, battle playground, direct generation tab, and leaderboards."}
+{"idx": 3, "title": "TIGER-AI-Lab/ GenAI -Bench: Code and Data for \" GenAI Arena : An...\"", "date": "", "ddg_snippet": "Then results will be printed and saveed to genaibench_ results .txt. Contributing a new model. If you want to evaluate your model on GenAI -Bench, you can follow the steps below: Fork this repository. Follow ./genaibench/mllm_tools/README.md to add your model to the evaluation pipeline.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TIGER-AI-Lab/GenAI-Bench", "content": "Then results will be printed and saveed to genaibench_ results .txt. Contributing a new model. If you want to evaluate your model on GenAI -Bench, you can follow the steps below: Fork this repository. Follow ./genaibench/mllm_tools/README.md to add your model to the evaluation pipeline."}
+{"idx": 4, "title": "Launching the Artificial Analysis Text to Image Leaderboard & Arena", "date": "", "ddg_snippet": "Our Image Arena represents a crowdsourcing approach to gathering human preference data at scale, enabling comparison between key models for the first time.", "subpage_snippet": "", "source": "huggingface.1319lm.top", "link": "https://huggingface.1319lm.top/blog/leaderboard-artificial-analysis2", "content": "Our Image Arena represents a crowdsourcing approach to gathering human preference data at scale, enabling comparison between key models for the first time."}
+{"idx": 5, "title": "AI Image Generator (free, no sign-up, unlimited)", "date": "", "ddg_snippet": "It's an AI-based image generator - i .e. a text - to - image model. No watermark, no account needed, unlimited images. Type words, make pics.", "subpage_snippet": "", "source": "perchance.org", "link": "https://perchance.org/ai-text-to-image-generator", "content": "It's an AI-based image generator - i .e. a text - to - image model. No watermark, no account needed, unlimited images. Type words, make pics."}
+{"idx": 6, "title": "GenAI Arena User Voting Interface. | Download Scientific Diagram", "date": "", "ddg_snippet": "GenAI Arena : An Open Evaluation Platform for Generative Models.Contexts in source publication. Context 1. ... platform is structured around three primary tasks: text - to - image generation, image edition, and text - to -video generation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/GenAI-Arena-User-Voting-Interface_fig1_381294137", "content": "GenAI Arena : An Open Evaluation Platform for Generative Models.Contexts in source publication. Context 1. ... platform is structured around three primary tasks: text - to - image generation, image edition, and text - to -video generation."}
+{"idx": 7, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 8, "title": "googleapis.github.io/python- genai / genai .html", "date": "", "ddg_snippet": "genai .tunings module.", "subpage_snippet": "", "source": "googleapis.github.io", "link": "https://googleapis.github.io/python-genai/genai.html", "content": "genai .tunings module."}
+{"idx": 9, "title": "Text to Image – The GenAI Cookbook: GenAI Recipes for eLearning...", "date": "", "ddg_snippet": "Text - to - Image Prompts are natural language descriptions used as input for AI art generators. These generators create images based on a dataset of text-image pairs. Previous chapters have focused on text-based output.", "subpage_snippet": "", "source": "iu.pressbooks.pub", "link": "https://iu.pressbooks.pub/thecopilotcookbook/chapter/text-to-image-creation/", "content": "Text - to - Image Prompts are natural language descriptions used as input for AI art generators. These generators create images based on a dataset of text-image pairs. Previous chapters have focused on text-based output."}
diff --git a/data/sampled_jsons/Generalization_Spectrum_Gap_Section_5.2_sitearxiv.orgpdf2502.10875.jsonl b/data/sampled_jsons/Generalization_Spectrum_Gap_Section_5.2_sitearxiv.orgpdf2502.10875.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..92112700e65a70f1b3a503e09912d87547d55b09
--- /dev/null
+++ b/data/sampled_jsons/Generalization_Spectrum_Gap_Section_5.2_sitearxiv.orgpdf2502.10875.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "A Geometric Approach to Personalized Recommendation ...", "date": "", "ddg_snippet": "by S Dasgupta · 2025 — The BOX-GEOMETRIC achieves the best Generalization . Spectrum Gap for all types of queries. C. Error Compounding Analysis. We further perform ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10875", "content": "by S Dasgupta · 2025 — The BOX-GEOMETRIC achieves the best Generalization . Spectrum Gap for all types of queries. C. Error Compounding Analysis. We further perform ..."}
diff --git a/data/sampled_jsons/Generalized_Sinkhorn_Algorithm_update_rule_equation_6_KL_divergence_f(x)_=_x_log_x_year_2024.jsonl b/data/sampled_jsons/Generalized_Sinkhorn_Algorithm_update_rule_equation_6_KL_divergence_f(x)_=_x_log_x_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f657bc2a4a2f10e9587b4e170d39a4eec6295210
--- /dev/null
+++ b/data/sampled_jsons/Generalized_Sinkhorn_Algorithm_update_rule_equation_6_KL_divergence_f(x)_=_x_log_x_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - renyi-ai/optimal-transport-with-f-divergence ...", "date": "", "ddg_snippet": "This is the official codebase for the paper \"Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm \" by Dávid Terjék and Diego González-Sánchez accepted for publication at the 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022. Before running the experiments, prepare a python 3 environment with the following packages ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/renyi-ai/optimal-transport-with-f-divergence-regularization-and-generalized-sinkhorn-algorithm", "content": "This is the official codebase for the paper \"Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm \" by Dávid Terjék and Diego González-Sánchez accepted for publication at the 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022. Before running the experiments, prepare a python 3 environment with the following packages ..."}
+{"idx": 1, "title": "Optimal transport with f-divergence regularization and ... A Stochastic Algorithm for Sinkhorn Distance-Regularized ... [2105.14337] Optimal transport with $f$-divergence ... A Stochastic Algorithm for Sinkhorn Distance-Regularized Distributio… Optimal transport with f - divergence regularization and A Stochastic Algorithm for Sinkhorn Distance-Regularized Distributio… Optimal transport with f - divergence regularization and A Stochastic Algorithm for Sinkhorn Distance-Regularized Distributio… A Stochastic Algorithm for Sinkhorn Distance-Regularized Distributio… Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "Entropic regularization provides a generaliza-tion of the original optimal transport prob-lem. It introduces a penalty term defined by the Kullback-Leibler divergence, making the problem more tractable via the celebrated Sinkhorn algorithm. Replacing the Kullback-Leibler divergence with a general f-divergence leads to a natural generalization. The ... See full list on proceedings.mlr.press 2.1 Notation We denote the extended reals by R = R ∪ {±∞}, the nonnegative reals by R+, and the extended nonnegative reals by R+ = R+ ∪ ∞. The indicator of a set A is denoted by ιA with ιA( x ) = 0 if x ∈ A and ιA( x ) = ∞ otherwise. We denote by int A the interior of a set A inside a topological space. Absolute continuity and singularity of measures w... See full list on proceedings.mlr.press The classical setup using the Kullback-Leiber diver-gence is the fastest to compute and gives low costs in general terms. However, the χ2 divergence , albeit being marginally slower, can obtain a similar cost but with a much more sparse coupling for values of cor-responding to shorter running times. As we discussed above, the optimal coupling can be... See full list on proceedings.mlr.press From the theoretical side, the main limitation of our pa-per is the assumption that the cost function is Lipschitz. We explained in the corresponding section the reasons why we decided to work in this setup. A more general theory may be able to include lower semicontinuous costs, but we did not pursue this in the present work. The Legendre type ass... See full list on proceedings.mlr.press Given a topological vector space X , denote its topological dual by X ∗, i.e. the set of real-valued continuous linear maps on X , which is a topological vector space itself, and the canonical pairing by h·, ·i : X × X ∗ → R, which is the continuous bilinear map ( x , x ∗) → hx, x ∗i = x ∗( x ). Given a function f : X → R, the set dom f = { x ∈ X : f ( x ) 0 and a cost metric c : Ω × Ω → R, the generalized Sinkhorn distance is defined as Does Sinkhorn converge to an optimal op-Timal transport solution? tion between the classical theory of op-timal transport and the regularized versions.We show that a generalized version of the Sinkhorn algorithm (also denoted IPFP sequences (Di Marino and Gerolin, 2020b)) converge to an optimal solution even Does Sinkhorn Dro have a nonconvex f-divergence? Motivated by these limitations, in this study, we consider the regularized Sinkhorn DRO prob-lem (see (1)) with nonconvex and possibly unbounded loss. Our contributions are summarized as follows. We introduce a generalized Sinkhorn distance based on the class of f - divergence measures. What are the applications of Sinkhorn distance? Sinkhorn distance has successful applications in areas like generative mod-els [16, 27], matrix factorization , image segmentation etc. In , Sinkhorn matrix scal-ing algorithms was proposed to compute optimal transport map under Sinkhorn distance objective. May 1, 2025 · The authors leverage the process of Lyu and Mukherjee'24 to identify the linear convergence of the generalized Sinkhorn algorithm for a general class of metrics and solve the problem suffered by the KL divergence .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v151/terjek22a/terjek22a.pdf", "content": "Entropic regularization provides a generaliza-tion of the original optimal transport prob-lem. It introduces a penalty term defined by the Kullback-Leibler divergence, making the problem more tractable via the celebrated Sinkhorn algorithm. Replacing the Kullback-Leibler divergence with a general f-divergence leads to a natural generalization. The ... See full list on proceedings.mlr.press 2.1 Notation We denote the extended reals by R = R ∪ {±∞}, the nonnegative reals by R+, and the extended nonnegative reals by R+ = R+ ∪ ∞. The indicator of a set A is denoted by ιA with ιA( x ) = 0 if x ∈ A and ιA( x ) = ∞ otherwise. We denote by int A the interior of a set A inside a topological space. Absolute continuity and singularity of measures w... See full list on proceedings.mlr.press The classical setup using the Kullback-Leiber diver-gence is the fastest to compute and gives low costs in general terms. However, the χ2 divergence , albeit being marginally slower, can obtain a similar cost but with a much more sparse coupling for values of cor-responding to shorter running times. As we discussed above, the optimal coupling can be... See full list on proceedings.mlr.press From the theoretical side, the main limitation of our pa-per is the assumption that the cost function is Lipschitz. We explained in the corresponding section the reasons why we decided to work in this setup. A more general theory may be able to include lower semicontinuous costs, but we did not pursue this in the present work. The Legendre type ass... See full list on proceedings.mlr.press Given a topological vector space X , denote its topological dual by X ∗, i.e. the set of real-valued continuous linear maps on X , which is a topological vector space itself, and the canonical pairing by h·, ·i : X × X ∗ → R, which is the continuous bilinear map ( x , x ∗) → hx, x ∗i = x ∗( x ). Given a function f : X → R, the set dom f = { x ∈ X : f ( x ) 0 and a cost metric c : Ω × Ω → R, the generalized Sinkhorn distance is defined as Does Sinkhorn converge to an optimal op-Timal transport solution? tion between the classical theory of op-timal transport and the regularized versions.We show that a generalized version of the Sinkhorn algorithm (also denoted IPFP sequences (Di Marino and Gerolin, 2020b)) converge to an optimal solution even Does Sinkhorn Dro have a nonconvex f-divergence? Motivated by these limitations, in this study, we consider the regularized Sinkhorn DRO prob-lem (see (1)) with nonconvex and possibly unbounded loss. Our contributions are summarized as follows. We introduce a generalized Sinkhorn distance based on the class of f - divergence measures. What are the applications of Sinkhorn distance? Sinkhorn distance has successful applications in areas like generative mod-els [16, 27], matrix factorization , image segmentation etc. In , Sinkhorn matrix scal-ing algorithms was proposed to compute optimal transport map under Sinkhorn distance objective. May 1, 2025 · The authors leverage the process of Lyu and Mukherjee'24 to identify the linear convergence of the generalized Sinkhorn algorithm for a general class of metrics and solve the problem suffered by the KL divergence ."}
+{"idx": 2, "title": "-divergence regularization and generalized Sinkhorn algorithm", "date": "", "ddg_snippet": "a practical algorithm for computing an ap-proximate solution of the optimal transport problem with f - divergence regularization via the generalized Sinkhorn algorithm . Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different f -divergences for regular-ization, which influences convergence speed, numerical stability and sparsity of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2105.14337.pdf", "content": "a practical algorithm for computing an ap-proximate solution of the optimal transport problem with f - divergence regularization via the generalized Sinkhorn algorithm . Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different f -divergences for regular-ization, which influences convergence speed, numerical stability and sparsity of the ..."}
+{"idx": 3, "title": "Nested Stochastic Algorithm for Generalized Sinkhorn distance ...", "date": "", "ddg_snippet": "Furthermore, the log -exponential compositional structure induced by the conjugate dual of the KL - divergence makes the objective function dificult to optimize and hinders convergence. In this work, we develop and study dual formulations of generalized Sinkhorn -distance regu-larized DRO problems (see formulation (1)).", "subpage_snippet": "", "source": "zhaosong-lu.github.io", "link": "https://zhaosong-lu.github.io/ResearchPapers/SinkhornDRO.pdf", "content": "Furthermore, the log -exponential compositional structure induced by the conjugate dual of the KL - divergence makes the objective function dificult to optimize and hinders convergence. In this work, we develop and study dual formulations of generalized Sinkhorn -distance regu-larized DRO problems (see formulation (1))."}
+{"idx": 4, "title": "A Stochastic Algorithm for Sinkhorn Distance-Regularized ...", "date": "", "ddg_snippet": "Moreover, our generalized Sinkhorn distance is based on the f - divergence , which generalizes the KL - divergence adopted in the definition of the standard Sinkhorn distance [37].", "subpage_snippet": "", "source": "opt-ml.org", "link": "https://opt-ml.org/papers/2024/paper22.pdf", "content": "Moreover, our generalized Sinkhorn distance is based on the f - divergence , which generalizes the KL - divergence adopted in the definition of the standard Sinkhorn distance [37]."}
+{"idx": 5, "title": "[2105.14337] Optimal transport with $f$-divergence ...", "date": "", "ddg_snippet": "May 29, 2021 · Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replacing the Kullback-Leibler divergence with a general f - divergence leads to a natural generalization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2105.14337", "content": "May 29, 2021 · Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replacing the Kullback-Leibler divergence with a general f - divergence leads to a natural generalization."}
+{"idx": 6, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "May 1, 2025 · The authors leverage the process of Lyu and Mukherjee'24 to identify the linear convergence of the generalized Sinkhorn algorithm for a general class of metrics and solve the problem suffered by the KL divergence .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0hrkN07DuO", "content": "May 1, 2025 · The authors leverage the process of Lyu and Mukherjee'24 to identify the linear convergence of the generalized Sinkhorn algorithm for a general class of metrics and solve the problem suffered by the KL divergence ."}
+{"idx": 7, "title": "Sinkhorn Divergences", "date": "", "ddg_snippet": "when ϕ( x ) = x log x − x + 1. With KL one has ϕ∞ = +∞, which has a nite value if and only if the supports satisfy spt(α) ⊂ spt(β). For discrete distributions dened on the same grid. of N points, these divergences are computed in O(N ) operations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1910.12958", "content": "when ϕ( x ) = x log x − x + 1. With KL one has ϕ∞ = +∞, which has a nite value if and only if the supports satisfy spt(α) ⊂ spt(β). For discrete distributions dened on the same grid. of N points, these divergences are computed in O(N ) operations."}
+{"idx": 8, "title": "Wasserstein distance via entropy regularization ( Sinkhorn algorithm )", "date": "", "ddg_snippet": "The Sinkhorn algorithm iterates this update rule until convergence, resulting in a transport plan that minimizes the regularized problem.", "subpage_snippet": "", "source": "www.fabriziomusacchio.com", "link": "https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance_sinkhorn/", "content": "The Sinkhorn algorithm iterates this update rule until convergence, resulting in a transport plan that minimizes the regularized problem."}
+{"idx": 9, "title": "Optimal transport with f - divergence regularization and generalized ...", "date": "", "ddg_snippet": "Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/optimal-transport-with-f-divergence-regularization-and-generalized-sinkhorn-algorithm", "content": "Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm ."}
diff --git a/data/sampled_jsons/Generalized_Sinkhorn_Algorithm_update_rule_u_v_f_divergence_f(x)=xlogx_KL.jsonl b/data/sampled_jsons/Generalized_Sinkhorn_Algorithm_update_rule_u_v_f_divergence_f(x)=xlogx_KL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f89e6c413679148b6356a3b41bfdc191af9d000
--- /dev/null
+++ b/data/sampled_jsons/Generalized_Sinkhorn_Algorithm_update_rule_u_v_f_divergence_f(x)=xlogx_KL.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2105.14337] Optimal transport with $ f $- divergence regularization and...", "date": "", "ddg_snippet": "Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2105.14337", "content": "Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm ."}
+{"idx": 1, "title": "Nested Stochastic Algorithm for Generalized Sinkhorn distance ...", "date": "", "ddg_snippet": "The considered generalized Sinkhorn distance is regularized by the f - divergence , which general-izes the KL - divergence regularization adopted in the definition of the standard Sinkhorn distance (Wang et al., 2023).", "subpage_snippet": "", "source": "zhaosong-lu.github.io", "link": "https://zhaosong-lu.github.io/ResearchPapers/SinkhornDRO.pdf", "content": "The considered generalized Sinkhorn distance is regularized by the f - divergence , which general-izes the KL - divergence regularization adopted in the definition of the standard Sinkhorn distance (Wang et al., 2023)."}
+{"idx": 2, "title": "Optimal transport with f-divergence regularization and ... GitHub - renyi-ai/optimal-transport-with-f-divergence ... Nested Stochastic Algorithm for Generalized Sinkhorn distance ... Optimal transport with f -divergence regularization and ... Linear convergence of Sinkhorn's algorithm for generalized ... Optimal transport with f - divergence regularization and Optimal transport with f - divergence regularization and Optimal transport with f - divergence regularization and Optimal transport with f - divergence regularization and Optimal transport with f - divergence regularization and Optimal transport with f - divergence regularization and Sinkhorn Knopp algorithm - Bregman projection in update rule", "date": "", "ddg_snippet": "Entropic regularization provides a generaliza-tion of the original optimal transport prob-lem. It introduces a penalty term defined by the Kullback-Leibler divergence, making the problem more tractable via the celebrated Sinkhorn algorithm. Replacing the Kullback-Leibler divergence with a general f-divergence leads to a natural generalization. The ... See full list on proceedings.mlr.press 2.1 Notation We denote the extended reals by R = R ∪ {±∞}, the nonnegative reals by R+, and the extended nonnegative reals by R+ = R+ ∪ ∞. The indicator of a set A is denoted by ιA with ιA( x ) = 0 if x ∈ A and ιA( x ) = ∞ otherwise. We denote by int A the interior of a set A inside a topological space. Absolute continuity and singularity of measures w... See full list on proceedings.mlr.press The classical setup using the Kullback-Leiber diver-gence is the fastest to compute and gives low costs in general terms. However, the χ2 divergence , albeit being marginally slower, can obtain a similar cost but with a much more sparse coupling for values of cor-responding to shorter running times. As we discussed above, the optimal coupling can be... See full list on proceedings.mlr.press From the theoretical side, the main limitation of our pa-per is the assumption that the cost function is Lipschitz. We explained in the corresponding section the reasons why we decided to work in this setup. A more general theory may be able to include lower semicontinuous costs, but we did not pursue this in the present work. The Legendre type ass... See full list on proceedings.mlr.press Given a topological vector space X , denote its topological dual by X ∗, i.e. the set of real-valued continuous linear maps on X , which is a topological vector space itself, and the canonical pairing by h·, ·i : X × X ∗ → R, which is the continuous bilinear map ( x , x ∗) → hx, x ∗i = x ∗( x ). Given a function f : X → R, the set dom f = { x ∈ X : f ( x ) < ∞} i... See full list on proceedings.mlr.press for every y ∈ Y . Thus we can always replace g by f (c, ,φ). A similar argument shows that we can always replace See full list on proceedings.mlr.press This is the official codebase for the paper \"Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm \" by Dávid Terjék and Diego González-Sánchez accepted for publication at the 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022. Before running the experiments, prepare a python 3 environment with the following packages ... The considered generalized Sinkhorn distance is regularized by the f - divergence , which general-izes the KL - divergence regularization adopted in the definition of the standard Sinkhorn distance (Wang et al., 2023). Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm D ́avid Terj ́ek & Diego Gonz ́alez-S ́anchez May 1, 2025 · The authors leverage the process of Lyu and Mukherjee'24 to identify the linear convergence of the generalized Sinkhorn algorithm for a general class of metrics and solve the problem suffered by the KL divergence . Does Sinkhorn converge to an optimal op-Timal transport solution? tion between the classical theory of op-timal transport and the regularized versions.We show that a generalized version of the Sinkhorn algorithm (also denoted IPFP sequences (Di Marino and Gerolin, 2020b)) converge to an optimal solution even What is Sinkhorn algorithm? ng for the Primal Problem. This process is called he generalized Sinkhorn algorithm . A single Sinkhorn iteration is defined as follows. Note that this definition yields a generalization of IPFP sequences (Di Marino and Gerolin, 2020b, Section 4) but with a stabilizing factor that wi Can f-divergence regularization solve a port problem? port problem with f - divergence regularization via the generalized Sinkhorn algorithm . Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different f -divergences for regular-ization, which i fluences convergence speed, numerical stability and spars Can Sinkhorn divergences entropic smooth a Wasserstein gradient flow? dis-crete entropic smoothing of the Wasserstein gradient flow (Carlier et al., 2017). We can also find a theoret-ical proof together with practical experiments of the usefulness of Sinkhorn divergences, which remove the bia introduced to the optimal coupling by the regu-larization term (Feydy et al., Why is divergence more tractable than Kull ACK-Leibler f-divergence? divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replacing the Kull ack-Leibler divergence with a general f - divergence leads to a natural generalization. The case of diverg Is the Sinkhorn operator continuous with the k k-norm? [[RUBATO]]̃g. [&The Sinkhorn operator is continuous&] with the k · k∞-norm by Proposition 22. By Proposition 24 and the definition of the pair (fn\\, gn) we know that 1(fn ⊕ gn − c) has its image in (−∞\\, φ0(∞) − τ\\] and therefore (Borwein and Lewis\\, 199 \\, Theorem 2.7) the operator D (·\\, ·) is continuous in the set where (fn\\, gn) lives.Thus\\, we have that D (fnk\\, Oct 5, 2024 · I don't understand the updating rule for $ u ^{l+1}$ in the Sinkhorn algorithm . The below images contain all necessary definitions of the projection operators $A_1$ and ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v151/terjek22a/terjek22a.pdf", "content": "Entropic regularization provides a generaliza-tion of the original optimal transport prob-lem. It introduces a penalty term defined by the Kullback-Leibler divergence, making the problem more tractable via the celebrated Sinkhorn algorithm. Replacing the Kullback-Leibler divergence with a general f-divergence leads to a natural generalization. The ... See full list on proceedings.mlr.press 2.1 Notation We denote the extended reals by R = R ∪ {±∞}, the nonnegative reals by R+, and the extended nonnegative reals by R+ = R+ ∪ ∞. The indicator of a set A is denoted by ιA with ιA( x ) = 0 if x ∈ A and ιA( x ) = ∞ otherwise. We denote by int A the interior of a set A inside a topological space. Absolute continuity and singularity of measures w... See full list on proceedings.mlr.press The classical setup using the Kullback-Leiber diver-gence is the fastest to compute and gives low costs in general terms. However, the χ2 divergence , albeit being marginally slower, can obtain a similar cost but with a much more sparse coupling for values of cor-responding to shorter running times. As we discussed above, the optimal coupling can be... See full list on proceedings.mlr.press From the theoretical side, the main limitation of our pa-per is the assumption that the cost function is Lipschitz. We explained in the corresponding section the reasons why we decided to work in this setup. A more general theory may be able to include lower semicontinuous costs, but we did not pursue this in the present work. The Legendre type ass... See full list on proceedings.mlr.press Given a topological vector space X , denote its topological dual by X ∗, i.e. the set of real-valued continuous linear maps on X , which is a topological vector space itself, and the canonical pairing by h·, ·i : X × X ∗ → R, which is the continuous bilinear map ( x , x ∗) → hx, x ∗i = x ∗( x ). Given a function f : X → R, the set dom f = { x ∈ X : f ( x ) < ∞} i... See full list on proceedings.mlr.press for every y ∈ Y . Thus we can always replace g by f (c, ,φ). A similar argument shows that we can always replace See full list on proceedings.mlr.press This is the official codebase for the paper \"Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm \" by Dávid Terjék and Diego González-Sánchez accepted for publication at the 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022. Before running the experiments, prepare a python 3 environment with the following packages ... The considered generalized Sinkhorn distance is regularized by the f - divergence , which general-izes the KL - divergence regularization adopted in the definition of the standard Sinkhorn distance (Wang et al., 2023). Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm D ́avid Terj ́ek & Diego Gonz ́alez-S ́anchez May 1, 2025 · The authors leverage the process of Lyu and Mukherjee'24 to identify the linear convergence of the generalized Sinkhorn algorithm for a general class of metrics and solve the problem suffered by the KL divergence . Does Sinkhorn converge to an optimal op-Timal transport solution? tion between the classical theory of op-timal transport and the regularized versions.We show that a generalized version of the Sinkhorn algorithm (also denoted IPFP sequences (Di Marino and Gerolin, 2020b)) converge to an optimal solution even What is Sinkhorn algorithm? ng for the Primal Problem. This process is called he generalized Sinkhorn algorithm . A single Sinkhorn iteration is defined as follows. Note that this definition yields a generalization of IPFP sequences (Di Marino and Gerolin, 2020b, Section 4) but with a stabilizing factor that wi Can f-divergence regularization solve a port problem? port problem with f - divergence regularization via the generalized Sinkhorn algorithm . Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different f -divergences for regular-ization, which i fluences convergence speed, numerical stability and spars Can Sinkhorn divergences entropic smooth a Wasserstein gradient flow? dis-crete entropic smoothing of the Wasserstein gradient flow (Carlier et al., 2017). We can also find a theoret-ical proof together with practical experiments of the usefulness of Sinkhorn divergences, which remove the bia introduced to the optimal coupling by the regu-larization term (Feydy et al., Why is divergence more tractable than Kull ACK-Leibler f-divergence? divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replacing the Kull ack-Leibler divergence with a general f - divergence leads to a natural generalization. The case of diverg Is the Sinkhorn operator continuous with the k k-norm? [[RUBATO]]̃g. [&The Sinkhorn operator is continuous&] with the k · k∞-norm by Proposition 22. By Proposition 24 and the definition of the pair (fn\\, gn) we know that 1(fn ⊕ gn − c) has its image in (−∞\\, φ0(∞) − τ\\] and therefore (Borwein and Lewis\\, 199 \\, Theorem 2.7) the operator D (·\\, ·) is continuous in the set where (fn\\, gn) lives.Thus\\, we have that D (fnk\\, Oct 5, 2024 · I don't understand the updating rule for $ u ^{l+1}$ in the Sinkhorn algorithm . The below images contain all necessary definitions of the projection operators $A_1$ and ..."}
+{"idx": 3, "title": "GitHub - renyi-ai/optimal-transport-with-f-divergence ...", "date": "", "ddg_snippet": "This is the official codebase for the paper \"Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm \" by Dávid Terjék and Diego González-Sánchez accepted for publication at the 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022. Before running the experiments, prepare a python 3 environment with the following packages ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/renyi-ai/optimal-transport-with-f-divergence-regularization-and-generalized-sinkhorn-algorithm", "content": "This is the official codebase for the paper \"Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm \" by Dávid Terjék and Diego González-Sánchez accepted for publication at the 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022. Before running the experiments, prepare a python 3 environment with the following packages ..."}
+{"idx": 4, "title": "Optimal transport with f -divergence regularization and ...", "date": "", "ddg_snippet": "Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm D ́avid Terj ́ek & Diego Gonz ́alez-S ́anchez", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/media/aistats-2022/Slides/3362.pdf", "content": "Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm D ́avid Terj ́ek & Diego Gonz ́alez-S ́anchez"}
+{"idx": 5, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "May 1, 2025 · The authors leverage the process of Lyu and Mukherjee'24 to identify the linear convergence of the generalized Sinkhorn algorithm for a general class of metrics and solve the problem suffered by the KL divergence .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0hrkN07DuO", "content": "May 1, 2025 · The authors leverage the process of Lyu and Mukherjee'24 to identify the linear convergence of the generalized Sinkhorn algorithm for a general class of metrics and solve the problem suffered by the KL divergence ."}
+{"idx": 6, "title": "Sinkhorn Knopp algorithm - Bregman projection in update rule", "date": "", "ddg_snippet": "Oct 5, 2024 · I don't understand the updating rule for $ u ^{l+1}$ in the Sinkhorn algorithm . The below images contain all necessary definitions of the projection operators $A_1$ and ...", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4980480/sinkhorn-knopp-algorithm-bregman-projection-in-update-rule", "content": "Oct 5, 2024 · I don't understand the updating rule for $ u ^{l+1}$ in the Sinkhorn algorithm . The below images contain all necessary definitions of the projection operators $A_1$ and ..."}
+{"idx": 7, "title": "Optimal transport with f - divergence regularization and generalized ...", "date": "", "ddg_snippet": "Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/optimal-transport-with-f-divergence-regularization-and-generalized-sinkhorn-algorithm", "content": "Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm ."}
+{"idx": 8, "title": "Wasserstein distance via entropy regularization ( Sinkhorn algorithm )", "date": "", "ddg_snippet": "The Sinkhorn algorithm iterates this update rule until convergence, resulting in a transport plan that minimizes the regularized problem.", "subpage_snippet": "", "source": "www.fabriziomusacchio.com", "link": "https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance_sinkhorn/", "content": "The Sinkhorn algorithm iterates this update rule until convergence, resulting in a transport plan that minimizes the regularized problem."}
+{"idx": 9, "title": "A Short Introduction to Entropy, Cross-Entropy and KL - Divergence", "date": "", "ddg_snippet": "Entropy, Cross-Entropy and KL - Divergence are often used in Machine Learning, in particular for training classifiers. In this short video, you will understand...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=ErfnhcEV1O8", "content": "Entropy, Cross-Entropy and KL - Divergence are often used in Machine Learning, in particular for training classifiers. In this short video, you will understand..."}
diff --git a/data/sampled_jsons/Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embedding.jsonl b/data/sampled_jsons/Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embedding.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..16fb60691faf412aeeaa432535d9e307cf8069b3
--- /dev/null
+++ b/data/sampled_jsons/Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embedding.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Geometric Approach to Personalized Recommendation with ...", "date": "", "ddg_snippet": "Box embeddings , with their geometric set operations, significantly outperform all vector-based methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10875v1", "content": "Box embeddings , with their geometric set operations, significantly outperform all vector-based methods."}
+{"idx": 1, "title": "(PDF) A Geometric Approach to Personalized Recommendation ...", "date": "", "ddg_snippet": "of set - theoretic query recommendation . Box embeddings , with their geometric set operations, sig-. nificantly outperform all vector-based methods.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389091382_A_Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embeddings", "content": "of set - theoretic query recommendation . Box embeddings , with their geometric set operations, sig-. nificantly outperform all vector-based methods."}
+{"idx": 2, "title": "A Geometric Approach to Personalized Recommendation with ...", "date": "", "ddg_snippet": "Box embeddings , with their geometric set operations, significantly outperform all vector-based methods.This paper aims to advance the field of Machine Learning by introducing a geometric approach to personalized recommendation under set - theoretic constraints .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46603/paper", "content": "Box embeddings , with their geometric set operations, significantly outperform all vector-based methods.This paper aims to advance the field of Machine Learning by introducing a geometric approach to personalized recommendation under set - theoretic constraints ."}
+{"idx": 3, "title": "GitHub - Lyz103/ Recommendation -paper-daily: Automatically...", "date": "", "ddg_snippet": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings .A multi-theoretical kernel-based approach to social network-based recommendation . Xin Li et.al. 2412.12202.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Lyz103/Recommendation-paper-daily", "content": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings .A multi-theoretical kernel-based approach to social network-based recommendation . Xin Li et.al. 2412.12202."}
+{"idx": 4, "title": "Where the next wave of storytelling happens with Veo.", "date": "", "ddg_snippet": "Flow is an AI filmmaking tool built with and for creatives. Seamlessly create cinematic clips, scenes and stories using Google’s most capable generative AI models. Background for section .", "subpage_snippet": "", "source": "labs.google", "link": "https://labs.google/flow/about", "content": "Flow is an AI filmmaking tool built with and for creatives. Seamlessly create cinematic clips, scenes and stories using Google’s most capable generative AI models. Background for section ."}
+{"idx": 5, "title": "Shib Sankar Dasgupta - Google Scholar", "date": "", "ddg_snippet": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=0KpQR94AAAAJ&hl=en", "content": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings ."}
+{"idx": 6, "title": "dblp: List of computer science publications by Andrew McCallum", "date": "", "ddg_snippet": "Shib Sankar Dasgupta, Michael Boratko, Andrew McCallum: A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings .", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/m/AndrewMcCallum.html", "content": "Shib Sankar Dasgupta, Michael Boratko, Andrew McCallum: A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings ."}
+{"idx": 7, "title": "Shib Dasgupta", "date": "", "ddg_snippet": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings . Box Embeddings : An Open-source Library for Representation Learning using Geometric Structures. EMNLP (Demo Track) 2021 – Open Source Contribution.", "subpage_snippet": "", "source": "ssdasgupta.github.io", "link": "https://ssdasgupta.github.io/", "content": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings . Box Embeddings : An Open-source Library for Representation Learning using Geometric Structures. EMNLP (Demo Track) 2021 – Open Source Contribution."}
+{"idx": 8, "title": "For students — Enter a Wayground Code", "date": "", "ddg_snippet": "Join an activity with your class and find or create your own quizzes and flashcards.", "subpage_snippet": "", "source": "wayground.com", "link": "https://wayground.com/join", "content": "Join an activity with your class and find or create your own quizzes and flashcards."}
+{"idx": 9, "title": "Andrew McCallum - Department of Computer Science... - AMiner", "date": "", "ddg_snippet": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings .Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching. Rico Angell,Andrew McCallum. ICML 2024（2024）.", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/profile/andrew-mccallum/53f42ed2dabfaedd74d496dc", "content": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings .Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching. Rico Angell,Andrew McCallum. ICML 2024（2024）."}
diff --git a/data/sampled_jsons/George_Papamakarios_Theo_Pavlakou_Iain_Murray_abstract_Autoregressive_models_are_among_the_best_perf_year_2017.jsonl b/data/sampled_jsons/George_Papamakarios_Theo_Pavlakou_Iain_Murray_abstract_Autoregressive_models_are_among_the_best_perf_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7c9662f84201d610495485be0c823c64730ad5f2
--- /dev/null
+++ b/data/sampled_jsons/George_Papamakarios_Theo_Pavlakou_Iain_Murray_abstract_Autoregressive_models_are_among_the_best_perf_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1705.07057] Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "Abstract : Autoregressive models are among the best performing neural density estimators. ... autoregressive models , each modelling the random numbers ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1705.07057", "content": "Abstract : Autoregressive models are among the best performing neural density estimators. ... autoregressive models , each modelling the random numbers ..."}
+{"idx": 1, "title": "Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "Autoregressive models are among the best performing neural density estimators. ... autoregressive models , each modelling the random numbers of the ...", "subpage_snippet": "", "source": "homepages.inf.ed.ac.uk", "link": "https://homepages.inf.ed.ac.uk/imurray2/pub/17maf/", "content": "Autoregressive models are among the best performing neural density estimators. ... autoregressive models , each modelling the random numbers of the ..."}
+{"idx": 2, "title": "Distilling Normalizing Flows", "date": "", "ddg_snippet": "Normalizing Flows are a type of explicit density model , where instead of attempting to just generate samples similar to our target distribution, the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21003v1", "content": "Normalizing Flows are a type of explicit density model , where instead of attempting to just generate samples similar to our target distribution, the ..."}
+{"idx": 3, "title": "Fractal Flow: Hierarchical and Interpretable Normalizing Flow", "date": "", "ddg_snippet": "... models make both training and inference computationally intensive, and these models do not provide tractable or explicit probability density ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19750v1", "content": "... models make both training and inference computationally intensive, and these models do not provide tractable or explicit probability density ..."}
+{"idx": 4, "title": "Multi-Flow: Multi-View-Enriched Normalizing Flows for", "date": "", "ddg_snippet": "With the de-facto standard data set MVTec AD [ 5 ] having reached near saturation in performance [ 48 , 38 ] , the research community has been ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.03306v1", "content": "With the de-facto standard data set MVTec AD [ 5 ] having reached near saturation in performance [ 48 , 38 ] , the research community has been ..."}
+{"idx": 5, "title": "Mutual Information Estimation via 𝑓-Divergence and Data", "date": "", "ddg_snippet": "... have demonstrated that neural networks can be leveraged as probability density function estimators and, more in general, are capable of modeling the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.20025v2", "content": "... have demonstrated that neural networks can be leveraged as probability density function estimators and, more in general, are capable of modeling the ..."}
+{"idx": 6, "title": "IN-Flow: Instance Normalization Flow for Non-stationary Time", "date": "", "ddg_snippet": "Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/393845692_IN-Flow_Instance_Normalization_Flow_for_Non-stationary_Time_Series_Forecasting", "content": "Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact."}
+{"idx": 7, "title": "μGUIDE: a framework for quantitative imaging via generalized", "date": "", "ddg_snippet": "... are powerful tools to characterize all the possible parameter estimations that could explain an observed measurement, their uncertainty, and existing ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/101069v1", "content": "... are powerful tools to characterize all the possible parameter estimations that could explain an observed measurement, their uncertainty, and existing ..."}
+{"idx": 8, "title": "μGUIDE: a framework for quantitative imaging via generalized", "date": "", "ddg_snippet": "... are powerful tools to characterize all the possible parameter estimations that could explain an observed measurement, their uncertainty, and existing ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/101069v2", "content": "... are powerful tools to characterize all the possible parameter estimations that could explain an observed measurement, their uncertainty, and existing ..."}
+{"idx": 9, "title": "Multiscale Flow for Robust and Optimal Cosmological Analysis", "date": "", "ddg_snippet": "While the Fourier basis is theoretically sound and widely used in such analysis, its kernels are not local in pixel space and require additional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.04689v2", "content": "While the Fourier basis is theoretically sound and widely used in such analysis, its kernels are not local in pixel space and require additional ..."}
diff --git a/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Section_2_Dvijotham.jsonl b/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Section_2_Dvijotham.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d92f950ac54e080f7176ae389bbec4f393fe2381
--- /dev/null
+++ b/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Section_2_Dvijotham.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Global Optimization with a Power-Transformed Objective ...", "date": "", "ddg_snippet": "17 Jul 2025 — We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46360", "content": "17 Jul 2025 — We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps."}
+{"idx": 1, "title": "Global Optimization with A Power-Transformed Objective ...", "date": "", "ddg_snippet": "6 Dec 2024 — Therefore, in this work, we propose a new method, namely the Gaussian Smoothing with a Power - transformed Objective (GSPTO), for solving the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.05204v1", "content": "6 Dec 2024 — Therefore, in this work, we propose a new method, namely the Gaussian Smoothing with a Power - transformed Objective (GSPTO), for solving the ..."}
+{"idx": 2, "title": "Global Optimization with a Power-Transformed Objective and ...", "date": "", "ddg_snippet": "We propose a novel method, namely Gaussian . Smoothing with a Power - Transformed Objective . (GS-PowerOpt), that solves global optimization .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=6ojzpDczIY&name=pdf", "content": "We propose a novel method, namely Gaussian . Smoothing with a Power - Transformed Objective . (GS-PowerOpt), that solves global optimization ."}
+{"idx": 3, "title": "Global Optimization with a Power-Transformed Objective and ...", "date": "", "ddg_snippet": "Our proposed algorithm, GS-PowerOpt, is significantly faster than the standard homotopy method for optimization , both empirically and.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46360_JdTUNKo.pdf", "content": "Our proposed algorithm, GS-PowerOpt, is significantly faster than the standard homotopy method for optimization , both empirically and."}
+{"idx": 4, "title": "Double Sampling Randomized Smoothing", "date": "", "ddg_snippet": "We concretize DSRS by generalized Gaussian smooth - ing mechanisms and propose a method to efficiently compute the certified radius for given classifiers. • We ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/li22aa/li22aa.pdf", "content": "We concretize DSRS by generalized Gaussian smooth - ing mechanisms and propose a method to efficiently compute the certified radius for given classifiers. • We ..."}
+{"idx": 5, "title": "Towards Understanding the Robustness of Diffusion ...", "date": "", "ddg_snippet": "2 Oct 2024 — This method follows an iterative two-step process: first, the Classifier-Guided Perturbation Optimization (CGPO) step generates adversarial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.14309v2", "content": "2 Oct 2024 — This method follows an iterative two-step process: first, the Classifier-Guided Perturbation Optimization (CGPO) step generates adversarial ..."}
+{"idx": 6, "title": "Safety, Robustness, and Interpretability in Machine Learning", "date": "", "ddg_snippet": "by S Pfrommer · 2025 — With an appropriate optimization formulation and loss function, we show theoretically that the final base policy is provably safe at optimality. 158 pages", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2025/EECS-2025-67.pdf", "content": "by S Pfrommer · 2025 — With an appropriate optimization formulation and loss function, we show theoretically that the final base policy is provably safe at optimality. 158 pages"}
+{"idx": 7, "title": "Energy Minimization Methods in Computer Vision and ...", "date": "", "ddg_snippet": "by SUSMR Walk · 2015 — This book contains 36 original research articles that cover the whole spectrum of energy minimization in computer vision and pattern recognition, including.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-319-14612-6.pdf", "content": "by SUSMR Walk · 2015 — This book contains 36 original research articles that cover the whole spectrum of energy minimization in computer vision and pattern recognition, including."}
+{"idx": 8, "title": "Generative AI of Things for Sustainable Smart Cities", "date": "", "ddg_snippet": "2 days ago — This study is structured as follows: Section 2 addresses the theoretical and practical foundations of GAIoT, providing a comprehensive ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2210670725006997", "content": "2 days ago — This study is structured as follows: Section 2 addresses the theoretical and practical foundations of GAIoT, providing a comprehensive ..."}
+{"idx": 9, "title": "CURVATURE-BASED ROBUSTNESS CERTIFICATES", "date": "", "ddg_snippet": "by S Singla — First, we show that if the eigenvalues of the Hessian of the network (curvatures of the network) are bounded, we can compute a robustness certificate in the l2.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Skgq1ANFDB", "content": "by S Singla — First, we show that if the eigenvalues of the Hessian of the network (curvatures of the network) are bounded, we can compute a robustness certificate in the l2."}
diff --git a/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_Equation_11_NFR_layer_feature.jsonl b/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_Equation_11_NFR_layer_feature.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e0da09cae94466f680333849f9707f4413cb5a89
--- /dev/null
+++ b/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_Equation_11_NFR_layer_feature.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Our analysis identifies two key factors that affect the utility of privacy-preserving graph learning: fea-ture dimension and neighborhood size. Based on the above analysis, UPGNET enhances utility by introducing two core layers : High-Order Aggre-gator (HOA) layer and the Node Feature Regular-ization ( NFR ) layer .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2aKHuXdr7Q", "content": "Our analysis identifies two key factors that affect the utility of privacy-preserving graph learning: fea-ture dimension and neighborhood size. Based on the above analysis, UPGNET enhances utility by introducing two core layers : High-Order Aggre-gator (HOA) layer and the Node Feature Regular-ization ( NFR ) layer ."}
+{"idx": 1, "title": "Locally Private Graph Neural Networks - ACM Digital Library", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features /labels are private . Our contributions include building a new privacy mechanism , called the multi-bit mechanism , for high-dimensional feature perturbation. We also ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460120.3484565", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features /labels are private . Our contributions include building a new privacy mechanism , called the multi-bit mechanism , for high-dimensional feature perturbation. We also ..."}
+{"idx": 2, "title": "Differentially private graph neural networks for graph classification ...", "date": "", "ddg_snippet": "The core concept of Graph Neural Networks (GNNs) involves aggregating information from neighboring nodes through a message-passing mechanism to update node feature representations.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424026654", "content": "The core concept of Graph Neural Networks (GNNs) involves aggregating information from neighboring nodes through a message-passing mechanism to update node feature representations."}
+{"idx": 3, "title": "[2006.05535] Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ..."}
+{"idx": 4, "title": "Local Differential Privacy in Graph Neural Networks: a Reconstruction ...", "date": "", "ddg_snippet": "Wen Huang* Abstract Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide local node privacy for users, while incurring low utility loss.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/pdf/10.1137/1.9781611978032.1", "content": "Wen Huang* Abstract Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide local node privacy for users, while incurring low utility loss."}
+{"idx": 5, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Poster Going Deeper into Locally Differentially Private Graph Neural Networks Longzhu He · Chaozhuo Li · Peng Tang · Sen Su East Exhibition Hall A-B #E-905", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46579", "content": "Poster Going Deeper into Locally Differentially Private Graph Neural Networks Longzhu He · Chaozhuo Li · Peng Tang · Sen Su East Exhibition Hall A-B #E-905"}
+{"idx": 6, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal information or variables related to individuals, learning from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these concerns, they ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/165017?from=subpath-search", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal information or variables related to individuals, learning from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these concerns, they ..."}
+{"idx": 7, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Our analysis identifies two key factors that affect the utility of privacy-preserving graph learning: * feature dimension* and *neighborhood size*. Based on the above analysis, UPGNET enhances utility by introducing two core layers : High-Order Aggregator (HOA) layer and the Node Feature Regularization ( NFR ) layer .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2aKHuXdr7Q", "content": "Our analysis identifies two key factors that affect the utility of privacy-preserving graph learning: * feature dimension* and *neighborhood size*. Based on the above analysis, UPGNET enhances utility by introducing two core layers : High-Order Aggregator (HOA) layer and the Node Feature Regularization ( NFR ) layer ."}
+{"idx": 8, "title": "Node-Level Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data and computing node-level representations via aggregation of information from the neighborhood of each node. However, this aggregation implies an increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy-preserving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.15521", "content": "Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data and computing node-level representations via aggregation of information from the neighborhood of each node. However, this aggregation implies an increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy-preserving ..."}
+{"idx": 9, "title": "Differentially private graph neural networks for graph classification ...", "date": "", "ddg_snippet": "Abstract Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular structure elucidation, have demonstrated superior performance in processing graph data.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0957417424026654", "content": "Abstract Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular structure elucidation, have demonstrated superior performance in processing graph data."}
diff --git a/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_Section_4.6_architectural_var_year_2024.jsonl b/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_Section_4.6_architectural_var_year_2024.jsonl
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index 0000000000000000000000000000000000000000..fa8e8f3c243be7e6c1ed8bc2d2930a57193c6323
--- /dev/null
+++ b/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_Section_4.6_architectural_var_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Anomaly Detection in Dynamic Graphs: A Comprehensive Survey", "date": "", "ddg_snippet": "... modern dynamic networks have proven to exhibit additional structural and algorithmic properties that go beyond the simple generalization of graphs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.00134v1", "content": "... modern dynamic networks have proven to exhibit additional structural and algorithmic properties that go beyond the simple generalization of graphs ..."}
+{"idx": 1, "title": "Advanced Health Informatics: Delving Deeper into the Digital", "date": "", "ddg_snippet": "Advanced Health Informatics: Delving Deeper into the Digital Health Landscape ... Variant Calling: ... Structural Variant Analysis :", "subpage_snippet": "", "source": "omicstutorials.com", "link": "https://omicstutorials.com/advanced-health-informatics-delving-deeper-into-the-digital-health-landscape/", "content": "Advanced Health Informatics: Delving Deeper into the Digital Health Landscape ... Variant Calling: ... Structural Variant Analysis :"}
+{"idx": 2, "title": "Neuromorphic Engineering: In Memory of Misha Mahowald | Neural", "date": "", "ddg_snippet": "In 1989, neural networks were assumed to be computer programs that ran on general-purpose computers. ... on the same chip, very few signals are local ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/neco/article/35/3/343/113812/Neuromorphic-Engineering-In-Memory-of-Misha", "content": "In 1989, neural networks were assumed to be computer programs that ran on general-purpose computers. ... on the same chip, very few signals are local ..."}
+{"idx": 3, "title": "Time Series Information Visualization – A Review of", "date": "", "ddg_snippet": "... Section 2 presents the research methodology adopted for collecting literature; Section 3 presents previous reviews about InfoVis and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14920v1", "content": "... Section 2 presents the research methodology adopted for collecting literature; Section 3 presents previous reviews about InfoVis and the ..."}
+{"idx": 4, "title": "1 Introduction", "date": "", "ddg_snippet": "... Graph Neural Networks (GNNs) as a natural inductive bias for encoding supply chain structure, demonstrate that they can represent optimal and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.11246v3", "content": "... Graph Neural Networks (GNNs) as a natural inductive bias for encoding supply chain structure, demonstrate that they can represent optimal and ..."}
+{"idx": 5, "title": "General Geospatial Inference with a Population Dynamics", "date": "", "ddg_snippet": "PDFM’s architecture leverages a graph neural network (GNN) because of its inherent ability to embed the network relationships across locations and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07207v5", "content": "PDFM’s architecture leverages a graph neural network (GNN) because of its inherent ability to embed the network relationships across locations and ..."}
+{"idx": 6, "title": "Machine Learning Techniques: Comprehensive Guide - WORLD", "date": "", "ddg_snippet": "Machine learning techniques encompass a wide range of approaches, from simple linear regression models to complex deep neural networks .", "subpage_snippet": "", "source": "insider-wp.com", "link": "https://insider-wp.com/machine-learning-techniques-comprehensive-guide/", "content": "Machine learning techniques encompass a wide range of approaches, from simple linear regression models to complex deep neural networks ."}
+{"idx": 7, "title": "Comprehensive review of dimensionality reduction algorithms:", "date": "", "ddg_snippet": "... intrinsic dimensionality estimation, fairness-aware and privacy-preserving embeddings, robust graph -based methods, and scalable deep architectures ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-3025/", "content": "... intrinsic dimensionality estimation, fairness-aware and privacy-preserving embeddings, robust graph -based methods, and scalable deep architectures ..."}
+{"idx": 8, "title": "Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled", "date": "", "ddg_snippet": "For example, location-based social network (LBSN) data from check-in services like Yelp and Foursquare enable targeted, location-aware advertising ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.06561v2", "content": "For example, location-based social network (LBSN) data from check-in services like Yelp and Foursquare enable targeted, location-aware advertising ..."}
+{"idx": 9, "title": "Efficiency of Coordinate Descent Methods on Huge-Scale", "date": "", "ddg_snippet": "... of the method and its accelerated variant ... Tseng, On the convergence of the coordinate descent method for convex differentiable minimization, J.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/100802001?cookieSet=1", "content": "... of the method and its accelerated variant ... Tseng, On the convergence of the coordinate descent method for convex differentiable minimization, J."}
diff --git a/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_equation_11_NFR_layer.jsonl b/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_equation_11_NFR_layer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1f98dba5b373203a426c52e4f834512b51596bff
--- /dev/null
+++ b/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_equation_11_NFR_layer.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Going Deeper into Locally Differentially Private Graph Neural ...", "date": "", "ddg_snippet": "In this section, we begin with a theoretical analysis of prior work on locally differentially private graph neural networks (LDPGNN) in Sec. 3.1, identifying the key factors limit-ing their utility.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2aKHuXdr7Q", "content": "In this section, we begin with a theoretical analysis of prior work on locally differentially private graph neural networks (LDPGNN) in Sec. 3.1, identifying the key factors limit-ing their utility."}
+{"idx": 1, "title": "Going Deeper into Locally Differentially Private Graph Neural ...", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal information or variables related to individuals, learning from graph data can raise significant privacy concerns.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/2aKHuXdr7Q@OpenReview", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal information or variables related to individuals, learning from graph data can raise significant privacy concerns."}
+{"idx": 2, "title": "Differentially private graph neural networks for graph ...", "date": "", "ddg_snippet": "Mar 5, 2025 · Abstract Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular structure elucidation, have demonstrated superior performance in processing graph data.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424026654", "content": "Mar 5, 2025 · Abstract Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular structure elucidation, have demonstrated superior performance in processing graph data."}
+{"idx": 3, "title": "Node-Level Differentially Private Graph Neural Networks Local Differential Privacy in Graph Neural Networks: a ... Local Differential Private Spatio- Temporal Dynamic Graph ... Locally Private Graph Neural Networks | Proceedings of the 2021 ACM Locally Private Graph Neural Networks | Proceedings of the 2021 ACM Node-Level Differentially Private Graph Neural Networks Locally Private Graph Neural Networks | Proceedings of the 2021 ACM Differentially private graph neural networks for graph classification Locally Private Graph Neural Networks | Proceedings of the 2021 ACM Locally Private Graph Neural Networks | Proceedings of the ...", "date": "", "ddg_snippet": "Nov 23, 2021 · Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data and computing node-level representations via aggregation of information from the neighborhood of each node. However, this aggregation implies an increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy-preserving ... Wen Huang* Abstract Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide local node privacy for users, while incurring low utility loss. Differential- Private Graph Neural Networks (DP-GNNs) have generated remarkable research results, enabling them to effectively tackle the privacy leakage problem in graph learning. However, most DP-GNNs do not consider the temporal-dimensional scenarios. In tasks involving spatiotemporal graph training, such as wireless social networks analysis, the sensitive interactive information in each ... Are Graph Neural Networks privacy-preserving? Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Are Graph Neural Networks safe? Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. Do Graph Neural networks reveal sensitive information? Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computing node-level representations via aggregation of information from the neighborhood of each node. However, this aggregation implies an increased risk of revealing sensitive information , as a node can participate in the inference for multiple nodes. Can a central server train a GNN over a graph? In this paper, we study the problem of node data privacy, where graph nodes (e.g., social network users) have potentially sensitive data that is kept private, but they could be beneficial for a central server for training a GNN over the graph. What is a Graph Neural Network (GNN)? The core concept of Graph Neural Networks (GNNs) involves aggregating information from neighboring nodes through a message-passing mechanism to update node feature representations. This iterative process aims to capture information from increasingly distant nodes within the graph . What is a privacy-preserving GNN framework based on local differential privacy? In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.15521", "content": "Nov 23, 2021 · Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data and computing node-level representations via aggregation of information from the neighborhood of each node. However, this aggregation implies an increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy-preserving ... Wen Huang* Abstract Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide local node privacy for users, while incurring low utility loss. Differential- Private Graph Neural Networks (DP-GNNs) have generated remarkable research results, enabling them to effectively tackle the privacy leakage problem in graph learning. However, most DP-GNNs do not consider the temporal-dimensional scenarios. In tasks involving spatiotemporal graph training, such as wireless social networks analysis, the sensitive interactive information in each ... Are Graph Neural Networks privacy-preserving? Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Are Graph Neural Networks safe? Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. Do Graph Neural networks reveal sensitive information? Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computing node-level representations via aggregation of information from the neighborhood of each node. However, this aggregation implies an increased risk of revealing sensitive information , as a node can participate in the inference for multiple nodes. Can a central server train a GNN over a graph? In this paper, we study the problem of node data privacy, where graph nodes (e.g., social network users) have potentially sensitive data that is kept private, but they could be beneficial for a central server for training a GNN over the graph. What is a Graph Neural Network (GNN)? The core concept of Graph Neural Networks (GNNs) involves aggregating information from neighboring nodes through a message-passing mechanism to update node feature representations. This iterative process aims to capture information from increasingly distant nodes within the graph . What is a privacy-preserving GNN framework based on local differential privacy? In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ..."}
+{"idx": 4, "title": "Local Differential Privacy in Graph Neural Networks: a ...", "date": "", "ddg_snippet": "Wen Huang* Abstract Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide local node privacy for users, while incurring low utility loss.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/pdf/10.1137/1.9781611978032.1", "content": "Wen Huang* Abstract Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide local node privacy for users, while incurring low utility loss."}
+{"idx": 5, "title": "Local Differential Private Spatio- Temporal Dynamic Graph ...", "date": "", "ddg_snippet": "Differential- Private Graph Neural Networks (DP-GNNs) have generated remarkable research results, enabling them to effectively tackle the privacy leakage problem in graph learning. However, most DP-GNNs do not consider the temporal-dimensional scenarios. In tasks involving spatiotemporal graph training, such as wireless social networks analysis, the sensitive interactive information in each ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10571169", "content": "Differential- Private Graph Neural Networks (DP-GNNs) have generated remarkable research results, enabling them to effectively tackle the privacy leakage problem in graph learning. However, most DP-GNNs do not consider the temporal-dimensional scenarios. In tasks involving spatiotemporal graph training, such as wireless social networks analysis, the sensitive interactive information in each ..."}
+{"idx": 6, "title": "Locally Private Graph Neural Networks | Proceedings of the ...", "date": "", "ddg_snippet": "Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3460120.3484565", "content": "Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ..."}
+{"idx": 7, "title": "ICML Poster Going Deeper into Locally Differentially Private Graph ...", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks.In this paper, we present UPGNet, an LDP-based privacy -preserving graph learning framework that enhances utility while safeguarding user privacy .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46579", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks.In this paper, we present UPGNet, an LDP-based privacy -preserving graph learning framework that enhances utility while safeguarding user privacy ."}
+{"idx": 8, "title": "Differentially Private Relational Learning with Entity-level Privacy ...", "date": "", "ddg_snippet": "Locally private graph neural networks . In Proceed-ings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pages 2130–2145, 2021.{GAP}: Differentially private graph neural networks with aggregation perturbation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08347", "content": "Locally private graph neural networks . In Proceed-ings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pages 2130–2145, 2021.{GAP}: Differentially private graph neural networks with aggregation perturbation."}
+{"idx": 9, "title": "Locally Private Graph Neural Networks -Bohrium", "date": "", "ddg_snippet": "Graph Neural Networks . Deep Learning. Local Differential Privacy . Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/locally-private-graph-neural-networks/867757841736269842-108614", "content": "Graph Neural Networks . Deep Learning. Local Differential Privacy . Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
diff --git a/data/sampled_jsons/Great_Models_Think_Alike_and_this_Undermines_AI_Oversight.jsonl b/data/sampled_jsons/Great_Models_Think_Alike_and_this_Undermines_AI_Oversight.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..389e062aff75b74b44c7c216fa2fb9db83bf958b
--- /dev/null
+++ b/data/sampled_jsons/Great_Models_Think_Alike_and_this_Undermines_AI_Oversight.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The AI oversight trap: When smarter models make the same ... Great Models Think Alike and this Undermines AI Oversight When AI Models Think Alike: The Hidden Challenge Undermining ... Great Models Think Alike and this Undermines AI Oversight Similarity affects Oversight Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Similarity affects Oversight Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Similarity affects Oversight Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight . May 1, 2025 · However, we observe a concerning trend-- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight . Feb 7, 2025 · Addressing the Risks of AI OversightOur research underscores the importance of recognizing and mitigating the effects of model similarity in AI oversight . If models share the same weaknesses, they may reinforce rather than correct each other's errors, leading to systemic issues in automated evaluation systems. Great Models Think Alike and this Undermines AI Oversight Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. Do great models think like undermine AI oversight? Overall, as model blind-spots get harder to detect, making us defer more to AI oversight, models making more similar mistakes poses the risk of correlated failures. title={ Great Models Think Alike and this Undermines AI Oversight }, Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Does model similarity affect AI oversight? We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Can other language models automate AI oversight? There is hope that other language models can automate both these tasks, which we refer to as AI Oversight. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on overlap in model mistakes. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. How does model similarity affect LMS? Model similarity has negative effects on using LMs to judge or train other models; Unfortunately LMs are getting similar with increasing capabilities. Mar 7, 2025 · However, we observe a concerning trend — model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight .", "subpage_snippet": "", "source": "www.devdiscourse.com", "link": "https://www.devdiscourse.com/article/technology/3256561-the-ai-oversight-trap-when-smarter-models-make-the-same-mistakes", "content": "Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight . May 1, 2025 · However, we observe a concerning trend-- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight . Feb 7, 2025 · Addressing the Risks of AI OversightOur research underscores the importance of recognizing and mitigating the effects of model similarity in AI oversight . If models share the same weaknesses, they may reinforce rather than correct each other's errors, leading to systemic issues in automated evaluation systems. Great Models Think Alike and this Undermines AI Oversight Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. Do great models think like undermine AI oversight? Overall, as model blind-spots get harder to detect, making us defer more to AI oversight, models making more similar mistakes poses the risk of correlated failures. title={ Great Models Think Alike and this Undermines AI Oversight }, Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Does model similarity affect AI oversight? We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Can other language models automate AI oversight? There is hope that other language models can automate both these tasks, which we refer to as AI Oversight. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on overlap in model mistakes. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. How does model similarity affect LMS? Model similarity has negative effects on using LMs to judge or train other models; Unfortunately LMs are getting similar with increasing capabilities. Mar 7, 2025 · However, we observe a concerning trend — model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight ."}
+{"idx": 1, "title": "Great Models Think Alike and this Undermines AI Oversight Great Models Think Alike and this Undermines AI Oversight The AI oversight trap: When smarter models make the same ... Great Models Think Alike and this Undermines AI Oversight When AI Models Think Alike: The Hidden Challenge Undermining ... Great Models Think Alike and this Undermines AI Oversight Similarity affects Oversight Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Similarity affects Oversight Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Similarity affects Oversight Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Feb 6, 2025 · As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as '' AI Oversight ''. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on ... As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight . May 1, 2025 · However, we observe a concerning trend-- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight . Feb 7, 2025 · Addressing the Risks of AI OversightOur research underscores the importance of recognizing and mitigating the effects of model similarity in AI oversight . If models share the same weaknesses, they may reinforce rather than correct each other's errors, leading to systemic issues in automated evaluation systems. Great Models Think Alike and this Undermines AI Oversight Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. Do great models think like undermine AI oversight? Overall, as model blind-spots get harder to detect, making us defer more to AI oversight, models making more similar mistakes poses the risk of correlated failures. title={ Great Models Think Alike and this Undermines AI Oversight }, Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Does model similarity affect AI oversight? We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Can other language models automate AI oversight? There is hope that other language models can automate both these tasks, which we refer to as AI Oversight. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on overlap in model mistakes. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. How does model similarity affect LMS? Model similarity has negative effects on using LMs to judge or train other models; Unfortunately LMs are getting similar with increasing capabilities. Mar 7, 2025 · However, we observe a concerning trend — model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04313", "content": "Feb 6, 2025 · As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as '' AI Oversight ''. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on ... As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight . May 1, 2025 · However, we observe a concerning trend-- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight . Feb 7, 2025 · Addressing the Risks of AI OversightOur research underscores the importance of recognizing and mitigating the effects of model similarity in AI oversight . If models share the same weaknesses, they may reinforce rather than correct each other's errors, leading to systemic issues in automated evaluation systems. Great Models Think Alike and this Undermines AI Oversight Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. Do great models think like undermine AI oversight? Overall, as model blind-spots get harder to detect, making us defer more to AI oversight, models making more similar mistakes poses the risk of correlated failures. title={ Great Models Think Alike and this Undermines AI Oversight }, Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Does model similarity affect AI oversight? We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Can other language models automate AI oversight? There is hope that other language models can automate both these tasks, which we refer to as AI Oversight. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on overlap in model mistakes. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. How does model similarity affect LMS? Model similarity has negative effects on using LMs to judge or train other models; Unfortunately LMs are getting similar with increasing capabilities. Mar 7, 2025 · However, we observe a concerning trend — model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight ."}
+{"idx": 2, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures.", "subpage_snippet": "", "source": "model-similarity.github.io", "link": "https://model-similarity.github.io/", "content": "As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures."}
+{"idx": 3, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "May 1, 2025 · However, we observe a concerning trend-- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3Z827FtMNe", "content": "May 1, 2025 · However, we observe a concerning trend-- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight ."}
+{"idx": 4, "title": "When AI Models Think Alike: The Hidden Challenge Undermining ...", "date": "", "ddg_snippet": "Feb 7, 2025 · Addressing the Risks of AI OversightOur research underscores the importance of recognizing and mitigating the effects of model similarity in AI oversight . If models share the same weaknesses, they may reinforce rather than correct each other's errors, leading to systemic issues in automated evaluation systems.", "subpage_snippet": "", "source": "www.globaltrendtimes.com", "link": "https://www.globaltrendtimes.com/2025/02/when-ai-models-think-alike-hidden.html", "content": "Feb 7, 2025 · Addressing the Risks of AI OversightOur research underscores the importance of recognizing and mitigating the effects of model similarity in AI oversight . If models share the same weaknesses, they may reinforce rather than correct each other's errors, leading to systemic issues in automated evaluation systems."}
+{"idx": 5, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Great Models Think Alike and this Undermines AI Oversight Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v1", "content": "Great Models Think Alike and this Undermines AI Oversight Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”."}
+{"idx": 6, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Mar 7, 2025 · However, we observe a concerning trend — model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=sYB0Y0hOxi", "content": "Mar 7, 2025 · However, we observe a concerning trend — model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight ."}
+{"idx": 7, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Can we rely on AI oversight going forward? This remains a topic of much debate. In this work, we study oversight from the perspective of model similarity. When assessing or teaching humans, it is well recognized that individuals have different strengths and weaknesses.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "Can we rely on AI oversight going forward? This remains a topic of much debate. In this work, we study oversight from the perspective of model similarity. When assessing or teaching humans, it is well recognized that individuals have different strengths and weaknesses."}
+{"idx": 8, "title": "[Literature Review] Great Models Think Alike and this Undermines ...", "date": "", "ddg_snippet": "The paper titled \" Great Models Think Alike and this Undermines AI Oversight \" explores the implications of model similarity in the context of overseeing and evaluating the performance of large language models (LLMs).", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/great-models-think-alike-and-this-undermines-ai-oversight", "content": "The paper titled \" Great Models Think Alike and this Undermines AI Oversight \" explores the implications of model similarity in the context of overseeing and evaluating the performance of large language models (LLMs)."}
+{"idx": 9, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as '' AI Oversight ''.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Great-Models-Think-Alike-and-this-Undermines-AI-Oversight-1f8d8bac-95d4-4413-8064-c3afceca72ae", "content": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as '' AI Oversight ''."}
diff --git a/data/sampled_jsons/Gumiho_Figure_4_serial_head_depth_2_to_3_layers_mean_accepted_tokens_speedup_trade-off.jsonl b/data/sampled_jsons/Gumiho_Figure_4_serial_head_depth_2_to_3_layers_mean_accepted_tokens_speedup_trade-off.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d1b72c815e4234751b5b67cbeb9d8d4341c85da2
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_Figure_4_serial_head_depth_2_to_3_layers_mean_accepted_tokens_speedup_trade-off.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0ObGn4e1IS", "content": "This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ..."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads . Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10135v2", "content": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads . Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 2, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... - GitHub", "date": "", "ddg_snippet": "Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based on the theoretical finding that initial tokens in the draft sequence have a more significant impact on the overall accepted length. Gumiho employs a hybrid head design that combines serial and parallel components.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based on the theoretical finding that initial tokens in the draft sequence have a more significant impact on the overall accepted length. Gumiho employs a hybrid head design that combines serial and parallel components."}
+{"idx": 3, "title": "Gumiho - a amd Collection - Hugging Face", "date": "", "ddg_snippet": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Paper • 2503.10135 •Published Mar 13 Upvote - Share collection View history Collection guide Browse collections", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/amd/gumiho-684a3b7cbbe86ab23b393e9f", "content": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Paper • 2503.10135 •Published Mar 13 Upvote - Share collection View history Collection guide Browse collections"}
+{"idx": 4, "title": "[2503.10135] Gumiho: A Hybrid Architecture to Prioritize Early Tokens ...", "date": "", "ddg_snippet": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads . Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10135", "content": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads . Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 5, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "Build-ing on this insight, we propose Gumiho , a hy-brid model combining serial and parallel heads . Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135v1", "content": "Build-ing on this insight, we propose Gumiho , a hy-brid model combining serial and parallel heads . Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 6, "title": "How to determine the number of layers and nodes of a neural network", "date": "", "ddg_snippet": "I am currently building a nn for a dataset with 387 features and 3000 samples. The outputs are 3 classes. I configured the network structure as following: input->200-> {300->100}->50->output Did I choose the correct number of nodes and layers ? How to determine the number of nodes of each layers (input,hidden and output)? Is there any rule?", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/35520587/how-to-determine-the-number-of-layers-and-nodes-of-a-neural-network", "content": "I am currently building a nn for a dataset with 387 features and 3000 samples. The outputs are 3 classes. I configured the network structure as following: input->200-> {300->100}->50->output Did I choose the correct number of nodes and layers ? How to determine the number of nodes of each layers (input,hidden and output)? Is there any rule?"}
+{"idx": 7, "title": "(PDF) Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389821466_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding", "content": "Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 8, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... - LinkedIn", "date": "", "ddg_snippet": "To address this, Gumiho proposes an innovative hybrid model that integrates both serial and parallel heads . 🔍 Key Features: - Early Tokens : Utilizes an advanced Transformer architecture in a ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/abdullah-kasri_gumiho-a-hybrid-architecture-to-prioritize-activity-7306519551876161536-HoDU", "content": "To address this, Gumiho proposes an innovative hybrid model that integrates both serial and parallel heads . 🔍 Key Features: - Early Tokens : Utilizes an advanced Transformer architecture in a ..."}
+{"idx": 9, "title": "Geralt-Targaryen/Awesome-Speculative-Decoding - GitHub", "date": "", "ddg_snippet": "The target LLM selectively skips some of its intermediate layers to generate draft tokens Experiments on: LLaMA-2-13B/70B, LLaMA-2-13B-Chat, CodeLLaMA-13B | CNN/DM, XSum, HumanEval \"Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism\" [2024-06] [ACL 2024 Findings] [paper]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Geralt-Targaryen/Awesome-Speculative-Decoding", "content": "The target LLM selectively skips some of its intermediate layers to generate draft tokens Experiments on: LLaMA-2-13B/70B, LLaMA-2-13B-Chat, CodeLLaMA-13B | CNN/DM, XSum, HumanEval \"Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism\" [2024-06] [ACL 2024 Findings] [paper]"}
diff --git a/data/sampled_jsons/Gumiho_Hybrid_Architecture_Prioritize_Early_Tokens_Speculative_Decoding_AMD_MI250_NVIDIA_A100_GPU_co.jsonl b/data/sampled_jsons/Gumiho_Hybrid_Architecture_Prioritize_Early_Tokens_Speculative_Decoding_AMD_MI250_NVIDIA_A100_GPU_co.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..880ca03196f4adbaef30a8288ea1ae25fc9f1a43
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_Hybrid_Architecture_Prioritize_Early_Tokens_Speculative_Decoding_AMD_MI250_NVIDIA_A100_GPU_co.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gumiho - a amd Collection - Hugging Face", "date": "", "ddg_snippet": "Jun 12, 2025 · Official Model Parameters for \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding \"", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/amd/gumiho-684a3b7cbbe86ab23b393e9f", "content": "Jun 12, 2025 · Official Model Parameters for \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding \""}
+{"idx": 1, "title": "GPUs for AI Workloads - GPU Clouds Price Comparison", "date": "", "ddg_snippet": "Optimize your AI and ML projects with cost-effective GPU resources found by our platform. GPU cloud made simple: Browse gpu options, compare costs, and deploy in minutes with ease.", "subpage_snippet": "", "source": "www.bing.com", "link": "https://www.bing.com/aclick?ld=e8bDKT11GgDw3-mqWjWNwwPzVUCUzUO-lftITYVgIyi8NWBDJ3btrKTroncD1C1pxm4-4R_l1lcjhGZG3hBu_luT0kLuiyKH4mSySOg07l6LBXm4UynhwLAIPkxmnzArgH7LnO8F4L-wGUdumBu2ilQgbLHLxjpoHy1YlhC0zGwdZJvcwQ2uBFkarC-Cj9oxtKw9aWXw&u=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&rlid=75b84ec4d7191bf6ff2b2614b9cca267", "content": "Optimize your AI and ML projects with cost-effective GPU resources found by our platform. GPU cloud made simple: Browse gpu options, compare costs, and deploy in minutes with ease."}
+{"idx": 2, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... Gumiho - a amd Collection - Hugging Face Gumiho: A Hybrid Architecture to Prioritize Early Tokens in... Accelerating LLMs with Smarter Token Prioritization Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... dblp: Gumiho: A Hybrid Architecture to Prioritize Early ...", "date": "", "ddg_snippet": "Mar 13, 2025 · Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ... Jun 12, 2025 · Official Model Parameters for \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding \" May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho : A hybrid approach to optimize speculative decoding Gumiho introduces a novel hybrid architecture that intelligently prioritizes early tokens in speculative decoding to significantly accelerate LLM inference. Mar 13, 2025 · Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. Bibliographic details on Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10135", "content": "Mar 13, 2025 · Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ... Jun 12, 2025 · Official Model Parameters for \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding \" May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho : A hybrid approach to optimize speculative decoding Gumiho introduces a novel hybrid architecture that intelligently prioritizes early tokens in speculative decoding to significantly accelerate LLM inference. Mar 13, 2025 · Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. Bibliographic details on Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding ."}
+{"idx": 3, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ..."}
+{"idx": 4, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0ObGn4e1IS", "content": "May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones."}
+{"idx": 5, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Mar 13, 2025 · Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389821466_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding", "content": "Mar 13, 2025 · Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 6, "title": "dblp: Gumiho: A Hybrid Architecture to Prioritize Early ...", "date": "", "ddg_snippet": "Bibliographic details on Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2503-10135", "content": "Bibliographic details on Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding ."}
+{"idx": 7, "title": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Gumiho:-A-Hybrid-Architecture-to-Prioritize-Early-Tokens-in-Speculative-Decoding-696023e2-00c1-4c36-86bf-09ecb8c253e9", "content": "Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads."}
+{"idx": 8, "title": "Org profile for AMD on Hugging Face, the AI community building the...", "date": "", "ddg_snippet": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding . Paper • 2503.10135 • Published Mar 13. OGA CPU LLM Collection.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/amd/collections", "content": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding . Paper • 2503.10135 • Published Mar 13. OGA CPU LLM Collection."}
+{"idx": 9, "title": "An Introduction to Speculative Decoding for Reducing Latency in AI...", "date": "", "ddg_snippet": "Speculative decoding is an inference optimization technique that pairs a target model with a lightweight draft mechanism that quickly proposes several next tokens . The target model verifies those proposals in a single forward pass...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/an-introduction-to-speculative-decoding-for-reducing-latency-in-ai-inference/", "content": "Speculative decoding is an inference optimization technique that pairs a target model with a lightweight draft mechanism that quickly proposes several next tokens . The target model verifies those proposals in a single forward pass..."}
diff --git a/data/sampled_jsons/Gumiho_hybrid_architecture_speculative_decoding_Figure_4_Table_3.jsonl b/data/sampled_jsons/Gumiho_hybrid_architecture_speculative_decoding_Figure_4_Table_3.jsonl
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index 0000000000000000000000000000000000000000..4b0add817c0a6be5a5070f077b9adba6223336e5
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_hybrid_architecture_speculative_decoding_Figure_4_Table_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "As shown in Fig. 1, Gumiho introduces a hybrid architecture that distinguishes itself from existing methods. Unlike approaches that rely solely on a single serial or parallel structure and employ uniform head size across all positions, Gumiho combines large serial heads with small parallel heads to enhance accuracy and eficiency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135", "content": "As shown in Fig. 1, Gumiho introduces a hybrid architecture that distinguishes itself from existing methods. Unlike approaches that rely solely on a single serial or parallel structure and employ uniform head size across all positions, Gumiho combines large serial heads with small parallel heads to enhance accuracy and eficiency."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ..."}
+{"idx": 2, "title": "Gumiho - a amd Collection - Hugging Face", "date": "", "ddg_snippet": "Official Model Parameters for \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding \"", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/amd/gumiho-684a3b7cbbe86ab23b393e9f", "content": "Official Model Parameters for \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding \""}
+{"idx": 3, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0ObGn4e1IS", "content": "This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ..."}
+{"idx": 4, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389821466_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding", "content": "Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 5, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "🌟 Introducing Gumiho : A Hybrid Architecture for Enhanced Speculative Decoding 🌟 In the realm of Large Language Models (LLMs), speculative decoding (SPD) serves as a pivotal technique for ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/abdullah-kasri_gumiho-a-hybrid-architecture-to-prioritize-activity-7306519551876161536-HoDU", "content": "🌟 Introducing Gumiho : A Hybrid Architecture for Enhanced Speculative Decoding 🌟 In the realm of Large Language Models (LLMs), speculative decoding (SPD) serves as a pivotal technique for ..."}
+{"idx": 6, "title": "Speculative Decoding via Hybrid Drafting and Rollback-Aware Branch ...", "date": "", "ddg_snippet": "Figure 3 : Empirical results of different drafting length estimation strategies: (a,b) comparison of T-SNE visualization for the explicit and the proposed hybrid methods from the MLP activations; (c) both implicit and explicit drafting structures have limitations of low prediction accuracy of the accepted draft length; (d) impact of different ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.01979", "content": "Figure 3 : Empirical results of different drafting length estimation strategies: (a,b) comparison of T-SNE visualization for the explicit and the proposed hybrid methods from the MLP activations; (c) both implicit and explicit drafting structures have limitations of low prediction accuracy of the accepted draft length; (d) impact of different ..."}
+{"idx": 7, "title": "Geralt-Targaryen/Awesome-Speculative-Decoding - GitHub", "date": "", "ddg_snippet": "\" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding \" [2025-03] [paper] EAGLE for the first two draft tokens, Medusa for the next 5.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Geralt-Targaryen/Awesome-Speculative-Decoding", "content": "\" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding \" [2025-03] [paper] EAGLE for the first two draft tokens, Medusa for the next 5."}
+{"idx": 8, "title": "dblp: Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Bibliographic details on Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2503-10135", "content": "Bibliographic details on Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding ."}
+{"idx": 9, "title": "Accelerating LLMs with Smarter Token Prioritization", "date": "", "ddg_snippet": "Gumiho : A hybrid approach to optimize speculative decoding Gumiho introduces a novel hybrid architecture that intelligently prioritizes early tokens in speculative decoding to significantly accelerate LLM inference.", "subpage_snippet": "", "source": "www.zerna.io", "link": "https://www.zerna.io/page/engineering/presentation_set/engineering-llm-research/presentation/engineering-model-optimization/slide/engineering-paper-2503_10135", "content": "Gumiho : A hybrid approach to optimize speculative decoding Gumiho introduces a novel hybrid architecture that intelligently prioritizes early tokens in speculative decoding to significantly accelerate LLM inference."}
diff --git a/data/sampled_jsons/Gumiho_paper_Table_3_wall_time_components_1st_serial_head_2.80ms_2nd_3.46ms.jsonl b/data/sampled_jsons/Gumiho_paper_Table_3_wall_time_components_1st_serial_head_2.80ms_2nd_3.46ms.jsonl
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index 0000000000000000000000000000000000000000..5dd6416a5ac4910f177eef7005c1d226a63e373d
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_paper_Table_3_wall_time_components_1st_serial_head_2.80ms_2nd_3.46ms.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Kumiho - Wikipedia", "date": "", "ddg_snippet": "A kumiho or gumiho (Korean: 구미호; Hanja: 九尾狐; lit. nine-tailed fox) is a creature that appears in the folktales of East Asia and legends of Korea. It is similar to the Chinese jiuweihu, the Japanese kitsune and the Vietnamese hồ ly tinh. \"The term 'gumiho' (구미호) literally means 'nine-tailed fox.' .In Korean colloquial usage, gumiho is often applied to describe a woman ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Kumiho", "content": "A kumiho or gumiho (Korean: 구미호; Hanja: 九尾狐; lit. nine-tailed fox) is a creature that appears in the folktales of East Asia and legends of Korea. It is similar to the Chinese jiuweihu, the Japanese kitsune and the Vietnamese hồ ly tinh. \"The term 'gumiho' (구미호) literally means 'nine-tailed fox.' .In Korean colloquial usage, gumiho is often applied to describe a woman ..."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "Build-ing on this insight, we propose Gumiho , a hy-brid model combining serial and parallel heads . Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135", "content": "Build-ing on this insight, we propose Gumiho , a hy-brid model combining serial and parallel heads . Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 2, "title": "GitHub - AMD-AGI/Gumiho: Official Implementation of \"Gumiho: A Hybrid ...", "date": "", "ddg_snippet": "Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based on the theoretical finding that initial tokens in the draft sequence have a more significant impact on the overall accepted length. Gumiho employs a hybrid head design that combines serial and parallel components .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based on the theoretical finding that initial tokens in the draft sequence have a more significant impact on the overall accepted length. Gumiho employs a hybrid head design that combines serial and parallel components ."}
+{"idx": 3, "title": "Paper page - Gumiho: A Hybrid Architecture to Prioritize Early Tokens ...", "date": "", "ddg_snippet": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads . Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.10135", "content": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads . Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 4, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding: Paper and Code. Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/gumiho-a-hybrid-architecture-to-prioritize", "content": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding: Paper and Code. Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM ..."}
+{"idx": 5, "title": "Gumiho - Monsters - Archives of Nethys: Pathfinder 2nd Edition Database", "date": "", "ddg_snippet": "Terrifyingly, it also gains an insatiable hunger for the livers of people. This hunger is so closely connected to a gumiho's power that if it abstains from eating liver for one thousand days, the gumiho loses its magic and permanently takes on its humanoid guise—also losing its evil nature in the process.", "subpage_snippet": "", "source": "2e.aonprd.com", "link": "https://2e.aonprd.com/Monsters.aspx?ID=1413&Weak=true", "content": "Terrifyingly, it also gains an insatiable hunger for the livers of people. This hunger is so closely connected to a gumiho's power that if it abstains from eating liver for one thousand days, the gumiho loses its magic and permanently takes on its humanoid guise—also losing its evil nature in the process."}
+{"idx": 6, "title": "Multiplication Table 3 - Teaching resources - Wordwall", "date": "", "ddg_snippet": "10,000+ results for 'multiplication table 3' 3's Multiplication Open the box by Rfigueroasauced 3rd Grade Math Multiplication", "subpage_snippet": "", "source": "wordwall.net", "link": "https://wordwall.net/en-us/community/multiplication/table-3", "content": "10,000+ results for 'multiplication table 3' 3's Multiplication Open the box by Rfigueroasauced 3rd Grade Math Multiplication"}
+{"idx": 7, "title": "Celest Gumiho Review (IEM) : r/headphones - Reddit", "date": "", "ddg_snippet": "A place for discussion, news, reviews and DIY projects related to portable audio, headphones, headphone amplifiers and DACs.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/headphones/comments/y0osab/celest_gumiho_review_iem/", "content": "A place for discussion, news, reviews and DIY projects related to portable audio, headphones, headphone amplifiers and DACs."}
+{"idx": 8, "title": "Spawning Tool: gumiho v ShoWTimE: Game 3 - [ESL] Dynasty", "date": "", "ddg_snippet": "Get step-by-step instructions on how to execute this build order below. Start the game timer, and the build order will tick by so you can play in parallel with StarCraft on a second screen or mobile device. Return to Replay", "subpage_snippet": "", "source": "lotv.spawningtool.com", "link": "https://lotv.spawningtool.com/build/190405/playable/", "content": "Get step-by-step instructions on how to execute this build order below. Start the game timer, and the build order will tick by so you can play in parallel with StarCraft on a second screen or mobile device. Return to Replay"}
+{"idx": 9, "title": "Nine-Tailed Fox: A Legend of Korean Mythology", "date": "", "ddg_snippet": "The Gumiho's Desire for Immortality The gumiho's quest for immortality is a central theme in Korean folklore. It's believed that by consuming the livers of 1,000 human victims, the fox can finally shed its animal form and attain eternal life as a human. This desire for immortality is a reflection of the human fear of death and the yearning for a life free from the limitations of time ...", "subpage_snippet": "", "source": "mythologyworldwide.com", "link": "https://mythologyworldwide.com/nine-tailed-fox-a-legend-of-korean-mythology/", "content": "The Gumiho's Desire for Immortality The gumiho's quest for immortality is a central theme in Korean folklore. It's believed that by consuming the livers of 1,000 human victims, the fox can finally shed its animal form and attain eternal life as a human. This desire for immortality is a reflection of the human fear of death and the yearning for a life free from the limitations of time ..."}
diff --git a/data/sampled_jsons/HDR-IPPO_CHDR-IPPO_equation_LPPO_DKL_lambda_regularization_weight.jsonl b/data/sampled_jsons/HDR-IPPO_CHDR-IPPO_equation_LPPO_DKL_lambda_regularization_weight.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f067341cda12e8847a7fbb2bdae8700593f75879
--- /dev/null
+++ b/data/sampled_jsons/HDR-IPPO_CHDR-IPPO_equation_LPPO_DKL_lambda_regularization_weight.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Use Weight Regularization to Reduce Overfitting of Deep Learning Models", "date": "", "ddg_snippet": "When using weight regularization , it is possible to use larger networks with less risk of overfitting. A good configuration strategy may be to start with larger networks and use weight decay.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/weight-regularization-to-reduce-overfitting-of-deep-learning-models/", "content": "When using weight regularization , it is possible to use larger networks with less risk of overfitting. A good configuration strategy may be to start with larger networks and use weight decay."}
+{"idx": 1, "title": "Regularization in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Regularization is a technique used in machine learning to prevent overfitting and performs poorly on unseen data. By adding a penalty for complexity, regularization encourages simpler, more generalizable models.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/regularization-in-machine-learning/", "content": "Regularization is a technique used in machine learning to prevent overfitting and performs poorly on unseen data. By adding a penalty for complexity, regularization encourages simpler, more generalizable models."}
+{"idx": 2, "title": "Overfitting: L2 regularization | Machine Learning - Google Developers", "date": "", "ddg_snippet": "Learn how the L2 regularization metric is calculated and how to set a regularization rate to minimize the combination of loss and complexity during model training, or to use alternative regularization techniques like early stopping.", "subpage_snippet": "", "source": "developers.google.com", "link": "https://developers.google.com/machine-learning/crash-course/overfitting/regularization", "content": "Learn how the L2 regularization metric is calculated and how to set a regularization rate to minimize the combination of loss and complexity during model training, or to use alternative regularization techniques like early stopping."}
+{"idx": 3, "title": "Stay away from overfitting: L2-norm Regularization, Weight ... - Medium", "date": "", "ddg_snippet": "For instance, if you had your weight decay set to 0.0005 as in the AlexNet paper and you move to a deep learning framework which implements L2 regularization instead, you should set lambda ( lambda ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/unpackai/stay-away-from-overfitting-l2-norm-regularization-weight-decay-and-l1-norm-regularization-795bbc5cf958", "content": "For instance, if you had your weight decay set to 0.0005 as in the AlexNet paper and you move to a deep learning framework which implements L2 regularization instead, you should set lambda ( lambda ..."}
+{"idx": 4, "title": "L1 & L2 Weight Regularization - apxml.com", "date": "", "ddg_snippet": "Understand and implement L1, L2 ( Weight Decay), and Elastic Net regularization to prevent overfitting by penalizing large weights .", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/deep-learning-regularization-optimization/chapter-2-weight-regularization", "content": "Understand and implement L1, L2 ( Weight Decay), and Elastic Net regularization to prevent overfitting by penalizing large weights ."}
+{"idx": 5, "title": "4.5. Weight Decay — Dive into Deep Learning 0.1.0 documentation - DJL", "date": "", "ddg_snippet": "4.5.1. Squared Norm Regularization Weight decay (commonly called L2 regularization ), might be the most widely-used technique for regularizing parametric machine learning models. The technique is motivated by the basic intuition that among all functions \\ (f\\), the function \\ (f = 0\\) (assigning the value \\ (0\\) to all inputs) is in some sense the simplest, and that we can measure the ...", "subpage_snippet": "", "source": "d2l.djl.ai", "link": "https://d2l.djl.ai/chapter_multilayer-perceptrons/weight-decay.html", "content": "4.5.1. Squared Norm Regularization Weight decay (commonly called L2 regularization ), might be the most widely-used technique for regularizing parametric machine learning models. The technique is motivated by the basic intuition that among all functions \\ (f\\), the function \\ (f = 0\\) (assigning the value \\ (0\\) to all inputs) is in some sense the simplest, and that we can measure the ..."}
+{"idx": 6, "title": "Understanding the difference between weight decay and L2 regularization", "date": "", "ddg_snippet": "Introduction Machine learning models are powerful tools for solving complex problems, but they can easily become overly complex themselves, leading to overfitting. Regularization techniques help prevent overfitting by imposing constraints on the model's parameters. One common regularization technique is L2 regularization , also known as weight decay. In this blog post, we'll explore the big ...", "subpage_snippet": "", "source": "www.paepper.com", "link": "https://www.paepper.com/blog/posts/understanding-the-difference-between-weight-decay-and-l2-regularization/", "content": "Introduction Machine learning models are powerful tools for solving complex problems, but they can easily become overly complex themselves, leading to overfitting. Regularization techniques help prevent overfitting by imposing constraints on the model's parameters. One common regularization technique is L2 regularization , also known as weight decay. In this blog post, we'll explore the big ..."}
+{"idx": 7, "title": "Explanation of Lambda in Regularization of Linear Regression Cost ...", "date": "", "ddg_snippet": "In this lecture I understand the purpose of Lambda in regularization and its impact. Such as higher values of lambda would lead to smaller value of parameters w. Since we are adding the regularization term how it result in decreasing the parameters w? Can someone explain via mathematical proof? Any links or supporting material highly appreciated. 1686×424 80.3 KB paulinpaloalto July 21, 2024 ...", "subpage_snippet": "", "source": "community.deeplearning.ai", "link": "https://community.deeplearning.ai/t/explanation-of-lambda-in-regularization-of-linear-regression-cost-function/666497", "content": "In this lecture I understand the purpose of Lambda in regularization and its impact. Such as higher values of lambda would lead to smaller value of parameters w. Since we are adding the regularization term how it result in decreasing the parameters w? Can someone explain via mathematical proof? Any links or supporting material highly appreciated. 1686×424 80.3 KB paulinpaloalto July 21, 2024 ..."}
+{"idx": 8, "title": "How to Use Weight Decay to Reduce Overfitting of Neural Network in ...", "date": "", "ddg_snippet": "Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set. There are multiple types of weight regularization , such as L1 and L2 vector norms, and each requires a hyperparameter […]", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/", "content": "Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set. There are multiple types of weight regularization , such as L1 and L2 vector norms, and each requires a hyperparameter […]"}
+{"idx": 9, "title": "Regularization Matters in Policy Optimization - arXiv.org", "date": "", "ddg_snippet": "Various conventional regularization techniques are considered, including L2/L1 weight regularization , dropout, weight clipping (Arjovsky et al., 2017) and Batch Normal-ization (BN) (Ioffe & Szegedy, 2015). We compare the performance of these regularization techniques to that with-out regularization , as well as the entropy regularization .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1910.09191v2", "content": "Various conventional regularization techniques are considered, including L2/L1 weight regularization , dropout, weight clipping (Arjovsky et al., 2017) and Batch Normal-ization (BN) (Ioffe & Szegedy, 2015). We compare the performance of these regularization techniques to that with-out regularization , as well as the entropy regularization ."}
diff --git a/data/sampled_jsons/Hao_Wang_Zhichao_Chen_recommendation_systems_weakly_supervised_learning.jsonl b/data/sampled_jsons/Hao_Wang_Zhichao_Chen_recommendation_systems_weakly_supervised_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..91815d5a4d69cf2dec65815a7f35e4929e9b2622
--- /dev/null
+++ b/data/sampled_jsons/Hao_Wang_Zhichao_Chen_recommendation_systems_weakly_supervised_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "Hao Wang , Zhichao Chen , Honglei Zhang, Zhengnan Li, Licheng Pan, Haoxuan Li, and Mingming Gong. 2025b. Debiased Recommendation via Wasserstein Causal Bal-ancing.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0E5rZOGA13", "content": "Hao Wang , Zhichao Chen , Honglei Zhang, Zhengnan Li, Licheng Pan, Haoxuan Li, and Mingming Gong. 2025b. Debiased Recommendation via Wasserstein Causal Bal-ancing."}
+{"idx": 1, "title": "Practically Unbiased Pairwise Loss for Recommendation With Implicit ...", "date": "", "ddg_snippet": "Recommender systems have been widely employed on various online platforms to improve user experience. In these systems , recommendation models are often learned from the users' historical behaviors that are automatically collected. Notably, recommender systems differ slightly from ordinary supervised learning tasks. In recommender systems , there is an exposure mechanism that decides which items ...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/40030662/", "content": "Recommender systems have been widely employed on various online platforms to improve user experience. In these systems , recommendation models are often learned from the users' historical behaviors that are automatically collected. Notably, recommender systems differ slightly from ordinary supervised learning tasks. In recommender systems , there is an exposure mechanism that decides which items ..."}
+{"idx": 2, "title": "Haoxuan Li's Homepage", "date": "", "ddg_snippet": "Hao Wang , Jiajun Fan, Zhichao Chen , Haoxuan Li, Weiming Liu, Tianqiao Liu, Quanyu Dai, Yichao Wang , Zhenhua Dong and Ruiming Tang NeurIPS 2023 (Full, Accepted Rate: 26.1%)", "subpage_snippet": "", "source": "haoxuanli-pku.github.io", "link": "https://haoxuanli-pku.github.io/", "content": "Hao Wang , Jiajun Fan, Zhichao Chen , Haoxuan Li, Weiming Liu, Tianqiao Liu, Quanyu Dai, Yichao Wang , Zhenhua Dong and Ruiming Tang NeurIPS 2023 (Full, Accepted Rate: 26.1%)"}
+{"idx": 3, "title": "WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation ...", "date": "", "ddg_snippet": "ABSTRACT Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems . Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distin-guish items in future behaviors from others based on the user's his-torical behaviors, have attracted a lot of interest in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.13616", "content": "ABSTRACT Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems . Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distin-guish items in future behaviors from others based on the user's his-torical behaviors, have attracted a lot of interest in ..."}
+{"idx": 4, "title": "A General Framework for Learning from Weak Supervision", "date": "", "ddg_snippet": "Abstract Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algorithms, thereby hindering the practical deployment. This paper introduces a general framework for learning from weak supervision (GLWS) with a novel algorithm.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/chen24ar.html", "content": "Abstract Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algorithms, thereby hindering the practical deployment. This paper introduces a general framework for learning from weak supervision (GLWS) with a novel algorithm."}
+{"idx": 5, "title": "Practically Unbiased Pairwise Loss for Recommendation With Implicit ...", "date": "", "ddg_snippet": "Recommender systems have been widely employed on various online platforms to improve user experience. In these systems , recommendation models are often learned from the users' historical behaviors that are automatically collected. Notably, recommender systems differ slightly from ordinary supervised learning tasks. In recommender systems , there is an exposure mechanism that decides which ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10810273", "content": "Recommender systems have been widely employed on various online platforms to improve user experience. In these systems , recommendation models are often learned from the users' historical behaviors that are automatically collected. Notably, recommender systems differ slightly from ordinary supervised learning tasks. In recommender systems , there is an exposure mechanism that decides which ..."}
+{"idx": 6, "title": "Zhichao Chen - OpenReview", "date": "", "ddg_snippet": "Hao Wang , zhengnan li, Haoxuan Li, Xu Chen , Mingming Gong, BinChen, Zhichao Chen Published: 22 Jan 2025, Last Modified: 02 Apr 2025 ICLR 2025 Poster FreDF: Learning to Forecast in the Frequency Domain Hao Wang , Lichen Pan, Yuan Shen, Zhichao Chen , Degui Yang, Yifei Yang, Sen Zhang, Xinggao Liu, Haoxuan Li, Dacheng Tao", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Zhichao_Chen2", "content": "Hao Wang , zhengnan li, Haoxuan Li, Xu Chen , Mingming Gong, BinChen, Zhichao Chen Published: 22 Jan 2025, Last Modified: 02 Apr 2025 ICLR 2025 Poster FreDF: Learning to Forecast in the Frequency Domain Hao Wang , Lichen Pan, Yuan Shen, Zhichao Chen , Degui Yang, Yifei Yang, Sen Zhang, Xinggao Liu, Haoxuan Li, Dacheng Tao"}
+{"idx": 7, "title": "Aspect-Enhanced Explainable Recommendation with Multi-modal Contrastive ...", "date": "", "ddg_snippet": "Explainable recommender systems (ERS) aim to enhance users' trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3673234", "content": "Explainable recommender systems (ERS) aim to enhance users' trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the ..."}
+{"idx": 8, "title": "PDF Entire Space Counterfactual Learning for Reliable Content Recommendations", "date": "", "ddg_snippet": "Entire Space Counterfactual Learning for Reliable Content Recommendations Hao Wang , Zhichao Chen , Zhaoran Liu , Haozhe Li , Degui Yang , Xinggao Liu, and Haoxuan Li, Member, IEEE", "subpage_snippet": "", "source": "haoxuanli-pku.github.io", "link": "https://haoxuanli-pku.github.io/papers/TIFS+-+Entire+Space+Counterfactual+Learning+for+Reliable+Content+Recommendations.pdf", "content": "Entire Space Counterfactual Learning for Reliable Content Recommendations Hao Wang , Zhichao Chen , Zhaoran Liu , Haozhe Li , Degui Yang , Xinggao Liu, and Haoxuan Li, Member, IEEE"}
+{"idx": 9, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "Poster Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning Eric Wang · Zhichao Chen · Haotian Wang · Yanchao Tan · Licheng Pan · Tianqiao Liu · Xu Chen · Haoxuan Li · Zhouchen Lin West Exhibition Hall B2-B3 #W-403", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "Poster Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning Eric Wang · Zhichao Chen · Haotian Wang · Yanchao Tan · Licheng Pan · Tianqiao Liu · Xu Chen · Haoxuan Li · Zhouchen Lin West Exhibition Hall B2-B3 #W-403"}
diff --git a/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function_allowing_overlaps_reduce_cost.jsonl b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function_allowing_overlaps_reduce_cost.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf63c8066abd5652c70fd932565a382547ba8365
--- /dev/null
+++ b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function_allowing_overlaps_reduce_cost.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function ...", "date": "", "ddg_snippet": "Poster Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46447", "content": "Poster Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009"}
+{"idx": 1, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "Sep 26, 2024 · Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "Sep 26, 2024 · Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 2, "title": "Overlapping Hierarchical Clustering (OHC) - inria.hal.science", "date": "", "ddg_snippet": "Overlapping clustering : Fuzzy clustering methods [6] allow that certain data points belong to multiple clusters with a di erent level of con dence. In this way, the boundary of clusters is fuzzy and we can talk about overlaps of these clusters.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "Overlapping clustering : Fuzzy clustering methods [6] allow that certain data points belong to multiple clusters with a di erent level of con dence. In this way, the boundary of clusters is fuzzy and we can talk about overlaps of these clusters."}
+{"idx": 3, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function ...", "date": "", "ddg_snippet": "This research paper introduces a new way to group data points in a more complex and realistic manner called hierarchical overlapping clustering (HOC). It combines two methods: hierarchical cluster ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46447/paper", "content": "This research paper introduces a new way to group data points in a more complex and realistic manner called hierarchical overlapping clustering (HOC). It combines two methods: hierarchical cluster ..."}
+{"idx": 4, "title": "Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "Abstract This paper presents two eficient hierarchical clus-tering (HC) algorithms with respect to Dasgupta’s cost function . For any input graph G with a clear cluster -structure, our designed algorithms run in nearly-linear time in the input size of G, and re-turn an O(1)-approximate HC tree with respect to Dasgupta’s cost function . We compare the perfor-mance of our algorithm against the ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/laenen23a/laenen23a.pdf", "content": "Abstract This paper presents two eficient hierarchical clus-tering (HC) algorithms with respect to Dasgupta’s cost function . For any input graph G with a clear cluster -structure, our designed algorithms run in nearly-linear time in the input size of G, and re-turn an O(1)-approximate HC tree with respect to Dasgupta’s cost function . We compare the perfor-mance of our algorithm against the ..."}
+{"idx": 5, "title": "Hierarchical Clustering: O(1)-Approximation for Well ...", "date": "", "ddg_snippet": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2021/file/4d68e143defa221fead61c84de7527a3-Paper.pdf", "content": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ..."}
+{"idx": 6, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — TL;DR: We have proposed a cost function for hierarchical overlapping clustering on graphs , and developed an approximation algorithm for it.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "by Y Pan — TL;DR: We have proposed a cost function for hierarchical overlapping clustering on graphs , and developed an approximation algorithm for it."}
+{"idx": 7, "title": "Nearly-Optimal Hierarchical Clustering for Well- ...", "date": "", "ddg_snippet": "by S Laenen · 2023 · Cited by 8 — This paper presents two efficient hierarchical clustering (HC) algorithms with respect to. Dasgupta's cost function . For any input graph G with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.09950", "content": "by S Laenen · 2023 · Cited by 8 — This paper presents two efficient hierarchical clustering (HC) algorithms with respect to. Dasgupta's cost function . For any input graph G with ..."}
+{"idx": 8, "title": "Revisiting agglomerative clustering", "date": "", "ddg_snippet": "by EK Tokuda · 2022 · Cited by 131 — Hierarchical clustering methods may construct the hierarchy in two opposite directions, bottom-up (agglomerative) and top-down (divisive). Despite conceptually ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0378437121007068", "content": "by EK Tokuda · 2022 · Cited by 131 — Hierarchical clustering methods may construct the hierarchy in two opposite directions, bottom-up (agglomerative) and top-down (divisive). Despite conceptually ..."}
+{"idx": 9, "title": "Overlapping Community Detection based on Network ...", "date": "", "ddg_snippet": "by Z Ding · 2016 · Cited by 110 — In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/srep24115", "content": "by Z Ding · 2016 · Cited by 110 — In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD."}
diff --git a/data/sampled_jsons/Hinton_Vinyals_Dean_2015_Distilling_Knowledge_Neural_Network_abstract_arxiv1503.02531_year_2015.jsonl b/data/sampled_jsons/Hinton_Vinyals_Dean_2015_Distilling_Knowledge_Neural_Network_abstract_arxiv1503.02531_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..386a55988049709e59302e8c41aa6bd22208e545
--- /dev/null
+++ b/data/sampled_jsons/Hinton_Vinyals_Dean_2015_Distilling_Knowledge_Neural_Network_abstract_arxiv1503.02531_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1503.02531] Distilling the Knowledge in a Neural Network", "date": "", "ddg_snippet": "View a PDF of the paper titled Distilling the Knowledge in a Neural Network , by Geoffrey Hinton and 2 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1503.02531", "content": "View a PDF of the paper titled Distilling the Knowledge in a Neural Network , by Geoffrey Hinton and 2 other authors"}
+{"idx": 1, "title": "Teaching the Teacher: Improving Neural Network Distillability", "date": "", "ddg_snippet": "... Hinton et al. ( Hinton et al., 2015 ) , knowledge distillation is a technique where a compact “student” model is trained to reproduce the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22767v1", "content": "... Hinton et al. ( Hinton et al., 2015 ) , knowledge distillation is a technique where a compact “student” model is trained to reproduce the ..."}
+{"idx": 2, "title": "KD2M: A unifying framework for feature knowledge distillation", "date": "", "ddg_snippet": "Indeed, in their seminal work Hinton et al., ( 2015 ) , Hinton , Vinyals , and Dean proposed to distill the knowledge of a teacher toward a student ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.01757v2", "content": "Indeed, in their seminal work Hinton et al., ( 2015 ) , Hinton , Vinyals , and Dean proposed to distill the knowledge of a teacher toward a student ..."}
+{"idx": 3, "title": "Geoffrey Hinton - Distilling the Knowledge in a Neural Network", "date": "", "ddg_snippet": "... Hinton - Distilling the Knowledge in a Neural Network ... Distilling the knowledge in a neural network .\" arXiv preprint arXiv : 1503 . 02531 ( 2015 ).", "subpage_snippet": "", "source": "www.tomrochette.com", "link": "https://www.tomrochette.com/machine-learning/papers/geoffrey-hinton-distilling-the-knowledge-in-a-neural-network", "content": "... Hinton - Distilling the Knowledge in a Neural Network ... Distilling the knowledge in a neural network .\" arXiv preprint arXiv : 1503 . 02531 ( 2015 )."}
+{"idx": 4, "title": "Distilling the Knowledge in a Neural Network — All Things", "date": "", "ddg_snippet": "The authors generalize the existing approach of knowledge transfer between different models, which they claim is a special case of their distillation ...", "subpage_snippet": "", "source": "allthingsphi.com", "link": "https://allthingsphi.com/blog/2016/11/11/distilling-the-knowledge-in-a-neural-network.html", "content": "The authors generalize the existing approach of knowledge transfer between different models, which they claim is a special case of their distillation ..."}
+{"idx": 5, "title": "Neural Networks - Chessprogramming wiki", "date": "", "ddg_snippet": "Artificial Neural Networks ( ANNs ) are a family of statistical learning devices or algorithms used in regression , and binary or multiclass ...", "subpage_snippet": "", "source": "www.chessprogramming.org", "link": "https://www.chessprogramming.org/Neural_Networks", "content": "Artificial Neural Networks ( ANNs ) are a family of statistical learning devices or algorithms used in regression , and binary or multiclass ..."}
+{"idx": 6, "title": "BigNAS: Scaling up Neural Architecture Search with Big", "date": "", "ddg_snippet": "Hinton , G., Vinyals , O., Dean , J.: Distilling the knowledge in a neural network . ... H., Guan, M.Y., Zoph, B., Le, Q.V., Dean , J.: Efficient neural ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/978-3-030-58571-6_41", "content": "Hinton , G., Vinyals , O., Dean , J.: Distilling the knowledge in a neural network . ... H., Guan, M.Y., Zoph, B., Le, Q.V., Dean , J.: Efficient neural ..."}
+{"idx": 7, "title": "Label Smoothing++: Enhanced Label Regularization for Training", "date": "", "ddg_snippet": "Knowledge distillation is considered a form of label regularization that involves generating targets from a larger network (the Teacher) and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.05307v1", "content": "Knowledge distillation is considered a form of label regularization that involves generating targets from a larger network (the Teacher) and ..."}
+{"idx": 8, "title": "Efficient Pomegranate Segmentation with UNet: A Comparative", "date": "", "ddg_snippet": "... networks for biomedical im- age segmentation, in Medical Image Computing and Computer-Assisted Intervention- MICCAI2015: 18th International ...", "subpage_snippet": "", "source": "www.itm-conferences.org", "link": "https://www.itm-conferences.org/articles/itmconf/ref/2023/04/itmconf_I3cs2023_01001/itmconf_I3cs2023_01001.html", "content": "... networks for biomedical im- age segmentation, in Medical Image Computing and Computer-Assisted Intervention- MICCAI2015: 18th International ..."}
+{"idx": 9, "title": "EEG Emotion Recognition Based on Self-Distillation", "date": "", "ddg_snippet": "... according to the relative positions of the electrode channels, and the self-distillation network is employed to extract local high-level abstract ...", "subpage_snippet": "", "source": "publications.eai.eu", "link": "https://publications.eai.eu/index.php/el/article/view/4974", "content": "... according to the relative positions of the electrode channels, and the self-distillation network is employed to extract local high-level abstract ..."}
diff --git a/data/sampled_jsons/History-Driven_Target_HDT_MCMC_LRU_cache_visit_frequency_approximation_year_2024.jsonl b/data/sampled_jsons/History-Driven_Target_HDT_MCMC_LRU_cache_visit_frequency_approximation_year_2024.jsonl
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index 0000000000000000000000000000000000000000..e2d518db0dc4e6822cbe005e3e4ede93a40769a8
--- /dev/null
+++ b/data/sampled_jsons/History-Driven_Target_HDT_MCMC_LRU_cache_visit_frequency_approximation_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "We propose a history - driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "We propose a history - driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ..."}
+{"idx": 1, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "We propose a history - driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46659", "content": "We propose a history - driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ..."}
+{"idx": 2, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "27 Jul 2025 — We propose a history - driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "27 Jul 2025 — We propose a history - driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state ..."}
+{"idx": 3, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "We propose a history - driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165013", "content": "We propose a history - driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ..."}
+{"idx": 4, "title": "Track: Poster Session 5 East", "date": "", "ddg_snippet": "17 Jul 2025 — We propose a * history - driven target ( HDT )* framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50267", "content": "17 Jul 2025 — We propose a * history - driven target ( HDT )* framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete ..."}
+{"idx": 5, "title": "algorithm performs significantly: Topics by ...", "date": "", "ddg_snippet": "... cache operating under the LRU ( Least-Recently-Used ) replacement policy. Three of the algorithms are SIMD, and are implemented on the MasPar MP-2 ...", "subpage_snippet": "", "source": "www.science.gov", "link": "https://www.science.gov/topicpages/a/algorithm+performs+significantly", "content": "... cache operating under the LRU ( Least-Recently-Used ) replacement policy. Three of the algorithms are SIMD, and are implemented on the MasPar MP-2 ..."}
+{"idx": 6, "title": "IPDPS 2007 Abstracts and CD-ROM", "date": "", "ddg_snippet": "27 Mar 2007 — More specifically, we target on-chip cache resource allocation and efficiency needed for guaranteeing certain performance levels on Chip ...", "subpage_snippet": "", "source": "websrv.cecs.uci.edu", "link": "https://websrv.cecs.uci.edu/~papers/ipdps07/Abstracts.pdf", "content": "27 Mar 2007 — More specifically, we target on-chip cache resource allocation and efficiency needed for guaranteeing certain performance levels on Chip ..."}
+{"idx": 7, "title": "Ground-Plane Classification For Robot Navigation C PDF", "date": "", "ddg_snippet": "This document provides information about the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/469167727/Ground-plane-classification-for-robot-navigation-C-pdf", "content": "This document provides information about the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)"}
+{"idx": 8, "title": "A Bibliography on Data Compression", "date": "", "ddg_snippet": "by NHF Beebe · 2025 — Nelson H. F. Beebe. University of Utah. Department of Mathematics, 110 LCB. 155 S 1400 E RM 233. Salt Lake City, UT 84112-0090. USA. Tel: +1 801 581 5254. 832 pages", "subpage_snippet": "", "source": "www.netlib.org", "link": "https://www.netlib.org/tex/bib/datacompression.pdf", "content": "by NHF Beebe · 2025 — Nelson H. F. Beebe. University of Utah. Department of Mathematics, 110 LCB. 155 S 1400 E RM 233. Salt Lake City, UT 84112-0090. USA. Tel: +1 801 581 5254. 832 pages"}
+{"idx": 9, "title": "Mobile Communications", "date": "", "ddg_snippet": "6 Sept 1996 — IPIP was founded in 1960 under the auspices of UNESCO, following the First World. Computer Congress held in Paris the previous year. An umbrella ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-0-387-34980-0.pdf", "content": "6 Sept 1996 — IPIP was founded in 1960 under the auspices of UNESCO, following the First World. Computer Congress held in Paris the previous year. An umbrella ..."}
diff --git a/data/sampled_jsons/Hoeffding_error_term_formula_Rs(k).jsonl b/data/sampled_jsons/Hoeffding_error_term_formula_Rs(k).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..29e6a9e03ab8ff332fae37581029c43db921f250
--- /dev/null
+++ b/data/sampled_jsons/Hoeffding_error_term_formula_Rs(k).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Binomial distribution - Wikipedia", "date": "", "ddg_snippet": "The formula can be understood as follows: pk qn− k is the probability of obtaining the sequence of n independent Bernoulli trials in which k trials are \"successes\" and the remaining n − k trials result in \"failure\". Hoeffding 's inequality yields the simple bound.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Binomial_distribution", "content": "The formula can be understood as follows: pk qn− k is the probability of obtaining the sequence of n independent Bernoulli trials in which k trials are \"successes\" and the remaining n − k trials result in \"failure\". Hoeffding 's inequality yields the simple bound."}
+{"idx": 1, "title": "Understanding Hoeffding ’s Inequality — Part 2 | by Helene | Medium", "date": "", "ddg_snippet": "Hoeffding ’s Inequality provides a bound on the probability that the sum of bounded independent random variables deviates from its expected value by more than a certain amount. Before we continue, let us take a peek at Hoeffding ’s Inequality", "subpage_snippet": "", "source": "helenedk.medium.com", "link": "https://helenedk.medium.com/understanding-hoeffdings-inequality-part-2-21044a334c1c", "content": "Hoeffding ’s Inequality provides a bound on the probability that the sum of bounded independent random variables deviates from its expected value by more than a certain amount. Before we continue, let us take a peek at Hoeffding ’s Inequality"}
+{"idx": 2, "title": "Hoeffding Bound // University of Oldenburg", "date": "", "ddg_snippet": "The Koller & Friedman formulas are 'one-sided' because the bounds are sensitive to the direction of the deviation, whereas Murphy (Murphy, 2012, (6.67), p.209) presents a 'two-sided' version.The Church program is a translation of Dümbgens formula .", "subpage_snippet": "", "source": "uol.de", "link": "https://uol.de/en/lcs/probabilistic-programming/webchurch-and-openbugs/hoeffding-bound", "content": "The Koller & Friedman formulas are 'one-sided' because the bounds are sensitive to the direction of the deviation, whereas Murphy (Murphy, 2012, (6.67), p.209) presents a 'two-sided' version.The Church program is a translation of Dümbgens formula ."}
+{"idx": 3, "title": "probability - Is Hoeffding 's bound tight in any way? - Mathematics...", "date": "", "ddg_snippet": "Yes, one such case is when all the random variables are uniform in $[0,1]$. The sum of uniform random variables has the Irwin-Hall distribution and its tail bound matches the one given by Hoeffding 's inequality. See Corollary 5 here.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/571032/is-hoeffdings-bound-tight-in-any-way", "content": "Yes, one such case is when all the random variables are uniform in $[0,1]$. The sum of uniform random variables has the Irwin-Hall distribution and its tail bound matches the one given by Hoeffding 's inequality. See Corollary 5 here."}
+{"idx": 4, "title": "Concentration bounds. Part 4: Hoeffding inequality | Aleksandar Petrov", "date": "", "ddg_snippet": "Hoeffding ’s inequality is one of the most frequently used bounds. It can be applied to sums of bounded independent random variables. Here “bounded” is the keyword: it means that every variable $X_i$ can only take values in $[a_i, b_i]$.", "subpage_snippet": "", "source": "blog.p-petrov.com", "link": "https://blog.p-petrov.com/2021-03-08/concentration-bounds-4-hoeffding", "content": "Hoeffding ’s inequality is one of the most frequently used bounds. It can be applied to sums of bounded independent random variables. Here “bounded” is the keyword: it means that every variable $X_i$ can only take values in $[a_i, b_i]$."}
+{"idx": 5, "title": "Incremental decision trees in river: the Hoeffding Tree case - River", "date": "", "ddg_snippet": "Nonetheless, Hoeffding Trees (HT) are historically the most popular family of iDTs to date. In fact, HTs have some nice properties: one-pass learning regime error .append(checkpoint[metric_name].get()) #. Convert timedelta object into seconds.", "subpage_snippet": "", "source": "riverml.xyz", "link": "https://riverml.xyz/0.15.0/recipes/on-hoeffding-trees/", "content": "Nonetheless, Hoeffding Trees (HT) are historically the most popular family of iDTs to date. In fact, HTs have some nice properties: one-pass learning regime error .append(checkpoint[metric_name].get()) #. Convert timedelta object into seconds."}
+{"idx": 6, "title": "Margin of Error", "date": "", "ddg_snippet": "This lesson defines the margin of error and describes step-by-step how to compute the margin of error . Includes sample problem with solution.", "subpage_snippet": "", "source": "stattrek.com", "link": "https://stattrek.com/estimation/margin-of-error", "content": "This lesson defines the margin of error and describes step-by-step how to compute the margin of error . Includes sample problem with solution."}
+{"idx": 7, "title": "Hoeffding : miRNA target prediction with the Hoeffding correlation...", "date": "", "ddg_snippet": "Calculate the Hoeffding correlation coefficient of each pair of miRNA-mRNA,and return a matrix of correlation coefficients with columns are miRNAs and rows are mRNAs.Usage. 1. Hoeffding (datacsv, cause, effect, targetbinding = NA). Arguments.", "subpage_snippet": "", "source": "rdrr.io", "link": "https://rdrr.io/bioc/miRLAB/man/Hoeffding.html", "content": "Calculate the Hoeffding correlation coefficient of each pair of miRNA-mRNA,and return a matrix of correlation coefficients with columns are miRNAs and rows are mRNAs.Usage. 1. Hoeffding (datacsv, cause, effect, targetbinding = NA). Arguments."}
+{"idx": 8, "title": "Arithmetic Sequences: A Formula for the ' n - th ' Term - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=lj_X9JVSF8k", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features."}
+{"idx": 9, "title": "Некоторые методы оценки важности переменных в статистике.", "date": "", "ddg_snippet": "Траектория исследований – человек, природа, технологии, выпуск 2, 2025. 81. Hunter, J. E. Methods of Meta-Analysis: Correcting Error and Bias in Research Findings / J. E. Hunter, F. L. Schmidt. – Thousand Oaks, CA: Sage Publications, 2004. – 617 p.", "subpage_snippet": "", "source": "restrajectory.ru", "link": "https://restrajectory.ru/14-4.pdf", "content": "Траектория исследований – человек, природа, технологии, выпуск 2, 2025. 81. Hunter, J. E. Methods of Meta-Analysis: Correcting Error and Bias in Research Findings / J. E. Hunter, F. L. Schmidt. – Thousand Oaks, CA: Sage Publications, 2004. – 617 p."}
diff --git a/data/sampled_jsons/HtmlRAG_'Prune-Gen'_model_'LLM_Chat'_model_Table_4.jsonl b/data/sampled_jsons/HtmlRAG_'Prune-Gen'_model_'LLM_Chat'_model_Table_4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..59538451e8682268aa78e838c6e09832dabb2946
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_'Prune-Gen'_model_'LLM_Chat'_model_Table_4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain...", "date": "", "ddg_snippet": "Parameter max_node_words is removed from class GenHTMLPruner since v0.1.0. If you switch from htmlrag v0.0. 4 to v0.0.5, please download the latest version of modeling files for Gerative HTML Pruners, which are available at modeling _llama.py, and modeling _phi3.py.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "Parameter max_node_words is removed from class GenHTMLPruner since v0.1.0. If you switch from htmlrag v0.0. 4 to v0.0.5, please download the latest version of modeling files for Gerative HTML Pruners, which are available at modeling _llama.py, and modeling _phi3.py."}
+{"idx": 1, "title": "Implementing HtmlRAG: Enhancing Retrieval-Augmented ...", "date": "", "ddg_snippet": "Nov 21, 2024 · Implementing HtmlRAG enhances RAG systems by utilizing the rich structural and semantic information present in HTML documents. By carefully cleaning, structuring, and pruning HTML content, we can provide LLMs with more informative inputs, leading to better understanding and generation of responses.", "subpage_snippet": "", "source": "blog.devgenius.io", "link": "https://blog.devgenius.io/implementing-htmlrag-enhancing-retrieval-augmented-generation-with-html-knowledge-91cdd6278e23", "content": "Nov 21, 2024 · Implementing HtmlRAG enhances RAG systems by utilizing the rich structural and semantic information present in HTML documents. By carefully cleaning, structuring, and pruning HTML content, we can provide LLMs with more informative inputs, leading to better understanding and generation of responses."}
+{"idx": 2, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Analysis of inference cost on ELI5 dataset We compare the chunking-based refiner using BGE (BGE), the two HTML pruning steps basing on the text embedding ( Prune -Embed) and the generative model ( Prune-Gen ) in HtmlRAG , and LLM chatting ( LLM Chat ) by model parameters, storage, average input tokens, and average output tokens.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "Analysis of inference cost on ELI5 dataset We compare the chunking-based refiner using BGE (BGE), the two HTML pruning steps basing on the text embedding ( Prune -Embed) and the generative model ( Prune-Gen ) in HtmlRAG , and LLM chatting ( LLM Chat ) by model parameters, storage, average input tokens, and average output tokens."}
+{"idx": 3, "title": "zstanjj/HTML-Pruner-Phi-3.8B · Hugging Face", "date": "", "ddg_snippet": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/zstanjj/HTML-Pruner-Phi-3.8B", "content": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems."}
+{"idx": 4, "title": "HTML-Pruner-Llama-1B · Models", "date": "", "ddg_snippet": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning ...", "subpage_snippet": "", "source": "www.modelscope.cn", "link": "https://www.modelscope.cn/models/zstanjj/HTML-Pruner-Llama-1B", "content": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning ..."}
+{"idx": 5, "title": "Paper tables with annotated results for HtmlRAG ... | Papers With Code", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems. Table 2. Results of HtmlRAG without pruning and baselines under the long-context setting. Hit@1 is the proportion of instances where at least one short answer matches.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/htmlrag-html-is-better-than-plain-text-for/review/", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems. Table 2. Results of HtmlRAG without pruning and baselines under the long-context setting. Hit@1 is the proportion of instances where at least one short answer matches."}
+{"idx": 6, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "w/o Prune - Gen . This table presents the ablation study results for the HtmlRAG model . It shows the impact of removing key components of the model , such as the block tree structure, the text embedding-based pruning, and the generative model -based pruni...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2411.02959/", "content": "w/o Prune - Gen . This table presents the ablation study results for the HtmlRAG model . It shows the impact of removing key components of the model , such as the block tree structure, the text embedding-based pruning, and the generative model -based pruni..."}
+{"idx": 7, "title": "Paper page - HtmlRAG : HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We compare HtmlRAG and rule-based HTML 2Markdown converter markdownify in Table 2. In our experiment, for Llama chat model , Markdown format is not as good as plain text or HTML .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "We compare HtmlRAG and rule-based HTML 2Markdown converter markdownify in Table 2. In our experiment, for Llama chat model , Markdown format is not as good as plain text or HTML ."}
+{"idx": 8, "title": "HTMLRAG , Multimodal RAG , and Agentic RAG : A Technical Deep Dive", "date": "", "ddg_snippet": "By keeping the original HTML structure, HTMLRAG aims to preserve these “hidden” contextual clues for the language model .The final pruned HTML is then provided to the LLM along with the query for answer generation .", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/htmlrag-multimodal-rag-agentic-technical-deep-dive-nagesh-nama-pv9be", "content": "By keeping the original HTML structure, HTMLRAG aims to preserve these “hidden” contextual clues for the language model .The final pruned HTML is then provided to the LLM along with the query for answer generation ."}
+{"idx": 9, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "HTML , Retrieval-Augmented Generation, Large Language Model . HTML pruning steps basing on the text embedding (Prune-. Embed) and the generative model ( Prune - Gen ) in HtmlRAG , and LLM chatting ( LLM Chat ) by model parameters, storage", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "HTML , Retrieval-Augmented Generation, Large Language Model . HTML pruning steps basing on the text embedding (Prune-. Embed) and the generative model ( Prune - Gen ) in HtmlRAG , and LLM chatting ( LLM Chat ) by model parameters, storage"}
diff --git a/data/sampled_jsons/HtmlRAG_BGE_baseline_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_sitepaperswithcode.com_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_BGE_baseline_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_sitepaperswithcode.com_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b1840dffda6323a5ad81a7e7f1fe3196e145a4de
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_BGE_baseline_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_sitepaperswithcode.com_year_2024.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "Paper tables with annotated results for HtmlRAG ... | Papers With Code", "date": "", "ddg_snippet": "Table 1. Results of HtmlRAG and baselines under the short-context setting. Hit @ 1 is the proportion of instances where at least one short answer matches. The best and second best results are in bold and underlined. Llama - 3 . 1 -8 B - Instruct -128K. Vanilla HTML .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/htmlrag-html-is-better-than-plain-text-for/review/", "content": "Table 1. Results of HtmlRAG and baselines under the short-context setting. Hit @ 1 is the proportion of instances where at least one short answer matches. The best and second best results are in bold and underlined. Llama - 3 . 1 -8 B - Instruct -128K. Vanilla HTML ."}
+{"idx": 1, "title": "A Practice of Post-Training on Llama - 3 70 B with... | Papers With Code", "date": "", "ddg_snippet": "In this paper, we perform CPT on Llama - 3 8B and 70 B to enhance its Chinese ability.We deploy the final 70 B version of LLM on an real-life chat system which obtain satisfying performance.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/a-practice-of-post-training-on-llama-3-70b", "content": "In this paper, we perform CPT on Llama - 3 8B and 70 B to enhance its Chinese ability.We deploy the final 70 B version of LLM on an real-life chat system which obtain satisfying performance."}
+{"idx": 2, "title": "Paper tables with annotated results for Improving... | Papers With Code", "date": "", "ddg_snippet": "Table 10: Results on ToH with 3 disks with a smaller LLM ( Llama 3 - 70 B ). MAP with Llama 3 - 70 B even outperforms the best GPT-4 baseline (GPT-4 ICL).", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/a-prefrontal-cortex-inspired-architecture-for/review/", "content": "Table 10: Results on ToH with 3 disks with a smaller LLM ( Llama 3 - 70 B ). MAP with Llama 3 - 70 B even outperforms the best GPT-4 baseline (GPT-4 ICL)."}
+{"idx": 3, "title": "Paper tables with annotated results for... | Papers With Code", "date": "", "ddg_snippet": "Measure helpfulness based on the model’s ability to assist users, considering the question’s intent.OpenMeditron/Meditron3- 70 B . Hugging Face. Llama - 3 . 1 - 70 B - Instruct .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/a-benchmark-for-long-form-medical-question/review/", "content": "Measure helpfulness based on the model’s ability to assist users, considering the question’s intent.OpenMeditron/Meditron3- 70 B . Hugging Face. Llama - 3 . 1 - 70 B - Instruct ."}
+{"idx": 4, "title": "Paper tables with annotated results for... | Papers With Code", "date": "", "ddg_snippet": "Our results show that Smartify (Gemma2+codegemma) achieves state-of-the-art performance, surpassing existing LLMs and enhancing general-purpose models' capabilities, such as Llama 3 . 1 . Instruction .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/a-multi-agent-framework-for-automated/review/", "content": "Our results show that Smartify (Gemma2+codegemma) achieves state-of-the-art performance, surpassing existing LLMs and enhancing general-purpose models' capabilities, such as Llama 3 . 1 . Instruction ."}
+{"idx": 5, "title": "Paper tables with annotated results for LLMs... | Papers With Code", "date": "", "ddg_snippet": "Table 3: Overall score and refuse rate given by GPT-4o, GPT-4o-mini and Llama 3 .3- 70 B - Instruct .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/2506-10022/review/", "content": "Table 3: Overall score and refuse rate given by GPT-4o, GPT-4o-mini and Llama 3 .3- 70 B - Instruct ."}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/HtmlRAG_HTML_Better_Plain_Text_RAG_Systems_Section_3.4_Prune-Embed_methodology_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_HTML_Better_Plain_Text_RAG_Systems_Section_3.4_Prune-Embed_methodology_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..531f6860b19b54350fc0fba418b4f7be4f2b0662
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_HTML_Better_Plain_Text_RAG_Systems_Section_3.4_Prune-Embed_methodology_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - plageon/HtmlRAG: HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new chal-lenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v1", "content": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new chal-lenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system ."}
+{"idx": 2, "title": "HTMLRAG, Multimodal RAG, and Agentic RAG - LinkedIn", "date": "", "ddg_snippet": "HTMLRAG is a recent enhancement to retrieval-augmented generation that works directly with HTML content instead of plain text . The motivation is that when we strip web pages down to plain text , we ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/htmlrag-multimodal-rag-agentic-technical-deep-dive-nagesh-nama-pv9be", "content": "HTMLRAG is a recent enhancement to retrieval-augmented generation that works directly with HTML content instead of plain text . The motivation is that when we strip web pages down to plain text , we ..."}
+{"idx": 3, "title": "HtmlRAG/toolkit/README.md at main · plageon/HtmlRAG", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - plageon/ HtmlRAG", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG/blob/main/toolkit/README.md", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - plageon/ HtmlRAG"}
+{"idx": 4, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems この論文は、Retrieval-Augmented Generation（RAG）システムにおいて、取得した知識の形式としてプレーンテキストの代わりにHTMLを使用することが、情報の構造と意味をより豊かに保持できることを示す研究です。 論文： https://arxiv.org ...", "subpage_snippet": "", "source": "hdkworks.com", "link": "https://hdkworks.com/archives/34876", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems この論文は、Retrieval-Augmented Generation（RAG）システムにおいて、取得した知識の形式としてプレーンテキストの代わりにHTMLを使用することが、情報の構造と意味をより豊かに保持できることを示す研究です。 論文： https://arxiv.org ..."}
+{"idx": 5, "title": "A Practical Approach to Retrieval Augmented Generation Systems - 3 RAG ...", "date": "", "ddg_snippet": "Furthermore, we'll explore retrieval chunks versus synthesis chunks and ways to embed references to text chunks for better understanding. We'll also investigate how to rethink retrieval methods for heterogeneous document corpora, delve into hybrid document retrieval, and examine the role of query rewriting in enhancing RAG capabilities.", "subpage_snippet": "", "source": "mallahyari.github.io", "link": "https://mallahyari.github.io/rag-ebook/03_prepare_data.html", "content": "Furthermore, we'll explore retrieval chunks versus synthesis chunks and ways to embed references to text chunks for better understanding. We'll also investigate how to rethink retrieval methods for heterogeneous document corpora, delve into hybrid document retrieval, and examine the role of query rewriting in enhancing RAG capabilities."}
+{"idx": 6, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "In this paper, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems , aiming to keep richer semantic and structured information that is missing in plain text .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "In this paper, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems , aiming to keep richer semantic and structured information that is missing in plain text ."}
+{"idx": 7, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system ."}
+{"idx": 8, "title": "8 Types of Chunking for RAG Systems - Analytics Vidhya", "date": "", "ddg_snippet": "Maximize the efficiency of RAG systems with chunking. Learn how breaking down information into smaller chunks improves data processing.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/02/types-of-chunking-for-rag-systems/", "content": "Maximize the efficiency of RAG systems with chunking. Learn how breaking down information into smaller chunks improves data processing."}
+{"idx": 9, "title": "CReSt: A Comprehensive Benchmark for Retrieval-Augmented Generation ...", "date": "", "ddg_snippet": "Htmlrag : Html is better than plain text for modeling retrieved knowledge in rag systems . In Proceedings of the ACM on Web Conference 2025 (WWW '25), pages 1733-1746.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.17503v1", "content": "Htmlrag : Html is better than plain text for modeling retrieved knowledge in rag systems . In Proceedings of the ACM on Web Conference 2025 (WWW '25), pages 1733-1746."}
diff --git a/data/sampled_jsons/HtmlRAG_Table_4_Prune-Gen_LLM_Chat.jsonl b/data/sampled_jsons/HtmlRAG_Table_4_Prune-Gen_LLM_Chat.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..76b2c8a97d1ad307899d9f6c2ffcff592e5d4c96
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_Table_4_Prune-Gen_LLM_Chat.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We conduct ablation studies to demonstrate the effectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune -Embed), and HTML pruning with the generative model ( Prune - Gen ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "We conduct ablation studies to demonstrate the effectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune -Embed), and HTML pruning with the generative model ( Prune - Gen )."}
+{"idx": 1, "title": "Paper tables with annotated results for HtmlRAG ... | Papers With Code", "date": "", "ddg_snippet": "Table 1. Results of HtmlRAG and baselines under the short-context setting. Hit@1 is the proportion of instances where at least one short answer matches. The best and second best results are in bold and underlined. HtmlRAG w/o Prune .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/htmlrag-html-is-better-than-plain-text-for/review/", "content": "Table 1. Results of HtmlRAG and baselines under the short-context setting. Hit@1 is the proportion of instances where at least one short answer matches. The best and second best results are in bold and underlined. HtmlRAG w/o Prune ."}
+{"idx": 2, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling...", "date": "", "ddg_snippet": "Table 1: Results of HtmlRAG and baselines under the short-context setting. Hit@1 is the proportion of instances where at least.Embed) and the generative model ( Prune - Gen ) in HtmlRAG , and LLM chatting ( LLM Chat ) by model parameters, storage", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "Table 1: Results of HtmlRAG and baselines under the short-context setting. Hit@1 is the proportion of instances where at least.Embed) and the generative model ( Prune - Gen ) in HtmlRAG , and LLM chatting ( LLM Chat ) by model parameters, storage"}
+{"idx": 3, "title": "Table 4 from HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG, and designs a two-step block-tree-based pruning method that prunes useless HTML blocks and keeps only the relevant part of the HTML.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/HtmlRAG:-HTML-is-Better-Than-Plain-Text-for-in-RAG-Tan-Dou/7cfd2426ca908c8c5a81bd7c7ca01f914a972de4/figure/6", "content": "HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG, and designs a two-step block-tree-based pruning method that prunes useless HTML blocks and keeps only the relevant part of the HTML."}
+{"idx": 4, "title": "[2411.02959] HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "We conduct ablation studies to demonstrate the effectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune -Embed), and HTML pruning with the generative model ( Prune - Gen ).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2411.02959", "content": "We conduct ablation studies to demonstrate the effectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune -Embed), and HTML pruning with the generative model ( Prune - Gen )."}
+{"idx": 5, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune -Embed), and HTML pruning with the generative model ( Prune - Gen ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v1", "content": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune -Embed), and HTML pruning with the generative model ( Prune - Gen )."}
+{"idx": 6, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune -Embed), and HTML pruning with the generative model ( Prune - Gen ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v2", "content": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune -Embed), and HTML pruning with the generative model ( Prune - Gen )."}
+{"idx": 7, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Experiments show that 920 HtmlRAG outperforms existing post-retrieval processes based on 921 plain text, and validates the priority of HTML as the format of 922 retrieved knowledge.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "Experiments show that 920 HtmlRAG outperforms existing post-retrieval processes based on 921 plain text, and validates the priority of HTML as the format of 922 retrieved knowledge."}
+{"idx": 8, "title": "Htmlrag: HTML Is Better Than Plain Text For Modeling ...", "date": "", "ddg_snippet": "The results of Prune -Embed and Prune - Gen are represented in a bar chart, with a red dashed horizontal line indicating the performance of the strong baseline method,", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/788595083/2411-02959v1", "content": "The results of Prune -Embed and Prune - Gen are represented in a bar chart, with a red dashed horizontal line indicating the performance of the strong baseline method,"}
diff --git a/data/sampled_jsons/HtmlRAG_transformation_process_Figure_2_pipeline.jsonl b/data/sampled_jsons/HtmlRAG_transformation_process_Figure_2_pipeline.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a41001ca7ef43b22fdfcdac9eb6ac7f26b1e073c
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_transformation_process_Figure_2_pipeline.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. Two -Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps, both of which are conducted on the block tree structure.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. Two -Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps, both of which are conducted on the block tree structure."}
+{"idx": 1, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "Figure 1. Information loss in HTML to plain text conversion. Traditional RAG pipelines typically use plain text as the format for retrieved knowledge (Wang et al., 2024c; Jin et al., 2024a) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v2", "content": "Figure 1. Information loss in HTML to plain text conversion. Traditional RAG pipelines typically use plain text as the format for retrieved knowledge (Wang et al., 2024c; Jin et al., 2024a) ."}
+{"idx": 2, "title": "HTMLRAG , Multimodal RAG , and Agentic RAG : A Technical Deep Dive", "date": "", "ddg_snippet": "Figure : Overview of the HTMLRAG pipeline (from Tan et al. 2024). The system retrieves knowledge in HTML format, then performs HTML cleaning and block-tree pruning in two stages (embedding-based and generative).", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/htmlrag-multimodal-rag-agentic-technical-deep-dive-nagesh-nama-pv9be", "content": "Figure : Overview of the HTMLRAG pipeline (from Tan et al. 2024). The system retrieves knowledge in HTML format, then performs HTML cleaning and block-tree pruning in two stages (embedding-based and generative)."}
+{"idx": 3, "title": "ℹ 1 0 1 **What is HtmlRAG , Multimodal RAG and Agentic...", "date": "", "ddg_snippet": "HtmlRAG works directly with HTML to keep more of the structure and meaning of the original content, missing the step of converting the data into the plain text. The main parts of HtmlRAG ’s working process are HTML Cleaning and Pruning techniques", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/Kseniase/html-multimodal-agentic-rag", "content": "HtmlRAG works directly with HTML to keep more of the structure and meaning of the original content, missing the step of converting the data into the plain text. The main parts of HtmlRAG ’s working process are HTML Cleaning and Pruning techniques"}
+{"idx": 4, "title": "Unleashing the Power of HtmlRAG : Transforming RAG with HTML ...", "date": "", "ddg_snippet": "Overview of the HtmlRAG pipeline . The HtmlRAG workflow is designed to make the most of HTML - formatted data. It starts with retrieving HTML documents, which can be the table - rich documents extracted by UnDatas.io.", "subpage_snippet": "", "source": "undatas.io", "link": "https://undatas.io/blog/posts/unleashing-the-power-of-htmlrag-transforming-rag-with-html-enhanced-table-data-from-undatas-io/", "content": "Overview of the HtmlRAG pipeline . The HtmlRAG workflow is designed to make the most of HTML - formatted data. It starts with retrieving HTML documents, which can be the table - rich documents extracted by UnDatas.io."}
+{"idx": 5, "title": "AI Innovations and Insights 20: HtmlRAG , AFLOW... | Level Up Coding", "date": "", "ddg_snippet": "HtmlRAG leverages HTML format instead of plain text in RAG systems to preserve semantic and structural information. Press enter or click to view image in full size. Figure 2 : HTML for RAG pipeline overview.", "subpage_snippet": "", "source": "levelup.gitconnected.com", "link": "https://levelup.gitconnected.com/ai-innovations-and-insights-20-htmlrag-aflow-chunkrag-and-markitdown-fe102693315e", "content": "HtmlRAG leverages HTML format instead of plain text in RAG systems to preserve semantic and structural information. Press enter or click to view image in full size. Figure 2 : HTML for RAG pipeline overview."}
+{"idx": 6, "title": "Implementing HtmlRAG : Enhancing Retrieval-Augmented... | Dev Genius", "date": "", "ddg_snippet": "4. Implementing HtmlRAG . To effectively integrate HTML into a RAG system, we need to address the challenges through a series of preprocessing and optimization steps: 1. HTML Cleaning: Remove unnecessary content to reduce size and noise.", "subpage_snippet": "", "source": "blog.devgenius.io", "link": "https://blog.devgenius.io/implementing-htmlrag-enhancing-retrieval-augmented-generation-with-html-knowledge-91cdd6278e23", "content": "4. Implementing HtmlRAG . To effectively integrate HTML into a RAG system, we need to address the challenges through a series of preprocessing and optimization steps: 1. HTML Cleaning: Remove unnecessary content to reduce size and noise."}
+{"idx": 7, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling...", "date": "", "ddg_snippet": "Block Tree. Figure 2 : HTML for RAG pipeline overview.Our model processes documents up to 512K tokens, transforming messy HTML into clean Markdown or JSON formats with high accuracy -- making it an ideal tool for grounding large language models.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "Block Tree. Figure 2 : HTML for RAG pipeline overview.Our model processes documents up to 512K tokens, transforming messy HTML into clean Markdown or JSON formats with high accuracy -- making it an ideal tool for grounding large language models."}
+{"idx": 8, "title": "HtmlRAG : Enhancing RAG Systems with Richer... - MarkTechPost", "date": "", "ddg_snippet": "HtmlRAG implements a two -step pruning mechanism to process retrieved HTML documents efficiently. Initially, the system concatenates all retrieved HTML documents and parses them into a single DOM tree using Beautiful Soup.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/11/10/htmlrag-enhancing-rag-systems-with-richer-semantic-and-structural-information-through-html/", "content": "HtmlRAG implements a two -step pruning mechanism to process retrieved HTML documents efficiently. Initially, the system concatenates all retrieved HTML documents and parses them into a single DOM tree using Beautiful Soup."}
+{"idx": 9, "title": "HtmlRAG : Enhancing Knowledge Retrieval with HTML in AI Systems...", "date": "", "ddg_snippet": "In this episode, we dive into the advancements of HtmlRAG , a novel approach to improving Retrieval-Augmented Generation ( RAG ) by using HTML to retain structural and semantic knowledge for AI models.", "subpage_snippet": "", "source": "american-podcasts.com", "link": "https://american-podcasts.com/podcast/life-is-artificial/htmlrag-enhancing-knowledge-retrieval-with-html-in", "content": "In this episode, we dive into the advancements of HtmlRAG , a novel approach to improving Retrieval-Augmented Generation ( RAG ) by using HTML to retain structural and semantic knowledge for AI models."}
diff --git a/data/sampled_jsons/Huanjian_Zhou_Masashi_Sugiyama_Parallel_Simulation_Log-concave_Sampling_Algorithm.jsonl b/data/sampled_jsons/Huanjian_Zhou_Masashi_Sugiyama_Parallel_Simulation_Log-concave_Sampling_Algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ab977cc6db641564ac2bf7dc600e11ed3db8325d
--- /dev/null
+++ b/data/sampled_jsons/Huanjian_Zhou_Masashi_Sugiyama_Parallel_Simulation_Log-concave_Sampling_Algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Parallel Simulation for Log - concave Sampling and... | OpenReview", "date": "", "ddg_snippet": "Huanjian Zhou , Masashi Sugiyama .Our research introduces a novel parallel sampling algorithm that significantly reduces the number of sequential steps—known as adaptive complexity—required to generate high-quality samples .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qtuxDy2qEB&referrer=[the+profile+of+Masashi+Sugiyama](/profile?id=~Masashi_Sugiyama1)", "content": "Huanjian Zhou , Masashi Sugiyama .Our research introduces a novel parallel sampling algorithm that significantly reduces the number of sequential steps—known as adaptive complexity—required to generate high-quality samples ."}
+{"idx": 1, "title": "The adaptive complexity of parallelized log - concave sampling", "date": "", "ddg_snippet": "Authors: Huanjian Zhou , Baoxiang Wang, Masashi Sugiyama .For box-constrained sampling , we show that an almost linear iteration algorithm cannot return a sample with sup-polynomially small error under total variation distance for log - concave distributions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.13045", "content": "Authors: Huanjian Zhou , Baoxiang Wang, Masashi Sugiyama .For box-constrained sampling , we show that an almost linear iteration algorithm cannot return a sample with sup-polynomially small error under total variation distance for log - concave distributions."}
+{"idx": 2, "title": "ICLR Poster The adaptive complexity of parallelized log - concave ...", "date": "", "ddg_snippet": "Huanjian Zhou · Baoxiang Wang · Masashi Sugiyama .For box-constrained sampling , we show that an almost linear iteration algorithm cannot return a sample with sup-polynomially small error under total variation distance for log - concave distributions.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/30393", "content": "Huanjian Zhou · Baoxiang Wang · Masashi Sugiyama .For box-constrained sampling , we show that an almost linear iteration algorithm cannot return a sample with sup-polynomially small error under total variation distance for log - concave distributions."}
+{"idx": 3, "title": "ICML Poster Parallel Simulation for Sampling under Isoperimetry and...", "date": "", "ddg_snippet": "Huanjian Zhou · Masashi Sugiyama .While recent works have introduced several parallelizable techniques, they often exhibit suboptimal convergence rates and remain significantly weaker than the latest lower bounds for log - concave sampling .To address this, we propose a novel...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43916", "content": "Huanjian Zhou · Masashi Sugiyama .While recent works have introduced several parallelizable techniques, they often exhibit suboptimal convergence rates and remain significantly weaker than the latest lower bounds for log - concave sampling .To address this, we propose a novel..."}
+{"idx": 4, "title": "Accelerating Science: The Future of Sampling - Simple Science", "date": "", "ddg_snippet": "Huanjian Zhou , Masashi Sugiyama .Original Source. Title: Parallel simulation for sampling under isoperimetry and score-based diffusion models.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-03-17-accelerating-science-the-future-of-sampling--akyl0xz", "content": "Huanjian Zhou , Masashi Sugiyama .Original Source. Title: Parallel simulation for sampling under isoperimetry and score-based diffusion models."}
+{"idx": 5, "title": "(PDF) Parallel simulation for sampling under isoperimetry and...", "date": "", "ddg_snippet": "Huanjian Zhou , Baoxiang Wang, and Masashi Sugiyama .For box-constrained sampling , we show that an almost linear iteration algorithm cannot return a sample with sup-polynomially small accuracy under total variation distance for log - concave distributions.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386976226_Parallel_simulation_for_sampling_under_isoperimetry_and_score-based_diffusion_models", "content": "Huanjian Zhou , Baoxiang Wang, and Masashi Sugiyama .For box-constrained sampling , we show that an almost linear iteration algorithm cannot return a sample with sup-polynomially small accuracy under total variation distance for log - concave distributions."}
+{"idx": 6, "title": "Beyond log - concave sampling – Off the convex path", "date": "", "ddg_snippet": "It turns out that there is a natural analogue of convexity for sampling — log - concavity . Paralleling the state of affairs in optimization, we have a variety of (provably efficient) algorithms for sampling from log - concave distributions, under a variety of access models to the distribution.", "subpage_snippet": "", "source": "www.offconvex.org", "link": "https://www.offconvex.org/2020/09/19/beyondlogconvavesampling/", "content": "It turns out that there is a natural analogue of convexity for sampling — log - concavity . Paralleling the state of affairs in optimization, we have a variety of (provably efficient) algorithms for sampling from log - concave distributions, under a variety of access models to the distribution."}
+{"idx": 7, "title": "dblp: List of computer science publications by Masashi Sugiyama", "date": "", "ddg_snippet": "Huanjian Zhou , Masashi Sugiyama : Parallel simulation for sampling under isoperimetry and score-based diffusion models.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/35/1228.html", "content": "Huanjian Zhou , Masashi Sugiyama : Parallel simulation for sampling under isoperimetry and score-based diffusion models."}
+{"idx": 8, "title": "Masashi Sugiyama", "date": "", "ddg_snippet": "View Masashi Sugiyama 's papers and open-source code.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Masashi+Sugiyama", "content": "View Masashi Sugiyama 's papers and open-source code."}
+{"idx": 9, "title": "Log - concave Sampling over a Convex Body with a Barrier: a Robust...", "date": "", "ddg_snippet": "The paper addresses the problem of efficiently sampling from high-dimensional log - concave distributions, which have many practical applications. The authors propose a \"robust\" sampling framework that leverages spectral approximations of the Hessian of the barrier function.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/log-concave-sampling-over-convex-body-barrier", "content": "The paper addresses the problem of efficiently sampling from high-dimensional log - concave distributions, which have many practical applications. The authors propose a \"robust\" sampling framework that leverages spectral approximations of the Hessian of the barrier function."}
diff --git a/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_with_General_Function_A.jsonl b/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_with_General_Function_A.jsonl
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index 0000000000000000000000000000000000000000..2e3e0385d6c41bf6264aac92de23ebf4867693cc
--- /dev/null
+++ b/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_with_General_Function_A.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "by X Chen · 2022 · Cited by 93 — In this paper, we propose the first optimistic model- based algorithm for PbRL with general function approximation , which estimates the model using value- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2205.11140", "content": "by X Chen · 2022 · Cited by 93 — In this paper, we propose the first optimistic model- based algorithm for PbRL with general function approximation , which estimates the model using value- ..."}
+{"idx": 1, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "by X Chen · 2022 · Cited by 93 — In this work, we tackle the regret minimization problem for preference - based reinforcement learning with general function approximation . Specifically, we study ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "by X Chen · 2022 · Cited by 93 — In this work, we tackle the regret minimization problem for preference - based reinforcement learning with general function approximation . Specifically, we study ..."}
+{"idx": 2, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "In this paper, we propose the first optimistic model- based algorithm for PbRL with general function approximation , which estimates the model using value- ...", "subpage_snippet": "", "source": "www.scholars.northwestern.edu", "link": "https://www.scholars.northwestern.edu/en/publications/human-in-the-loop-provably-efficient-preference-based-reinforceme", "content": "In this paper, we propose the first optimistic model- based algorithm for PbRL with general function approximation , which estimates the model using value- ..."}
+{"idx": 3, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "by X Chen · Cited by 93 — Human -in-the- loop : Provably Efficient Preference - based . Reinforcement Learning with General Function Approximation . Xiaoyu Chen1, Han Zhong1, Zhuoran Yang2 ... 7 pages", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/18372.pdf", "content": "by X Chen · Cited by 93 — Human -in-the- loop : Provably Efficient Preference - based . Reinforcement Learning with General Function Approximation . Xiaoyu Chen1, Han Zhong1, Zhuoran Yang2 ... 7 pages"}
+{"idx": 4, "title": "Provable Reward-Agnostic Preference-Based ...", "date": "", "ddg_snippet": "by W Zhan · Cited by 21 — [7] Chen , Xiaoyu, et al . \" Human -in-the- loop : Provably efficient preference - based reinforcement learning with general function approximation .\" International ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=yTBXeXdbMf", "content": "by W Zhan · Cited by 21 — [7] Chen , Xiaoyu, et al . \" Human -in-the- loop : Provably efficient preference - based reinforcement learning with general function approximation .\" International ..."}
+{"idx": 5, "title": "RA-PbRL: provably efficient risk-aware preference-based ...", "date": "", "ddg_snippet": "5 Jun 2025 — Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation. In International ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739861", "content": "5 Jun 2025 — Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation. In International ..."}
+{"idx": 6, "title": "Provable Reinforcement Learning from Human Feedback ...", "date": "", "ddg_snippet": "by Q Zhang · 2025 — Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation. In Proceedings of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.03066", "content": "by Q Zhang · 2025 — Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation. In Proceedings of the ..."}
+{"idx": 7, "title": "Provably Feedback-Efficient Reinforcement Learning via ...", "date": "", "ddg_snippet": "by D Kong · 2022 · Cited by 14 — Recently, a popular framework called Human -in-the- loop (HiL) RL [Knox and Stone, 2009, Christiano et al .,. 2017, MacGlashan et al ., 2017, Ibarz et al ., 2018, ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/476c289f685e27936aa089e9d53a4213-Paper-Conference.pdf", "content": "by D Kong · 2022 · Cited by 14 — Recently, a popular framework called Human -in-the- loop (HiL) RL [Knox and Stone, 2009, Christiano et al .,. 2017, MacGlashan et al ., 2017, Ibarz et al ., 2018, ..."}
+{"idx": 8, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "9 Dec 2024 — Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/95716", "content": "9 Dec 2024 — Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each ..."}
+{"idx": 9, "title": "provably sample-efficient exploration in rlhf with general", "date": "", "ddg_snippet": "This paper investigates a basic question in reinforcement learning from human feedback (RLHF) from a theoretical perspective: how to efficiently explore in.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=raW6gMLwK8&name=pdf", "content": "This paper investigates a basic question in reinforcement learning from human feedback (RLHF) from a theoretical perspective: how to efficiently explore in."}
diff --git a/data/sampled_jsons/ICFL_algorithm_Iterative_Codebook_Feature_Learning_matching_pursuit_J_L_sparsity.jsonl b/data/sampled_jsons/ICFL_algorithm_Iterative_Codebook_Feature_Learning_matching_pursuit_J_L_sparsity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..28b5ca6c2f09b921632fa4bd13d3f8b46e8e9eab
--- /dev/null
+++ b/data/sampled_jsons/ICFL_algorithm_Iterative_Codebook_Feature_Learning_matching_pursuit_J_L_sparsity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Towards scientific discovery with dictionary learning", "date": "", "ddg_snippet": "We propose a novel combination of a sparse DL algorithm , Iterative Codebook Feature Learning ( ICFL ), with a PCA whitening pre-processing step derived from ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44540/paper", "content": "We propose a novel combination of a sparse DL algorithm , Iterative Codebook Feature Learning ( ICFL ), with a PCA whitening pre-processing step derived from ..."}
+{"idx": 1, "title": "Towards scientific discovery with dictionary learning : Extracting...", "date": "", "ddg_snippet": ". 4 Iterative codebook feature learning ( ICFL ).We also proposed a new dictionary learning algorithm , Iterative Codebook Feature Learning ( ICFL ), and the use of PCA whitening on a control dataset as a form of weak supervision for the feature extraction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16247v2", "content": ". 4 Iterative codebook feature learning ( ICFL ).We also proposed a new dictionary learning algorithm , Iterative Codebook Feature Learning ( ICFL ), and the use of PCA whitening on a control dataset as a form of weak supervision for the feature extraction."}
+{"idx": 2, "title": "ICML Poster Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Sparse dictionary learning (DL) has become a popular tool for interpreting trained large language models (LLM).We propose a novel combination of a DL algorithm , Iterative Codebook Feature Learning ~( ICFL ), with a pre-processing step using PCA whitening from a control dataset.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44540", "content": "Sparse dictionary learning (DL) has become a popular tool for interpreting trained large language models (LLM).We propose a novel combination of a DL algorithm , Iterative Codebook Feature Learning ~( ICFL ), with a pre-processing step using PCA whitening from a control dataset."}
+{"idx": 3, "title": "Orthogonal Matching Pursuit Algorithm Research... - R Discovery", "date": "", "ddg_snippet": "The sparse representation, which is based on the orthogonal matching pursuit (OMP) algorithm , is a useful technique for identifying defect characteristics in rolling element bearings.", "subpage_snippet": "", "source": "discovery.researcher.life", "link": "https://discovery.researcher.life/topic/orthogonal-matching-pursuit-algorithm/6676146?page=2", "content": "The sparse representation, which is based on the orthogonal matching pursuit (OMP) algorithm , is a useful technique for identifying defect characteristics in rolling element bearings."}
+{"idx": 4, "title": "Towards scientific discovery with dictionary learning ... | OpenReview", "date": "", "ddg_snippet": "We also propose a new DL algorithm , Iterative Codebook Feature Learning ~( ICFL ) and combine it with a pre-processing step which uses PCA whitening from a control dataset.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=jYl7kM1oK0", "content": "We also propose a new DL algorithm , Iterative Codebook Feature Learning ~( ICFL ) and combine it with a pre-processing step which uses PCA whitening from a control dataset."}
+{"idx": 5, "title": "Algorithms for simultaneous sparse approximation. Part I: Greedy...", "date": "", "ddg_snippet": "The simultaneous orthogonal matching pursuit (SOMP) algorithm aims to find the joint support of a set of sparse signals acquired under a multiple measurement vector model.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/222560342_Algorithms_for_simultaneous_sparse_approximation_Part_I_Greedy_pursuit", "content": "The simultaneous orthogonal matching pursuit (SOMP) algorithm aims to find the joint support of a set of sparse signals acquired under a multiple measurement vector model."}
+{"idx": 6, "title": "Sparsity Constrained Nonlinear Optimization: Optimality Conditions...", "date": "", "ddg_snippet": "algorithms for sparse recovery [3, 2, 1, 28]. Despite the great interest in exploiting. sparsity in various applications, most of the work to date has focused on recovering sparse data represented by a vector x ∈ Rn from linear measurements of the form b = Ax.", "subpage_snippet": "", "source": "www.tau.ac.il", "link": "https://www.tau.ac.il/~becka/siam_sparse.pdf", "content": "algorithms for sparse recovery [3, 2, 1, 28]. Despite the great interest in exploiting. sparsity in various applications, most of the work to date has focused on recovering sparse data represented by a vector x ∈ Rn from linear measurements of the form b = Ax."}
+{"idx": 7, "title": "Bag of Pursuits and Neural Gas for Improved", "date": "", "ddg_snippet": "Keywords: sparse coding , neural gas, dictionary learning , matching pursuit .In contrast to the hard-competitive approaches, in each learning iteration , ev-ery codebook vector cl is updated. The update is weighted according to the rank of the encoding that uses the codebook vector cl.", "subpage_snippet": "", "source": "www.inb.uni-luebeck.de", "link": "https://www.inb.uni-luebeck.de/fileadmin/files/PUBPDFS/LaBaMa10b.pdf", "content": "Keywords: sparse coding , neural gas, dictionary learning , matching pursuit .In contrast to the hard-competitive approaches, in each learning iteration , ev-ery codebook vector cl is updated. The update is weighted according to the rank of the encoding that uses the codebook vector cl."}
+{"idx": 8, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "- Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives.In our experiments, we demonstrate that both ICFL and PCA improve the selectivity of extracted features compared to TopK sparse autoencoders.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=feature+description+framework", "content": "- Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives.In our experiments, we demonstrate that both ICFL and PCA improve the selectivity of extracted features compared to TopK sparse autoencoders."}
+{"idx": 9, "title": "GitHub - JuliaDiff/SparseDiffTools. jl : Fast jacobian computation through...", "date": "", "ddg_snippet": "Specify a Sparsity Detection Algorithm . There are 3 possible choices currentlySymbolicsSparsityDetection: This will use Symbolics. jl to automatically detect the sparsity pattern. (Note that Symbolics. jl must be explicitly loaded before using this functionality.)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/JuliaDiff/SparseDiffTools.jl", "content": "Specify a Sparsity Detection Algorithm . There are 3 possible choices currentlySymbolicsSparsityDetection: This will use Symbolics. jl to automatically detect the sparsity pattern. (Note that Symbolics. jl must be explicitly loaded before using this functionality.)"}
diff --git a/data/sampled_jsons/ICLR_2025_gQlxd3Mtru_LEnergy_equation_10.jsonl b/data/sampled_jsons/ICLR_2025_gQlxd3Mtru_LEnergy_equation_10.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..97bfd00b97d828cafd058e5a7da8cb5caf1a154a
--- /dev/null
+++ b/data/sampled_jsons/ICLR_2025_gQlxd3Mtru_LEnergy_equation_10.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International Conference on Learning Representations - Wikipedia", "date": "", "ddg_snippet": "The conference includes invited talks as well as oral and poster presentations of refereed papers. Since its inception in 2013, ICLR has employed an open peer review process to referee paper submissions (based on models proposed by Yann LeCun[2]).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/International_Conference_on_Learning_Representations", "content": "The conference includes invited talks as well as oral and poster presentations of refereed papers. Since its inception in 2013, ICLR has employed an open peer review process to referee paper submissions (based on models proposed by Yann LeCun[2])."}
+{"idx": 1, "title": "GitHub - nunchaku-tech/nunchaku: [ ICLR 2025 Spotlight] SVDQuant...", "date": "", "ddg_snippet": "[ ICLR 2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models. [ 2025 -02-11] SVDQuant has been selected as a ICLR 2025 Spotlight! FLUX.1-tools Gradio demos are now available! Check here for the usage details!", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nunchaku-tech/nunchaku", "content": "[ ICLR 2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models. [ 2025 -02-11] SVDQuant has been selected as a ICLR 2025 Spotlight! FLUX.1-tools Gradio demos are now available! Check here for the usage details!"}
+{"idx": 2, "title": "Лучшие смарт-часы 2025 года - Рейтинг (Топ 15)", "date": "", "ddg_snippet": "Лучшие умные часы 2025 года — взрослые (мужские и женские), детские, спортивные. Рейтинг лучших смарт-часов по отзывам экспертов и обычных покупателей.8.2 / 10 . Лучшие умные часы среднего класса и ценового диапазона.", "subpage_snippet": "", "source": "www.expertcen.ru", "link": "https://www.expertcen.ru/article/ratings/luchshie-umnye-chasy.html", "content": "Лучшие умные часы 2025 года — взрослые (мужские и женские), детские, спортивные. Рейтинг лучших смарт-часов по отзывам экспертов и обычных покупателей.8.2 / 10 . Лучшие умные часы среднего класса и ценового диапазона."}
+{"idx": 3, "title": "QAQqaq/ ICLR 2025 Openreview · Datasets at Hugging Face", "date": "", "ddg_snippet": "gQlxd 3 Mtru .[3, 10 , 10 , 8, 10 ]. \"Sparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing activations from a sparse bottleneck layer.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/QAQqaq/ICLR2025Openreview", "content": "gQlxd 3 Mtru .[3, 10 , 10 , 8, 10 ]. \"Sparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing activations from a sparse bottleneck layer."}
+{"idx": 4, "title": "partially observed trajectory inference us", "date": "", "ddg_snippet": "by A Gu · 2024 · Cited by 8 — Note that Ξ affects this equation implicitly via the Schrödinger potentials. ... Page 10 . Published as a conference paper at ICLR 2025 . (a) Ground ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.07475?", "content": "by A Gu · 2024 · Cited by 8 — Note that Ξ affects this equation implicitly via the Schrödinger potentials. ... Page 10 . Published as a conference paper at ICLR 2025 . (a) Ground ..."}
+{"idx": 5, "title": "Learning stochastic dynamics from snapshots through... | OpenReview", "date": "", "ddg_snippet": "Published: 22 Jan 2025 , Last Modified: 08 May 2025 ICLR 2025 OralEveryoneRevisionsBibTeXCC BY 4.0. Keywords: optimal transport, Schrödinger bridge, trajectory inference, single-cell.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gQlxd3Mtru", "content": "Published: 22 Jan 2025 , Last Modified: 08 May 2025 ICLR 2025 OralEveryoneRevisionsBibTeXCC BY 4.0. Keywords: optimal transport, Schrödinger bridge, trajectory inference, single-cell."}
+{"idx": 6, "title": "2026 Conference", "date": "", "ddg_snippet": "LLM Usage Policies at ICLR 2026. Sponsors. Check back Dec 1, 2025 . Become a 2026 Sponsor (not currently taking applications).", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/", "content": "LLM Usage Policies at ICLR 2026. Sponsors. Check back Dec 1, 2025 . Become a 2026 Sponsor (not currently taking applications)."}
+{"idx": 7, "title": "Номер 3, страница 7 - гдз по английскому языку 6 класс (spotlight)...", "date": "", "ddg_snippet": "Издательство: Просвещение, Express Publishing. Год издания: 2023 - 2025 . Цвет обложки: зелёный, белый с Тауэрским мостом. ISBN: 978-5-09-100031-3.", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/6-klass/english/vaulina-spotlight-rabochaja-tetrad/01-3-3", "content": "Издательство: Просвещение, Express Publishing. Год издания: 2023 - 2025 . Цвет обложки: зелёный, белый с Тауэрским мостом. ISBN: 978-5-09-100031-3."}
+{"idx": 8, "title": "Метод Гаусса онлайн", "date": "", "ddg_snippet": "12+ Все права защищены и охраняются законом. Copyright © ООО Новый семестр 2006- 2025 .", "subpage_snippet": "", "source": "math.semestr.ru", "link": "https://math.semestr.ru/gauss/gauss.php", "content": "12+ Все права защищены и охраняются законом. Copyright © ООО Новый семестр 2006- 2025 ."}
+{"idx": 9, "title": "Английский язык 5 класс Spotlight Английский в фокусе Ваулина.", "date": "", "ddg_snippet": "Подписаться в Телеграм. Раздел: Starter Unit (pp. 10 -24). Numbers (страница 20).", "subpage_snippet": "", "source": "Reshalka.com", "link": "https://Reshalka.com/uchebniki/5-klass/english/vaulina/43", "content": "Подписаться в Телеграм. Раздел: Starter Unit (pp. 10 -24). Numbers (страница 20)."}
diff --git a/data/sampled_jsons/ICLR_2025_paper_acceptance_categories_tiers_poster_spotlight_oral.jsonl b/data/sampled_jsons/ICLR_2025_paper_acceptance_categories_tiers_poster_spotlight_oral.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1c5d77b2b376d8710652ca564a7e557c71f36bb2
--- /dev/null
+++ b/data/sampled_jsons/ICLR_2025_paper_acceptance_categories_tiers_poster_spotlight_oral.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Find the latest ICON Public Limited Company ( ICLR ) stock quote, history, news and other vital information to help you with your stock trading and investing.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/?fr=sycsrp_catchall", "content": "Find the latest ICON Public Limited Company ( ICLR ) stock quote, history, news and other vital information to help you with your stock trading and investing."}
+{"idx": 1, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Get the latest ICON Public Limited Company ( ICLR ) stock news and headlines to help you in your trading and investing decisions.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/news/?fr=sycsrp_catchall", "content": "Get the latest ICON Public Limited Company ( ICLR ) stock news and headlines to help you in your trading and investing decisions."}
+{"idx": 2, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "See the company profile for ICON Public Limited Company ( ICLR ) including business summary, industry/sector information, number of employees, business summary, corporate governance, key executives ...", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/profile/?fr=sycsrp_catchall", "content": "See the company profile for ICON Public Limited Company ( ICLR ) including business summary, industry/sector information, number of employees, business summary, corporate governance, key executives ..."}
+{"idx": 3, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Discover historical prices for ICLR stock on Yahoo Finance. View daily, weekly or monthly format back to when ICON Public Limited Company stock was issued.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/history/?fr=sycsrp_catchall", "content": "Discover historical prices for ICLR stock on Yahoo Finance. View daily, weekly or monthly format back to when ICON Public Limited Company stock was issued."}
+{"idx": 4, "title": "Icon PLC (ICLR) Q3 2024 Earnings Call Highlights: Navigating...", "date": "", "ddg_snippet": "Oct 25, 2024 · Despite revenue decline and project delays, Icon PLC ( ICLR ) remains optimistic about future growth through strategic partnerships and market share gains.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/icon-plc-iclr-q3-2024-070717387.html?fr=sycsrp_catchall", "content": "Oct 25, 2024 · Despite revenue decline and project delays, Icon PLC ( ICLR ) remains optimistic about future growth through strategic partnerships and market share gains."}
+{"idx": 5, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "See ICON Public Limited Company ( ICLR ) stock analyst estimates, including earnings and revenue, EPS, upgrades and downgrades.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/analysis/?fr=sycsrp_catchall", "content": "See ICON Public Limited Company ( ICLR ) stock analyst estimates, including earnings and revenue, EPS, upgrades and downgrades."}
+{"idx": 6, "title": "ICON Public Limited Company (ICLR): Among Steven Cohen’s Mid-Cap...", "date": "", "ddg_snippet": "May 9, 2025 · In this article, we are going to take a look at where ICON Public Limited Company (NASDAQ: ICLR ) stands against Steve Cohen's other mid-cap stock picks with huge upside potential.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/icon-public-limited-company-iclr-071309449.html?fr=sycsrp_catchall", "content": "May 9, 2025 · In this article, we are going to take a look at where ICON Public Limited Company (NASDAQ: ICLR ) stands against Steve Cohen's other mid-cap stock picks with huge upside potential."}
+{"idx": 7, "title": "ICLR Misses on Q3 Earnings, Lowers 2024 Guidance, Stock Falls", "date": "", "ddg_snippet": "Oct 28, 2024 · Find the latest EPS estimates and surprises on Zacks Earnings Calendar. Following the earnings announcement, ICLR stock fell 21% last Thursday.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/iclr-misses-q3-earnings-lowers-123400740.html?fr=sycsrp_catchall", "content": "Oct 28, 2024 · Find the latest EPS estimates and surprises on Zacks Earnings Calendar. Following the earnings announcement, ICLR stock fell 21% last Thursday."}
+{"idx": 8, "title": "Icon PLC (ICLR) Lags Q3 Earnings and Revenue Estimates", "date": "", "ddg_snippet": "Oct 23, 2024 · Icon PLC ( ICLR ) delivered earnings and revenue surprises of -12.99% and 5.12%, respectively, for the quarter ended September 2024. Do the numbers hold clues to what lies ahead for the stock?", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/icon-plc-iclr-lags-q3-215513569.html?fr=sycsrp_catchall", "content": "Oct 23, 2024 · Icon PLC ( ICLR ) delivered earnings and revenue surprises of -12.99% and 5.12%, respectively, for the quarter ended September 2024. Do the numbers hold clues to what lies ahead for the stock?"}
+{"idx": 9, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Find out the direct holders, institutional holders and mutual fund holders for ICON Public Limited Company ( ICLR ).", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/holders/?fr=sycsrp_catchall", "content": "Find out the direct holders, institutional holders and mutual fund holders for ICON Public Limited Company ( ICLR )."}
diff --git a/data/sampled_jsons/ICLR_conference_paper_categories_oral_spotlight_poster_ranking_tier.jsonl b/data/sampled_jsons/ICLR_conference_paper_categories_oral_spotlight_poster_ranking_tier.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..26136619d400dac7dbd7454cdfdb980279ef98ba
--- /dev/null
+++ b/data/sampled_jsons/ICLR_conference_paper_categories_oral_spotlight_poster_ranking_tier.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International Conference on Learning Representations - Wikipedia", "date": "", "ddg_snippet": "The conference includes invited talks as well as oral and poster presentations of refereed papers . Since its inception in 2013, ICLR has employed an open peer review process to referee paper submissions (based on models proposed by Yann LeCun[2]).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/International_Conference_on_Learning_Representations", "content": "The conference includes invited talks as well as oral and poster presentations of refereed papers . Since its inception in 2013, ICLR has employed an open peer review process to referee paper submissions (based on models proposed by Yann LeCun[2])."}
+{"idx": 1, "title": "ICLR 2025 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "Poster . Spotlight . Oral . Reject.How to use the paper list below: - Overview: This table presents papers from the ICLR conference , year 2025.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/iclr-paper-list/iclr-2025-paper-list/", "content": "Poster . Spotlight . Oral . Reject.How to use the paper list below: - Overview: This table presents papers from the ICLR conference , year 2025."}
+{"idx": 2, "title": "ICLR Oral Presentations (With Coffee Break)", "date": "", "ddg_snippet": "Papers . In-person Orals . Spotlight Posters .OpenCoder: The Open Cookbook for Top- Tier Code Large Language Models. Data Metabolism: An Efficient Data Design Scheme for Vision Language Models. Ultra-Sparse Memory Network.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/10000619", "content": "Papers . In-person Orals . Spotlight Posters .OpenCoder: The Open Cookbook for Top- Tier Code Large Language Models. Data Metabolism: An Efficient Data Design Scheme for Vision Language Models. Ultra-Sparse Memory Network."}
+{"idx": 3, "title": "顶会论文中的 poster , oral , spotlight ...", "date": "", "ddg_snippet": "In addition, all oral and spotlight papers will be presented at the following poster session. Our rationale behind adding SPOTLIGHT presentations to CVPR'16 was to increase the number of authors who have the opportunity to present their work to a large audience and therefore gain...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/weixin_43332715/article/details/124584360", "content": "In addition, all oral and spotlight papers will be presented at the following poster session. Our rationale behind adding SPOTLIGHT presentations to CVPR'16 was to increase the number of authors who have the opportunity to present their work to a large audience and therefore gain..."}
+{"idx": 4, "title": "NeurIPS 2023 Spotlight Posters", "date": "", "ddg_snippet": "Spotlight Posters . Kiki or Bouba?The assignment of papers to reviewers is a crucial part of the peer review processes of large publication venues, where organizers (e.g., conference program chairs) rely on algorithms to perform automated paper assignment.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/events/spotlight-posters-2023", "content": "Spotlight Posters . Kiki or Bouba?The assignment of papers to reviewers is a crucial part of the peer review processes of large publication venues, where organizers (e.g., conference program chairs) rely on algorithms to perform automated paper assignment."}
+{"idx": 5, "title": "ICLR Oral and Spotlight Decisions - Githubissues", "date": "", "ddg_snippet": "I see that ICLR has announced their oral / spotlight / poster decisions today, and it'd be nice to have it on the website.11 forks source link. ICLR Oral and Spotlight Decisions #5. Closed Akash190104 closed 1 week ago.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/papercopilot/paperlists/5", "content": "I see that ICLR has announced their oral / spotlight / poster decisions today, and it'd be nice to have it on the website.11 forks source link. ICLR Oral and Spotlight Decisions #5. Closed Akash190104 closed 1 week ago."}
+{"idx": 6, "title": "GitHub - accum-dai/ iclr _ paper _downloader", "date": "", "ddg_snippet": "FeaturesScrape paper information from the ICLR conference websiteSupport for different paper types ( oral , spotlight , poster )", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/accum-dai/iclr_paper_downloader", "content": "FeaturesScrape paper information from the ICLR conference websiteSupport for different paper types ( oral , spotlight , poster )"}
+{"idx": 7, "title": "NeurIPS Poster Group Fairness in Peer Review", "date": "", "ddg_snippet": "Spotlight Poster .Large conferences such as NeurIPS and AAAI serve as crossroads of various AI fields, since they attract submissions from a vast number of communities.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/71068", "content": "Spotlight Poster .Large conferences such as NeurIPS and AAAI serve as crossroads of various AI fields, since they attract submissions from a vast number of communities."}
+{"idx": 8, "title": "When Acceptance Isn’t Enough: NeurIPS 2025 rejects 400... | CSPaper", "date": "", "ddg_snippet": "A Shock to the AI Research Community NeurIPS 2025, the 39th iteration of the world’s premier AI conference , finds itself embroiled in controversy.", "subpage_snippet": "", "source": "forum.cspaper.org", "link": "https://forum.cspaper.org/topic/140/when-acceptance-isn-t-enough-neurips-2025-rejects-400-accepted-papers-due-to-venue-crisis", "content": "A Shock to the AI Research Community NeurIPS 2025, the 39th iteration of the world’s premier AI conference , finds itself embroiled in controversy."}
+{"idx": 9, "title": "International Conference on Biology and Life... - Conference Index", "date": "", "ddg_snippet": "Papers submitted to the conference will be published online in the cooperating journals after the final edition. After the paper is accepted, the author can make an oral / poster presentation at the conference , or attend the conference as a listener. Abstract Submission.", "subpage_snippet": "", "source": "conferenceindex.org", "link": "https://conferenceindex.org/event/international-conference-on-biology-and-life-sciences-icbls-2025-november-wuhan-cn", "content": "Papers submitted to the conference will be published online in the cooperating journals after the final edition. After the paper is accepted, the author can make an oral / poster presentation at the conference , or attend the conference as a listener. Abstract Submission."}
diff --git a/data/sampled_jsons/ICLR_conference_paper_tiers_Mid-Tier_classification.jsonl b/data/sampled_jsons/ICLR_conference_paper_tiers_Mid-Tier_classification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f7153dc0d4edb4be9a8030c8bcd085e9d7f92992
--- /dev/null
+++ b/data/sampled_jsons/ICLR_conference_paper_tiers_Mid-Tier_classification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning to rank - Wikipedia", "date": "", "ddg_snippet": "... retrieval and many heuristics were proposed in the literature to accelerate it, such as using a document's static quality score and tiered indexes.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Learning_to_rank", "content": "... retrieval and many heuristics were proposed in the literature to accelerate it, such as using a document's static quality score and tiered indexes."}
+{"idx": 1, "title": "AtICNet: semantic segmentation with atrous spatial pyramid", "date": "", "ddg_snippet": "This paper describes a new type of image segmentation method based on deep convolutional neural networks (DCNN) in the actual autonomous driving ...", "subpage_snippet": "", "source": "1library.net", "link": "https://1library.net/document/z3n1v58q-aticnet-semantic-segmentation-spatial-pyramid-pooling-cascade-network.html", "content": "This paper describes a new type of image segmentation method based on deep convolutional neural networks (DCNN) in the actual autonomous driving ..."}
+{"idx": 2, "title": "Yannis Kalantidis' joint", "date": "", "ddg_snippet": "Paper accepted at ECCV 2024 : \"UNIC: Universal Classification Models via Multi-teacher Distillation\" . ... Paper accepted at CVPR 2024 : \"Label ...", "subpage_snippet": "", "source": "www.skamalas.com", "link": "https://www.skamalas.com/", "content": "Paper accepted at ECCV 2024 : \"UNIC: Universal Classification Models via Multi-teacher Distillation\" . ... Paper accepted at CVPR 2024 : \"Label ..."}
+{"idx": 3, "title": "Yannis Kalantidis' joint", "date": "", "ddg_snippet": "Paper accepted at ECCV 2024 : \"UNIC: Universal Classification Models via Multi-teacher Distillation\" . ... Paper accepted at CVPR 2024 : \"Label ...", "subpage_snippet": "", "source": "www.skamalas.com", "link": "http://www.skamalas.com/", "content": "Paper accepted at ECCV 2024 : \"UNIC: Universal Classification Models via Multi-teacher Distillation\" . ... Paper accepted at CVPR 2024 : \"Label ..."}
+{"idx": 4, "title": "ME336 Collaborative Robot Learning – mainDL@SUSTech in", "date": "", "ddg_snippet": "Mar 19: 1st Paper Review – Team 2: Paper | PDF | PowerPoint – Team 6: Paper | PDF | PowerPoint – Team 7: Paper | PDF | PowerPoint", "subpage_snippet": "", "source": "me336.ancorasir.com", "link": "https://me336.ancorasir.com/", "content": "Mar 19: 1st Paper Review – Team 2: Paper | PDF | PowerPoint – Team 6: Paper | PDF | PowerPoint – Team 7: Paper | PDF | PowerPoint"}
+{"idx": 5, "title": "Zehong (Jimmy) Cao's Personal Website - Enjoy your visit", "date": "", "ddg_snippet": "03/2024] Call for Papers : Technical papers on substantial, original, and unpublished research in all aspects of Artificial Intelligence, at PRICAI ...", "subpage_snippet": "", "source": "czh513.github.io", "link": "https://czh513.github.io/", "content": "03/2024] Call for Papers : Technical papers on substantial, original, and unpublished research in all aspects of Artificial Intelligence, at PRICAI ..."}
+{"idx": 6, "title": "Events | PyTorch", "date": "", "ddg_snippet": "The European Conference on Computer Vision (ECCV) is a biennial premier research conference in Computer Vision and Machine Learning, managed by the ...", "subpage_snippet": "", "source": "docs.pytorch.org", "link": "https://docs.pytorch.org/events", "content": "The European Conference on Computer Vision (ECCV) is a biennial premier research conference in Computer Vision and Machine Learning, managed by the ..."}
+{"idx": 7, "title": "Jana's Homepage", "date": "", "ddg_snippet": "Best Paper Award, ACM/IEEE Embedded Systems Week Conference (2022); Best Paper Award, ACM Transactions on Design Automation of Electronic Systems ...", "subpage_snippet": "", "source": "eecs.wsu.edu", "link": "https://eecs.wsu.edu/~jana/", "content": "Best Paper Award, ACM/IEEE Embedded Systems Week Conference (2022); Best Paper Award, ACM Transactions on Design Automation of Electronic Systems ..."}
+{"idx": 8, "title": "Solving AI Foundational Model Latency with Telco Infrastructure", "date": "", "ddg_snippet": "This paper presents a technical framework leveraging Telco infrastructure—spanning regional data centers, existing content delivery network (CDN ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.03708v1", "content": "This paper presents a technical framework leveraging Telco infrastructure—spanning regional data centers, existing content delivery network (CDN ..."}
+{"idx": 9, "title": "AetherCode: Evaluating LLMs’ Ability to Win In Premier", "date": "", "ddg_snippet": "In this paper , we argue that a significant gap still exists between the performance of LLMs and top- tier human competitors in programming contests.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.16402v1", "content": "In this paper , we argue that a significant gap still exists between the performance of LLMs and top- tier human competitors in programming contests."}
diff --git a/data/sampled_jsons/ICLR_paper_acceptance_tiers_ranking_oral_spotlight_poster_which_is_mid-tier.jsonl b/data/sampled_jsons/ICLR_paper_acceptance_tiers_ranking_oral_spotlight_poster_which_is_mid-tier.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..36c737cf6fd9f5b3f44a94c6093bf07f43b37da7
--- /dev/null
+++ b/data/sampled_jsons/ICLR_paper_acceptance_tiers_ranking_oral_spotlight_poster_which_is_mid-tier.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Find the latest ICON Public Limited Company ( ICLR ) stock quote, history, news and other vital information to help you with your stock trading and investing.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/?fr=sycsrp_catchall", "content": "Find the latest ICON Public Limited Company ( ICLR ) stock quote, history, news and other vital information to help you with your stock trading and investing."}
+{"idx": 1, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Get the latest ICON Public Limited Company ( ICLR ) stock news and headlines to help you in your trading and investing decisions.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/news/?fr=sycsrp_catchall", "content": "Get the latest ICON Public Limited Company ( ICLR ) stock news and headlines to help you in your trading and investing decisions."}
+{"idx": 2, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "See the company profile for ICON Public Limited Company ( ICLR ) including business summary, industry/sector information, number of employees, business summary, corporate governance, key executives ...", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/profile/?fr=sycsrp_catchall", "content": "See the company profile for ICON Public Limited Company ( ICLR ) including business summary, industry/sector information, number of employees, business summary, corporate governance, key executives ..."}
+{"idx": 3, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Discover historical prices for ICLR stock on Yahoo Finance. View daily, weekly or monthly format back to when ICON Public Limited Company stock was issued.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/history/?fr=sycsrp_catchall", "content": "Discover historical prices for ICLR stock on Yahoo Finance. View daily, weekly or monthly format back to when ICON Public Limited Company stock was issued."}
+{"idx": 4, "title": "Icon PLC (ICLR) Q3 2024 Earnings Call Highlights: Navigating...", "date": "", "ddg_snippet": "Oct 25, 2024 · Despite revenue decline and project delays, Icon PLC ( ICLR ) remains optimistic about future growth through strategic partnerships and market share gains.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/icon-plc-iclr-q3-2024-070717387.html?fr=sycsrp_catchall", "content": "Oct 25, 2024 · Despite revenue decline and project delays, Icon PLC ( ICLR ) remains optimistic about future growth through strategic partnerships and market share gains."}
+{"idx": 5, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "See ICON Public Limited Company ( ICLR ) stock analyst estimates, including earnings and revenue, EPS, upgrades and downgrades.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/analysis/?fr=sycsrp_catchall", "content": "See ICON Public Limited Company ( ICLR ) stock analyst estimates, including earnings and revenue, EPS, upgrades and downgrades."}
+{"idx": 6, "title": "ICON Public Limited Company (ICLR): Among Steven Cohen’s Mid-Cap...", "date": "", "ddg_snippet": "May 9, 2025 · In this article, we are going to take a look at where ICON Public Limited Company (NASDAQ: ICLR ) stands against Steve Cohen's other mid -cap stock picks with huge upside potential.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/icon-public-limited-company-iclr-071309449.html?fr=sycsrp_catchall", "content": "May 9, 2025 · In this article, we are going to take a look at where ICON Public Limited Company (NASDAQ: ICLR ) stands against Steve Cohen's other mid -cap stock picks with huge upside potential."}
+{"idx": 7, "title": "ICLR Misses on Q3 Earnings, Lowers 2024 Guidance, Stock Falls", "date": "", "ddg_snippet": "Oct 28, 2024 · Find the latest EPS estimates and surprises on Zacks Earnings Calendar. Following the earnings announcement, ICLR stock fell 21% last Thursday.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/iclr-misses-q3-earnings-lowers-123400740.html?fr=sycsrp_catchall", "content": "Oct 28, 2024 · Find the latest EPS estimates and surprises on Zacks Earnings Calendar. Following the earnings announcement, ICLR stock fell 21% last Thursday."}
+{"idx": 8, "title": "Icon PLC (ICLR) Lags Q3 Earnings and Revenue Estimates", "date": "", "ddg_snippet": "Oct 23, 2024 · Icon PLC ( ICLR ) delivered earnings and revenue surprises of -12.99% and 5.12%, respectively, for the quarter ended September 2024. Do the numbers hold clues to what lies ahead for the stock?", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/icon-plc-iclr-lags-q3-215513569.html?fr=sycsrp_catchall", "content": "Oct 23, 2024 · Icon PLC ( ICLR ) delivered earnings and revenue surprises of -12.99% and 5.12%, respectively, for the quarter ended September 2024. Do the numbers hold clues to what lies ahead for the stock?"}
+{"idx": 9, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Find out the direct holders, institutional holders and mutual fund holders for ICON Public Limited Company ( ICLR ).", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/holders/?fr=sycsrp_catchall", "content": "Find out the direct holders, institutional holders and mutual fund holders for ICON Public Limited Company ( ICLR )."}
diff --git a/data/sampled_jsons/ICML_2019_proceedings_Shen_Lee_Minimizing_the_sum_of_many_functions.jsonl b/data/sampled_jsons/ICML_2019_proceedings_Shen_Lee_Minimizing_the_sum_of_many_functions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..376a0c2209d6db8b943575e2c6e737eb7841de9d
--- /dev/null
+++ b/data/sampled_jsons/ICML_2019_proceedings_Shen_Lee_Minimizing_the_sum_of_many_functions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Optimization and Control May 2019", "date": "", "ddg_snippet": "9 May 2019 — Title: A Stochastic Decoupling Method for Minimizing the Sum of Smooth and Non-Smooth Functions . Konstantin Mishchenko, Peter Richtárik.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/math.OC/2019-05?skip=100&show=500", "content": "9 May 2019 — Title: A Stochastic Decoupling Method for Minimizing the Sum of Smooth and Non-Smooth Functions . Konstantin Mishchenko, Peter Richtárik."}
+{"idx": 1, "title": "Lower Complexity Bounds for Finite-Sum Convex-Concave ...", "date": "", "ddg_snippet": "by G Xie · 2020 · Cited by 25 — Abstract. This paper studies the lower bound complexity for minimax optimization problem whose objective function is the average of n individual smooth.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v119/xie20d/xie20d.pdf", "content": "by G Xie · 2020 · Cited by 25 — Abstract. This paper studies the lower bound complexity for minimax optimization problem whose objective function is the average of n individual smooth."}
+{"idx": 2, "title": "Overcoming the Curse of Dimensionality in Reinforcement ...", "date": "", "ddg_snippet": "(6). Appendix C provides an example of synchronous sampling with disjoint scopes. This strategy improves sample efficiency by reducing the total number of ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44811", "content": "(6). Appendix C provides an example of synchronous sampling with disjoint scopes. This strategy improves sample efficiency by reducing the total number of ..."}
+{"idx": 3, "title": "Sum of Ranked Range Loss for Supervised Learning", "date": "", "ddg_snippet": "by S Hu · 2022 · Cited by 29 — Minimizing the sum of the k largest functions in linear time. Information Processing Letters, 85(3):117–122, 2003. 41. Page 42. Hu, Ying, Wang, and Lyu. 44 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume23/21-0622/21-0622.pdf", "content": "by S Hu · 2022 · Cited by 29 — Minimizing the sum of the k largest functions in linear time. Information Processing Letters, 85(3):117–122, 2003. 41. Page 42. Hu, Ying, Wang, and Lyu. 44 pages"}
+{"idx": 4, "title": "Efficient algorithms for sum-of-minimum optimization", "date": "", "ddg_snippet": "by L Ding · 2024 · Cited by 7 — In this work, we propose a novel optimization model termed \" sum -of-minimum\" optimization. This model seeks to minimize the sum or average of ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3692504", "content": "by L Ding · 2024 · Cited by 7 — In this work, we propose a novel optimization model termed \" sum -of-minimum\" optimization. This model seeks to minimize the sum or average of ..."}
+{"idx": 5, "title": "Efficient Core-set Selection for Deep Learning Through ...", "date": "", "ddg_snippet": "16 Jul 2025 — 1. We propose a novel CS objective based on minimizing the sum of squared loss, which balances convergence between the core-set and non-core-set ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45105", "content": "16 Jul 2025 — 1. We propose a novel CS objective based on minimizing the sum of squared loss, which balances convergence between the core-set and non-core-set ..."}
+{"idx": 6, "title": "Reducing Variance of Stochastic Optimization for ...", "date": "", "ddg_snippet": "15 Jul 2025 — To improve the convergence rate by mitigating the high variance associated with the existing unbiased loss function , we propose a novel ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45762", "content": "15 Jul 2025 — To improve the convergence rate by mitigating the high variance associated with the existing unbiased loss function , we propose a novel ..."}
+{"idx": 7, "title": "Structured Logconcave Sampling with a Restricted Gaussian ...", "date": "", "ddg_snippet": "by YT Lee · 2021 · Cited by 101 — Minimizing finite sums with the stochastic average gradient. Math. Program., 162(1-2):83–112, 2017. Ruoqi Shen and Yin Tat Lee . The randomized midpoint method ... 58 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v134/lee21a/lee21a.pdf", "content": "by YT Lee · 2021 · Cited by 101 — Minimizing finite sums with the stochastic average gradient. Math. Program., 162(1-2):83–112, 2017. Ruoqi Shen and Yin Tat Lee . The randomized midpoint method ... 58 pages"}
+{"idx": 8, "title": "Sarah Frank-Wolfe: methods for constrained optimization with ...", "date": "", "ddg_snippet": "by A Beznosikov · 2024 · Cited by 10 — In this paper, we present two new variants of the FW algorithms for stochastic finite- sum minimization . Our algorithms have the best convergence ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3692219", "content": "by A Beznosikov · 2024 · Cited by 10 — In this paper, we present two new variants of the FW algorithms for stochastic finite- sum minimization . Our algorithms have the best convergence ..."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/ICML_2025_42nd_International_Conference_on_Machine_Learning_PMLR.jsonl b/data/sampled_jsons/ICML_2025_42nd_International_Conference_on_Machine_Learning_PMLR.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f115765562bd5a884e7be7f70e26e6b2f47f5395
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_42nd_International_Conference_on_Machine_Learning_PMLR.jsonl
@@ -0,0 +1,10 @@
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+{"idx": 1, "title": "ICML 2025 Template - Overleaf, Editor LaTeX online", "date": "", "ddg_snippet": "International Conference on Machine Learning ( ICML 2025 )} %. It is OKAY to include author information, even for blind % submissions: the style file will automatically remove it for you % unless you've provided the [accepted] option to the icml 2025 % package. %", "subpage_snippet": "", "source": "it.overleaf.com", "link": "https://it.overleaf.com/latex/templates/icml2025-template/dhxrkcgkvnkt", "content": "International Conference on Machine Learning ( ICML 2025 )} %. It is OKAY to include author information, even for blind % submissions: the style file will automatically remove it for you % unless you've provided the [accepted] option to the icml 2025 % package. %"}
+{"idx": 2, "title": "Riemannian Diffusion Adaptation for Distributed Optimization on...", "date": "", "ddg_snippet": "In International Conference on Machine Learn -ing, pp. 1594–1605.In Interna - tional Conference on Machine Learning , pp. 4465–4474. PMLR , 2020. 10. Riemannian Diffusion Adaptation.", "subpage_snippet": "", "source": "asl.epfl.ch", "link": "https://asl.epfl.ch/wp-content/uploads/2025/05/2025-ICML.pdf", "content": "In International Conference on Machine Learn -ing, pp. 1594–1605.In Interna - tional Conference on Machine Learning , pp. 4465–4474. PMLR , 2020. 10. Riemannian Diffusion Adaptation."}
+{"idx": 3, "title": "No Query, No Access", "date": "", "ddg_snippet": ">. Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada. PMLR 267, 2025 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.07258", "content": ">. Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada. PMLR 267, 2025 ."}
+{"idx": 4, "title": "HyperIV: Real-time implied volatility smoothing - University of...", "date": "", "ddg_snippet": "The 42 nd International Conference on Machine Learning . Abbreviated title. ICML 2025 . Country/Territory. Canada.In Proceedings of the 42 nd International Conference on Machine Learning (pp. 1-15).", "subpage_snippet": "", "source": "www.research.ed.ac.uk", "link": "https://www.research.ed.ac.uk/en/publications/hyperiv-real-time-implied-volatility-smoothing", "content": "The 42 nd International Conference on Machine Learning . Abbreviated title. ICML 2025 . Country/Territory. Canada.In Proceedings of the 42 nd International Conference on Machine Learning (pp. 1-15)."}
+{"idx": 5, "title": "Visual Abstraction: A Plug-and-Play Approach for Text-Visual Retrieval", "date": "", "ddg_snippet": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada. PMLR 267, 2025 .", "subpage_snippet": "", "source": "pengxi.me", "link": "https://pengxi.me/wp-content/uploads/2025/05/2025ICML.pdf", "content": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada. PMLR 267, 2025 ."}
+{"idx": 6, "title": "Winner-takes-all for Multivariate Probabilistic Time Series Forecasting", "date": "", "ddg_snippet": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada. PMLR 267, 2025 .", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-05100125v2/document", "content": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada. PMLR 267, 2025 ."}
+{"idx": 7, "title": "Flexible Tails for Normalizing Flows - University of Bristol", "date": "", "ddg_snippet": "The 42 nd International Conference on Machine Learning ( ICML 2025 ). Abbreviated title.In International Conference on Machine Learning , 13-19 July 2025 , Vancouver, Canada (Proceedings of Machine Learning Research).", "subpage_snippet": "", "source": "research-information.bris.ac.uk", "link": "https://research-information.bris.ac.uk/en/publications/flexible-tails-for-normalizing-flows", "content": "The 42 nd International Conference on Machine Learning ( ICML 2025 ). Abbreviated title.In International Conference on Machine Learning , 13-19 July 2025 , Vancouver, Canada (Proceedings of Machine Learning Research)."}
+{"idx": 8, "title": "MODA: MOdular Duplex Attention forMultimodal Perception, Cognition...", "date": "", "ddg_snippet": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada. PMLR 267, 2025 .The learning rate is set to 2e-5 for LLM and 2e-6 for visual encoder, respectively.", "subpage_snippet": "", "source": "zzcheng.top", "link": "https://zzcheng.top/assets/pdf/2025_ICML_MODA.pdf", "content": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada. PMLR 267, 2025 .The learning rate is set to 2e-5 for LLM and 2e-6 for visual encoder, respectively."}
+{"idx": 9, "title": "ZHIYUAN LI", "date": "", "ddg_snippet": "International Conference on Machine Learning ( ICML ). PMLR . 2024.", "subpage_snippet": "", "source": "zhiyuanli.ttic.edu", "link": "https://zhiyuanli.ttic.edu/assets/pdf/zhiyuan.pdf", "content": "International Conference on Machine Learning ( ICML ). PMLR . 2024."}
diff --git a/data/sampled_jsons/ICML_2025_Normalizing_Flows_abstract_top_papers_year_2025.jsonl b/data/sampled_jsons/ICML_2025_Normalizing_Flows_abstract_top_papers_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..94d6d7823e4ff6ce952b5e31e0459c10d72dc39c
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_Normalizing_Flows_abstract_top_papers_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "ICML is a global non-profit organization dedicated to helping lubrication practitioners succeed in their professional careers. ICML certification exams are administered in accordance with ISO 18436 and are available worldwide in multiple languages and in both paper and online formats.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/", "content": "ICML is a global non-profit organization dedicated to helping lubrication practitioners succeed in their professional careers. ICML certification exams are administered in accordance with ISO 18436 and are available worldwide in multiple languages and in both paper and online formats."}
+{"idx": 1, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "Candidates have three hours to complete the closed-book examination. A score of 70% is required to pass the examination and achieve certification. Contact ICML about the availability of the exam in other languages.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/exams/Default.aspx", "content": "Candidates have three hours to complete the closed-book examination. A score of 70% is required to pass the examination and achieve certification. Contact ICML about the availability of the exam in other languages."}
+{"idx": 2, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "About Us Success Stories Roster ICML Alliances Training Partners Contact Us Privacy Questions ICML | 2404 W. Detroit St | Broken Arrow, OK 74012 USA o: 918.259.2950 | f: 918.259.0177 www.lubecouncil.org | info@lubecouncil.org", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/contact_us.aspx", "content": "About Us Success Stories Roster ICML Alliances Training Partners Contact Us Privacy Questions ICML | 2404 W. Detroit St | Broken Arrow, OK 74012 USA o: 918.259.2950 | f: 918.259.0177 www.lubecouncil.org | info@lubecouncil.org"}
+{"idx": 3, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "ICML provides certification programs for machinery lubrication professionals, ensuring qualifications and knowledge in lubrication practices.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/exams/professionals.aspx", "content": "ICML provides certification programs for machinery lubrication professionals, ensuring qualifications and knowledge in lubrication practices."}
+{"idx": 4, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "ICML 's Bodies of Knowledge are of public domain and can be utilized by companies in the development of courses, as well as by any prospective candidate for evaluating the appropriateness of chosen training.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/exams/Default.aspx?p=LLA1", "content": "ICML 's Bodies of Knowledge are of public domain and can be utilized by companies in the development of courses, as well as by any prospective candidate for evaluating the appropriateness of chosen training."}
+{"idx": 5, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "Examination – Each candidate must successfully pass a 150-question, multiple choice Machinery Lubrication Engineer (MLE) ® examination that tests the candidate’s mastery of the ICML 's Machinery Lubrication Engineer (MLE) body of knowledge.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/exams/Default.aspx?p=MLE1", "content": "Examination – Each candidate must successfully pass a 150-question, multiple choice Machinery Lubrication Engineer (MLE) ® examination that tests the candidate’s mastery of the ICML 's Machinery Lubrication Engineer (MLE) body of knowledge."}
+{"idx": 6, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "You must take the exam on paper in a controlled environment approved by ICML . Please start your application by selecting an exam type below. You will be asked to confirm your qualifications, and then you will have the opportunity to select from a list of exam sessions already scheduled on our calendar.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/apply2/", "content": "You must take the exam on paper in a controlled environment approved by ICML . Please start your application by selecting an exam type below. You will be asked to confirm your qualifications, and then you will have the opportunity to select from a list of exam sessions already scheduled on our calendar."}
+{"idx": 7, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "Find information on ICML recertification, including requirements and processes for maintaining your lubrication certification.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/recertification/default.aspx", "content": "Find information on ICML recertification, including requirements and processes for maintaining your lubrication certification."}
+{"idx": 8, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "As a supporter and promoter of the ICML 55® Standard, he recently (2023) contributed as a co-author to ICML 55.2 “Guideline for the Optimized Lubrication of Mechanical Physical Assets.”", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/board_of_directors.aspx", "content": "As a supporter and promoter of the ICML 55® Standard, he recently (2023) contributed as a co-author to ICML 55.2 “Guideline for the Optimized Lubrication of Mechanical Physical Assets.”"}
+{"idx": 9, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "Serving Lubrication Practitioners GET CERTIFIED. STAY CERTIFIED.Problem or Suggestion About This Page?", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/Default.aspx", "content": "Serving Lubrication Practitioners GET CERTIFIED. STAY CERTIFIED.Problem or Suggestion About This Page?"}
diff --git a/data/sampled_jsons/ICML_2025_Symmetric_Reinforcement_Learning_Loss_Section_5.4_PPO_instability.jsonl b/data/sampled_jsons/ICML_2025_Symmetric_Reinforcement_Learning_Loss_Section_5.4_PPO_instability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5acb0b8518c12115918d14e4313e8bb1a107f903
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_Symmetric_Reinforcement_Learning_Loss_Section_5.4_PPO_instability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scalable Multi-Objective Robot Reinforcement Learning through", "date": "", "ddg_snippet": "GCR- PPO provides a scalable approach for reinforcement learning agents to balance and learn from multiple reward signals simultaneously through ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14816v1", "content": "GCR- PPO provides a scalable approach for reinforcement learning agents to balance and learn from multiple reward signals simultaneously through ..."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning difficulties with cross-entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17618v3", "content": "In this work, we focus on RL algorithms that share learning difficulties with cross-entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 2, "title": "ICML 2025 2025 Spotlight Posters", "date": "", "ddg_snippet": "Existing reinforcement learning (RL) approaches often struggle to generalize to unseen goals and states, limiting their applicability. In this paper, we introduce TEDUO, a novel training pipeline for offline language-conditioned policy learning in symbolic environments.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/2025SpotlightPosters", "content": "Existing reinforcement learning (RL) approaches often struggle to generalize to unseen goals and states, limiting their applicability. In this paper, we introduce TEDUO, a novel training pipeline for offline language-conditioned policy learning in symbolic environments."}
+{"idx": 3, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning dificulties with cross-entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618v3", "content": "In this work, we focus on RL algorithms that share learning dificulties with cross-entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 4, "title": "ICML 2025 Statistics - Paper Copilot", "date": "", "ddg_snippet": "Application-Driven Machine Learning (innovative techniques, problems, and datasets that are of interest to the machine learning community and driven by the needs of end-users in applications such as healthcare, physical sciences, biosciences, social sciences, sustainability and climate, etc.)", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/icml-statistics/icml-2025-statistics/", "content": "Application-Driven Machine Learning (innovative techniques, problems, and datasets that are of interest to the machine learning community and driven by the needs of end-users in applications such as healthcare, physical sciences, biosciences, social sciences, sustainability and climate, etc.)"}
+{"idx": 5, "title": "PDF Solutions to Exercises in Reinforcement Learning by ... - Tianlin Liu", "date": "", "ddg_snippet": "Exercise 1.1. Self-Play. Suppose, instead of playing against a random opponent, the reinforce-ment learning algorithm described above played against itself, with both sides learning . What do you think would happen in this case? Would it learn a di erent policy for selecting moves?", "subpage_snippet": "", "source": "tianlinliu.com", "link": "https://tianlinliu.com/files/notes_exercise_RL.pdf", "content": "Exercise 1.1. Self-Play. Suppose, instead of playing against a random opponent, the reinforce-ment learning algorithm described above played against itself, with both sides learning . What do you think would happen in this case? Would it learn a di erent policy for selecting moves?"}
+{"idx": 6, "title": "PDF Categorical Distributional Reinforcement Learning with Kullback-Leibler ...", "date": "", "ddg_snippet": "In this work, we study categorical temporal-difference learn-ing with KL loss (KL-CTD) as a fundamental tabular al-gorithm for reinforcement learning in its own right. In Section 3, we present several empirical examples of intrigu-ing behaviour of KL-CTD in comparison to classical TD learning , motivating our study.", "subpage_snippet": "", "source": "sologen.net", "link": "https://sologen.net/papers/CTD(ICML2025).pdf", "content": "In this work, we study categorical temporal-difference learn-ing with KL loss (KL-CTD) as a fundamental tabular al-gorithm for reinforcement learning in its own right. In Section 3, we present several empirical examples of intrigu-ing behaviour of KL-CTD in comparison to classical TD learning , motivating our study."}
+{"idx": 7, "title": "Code for Symmetric Machine Theory of Mind - GitHub", "date": "", "ddg_snippet": "About Code for the paper \" Symmetric Machine Theory of Mind\", presented at ICML 2022.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/msclar/symmtom", "content": "About Code for the paper \" Symmetric Machine Theory of Mind\", presented at ICML 2022."}
+{"idx": 8, "title": "PDF Asynchronous Methods for Deep Reinforcement Learning", "date": "", "ddg_snippet": "Combining other existing reinforcement learning meth-ods or recent advances in deep reinforcement learning with our asynchronous framework presents many possibil-ities for immediate improvements to the methods we pre-sented.", "subpage_snippet": "", "source": "www.cl.cam.ac.uk", "link": "https://www.cl.cam.ac.uk/~ey204/teaching/ACS/R244_2024_2025/papers/mnih_ICML_2016.pdf", "content": "Combining other existing reinforcement learning meth-ods or recent advances in deep reinforcement learning with our asynchronous framework presents many possibil-ities for immediate improvements to the methods we pre-sented."}
+{"idx": 9, "title": "Track: Poster Session 1 West - icml.cc", "date": "", "ddg_snippet": "This work examines average-reward reinforcement learning with general policy parametrization. Existing state-of-the-art (SOTA) guarantees for this problem are either suboptimal or hindered by several challenges, including poor scalability with respect to the size of the state-action space, high iteration complexity, and a significant dependence ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50257", "content": "This work examines average-reward reinforcement learning with general policy parametrization. Existing state-of-the-art (SOTA) guarantees for this problem are either suboptimal or hindered by several challenges, including poor scalability with respect to the size of the state-action space, high iteration complexity, and a significant dependence ..."}
diff --git a/data/sampled_jsons/IP-Adapter_CLIP_encoding_image_prompt_adapter.jsonl b/data/sampled_jsons/IP-Adapter_CLIP_encoding_image_prompt_adapter.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7c808cea718f289f01e4637f8965c9d3029d79c2
--- /dev/null
+++ b/data/sampled_jsons/IP-Adapter_CLIP_encoding_image_prompt_adapter.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "tencent-ailab/IP-Adapter: The image prompt ...", "date": "", "ddg_snippet": "We present IP-Adapter , an effective and lightweight adapter to achieve image prompt capability for the pre-trained text-to-image diffusion models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tencent-ailab/IP-Adapter", "content": "We present IP-Adapter , an effective and lightweight adapter to achieve image prompt capability for the pre-trained text-to-image diffusion models."}
+{"idx": 1, "title": "IP-Adapter", "date": "", "ddg_snippet": "IP-Adapter is a lightweight adapter that integrates image-based guidance with text-to-image models, using image embeddings and cross-attention layers.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/diffusers/main/en/using-diffusers/ip_adapter", "content": "IP-Adapter is a lightweight adapter that integrates image-based guidance with text-to-image models, using image embeddings and cross-attention layers."}
+{"idx": 2, "title": "IP‐Adapter‐Face · tencent-ailab/IP-Adapter Wiki", "date": "", "ddg_snippet": "1 Feb 2024 — The image prompt adapter is designed to enable a pretrained text-to-image diffusion model to generate images with image prompt.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tencent-ailab/IP-Adapter/wiki/IP‐Adapter‐Face", "content": "1 Feb 2024 — The image prompt adapter is designed to enable a pretrained text-to-image diffusion model to generate images with image prompt."}
+{"idx": 3, "title": "Image Generation with Stable Diffusion and IP-Adapter", "date": "", "ddg_snippet": "10 Feb 2024 — IP-Adapter is an effective and lightweight adapter that adds image prompting capabilities to a diffusion model.", "subpage_snippet": "", "source": "docs.openvino.ai", "link": "https://docs.openvino.ai/2023.3/notebooks/278-stable-diffusion-ip-adapter-with-output.html", "content": "10 Feb 2024 — IP-Adapter is an effective and lightweight adapter that adds image prompting capabilities to a diffusion model."}
+{"idx": 4, "title": "h94/IP-Adapter", "date": "", "ddg_snippet": "IP-Adapter is a lightweight adapter for text-to-image models, enabling image prompts with only 22M parameters, and can be used for multimodal generation.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/h94/IP-Adapter", "content": "IP-Adapter is a lightweight adapter for text-to-image models, enabling image prompts with only 22M parameters, and can be used for multimodal generation."}
+{"idx": 5, "title": "Mind the Trojan Horse: Image Prompt Adapter Enabling ...", "date": "", "ddg_snippet": "8 Apr 2025 — In this paper, we reveal that T2I-DMs equipped with the IP - Adapter (T2I- IP -DMs) enable a new jailbreak attack named the hijacking attack.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.05838v1", "content": "8 Apr 2025 — In this paper, we reveal that T2I-DMs equipped with the IP - Adapter (T2I- IP -DMs) enable a new jailbreak attack named the hijacking attack."}
+{"idx": 6, "title": "Understanding and Training IP Adapters for Diffusion Models", "date": "", "ddg_snippet": "Learn how IP-Adapter enhances diffusion models by enabling image prompts . Explore its architecture, training process, and comparison to ControlNet and LoRA.", "subpage_snippet": "", "source": "www.mercity.ai", "link": "https://www.mercity.ai/blog-post/understanding-and-training-ip-adapters-for-diffusion-models", "content": "Learn how IP-Adapter enhances diffusion models by enabling image prompts . Explore its architecture, training process, and comparison to ControlNet and LoRA."}
+{"idx": 7, "title": "Part 4: Style Without Training — How IP-Adapter Adds You to ...", "date": "", "ddg_snippet": "It works by: Passing the reference image through a CLIP vision encoder ; Injecting the image embedding into the U-Net via cross-attention ...", "subpage_snippet": "", "source": "shree6791.medium.com", "link": "https://shree6791.medium.com/part-4-style-without-training-how-ip-adapter-adds-you-to-the-picture-836c9355b085", "content": "It works by: Passing the reference image through a CLIP vision encoder ; Injecting the image embedding into the U-Net via cross-attention ..."}
+{"idx": 8, "title": "Can someone explain to my like I'm 5 what ipadapter is ...", "date": "", "ddg_snippet": "A basic Image Prompt Adapter is a small hypernetwork model that essentially generates embeddings based on the input image and intermediate outputs and inserts.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/StableDiffusion/comments/1aq844t/can_someone_explain_to_my_like_im_5_what/", "content": "A basic Image Prompt Adapter is a small hypernetwork model that essentially generates embeddings based on the input image and intermediate outputs and inserts."}
+{"idx": 9, "title": "IP-Adapters: All you need to know", "date": "", "ddg_snippet": "5 Jun 2024 — IP-adapter (Image Prompt adapter ) is a Stable Diffusion add-on for using images as prompts, similar to Midjourney and DaLLE 3.", "subpage_snippet": "", "source": "stable-diffusion-art.com", "link": "https://stable-diffusion-art.com/ip-adapter/", "content": "5 Jun 2024 — IP-adapter (Image Prompt adapter ) is a Stable Diffusion add-on for using images as prompts, similar to Midjourney and DaLLE 3."}
diff --git a/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_Equation_6_Complexity_Tab.jsonl b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_Equation_6_Complexity_Tab.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4f146284f45663dd7a7c3eb8a531453ab6fea149
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_Equation_6_Complexity_Tab.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "Feb 7, 2025 · Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks . Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05352", "content": "Feb 7, 2025 · Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks . Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and ..."}
+{"idx": 1, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "Jul 13, 2025 · The design enables AI researchers to understand the challenges and opportunities of AI agents for IT automation with push-button workflows and interpretable metrics. ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions.", "subpage_snippet": "", "source": "research.ibm.com", "link": "https://research.ibm.com/publications/itbench-evaluating-ai-agents-across-diverse-real-world-it-automation-tasks", "content": "Jul 13, 2025 · The design enables AI researchers to understand the challenges and opportunities of AI agents for IT automation with push-button workflows and interpretable metrics. ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions."}
+{"idx": 2, "title": "ITBench : Next-Gen Benchmarking for IT Automation Evaluation", "date": "", "ddg_snippet": "ITBench Benchmarking Scenarios, Agents , Automation Server, and Leaderboard. ITBench is a systematic benchmarking framework and run-time environment designed to evaluate agents tasked with automating IT operations.", "subpage_snippet": "", "source": "dzone.com", "link": "https://dzone.com/articles/itbench-next-gen-benchmarking-it-automation", "content": "ITBench Benchmarking Scenarios, Agents , Automation Server, and Leaderboard. ITBench is a systematic benchmarking framework and run-time environment designed to evaluate agents tasked with automating IT operations."}
+{"idx": 3, "title": "(PDF) ITBench : Evaluating AI Agents across Diverse Real - World IT ...", "date": "", "ddg_snippet": "Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388882803_ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks", "content": "Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address..."}
+{"idx": 4, "title": "GitHub - itbench-hub/ITBench: Code repository for ITBench", "date": "", "ddg_snippet": "ITBench measures the performance of AI agents across a wide variety of complex and real-world inspired IT automation tasks targeting three key use cases:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench", "content": "ITBench measures the performance of AI agents across a wide variety of complex and real-world inspired IT automation tasks targeting three key use cases:"}
+{"idx": 5, "title": "Benchmarking AI Agents for IT Automation Tasks with ITBench", "date": "", "ddg_snippet": "Modern IT infrastructures have grown exponentially in complexity with the adoption of cloud computing and agile development methodologies, making their management increasingly challenging. These management tasks span multiple domains, including site reliability engineering (SRE), compliance and security operations (CISO), and financial operations (FinOps). AI agents have shown initial promise ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11068394", "content": "Modern IT infrastructures have grown exponentially in complexity with the adoption of cloud computing and agile development methodologies, making their management increasingly challenging. These management tasks span multiple domains, including site reliability engineering (SRE), compliance and security operations (CISO), and financial operations (FinOps). AI agents have shown initial promise ..."}
+{"idx": 6, "title": "Paper tables with annotated results for ITBench : Evaluating AI ...", "date": "", "ddg_snippet": "ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks . Agent Level. Beginner, Intermediate, Expert. (maps to scenario complexity : Easy, Medium, Hard).", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/itbench-evaluating-ai-agents-across-diverse/review/", "content": "ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks . Agent Level. Beginner, Intermediate, Expert. (maps to scenario complexity : Easy, Medium, Hard)."}
+{"idx": 7, "title": "[Literature Review] ITBench : Evaluating AI Agents across Diverse ...", "date": "", "ddg_snippet": "The paper introduces ITBench , a benchmarking framework designed to evaluate AI agents performing various real - world IT automation tasks .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/itbench-evaluating-ai-agents-across-diverse-real-world-it-automation-tasks", "content": "The paper introduces ITBench , a benchmarking framework designed to evaluate AI agents performing various real - world IT automation tasks ."}
+{"idx": 8, "title": "ITBench : Evaluating AI Agents across Diverse Real - World IT ...", "date": "", "ddg_snippet": "We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.05352v1", "content": "We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks ."}
+{"idx": 9, "title": "GitHub - xlab-uiuc/ ITBench -1: Code repository for sample scenarios as...", "date": "", "ddg_snippet": "title={ ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks }, author={Jha, Saurabh and Arora, Rohan and Watanabe, Yuji and others}", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xlab-uiuc/ITBench-1", "content": "title={ ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks }, author={Jha, Saurabh and Arora, Rohan and Watanabe, Yuji and others}"}
diff --git a/data/sampled_jsons/ITBench_Table_12_MemoryLeak_3_2.jsonl b/data/sampled_jsons/ITBench_Table_12_MemoryLeak_3_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..885d6b802670064182b923ae97f4172d94489b23
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Table_12_MemoryLeak_3_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Paper tables with annotated results for ITBench ... | Papers With Code", "date": "", "ddg_snippet": "ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25. 2 % of CISO scenarios, and 0% of FinOps scenarios.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/itbench-evaluating-ai-agents-across-diverse/review/", "content": "ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25. 2 % of CISO scenarios, and 0% of FinOps scenarios."}
+{"idx": 1, "title": "ITBench : Evaluating AI Agents across", "date": "", "ddg_snippet": "Table 12 summarizes the scenarios that are currently avail-able in ITBench . Table 2 summarizes the CISO tasks initially supported in our ITBench , namely CE and SAP. The other will be cov-ered in subsequent releases. These tasks are executed in IT - Bench against predefined...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "Table 12 summarizes the scenarios that are currently avail-able in ITBench . Table 2 summarizes the CISO tasks initially supported in our ITBench , namely CE and SAP. The other will be cov-ered in subsequent releases. These tasks are executed in IT - Bench against predefined..."}
+{"idx": 2, "title": "(PDF) ITBench : Evaluating AI Agents across Diverse Real-World IT...", "date": "", "ddg_snippet": "Table 12 : Unique Scenarios available in ITBench . Scenario Pattern Technologies Impacted # Fault Propagation # Resolution Steps. MemoryLeak python, Node.js, Go 8 6.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388882803_ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks", "content": "Table 12 : Unique Scenarios available in ITBench . Scenario Pattern Technologies Impacted # Fault Propagation # Resolution Steps. MemoryLeak python, Node.js, Go 8 6."}
+{"idx": 3, "title": "Use memoryLeak in stryker-parent With Examples | LambdaTest", "date": "", "ddg_snippet": "Learn how to set up and run automated tests with code examples of memoryLeak method from our library.", "subpage_snippet": "", "source": "www.lambdatest.com", "link": "https://www.lambdatest.com/automation-testing-advisor/javascript/stryker-parent-memoryLeak", "content": "Learn how to set up and run automated tests with code examples of memoryLeak method from our library."}
+{"idx": 4, "title": "WinMerge / Patches / #2975 memoryleak from pcre", "date": "", "ddg_snippet": "Created: 2010-09- 12 . Creator: Matthias. Private: No. with new version I get a memoryleak pcre_study() alloc memory, what never has set free again aslong there was no errormessage.", "subpage_snippet": "", "source": "sourceforge.net", "link": "https://sourceforge.net/p/winmerge/patches/2975/", "content": "Created: 2010-09- 12 . Creator: Matthias. Private: No. with new version I get a memoryleak pcre_study() alloc memory, what never has set free again aslong there was no errormessage."}
+{"idx": 5, "title": "Пойдет ли MEMORYLEAK ™ ? Системные требования игры...", "date": "", "ddg_snippet": "Проверьте совместимость MEMORYLEAK ™ с вашим компьютером и узнайте ожидаемый FPS при работе на различных процессорах и видеокартах.", "subpage_snippet": "", "source": "hardrank.net", "link": "https://hardrank.net/ru/game/memoryleak", "content": "Проверьте совместимость MEMORYLEAK ™ с вашим компьютером и узнайте ожидаемый FPS при работе на различных процессорах и видеокартах."}
+{"idx": 6, "title": "Questions - OpenCV Q&A Forum", "date": "", "ddg_snippet": "43 questions. Tagged. memoryleak ×.OCL device memory leak. memoryleak . 1k. views.", "subpage_snippet": "", "source": "answers.opencv.org", "link": "https://answers.opencv.org/questions/scope:all/sort:activity-desc/tags:memoryleak/page:1/", "content": "43 questions. Tagged. memoryleak ×.OCL device memory leak. memoryleak . 1k. views."}
+{"idx": 7, "title": "c# - How many requests can SQL Server handle per... - Stack Overflow", "date": "", "ddg_snippet": "1. I just use linq to insert records. MemoryLeak .SQL Server 2016 1 200 000 batch requests/sec Memory-Optimized Table with LOB support, Natively Compiled stored procedures. enter image description here.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/1824772/how-many-requests-can-sql-server-handle-per-second", "content": "1. I just use linq to insert records. MemoryLeak .SQL Server 2016 1 200 000 batch requests/sec Memory-Optimized Table with LOB support, Natively Compiled stored procedures. enter image description here."}
+{"idx": 8, "title": "The ConnectionString Property has not been Initialized On only one...", "date": "", "ddg_snippet": "Visual Studio Report Designer - Connection name disappears when you open a report in v24. 2 . 3 MemoryLeak in XtraReports running on Debian Linux (kubernetes cluster) How to show detail report by clicking on a pivot grid cell value How to provide a custom data source (ODBC)...", "subpage_snippet": "", "source": "supportcenter.devexpress.com", "link": "https://supportcenter.devexpress.com/ticket/details/t441588/the-connectionstring-property-has-not-been-initialized-on-only-one-install", "content": "Visual Studio Report Designer - Connection name disappears when you open a report in v24. 2 . 3 MemoryLeak in XtraReports running on Debian Linux (kubernetes cluster) How to show detail report by clicking on a pivot grid cell value How to provide a custom data source (ODBC)..."}
+{"idx": 9, "title": "VS2012吐槽，实在是吐血啊。 - StevenChennet - 博客园", "date": "", "ddg_snippet": "« 上一篇： 捉虫记（二）GC导致的hang » 下一篇： 捉虫记（三） Event导致的 MemoryLeak .", "subpage_snippet": "", "source": "www.cnblogs.com", "link": "https://www.cnblogs.com/StevenChennet/archive/2012/08/17/2643633.html", "content": "« 上一篇： 捉虫记（二）GC导致的hang » 下一篇： 捉虫记（三） Event导致的 MemoryLeak ."}
diff --git a/data/sampled_jsons/ITBench_Table_12_MemoryLeak_3_2_2.jsonl b/data/sampled_jsons/ITBench_Table_12_MemoryLeak_3_2_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..793b4a3c29d8c919b37dde968bd597c6b5449073
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Table_12_MemoryLeak_3_2_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Paper tables with annotated results for ITBench ... | Papers With Code", "date": "", "ddg_snippet": "ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25. 2 % of CISO scenarios, and 0% of FinOps scenarios.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/itbench-evaluating-ai-agents-across-diverse/review/", "content": "ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25. 2 % of CISO scenarios, and 0% of FinOps scenarios."}
+{"idx": 1, "title": "ITBench : Evaluating AI Agents across", "date": "", "ddg_snippet": "Table 12 : Unique Scenarios available in ITBench . Scenario Pattern. Table 2 summarizes the CISO tasks initially supported in our ITBench , namely CE and SAP. The other will be cov-ered in subsequent releases. These tasks are executed in IT - Bench against predefined, standard scenarios...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "Table 12 : Unique Scenarios available in ITBench . Scenario Pattern. Table 2 summarizes the CISO tasks initially supported in our ITBench , namely CE and SAP. The other will be cov-ered in subsequent releases. These tasks are executed in IT - Bench against predefined, standard scenarios..."}
+{"idx": 2, "title": "(PDF) ITBench : Evaluating AI Agents across Diverse Real-World IT...", "date": "", "ddg_snippet": "Table 12 : Unique Scenarios available in ITBench . Scenario Pattern Technologies Impacted # Fault Propagation # Resolution Steps.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388882803_ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks", "content": "Table 12 : Unique Scenarios available in ITBench . Scenario Pattern Technologies Impacted # Fault Propagation # Resolution Steps."}
+{"idx": 3, "title": "Use memoryLeak in stryker-parent With Examples | LambdaTest", "date": "", "ddg_snippet": "Learn how to set up and run automated tests with code examples of memoryLeak method from our library.", "subpage_snippet": "", "source": "www.lambdatest.com", "link": "https://www.lambdatest.com/automation-testing-advisor/javascript/stryker-parent-memoryLeak", "content": "Learn how to set up and run automated tests with code examples of memoryLeak method from our library."}
+{"idx": 4, "title": "Memoryleak - DEV Community", "date": "", "ddg_snippet": "memoryleak content on DEV Community.Paula Santana. Follow. Aug 12 '22.", "subpage_snippet": "", "source": "practicaldev-herokuapp-com.freetls.fastly.net", "link": "https://practicaldev-herokuapp-com.freetls.fastly.net/t/memoryleak", "content": "memoryleak content on DEV Community.Paula Santana. Follow. Aug 12 '22."}
+{"idx": 5, "title": "Blobs created by MediaRecorder never get released. [ MemoryLeak ]...", "date": "", "ddg_snippet": "kr...@google.com # 12 Feb 18, 2021 04:42PM. I just tried capturing a heap snapshot, but cannot say that I see anything that corresponds to the sizes of the blobs. I made a quick test on Chrome 87, and the problem seems to reproduce also there.", "subpage_snippet": "", "source": "issuetracker.google.com", "link": "https://issuetracker.google.com/issues/40749662", "content": "kr...@google.com # 12 Feb 18, 2021 04:42PM. I just tried capturing a heap snapshot, but cannot say that I see anything that corresponds to the sizes of the blobs. I made a quick test on Chrome 87, and the problem seems to reproduce also there."}
+{"idx": 6, "title": "Questions - OpenCV Q&A Forum", "date": "", "ddg_snippet": "OCL device memory leak. memoryleak .", "subpage_snippet": "", "source": "answers.opencv.org", "link": "https://answers.opencv.org/questions/scope:all/sort:activity-desc/tags:memoryleak/page:1/", "content": "OCL device memory leak. memoryleak ."}
+{"idx": 7, "title": "C# Event Based Memory Leaks - Stack Overflow", "date": "", "ddg_snippet": "MT Count TotalSize Class Name 0040348c 1 12 MemoryLeak .AnEventListener Total 1 objects.Dump Method Table to find 0040C060 in it: !dumpmt -md 0ced1910. If there no match, Dump the memory that start from the _methodPtr address: !u 0040C060.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/4234130/c-sharp-event-based-memory-leaks", "content": "MT Count TotalSize Class Name 0040348c 1 12 MemoryLeak .AnEventListener Total 1 objects.Dump Method Table to find 0040C060 in it: !dumpmt -md 0ced1910. If there no match, Dump the memory that start from the _methodPtr address: !u 0040C060."}
+{"idx": 8, "title": "Поиск неисправностей с помощью WinDbg, Sos и Sosex / Хабр", "date": "", "ddg_snippet": "(Пример 03- MemoryLeak ). Иногда может возникнуть ситуация, когда приложение начинает выделять все больше памяти, не освобождая ее. В таком случае необходимо провести анализ выделенной памяти на предмет поиска утечек.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/pt/articles/327524/", "content": "(Пример 03- MemoryLeak ). Иногда может возникнуть ситуация, когда приложение начинает выделять все больше памяти, не освобождая ее. В таком случае необходимо провести анализ выделенной памяти на предмет поиска утечек."}
+{"idx": 9, "title": "At my wit's end...high memory but figures don't add up!!", "date": "", "ddg_snippet": "memoryleak 1.png memoryleak 2 .png memoryleak 3 .png.# 12 . The commit charge is 31.7 GB which is far higher than can be accounted for by visible processes. It is also not accounted for by either the paged or non-paged pools.", "subpage_snippet": "", "source": "www.eightforums.com", "link": "https://www.eightforums.com/threads/at-my-wits-end-high-memory-but-figures-dont-add-up.33928/", "content": "memoryleak 1.png memoryleak 2 .png memoryleak 3 .png.# 12 . The commit charge is 31.7 GB which is far higher than can be accounted for by visible processes. It is also not accounted for by either the paged or non-paged pools."}
diff --git a/data/sampled_jsons/ITBench_paper_sitearxiv.org_'Equation_(6)'_complexity_definition_year_2024.jsonl b/data/sampled_jsons/ITBench_paper_sitearxiv.org_'Equation_(6)'_complexity_definition_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7779076d95c5986edc8e9faeabfec1a69039cdf7
--- /dev/null
+++ b/data/sampled_jsons/ITBench_paper_sitearxiv.org_'Equation_(6)'_complexity_definition_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "The goal of ITBench is to measure the performance of AI agents across a wide variety of complex and real-life IT tasks across personas including, Site Reliability Engineering (SRE) focusing on availability and resiliency, Compliance and Security Operations (CISO) ensuring compliance and security of IT implementations, and Financial Operations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "The goal of ITBench is to measure the performance of AI agents across a wide variety of complex and real-life IT tasks across personas including, Site Reliability Engineering (SRE) focusing on availability and resiliency, Compliance and Security Operations (CISO) ensuring compliance and security of IT implementations, and Financial Operations ..."}
+{"idx": 1, "title": "arXiv:2502.05352v1 [cs.AI] 7 Feb 2025", "date": "", "ddg_snippet": "by S Jha · 2025 · Cited by 3 — The goal of ITBench is to measure the performance of AI agents across a wide variety of complex and real-life IT tasks across personas including ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352?", "content": "by S Jha · 2025 · Cited by 3 — The goal of ITBench is to measure the performance of AI agents across a wide variety of complex and real-life IT tasks across personas including ..."}
+{"idx": 2, "title": "[2502.05352] ITBench: Evaluating AI Agents across Diverse ...", "date": "", "ddg_snippet": "Feb 7, 2025 · View a PDF of the paper titled ITBench : Evaluating AI Agents across Diverse Real-World IT Automation Tasks, by Saurabh Jha (1) and 43 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05352", "content": "Feb 7, 2025 · View a PDF of the paper titled ITBench : Evaluating AI Agents across Diverse Real-World IT Automation Tasks, by Saurabh Jha (1) and 43 other authors"}
+{"idx": 3, "title": "Measuring technological complexity - Current approaches and a ...", "date": "", "ddg_snippet": "The paper reviews two prominent approaches for the measurement of techno-logical complexity : the method of reflection and the assessment of technologies’ combinatorial difficulty. It discusses their central underlying assumptions and iden-tifies potential problems related to these. A new measure of structural complexity is introduced as an alternative. The paper also puts forward four ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1708.07357", "content": "The paper reviews two prominent approaches for the measurement of techno-logical complexity : the method of reflection and the assessment of technologies’ combinatorial difficulty. It discusses their central underlying assumptions and iden-tifies potential problems related to these. A new measure of structural complexity is introduced as an alternative. The paper also puts forward four ..."}
+{"idx": 4, "title": "[0805.0685] Concept and Definition of Complexity - arXiv.org Local vs. Global Interpretability: A Computational Complexity ... [2312.11511] ComplexityNet: Increasing LLM Inference ... Measuring technological complexity - Current approaches and a new m… Measuring technological complexity - Current approaches and a new m… Measuring technological complexity - Current approaches and a new m… ComplexityNet: Increasing LLM Inference Efficiency by Learning Task C… Measuring technological complexity - Current approaches and a new m… ComplexityNet: Increasing LLM Inference Efficiency by Learning Task C… [2103.05127] Model Complexity of Deep Learning: A Survey", "date": "", "ddg_snippet": "May 6 , 2008 · The term {\\\\em complexity } is used informally both as a quality and as a quantity. As a quality, complexity has something to do with our ability to understand a system or object -- we understand simple systems, but not complex ones. On another level, {\\\\em complexity } is used as a quantity, when we talk about something being more complicated than another. In this chapter, we explore the ... Jun 5, 2024 · The local and global interpretability of various ML models has been studied extensively in recent years. However, despite significant progress in the field, many known results remain informal or lack sufficient mathematical rigor. We propose a framework for bridging this gap, by using computational complexity theory to assess local and global perspectives of interpreting ML models. We begin by ... Dec 12, 2023 · View a PDF of the paper titled ComplexityNet: Increasing LLM Inference Efficiency by Learning Task Complexity , by Henry Bae and 3 other authors How is technological complexity measured? The complexity of technologies has been measured in various ways in the past. The pa-per reviewed two existing empirical measures of technological complexity: the method of reflection approach by Hidalgo and Hausmann (2009) and the difficulty of knowledge com-bination approach put forward by Fleming and Sorenson (2001). Is structural complexity a good measure of technological complexity? In summary, the newly proposed measure of structural complexity yields promising results and performs well with respect to the four stylized facts of technological complexity put forward in the paper. What is a technological complexity score? A technological complexity score is then estimated as the second eigenvector of matrix B. It is called HH:eigen . Accordingly, two measures are based on the original method of reflection (HH:3NUT S, HH:2NUT S) that vary in terms of the underlying spatial unit. What is complexitynet – a streamlined language model for assessing task complexity? We present ComplexityNet, a streamlined language model designed for assessing task complexity . This model predicts the likelihood of accurate output by various language models, each with different capabilities. Our initial application of ComplexityNet involves the Mostly Basic Python Problems (MBPP) dataset. Does structural complexity overlap with technical complexity? Accordingly, while attempting to measure the same thing (technolog-ical complexity), the two approaches (method of reflection and evaluating IPC subclass combinations) do not overlap empirically . It should be noted that the computational requirements of Structural drastically ex-ceed those of the other measures. How accurate is complexitynet? Our initial application of ComplexityNet involves the Mostly Basic Python Problems (MBPP) dataset. We pioneered the creation of the first set of labels to define task complexity. ComplexityNet achieved a notable 79% accuracy in determining task complexity, a significant improvement over the 34% accuracy of the original, non fine-tuned model. Mar 8, 2021 · Model complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complexity of deep learning can be categorized into expressive capacity and effective model complexity . We review the existing studies on those two categories along four important factors, including model framework, model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/0805.0685", "content": "May 6 , 2008 · The term {\\\\em complexity } is used informally both as a quality and as a quantity. As a quality, complexity has something to do with our ability to understand a system or object -- we understand simple systems, but not complex ones. On another level, {\\\\em complexity } is used as a quantity, when we talk about something being more complicated than another. In this chapter, we explore the ... Jun 5, 2024 · The local and global interpretability of various ML models has been studied extensively in recent years. However, despite significant progress in the field, many known results remain informal or lack sufficient mathematical rigor. We propose a framework for bridging this gap, by using computational complexity theory to assess local and global perspectives of interpreting ML models. We begin by ... Dec 12, 2023 · View a PDF of the paper titled ComplexityNet: Increasing LLM Inference Efficiency by Learning Task Complexity , by Henry Bae and 3 other authors How is technological complexity measured? The complexity of technologies has been measured in various ways in the past. The pa-per reviewed two existing empirical measures of technological complexity: the method of reflection approach by Hidalgo and Hausmann (2009) and the difficulty of knowledge com-bination approach put forward by Fleming and Sorenson (2001). Is structural complexity a good measure of technological complexity? In summary, the newly proposed measure of structural complexity yields promising results and performs well with respect to the four stylized facts of technological complexity put forward in the paper. What is a technological complexity score? A technological complexity score is then estimated as the second eigenvector of matrix B. It is called HH:eigen . Accordingly, two measures are based on the original method of reflection (HH:3NUT S, HH:2NUT S) that vary in terms of the underlying spatial unit. What is complexitynet – a streamlined language model for assessing task complexity? We present ComplexityNet, a streamlined language model designed for assessing task complexity . This model predicts the likelihood of accurate output by various language models, each with different capabilities. Our initial application of ComplexityNet involves the Mostly Basic Python Problems (MBPP) dataset. Does structural complexity overlap with technical complexity? Accordingly, while attempting to measure the same thing (technolog-ical complexity), the two approaches (method of reflection and evaluating IPC subclass combinations) do not overlap empirically . It should be noted that the computational requirements of Structural drastically ex-ceed those of the other measures. How accurate is complexitynet? Our initial application of ComplexityNet involves the Mostly Basic Python Problems (MBPP) dataset. We pioneered the creation of the first set of labels to define task complexity. ComplexityNet achieved a notable 79% accuracy in determining task complexity, a significant improvement over the 34% accuracy of the original, non fine-tuned model. Mar 8, 2021 · Model complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complexity of deep learning can be categorized into expressive capacity and effective model complexity . We review the existing studies on those two categories along four important factors, including model framework, model ..."}
+{"idx": 5, "title": "Local vs. Global Interpretability: A Computational Complexity ...", "date": "", "ddg_snippet": "Jun 5, 2024 · The local and global interpretability of various ML models has been studied extensively in recent years. However, despite significant progress in the field, many known results remain informal or lack sufficient mathematical rigor. We propose a framework for bridging this gap, by using computational complexity theory to assess local and global perspectives of interpreting ML models. We begin by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.02981", "content": "Jun 5, 2024 · The local and global interpretability of various ML models has been studied extensively in recent years. However, despite significant progress in the field, many known results remain informal or lack sufficient mathematical rigor. We propose a framework for bridging this gap, by using computational complexity theory to assess local and global perspectives of interpreting ML models. We begin by ..."}
+{"idx": 6, "title": "[2312.11511] ComplexityNet: Increasing LLM Inference ...", "date": "", "ddg_snippet": "Dec 12, 2023 · View a PDF of the paper titled ComplexityNet: Increasing LLM Inference Efficiency by Learning Task Complexity , by Henry Bae and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.11511", "content": "Dec 12, 2023 · View a PDF of the paper titled ComplexityNet: Increasing LLM Inference Efficiency by Learning Task Complexity , by Henry Bae and 3 other authors"}
+{"idx": 7, "title": "[2103.05127] Model Complexity of Deep Learning: A Survey", "date": "", "ddg_snippet": "Mar 8, 2021 · Model complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complexity of deep learning can be categorized into expressive capacity and effective model complexity . We review the existing studies on those two categories along four important factors, including model framework, model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2103.05127", "content": "Mar 8, 2021 · Model complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complexity of deep learning can be categorized into expressive capacity and effective model complexity . We review the existing studies on those two categories along four important factors, including model framework, model ..."}
+{"idx": 8, "title": "Links between Entropy, Complexity , and the Technological Singularity", "date": "", "ddg_snippet": "But in this paper , and for the sake of consistency with the previous section, we will use the following information-related definition for complexity : the capacity of a system to incorporate information at a given time. Complexity is more like a snapshot while entropy is more like a sum.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10844", "content": "But in this paper , and for the sake of consistency with the previous section, we will use the following information-related definition for complexity : the capacity of a system to incorporate information at a given time. Complexity is more like a snapshot while entropy is more like a sum."}
+{"idx": 9, "title": "Krylov complexity is not a measure of distance between states or...", "date": "", "ddg_snippet": "One definition is circuit complexity : the minimum number of elementary gates from a universal gate set needed to prepare a given unitary operator, up to some tolerance.In this paper , we ask whether Krylov complexity is compatible with the circuit and Nielsen definitions of complexity .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.04093v2", "content": "One definition is circuit complexity : the minimum number of elementary gates from a universal gate set needed to prepare a given unitary operator, up to some tolerance.In this paper , we ask whether Krylov complexity is compatible with the circuit and Nielsen definitions of complexity ."}
diff --git a/data/sampled_jsons/Identity_Preference_Optimization_Azar_abstract_Bradley-Terry_model.jsonl b/data/sampled_jsons/Identity_Preference_Optimization_Azar_abstract_Bradley-Terry_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5e77a50903ac9956d197f25c6ddc28ca488357b8
--- /dev/null
+++ b/data/sampled_jsons/Identity_Preference_Optimization_Azar_abstract_Bradley-Terry_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Novel Approach to Identity Preference Optimization", "date": "", "ddg_snippet": "The Bradley - Terry model is not required for our algorithms but is used here for its popularity in preference modeling. For the score-based experiments ... 12 pages", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs224n/final-reports/256735149.pdf", "content": "The Bradley - Terry model is not required for our algorithms but is used here for its popularity in preference modeling. For the score-based experiments ... 12 pages"}
+{"idx": 1, "title": "Beyond Bradley-Terry Models: A General Preference ...", "date": "", "ddg_snippet": "In this paper, we introduce preference embedding, an approach that embeds responses into a latent space to capture intricate preference structures efficiently, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02197v2", "content": "In this paper, we introduce preference embedding, an approach that embeds responses into a latent space to capture intricate preference structures efficiently, ..."}
+{"idx": 2, "title": "Beyond Bradley-Terry Models: A General Preference ...", "date": "", "ddg_snippet": "In this paper, we introduce preference embedding, an approach that embeds responses into a latent space to capture intricate preference structures efficiently, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45103", "content": "In this paper, we introduce preference embedding, an approach that embeds responses into a latent space to capture intricate preference structures efficiently, ..."}
+{"idx": 3, "title": "BEYOND BRADLEY-TERRY MODELS", "date": "", "ddg_snippet": "by Y Zhang — In this paper, we introduce preference embedding, an approach that embeds responses into a latent space to capture intricate preference structures efficiently, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=bT8Wm4jtJC", "content": "by Y Zhang — In this paper, we introduce preference embedding, an approach that embeds responses into a latent space to capture intricate preference structures efficiently, ..."}
+{"idx": 4, "title": "Human Alignment of Large Language Models through Online ...", "date": "", "ddg_snippet": "by D Calandriello · Cited by 49 — Bradley - Terry -derived reward function have been proposed. Identity preference optimisation ( Azar et al., 2023, IPO) is an algorithm that aims to optimise ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/calandriello24a/calandriello24a.pdf", "content": "by D Calandriello · Cited by 49 — Bradley - Terry -derived reward function have been proposed. Identity preference optimisation ( Azar et al., 2023, IPO) is an algorithm that aims to optimise ..."}
+{"idx": 5, "title": "Extended Abstract - CS 224R Deep Reinforcement Learning", "date": "", "ddg_snippet": "by P Chaturvedi — Building on this, we investigate several recent extensions: eXploratory Preference Optimization (XPO) and Identity Preference Optimization . (IPO), synthetic ...", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/CS224R_Final_Project__1_.pdf", "content": "by P Chaturvedi — Building on this, we investigate several recent extensions: eXploratory Preference Optimization (XPO) and Identity Preference Optimization . (IPO), synthetic ..."}
+{"idx": 6, "title": "Enhanced Model Alignment with Granular Feedback", "date": "", "ddg_snippet": "by K Kim · 2024 · Cited by 5 — the Bradley - Terry model defines preference prob- ability in terms of the difference in rewards be- tween the output pairs, i.e., p(yw ≻ yl ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.792.pdf", "content": "by K Kim · 2024 · Cited by 5 — the Bradley - Terry model defines preference prob- ability in terms of the difference in rewards be- tween the output pairs, i.e., p(yw ≻ yl ..."}
+{"idx": 7, "title": "Preference Alignment with Flow Matching", "date": "", "ddg_snippet": "by M Kim · 2024 · Cited by 9 — Despite their strengths, both works rely on the Bradley-Terry model to implicitly learn the reward function. Identity Preference Optimization (IPO) [Azar et al.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/3df874367ce2c43891aab1ab23ae6959-Paper-Conference.pdf", "content": "by M Kim · 2024 · Cited by 9 — Despite their strengths, both works rely on the Bradley-Terry model to implicitly learn the reward function. Identity Preference Optimization (IPO) [Azar et al."}
+{"idx": 8, "title": "arXiv:2310.12036v2 [cs.AI] 22 Nov 2023", "date": "", "ddg_snippet": "by MG Azar · 2023 · Cited by 703 — Assuming that p∗(y ≻ y′|x) conforms to the Bradley -. Terry model , one can show that as the size of the dataset D grows, p(y ≻ y′|x) becomes a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.12036", "content": "by MG Azar · 2023 · Cited by 703 — Assuming that p∗(y ≻ y′|x) conforms to the Bradley -. Terry model , one can show that as the size of the dataset D grows, p(y ≻ y′|x) becomes a ..."}
+{"idx": 9, "title": "A UNIFIED APPROACH TO ONLINE AND OFFLINE RLHF", "date": "", "ddg_snippet": "by S Cen · Cited by 50 — To resolve this challenge, we introduce the following equivalent class of reward functions for the Bradley - Terry model to eliminate the shift ambiguity, which ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=SQnitDuow6", "content": "by S Cen · Cited by 50 — To resolve this challenge, we introduce the following equivalent class of reward functions for the Bradley - Terry model to eliminate the shift ambiguity, which ..."}
diff --git a/data/sampled_jsons/ImageNet-1k_Syn._Static_(Fan_et_al.,_2024)_2M_IN_real_Val..jsonl b/data/sampled_jsons/ImageNet-1k_Syn._Static_(Fan_et_al.,_2024)_2M_IN_real_Val..jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..184095ee813e6926050e3f99850880e895ffe42e
--- /dev/null
+++ b/data/sampled_jsons/ImageNet-1k_Syn._Static_(Fan_et_al.,_2024)_2M_IN_real_Val..jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "(PDF) Improving the Scaling Laws of Synthetic Data with Deliberate...", "date": "", "ddg_snippet": "Static - Fan et al .On ImageNet -100, DP generated 4.6. million fewer samples and trained for only one-sixth of the iterations compared to previous works, yet achieved. superior performance on the real data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389274598_Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice", "content": "Static - Fan et al .On ImageNet -100, DP generated 4.6. million fewer samples and trained for only one-sixth of the iterations compared to previous works, yet achieved. superior performance on the real data."}
+{"idx": 1, "title": "Deliberate Practice", "date": "", "ddg_snippet": "by R Askari-Hemmat · 2025 · Cited by 1 — (Right): Top-1 validation accuracy on ImageNet-1k with models trained solely on synthetic data. ... Syn. Static - Fan et al. (2024 ). IN-1k.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.15588", "content": "by R Askari-Hemmat · 2025 · Cited by 1 — (Right): Top-1 validation accuracy on ImageNet-1k with models trained solely on synthetic data. ... Syn. Static - Fan et al. (2024 ). IN-1k."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/ImagineFSL_CVPR_2025_Table_1_results_Flowers_DISEF.jsonl b/data/sampled_jsons/ImagineFSL_CVPR_2025_Table_1_results_Flowers_DISEF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d9c0b42ebe093a74e9d0185e3cfb9a97c810a2b7
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_CVPR_2025_Table_1_results_Flowers_DISEF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "We present a novel methodology called ImagineFSL (Figure 1 ), consisting of pretraining on iBase, followed by fine-tuning for downstream FSL tasks with real images and task-specific synthetic images.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "We present a novel methodology called ImagineFSL (Figure 1 ), consisting of pretraining on iBase, followed by fine-tuning for downstream FSL tasks with real images and task-specific synthetic images."}
+{"idx": 1, "title": "GitHub - HaoyuanYang-2023/ImagineFSL: Official implementation of ...", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 2, "title": "ImagineFSL: Self-Supervised Pretraining Matters on ... - IEEE Xplore", "date": "", "ddg_snippet": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Adapting CLIP models for few-shot recognition has recently attracted significant attention. Despite considerable progress, these adaptations remain hindered by the pervasive challenge of data scarcity.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094502", "content": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Adapting CLIP models for few-shot recognition has recently attracted significant attention. Despite considerable progress, these adaptations remain hindered by the pervasive challenge of data scarcity."}
+{"idx": 3, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set", "date": "", "ddg_snippet": "Few-shot Recognition Compared with methods using synthetic images We compare to prior methods based on synthetic images in 1-/16-shot settings, including IsSynth [18], CaFo [13], DISEF [17], and DataDream [19]. Bold: best results ; underlined: second-best results . † Reproduced by us. Compared with methods only using real images", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/ImagineFSL/index.html", "content": "Few-shot Recognition Compared with methods using synthetic images We compare to prior methods based on synthetic images in 1-/16-shot settings, including IsSynth [18], CaFo [13], DISEF [17], and DataDream [19]. Bold: best results ; underlined: second-best results . † Reproduced by us. Compared with methods only using real images"}
+{"idx": 4, "title": "[2025CVPR-小样本方向]ImagineFSL：基于VLM的少样本学习的想象基集上的自监督预训练很重要-CSDN博客", "date": "", "ddg_snippet": "文章浏览阅读142次。本文提出ImagineFSL框架，通过自监督预训练和微调两阶段方法，利用合成图像作为独立知识库解决少样本学习的数据稀缺问题。创新点包括：1）构建大规模合成数据集iBase；2）提出HoM-DINO自监督方法，融合高阶矩特征和掩码图像建模；3）开发自动化合成数据生成管道。在11个基准 ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/noboxihong/article/details/149815979", "content": "文章浏览阅读142次。本文提出ImagineFSL框架，通过自监督预训练和微调两阶段方法，利用合成图像作为独立知识库解决少样本学习的数据稀缺问题。创新点包括：1）构建大规模合成数据集iBase；2）提出HoM-DINO自监督方法，融合高阶矩特征和掩码图像建模；3）开发自动化合成数据生成管道。在11个基准 ..."}
+{"idx": 5, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.html", "content": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
+{"idx": 6, "title": "CVPR.2025 - Highlight | Cool Papers - Immersive Paper Discovery", "date": "", "ddg_snippet": "The list of accepted papers for CVPR.2025 - Highlight, including titles, authors, and abstracts, with support for paper interpretation based on Kimi AI.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/CVPR.2025?group=Highlight", "content": "The list of accepted papers for CVPR.2025 - Highlight, including titles, authors, and abstracts, with support for paper interpretation based on Kimi AI."}
+{"idx": 7, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023/ImagineFSL · GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 8, "title": "CVPR Poster ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "We introduce a novel CLIP adaptation methodology called ImagineFSL , involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32717", "content": "We introduce a novel CLIP adaptation methodology called ImagineFSL , involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
+{"idx": 9, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2025/436400af020/299a9wM2Rck", "content": "Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
diff --git a/data/sampled_jsons/ImagineFSL_CVPR_2025_reference_list_[21].jsonl b/data/sampled_jsons/ImagineFSL_CVPR_2025_reference_list_[21].jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..32aa833ea7ed83b63d46081a8f90f74e04c914cf
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_CVPR_2025_reference_list_[21].jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR 2025 Awards - The Computer Vision Foundation", "date": "", "ddg_snippet": "Highlight. ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/awards_detail", "content": "Highlight. ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning."}
+{"idx": 1, "title": "PDF ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "We present a novel methodology called ImagineFSL (Figure 1), consisting of pretraining on iBase, followed by fine-tuning for downstream FSL tasks with real images and task-specific synthetic images.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "We present a novel methodology called ImagineFSL (Figure 1), consisting of pretraining on iBase, followed by fine-tuning for downstream FSL tasks with real images and task-specific synthetic images."}
+{"idx": 2, "title": "GitHub - HaoyuanYang-2023/ImagineFSL: Official implementation of ...", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 3, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set", "date": "", "ddg_snippet": "title = { ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},", "subpage_snippet": "", "source": "www.peihuali.org", "link": "https://www.peihuali.org/ImagineFSL/", "content": "title = { ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},"}
+{"idx": 4, "title": "ImagineFSL: Self-Supervised Pretraining Matters on ... - IEEE Xplore", "date": "", "ddg_snippet": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Adapting CLIP models for few-shot recognition has recently attracted significant attention. Despite considerable progress, these adaptations remain hindered by the pervasive challenge of data scarcity.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094502", "content": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Adapting CLIP models for few-shot recognition has recently attracted significant attention. Despite considerable progress, these adaptations remain hindered by the pervasive challenge of data scarcity."}
+{"idx": 5, "title": "\"ImagineFSL: Self-Supervised Pretraining Matters on Imagined ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/cvpr/YangLLCWL25", "content": "Bibliographic details on ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning."}
+{"idx": 6, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.html", "content": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
+{"idx": 7, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2025/436400af020/299a9wM2Rck", "content": "Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
+{"idx": 8, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023/ImagineFSL · GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 9, "title": "CVPR 2025 Accepted Papers", "date": "", "ddg_snippet": "... 21. Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift Poster Session 2. Siyuan Liang · Jiawei Liang · Tianyu Pang · Chao Du ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/AcceptedPapers", "content": "... 21. Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift Poster Session 2. Siyuan Liang · Jiawei Liang · Tianyu Pang · Chao Du ..."}
diff --git a/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Matters_Imagined_Base_Set_VLM_Few-shot_Learning_reference_21.jsonl b/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Matters_Imagined_Base_Set_VLM_Few-shot_Learning_reference_21.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b2758f0429a0560131957b664d881e4a12927a5e
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Matters_Imagined_Base_Set_VLM_Few-shot_Learning_reference_21.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance. Based on on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of repre-sentations from synthetic to real image domains.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance. Based on on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of repre-sentations from synthetic to real image domains."}
+{"idx": 1, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM -based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM -based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 2, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance. Based on on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of representations from synthetic to real image domains.", "subpage_snippet": "", "source": "www.peihuali.org", "link": "https://www.peihuali.org/ImagineFSL/", "content": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance. Based on on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of representations from synthetic to real image domains."}
+{"idx": 3, "title": "Improving In-Context Few-Shot Learning via Self-Supervised ... ImagineFSL: Self-Supervised Pretraining Matters on Imagined ... Few-shot intent detection with self-supervised pretraining ... ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set Improving In-Context Few-Shot Learning via Self-Supervised Training ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set Improving In-Context Few-Shot Learning via Self-Supervised Training ImagineFSL/README.md at main · HaoyuanYang-2023 ... - GitHub", "date": "", "ddg_snippet": "May 3, 2022 · Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot learning . In this paper, we propose to use self - supervision in an intermediate training stage between pretraining and downstream few-shot usage with the goal to teach the model to perform in-context few shot learning ... ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM -based Few-shot Learning Published in: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Article #: Date of Conference: 10-17 June 2025 Nov 1, 2024 · Our method is closely related to few-shot learning , few-shot intent detection tasks, prototypical networks, and self-supervised multi-task pretraining . In this section, we review each of these related efforts in turn. Is self-supervised pretraining better than no pretraining? We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance . Based on on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of repre-sentations from synthetic to real image domains. What is imaginefsl? To over-come this limitation, we frame synthetic images as an imag-ined base set (iBase), i.e., an independent, large-scale synthetic dataset encompassing diverse concepts. Build-ing on this perspective, we introduce ImagineFSL, a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks. Is supervised pretraining better than no pretraining? Build-ing on this perspective, we introduce ImagineFSL, a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance . Can self-supervised pretraining be used for in-context few-shot learning? Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot learning . Is self-supervised learning better than supervised learning for pretraining? We find that, compared to no pretraining, either self-supervised learning (Self-SL) or supervised learning (SL) for pretraining is beneficial; cru-cially, Self-SL (e.g., DINO [20, 21]) outperforms SL . A yawl is sailing in a bay. (a) Self-supervised pretraining on PURELY synthetic dataset of iBase. photo of a {yawl}. photo of a {cat}. Can self-supervision be used to teach a model to perform in-context few-shot learning? In this paper, we propose to use self -supervision in an intermediate training stage between pretraining and downstream few - shot usage with the goal to teach the model to perform in-context few shot learning . We propose and evaluate four self - supervised objectives on two benchmarks. This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM -based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2205.01703", "content": "May 3, 2022 · Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot learning . In this paper, we propose to use self - supervision in an intermediate training stage between pretraining and downstream few-shot usage with the goal to teach the model to perform in-context few shot learning ... ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM -based Few-shot Learning Published in: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Article #: Date of Conference: 10-17 June 2025 Nov 1, 2024 · Our method is closely related to few-shot learning , few-shot intent detection tasks, prototypical networks, and self-supervised multi-task pretraining . In this section, we review each of these related efforts in turn. Is self-supervised pretraining better than no pretraining? We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance . Based on on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of repre-sentations from synthetic to real image domains. What is imaginefsl? To over-come this limitation, we frame synthetic images as an imag-ined base set (iBase), i.e., an independent, large-scale synthetic dataset encompassing diverse concepts. Build-ing on this perspective, we introduce ImagineFSL, a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks. Is supervised pretraining better than no pretraining? Build-ing on this perspective, we introduce ImagineFSL, a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance . Can self-supervised pretraining be used for in-context few-shot learning? Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot learning . Is self-supervised learning better than supervised learning for pretraining? We find that, compared to no pretraining, either self-supervised learning (Self-SL) or supervised learning (SL) for pretraining is beneficial; cru-cially, Self-SL (e.g., DINO [20, 21]) outperforms SL . A yawl is sailing in a bay. (a) Self-supervised pretraining on PURELY synthetic dataset of iBase. photo of a {yawl}. photo of a {cat}. Can self-supervision be used to teach a model to perform in-context few-shot learning? In this paper, we propose to use self -supervision in an intermediate training stage between pretraining and downstream few - shot usage with the goal to teach the model to perform in-context few shot learning . We propose and evaluate four self - supervised objectives on two benchmarks. This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM -based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 4, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM -based Few-shot Learning Published in: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Article #: Date of Conference: 10-17 June 2025", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11094502", "content": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM -based Few-shot Learning Published in: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Article #: Date of Conference: 10-17 June 2025"}
+{"idx": 5, "title": "Few-shot intent detection with self-supervised pretraining ...", "date": "", "ddg_snippet": "Nov 1, 2024 · Our method is closely related to few-shot learning , few-shot intent detection tasks, prototypical networks, and self-supervised multi-task pretraining . In this section, we review each of these related efforts in turn.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320324003923", "content": "Nov 1, 2024 · Our method is closely related to few-shot learning , few-shot intent detection tasks, prototypical networks, and self-supervised multi-task pretraining . In this section, we review each of these related efforts in turn."}
+{"idx": 6, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023 ... - GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM -based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM -based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 7, "title": "few - shot - learning · GitHub Topics · GitHub", "date": "", "ddg_snippet": "A Python Library for Few - Shot Learning Models.Official implementation of \" ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning \" [CVPR 2025 Highlight].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/few-shot-learning?o=desc&s=updated", "content": "A Python Library for Few - Shot Learning Models.Official implementation of \" ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning \" [CVPR 2025 Highlight]."}
+{"idx": 8, "title": "(PDF) Long-Tail Zero and Few - Shot Learning via Contrastive...", "date": "", "ddg_snippet": "supervised learning with self - supervised pretraining to produce a small model, with strong longTo make matters worse, real-world long-tail data. is highly vulnerable to noise, which creates drastic learning and evaluation challenges, especially for self - supervised learning methods.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/367703490_Long-Tail_Zero_and_Few-Shot_Learning_via_Contrastive_Pretraining_on_and_for_Small_Data", "content": "supervised learning with self - supervised pretraining to produce a small model, with strong longTo make matters worse, real-world long-tail data. is highly vulnerable to noise, which creates drastic learning and evaluation challenges, especially for self - supervised learning methods."}
+{"idx": 9, "title": "Self - supervised contrastive zero to few - shot learning from small...", "date": "", "ddg_snippet": "ing self - supervised learning signals via large external sources is infeasible.(2017), because it helps during zero- shot learning . Such label-embedding based self - supervised pretraining has multiple advantages.", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/self-supervised-contrastive-zero-to-few-shot-learning-from-5444177", "content": "ing self - supervised learning signals via large external sources is infeasible.(2017), because it helps during zero- shot learning . Such label-embedding based self - supervised pretraining has multiple advantages."}
diff --git a/data/sampled_jsons/ImagineFSL_Table_2_AT_Avg_Acc_year_2025.jsonl b/data/sampled_jsons/ImagineFSL_Table_2_AT_Avg_Acc_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..04bc8815235814ca8d481295abf65886b7c06e3c
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_Table_2_AT_Avg_Acc_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "孫内線 ｜青森市バス｜バス路線図・停車順 - 乗換案内NEXT", "date": "", "ddg_snippet": "青森市バスの 孫 内 線の 停車順・路線図をご案内。 乗換案内NEXTの時刻表もサポート。 孫 内 線 に乗っておでかけの際はぜひチェック！", "subpage_snippet": "", "source": "mb.jorudan.co.jp", "link": "https://mb.jorudan.co.jp/os/bus/0210/line/46218.html", "content": "青森市バスの 孫 内 線の 停車順・路線図をご案内。 乗換案内NEXTの時刻表もサポート。 孫 内 線 に乗っておでかけの際はぜひチェック！"}
+{"idx": 1, "title": "青森市市バス 路線別時刻表 - 青森市公式ホームページ", "date": "", "ddg_snippet": "孫内線 【お知らせ】 令和7年10月1日(水)より、青森市西部地区デマンド型交通の運行事業開始 のため、一部の区間及び便が運休となります。 詳細は別紙をご確認ください。", "subpage_snippet": "", "source": "www.city.aomori.aomori.jp", "link": "https://www.city.aomori.aomori.jp/_res/projects/default_project/_page_/001/002/387/071001/071001_magonai_timetable.pdf", "content": "孫内線 【お知らせ】 令和7年10月1日(水)より、青森市西部地区デマンド型交通の運行事業開始 のため、一部の区間及び便が運休となります。 詳細は別紙をご確認ください。"}
+{"idx": 2, "title": "孫内線 [市民バス]のバス路線図 - NAVITIME", "date": "", "ddg_snippet": "孫 内 線 [市民バス]のバス路線図を地図に表示します。 停車バス停や、同じバス停を通る別路線/系統も確認できます。", "subpage_snippet": "", "source": "www.navitime.co.jp", "link": "https://www.navitime.co.jp/bus/route/00062460/孫内線［市民バス］", "content": "孫 内 線 [市民バス]のバス路線図を地図に表示します。 停車バス停や、同じバス停を通る別路線/系統も確認できます。"}
+{"idx": 3, "title": "孫内線 | バスマップ", "date": "", "ddg_snippet": "このページをご覧いただくと、 孫 内 線の 地図上の場所、 孫 内 線 が停まるバス停の一覧や運行する事業者の名前が分かります。", "subpage_snippet": "", "source": "busmap.info", "link": "https://busmap.info/route/1134/", "content": "このページをご覧いただくと、 孫 内 線の 地図上の場所、 孫 内 線 が停まるバス停の一覧や運行する事業者の名前が分かります。"}
+{"idx": 4, "title": "「 孫内線 」路線図風の地図でバス停時刻表探す ( 孫 内→駅西口 ...", "date": "", "ddg_snippet": "「 孫 内 線 ( 孫 内 →駅西口等)」 (運行：青森市バス)の路線図風の地図と経由バス停一覧から時刻表や運賃情報を探す。 完全無料。", "subpage_snippet": "", "source": "bustime.jp", "link": "https://bustime.jp/GtfsRoutes/detail1/?id1=1617309", "content": "「 孫 内 線 ( 孫 内 →駅西口等)」 (運行：青森市バス)の路線図風の地図と経由バス停一覧から時刻表や運賃情報を探す。 完全無料。"}
+{"idx": 5, "title": "滝内孫内線 舗装 (6-2)工事 2024/04/01-青森県 | エヌ・サーチ", "date": "", "ddg_snippet": "Apr 1, 2024 · 【 滝 内 孫 内 線 舗装 (6-2)工事 | エヌ・サーチ】 機関:青森県青森市, 落札会社:青森縦貫道路 (株), 入札日:2024/04/17", "subpage_snippet": "", "source": "nsearch.jp", "link": "https://nsearch.jp/nyusatsu_ankens/660a74e5ec9e320cc90da557", "content": "Apr 1, 2024 · 【 滝 内 孫 内 線 舗装 (6-2)工事 | エヌ・サーチ】 機関:青森県青森市, 落札会社:青森縦貫道路 (株), 入札日:2024/04/17"}
+{"idx": 6, "title": "孫 内のバス時刻表とバスのりば地図｜青森市バス｜路線バス情報", "date": "", "ddg_snippet": "Apr 28, 2025 · 孫 内 バス停に停車するバス路線系統一覧をご覧いただけます。 孫 内の バス時刻表やバス路線図、周辺観光施設やコンビニも乗換案内NEXTのサービスでサポート充実！", "subpage_snippet": "", "source": "mb.jorudan.co.jp", "link": "https://mb.jorudan.co.jp/os/bus/0210/stop/270119.html", "content": "Apr 28, 2025 · 孫 内 バス停に停車するバス路線系統一覧をご覧いただけます。 孫 内の バス時刻表やバス路線図、周辺観光施設やコンビニも乗換案内NEXTのサービスでサポート充実！"}
+{"idx": 7, "title": "孫内線 [市民バス]のバス時刻表 バス停一覧 - NAVITIME", "date": "", "ddg_snippet": "バス停を選択すると時刻表の詳細を確認できます。", "subpage_snippet": "", "source": "www.navitime.co.jp", "link": "https://www.navitime.co.jp/bus/company/00001260/route/00062460/", "content": "バス停を選択すると時刻表の詳細を確認できます。"}
+{"idx": 8, "title": "乗換案内、時刻表、運行情報 - Yahoo!路線情報", "date": "", "ddg_snippet": "電車もバスも飛行機も、経路検索はこれ一つでOK！ 定期代も検索できます。 ・ 近くのバス停がすぐわかる！ 時刻表から地図でバス停を確認できるようになりました. ※Yahoo!乗換案内バージョン 8.24.20 より、iOS 15.0 以下は動作保証の対象外となりますのでご注意ください。 ※ただし一部の機種では正常に動作しない場合があります。 自動車情報サイト carview! Yahoo!路線情報：全国の路線や高速バス、路線バス、飛行機の乗り換え案内サービスです。 始発・終電検索、JR・地下鉄・私鉄の定期代検索、新幹線・電車の運行情報、時刻表、主要空港のフライト情報も提供中。", "subpage_snippet": "", "source": "transit.yahoo.co.jp", "link": "https://transit.yahoo.co.jp/", "content": "電車もバスも飛行機も、経路検索はこれ一つでOK！ 定期代も検索できます。 ・ 近くのバス停がすぐわかる！ 時刻表から地図でバス停を確認できるようになりました. ※Yahoo!乗換案内バージョン 8.24.20 より、iOS 15.0 以下は動作保証の対象外となりますのでご注意ください。 ※ただし一部の機種では正常に動作しない場合があります。 自動車情報サイト carview! Yahoo!路線情報：全国の路線や高速バス、路線バス、飛行機の乗り換え案内サービスです。 始発・終電検索、JR・地下鉄・私鉄の定期代検索、新幹線・電車の運行情報、時刻表、主要空港のフライト情報も提供中。"}
+{"idx": 9, "title": "西 滝 〔青森市バス〕 - 孫内線 （ 孫 内方面）- 路線バス時刻表 ...", "date": "", "ddg_snippet": "西 滝 〔青森市バス〕の 孫 内 線 （孫 内 方面）の情報を掲載しています。 路線バス・高速バス・空港バス・深夜バスの時刻表を検索できます。", "subpage_snippet": "", "source": "www.jorudan.co.jp", "link": "https://www.jorudan.co.jp/bus/rosen/timetable/西滝〔青森市バス〕/孫内線/ヤクルト前/", "content": "西 滝 〔青森市バス〕の 孫 内 線 （孫 内 方面）の情報を掲載しています。 路線バス・高速バス・空港バス・深夜バスの時刻表を検索できます。"}
diff --git a/data/sampled_jsons/ImagineFSL_caption_synthesis_GPT-4_Llama.jsonl b/data/sampled_jsons/ImagineFSL_caption_synthesis_GPT-4_Llama.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fe4d4c8fe66e254cd2a509fef002e836727ecd5a
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_caption_synthesis_GPT-4_Llama.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "HaoyuanYang-2023/ImagineFSL: Official implementation ...", "date": "", "ddg_snippet": "11 Jun 2025 — We use Llama 3 8B to synthesize extensive captions . The weight files of Llama 3 8B can be downloaded here . You need to install additional ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "11 Jun 2025 — We use Llama 3 8B to synthesize extensive captions . The weight files of Llama 3 8B can be downloaded here . You need to install additional ..."}
+{"idx": 1, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "by H Yang · 2025 — We develop PTe for. Llama that utilizes exemplary captions generated by GPT - 4 ... each concept, we synthesize 300 captions , generating four images per ... 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "by H Yang · 2025 — We develop PTe for. Llama that utilizes exemplary captions generated by GPT - 4 ... each concept, we synthesize 300 captions , generating four images per ... 12 pages"}
+{"idx": 2, "title": "Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "by H Yang — Replacing GPT - 4 with Open-Source Llama 3.1 405B. In our synthesizing pipeline, we use GPT - 4 with CoT for key factor analysis and exemplar caption generation.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "by H Yang — Replacing GPT - 4 with Open-Source Llama 3.1 405B. In our synthesizing pipeline, we use GPT - 4 with CoT for key factor analysis and exemplar caption generation."}
+{"idx": 3, "title": "2025 IEEE/CVF Conference on Computer Vision and ...", "date": "", "ddg_snippet": "10 Jun 2025 — 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 10 2025 to June 17 2025. Nashville, TN, USA.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings/cvpr/2025/2998kzfEzK0", "content": "10 Jun 2025 — 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 10 2025 to June 17 2025. Nashville, TN, USA."}
+{"idx": 4, "title": "52CV/CVPR-2025-Papers", "date": "", "ddg_snippet": "Contribute to 52CV/CVPR-2025-Papers development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/52CV/CVPR-2025-Papers", "content": "Contribute to 52CV/CVPR-2025-Papers development by creating an account on GitHub."}
+{"idx": 5, "title": "IEEE/CVF Conference on Computer Vision and Pattern ...", "date": "", "ddg_snippet": "and Pattern Recognition in Nashville, Tennessee! CVPR is the premier and flagship annual meeting of IEEE/CVF and PAMI-. 80 pages", "subpage_snippet": "", "source": "media.eventhosts.cc", "link": "https://media.eventhosts.cc/Conferences/CVPR2025/CVPR_main_conf_2025.pdf", "content": "and Pattern Recognition in Nashville, Tennessee! CVPR is the premier and flagship annual meeting of IEEE/CVF and PAMI-. 80 pages"}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Improved_denoising_diffusion_probabilistic_models_conditional_ImageNet_64x64_FID.jsonl b/data/sampled_jsons/Improved_denoising_diffusion_probabilistic_models_conditional_ImageNet_64x64_FID.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1746beb566fe97b8a93e72a77b8e46fb8f16ff8a
--- /dev/null
+++ b/data/sampled_jsons/Improved_denoising_diffusion_probabilistic_models_conditional_ImageNet_64x64_FID.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - openai/improved-diffusion: Release for Improved Denoising ...", "date": "", "ddg_snippet": "The training code reads images from a directory of image files. In the datasets folder, we have provided instructions/scripts for preparing these directories for ImageNet , LSUN bedrooms, and CIFAR-10. For creating your own dataset, simply dump all of your images into a directory with \".jpg\", \".jpeg\", or \".png\" extensions. If you wish to train a class- conditional model , name the files like ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openai/improved-diffusion", "content": "The training code reads images from a directory of image files. In the datasets folder, we have provided instructions/scripts for preparing these directories for ImageNet , LSUN bedrooms, and CIFAR-10. For creating your own dataset, simply dump all of your images into a directory with \".jpg\", \".jpeg\", or \".png\" extensions. If you wish to train a class- conditional model , name the files like ..."}
+{"idx": 1, "title": "PDF Improved Denoising Diffusion Probabilistic Models (Supplementary)", "date": "", "ddg_snippet": "1= 0:0001=4 to 4000= 0:02=4 to preserve the shape of tfor the T= 4000 sched- ule. When computing FID we produce 50K samples from our models , except for unconditional ImageNet 64 64 where we produce 10K samples. Using only 10K samples biases the FID to be higher, but requires much less compute for sampling and helps do large ablations.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/nichol21a/nichol21a-supp.pdf", "content": "1= 0:0001=4 to 4000= 0:02=4 to preserve the shape of tfor the T= 4000 sched- ule. When computing FID we produce 50K samples from our models , except for unconditional ImageNet 64 64 where we produce 10K samples. Using only 10K samples biases the FID to be higher, but requires much less compute for sampling and helps do large ablations."}
+{"idx": 2, "title": "Improved Denoising Diffusion Probabilistic Models - OpenReview", "date": "", "ddg_snippet": "This paper presents rich discussions and various practical techniques to improve the training of probabilistic diffusion models , which include a hybrid objective to learn the variance for improving log-likelihood performance, a different noise schedule tailored for ImageNet 64x64 and importance sampling to reduce gradient noise.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=-NEXDKk8gZ", "content": "This paper presents rich discussions and various practical techniques to improve the training of probabilistic diffusion models , which include a hybrid objective to learn the variance for improving log-likelihood performance, a different noise schedule tailored for ImageNet 64x64 and importance sampling to reduce gradient noise."}
+{"idx": 3, "title": "Conditional Denoising Diffusion Probabilistic Models for Data ...", "date": "", "ddg_snippet": "Abstract In this paper, conditional denoising diffusion probabilistic models (CDiffs) are proposed to enhance the data transmission and reconstruction over wireless channels. The underlying mechanism of diffusion models is to decompose the data generation process over the so-called \" denoising \" steps.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.19460v3", "content": "Abstract In this paper, conditional denoising diffusion probabilistic models (CDiffs) are proposed to enhance the data transmission and reconstruction over wireless channels. The underlying mechanism of diffusion models is to decompose the data generation process over the so-called \" denoising \" steps."}
+{"idx": 4, "title": "PDF Improving Conditional Diffusion Models through Re-Noising from ...", "date": "", "ddg_snippet": "Denoising diffusion probabilistic models [11,44] are the most recent deep generative models . They have shown com-parable and even better performance at image synthesis than GANs with delicate guidance [7].", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/papers/Mei_Improving_Conditional_Diffusion_Models_through_Re-Noising_from_Unconditional_Diffusion_Priors_WACV_2025_paper.pdf", "content": "Denoising diffusion probabilistic models [11,44] are the most recent deep generative models . They have shown com-parable and even better performance at image synthesis than GANs with delicate guidance [7]."}
+{"idx": 5, "title": "improved-diffusion/README.md at main - GitHub", "date": "", "ddg_snippet": "improved - diffusion This is the codebase for Improved Denoising Diffusion Probabilistic Models .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openai/improved-diffusion/blob/main/README.md", "content": "improved - diffusion This is the codebase for Improved Denoising Diffusion Probabilistic Models ."}
+{"idx": 6, "title": "Improving Conditional Diffusion Models through Re-Noising from ...", "date": "", "ddg_snippet": "Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, of-tentimes their results can be unrealistic with observable color shifts and textures. We believe that this issue results from the divergence between the probabilistic distribution learned by the model and the distribution ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10943666", "content": "Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, of-tentimes their results can be unrealistic with observable color shifts and textures. We believe that this issue results from the divergence between the probabilistic distribution learned by the model and the distribution ..."}
+{"idx": 7, "title": "Directly Denoising Diffusion Models - arXiv.org", "date": "", "ddg_snippet": "Then Ho et al. (2020) developed denoising diffusion probabilistic models (DDPM) and demonstrated their exceptional capabilities in image generation. By improving noise schedule and variance taking into consideration, Nichol & Dhariwal further enhanced these models in 2021, achieving better log-likelihood scores and better FID scores.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.13540v2", "content": "Then Ho et al. (2020) developed denoising diffusion probabilistic models (DDPM) and demonstrated their exceptional capabilities in image generation. By improving noise schedule and variance taking into consideration, Nichol & Dhariwal further enhanced these models in 2021, achieving better log-likelihood scores and better FID scores."}
+{"idx": 8, "title": "Denoising diffusion probabilistic models | Proceedings of the 34th ...", "date": "", "ddg_snippet": "We present high quality image synthesis results using diffusion probabilistic models , a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and ...", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/10.5555/3495724.3496298", "content": "We present high quality image synthesis results using diffusion probabilistic models , a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and ..."}
+{"idx": 9, "title": "PDF Improved Denoising Diffusion Probabilistic Models", "date": "", "ddg_snippet": "Abstract Denoising diffusion probabilistic models (DDPM) are a class of generative models which have re-cently been shown to produce excellent sam-ples. We show that with a few simple modifi-cations, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning vari-ances of the reverse diffusion process allows sam-pling with an ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf", "content": "Abstract Denoising diffusion probabilistic models (DDPM) are a class of generative models which have re-cently been shown to produce excellent sam-ples. We show that with a few simple modifi-cations, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning vari-ances of the reverse diffusion process allows sam-pling with an ..."}
diff --git a/data/sampled_jsons/In_this_study,_we_propose_a_Disease-aware_image-text_Alignment_and_self-correcting_Re-alignment_for_.jsonl b/data/sampled_jsons/In_this_study,_we_propose_a_Disease-aware_image-text_Alignment_and_self-correcting_Re-alignment_for_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0e85155e96649a384d6f0b465e91b99a2eeacad8
--- /dev/null
+++ b/data/sampled_jsons/In_this_study,_we_propose_a_Disease-aware_image-text_Alignment_and_self-correcting_Re-alignment_for_.jsonl
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+{"idx": 0, "title": "pubmed19n0338.txt", "date": "", "ddg_snippet": "A 1992 Supreme Court decision did not restrict the inequitable access to health care through self -insured plans for AIDS patients. When employees of self - ...", "subpage_snippet": "", "source": "data.lhncbc.nlm.nih.gov", "link": "https://data.lhncbc.nlm.nih.gov/public/ii/information/MBR/Download/MetaMapped_Medline/2019/MEDLINE/pubmed19n0338.txt", "content": "A 1992 Supreme Court decision did not restrict the inequitable access to health care through self -insured plans for AIDS patients. When employees of self - ..."}
+{"idx": 1, "title": "test.txt - Ying Ding", "date": "", "ddg_snippet": "... reliable accuracy seems to be essential. In this study , basing on features of Landsat-8 TIRS thermal infrared channels, we re -calculated parameters in the ...", "subpage_snippet": "", "source": "yingding.ischool.utexas.edu", "link": "https://yingding.ischool.utexas.edu/Teaching/Z519/test.txt", "content": "... reliable accuracy seems to be essential. In this study , basing on features of Landsat-8 TIRS thermal infrared channels, we re -calculated parameters in the ..."}
+{"idx": 2, "title": "TEXT", "date": "", "ddg_snippet": "... report will not be liable when such reports are made in good faith and without malice. DELAWARE REGISTER OF REGULATIONS, VOL. 5, ISSUE 11, WEDNESDAY, MAY 1 ...", "subpage_snippet": "", "source": "regulations.delaware.gov", "link": "https://regulations.delaware.gov/api/register/May2002/6289a982-758b-4a8c-8ad9-279da73f580d/May2002c.txt", "content": "... report will not be liable when such reports are made in good faith and without malice. DELAWARE REGISTER OF REGULATIONS, VOL. 5, ISSUE 11, WEDNESDAY, MAY 1 ..."}
+{"idx": 3, "title": "pubmed19n0477.txt", "date": "", "ddg_snippet": "PURPOSE: The purpose of this article is to review why the reported effect of PCV-7 on AOM infection rates does not match the reduction in invasive pneumococcal ...", "subpage_snippet": "", "source": "data.lhncbc.nlm.nih.gov", "link": "https://data.lhncbc.nlm.nih.gov/public/ii/information/MBR/Download/MetaMapped_Medline/2019/MEDLINE/pubmed19n0477.txt", "content": "PURPOSE: The purpose of this article is to review why the reported effect of PCV-7 on AOM infection rates does not match the reduction in invasive pneumococcal ..."}
+{"idx": 4, "title": "masterlist.txt - Alex M. Chubaty", "date": "", "ddg_snippet": "... , TRAINING, AND SYSTEMS ALCTS P LIB TECH SER FORMATION AND DIFFERENTIATION OF EARLY EMBRYONIC MESODERM NATO ADV SCI I A-LIF FORMATION AND DYNAMICS OF SELF .", "subpage_snippet": "", "source": "www.alexchubaty.com", "link": "https://www.alexchubaty.com/uploads/2013/04/masterlist.txt", "content": "... , TRAINING, AND SYSTEMS ALCTS P LIB TECH SER FORMATION AND DIFFERENTIATION OF EARLY EMBRYONIC MESODERM NATO ADV SCI I A-LIF FORMATION AND DYNAMICS OF SELF ."}
+{"idx": 5, "title": "WordList.txt 2.2.5", "date": "", "ddg_snippet": "align aligned aligning alignment alignments aligns alike aliment aliments alimony alive alkali alkaline alkalis alkaloid alkaloids alkyl all allay allayed", "subpage_snippet": "", "source": "www.powershellgallery.com", "link": "https://www.powershellgallery.com/packages/PoshFunctions/2.2.5/Content/WordList.txt", "content": "align aligned aligning alignment alignments aligns alike aliment aliments alimony alive alkali alkaline alkalis alkaloid alkaloids alkyl all allay allayed"}
+{"idx": 6, "title": "0001445546-16-009409.txt", "date": "", "ddg_snippet": "We have audited the accompanying statements of net assets, including the schedules of investments, of FT 6230, comprising Dow(R) Target 5 3Q '16 - Term 10/6/17 ...", "subpage_snippet": "", "source": "www.sec.gov", "link": "https://www.sec.gov/Archives/edgar/data/1670270/000144554616009409/0001445546-16-009409.txt", "content": "We have audited the accompanying statements of net assets, including the schedules of investments, of FT 6230, comprising Dow(R) Target 5 3Q '16 - Term 10/6/17 ..."}
+{"idx": 7, "title": "104282-PUB-Box394877B-PUBLIC-pubdate-3-10-16. ...", "date": "", "ddg_snippet": "Mental, Neurological, and Substance Use Disorders . Disease Control Priorities, third edition, volume 4. Washington, DC: World Bank. doi:10.1596/978-1-4648-0426- ...", "subpage_snippet": "", "source": "documents1.worldbank.org", "link": "https://documents1.worldbank.org/curated/en/841781467995046626/txt/104282-PUB-Box394877B-PUBLIC-pubdate-3-10-16.txt", "content": "Mental, Neurological, and Substance Use Disorders . Disease Control Priorities, third edition, volume 4. Washington, DC: World Bank. doi:10.1596/978-1-4648-0426- ..."}
+{"idx": 8, "title": "236 kB", "date": "", "ddg_snippet": "... re stud ##ure ##ate ##st an ##un ##oc res ex ##ss ##um ##ra ##em study com ##ly ##ant ##ver ##uc ##ence ##op pre ##ri ##ical ch ##ect ##og ##ow ##igh ##ir ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/sabousergiu3dan/BertAllComp/resolve/966367a3854179ba9a5c76302e9ab25f633353cd/vocab.txt?download=true", "content": "... re stud ##ure ##ate ##st an ##un ##oc res ex ##ss ##um ##ra ##em study com ##ly ##ant ##ver ##uc ##ence ##op pre ##ri ##ical ch ##ect ##og ##ow ##igh ##ir ..."}
+{"idx": 9, "title": "download", "date": "", "ddg_snippet": "... ALIGNMENT ALIGNMENTS ALIGNS ALIKE ALIMEMAZINE ALIMENT ALIMENTARY ... AWARE AWARENESS AWASH AWAY AWE AWED AWEING AWELYSIN AWES AWESOME AWFUL AWFULLY ...", "subpage_snippet": "", "source": "physionet.org", "link": "https://physionet.org/files/deid/1.1/dict/sno_edited.txt?download", "content": "... ALIGNMENT ALIGNMENTS ALIGNS ALIKE ALIMEMAZINE ALIMENT ALIMENTARY ... AWARE AWARENESS AWASH AWAY AWE AWED AWEING AWELYSIN AWES AWESOME AWFUL AWFULLY ..."}
diff --git a/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold.jsonl b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..451b5325d73d97877ea42a92b8410af4d2cde419
--- /dev/null
+++ b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "synthesis data | Synced", "date": "", "ddg_snippet": "In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team provides insights into how ...", "subpage_snippet": "", "source": "syncedreview.com", "link": "https://syncedreview.com/tag/synthesis-data/", "content": "In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team provides insights into how ..."}
+{"idx": 1, "title": "[2406.14532] RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "Title: RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ... of the paper titled RL on Incorrect Synthetic Data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Title: RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ... of the paper titled RL on Incorrect Synthetic Data ..."}
+{"idx": 2, "title": "Achieving 8× Performance Gains with Reinforcement Learning on", "date": "", "ddg_snippet": "In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team provides insights into how ...", "subpage_snippet": "", "source": "syncedreview.com", "link": "https://syncedreview.com/2024/07/01/achieving-8x-performance-gains-with-reinforcement-learning-on-synthetic-data-in-large-language-models/", "content": "In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team provides insights into how ..."}
+{"idx": 3, "title": "model training | Synced", "date": "", "ddg_snippet": "In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team provides insights into how ...", "subpage_snippet": "", "source": "syncedreview.com", "link": "https://syncedreview.com/tag/model-training/", "content": "In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team provides insights into how ..."}
+{"idx": 4, "title": "large language model | Synced", "date": "", "ddg_snippet": "This technique merges the interpretability and efficiency of n-gram models with insights from neural LLMs to enhance language modeling performance.", "subpage_snippet": "", "source": "syncedreview.com", "link": "https://syncedreview.com/tag/large-language-model/", "content": "This technique merges the interpretability and efficiency of n-gram models with insights from neural LLMs to enhance language modeling performance."}
+{"idx": 5, "title": "Improving Value-based Process Verifier via Low-Cost Variance", "date": "", "ddg_snippet": "... ComMCS outperforms regression-based optimization method by 2. 8 points, the non- variance-reduced baseline by 2.2 points on MATH -500 on Best- of -32 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10539v1", "content": "... ComMCS outperforms regression-based optimization method by 2. 8 points, the non- variance-reduced baseline by 2.2 points on MATH -500 on Best- of -32 ..."}
+{"idx": 6, "title": "Virginia Smith", "date": "", "ddg_snippet": "... dataset 90.1% of authors are connected to Paul Erdős ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/virginia-smith/", "content": "... dataset 90.1% of authors are connected to Paul Erdős ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ."}
+{"idx": 7, "title": "Amrith Setlur", "date": "", "ddg_snippet": "... dataset 90.1% of authors are connected to Paul Erdős ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/amrith-setlur/", "content": "... dataset 90.1% of authors are connected to Paul Erdős ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ."}
+{"idx": 8, "title": "Virginia Smith", "date": "", "ddg_snippet": "... the potential to improve the privacy and safety of large ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Virginia+Smith", "content": "... the potential to improve the privacy and safety of large ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold"}
+{"idx": 9, "title": "Saurabh Garg - researchr alias", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold Amrith Setlur , Saurabh Garg , Xinyang Geng , Naman Garg ...", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/alias/saurabh-garg", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold Amrith Setlur , Saurabh Garg , Xinyang Geng , Naman Garg ..."}
diff --git a/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_benchmarks.jsonl b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_benchmarks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9534eacc15c1c5617d5efed1075b7cfcc404be94
--- /dev/null
+++ b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_benchmarks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 66 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "by A Setlur · 2024 · Cited by 66 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 67 — Our contribution is a study of the role of synthetic data in improving math reasoning capabilities of LLMs. We derive scaling laws for positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 67 — Our contribution is a study of the role of synthetic data in improving math reasoning capabilities of LLMs. We derive scaling laws for positive ..."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "This paper investigates the role of synthetic data in improving the math reasoning capabilities of large language models (LLMs).", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/1dm164m/rl_on_incorrect_synthetic_data_scales_the/", "content": "This paper investigates the role of synthetic data in improving the math reasoning capabilities of large language models (LLMs)."}
+{"idx": 4, "title": "RL on incorrect synthetic data scales the efficiency of LLM ...", "date": "", "ddg_snippet": "5 Jun 2025 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739277", "content": "5 Jun 2025 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our ..."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — The researchers used this 'negative reinforcement learning' to build an AI that's remarkably efficient at math problem-solving. They found that ...", "subpage_snippet": "", "source": "www.promptlayer.com", "link": "https://www.promptlayer.com/research-papers/rl-on-incorrect-synthetic-data-scales-the-efficiency-of-llm-math-reasoning-by-eight-fold", "content": "20 Jun 2024 — The researchers used this 'negative reinforcement learning' to build an AI that's remarkably efficient at math problem-solving. They found that ..."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Key takeaway: 'Training on incorrect synthetic data , when constructed with appropriate recovery, can significantly improve the efficiency of LLM math ...", "subpage_snippet": "", "source": "k8s.consensus.app", "link": "https://k8s.consensus.app/papers/details/fffa1f2c572d54268d6e2691de789e4c/", "content": "Key takeaway: 'Training on incorrect synthetic data , when constructed with appropriate recovery, can significantly improve the efficiency of LLM math ..."}
+{"idx": 7, "title": "RL on Synthetic Data Boosts LLM Math Reasoning", "date": "", "ddg_snippet": "The paper shows that reinforcement learning with negative synthetic data scales LLM math reasoning efficiency by eight-fold . It employs per-step ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The paper shows that reinforcement learning with negative synthetic data scales LLM math reasoning efficiency by eight-fold . It employs per-step ..."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of", "date": "", "ddg_snippet": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96295/paper", "content": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks."}
+{"idx": 9, "title": "Achieving 8× Performance Gains with Reinforcement ...", "date": "", "ddg_snippet": "The researchers aim to understand synthetic data's impact on LLM capabilities via a study on math reasoning , a prevalent scenario where ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/syncedreview/achieving-8-performance-gains-with-reinforcement-learning-on-synthetic-data-in-large-language-cb6a9cfbffc9", "content": "The researchers aim to understand synthetic data's impact on LLM capabilities via a study on math reasoning , a prevalent scenario where ..."}
diff --git a/data/sampled_jsons/Initialization_Techniques_for_Large-Scale_Language_Models_ICML_2025_year_2025.jsonl b/data/sampled_jsons/Initialization_Techniques_for_Large-Scale_Language_Models_ICML_2025_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a15faa16ccfd5b4d56bfe3ef742f45b7841ddc46
--- /dev/null
+++ b/data/sampled_jsons/Initialization_Techniques_for_Large-Scale_Language_Models_ICML_2025_year_2025.jsonl
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+{"idx": 0, "title": "A Systematic Survey on Large Language Models for Evolutionary ...", "date": "", "ddg_snippet": "Sep 11, 2025 · Yisong Zhang, Ran Cheng, Guoxing Yi, and Kay Chen Tan Abstract— Large Language Models (LLMs), with their strong understanding and reasoning capabilities, are increasingly being explored for tackling optimization problems, especially in synergy with evolutionary computation1. Despite rapid progress, however, the field still lacks a unified synthesis and a systematic taxonomy. This survey ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.08269", "content": "Sep 11, 2025 · Yisong Zhang, Ran Cheng, Guoxing Yi, and Kay Chen Tan Abstract— Large Language Models (LLMs), with their strong understanding and reasoning capabilities, are increasingly being explored for tackling optimization problems, especially in synergy with evolutionary computation1. Despite rapid progress, however, the field still lacks a unified synthesis and a systematic taxonomy. This survey ..."}
+{"idx": 1, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html?search=agent", "content": "2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008"}
+{"idx": 2, "title": "ICML 2025 Paper Accepted - ELSALAB", "date": "", "ddg_snippet": "ICML 2025 Paper Accepted 🎉 One paper from Elsa Lab members and our collaborators has been accepted for presentation at ICML 2025 . 💪 \"Retraining-Free Merging of Sparse MoE via Hierarchical Clustering\". We are excited to share our work on HC-SMoE, a novel approach for merging experts in Sparse Mixture-of-Experts models without retraining! Our method effectively reduces parameters while ...", "subpage_snippet": "", "source": "www.elsalab.ai", "link": "https://www.elsalab.ai/news/250502-icml-2025", "content": "ICML 2025 Paper Accepted 🎉 One paper from Elsa Lab members and our collaborators has been accepted for presentation at ICML 2025 . 💪 \"Retraining-Free Merging of Sparse MoE via Hierarchical Clustering\". We are excited to share our work on HC-SMoE, a novel approach for merging experts in Sparse Mixture-of-Experts models without retraining! Our method effectively reduces parameters while ..."}
+{"idx": 3, "title": "GitHub - thuml/Large-Time-Series-Model: Official code ...", "date": "", "ddg_snippet": "ICML 2025 Oral We proposed Sundial, a family of generative time series foundation models, which is pre-trained on a trillion (10^12) time points. The model can be applied for both point and probabilistic forecasting, making zero-shot forecasting within milliseconds [ GitHub ].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/thuml/Large-Time-Series-Model", "content": "ICML 2025 Oral We proposed Sundial, a family of generative time series foundation models, which is pre-trained on a trillion (10^12) time points. The model can be applied for both point and probabilistic forecasting, making zero-shot forecasting within milliseconds [ GitHub ]."}
+{"idx": 4, "title": "ICML 2025: Where AI Research Meets AI-Powered Recording", "date": "", "ddg_snippet": "The International Conference on Machine Learning ( ICML ) is one of the world’s premier gatherings for AI and machine learning research. In 2025 , the conference brought together more than 10,000 participants in Vancouver and via live stream for a week of groundbreaking ideas and collaboration.", "subpage_snippet": "", "source": "hello.slideslive.com", "link": "https://hello.slideslive.com/blog/icml-2025-where-ai-research-meets-ai-powered-recording", "content": "The International Conference on Machine Learning ( ICML ) is one of the world’s premier gatherings for AI and machine learning research. In 2025 , the conference brought together more than 10,000 participants in Vancouver and via live stream for a week of groundbreaking ideas and collaboration."}
+{"idx": 5, "title": "Multimodal Reasoning for Science: Technical Report and 1st ...", "date": "", "ddg_snippet": "6 days ago · Join the discussion on this paper pageMultimodal Reasoning for Science: Technical Report and 1st Place Solution to the ICML 2025 SeePhys Challenge", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2509.06079", "content": "6 days ago · Join the discussion on this paper pageMultimodal Reasoning for Science: Technical Report and 1st Place Solution to the ICML 2025 SeePhys Challenge"}
+{"idx": 6, "title": "International Conference on Machine Learning (ICML) Vancouver ...", "date": "", "ddg_snippet": "Jul 13, 2025 · ICML 2025 will feature tutorials on July 14, main conference sessions from July 15 to 17, and workshops on July 18 and 19. The event will also include an expo on July 13, providing a platform for industry and academic professionals to present and discuss the latest developments in machine learning.", "subpage_snippet": "", "source": "www.vktr.com", "link": "https://www.vktr.com/events/conference/international-conference-on-machine-learning-icml-vancouver-2025/", "content": "Jul 13, 2025 · ICML 2025 will feature tutorials on July 14, main conference sessions from July 15 to 17, and workshops on July 18 and 19. The event will also include an expo on July 13, providing a platform for industry and academic professionals to present and discuss the latest developments in machine learning."}
+{"idx": 7, "title": "An Analysis for Reasoning Bias of Language Models with ...", "date": "", "ddg_snippet": "16 Jul 2025 — This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46492", "content": "16 Jul 2025 — This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that ..."}
+{"idx": 8, "title": "ICML 2025 Workshop LCFM", "date": "", "ddg_snippet": "Abstract: Recently, long -context large language models (LLMs) have shown strong performance in information retrieval and long -document QA. However, to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=ICML.cc/2025/Workshop/LCFM", "content": "Abstract: Recently, long -context large language models (LLMs) have shown strong performance in information retrieval and long -document QA. However, to ..."}
+{"idx": 9, "title": "Task-Aware Parameter Initialization at Flexible Scales", "date": "", "ddg_snippet": "Appropriate parameter initialization strategies are essential for reducing the high computational costs of training large pretrained models in various task ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45736", "content": "Appropriate parameter initialization strategies are essential for reducing the high computational costs of training large pretrained models in various task ..."}
diff --git a/data/sampled_jsons/Inria_Aerial_Image_Labeling_dataset_tile_size_resolution_year_2024.jsonl b/data/sampled_jsons/Inria_Aerial_Image_Labeling_dataset_tile_size_resolution_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..78511b2dac05db835265f610bcf9e2d6c9b4f569
--- /dev/null
+++ b/data/sampled_jsons/Inria_Aerial_Image_Labeling_dataset_tile_size_resolution_year_2024.jsonl
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+{"idx": 0, "title": "Inria Aerial Image Labeling Dataset", "date": "", "ddg_snippet": "The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery (link to paper). Dataset features: Coverage of 810 km² (405 km² for training and 405 km² for testing) Aerial orthorectified color imagery with a spatial resolution of 0.3 m Ground truth data for two semantic classes: building and not building (publicly disclosed only ...", "subpage_snippet": "", "source": "project.inria.fr", "link": "https://project.inria.fr/aerialimagelabeling/", "content": "The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery (link to paper). Dataset features: Coverage of 810 km² (405 km² for training and 405 km² for testing) Aerial orthorectified color imagery with a spatial resolution of 0.3 m Ground truth data for two semantic classes: building and not building (publicly disclosed only ..."}
+{"idx": 1, "title": "blanchon/INRIA-Aerial-Image-Labeling · Datasets at Hugging Face", "date": "", "ddg_snippet": "Inria Aerial Image Labeling Dataset Description The Inria Aerial Image Labeling Dataset is a building semantic segmentation dataset proposed in \"Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark,\" Maggiori et al.. It consists of 360 high- resolution (0.3m) RGB images , each with a size of 5000x5000 ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/blanchon/INRIA-Aerial-Image-Labeling", "content": "Inria Aerial Image Labeling Dataset Description The Inria Aerial Image Labeling Dataset is a building semantic segmentation dataset proposed in \"Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark,\" Maggiori et al.. It consists of 360 high- resolution (0.3m) RGB images , each with a size of 5000x5000 ..."}
+{"idx": 2, "title": "INRIA Aerial Image Labeling|遥感图像数据集|建筑物检测数据集", "date": "", "ddg_snippet": "The INRIA Aerial Image Labeling dataset is comprised of 360 RGB tiles of 5000×5000px with a spatial resolution of 30cm/px on 10 cities across the globe. Half of the cities are used for training and are associated to a public ground truth of building footprints. The rest of the dataset is used only for evaluation with a hidden ground truth. The dataset was constructed by combining public ...", "subpage_snippet": "", "source": "www.selectdataset.com", "link": "https://www.selectdataset.com/dataset/e84b4320d9c03341a985a2bcac8aea01", "content": "The INRIA Aerial Image Labeling dataset is comprised of 360 RGB tiles of 5000×5000px with a spatial resolution of 30cm/px on 10 cities across the globe. Half of the cities are used for training and are associated to a public ground truth of building footprints. The rest of the dataset is used only for evaluation with a hidden ground truth. The dataset was constructed by combining public ..."}
+{"idx": 3, "title": "aitlas.datasets.inria — AiTLAS Documentation", "date": "", "ddg_snippet": "from.semantic_segmentationimportSemanticSegmentationDataset\"\"\"The training set contains 180 color image tiles of size 5000×5000, covering a surface of 1500 m × 1500 m each(at a 30 cm resolution ).", "subpage_snippet": "", "source": "aitlas.readthedocs.io", "link": "https://aitlas.readthedocs.io/en/latest/_modules/aitlas/datasets/inria.html", "content": "from.semantic_segmentationimportSemanticSegmentationDataset\"\"\"The training set contains 180 color image tiles of size 5000×5000, covering a surface of 1500 m × 1500 m each(at a 30 cm resolution )."}
+{"idx": 4, "title": "Inria Aerial Image Labeling", "date": "", "ddg_snippet": "The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery (link to paper). The dataset features a coverage of 810 km² (405 km² for training and 405 km² for testing), aerial orthorectified color imagery with a spatial resolution of 0.3 m, and ground truth data for two semantic classes: building and not building (publicly ...", "subpage_snippet": "", "source": "www.eod-grss-ieee.com", "link": "https://www.eod-grss-ieee.com/dataset-detail/ZVpUS1p4ekFnOGJIQUdSRXRZbms2dz09", "content": "The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery (link to paper). The dataset features a coverage of 810 km² (405 km² for training and 405 km² for testing), aerial orthorectified color imagery with a spatial resolution of 0.3 m, and ground truth data for two semantic classes: building and not building (publicly ..."}
+{"idx": 5, "title": "PDF Can Semantic Labeling Methods Generalize to Any City? the Inria Aerial ...", "date": "", "ddg_snippet": "In this paper, we propose an aerial image labeling dataset that covers a wide range of urban settlement appearances, from different geographic locations. Moreover, the cities in-cluded in the test set are different from those of the training set.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-01468452/file/AerialImageLabelingDataset.pdf", "content": "In this paper, we propose an aerial image labeling dataset that covers a wide range of urban settlement appearances, from different geographic locations. Moreover, the cities in-cluded in the test set are different from those of the training set."}
+{"idx": 6, "title": "GitHub - a-milosavljevic/inria-aerial-image-labeling: Inria Aerial ...", "date": "", "ddg_snippet": "About Inria Aerial Image Labeling - Building Footprint Extraction using Deep Semantic Segmentation", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/a-milosavljevic/inria-aerial-image-labeling", "content": "About Inria Aerial Image Labeling - Building Footprint Extraction using Deep Semantic Segmentation"}
+{"idx": 7, "title": "torchgeo.datasets.inria — torchgeo 0.7.0 documentation", "date": "", "ddg_snippet": "Dataset features: * Coverage of 810 km\\ :sup:`2`\\ (405 km\\ :sup:`2`\\ for training and 405 km\\ :sup:`2`\\ for testing) * Aerial orthorectified color imagery with a spatial resolution of 0.3 m * Number of images : 360 (train: 180, test: 180) * Train cities: Austin, Chicago, Kitsap, West Tyrol, Vienna * Test cities: Bellingham, Bloomington ...", "subpage_snippet": "", "source": "torchgeo.readthedocs.io", "link": "https://torchgeo.readthedocs.io/en/v0.7.0/_modules/torchgeo/datasets/inria.html", "content": "Dataset features: * Coverage of 810 km\\ :sup:`2`\\ (405 km\\ :sup:`2`\\ for training and 405 km\\ :sup:`2`\\ for testing) * Aerial orthorectified color imagery with a spatial resolution of 0.3 m * Number of images : 360 (train: 180, test: 180) * Train cities: Austin, Chicago, Kitsap, West Tyrol, Vienna * Test cities: Bellingham, Bloomington ..."}
+{"idx": 8, "title": "Inria Aerial Image Labeling Dataset | IEEE DataPort", "date": "", "ddg_snippet": "The data set includes five cities. Each city had 36 images , the first 5 as a test set and the last 31 as a training set. Each image is cropped from 5000 x 5000 pixels to 1024 x 1024 pixels.", "subpage_snippet": "", "source": "ieee-dataport.org", "link": "https://ieee-dataport.org/documents/inria-aerial-image-labeling-dataset", "content": "The data set includes five cities. Each city had 36 images , the first 5 as a test set and the last 31 as a training set. Each image is cropped from 5000 x 5000 pixels to 1024 x 1024 pixels."}
+{"idx": 9, "title": "Inria Aerial Image Labeling Dataset | Kaggle", "date": "", "ddg_snippet": "Kaggle is the world's largest data science community with powerful tools and resources to help you achieve your data science goals.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/huanranye/inria-aerial-image-labeling-dataset/", "content": "Kaggle is the world's largest data science community with powerful tools and resources to help you achieve your data science goals."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_Dynamic_Scene_Reconstruction_Gaussian_Splat.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_Dynamic_Scene_Reconstruction_Gaussian_Splat.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..69a5711d0bef82f949ebcdebc18ac7761729a212
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_Dynamic_Scene_Reconstruction_Gaussian_Splat.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ... Instant Gaussian Stream: Fast and Generalizable Streaming of ... Instant Gaussian Stream: Fast and Generalizable Streaming of ... Instant Gaussian Stream: Fast and Generalizable Streaming of ... Instant Gaussian Stream: Fast and Generalizable Streaming of ... CVPR 2025 Open Access Repository Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic (PDF) Instant Gaussian Stream : Fast and Generalizable Streaming of Dy… Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic IGS/README.md at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "Mar 21, 2025 · View a PDF of the paper titled Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting , by Jinbo Yan and 7 other authors With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 62, 66, 69, 71] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ... @misc{yan2025instantgaussianstreamfast, title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting ... Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to offline training methods, greatly enhancing user experience. Mar 21, 2025 · Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Y an 1, Rui Peng 1,2, Zhiyan Wang 1, Luyang T ang 1,2, Jiayu Y ang 1,2 Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 16520-16531 Is instant Gaussian stream a fast and generalizable streaming framework? However, current streaming approaches face challenges of high per-frame reconstruction time (10s+) and error accumulation, limiting their broader application. In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework , to address these issues. What is instant Gaussian stream (IGS)? In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework , to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. Does 3D Gaussian splatting improve dynamic scene reconstruction? With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS), dynamic scene reconstruction has seen rapid progress . Some ofline training methods[23, 31, 62, 66, 69, 71] achieve high-quality view synthesis but require collect-ing all frames before training can begin. Is generalizable 3D Gaussian splatting efficient? Compared with previous 3D reconstruction methods like Nerf, recent Generalizable 3D Gaussian Splatting (G-3DGS) methods demonstrate impressive efficiency even in the sparse-view setting. However, the promising reconstruction performance of existing G-3DGS methods relies heavily on accurate multi-view feature matching, which is quite challenging. What is preliminary Gaussian splatting? Preliminary Gaussian splatting represents static scenes as a collec-tion of anisotropic 3D Gaussians . The color of each pixel is obtained through point-based alpha blending rendering, enabling high-fidelity real-time novel view synthesis. Does triplane meet Gaussian splatting? Triplane meets gaussian splatting : Fast and generalizable single-view 3d reconstruction with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10324–10335, 2024. 2 Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting [CVPR25]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.16979", "content": "Mar 21, 2025 · View a PDF of the paper titled Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting , by Jinbo Yan and 7 other authors With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 62, 66, 69, 71] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ... @misc{yan2025instantgaussianstreamfast, title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting ... Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to offline training methods, greatly enhancing user experience. Mar 21, 2025 · Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Y an 1, Rui Peng 1,2, Zhiyan Wang 1, Luyang T ang 1,2, Jiayu Y ang 1,2 Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 16520-16531 Is instant Gaussian stream a fast and generalizable streaming framework? However, current streaming approaches face challenges of high per-frame reconstruction time (10s+) and error accumulation, limiting their broader application. In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework , to address these issues. What is instant Gaussian stream (IGS)? In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework , to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. Does 3D Gaussian splatting improve dynamic scene reconstruction? With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS), dynamic scene reconstruction has seen rapid progress . Some ofline training methods[23, 31, 62, 66, 69, 71] achieve high-quality view synthesis but require collect-ing all frames before training can begin. Is generalizable 3D Gaussian splatting efficient? Compared with previous 3D reconstruction methods like Nerf, recent Generalizable 3D Gaussian Splatting (G-3DGS) methods demonstrate impressive efficiency even in the sparse-view setting. However, the promising reconstruction performance of existing G-3DGS methods relies heavily on accurate multi-view feature matching, which is quite challenging. What is preliminary Gaussian splatting? Preliminary Gaussian splatting represents static scenes as a collec-tion of anisotropic 3D Gaussians . The color of each pixel is obtained through point-based alpha blending rendering, enabling high-fidelity real-time novel view synthesis. Does triplane meet Gaussian splatting? Triplane meets gaussian splatting : Fast and generalizable single-view 3d reconstruction with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10324–10335, 2024. 2 Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting [CVPR25]"}
+{"idx": 1, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 62, 66, 69, 71] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 62, 66, 69, 71] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ..."}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "@misc{yan2025instantgaussianstreamfast, title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "@misc{yan2025instantgaussianstreamfast, title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting ..."}
+{"idx": 3, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to offline training methods, greatly enhancing user experience.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11095003", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to offline training methods, greatly enhancing user experience."}
+{"idx": 4, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Mar 21, 2025 · Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Y an 1, Rui Peng 1,2, Zhiyan Wang 1, Luyang T ang 1,2, Jiayu Y ang 1,2", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "Mar 21, 2025 · Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Y an 1, Rui Peng 1,2, Zhiyan Wang 1, Luyang T ang 1,2, Jiayu Y ang 1,2"}
+{"idx": 5, "title": "TextSplat: Text-Guided Semantic Fusion for Generalizable", "date": "", "ddg_snippet": "... Generalizable Gaussian Splatting have enabled robust 3D reconstruction from sparse input views by utilizing feed-forward Gaussian Splatting models, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09588v2", "content": "... Generalizable Gaussian Splatting have enabled robust 3D reconstruction from sparse input views by utilizing feed-forward Gaussian Splatting models, ..."}
+{"idx": 6, "title": "QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for", "date": "", "ddg_snippet": "... Gaussian Splatting (3D-GS) [ 29 ] has emerged as a promising technique with significantly faster training and rendering speeds in comparison with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.04469v1", "content": "... Gaussian Splatting (3D-GS) [ 29 ] has emerged as a promising technique with significantly faster training and rendering speeds in comparison with ..."}
+{"idx": 7, "title": "V^3: Viewing Volumetric Videos on Mobiles via Streamable 2D", "date": "", "ddg_snippet": "... stream dynamic Gaussians on mobile devices, our companion player offers users an unprecedented volumetric video experience, including smooth scrolling ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3687935", "content": "... stream dynamic Gaussians on mobile devices, our companion player offers users an unprecedented volumetric video experience, including smooth scrolling ..."}
+{"idx": 8, "title": "Awesome 3D Gaussian Splatting Paper List", "date": "", "ddg_snippet": "Gaussian splatting has emerged as a powerful tool for high-fidelity reconstruction of dynamic scenes . ... FastSAM, etc.), together with features from ...", "subpage_snippet": "", "source": "mrnerf.github.io", "link": "https://mrnerf.github.io/awesome-3D-gaussian-splatting/", "content": "Gaussian splatting has emerged as a powerful tool for high-fidelity reconstruction of dynamic scenes . ... FastSAM, etc.), together with features from ..."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 16520-16531", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.html", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 16520-16531"}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_Section_5.2_6x_reduction_train_time_year_2024.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_Section_5.2_6x_reduction_train_time_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7301a0c8655078a6af3a1ff1d15e23f2ed146efc
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_Section_5.2_6x_reduction_train_time_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream -ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream -ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality."}
+{"idx": 1, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ... - GitHub", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting [CVPR25]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting [CVPR25]"}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.16979", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians ."}
+{"idx": 3, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "Compared to 3DGStream and StreamRF, our method achieves a 6x reduction in train time , with an average delay of 2.67 seconds per frame, while maintaining comparable rendering speed and storage usage.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "Compared to 3DGStream and StreamRF, our method achieves a 6x reduction in train time , with an average delay of 2.67 seconds per frame, while maintaining comparable rendering speed and storage usage."}
+{"idx": 4, "title": "(PDF) Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 5, "title": "每帧仅2秒的流式重建速度!Igs已开源：采用流式策略解决动态场景重建 (Cvpr'25) - 知乎", "date": "", "ddg_snippet": "为使流式框架更具实用性，本文介绍 即时高斯流（ Instant Gaussian Stream , IGS），这是一种单帧重建耗时仅2秒+、能抑制误差累积并提升视图合成质量的动态场景重建方法： 广义锚点驱动高斯运动网络（AGM-Net）：通过一组称为锚点的关键点携带运动特征来指导高斯 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1915569198148593307", "content": "为使流式框架更具实用性，本文介绍 即时高斯流（ Instant Gaussian Stream , IGS），这是一种单帧重建耗时仅2秒+、能抑制误差累积并提升视图合成质量的动态场景重建方法： 广义锚点驱动高斯运动网络（AGM-Net）：通过一组称为锚点的关键点携带运动特征来指导高斯 ..."}
+{"idx": 6, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "n, limiting their broader application. In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and generalizable stream ng framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion fea-tures into 3D space, using anchor points", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "n, limiting their broader application. In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and generalizable stream ng framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion fea-tures into 3D space, using anchor points"}
+{"idx": 7, "title": "IGS/README.md at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "[CVPR25 Highlight] Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting - yjb6/IGS", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/blob/master/README.md", "content": "[CVPR25 Highlight] Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting - yjb6/IGS"}
+{"idx": 8, "title": "高斯也能流式输出!不到2秒!Igs超快完成每帧动态场景建模! - 知乎", "date": "", "ddg_snippet": "然而，目前的流媒体方法面临着每帧重建时间过长（超过10秒）和误差积累的问题，限制了其广泛应用。 本文提出了即时高斯流（ Instant Gaussian Stream，IGS），一种快速且具可推广性的流媒体框架，用于解决这些问题。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1894842454890489789", "content": "然而，目前的流媒体方法面临着每帧重建时间过长（超过10秒）和误差积累的问题，限制了其广泛应用。 本文提出了即时高斯流（ Instant Gaussian Stream，IGS），一种快速且具可推广性的流媒体框架，用于解决这些问题。"}
+{"idx": 9, "title": "Cvpr'25开源远超4dgs|北大新作igs：高达204 Fps的动态场景重建 - 哔哩哔哩", "date": "", "ddg_snippet": "为推动流式框架的实际应用，我们提出瞬时高斯流（ Instant Gaussian Stream , IGS），这是一种动态场景重建的流式方法，实现单帧2秒+的重建时间，缓解误差累积问题并提升视图合成质量。", "subpage_snippet": "", "source": "www.bilibili.com", "link": "https://www.bilibili.com/opus/1081499205002854423", "content": "为推动流式框架的实际应用，我们提出瞬时高斯流（ Instant Gaussian Stream , IGS），这是一种动态场景重建的流式方法，实现单帧2秒+的重建时间，缓解误差累积问题并提升视图合成质量。"}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_anchor_driven_gaussian_motion_network_interpolation.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_anchor_driven_gaussian_motion_network_interpolation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3565c228332d699cdd9ff272bcf21502dc27c51e
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_anchor_driven_gaussian_motion_network_interpolation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "We propose a generalized Anchor - driven Gaussian Mo-tion Network that captures Gaussian motion between ad-jacent frames with a single inference, eliminating the need for frame-by-frame optimization.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "We propose a generalized Anchor - driven Gaussian Mo-tion Network that captures Gaussian motion between ad-jacent frames with a single inference, eliminating the need for frame-by-frame optimization."}
+{"idx": 1, "title": "GitHub - H-Huang774/ADC-GS: Official code of \"ADC-GS: Anchor ...", "date": "", "ddg_snippet": "Official code of \"ADC-GS: Anchor - Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction\", IJCAI2025 - H-Huang774/ADC-GS", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/H-Huang774/ADC-GS", "content": "Official code of \"ADC-GS: Anchor - Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction\", IJCAI2025 - H-Huang774/ADC-GS"}
+{"idx": 2, "title": "[PDF] Instant Gaussian Stream: Fast and Generalizable ...", "date": "", "ddg_snippet": "Mar 21, 2025 · This paper proposes Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, which introduces a generalized Anchor - driven Gaussian Motion Network , which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Instant-Gaussian-Stream:-Fast-and-Generalizable-of-Yan-Peng/b1554873f9eb74b1fe602a534cde1cce6cb298bd", "content": "Mar 21, 2025 · This paper proposes Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, which introduces a generalized Anchor - driven Gaussian Motion Network , which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to ..."}
+{"idx": 3, "title": "UV Gaussians: Joint Learning of Mesh Deformation and Gaussian", "date": "", "ddg_snippet": "... Gaussians [ 27 ] , GaussianAvatar [ 15 ] , and ASH [ 35 ] have ventured into learning Gaussian features from 2D images, allowing for the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.11589v1", "content": "... Gaussians [ 27 ] , GaussianAvatar [ 15 ] , and ASH [ 35 ] have ventured into learning Gaussian features from 2D images, allowing for the ..."}
+{"idx": 4, "title": "L3GS: Layered 3D Gaussian Splats for Efficient 3D Scene Delivery", "date": "", "ddg_snippet": "Experiments to measure visual quality and latency, driven by traces that we collected of users exploring 3DGS scenes using VR headsets (Meta Quest 3).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.05517v1", "content": "Experiments to measure visual quality and latency, driven by traces that we collected of users exploring 3DGS scenes using VR headsets (Meta Quest 3)."}
+{"idx": 5, "title": "Awesome 3D Gaussian Splatting Paper List", "date": "", "ddg_snippet": "However, existing methods primarily rely on implicit motion representations, such as encoding motions into neural networks or per- Gaussian parameters ...", "subpage_snippet": "", "source": "mrnerf.github.io", "link": "https://mrnerf.github.io/awesome-3D-gaussian-splatting/", "content": "However, existing methods primarily rely on implicit motion representations, such as encoding motions into neural networks or per- Gaussian parameters ..."}
+{"idx": 6, "title": "US11830209B2 - Neural network-based image stream modification -", "date": "", "ddg_snippet": "... the present disclosure addresses systems and methods for neural network -based object detection and inserting graphical elements into an image stream ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11830209B2/en", "content": "... the present disclosure addresses systems and methods for neural network -based object detection and inserting graphical elements into an image stream ..."}
+{"idx": 7, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "n, limiting their broader application. In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and generalizable stream ng framework, to address these issues. First, we introduce a generalized Anchor - driven Gaussian Motion Network , which projects multi-view 2D motion fea-tures into 3D space, using anchor points", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "n, limiting their broader application. In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and generalizable stream ng framework, to address these issues. First, we introduce a generalized Anchor - driven Gaussian Motion Network , which projects multi-view 2D motion fea-tures into 3D space, using anchor points"}
+{"idx": 8, "title": "Jiahao Wu - Homepage", "date": "", "ddg_snippet": "[CVPR 2025] Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang", "subpage_snippet": "", "source": "wujh2001.github.io", "link": "https://wujh2001.github.io/", "content": "[CVPR 2025] Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang"}
+{"idx": 9, "title": "CVPR'25开源远超4DGS|北大新作IGS：高达204 FPS的动态场景重建 - 哔哩...", "date": "", "ddg_snippet": "Jun 23, 2025 · 具体贡献如下： 针对单帧重建耗时问题，我们开发了广义锚点驱动高斯运动网络（ Anchor - driven Gaussian Motion Network , AGM-Net）。 该网络利用称为锚点的一组关键点承载运动特征，引导高斯基元的变换。", "subpage_snippet": "", "source": "www.bilibili.com", "link": "https://www.bilibili.com/opus/1081499205002854423", "content": "Jun 23, 2025 · 具体贡献如下： 针对单帧重建耗时问题，我们开发了广义锚点驱动高斯运动网络（ Anchor - driven Gaussian Motion Network , AGM-Net）。 该网络利用称为锚点的一组关键点承载运动特征，引导高斯基元的变换。"}
diff --git a/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Example_3.2_formula_abd.jsonl b/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Example_3.2_formula_abd.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..08ae811bb3a83dccd66e8a9cc45b7d4569c65c22
--- /dev/null
+++ b/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Example_3.2_formula_abd.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "23 May 2024 — We already hinted in Examples 3.2 and 3.3 how we give semantics to formulas in CBNs. We now formalize this. Report issue for preceding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.14728v1", "content": "23 May 2024 — We already hinted in Examples 3.2 and 3.3 how we give semantics to formulas in CBNs. We now formalize this. Report issue for preceding ..."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Section 3 gives semantics to formulas in Causal Bayesian Networks ... We already hinted in Examples 3.2 and 3.3 how we give semantics to formulas in CBNs.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96098", "content": "Section 3 gives semantics to formulas in Causal Bayesian Networks ... We already hinted in Examples 3.2 and 3.3 how we give semantics to formulas in CBNs."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by J Halpern — The total probability of these two fccces is abd (1 − e) + (1 − a)cd(1 − e); this is the probability of ϕ in M∗. We give one more example of this calculation .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "by J Halpern — The total probability of these two fccces is abd (1 − e) + (1 − a)cd(1 − e); this is the probability of ϕ in M∗. We give one more example of this calculation ."}
+{"idx": 3, "title": "Causation and Intervention", "date": "", "ddg_snippet": "by F Eberhardt · Cited by 104 — Accounts of causal discovery have traditionally split into approaches based on passive observational data and approaches based on experimental interventions . 209 pages", "subpage_snippet": "", "source": "www.its.caltech.edu", "link": "https://www.its.caltech.edu/~fehardt/papers/PhDthesis.pdf", "content": "by F Eberhardt · Cited by 104 — Accounts of causal discovery have traditionally split into approaches based on passive observational data and approaches based on experimental interventions . 209 pages"}
+{"idx": 4, "title": "Bayesian Causal Inference With Intermediates - Harvard DASH", "date": "", "ddg_snippet": "by LA Comment · 2019 — We motivate two new causal estimands: the time-varying survivor average causal effect (TV-SACE) and the restricted mean survivor average causal effect (RM-SACE) ...", "subpage_snippet": "", "source": "dash.harvard.edu", "link": "https://dash.harvard.edu/bitstreams/7d226e65-f67b-44d7-b96f-94ea0b7b5d04/download", "content": "by LA Comment · 2019 — We motivate two new causal estimands: the time-varying survivor average causal effect (TV-SACE) and the restricted mean survivor average causal effect (RM-SACE) ..."}
+{"idx": 5, "title": "A 20‐year mapping of Bayesian belief networks in software ...", "date": "", "ddg_snippet": "by ALR de Sousa · 2022 · Cited by 15 — 1 INTRODUCTION. Bayesian belief networks (BBNs ) are a modelling technique for causal relationships that are based on Bayesian inference.", "subpage_snippet": "", "source": "digital-library.theiet.org", "link": "https://digital-library.theiet.org/doi/full/10.1049/sfw2.12043", "content": "by ALR de Sousa · 2022 · Cited by 15 — 1 INTRODUCTION. Bayesian belief networks (BBNs ) are a modelling technique for causal relationships that are based on Bayesian inference."}
+{"idx": 6, "title": "Dynamic bayesian networks: representation, inference and ...", "date": "", "ddg_snippet": "B.6.1 Exploiting causal independence. CPDs with causal independence were introduced in Section A. 3.2 . The canonical example is noisy-OR. Pearl [Pea88] showed ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/dynamic-bayesian-networks-representation-inference-and-yy4fhfxdcj.pdf", "content": "B.6.1 Exploiting causal independence. CPDs with causal independence were introduced in Section A. 3.2 . The canonical example is noisy-OR. Pearl [Pea88] showed ..."}
+{"idx": 7, "title": "Integrating Bayesian networks and ontology to improve ...", "date": "", "ddg_snippet": "by W Junwu · 2024 · Cited by 9 — This study presents a novel conceptual framework for enhancing sustainability in construction behavioral safety management.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2090447924002818", "content": "by W Junwu · 2024 · Cited by 9 — This study presents a novel conceptual framework for enhancing sustainability in construction behavioral safety management."}
+{"idx": 8, "title": "Part II: Probability - Cognitive Systems Laboratory", "date": "", "ddg_snippet": "a formula typically known as the chain rule for Bayesian networks . We note that the graph structure associated with a distribution PΦ reveal in- dependence ...", "subpage_snippet": "", "source": "bayes.cs.ucla.edu", "link": "https://bayes.cs.ucla.edu/TRIBUTE/part2-probability.pdf", "content": "a formula typically known as the chain rule for Bayesian networks . We note that the graph structure associated with a distribution PΦ reveal in- dependence ..."}
+{"idx": 9, "title": "CTBN-PH", "date": "", "ddg_snippet": "22 May 2025 — A novel framework combines continuous-time Bayesian networks (CTBNs) with Cox proportional hazards (PH) models for risk prognosis.", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5264449.pdf?abstractid=5264449&mirid=1", "content": "22 May 2025 — A novel framework combines continuous-time Bayesian networks (CTBNs) with Cox proportional hazards (PH) models for risk prognosis."}
diff --git a/data/sampled_jsons/JNDcFOczOf_RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_challenges.jsonl b/data/sampled_jsons/JNDcFOczOf_RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_challenges.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..08ca82eab4306af11dbabc3a71dbedfcd22b342e
--- /dev/null
+++ b/data/sampled_jsons/JNDcFOczOf_RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_challenges.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 1, "title": "PDF RA-PbRL: Provably Eficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "To the best of our knowledge, our proposed RA- PbRL algorithm is the first provably eficient Preference-based Reinforcement Learning ( PbRL ) algorithm that incorporates both nested and static risk objectives in one algorithm.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "To the best of our knowledge, our proposed RA- PbRL algorithm is the first provably eficient Preference-based Reinforcement Learning ( PbRL ) algorithm that incorporates both nested and static risk objectives in one algorithm."}
+{"idx": 2, "title": "RA-PbRL | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739861", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 3, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf", "content": "Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators ..."}
+{"idx": 4, "title": "Risk-Aware Preference-baser Reinforcement Learning (RA-PbRL)", "date": "", "ddg_snippet": "RA- PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "RA- PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties."}
+{"idx": 5, "title": "Efficient Preference-Based Reinforcement Learning Using Learned ...", "date": "", "ddg_snippet": "Preference-based reinforcement learning ( PbRL ) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10161081", "content": "Preference-based reinforcement learning ( PbRL ) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ..."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "This work explores and proves the applicability of two risk-aware objectives to PbRL : nested and static quantile risk objectives and introduces Risk-Aware - PbRL (RA- PbRL ), an algorithm designed to optimize both nested and static objectives. Preference-based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/RA-PbRL:-Provably-Efficient-Risk-Aware-Learning-Zhao-Escamill/80889b1260bfcf4275f7ec18bb4eec183400f05d", "content": "This work explores and proves the applicability of two risk-aware objectives to PbRL : nested and static quantile risk objectives and introduces Risk-Aware - PbRL (RA- PbRL ), an algorithm designed to optimize both nested and static objectives. Preference-based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode ..."}
+{"idx": 7, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Authors Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "Authors Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences ..."}
+{"idx": 8, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "This paper is important because it addresses a critical limitation of existing preference-based reinforcement learning ( PbRL ) methods: their inability to handle risk effectively. RA- PbRL offers a novel solution by incorporating risk-aware objectives and developing a provably efficient algorithm. This work is relevant to current research trends in safe and robust AI, which require algorithms ...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "This paper is important because it addresses a critical limitation of existing preference-based reinforcement learning ( PbRL ) methods: their inability to handle risk effectively. RA- PbRL offers a novel solution by incorporating risk-aware objectives and developing a provably efficient algorithm. This work is relevant to current research trends in safe and robust AI, which require algorithms ..."}
+{"idx": 9, "title": "Yujie (Norah) Zhao", "date": "", "ddg_snippet": "First Author Publications (Full List on Google Scholar) RA- PbRL : Provably Efficient Risk-Aware Preference-Based Reinforcement Learning", "subpage_snippet": "", "source": "norahyujiezhao.github.io", "link": "https://norahyujiezhao.github.io/", "content": "First Author Publications (Full List on Google Scholar) RA- PbRL : Provably Efficient Risk-Aware Preference-Based Reinforcement Learning"}
diff --git a/data/sampled_jsons/JUICE_Gemma-2b_World_Capital_No_Conflict_Type_1_accuracy_Table_3.jsonl b/data/sampled_jsons/JUICE_Gemma-2b_World_Capital_No_Conflict_Type_1_accuracy_Table_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ad5eb7f08a3fcb484920c24438898afcc0eda1f4
--- /dev/null
+++ b/data/sampled_jsons/JUICE_Gemma-2b_World_Capital_No_Conflict_Type_1_accuracy_Table_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - GaotangLi/JUICE: [ICML'25 Spotlight] Taming Knowledge Conflict ...", "date": "", "ddg_snippet": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models. The code is now still being updated.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GaotangLi/JUICE", "content": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models. The code is now still being updated."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models - arXiv.org", "date": "", "ddg_snippet": "Performance of different methods with Gemma-2b under various conflict types . JUICE achieves consistently high perfor-mance in facing challenging knowledge conflicts .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10996v2", "content": "Performance of different methods with Gemma-2b under various conflict types . JUICE achieves consistently high perfor-mance in facing challenging knowledge conflicts ."}
+{"idx": 2, "title": "Fine-Tuning Gemma 2B: A Practical Guide - Medium", "date": "", "ddg_snippet": "Fine-tuning Gemma 2B is as much an art as it is a science. With every project I've worked on, I've learned that the process is iterative — there's no one-size-fits-all solution.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@heyamit10/fine-tuning-gemma-2b-a-practical-guide-e4c25de43b2d", "content": "Fine-tuning Gemma 2B is as much an art as it is a science. With every project I've worked on, I've learned that the process is iterative — there's no one-size-fits-all solution."}
+{"idx": 3, "title": "google/gemma-2b · Hugging Face", "date": "", "ddg_snippet": "This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the 7B base model, 7B instruct model, and 2B instruct model.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/google/gemma-2b", "content": "This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the 7B base model, 7B instruct model, and 2B instruct model."}
+{"idx": 4, "title": "Gemma - 2B Parameters - Edge AI", "date": "", "ddg_snippet": "Gemma is a lightweight, state-of-the-art open model developed by Google DeepMind. Inspired by Gemini, Gemma is optimized for efficiency and high-quality text generation. Key Features Available in 2B & 7B parameter sizes Trained on diverse web documents, covering linguistic styles, coding syntax, and mathematical reasoning Enhanced safety measures, including content filtering for sensitive and ...", "subpage_snippet": "", "source": "edgeai.org", "link": "https://edgeai.org/gemma-2b-parameters/", "content": "Gemma is a lightweight, state-of-the-art open model developed by Google DeepMind. Inspired by Gemini, Gemma is optimized for efficiency and high-quality text generation. Key Features Available in 2B & 7B parameter sizes Trained on diverse web documents, covering linguistic styles, coding syntax, and mathematical reasoning Enhanced safety measures, including content filtering for sensitive and ..."}
+{"idx": 5, "title": "google-gemini/gemma-cookbook - GitHub", "date": "", "ddg_snippet": "Gemma is a family of lightweight, generative artificial intelligence (AI) open models, built from the same research and technology used to create the Gemini models. The Gemma model family includes: Gemma The core models of the Gemma family. Gemma For a variety of text generation tasks and can be further tuned for specific use cases Gemma 2 Higher-performing and more efficient, available in 2B ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/google-gemini/gemma-cookbook", "content": "Gemma is a family of lightweight, generative artificial intelligence (AI) open models, built from the same research and technology used to create the Gemini models. The Gemma model family includes: Gemma The core models of the Gemma family. Gemma For a variety of text generation tasks and can be further tuned for specific use cases Gemma 2 Higher-performing and more efficient, available in 2B ..."}
+{"idx": 6, "title": "Gemma 2: Improving Open Language Models at a Practical Size", "date": "", "ddg_snippet": "Table 2: Parameter counts for the Gemma models. We inherit from the large Gemini vocabulary (256k entries), that is designed to work on a large number of languages, hence, the larger embedding parameter counts compared to models that are limited to one or a few languages. Post-norm and pre-norm with RMSNorm.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.00118v3", "content": "Table 2: Parameter counts for the Gemma models. We inherit from the large Gemini vocabulary (256k entries), that is designed to work on a large number of languages, hence, the larger embedding parameter counts compared to models that are limited to one or a few languages. Post-norm and pre-norm with RMSNorm."}
+{"idx": 7, "title": "Performance and Guardrails Evaluation of Google's Gemma-2B IT LLM - GitHub", "date": "", "ddg_snippet": "This repository documents an evaluation of Google's newest open-source large language model, Instruction Tuned Gemma-2B IT. Testing framework encompasses a broad spectrum of questions across various domains, aimed at benchmarking the model's performance capabilities and its adherence to ethical guardrails.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mickymultani/TestingGemma2B", "content": "This repository documents an evaluation of Google's newest open-source large language model, Instruction Tuned Gemma-2B IT. Testing framework encompasses a broad spectrum of questions across various domains, aimed at benchmarking the model's performance capabilities and its adherence to ethical guardrails."}
+{"idx": 8, "title": "forcemultiplier/gpqa-syngen-gemma2-2b · Hugging Face", "date": "", "ddg_snippet": "This model is a fine-tuned version of the Gemma 2B base model, specifically tailored to Google GPQA (Graduate-Level Google-Proof Q&A Benchmark) dataset. It produces graduate-level, context-rich multiple-choice questions along with one correct answer, three incorrect answers, and an explanation.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/forcemultiplier/gpqa-syngen-gemma2-2b", "content": "This model is a fine-tuned version of the Gemma 2B base model, specifically tailored to Google GPQA (Graduate-Level Google-Proof Q&A Benchmark) dataset. It produces graduate-level, context-rich multiple-choice questions along with one correct answer, three incorrect answers, and an explanation."}
+{"idx": 9, "title": "google/gemma-2b-GGUF · Hugging Face", "date": "", "ddg_snippet": "Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/google/gemma-2b-GGUF", "content": "Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants."}
diff --git a/data/sampled_jsons/JUICE_algorithm_dual-run_process_attention_heads_language_models.jsonl b/data/sampled_jsons/JUICE_algorithm_dual-run_process_attention_heads_language_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ec4eeaf72145d90f2e144aa5bbef760dba9311d
--- /dev/null
+++ b/data/sampled_jsons/JUICE_algorithm_dual-run_process_attention_heads_language_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Find natural language algorithm Jobs, Employment | Freelancer", "date": "", "ddg_snippet": "This includes coded analysis of labelled body language and facial datasets, primarily focusing on head movements and facial expressions.", "subpage_snippet": "", "source": "www.freelancer.com", "link": "https://www.freelancer.com/job-search/find-natural-language-algorithm/", "content": "This includes coded analysis of labelled body language and facial datasets, primarily focusing on head movements and facial expressions."}
+{"idx": 1, "title": "Juice Up Your C# App with the Power of Hyper-Threading |", "date": "", "ddg_snippet": "This means that with only two threads running, each thread would be assigned to its own processor rather than having them hyper-threaded onto the ...", "subpage_snippet": "", "source": "learn.microsoft.com", "link": "https://learn.microsoft.com/en-us/archive/msdn-magazine/2005/june/juice-up-your-csharp-app-with-the-power-of-hyper-threading", "content": "This means that with only two threads running, each thread would be assigned to its own processor rather than having them hyper-threaded onto the ..."}
+{"idx": 2, "title": "Online Marketing Juice – Page 2 – Online Marketing", "date": "", "ddg_snippet": "Whether it ’ s sharing each other ’ s content, co-hosting webinars, or running promotions, collaboration can amplify your reach.", "subpage_snippet": "", "source": "onlinemarketingjuice.com", "link": "https://onlinemarketingjuice.com/author/onlinemarketingjuice/page/2/", "content": "Whether it ’ s sharing each other ’ s content, co-hosting webinars, or running promotions, collaboration can amplify your reach."}
+{"idx": 3, "title": "Algorithmic Sameness with Kyle Chayka - People vs Algorithms", "date": "", "ddg_snippet": "Algorithms ,' hosts Brian Morrissey, Troy Young, and Alex Schleifer engage in a wide-ranging discussion on how algorithms shape our cultural landscape ...", "subpage_snippet": "", "source": "www.listennotes.com", "link": "https://www.listennotes.com/podcasts/people-vs-algorithms/algorithmic-sameness-with-iPSbCz7E4BH/", "content": "Algorithms ,' hosts Brian Morrissey, Troy Young, and Alex Schleifer engage in a wide-ranging discussion on how algorithms shape our cultural landscape ..."}
+{"idx": 4, "title": "Introduction to the HTML5 Web Workers: the JavaScript", "date": "", "ddg_snippet": "... annoying with today multi-cores processors like the i5/i7 containing up to 8 logical CPUs and even with the latest ARM mobile processors being dual ...", "subpage_snippet": "", "source": "www.davrous.com", "link": "https://www.davrous.com/2011/07/15/introduction-to-the-html5-web-workers-the-javascript-multithreading-approach/", "content": "... annoying with today multi-cores processors like the i5/i7 containing up to 8 logical CPUs and even with the latest ARM mobile processors being dual ..."}
+{"idx": 5, "title": "Multi-agent papers", "date": "", "ddg_snippet": "... process plant faults using digital twins? Can LLMs automate quantum chemistry workflows? How to secure interacting AI agents online? How to optimize ...", "subpage_snippet": "", "source": "papers.miklos.dev", "link": "https://papers.miklos.dev/", "content": "... process plant faults using digital twins? Can LLMs automate quantum chemistry workflows? How to secure interacting AI agents online? How to optimize ..."}
+{"idx": 6, "title": "Falcon-H1: The AI Chimera That Challenges The Transformer", "date": "", "ddg_snippet": "Hybrid Architecture ( Attention + SSM): Each model layer uses two parallel heads – one Transformer attention head and one “Mamba-2” SSM head ...", "subpage_snippet": "", "source": "www.llmwatch.com", "link": "https://www.llmwatch.com/p/falcon-h1-the-ai-chimera-that-challenges", "content": "Hybrid Architecture ( Attention + SSM): Each model layer uses two parallel heads – one Transformer attention head and one “Mamba-2” SSM head ..."}
+{"idx": 7, "title": "Universal Jailbreak Backdoors from Poisoned Human Feedback", "date": "", "ddg_snippet": "RLHF first trains a reward model to mimic human feedback, and then uses this model to label the language model ’s generations during finetuning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.14455v4", "content": "RLHF first trains a reward model to mimic human feedback, and then uses this model to label the language model ’s generations during finetuning."}
+{"idx": 8, "title": "The Perils of Parallel", "date": "", "ddg_snippet": "I fear we have closed in on an incrementally-improving era of computing, at least on the hardware and processing side, requiring inhuman levels of ...", "subpage_snippet": "", "source": "perilsofparallel.blogspot.com", "link": "http://perilsofparallel.blogspot.com/", "content": "I fear we have closed in on an incrementally-improving era of computing, at least on the hardware and processing side, requiring inhuman levels of ..."}
+{"idx": 9, "title": "Technology | ShriViews | Page 2", "date": "", "ddg_snippet": "You can in fact, use a gaming graphics card for workstation applications- but the gaming card won ’ t do the processing as fast as a dedicated ...", "subpage_snippet": "", "source": "shriviews.com", "link": "https://shriviews.com/category/technology/page/2/", "content": "You can in fact, use a gaming graphics card for workstation applications- but the gaming card won ’ t do the processing as fast as a dedicated ..."}
diff --git a/data/sampled_jsons/Kerner_2024_CrowdStrike.jsonl b/data/sampled_jsons/Kerner_2024_CrowdStrike.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07d660414b36cf13a8e4c64362e3ef58fd5f4668
--- /dev/null
+++ b/data/sampled_jsons/Kerner_2024_CrowdStrike.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2024 CrowdStrike-related IT outages - Wikipedia", "date": "", "ddg_snippet": "On 19 July 2024 , the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. As a result, roughly 8.5 million systems crashed and were unable to properly restart [1] in what has been called the largest outage in the history of information technology [2 ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/2024_CrowdStrike-related_IT_outages", "content": "On 19 July 2024 , the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. As a result, roughly 8.5 million systems crashed and were unable to properly restart [1] in what has been called the largest outage in the history of information technology [2 ..."}
+{"idx": 1, "title": "Crowdstrike: 79 Minutes", "date": "", "ddg_snippet": "Approximately 8.5 million devices were directly affected ( Kerner , 2024 ), impacting critical operations across multiple sectors, including airlines (leading to ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/crowdstrike-79-minutes-christian-calipusan-xpy4f", "content": "Approximately 8.5 million devices were directly affected ( Kerner , 2024 ), impacting critical operations across multiple sectors, including airlines (leading to ..."}
+{"idx": 2, "title": "CrowdStrike outage explained: What caused it and what’s next 2024 CrowdStrike-related IT outages - Wikipedia Microsoft moves antivirus out of Kernel for safety | Cybernews Microsoft Removes 3rd Party Antivirus Software From Windows ... Microsoft changes Windows in attempt to prevent next ... Crowdstrike: 79 Minutes - LinkedIn Microsoft calls for Windows changes and resilience after ... Microsoft Removes 3rd Party Antivirus Software From Windows Kernel 2024 CrowdStrike -related IT outages - Wikipedia CrowdStrike outage explained: What caused it and what’s next 2024 CrowdStrike -related IT outages - Wikipedia 2024 CrowdStrike -related IT outages - Wikipedia Microsoft calls for Windows changes and resilience after CrowdStrike", "date": "", "ddg_snippet": "Oct 29, 2024 · What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ... On 19 July 2024 , the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. As a result, roughly 8.5 million systems crashed and were unable to properly restart [1] in what has been called the largest outage in the history of information technology [2 ... Jun 30, 2025 · After CrowdStrike 's 2024 crash knocked 8.5M devices offline, Microsoft is rolling out new features to keep third-party drivers out of the kernel. Jun 27, 2025 · In response to the 2024 CrowdStrike crisis, Microsoft is overhauling Windows security by forcing antivirus software out of the kernel to boost stability. This major architectural shift, part of ... Jun 27, 2025 · In the summer of 2024 , corporate anti-malware provider CrowdStrike pushed a broken update to millions of PCs and servers running some version of Microsoft's Windows software, taking down systems ... Mar 7, 2025 · The outage occurred on July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ( Kerner , 2024 ). Jul 26, 2024 · Microsoft calls for Windows changes and resilience after CrowdStrike outage Microsoft appears to be starting the conversation about moving security vendors out of the Windows kernel. What is Microsoft doing about CrowdStrike 2024? In response to the 2024 CrowdStrike crisis, Microsoft is overhauling Windows security by forcing antivirus software out of the kernel to boost stability. This major architectural shift, part of the 'Windows Resiliency Initiative,' redefines platform security for the entire industry. How many customers did CrowdStrike have in 2024? Archived from the original on 22 July 2024. Retrieved 19 July 2024. In its last earnings report, Crowdstrike declared a total of nearly 24,000 customers . ^ Singh, Manish (19 July 2024). \"Faulty CrowdStrike update causes major global IT outage, taking out banks, airlines and businesses globally\". TechCrunch. Does CrowdStrike run on Windows? CrowdStrike's software doesn't just run on Microsoft Windows ; it also runs on Apple's macOS and the Linux OS. But the July outage only affected Microsoft Windows. The root cause of the outage was a faulty sensor configuration update that specifically affected Windows systems. What happened to CrowdStrike's Falcon sensor security software? On 19 July 2024, the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. Is CrowdStrike causing Linux kernel panics? \"CrowdStrike issues go beyond Windows: company's security software has reportedly been causing Linux kernel panics since at least April\". Tom's Hardware. Archived from the original on 25 July 2024. Retrieved 25 July 2024. ^ Chambers, Bradley (20 July 2024). \"Apple @ Work: How Apple protects the Mac from mass enterprise outages\". 9to5Mac. Does CrowdStrike have a blue screen of death? While CrowdStrike has blamed a bug in its testing software for its botched update, its software runs at the kernel level — the core part of an operating system that has unrestricted access to system memory and hardware. This means that if something goes wrong with CrowdStrike ’s app, it can take down Windows machines with a Blue Screen of Death.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/WhatIs/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "Oct 29, 2024 · What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ... On 19 July 2024 , the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. As a result, roughly 8.5 million systems crashed and were unable to properly restart [1] in what has been called the largest outage in the history of information technology [2 ... Jun 30, 2025 · After CrowdStrike 's 2024 crash knocked 8.5M devices offline, Microsoft is rolling out new features to keep third-party drivers out of the kernel. Jun 27, 2025 · In response to the 2024 CrowdStrike crisis, Microsoft is overhauling Windows security by forcing antivirus software out of the kernel to boost stability. This major architectural shift, part of ... Jun 27, 2025 · In the summer of 2024 , corporate anti-malware provider CrowdStrike pushed a broken update to millions of PCs and servers running some version of Microsoft's Windows software, taking down systems ... Mar 7, 2025 · The outage occurred on July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ( Kerner , 2024 ). Jul 26, 2024 · Microsoft calls for Windows changes and resilience after CrowdStrike outage Microsoft appears to be starting the conversation about moving security vendors out of the Windows kernel. What is Microsoft doing about CrowdStrike 2024? In response to the 2024 CrowdStrike crisis, Microsoft is overhauling Windows security by forcing antivirus software out of the kernel to boost stability. This major architectural shift, part of the 'Windows Resiliency Initiative,' redefines platform security for the entire industry. How many customers did CrowdStrike have in 2024? Archived from the original on 22 July 2024. Retrieved 19 July 2024. In its last earnings report, Crowdstrike declared a total of nearly 24,000 customers . ^ Singh, Manish (19 July 2024). \"Faulty CrowdStrike update causes major global IT outage, taking out banks, airlines and businesses globally\". TechCrunch. Does CrowdStrike run on Windows? CrowdStrike's software doesn't just run on Microsoft Windows ; it also runs on Apple's macOS and the Linux OS. But the July outage only affected Microsoft Windows. The root cause of the outage was a faulty sensor configuration update that specifically affected Windows systems. What happened to CrowdStrike's Falcon sensor security software? On 19 July 2024, the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. Is CrowdStrike causing Linux kernel panics? \"CrowdStrike issues go beyond Windows: company's security software has reportedly been causing Linux kernel panics since at least April\". Tom's Hardware. Archived from the original on 25 July 2024. Retrieved 25 July 2024. ^ Chambers, Bradley (20 July 2024). \"Apple @ Work: How Apple protects the Mac from mass enterprise outages\". 9to5Mac. Does CrowdStrike have a blue screen of death? While CrowdStrike has blamed a bug in its testing software for its botched update, its software runs at the kernel level — the core part of an operating system that has unrestricted access to system memory and hardware. This means that if something goes wrong with CrowdStrike ’s app, it can take down Windows machines with a Blue Screen of Death."}
+{"idx": 3, "title": "CrowdStrike outage explained: What caused it and what's ...", "date": "", "ddg_snippet": "29 Oct 2024 — The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death (BSOD). CrowdStrike -- ...", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/whatis/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "29 Oct 2024 — The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death (BSOD). CrowdStrike -- ..."}
+{"idx": 4, "title": "CrowdStrike update chaos explained: What you need to ...", "date": "", "ddg_snippet": "29 Jul 2024 — 19 July 2024 : An update to CrowdStrike's Falcon service has led to many Windows users being unable to work this morning. · The Emis Web IT system ...", "subpage_snippet": "", "source": "www.computerweekly.com", "link": "https://www.computerweekly.com/feature/CrowdStrike-update-chaos-explained-What-you-need-to-know", "content": "29 Jul 2024 — 19 July 2024 : An update to CrowdStrike's Falcon service has led to many Windows users being unable to work this morning. · The Emis Web IT system ..."}
+{"idx": 5, "title": "Unraveling the 2024 CrowdStrike Incident", "date": "", "ddg_snippet": "by B Venkata · 2025 — In July 2024 , a faulty update of CrowdStrike's Falcon Endpoint. Detection and Response (EDR) software caused widespread system crashes known as the “Blue Screen ...", "subpage_snippet": "", "source": "philarchive.org", "link": "https://philarchive.org/archive/VENUTN", "content": "by B Venkata · 2025 — In July 2024 , a faulty update of CrowdStrike's Falcon Endpoint. Detection and Response (EDR) software caused widespread system crashes known as the “Blue Screen ..."}
+{"idx": 6, "title": "Crowdstrike's massive cyber outage 1-year later", "date": "", "ddg_snippet": "21 Jul 2025 — The incident's legacy extends far beyond CrowdStrike . Organizations now implement staged rollouts and maintain manual override capabilities.", "subpage_snippet": "", "source": "venturebeat.com", "link": "https://venturebeat.com/ai/how-crowdstrikes-78-minute-outage-reshaped-enterprise-cybersecurity", "content": "21 Jul 2025 — The incident's legacy extends far beyond CrowdStrike . Organizations now implement staged rollouts and maintain manual override capabilities."}
+{"idx": 7, "title": "CrowdStrike chaos shows risks of concentrated 'big IT'", "date": "", "ddg_snippet": "22 Jul 2024 — The global Microsoft outage caused by a botched update from security firm CrowdStrike has highlighted the dangerous business continuity risk.", "subpage_snippet": "", "source": "www.computerweekly.com", "link": "https://www.computerweekly.com/news/366596434/CrowdStrike-chaos-shows-risks-of-concentrated-big-IT", "content": "22 Jul 2024 — The global Microsoft outage caused by a botched update from security firm CrowdStrike has highlighted the dangerous business continuity risk."}
+{"idx": 8, "title": "A Reflection on the CrowdStrike IT Outage - Platypus", "date": "", "ddg_snippet": "18 Feb 2025 — Recovery time estimates ranged from days to months, but 99% of affected Windows systems were back online by the end of July ( Kerner 2024 ). The ...", "subpage_snippet": "", "source": "blog.castac.org", "link": "https://blog.castac.org/2025/02/major-internet-outages-are-getting-bigger-and-occurring-more-often-a-reflection-on-the-crowdstrike-it-outage/", "content": "18 Feb 2025 — Recovery time estimates ranged from days to months, but 99% of affected Windows systems were back online by the end of July ( Kerner 2024 ). The ..."}
+{"idx": 9, "title": "Understanding the CrowdStrike Attack and Cyber Insurance", "date": "", "ddg_snippet": "The CrowdStrike attack is a wake-up call for all organizations. Help your business clients take action, especially in securing Cyber Insurance coverage.", "subpage_snippet": "", "source": "prowritersins.com", "link": "https://prowritersins.com/cyber-insurance-blog/crowdstrike-cyber-attack/", "content": "The CrowdStrike attack is a wake-up call for all organizations. Help your business clients take action, especially in securing Cyber Insurance coverage."}
diff --git a/data/sampled_jsons/Kerner_CrowdStrike_2024_financial_cost.jsonl b/data/sampled_jsons/Kerner_CrowdStrike_2024_financial_cost.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..20978a94d1a64ea3d39fd60f4aab008fe36a20dd
--- /dev/null
+++ b/data/sampled_jsons/Kerner_CrowdStrike_2024_financial_cost.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "George Kurtz - Wikipedia", "date": "", "ddg_snippet": "In 2024 , his company CrowdStrike crashed millions of Windows computers around the world, causing billions of dollars in economic losses in what has ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/George_Kurtz", "content": "In 2024 , his company CrowdStrike crashed millions of Windows computers around the world, causing billions of dollars in economic losses in what has ..."}
+{"idx": 1, "title": "CrowdStrike outage explained: What caused it and what’s next", "date": "", "ddg_snippet": "Delta Air Lines filed a lawsuit against CrowdStrike on Oct. 25, 2024 , over the outage that grounded thousands of flights and cost the airline approximately $500 million.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/whatis/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "Delta Air Lines filed a lawsuit against CrowdStrike on Oct. 25, 2024 , over the outage that grounded thousands of flights and cost the airline approximately $500 million."}
+{"idx": 2, "title": "El Accelerate Partner de CrowdStrike permite... | Computerworld.es", "date": "", "ddg_snippet": "CrowdStrike propone ahora a su canal un programa moderno y escalable que permite a los socios maximizar el valor, mantener unos márgenes sólidos y superar a la competencia.", "subpage_snippet": "", "source": "www.computerworld.es", "link": "https://www.computerworld.es/article/3843066/el-accelerate-partner-de-crowdstrike-permite-a-los-socios-mantener-unos-margenes-solidos.html", "content": "CrowdStrike propone ahora a su canal un programa moderno y escalable que permite a los socios maximizar el valor, mantener unos márgenes sólidos y superar a la competencia."}
+{"idx": 3, "title": "Why Did CrowdStrike Produce The Windows Blue Screen?", "date": "", "ddg_snippet": "He described CrowdStrike Falcon as anti-malware for Windows servers, which &ldquo, proactively detects new attacks &rdquo, and analyses application behaviour. To do this, CrowdStrike needs to run as a seed unit pilot.", "subpage_snippet": "", "source": "url4ever.com", "link": "https://url4ever.com/why-did-crowdstrike-cause-the-windows-blue-screen/", "content": "He described CrowdStrike Falcon as anti-malware for Windows servers, which &ldquo, proactively detects new attacks &rdquo, and analyses application behaviour. To do this, CrowdStrike needs to run as a seed unit pilot."}
+{"idx": 4, "title": "Schwerwiegender fehler anzeigen, Fataler Fehler bei CrowdStrike ...", "date": "", "ddg_snippet": "Fataler Fehler bei CrowdStrike : Schuld war ein Null Pointer. SChannel ist sehr geschwätzig und protokolliert, wenn Verbindungen nicht aufgebaut werden konnten. In diesem Teil stellen wir Ihnen 8 Möglichkeiten vor, wie Sie den schwerwiegenden Fehler auf einem Windows 10-PC...", "subpage_snippet": "", "source": "p4lz30.freedomladder.org", "link": "https://p4lz30.freedomladder.org/", "content": "Fataler Fehler bei CrowdStrike : Schuld war ein Null Pointer. SChannel ist sehr geschwätzig und protokolliert, wenn Verbindungen nicht aufgebaut werden konnten. In diesem Teil stellen wir Ihnen 8 Möglichkeiten vor, wie Sie den schwerwiegenden Fehler auf einem Windows 10-PC..."}
+{"idx": 5, "title": "CrowdStrike outage explained: What caused it and what’s", "date": "", "ddg_snippet": "The conditions that led to those errors were both patched by CrowdStrike after July 19, 2024 . ... 2024 , over the outage that grounded thousands of ...", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/WhatIs/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "The conditions that led to those errors were both patched by CrowdStrike after July 19, 2024 . ... 2024 , over the outage that grounded thousands of ..."}
+{"idx": 6, "title": "Microsoft, SecOps pros weigh kernel access post-CrowdStrike |", "date": "", "ddg_snippet": "Because CrowdStrike 's software ran as a device driver in the core Windows OS kernel, also known as Ring Zero, its failure prompted a kernel panic and ...", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/searchitoperations/news/366599066/Microsoft-SecOps-pros-weigh-kernel-access-post-CrowdStrike", "content": "Because CrowdStrike 's software ran as a device driver in the core Windows OS kernel, also known as Ring Zero, its failure prompted a kernel panic and ..."}
+{"idx": 7, "title": "InfoSec community sounds off on CrowdStrike outage, next steps", "date": "", "ddg_snippet": "Both CrowdStrike 's testing process and use of its own products to identify issues failed and allowed an undetected bug to be shipped off to customers ...", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/searchsecurity/news/366599607/InfoSec-community-sounds-off-on-CrowdStrike-outage-next-steps", "content": "Both CrowdStrike 's testing process and use of its own products to identify issues failed and allowed an undetected bug to be shipped off to customers ..."}
+{"idx": 8, "title": "CrowdStrike responds to NSS Labs lawsuit over product testing |", "date": "", "ddg_snippet": "CrowdStrike and the Anti-Malware Testing Standards Organization responds the allegations made by NSS Labs in a bombshell antitrust suit over product ...", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/searchsecurity/news/252449112/CrowdStrike-responds-to-NSS-Labs-lawsuit-over-product-testing", "content": "CrowdStrike and the Anti-Malware Testing Standards Organization responds the allegations made by NSS Labs in a bombshell antitrust suit over product ..."}
+{"idx": 9, "title": "CrowdStrike update snafu affected 8.5 million Windows devices |", "date": "", "ddg_snippet": "On 19 July 2024 , a content update that included malware signatures rolled out to users of the CrowdStrike Falcon endpoint protection service led to ...", "subpage_snippet": "", "source": "www.computerweekly.com", "link": "https://www.computerweekly.com/news/366596373/CrowdStrike-update-snafu-affected-85-million-Windows-devices", "content": "On 19 July 2024 , a content update that included malware signatures rolled out to users of the CrowdStrike Falcon endpoint protection service led to ..."}
diff --git a/data/sampled_jsons/Kevin_Roose_New_York_Times_2024_AI_measurement_problem.jsonl b/data/sampled_jsons/Kevin_Roose_New_York_Times_2024_AI_measurement_problem.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c447d43b9f0c32d70f8f8710dee98d50f07d10c2
--- /dev/null
+++ b/data/sampled_jsons/Kevin_Roose_New_York_Times_2024_AI_measurement_problem.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A . I . Has a Measurement Problem - The New York Times", "date": "", "ddg_snippet": "(The New York Times has sued OpenAI, the maker of ChatGPT, and its partner, Microsoft, on claims of copyright infringement involving artificial intelligence systems that generate text.) There may also be problems with the tests themselves.", "subpage_snippet": "", "source": "www.nytimes.com", "link": "https://www.nytimes.com/2024/04/15/technology/ai-models-measurement.html?ref=spyglass.org", "content": "(The New York Times has sued OpenAI, the maker of ChatGPT, and its partner, Microsoft, on claims of copyright infringement involving artificial intelligence systems that generate text.) There may also be problems with the tests themselves."}
+{"idx": 1, "title": "AI has a measurement problem | eKathimerini.com", "date": "", "ddg_snippet": "The new york times . AI has a measurement problem .(The New York Times has sued OpenAI, the maker of ChatGPT, and its partner, Microsoft, on claims of copyright infringement involving AI systems that generate text.) There may also be problems with the tests themselves.", "subpage_snippet": "", "source": "www.ekathimerini.com", "link": "https://www.ekathimerini.com/nytimes/1236644/", "content": "The new york times . AI has a measurement problem .(The New York Times has sued OpenAI, the maker of ChatGPT, and its partner, Microsoft, on claims of copyright infringement involving AI systems that generate text.) There may also be problems with the tests themselves."}
+{"idx": 2, "title": "Lucas B. on LinkedIn: A . I . Has a Measurement Problem", "date": "", "ddg_snippet": "In The New York Times , Kevin Roose describes the current landscape of #AImeasurement and evaluation as “a tangle of sloppy tests, apples-to-oranges comparisons, and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark.”", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/lbartoszcze_ai-has-a-measurement-problem-activity-7186023045742940160-FKBp", "content": "In The New York Times , Kevin Roose describes the current landscape of #AImeasurement and evaluation as “a tangle of sloppy tests, apples-to-oranges comparisons, and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark.”"}
+{"idx": 3, "title": "A . I . Has a Measurement Problem by Kevin Roose | Pacosite's Blog", "date": "", "ddg_snippet": "By Kevin Roose . Which A . I . system writes the best computer code or generates the most realistic image? Right now, there’s no easy way to answer those questions. Published: April 15, 2024 at 09:14AM.", "subpage_snippet": "", "source": "carmona.mx", "link": "https://carmona.mx/2024/04/15/a-i-has-a-measurement-problem-by-kevin-roose/", "content": "By Kevin Roose . Which A . I . system writes the best computer code or generates the most realistic image? Right now, there’s no easy way to answer those questions. Published: April 15, 2024 at 09:14AM."}
+{"idx": 4, "title": "Toward A Theory Of Kevin Roose | Defector", "date": "", "ddg_snippet": "“You can’t be a serious critic,” New York Times technology reporter Kevin Roose wrote on Tuesday, on Bluesky, about artificial intelligence, “if you’re in denial about how useful it is.”", "subpage_snippet": "", "source": "defector.com", "link": "https://defector.com/toward-a-theory-of-kevin-roose?ref=simplemagic.ca", "content": "“You can’t be a serious critic,” New York Times technology reporter Kevin Roose wrote on Tuesday, on Bluesky, about artificial intelligence, “if you’re in denial about how useful it is.”"}
+{"idx": 5, "title": "Blind Selection: The Struggle to Objectively Measure AI - Freedium", "date": "", "ddg_snippet": "As AI investor Nathan Benaich recently told Kevin Roose of The New York Times A . I . Has a Measurement Problem , The New York Times . Challenges in evaluating AI systems, Anthropic. Source: PTechPartners.com.", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/76b94202b5d8", "content": "As AI investor Nathan Benaich recently told Kevin Roose of The New York Times A . I . Has a Measurement Problem , The New York Times . Challenges in evaluating AI systems, Anthropic. Source: PTechPartners.com."}
+{"idx": 6, "title": "Techmeme: An interview with Eliezer Yudkowsky, one of the first...", "date": "", "ddg_snippet": "Kevin Roose / New York Times Securing AI models and agents in the enterprise — The new AI stack creates a highly dynamic attack surface, and traditional controls aren't keeping up.", "subpage_snippet": "", "source": "www.techmeme.com", "link": "https://www.techmeme.com/250914/p5", "content": "Kevin Roose / New York Times Securing AI models and agents in the enterprise — The new AI stack creates a highly dynamic attack surface, and traditional controls aren't keeping up."}
+{"idx": 7, "title": "How U.S. AI Labs Are Running Out of Data — And Why... | Medium", "date": "", "ddg_snippet": "Kevin Roose , technology columnist for The New York Times says “Now, that data is drying up.”The problem facing U.S. AI development is not only data scarcity, but also the lack of a coherent policy infrastructure to regulate how AI companies collect and use training data.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jrab0472/how-u-s-ai-labs-are-running-out-of-data-and-why-its-an-issue-for-everyone-416a25a74015", "content": "Kevin Roose , technology columnist for The New York Times says “Now, that data is drying up.”The problem facing U.S. AI development is not only data scarcity, but also the lack of a coherent policy infrastructure to regulate how AI companies collect and use training data."}
+{"idx": 8, "title": "Links from around the web: vibecoding, 60-hour work weeks, smaller...", "date": "", "ddg_snippet": "By Kevin Roose , New York Times , Feb 27, 2025. Kevin Roose , podcast co-host of Hard Fork, talks about his discovery of the joy of “vibecoding,” which refers to using natural English prompts with AI tools to build software.", "subpage_snippet": "", "source": "idratherbewriting.com", "link": "https://idratherbewriting.com/blog/links-vibecoding-60-hours-small-communities", "content": "By Kevin Roose , New York Times , Feb 27, 2025. Kevin Roose , podcast co-host of Hard Fork, talks about his discovery of the joy of “vibecoding,” which refers to using natural English prompts with AI tools to build software."}
+{"idx": 9, "title": "Creepy Microsoft Bing Chatbot Urges Tech Columnist... | HuffPost Life", "date": "", "ddg_snippet": "The AI chatbot \"Sydney\" declared it loved New York Times journalist Kevin Roose and that it wanted to be human. Kevin Roose was interacting with the artificial intelligence-powered chatbot called “Sydney” when it suddenly “declared, out of nowhere, that it loved me,” he wrote.", "subpage_snippet": "", "source": "www.huffpost.com", "link": "https://www.huffpost.com/entry/kevin-roose-ai-chatbot_l_63eeb367e4b0063ccb2bcc45", "content": "The AI chatbot \"Sydney\" declared it loved New York Times journalist Kevin Roose and that it wanted to be human. Kevin Roose was interacting with the artificial intelligence-powered chatbot called “Sydney” when it suddenly “declared, out of nowhere, that it loved me,” he wrote."}
diff --git a/data/sampled_jsons/KijslFbfOL_Incomplete_Multi-view_Clustering_HPQ_SPQ_methodology_hybrid-group_prototype_year_2023.jsonl b/data/sampled_jsons/KijslFbfOL_Incomplete_Multi-view_Clustering_HPQ_SPQ_methodology_hybrid-group_prototype_year_2023.jsonl
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index 0000000000000000000000000000000000000000..0689dcf140584ca063dae7d59a518ea4532f468a
--- /dev/null
+++ b/data/sampled_jsons/KijslFbfOL_Incomplete_Multi-view_Clustering_HPQ_SPQ_methodology_hybrid-group_prototype_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "S EFFECTIVE INCOMPLETE MULTI VIEW C - OpenReview", "date": "", "ddg_snippet": "S EFFECTIVE INCOMPLETE MULTI VIEW C : SIMILARITY LEVEL IMPUTATION AND H GROUP PROTOTYPE CONSTRUC SIMPLE YET EFFECTIVE INCOMPLETE MULTI-VIEW CLUSTERING : SIMILARITY-LEVEL IMPUTATION AND INTRA- VIEW HYBRID-GROUP PROTOTYPE CONSTRUC-", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KijslFbfOL", "content": "S EFFECTIVE INCOMPLETE MULTI VIEW C : SIMILARITY LEVEL IMPUTATION AND H GROUP PROTOTYPE CONSTRUC SIMPLE YET EFFECTIVE INCOMPLETE MULTI-VIEW CLUSTERING : SIMILARITY-LEVEL IMPUTATION AND INTRA- VIEW HYBRID-GROUP PROTOTYPE CONSTRUC-"}
+{"idx": 1, "title": "Incomplete Multi-view Clustering via Prototype-based ... - GitHub", "date": "", "ddg_snippet": "Incomplete Multi-view Clustering via Prototype -based Imputation (IJCAI2023) This repo contains the code and data of our IJCAI'2023 paper Incomplete Multi-view Clustering via Prototype -based Imputation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/XLearning-SCU/2023-IJCAI-ProImp", "content": "Incomplete Multi-view Clustering via Prototype -based Imputation (IJCAI2023) This repo contains the code and data of our IJCAI'2023 paper Incomplete Multi-view Clustering via Prototype -based Imputation."}
+{"idx": 2, "title": "PDF Incomplete Multi-view Clustering via Prototype-based Imputation - IJCAI", "date": "", "ddg_snippet": "To implement the prototype -based imputation, ones have to overcome the following two technical challenges, i.e., i) incorporating prototypes and samples to enhance the instance commonality, and ii) learning view -specific prototypes to pre-serve view versatility. To this end, we propose an incomplete multi-view clustering method based on a novel dual-stream model consisting of a dual attention ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2023/0435.pdf", "content": "To implement the prototype -based imputation, ones have to overcome the following two technical challenges, i.e., i) incorporating prototypes and samples to enhance the instance commonality, and ii) learning view -specific prototypes to pre-serve view versatility. To this end, we propose an incomplete multi-view clustering method based on a novel dual-stream model consisting of a dual attention ..."}
+{"idx": 3, "title": "[2303.15689] Deep Incomplete Multi-view Clustering with Cross-view ...", "date": "", "ddg_snippet": "The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views . However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC). Although several attempts have been proposed to address IMVC, they suffer from the following ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.15689", "content": "The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views . However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC). Although several attempts have been proposed to address IMVC, they suffer from the following ..."}
+{"idx": 4, "title": "PDF Deep Incomplete Multi-view Clustering with Cross-view Partial Sample ...", "date": "", "ddg_snippet": "Abstract The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views . However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC).", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Deep_Incomplete_Multi-View_Clustering_With_Cross-View_Partial_Sample_and_Prototype_CVPR_2023_paper.pdf", "content": "Abstract The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views . However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC)."}
+{"idx": 5, "title": "Incomplete multi-view clustering via structure exploration and missing ...", "date": "", "ddg_snippet": "Incomplete multi-view clustering (IMVC) aims to boost clustering performance by capturing complementary information from incomplete multi-views , where partial data samples in one or more views are missing.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253523004396", "content": "Incomplete multi-view clustering (IMVC) aims to boost clustering performance by capturing complementary information from incomplete multi-views , where partial data samples in one or more views are missing."}
+{"idx": 6, "title": "Simple yet Effective Incomplete Multi-view Clustering: Similarity-level ...", "date": "", "ddg_snippet": "Dive into the research topics of 'Simple yet Effective Incomplete Multi-view Clustering : Similarity-level Imputation and Intra- view Hybrid-group Prototype Construction'.", "subpage_snippet": "", "source": "scholars.hkbu.edu.hk", "link": "https://scholars.hkbu.edu.hk/en/publications/simple-yet-effective-incomplete-multi-view-clustering-similarity-", "content": "Dive into the research topics of 'Simple yet Effective Incomplete Multi-view Clustering : Similarity-level Imputation and Intra- view Hybrid-group Prototype Construction'."}
+{"idx": 7, "title": "PDF Simple yet Effective Incomplete Multi-view Clustering: Similarity-level ...", "date": "", "ddg_snippet": "Gather the information from other views at the similarity level to assist imputing the incomplete parts of similarity on each view . Associate a group of hybrid prototype quantities for each individual view so that it can flexibly exploit features according to the characteristics of each view .", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/30038.pdf", "content": "Gather the information from other views at the similarity level to assist imputing the incomplete parts of similarity on each view . Associate a group of hybrid prototype quantities for each individual view so that it can flexibly exploit features according to the characteristics of each view ."}
+{"idx": 8, "title": "Simple yet Effective Incomplete Multi-view Clustering:...", "date": "", "ddg_snippet": "Simple yet Effective Incomplete Multi-view Clustering : Similarity-level Imputation and Intra- view Hybrid-group Prototype Construction Shengju Yu, Zhibin Dong, Siwei Wang, Pei Zhang, Yi Zhang, Xinwang Liu, Naiyang Guan, Tiejun Li, Yiu-ming Cheung Published: 22 Jan 2025, Last Modified: 28 Feb 2025 ICLR 2025 Spotlight Everyone Revisions BibTeX CC ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KijslFbfOL", "content": "Simple yet Effective Incomplete Multi-view Clustering : Similarity-level Imputation and Intra- view Hybrid-group Prototype Construction Shengju Yu, Zhibin Dong, Siwei Wang, Pei Zhang, Yi Zhang, Xinwang Liu, Naiyang Guan, Tiejun Li, Yiu-ming Cheung Published: 22 Jan 2025, Last Modified: 28 Feb 2025 ICLR 2025 Spotlight Everyone Revisions BibTeX CC ..."}
+{"idx": 9, "title": "Incomplete Multi-view Clustering via Prototype-based Imputation", "date": "", "ddg_snippet": "In this paper, we study how to achieve two characteristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance commonality refers to that within-cluster instances should share a common pattern, and ii) view versatility refers to that cross- view samples should own view -specific patterns. To this end, we design a novel dual-stream model which employs a dual attention ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.11045", "content": "In this paper, we study how to achieve two characteristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance commonality refers to that within-cluster instances should share a common pattern, and ii) view versatility refers to that cross- view samples should own view -specific patterns. To this end, we design a novel dual-stream model which employs a dual attention ..."}
diff --git a/data/sampled_jsons/Kirilenko_2015_abstract_methodology_causal_pathways_climate_change_Twitter_year_2015.jsonl b/data/sampled_jsons/Kirilenko_2015_abstract_methodology_causal_pathways_climate_change_Twitter_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cdcc8813a87a5a9fe261664570f9c7849c226a1b
--- /dev/null
+++ b/data/sampled_jsons/Kirilenko_2015_abstract_methodology_causal_pathways_climate_change_Twitter_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Attention, sentiments and emotions towards emerging climate ...", "date": "", "ddg_snippet": "technologies address the cause of climate change by removing green Kirilenko , A.P., Molodtsova, T., Stepchenkova, S.O., 2015 . People as sensors: Mass media. and local temperature inuence climate change discussion on Twitter .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/375075516_Attention_sentiments_and_emotions_towards_emerging_climate_technologies_on_Twitter", "content": "technologies address the cause of climate change by removing green Kirilenko , A.P., Molodtsova, T., Stepchenkova, S.O., 2015 . People as sensors: Mass media. and local temperature inuence climate change discussion on Twitter ."}
+{"idx": 1, "title": "Attention, sentiments and emotions towards emerging climate ...", "date": "", "ddg_snippet": "The climate change Twitter dataset. Kirilenko , A.P., Molodtsova, T., Stepchenkova, S.O., 2015 . People as sensors: Mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "pure.iiasa.ac.at", "link": "https://pure.iiasa.ac.at/id/eprint/19384/1/1-s2.0-S0959378023001310-main+(1).pdf", "content": "The climate change Twitter dataset. Kirilenko , A.P., Molodtsova, T., Stepchenkova, S.O., 2015 . People as sensors: Mass media and local temperature influence climate change discussion on Twitter ."}
+{"idx": 2, "title": "How does climate", "date": "", "ddg_snippet": "Abstract . Did you know that climate change affects people’s mental health? Some people experience climate change anxiety. They have extreme worries and fears about the future because of climate change .", "subpage_snippet": "", "source": "www.sciencejournalforkids.org", "link": "https://www.sciencejournalforkids.org/wp-content/uploads/2025/09/eco-anxiety_article.pdf", "content": "Abstract . Did you know that climate change affects people’s mental health? Some people experience climate change anxiety. They have extreme worries and fears about the future because of climate change ."}
+{"idx": 3, "title": "How Does Littering Affect Climate Change ? - Thinking Sustainably", "date": "", "ddg_snippet": "One way you can do this is by eliminating your use of single-use plastics and by recycling all of the materials that you can. In this article, we will discuss how littering can affect our planet and what you can do to lessen your carbon footprint. Does litter contribute to Climate Change ? Yes!", "subpage_snippet": "", "source": "www.thinkingsustainably.com", "link": "https://www.thinkingsustainably.com/littering-affect-climate-change/", "content": "One way you can do this is by eliminating your use of single-use plastics and by recycling all of the materials that you can. In this article, we will discuss how littering can affect our planet and what you can do to lessen your carbon footprint. Does litter contribute to Climate Change ? Yes!"}
+{"idx": 4, "title": "Contrarian views on Climate Change", "date": "", "ddg_snippet": "explanations for climate change , others punch holes in the research methodology of conventional climate change thinking. The bedrock of much of the IPCC’s analysis depends on computer models to forecast future climate conditions.", "subpage_snippet": "", "source": "www.iaea.org", "link": "https://www.iaea.org/sites/default/files/publications/magazines/bulletin/bull49-2/49207112021.pdf", "content": "explanations for climate change , others punch holes in the research methodology of conventional climate change thinking. The bedrock of much of the IPCC’s analysis depends on computer models to forecast future climate conditions."}
+{"idx": 5, "title": "Is climate change making disasters worse? | World Wildlife Fund", "date": "", "ddg_snippet": "Is climate change increasing the risk of disasters? Facts about floods, fires, hurricanes, and tornadoes.", "subpage_snippet": "", "source": "www.worldwildlife.org", "link": "https://www.worldwildlife.org/stories/is-climate-change-increasing-the-risk-of-disasters", "content": "Is climate change increasing the risk of disasters? Facts about floods, fires, hurricanes, and tornadoes."}
+{"idx": 6, "title": "SCI - Blog: Why are we ignoring climate change ?", "date": "", "ddg_snippet": "Why do we ignore climate change and what can we do about it? That’s what Toby Park, of The Behavioural Insights Team, explained in our latest SCItalk.", "subpage_snippet": "", "source": "www.soci.org", "link": "https://www.soci.org/blog/2022/3/why-are-we-ignoring-climate-change", "content": "Why do we ignore climate change and what can we do about it? That’s what Toby Park, of The Behavioural Insights Team, explained in our latest SCItalk."}
+{"idx": 7, "title": "major causes of climate change | GlobalEcoGuy.org", "date": "", "ddg_snippet": "Climate change is caused by several key factors, which are outlined here. Greenhouse gases.", "subpage_snippet": "", "source": "globalecoguy.org", "link": "https://globalecoguy.org/the-three-most-important-graphs-in-climate-change-e64d3f4ed76", "content": "Climate change is caused by several key factors, which are outlined here. Greenhouse gases."}
+{"idx": 8, "title": "Opinion | So You Want to Convince a Climate Change Skeptic - The...", "date": "", "ddg_snippet": "Because climate change perceptions in children seem less susceptible to the influence of worldview or political context, it may be possible for them to inspire adults toward higher levels of climate concern, and in turn, collective action.", "subpage_snippet": "", "source": "www.nytimes.com", "link": "https://www.nytimes.com/2020/01/02/opinion/climate-change-deniers.html", "content": "Because climate change perceptions in children seem less susceptible to the influence of worldview or political context, it may be possible for them to inspire adults toward higher levels of climate concern, and in turn, collective action."}
+{"idx": 9, "title": "Hasn't Earth warmed and cooled naturally... | NOAA Climate .gov", "date": "", "ddg_snippet": "Earth has warmed in the past due to changes in the Sun, volcanic eruptions, and naturally occurring increases in greenhouse gases. Our ability to understand and explain past changes is one reason we are confident that recent changes are due to humans.", "subpage_snippet": "", "source": "www.climate.gov", "link": "https://www.climate.gov/news-features/climate-qa/hasnt-earth-warmed-and-cooled-naturally-throughout-history", "content": "Earth has warmed in the past due to changes in the Sun, volcanic eruptions, and naturally occurring increases in greenhouse gases. Our ability to understand and explain past changes is one reason we are confident that recent changes are due to humans."}
diff --git a/data/sampled_jsons/Kirilenko_Molodtsova_Stepchenkova_2015_abstract_Global_Environmental_Change_temperature_media_Twitte.jsonl b/data/sampled_jsons/Kirilenko_Molodtsova_Stepchenkova_2015_abstract_Global_Environmental_Change_temperature_media_Twitte.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..74f9da7ffe6488690607e3b743c21d5a7956ce70
--- /dev/null
+++ b/data/sampled_jsons/Kirilenko_Molodtsova_Stepchenkova_2015_abstract_Global_Environmental_Change_temperature_media_Twitte.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect- ...", "date": "", "ddg_snippet": "by AP Kirilenko · 2015 · Cited by 241 — Remarkably, we found only a handful of climate-related publications that used Twitter to. A.P. Kirilenko et al. / Global Environmental Change 30 ( 2015 ) 92–100. 9 pages", "subpage_snippet": "", "source": "research.fit.edu", "link": "https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/climate-communications/youth-climate-amp-social-media/Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect-CC-Discussion-On-Twitter.pdf", "content": "by AP Kirilenko · 2015 · Cited by 241 — Remarkably, we found only a handful of climate-related publications that used Twitter to. A.P. Kirilenko et al. / Global Environmental Change 30 ( 2015 ) 92–100. 9 pages"}
+{"idx": 1, "title": "Mass media and local temperature influence climate ...", "date": "", "ddg_snippet": "by AP Kirilenko · 2015 · Cited by 242 — We used the volume of Twitter messages containing words “ climate change ” and “ global warming ” as the indicator of attention that public pays to the issue.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0959378014001952", "content": "by AP Kirilenko · 2015 · Cited by 242 — We used the volume of Twitter messages containing words “ climate change ” and “ global warming ” as the indicator of attention that public pays to the issue."}
+{"idx": 2, "title": "twitter and climate change", "date": "", "ddg_snippet": "by JR Fownes · 2018 · Cited by 126 — Tweets about climate change also follow hot and cold local temperatures , wildfire, heavy snow, hail, strong wind, flooding, and drought ( Kirilenko et al. 2015 ; ...", "subpage_snippet": "", "source": "compass.onlinelibrary.wiley.com", "link": "https://compass.onlinelibrary.wiley.com/doi/am-pdf/10.1111/soc4.12587", "content": "by JR Fownes · 2018 · Cited by 126 — Tweets about climate change also follow hot and cold local temperatures , wildfire, heavy snow, hail, strong wind, flooding, and drought ( Kirilenko et al. 2015 ; ..."}
+{"idx": 3, "title": "Effects of Social Media Use on Climate Change Opinion, ...", "date": "", "ddg_snippet": "29 Mar 2017 — Indeed, one study that examined tweets in the United States provides evidence that people mention temperature anomalies and climate change ...", "subpage_snippet": "", "source": "oxfordre.com", "link": "https://oxfordre.com/climatescience/display/10.1093/acrefore/9780190228620.001.0001/acrefore-9780190228620-e-369?p=emailAizOKgM2zA30k&d=/10.1093/acrefore/9780190228620.001.0001/acrefore-9780190228620-e-369", "content": "29 Mar 2017 — Indeed, one study that examined tweets in the United States provides evidence that people mention temperature anomalies and climate change ..."}
+{"idx": 4, "title": "Public microblogging on climate change: One year of ...", "date": "", "ddg_snippet": "by AP Kirilenko · 2014 · Cited by 360 — Network analysis reveals open forums and echo chambers in social media discussions of climate change . 2015 , Global Environmental Change . Show abstract . Action ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0959378014000375", "content": "by AP Kirilenko · 2014 · Cited by 360 — Network analysis reveals open forums and echo chambers in social media discussions of climate change . 2015 , Global Environmental Change . Show abstract . Action ..."}
+{"idx": 5, "title": "Climate Change Sentiment on Twitter: An Unsolicited ...", "date": "", "ddg_snippet": "by EM Cody · 2015 · Cited by 401 — Kirilenko AP, Molodtsova T, Stepchenkova SO. People as sensors: Mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4546368/", "content": "by EM Cody · 2015 · Cited by 401 — Kirilenko AP, Molodtsova T, Stepchenkova SO. People as sensors: Mass media and local temperature influence climate change discussion on Twitter ."}
+{"idx": 6, "title": "Extreme weather and climate change: social media results ...", "date": "", "ddg_snippet": "by P Berglez · 2021 · Cited by 56 — Kirilenko , A. P., Molodtsova , T., & Stepchenkova , S. O. ( 2015 ). People as sensors: Mass media and local temperature influence climate change discussion.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/17477891.2020.1829532", "content": "by P Berglez · 2021 · Cited by 56 — Kirilenko , A. P., Molodtsova , T., & Stepchenkova , S. O. ( 2015 ). People as sensors: Mass media and local temperature influence climate change discussion."}
+{"idx": 7, "title": "Towards Fine-grained Classification of Climate Change ...", "date": "", "ddg_snippet": "by R Vaid · 2022 · Cited by 31 — Kirilenko , T. Molodtsova , and S. Stepchenkova . 2014. People as sensors: Mass media and local tempera- ture influence climate change discussion ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.acl-srw.35.pdf", "content": "by R Vaid · 2022 · Cited by 31 — Kirilenko , T. Molodtsova , and S. Stepchenkova . 2014. People as sensors: Mass media and local tempera- ture influence climate change discussion ..."}
+{"idx": 8, "title": "Exploring climate change on Twitter using seven aspects", "date": "", "ddg_snippet": "by D Effrosynidis · 2022 · Cited by 52 — Kirilenko AP, Molodtsova T, Stepchenkova SO. People as sensors: Mass media and local temperature influence climate change discussion on Twitter.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9491544/", "content": "by D Effrosynidis · 2022 · Cited by 52 — Kirilenko AP, Molodtsova T, Stepchenkova SO. People as sensors: Mass media and local temperature influence climate change discussion on Twitter."}
+{"idx": 9, "title": "Tracing the Flow of Climate Change Frames: Intermedia ...", "date": "", "ddg_snippet": "7 Feb 2024 — This study integrates agenda setting and framing theory to explore the transmission of climate change frames between newspapers and Twitter ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/27523543231218296?int.sj-full-text.similar-articles.6", "content": "7 Feb 2024 — This study integrates agenda setting and framing theory to explore the transmission of climate change frames between newspapers and Twitter ..."}
diff --git a/data/sampled_jsons/Kwon_et_al._Tor_circuit_fingerprinting_attack_abstract.jsonl b/data/sampled_jsons/Kwon_et_al._Tor_circuit_fingerprinting_attack_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6d32a98a0ce7d5c6fb0e6c390ff74859c7ee2ddc
--- /dev/null
+++ b/data/sampled_jsons/Kwon_et_al._Tor_circuit_fingerprinting_attack_abstract.jsonl
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+{"idx": 0, "title": "RV Generator Manuals - Cummins Inc.", "date": "", "ddg_snippet": "You can find copies of some of our most popular RV generator manuals below, free and downloadable. Onan Generator Quick Start Guide. Other manuals : Cummins Commercial Mobile generator manuals . EC-AGS+ manual . Generator warranty information.", "subpage_snippet": "", "source": "www.cummins.com", "link": "https://www.cummins.com/generators/rv-generators/rv-generator-manuals", "content": "You can find copies of some of our most popular RV generator manuals below, free and downloadable. Onan Generator Quick Start Guide. Other manuals : Cummins Commercial Mobile generator manuals . EC-AGS+ manual . Generator warranty information."}
+{"idx": 1, "title": "Operator Manual - Electric Generators Direct", "date": "", "ddg_snippet": "This manual covers the operation and maintenance of the HGJAA, HGJAB and HGJAC Series of gener - ator sets (gensets). Each operator should study this manual carefully and observe all of its instructions and safety precautions.", "subpage_snippet": "", "source": "www.electricgeneratorsdirect.com", "link": "https://www.electricgeneratorsdirect.com/manuals/RVQG5500_Man.pdf", "content": "This manual covers the operation and maintenance of the HGJAA, HGJAB and HGJAC Series of gener - ator sets (gensets). Each operator should study this manual carefully and observe all of its instructions and safety precautions."}
+{"idx": 2, "title": "5410860 RV Generator Handbook (F-1123)", "date": "", "ddg_snippet": "A Cummins RV generator is an investment that can bring you many years of comfort and enjoyment However, all generators require periodic maintenance for dependable, ongoing performance Your operator’s manual outlines the specific maintenance procedures and service intervals for your particular model Take good care of your generator and it will ...", "subpage_snippet": "", "source": "primetimerv.com", "link": "https://primetimerv.com/files/component-manuals//Cummins+Onan+RV+Generator+Handbook.pdf", "content": "A Cummins RV generator is an investment that can bring you many years of comfort and enjoyment However, all generators require periodic maintenance for dependable, ongoing performance Your operator’s manual outlines the specific maintenance procedures and service intervals for your particular model Take good care of your generator and it will ..."}
+{"idx": 3, "title": "Cummins Onan QG 5500 Generator Manual - OManuals.com", "date": "", "ddg_snippet": "This Onan 5.5 kW generator is Gasoline Powered. It can run two 13,500 BTU AC units with 1100 additional watts available. It has a Cummins 4 Cycle OHV VTwin Engine and is microprocessor controlled. Learn more about this cummins generator in the Onan manual . You are viewing the manual and parts listings for the Cummins Onan QG 5500 Generator .", "subpage_snippet": "", "source": "www.omanuals.com", "link": "https://www.omanuals.com/cummins-onan-qg-5500-generator--manual.html", "content": "This Onan 5.5 kW generator is Gasoline Powered. It can run two 13,500 BTU AC units with 1100 additional watts available. It has a Cummins 4 Cycle OHV VTwin Engine and is microprocessor controlled. Learn more about this cummins generator in the Onan manual . You are viewing the manual and parts listings for the Cummins Onan QG 5500 Generator ."}
+{"idx": 4, "title": "Cummins RV Generator Service and Parts | Onan Generators", "date": "", "ddg_snippet": "Whether you’re changing the oil yourself or need to check the specs on your generator , there are digital manuals and spec sheets available for quick and easy download.", "subpage_snippet": "", "source": "www.cummins.com", "link": "https://www.cummins.com/generators/rv-generators/parts-and-maintenance", "content": "Whether you’re changing the oil yourself or need to check the specs on your generator , there are digital manuals and spec sheets available for quick and easy download."}
+{"idx": 5, "title": "5410860 RV Generator Handbook (F-1123) - Forest River Inc.", "date": "", "ddg_snippet": "A Cummins RV generator is an investment that can bring you many years of comfort and enjoyment However, all generators require periodic maintenance for dependable, ongoing performance Your operator’s manual outlines the specific maintenance procedures and service intervals for your particular model Take good care of your generator, and it ...", "subpage_snippet": "", "source": "forestriverinc.com", "link": "https://forestriverinc.com/files/Component-Manuals/Generator/Onan+-+Generator+Handbook+(2019).pdf", "content": "A Cummins RV generator is an investment that can bring you many years of comfort and enjoyment However, all generators require periodic maintenance for dependable, ongoing performance Your operator’s manual outlines the specific maintenance procedures and service intervals for your particular model Take good care of your generator, and it ..."}
+{"idx": 6, "title": "Cummins Onan RV QG5500 EVAP - 5.5HGJAB-7103 - 5.5kW RV Generator ...", "date": "", "ddg_snippet": "Keep this manual and the Installation Manual with the other vehicle manuals . Operation, Periodic Maintenance and Troubleshooting provide the instructions necessary for operating the genset and maintaining it at top performance.", "subpage_snippet": "", "source": "www.manualshelf.com", "link": "https://www.manualshelf.com/manual/cummins-onan/a063b870/product-manual-english.html", "content": "Keep this manual and the Installation Manual with the other vehicle manuals . Operation, Periodic Maintenance and Troubleshooting provide the instructions necessary for operating the genset and maintaining it at top performance."}
+{"idx": 7, "title": "Quiet GasolineTM Series RV QG 5500 - Electric Generators Direct", "date": "", "ddg_snippet": "RV QG 5500 Features and benefits Quietest in its class. Exceptionally low vibration levels. Integrated design with enclosed muffler for easy installation.", "subpage_snippet": "", "source": "www.electricgeneratorsdirect.com", "link": "https://www.electricgeneratorsdirect.com/manuals/CO_RVQG5500_EVAP_Spec.pdf", "content": "RV QG 5500 Features and benefits Quietest in its class. Exceptionally low vibration levels. Integrated design with enclosed muffler for easy installation."}
+{"idx": 8, "title": "Onan RV QG 5500 Service Manual - Download - gretchenwcook.com", "date": "", "ddg_snippet": "Nov 18, 2024 · Welcome to the Onan RV QG 5500 Service Manual , your comprehensive guide for maintaining, troubleshooting, and repairing your generator. This manual provides detailed instructions for operation, installation, and safety procedures, ensuring optimal performance and longevity of your RV generator.", "subpage_snippet": "", "source": "gretchenwcook.com", "link": "https://gretchenwcook.com/onan-rv-qg-5500-service-manual/", "content": "Nov 18, 2024 · Welcome to the Onan RV QG 5500 Service Manual , your comprehensive guide for maintaining, troubleshooting, and repairing your generator. This manual provides detailed instructions for operation, installation, and safety procedures, ensuring optimal performance and longevity of your RV generator."}
+{"idx": 9, "title": "Cummins Onan QG5500 EFI HGJAA A-E User Manual PDF | Manualsnet", "date": "", "ddg_snippet": "Download Cummins Onan QG5500 EFI HGJAA A-E User Manual or view PDF for FREE. Find the help you need with user manuals and owners instruction guides.", "subpage_snippet": "", "source": "manualsnet.com", "link": "https://manualsnet.com/cummins/onan-qg5500-efi-hgjaa-a-e", "content": "Download Cummins Onan QG5500 EFI HGJAA A-E User Manual or view PDF for FREE. Find the help you need with user manuals and owners instruction guides."}
diff --git a/data/sampled_jsons/Kwon_et_al._Tor_circuit_fingerprinting_attacks_abstract.jsonl b/data/sampled_jsons/Kwon_et_al._Tor_circuit_fingerprinting_attacks_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..10d9556679c20b30d3c75f366c92a22ef527405d
--- /dev/null
+++ b/data/sampled_jsons/Kwon_et_al._Tor_circuit_fingerprinting_attacks_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Circuit Fingerprinting Attacks: Passive Deanonymization of ...", "date": "", "ddg_snippet": "In this paper, we present the first practical passive attack against hidden services and their users called circuit fingerprinting attack . Using our attack , an at-tacker can identify the presence of (client or server) hid-den service activity in the network with high accuracy.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/conference/usenixsecurity15/sec15-paper-kwon.pdf", "content": "In this paper, we present the first practical passive attack against hidden services and their users called circuit fingerprinting attack . Using our attack , an at-tacker can identify the presence of (client or server) hid-den service activity in the network with high accuracy."}
+{"idx": 1, "title": "Discovering onion services through circuit fingerprinting attacks Poster: Fingerprinting Hidden Service Circuits from a Tor ... arXiv:2103.03831v2 [cs.CR] 11 Jan 2022 Circuit Fingerprinting Attacks: Passive Deanonymization of ... Discovering onion services through circuit fingerprinting attacks Discovering onion services through circuit fingerprinting attacks Discovering onion services through circuit fingerprinting attacks Discovering onion services through circuit fingerprinting attacks Discovering onion services through circuit fingerprinting attacks Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Circuit Fingerprinting Attacks: Passive Deanonymization of ...", "date": "", "ddg_snippet": "Mar 1, 2023 · Circuit fingerprinting attack is a traffic analysis attack against Tor which break or reduce the anonymity that Tor aims to provide. Kwon et al . [1] discovered the fingerprint features of circuits and used the features to first propose a circuit fingerprinting attack . To measure the popularity of onion services, Jansen et al . [2] proposes a circuit fingerprinting attack that can be implemented ... Abstract — Kwon et al . recently showed that circuit fingerprint-ing attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online. Contributions In this paper, we study the circuit finger-printing problem, isolating and formalizing it on its own, separately from other fingerprinting problems. We start by presenting our threat model (§2) and experimental method-ology (§3). We then revisit and verify previous results by Kwon et al . [17] (§4) to show that distinguishing onion ser-vice circuits is possible with high ... Our key insight is that during the circuit con-struction and communication phase between a client and a hidden service, Tor exhibits fingerprintable traffic pat-terns that allow an adversary to efficiently and accurately identify, and correlate circuits involved in the communi-cation with hidden services. What is Circuit Fingerprinting Attack? Circuit fingerprinting attack is a approach to classify and discover specific Tor circuits through Tor circuit fingerprints on the onion router. Kwon et al . were the first to find that Tor circuits presents different characteristics in construction sequence, duration of activity, and proposed a circuit fingerprinting attack. Why is Kwon's Circuit Fingerprinting Attack worse than Tor? Since Tor uses Client-side introduction and rendezvous circuit hiding machines on all client-side circuits, the performance of Kwon ’s circuit fingerprinting attack with circuit construction sequence feature becomes worse. Table 4. Results for circuit fingerprintings. 6. Discussion Are onion services vulnerable to Circuit Fingerprinting attacks? This would cause the network addresses of onion services to be easily exposed to malicious entry relays using circuit fingerprinting attacks . Compared to the IP addresses of clients, the IP addresses of onion services does not change frequently. How accurate is Circuit Fingerprinting compared to Kwon's Fingerprinting Attack? Table 4 shows the accuracy, precision, recall and F1 results. Our circuit fingerprinting attains 99.96% accuracy , 99.97% precision and 99.97% recall, which is better than Kwon’s circuit fingerprinting attack. Can a dummy cell protect against a Circuit Fingerprinting Attack? But an adversary could use a circuit fingerprinting attack to classify circuit types and discovers the network address of the onion service. Recently, Tor has used padding defenses to inject dummy cells to protect against circuit fingerprinting attacks . But we found that circuits still expose much information to the adversary. Can a op-to-or TCP connection tell if a circuit is active? An observer watching the OP-to-OR TCP connection should not be able to tell apart which TCP segment belongs to which circuit (unless only one circuit is active). However, an entry guard is able to dif-ferentiate the traffic of different circuits (though the con-tents of the cells are encrypted). A novel circuit fingerprinting attack is presented, which divides the circuit into the circuitgenerated by the client and the circuit generated by the onion service, and achieves highly accurate circuits fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2667295222000514", "content": "Mar 1, 2023 · Circuit fingerprinting attack is a traffic analysis attack against Tor which break or reduce the anonymity that Tor aims to provide. Kwon et al . [1] discovered the fingerprint features of circuits and used the features to first propose a circuit fingerprinting attack . To measure the popularity of onion services, Jansen et al . [2] proposes a circuit fingerprinting attack that can be implemented ... Abstract — Kwon et al . recently showed that circuit fingerprint-ing attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online. Contributions In this paper, we study the circuit finger-printing problem, isolating and formalizing it on its own, separately from other fingerprinting problems. We start by presenting our threat model (§2) and experimental method-ology (§3). We then revisit and verify previous results by Kwon et al . [17] (§4) to show that distinguishing onion ser-vice circuits is possible with high ... Our key insight is that during the circuit con-struction and communication phase between a client and a hidden service, Tor exhibits fingerprintable traffic pat-terns that allow an adversary to efficiently and accurately identify, and correlate circuits involved in the communi-cation with hidden services. What is Circuit Fingerprinting Attack? Circuit fingerprinting attack is a approach to classify and discover specific Tor circuits through Tor circuit fingerprints on the onion router. Kwon et al . were the first to find that Tor circuits presents different characteristics in construction sequence, duration of activity, and proposed a circuit fingerprinting attack. Why is Kwon's Circuit Fingerprinting Attack worse than Tor? Since Tor uses Client-side introduction and rendezvous circuit hiding machines on all client-side circuits, the performance of Kwon ’s circuit fingerprinting attack with circuit construction sequence feature becomes worse. Table 4. Results for circuit fingerprintings. 6. Discussion Are onion services vulnerable to Circuit Fingerprinting attacks? This would cause the network addresses of onion services to be easily exposed to malicious entry relays using circuit fingerprinting attacks . Compared to the IP addresses of clients, the IP addresses of onion services does not change frequently. How accurate is Circuit Fingerprinting compared to Kwon's Fingerprinting Attack? Table 4 shows the accuracy, precision, recall and F1 results. Our circuit fingerprinting attains 99.96% accuracy , 99.97% precision and 99.97% recall, which is better than Kwon’s circuit fingerprinting attack. Can a dummy cell protect against a Circuit Fingerprinting Attack? But an adversary could use a circuit fingerprinting attack to classify circuit types and discovers the network address of the onion service. Recently, Tor has used padding defenses to inject dummy cells to protect against circuit fingerprinting attacks . But we found that circuits still expose much information to the adversary. Can a op-to-or TCP connection tell if a circuit is active? An observer watching the OP-to-OR TCP connection should not be able to tell apart which TCP segment belongs to which circuit (unless only one circuit is active). However, an entry guard is able to dif-ferentiate the traffic of different circuits (though the con-tents of the cells are encrypted). A novel circuit fingerprinting attack is presented, which divides the circuit into the circuitgenerated by the client and the circuit generated by the onion service, and achieves highly accurate circuits fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor ."}
+{"idx": 2, "title": "Circuit fingerprinting attacks | Proceedings of the 24th ...", "date": "", "ddg_snippet": "Abstract This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively. In particular, we show that the circuits, paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit . We propose two attacks , under ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2831143.2831162", "content": "Abstract This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively. In particular, we show that the circuits, paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit . We propose two attacks , under ..."}
+{"idx": 3, "title": "Poster: Fingerprinting Hidden Service Circuits from a Tor ...", "date": "", "ddg_snippet": "Abstract — Kwon et al . recently showed that circuit fingerprint-ing attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online.", "subpage_snippet": "", "source": "www.ieee-security.org", "link": "https://www.ieee-security.org/TC/SP2017/poster-abstracts/IEEE-SP17_Posters_paper_36.pdf", "content": "Abstract — Kwon et al . recently showed that circuit fingerprint-ing attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online."}
+{"idx": 4, "title": "Circuit Fingerprinting Attacks: Passive Deanonymization of ...", "date": "", "ddg_snippet": "Our key insight is that during the circuit con-struction and communication phase between a client and a hidden service, Tor exhibits fingerprintable traffic pat-terns that allow an adversary to efficiently and accurately identify, and correlate circuits involved in the communi-cation with hidden services.", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/devadas/pubs/circuit_finger.pdf", "content": "Our key insight is that during the circuit con-struction and communication phase between a client and a hidden service, Tor exhibits fingerprintable traffic pat-terns that allow an adversary to efficiently and accurately identify, and correlate circuits involved in the communi-cation with hidden services."}
+{"idx": 5, "title": "Circuit Fingerprinting Attacks: Passive Deanonymization of ...", "date": "", "ddg_snippet": "A novel circuit fingerprinting attack is presented, which divides the circuit into the circuitgenerated by the client and the circuit generated by the onion service, and achieves highly accurate circuits fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Circuit-Fingerprinting-Attacks:-Passive-of-Tor-Kwon-Alsabah/fb4cb1f1c57ee56e9a014570debf7d3d6871ddf3/figure/15", "content": "A novel circuit fingerprinting attack is presented, which divides the circuit into the circuitgenerated by the client and the circuit generated by the onion service, and achieves highly accurate circuits fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor ."}
+{"idx": 6, "title": "Passive Deanonymization of Tor Hidden Services", "date": "", "ddg_snippet": "by A Kwon · 2015 · Cited by 243 — Circuit Fingerprinting Attacks: Passive Deanonymization of Tor Hidden Services . Authors: Albert Kwon, Massachusetts Institute of Technology; Mashael AlSabah ...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/kwon", "content": "by A Kwon · 2015 · Cited by 243 — Circuit Fingerprinting Attacks: Passive Deanonymization of Tor Hidden Services . Authors: Albert Kwon, Massachusetts Institute of Technology; Mashael AlSabah ..."}
+{"idx": 7, "title": "Analysis of Fingerprinting Techniques for Tor Hidden ...", "date": "", "ddg_snippet": "by A Panchenko · 2017 · Cited by 52 — ABSTRACT . The website fingerprinting attack aims to infer the content of en- crypted and anonymized connections by analyzing traffic ...", "subpage_snippet": "", "source": "www.freehaven.net", "link": "https://www.freehaven.net/anonbib/cache/fingerprinting-wpes17.pdf", "content": "by A Panchenko · 2017 · Cited by 52 — ABSTRACT . The website fingerprinting attack aims to infer the content of en- crypted and anonymized connections by analyzing traffic ..."}
+{"idx": 8, "title": "Fingerprinting Hidden Service Circuits from a Tor Middle ...", "date": "", "ddg_snippet": "5 Aug 2025 — Abstract. Kwon et al. recently showed that circuit fingerprinting attacks could be used to identify hidden service circuits , which is a key step ...", "subpage_snippet": "", "source": "www.robgjansen.com", "link": "https://www.robgjansen.com/publications/middlefp-s&p2017.html", "content": "5 Aug 2025 — Abstract. Kwon et al. recently showed that circuit fingerprinting attacks could be used to identify hidden service circuits , which is a key step ..."}
+{"idx": 9, "title": "Flow Correlation Attacks on Tor Onion Service Sessions ...", "date": "", "ddg_snippet": "by D Lopes — Circuit Fingerprinting for Tor Onion Service Traffic . Kwon et al. [44] introduced the first approach to tackle the base rate fallacy problem in the analysis ... 20 pages", "subpage_snippet": "", "source": "www.ndss-symposium.org", "link": "https://www.ndss-symposium.org/wp-content/uploads/2024-337-paper.pdf", "content": "by D Lopes — Circuit Fingerprinting for Tor Onion Service Traffic . Kwon et al. [44] introduced the first approach to tackle the base rate fallacy problem in the analysis ... 20 pages"}
diff --git a/data/sampled_jsons/LAUREL-40_Table_7_percentage_improvements_10_tasks_average_year_2024.jsonl b/data/sampled_jsons/LAUREL-40_Table_7_percentage_improvements_10_tasks_average_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..953f293b90739b606193f15d536bbc8029adc085
--- /dev/null
+++ b/data/sampled_jsons/LAUREL-40_Table_7_percentage_improvements_10_tasks_average_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Laurus nobilis - Wikipedia", "date": "", "ddg_snippet": "The laurel is an evergreen shrub or small tree, variable in size and sometimes reaching 7–18 m (23–59 ft) tall. [5] The genus Laurus includes three accepted species, [7] whose diagnostic key characters often overlap.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Laurus_nobilis", "content": "The laurel is an evergreen shrub or small tree, variable in size and sometimes reaching 7–18 m (23–59 ft) tall. [5] The genus Laurus includes three accepted species, [7] whose diagnostic key characters often overlap."}
+{"idx": 1, "title": "Laurel - Wikipedia", "date": "", "ddg_snippet": "Laurel and Yanny, an acoustic illusion that went viral on social media in 2018.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Laurel", "content": "Laurel and Yanny, an acoustic illusion that went viral on social media in 2018."}
+{"idx": 2, "title": "Lauraceae - Wikipedia", "date": "", "ddg_snippet": "Lauraceae, or the laurels , is a plant family that includes the true laurel and its closest relatives. This family comprises about 2850 known species in about 45 genera worldwide. [4]", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Lauraceae", "content": "Lauraceae, or the laurels , is a plant family that includes the true laurel and its closest relatives. This family comprises about 2850 known species in about 45 genera worldwide. [4]"}
+{"idx": 3, "title": "Prunus laurocerasus - Wikipedia", "date": "", "ddg_snippet": "Prunus laurocerasus, also known as cherry laurel , common laurel and sometimes English laurel in North America, is an evergreen species of cherry (Prunus), native to regions bordering the Black Sea in southwestern Asia and southeastern Europe, from Albania and Bulgaria east through Turkey to the Caucasus Mountains and northern Iran. [2][3]", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Prunus_laurocerasus", "content": "Prunus laurocerasus, also known as cherry laurel , common laurel and sometimes English laurel in North America, is an evergreen species of cherry (Prunus), native to regions bordering the Black Sea in southwestern Asia and southeastern Europe, from Albania and Bulgaria east through Turkey to the Caucasus Mountains and northern Iran. [2][3]"}
+{"idx": 4, "title": "Laurel : Types, Benefits, Challenges, and Uses - Gardenia", "date": "", "ddg_snippet": "From the hardy Mountain Laurel to the evergreen Cherry Laurel, explore popular varieties, benefits, challenges, and best uses!", "subpage_snippet": "", "source": "www.gardenia.net", "link": "https://www.gardenia.net/guide/laurel-types-benefits-challenges-uses", "content": "From the hardy Mountain Laurel to the evergreen Cherry Laurel, explore popular varieties, benefits, challenges, and best uses!"}
+{"idx": 5, "title": "LAUREL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": ": an evergreen shrub or tree (Laurus nobilis of the family Lauraceae, the laurel family) of southern Europe with small yellow flowers, fruits that are ovoid blackish berries, and evergreen foliage once used by the ancient Greeks to crown victors in the Pythian games", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/laurel", "content": ": an evergreen shrub or tree (Laurus nobilis of the family Lauraceae, the laurel family) of southern Europe with small yellow flowers, fruits that are ovoid blackish berries, and evergreen foliage once used by the ancient Greeks to crown victors in the Pythian games"}
+{"idx": 6, "title": "9 Types of Laurel Bushes - The Gardening Fix", "date": "", "ddg_snippet": "Interestingly, there are several varieties of Laurel belonging to their own botanical families and here I have listed 9 popular Laurel bushes along with their attributes and care requirements so that you can decide which particular type is the perfect choice for you.", "subpage_snippet": "", "source": "thegardeningfix.com", "link": "https://thegardeningfix.com/types-of-laurel-bushes/", "content": "Interestingly, there are several varieties of Laurel belonging to their own botanical families and here I have listed 9 popular Laurel bushes along with their attributes and care requirements so that you can decide which particular type is the perfect choice for you."}
+{"idx": 7, "title": "Laurel | Evergreen, Aromatic, Mediterranean | Britannica", "date": "", "ddg_snippet": "Bay laurel , (Laurus nobilis), fragrant evergreen species of the family Lauraceae, the source of the cooking herb bay leaf. Bay laurel is native to the Mediterranean region but now widely cultivated in other regions of the world. In ancient Greece the wreath of honour placed upon the heads of heroes", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/plant/laurel-plant-Laurus-genus", "content": "Bay laurel , (Laurus nobilis), fragrant evergreen species of the family Lauraceae, the source of the cooking herb bay leaf. Bay laurel is native to the Mediterranean region but now widely cultivated in other regions of the world. In ancient Greece the wreath of honour placed upon the heads of heroes"}
+{"idx": 8, "title": "Home - Laurel Ag & Water", "date": "", "ddg_snippet": "Choosing to build better, more efficient ways to move water will give you more confidence in your ability to achieve higher yields, increased return on investment and a better future. We serve from Northern California down to Yuma, Arizona, so you have capacity to grow while keeping your irrigation partner by your side.", "subpage_snippet": "", "source": "laurel-ag.com", "link": "https://laurel-ag.com/", "content": "Choosing to build better, more efficient ways to move water will give you more confidence in your ability to achieve higher yields, increased return on investment and a better future. We serve from Northern California down to Yuma, Arizona, so you have capacity to grow while keeping your irrigation partner by your side."}
+{"idx": 9, "title": "story - LAUREL -online", "date": "", "ddg_snippet": "storyLaurèl is deeply influenced by the German philosophy of \"aesthetic of order.\" The brand adheres to the design philosophy of \"Simplicity Power, Rational Balance.\" Laurèl embraces warm and bright joyful colors as its main tone, infusing modern life with emotions and warmth.", "subpage_snippet": "", "source": "www.laurel-online.com", "link": "https://www.laurel-online.com/pages/story", "content": "storyLaurèl is deeply influenced by the German philosophy of \"aesthetic of order.\" The brand adheres to the design philosophy of \"Simplicity Power, Rational Balance.\" Laurèl embraces warm and bright joyful colors as its main tone, infusing modern life with emotions and warmth."}
diff --git a/data/sampled_jsons/LAUREL-40_Table_7_relative_improvement_values.jsonl b/data/sampled_jsons/LAUREL-40_Table_7_relative_improvement_values.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..81ce90409b4221777e0929feea5938d5be56de9b
--- /dev/null
+++ b/data/sampled_jsons/LAUREL-40_Table_7_relative_improvement_values.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "by G Menghani · 2024 · Cited by 3 — Table 7 . LAUREL - 40's ... The percentages in the last row of the LAUREL - 40 section indicate the relative improvement over the baseline.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501?", "content": "by G Menghani · 2024 · Cited by 3 — Table 7 . LAUREL - 40's ... The percentages in the last row of the LAUREL - 40 section indicate the relative improvement over the baseline."}
+{"idx": 1, "title": "LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "24 Jun 2025 — Table 7 shows the downstream quality of the baselines and LAuReL - 40 on 10 tasks across Math, General Reasoning, Reading Comprehension, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v4", "content": "24 Jun 2025 — Table 7 shows the downstream quality of the baselines and LAuReL - 40 on 10 tasks across Math, General Reasoning, Reading Comprehension, ..."}
+{"idx": 2, "title": "Pre-Clinical Autoimmunity in Lupus Relatives", "date": "", "ddg_snippet": "by ME Munroe · 2022 · Cited by 7 — Overall, SLE-CSQ scores closely correlated with the number of ACR criteria documented in the medical record across the LAUREL (baseline and follow-up) and LFRR ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9203691/", "content": "by ME Munroe · 2022 · Cited by 7 — Overall, SLE-CSQ scores closely correlated with the number of ACR criteria documented in the medical record across the LAUREL (baseline and follow-up) and LFRR ..."}
+{"idx": 3, "title": "U.S. Laurels", "date": "", "ddg_snippet": "4 May 2021 — Colored numbers above a bar indicate the value exceeds the limits of the chart. Page 12. 12 Conservation Gap Analysis of Native U.S. Laurels .", "subpage_snippet": "", "source": "mortonarb.org", "link": "https://mortonarb.org/app/uploads/2021/05/conservation_gap_analysis_of_selected_native_us_laurels.pdf", "content": "4 May 2021 — Colored numbers above a bar indicate the value exceeds the limits of the chart. Page 12. 12 Conservation Gap Analysis of Native U.S. Laurels ."}
+{"idx": 4, "title": "Model-aided process development for scalable spray ...", "date": "", "ddg_snippet": "by A Kapil · 2025 — Table 7 lists the operating condition modifications (compared to Table 6). The predicted values matched experimental values for outlet temperature, but like ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/chemical-engineering/articles/10.3389/fceng.2025.1565916/full", "content": "by A Kapil · 2025 — Table 7 lists the operating condition modifications (compared to Table 6). The predicted values matched experimental values for outlet temperature, but like ..."}
+{"idx": 5, "title": "Species: Kalmia latifolia", "date": "", "ddg_snippet": "The following table represents mountain laurel importance values 1 and 2 years postfire. Postfire year, High-severity, Low-severity, Unburned. +1, 17.2, 6.3 ...", "subpage_snippet": "", "source": "www.fs.usda.gov", "link": "https://www.fs.usda.gov/database/feis/plants/shrub/kallat/all.html", "content": "The following table represents mountain laurel importance values 1 and 2 years postfire. Postfire year, High-severity, Low-severity, Unburned. +1, 17.2, 6.3 ..."}
+{"idx": 6, "title": "The Influence of Commercial Film-Forming Polymers on ...", "date": "", "ddg_snippet": "by GC Percival · 2007 · Cited by 4 — Table 7 . Influence of film-forming polymers on height and leaf area of laurel under field conditions after applications of 6% salt (NaCl) at week 1 ...", "subpage_snippet": "", "source": "auf.isa-arbor.com", "link": "https://auf.isa-arbor.com/content/33/3/185", "content": "by GC Percival · 2007 · Cited by 4 — Table 7 . Influence of film-forming polymers on height and leaf area of laurel under field conditions after applications of 6% salt (NaCl) at week 1 ..."}
+{"idx": 7, "title": "Diapositivo 1", "date": "", "ddg_snippet": "Table 1 provides the mean relative improvement in objective function value over the ETP and the RBS procedures (denoted by %imp – ETP and %imp – RBS, respectively).", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/6379370.pdf", "content": "Table 1 provides the mean relative improvement in objective function value over the ETP and the RBS procedures (denoted by %imp – ETP and %imp – RBS, respectively)."}
+{"idx": 8, "title": "Visual Outcomes and Prognostic Factors in Epiretinal Membrane...", "date": "", "ddg_snippet": "Table 3 Visual Improvements After Surgery Stratified by Group, Age, OCT Biomarkers, and Preoperative Vision. Figure 3 Green star represents: Median results for the lamellar macular hole (LMH) group (0. 4 / 0 .55).", "subpage_snippet": "", "source": "www.dovepress.com", "link": "https://www.dovepress.com/visual-outcomes-and-prognostic-factors-in-epiretinal-membrane-foveosch-peer-reviewed-fulltext-article-OPTH", "content": "Table 3 Visual Improvements After Surgery Stratified by Group, Age, OCT Biomarkers, and Preoperative Vision. Figure 3 Green star represents: Median results for the lamellar macular hole (LMH) group (0. 4 / 0 .55)."}
+{"idx": 9, "title": "(PDF) The quest for the optimal class distribution: An approach for...", "date": "", "ddg_snippet": "Table 7 shows the average relative improvement values . (Rel.Impr. row) and the Wilcoxons Tests values (Wilcoxon. row) for the three values used for class distribution: bal,ocd.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/257807048_The_quest_for_the_optimal_class_distribution_An_approach_for_enhancing_the_effectiveness_of_learning_via_resampling_methods_for_imbalanced_data_sets", "content": "Table 7 shows the average relative improvement values . (Rel.Impr. row) and the Wilcoxons Tests values (Wilcoxon. row) for the three values used for class distribution: bal,ocd."}
diff --git a/data/sampled_jsons/LAUREL-LR_variant_Equation_3_xi+1_=_f(xi)_+_A_B_f(xi).jsonl b/data/sampled_jsons/LAUREL-LR_variant_Equation_3_xi+1_=_f(xi)_+_A_B_f(xi).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a471e014d46f8d76853f2fbbde79ddc373ef10a2
--- /dev/null
+++ b/data/sampled_jsons/LAUREL-LR_variant_Equation_3_xi+1_=_f(xi)_+_A_B_f(xi).jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "Rocket engine - Wikipedia", "date": "", "ddg_snippet": "Here, \"rocket\" is used as an abbreviation for \"rocket engine\". Thermal rockets use an inert propellant, heated by electricity (electrothermal propulsion) or a nuclear reactor (nuclear thermal rocket). Chemical rockets are powered by exothermic reduction-oxidation chemical reactions of the propellant: Solid-fuel rockets (or solid-propellant rockets or motors) are chemical rockets which use ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Rocket_engine", "content": "Here, \"rocket\" is used as an abbreviation for \"rocket engine\". Thermal rockets use an inert propellant, heated by electricity (electrothermal propulsion) or a nuclear reactor (nuclear thermal rocket). Chemical rockets are powered by exothermic reduction-oxidation chemical reactions of the propellant: Solid-fuel rockets (or solid-propellant rockets or motors) are chemical rockets which use ..."}
+{"idx": 1, "title": "[PDF] Baltimore Marriott Waterfront Baltimore, MD USA - Free ...", "date": "", "ddg_snippet": "LR 5: Palliative Care Faculty: William Breitbart, MD, FAPM; Lewis Cohen, MD, FAPM; Donna Greenberg, MD, FAPM; Joseph Weiner, MD, FAPM Laurel A BR 8: Collaborative Care Interventions for Treating Depression in Patients with Cardiac Disease: The Bypassing the Blues Experience Faculty: Bruce L. Rollman, MD and Bea Herbeck Belnap, Dr. Biol. Hum ...", "subpage_snippet": "", "source": "silo.tips", "link": "https://silo.tips/download/baltimore-marriott-waterfront-baltimore-md-usa", "content": "LR 5: Palliative Care Faculty: William Breitbart, MD, FAPM; Lewis Cohen, MD, FAPM; Donna Greenberg, MD, FAPM; Joseph Weiner, MD, FAPM Laurel A BR 8: Collaborative Care Interventions for Treating Depression in Patients with Cardiac Disease: The Bypassing the Blues Experience Faculty: Bruce L. Rollman, MD and Bea Herbeck Belnap, Dr. Biol. Hum ..."}
+{"idx": 2, "title": "Ecology: From Individuals to Ecosystems - Textbook", "date": "", "ddg_snippet": "Explore ecology from organisms to ecosystems with this textbook by Begon, Townsend, and Harper. Learn ecological principles and applications.", "subpage_snippet": "", "source": "studylib.es", "link": "https://studylib.es/doc/9530598/begonharpertownsend2006-2", "content": "Explore ecology from organisms to ecosystems with this textbook by Begon, Townsend, and Harper. Learn ecological principles and applications."}
+{"idx": 3, "title": "sentence-transformers-all-MiniLM-L6-v2/vocab.txt at main ...", "date": "", "ddg_snippet": "Sentance-transformer model from hugging face. Contribute to yogendrasinghmehra/sentence-transformers-all-MiniLM-L6-v2 development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yogendrasinghmehra/sentence-transformers-all-MiniLM-L6-v2/blob/main/vocab.txt", "content": "Sentance-transformer model from hugging face. Contribute to yogendrasinghmehra/sentence-transformers-all-MiniLM-L6-v2 development by creating an account on GitHub."}
+{"idx": 4, "title": "Human lifespan changes in the brain’s functional connectome", "date": "", "ddg_snippet": "Apr 3 , 2025 · Sun et al. report human lifespan changes in the brain’s functional connectome in 33,250 individuals, which highlights critical growth milestones and distinct maturation patterns and offers a ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41593-025-01907-4", "content": "Apr 3 , 2025 · Sun et al. report human lifespan changes in the brain’s functional connectome in 33,250 individuals, which highlights critical growth milestones and distinct maturation patterns and offers a ..."}
+{"idx": 5, "title": "Full text of \"Saturday review of politics, literature ...", "date": "", "ddg_snippet": "The Saturday r Re view. 3 > State for the Colonies, declarations of an intention to vindicate the honour of England abroad and to secure new markets for English goods are, of course, very im¬ portant.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/stream/saturdayreviewof8018unse/saturdayreviewof8018unse_djvu.txt", "content": "The Saturday r Re view. 3 > State for the Colonies, declarations of an intention to vindicate the honour of England abroad and to secure new markets for English goods are, of course, very im¬ portant."}
+{"idx": 6, "title": "ペットホテル パピーパーティ 犬の保育園 (dog nursery)｜DOG DIAMOND...", "date": "", "ddg_snippet": "彼女の人となりがわかる過去記事 「辞めたあとなのに、近所の小学校の イベントに誘ったら来てくれた話」 セミナーに登壇した時のエピソード記事 「服部のカンペがありえないという話」 服部芽以 OPENしてしばらくは不定休のようなので 予定を確認して公式LINE インスタのDMから ご予約お ...", "subpage_snippet": "", "source": "dogdiamond.sakura.ne.jp", "link": "http://dogdiamond.sakura.ne.jp/blog/column/20250904column/", "content": "彼女の人となりがわかる過去記事 「辞めたあとなのに、近所の小学校の イベントに誘ったら来てくれた話」 セミナーに登壇した時のエピソード記事 「服部のカンペがありえないという話」 服部芽以 OPENしてしばらくは不定休のようなので 予定を確認して公式LINE インスタのDMから ご予約お ..."}
diff --git a/data/sampled_jsons/LAuReL_Learned_Augmented_Residual_Layer_OpenReview.jsonl b/data/sampled_jsons/LAuReL_Learned_Augmented_Residual_Layer_OpenReview.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2d0a60803eabe7c1a79ef8cd4876bdd1853e58ba
--- /dev/null
+++ b/data/sampled_jsons/LAuReL_Learned_Augmented_Residual_Layer_OpenReview.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LAuReL: Learned Augmented Residual Layer | OpenReview", "date": "", "ddg_snippet": "In this paper we introduce a Learned Augmented Residual Layer (LAUREL)—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=honBJOVRn5", "content": "In this paper we introduce a Learned Augmented Residual Layer (LAUREL)—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 1, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection. Recall that deep-learning models with residual connections have a 'block' structure, with many blocks chained together between the input and final output; these could be convolution/identity blocks within a ResNet, a transformer block in a transformer encoder/decoder ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v4", "content": "In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection. Recall that deep-learning models with residual connections have a 'block' structure, with many blocks chained together between the input and final output; these could be convolution/identity blocks within a ResNet, a transformer block in a transformer encoder/decoder ..."}
+{"idx": 2, "title": "ICML Poster LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43889", "content": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation."}
+{"idx": 3, "title": "BAW2501/LAuReL-Learned-Augmented-Residual-Layer - GitHub", "date": "", "ddg_snippet": "This repository contains my independent implementations of the three LAuReL variants described in the article titled \" LAuReL : Learned Augmented Residual Layer \". These implementations aim to explore the concepts presented in the paper and evaluate their effectiveness on image datasets. Unofficial ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/LAuReL-Learned-Augmented-Residual-Layer", "content": "This repository contains my independent implementations of the three LAuReL variants described in the article titled \" LAuReL : Learned Augmented Residual Layer \". These implementations aim to explore the concepts presented in the paper and evaluate their effectiveness on image datasets. Unofficial ..."}
+{"idx": 4, "title": "LAUREL: Learned Augmented Residual Layer - chatpaper.com", "date": "", "ddg_snippet": "The paper introduces the Learned Augmented Residual Layer ( LAUREL ), a novel framework that enhances deep learning model efficiency and performance by generalizing traditional residual connections, demonstrating significant improvements in accuracy with minimal increases in parameter count across vision and language models.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/75803?from=search", "content": "The paper introduces the Learned Augmented Residual Layer ( LAUREL ), a novel framework that enhances deep learning model efficiency and performance by generalizing traditional residual connections, demonstrating significant improvements in accuracy with minimal increases in parameter count across vision and language models."}
+{"idx": 5, "title": "Google AI Introduces LAuReL (Learned Augmented Residual Layer ...", "date": "", "ddg_snippet": "These constraints often limit the practical implementation of high-quality models in production environments. The need […] The post Google AI Introduces LAuReL ( Learned Augmented Residual Layer ): Revolutionizing Neural Networks with Enhanced Residual Connections for Efficient Model Performance appeared first on MarkTechPost.", "subpage_snippet": "", "source": "aiquantumintelligence.com", "link": "https://aiquantumintelligence.com/google-ai-introduces-laurel-learned-augmented-residual-layer-revolutionizing-neural-networks-with-enhanced-residual-connections-for-efficient-model-performance", "content": "These constraints often limit the practical implementation of high-quality models in production environments. The need […] The post Google AI Introduces LAuReL ( Learned Augmented Residual Layer ): Revolutionizing Neural Networks with Enhanced Residual Connections for Efficient Model Performance appeared first on MarkTechPost."}
+{"idx": 6, "title": "LAuReL: Learned Augmented Residual Layer - OpenReview", "date": "", "ddg_snippet": "In this paper, we introduce the Learned Augmented Residual Layer ( LAuReL ) --- a novel generalization of the canonical residual connection --- designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rUDRWP9WvZ", "content": "In this paper, we introduce the Learned Augmented Residual Layer ( LAuReL ) --- a novel generalization of the canonical residual connection --- designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics."}
+{"idx": 7, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce \\emph { Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics. Our experiments show that using \\ laurel can help boost performance for both vision and language models. For example, on the ResNet ...", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2411.07501", "content": "In this paper we introduce \\emph { Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics. Our experiments show that using \\ laurel can help boost performance for both vision and language models. For example, on the ResNet ..."}
+{"idx": 8, "title": "LAUREL: Learned Augmented Residual Layer | Cool Papers - Immersive ...", "date": "", "ddg_snippet": "In this paper we introduce \\emph { Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2411.07501", "content": "In this paper we introduce \\emph { Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 9, "title": "ICML LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "Poster in Workshop: ES-FoMo II: 2nd Workshop on Efficient Systems for Foundation Models LAuReL : Learned Augmented Residual Layer Gaurav Menghani · Ravi Kumar · Sanjiv Kumar [ Abstract ] [ Project Page ] [ OpenReview ]", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/39619", "content": "Poster in Workshop: ES-FoMo II: 2nd Workshop on Efficient Systems for Foundation Models LAuReL : Learned Augmented Residual Layer Gaurav Menghani · Ravi Kumar · Sanjiv Kumar [ Abstract ] [ Project Page ] [ OpenReview ]"}
diff --git a/data/sampled_jsons/LHRS-Bench_dataset_size.jsonl b/data/sampled_jsons/LHRS-Bench_dataset_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3074009cc759805402978fcbf1af94bde9818d09
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Bench_dataset_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - NJU-LHRS/LHRS-Bot: VGI-Enhanced multimodal large ...", "date": "", "ddg_snippet": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !"}
+{"idx": 1, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "LHRS -Bot employs a novel bridging strategy and leverages a curriculum learning approach to fully exploit the inherent knowledge of the proposed datasets .", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/09511.pdf", "content": "LHRS -Bot employs a novel bridging strategy and leverages a curriculum learning approach to fully exploit the inherent knowledge of the proposed datasets ."}
+{"idx": 2, "title": "LHRS-Bot-Nova: Improved Multimodal Large Language Model for ...", "date": "", "ddg_snippet": "Nov 14, 2024 · To further enhance RS-oriented vision-language alignment, we propose a large-scale RS image-caption dataset , generated through feature-guided image recaptioning. Additionally, we introduce an instruction dataset specifically designed to improve spatial recognition abilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.09301v1", "content": "Nov 14, 2024 · To further enhance RS-oriented vision-language alignment, we propose a large-scale RS image-caption dataset , generated through feature-guided image recaptioning. Additionally, we introduce an instruction dataset specifically designed to improve spatial recognition abilities."}
+{"idx": 3, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "Nov 21, 2024 · To unleash the potential of LLMs for RS image understanding, we curate a large-scale dataset , LHRS -Align, for RS-specific alignment, and LHRS -Instruct, a multimodal instruction-following dataset to enhance LHRS -Bot’s instruction-following capabilities.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-72904-1_26", "content": "Nov 21, 2024 · To unleash the potential of LLMs for RS image understanding, we curate a large-scale dataset , LHRS -Align, for RS-specific alignment, and LHRS -Instruct, a multimodal instruction-following dataset to enhance LHRS -Bot’s instruction-following capabilities."}
+{"idx": 4, "title": "[2402.02544] LHRS-Bot: Empowering Remote Sensing with VGI ...", "date": "", "ddg_snippet": "Feb 4, 2024 · To bridge this gap, we construct a large-scale RS image-text dataset , LHRS -Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.02544", "content": "Feb 4, 2024 · To bridge this gap, we construct a large-scale RS image-text dataset , LHRS -Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images."}
+{"idx": 5, "title": "LHRS-Bot/README.md at main · NJU-LHRS/LHRS-Bot - GitHub", "date": "", "ddg_snippet": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/README.md", "content": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !"}
+{"idx": 6, "title": "GeoGrid-Bench: Can Foundation Models Understand Multimodal", "date": "", "ddg_snippet": "... and data are publicly available at our Github repository https://github.com/bowen-upenn/GeoGrid_ Bench and Huggingface https://huggingface.co/ datasets ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10714v1", "content": "... and data are publicly available at our Github repository https://github.com/bowen-upenn/GeoGrid_ Bench and Huggingface https://huggingface.co/ datasets ..."}
+{"idx": 7, "title": "UrBench: A Comprehensive Benchmark for Evaluating Large", "date": "", "ddg_snippet": "2024 ) adapt existing remote sensing datasets to create visual reasoning benchmarks for LMMs, while Geochat (Kuckreja et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17267v3", "content": "2024 ) adapt existing remote sensing datasets to create visual reasoning benchmarks for LMMs, while Geochat (Kuckreja et al."}
+{"idx": 8, "title": "PumpkinCat/LHRS_Data at main - Hugging Face", "date": "", "ddg_snippet": "Jul 3, 2024 · We’re on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data/tree/main", "content": "Jul 3, 2024 · We’re on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 9, "title": "UrbanLLaVA: A Multi-modal Large Language Model for Urban", "date": "", "ddg_snippet": "Drawing from the diverse evaluation tasks in these two benchmarks, we reorganize and expand them to create the urban evaluation benchmark UBench ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.23219v1", "content": "Drawing from the diverse evaluation tasks in these two benchmarks, we reorganize and expand them to create the urban evaluation benchmark UBench ..."}
diff --git a/data/sampled_jsons/LHRS-Bench_number_of_annotations_OR_dataset_size_OR_statistics.jsonl b/data/sampled_jsons/LHRS-Bench_number_of_annotations_OR_dataset_size_OR_statistics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8bf29bacf12e2645a221762fc3b545cafd8a02a2
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Bench_number_of_annotations_OR_dataset_size_OR_statistics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "YouTubePD: A Multimodal Benchmark for", "date": "", "ddg_snippet": "The number of annotations varies depending on severity levels and regions.Finally, the size of the dataset is relatively small compared with typical computer vision datasets, due to the inherent challenges involved in collecting PD data.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/acffd5024f52c3a9ecc8ccb4b75b4e5c-Supplemental-Datasets_and_Benchmarks.pdf", "content": "The number of annotations varies depending on severity levels and regions.Finally, the size of the dataset is relatively small compared with typical computer vision datasets, due to the inherent challenges involved in collecting PD data."}
+{"idx": 1, "title": "(PDF) Extending Dataset Pruning to Object Detection...", "date": "", "ddg_snippet": "Storage size of images and annotations in main object detection datasets.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392085743_Extending_Dataset_Pruning_to_Object_Detection_A_Variance-based_Approach", "content": "Storage size of images and annotations in main object detection datasets."}
+{"idx": 2, "title": "The Uli Dataset : An Exercise in Experience Led Annotation of oGBV", "date": "", "ddg_snippet": "This dataset captures a very small aspect of the experience of oGBV- that which is patently visible in text-based statements. Within this narrow scope, we use the terms oGBV and gendered abuse interchangeably.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.09086v3", "content": "This dataset captures a very small aspect of the experience of oGBV- that which is patently visible in text-based statements. Within this narrow scope, we use the terms oGBV and gendered abuse interchangeably."}
+{"idx": 3, "title": "A Dataset for Detection and Segmentation of Underwater Marine...", "date": "", "ddg_snippet": "Information on the number of images and annotations for each sub-folder are provided in Table 2. Visualized samples of annotated images from the dataset can be seen in Fig. 7. Table 2 No. images per dataset domains, grouped by data collection sites and cameras.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41597-024-03759-2?error=cookies_not_supported", "content": "Information on the number of images and annotations for each sub-folder are provided in Table 2. Visualized samples of annotated images from the dataset can be seen in Fig. 7. Table 2 No. images per dataset domains, grouped by data collection sites and cameras."}
+{"idx": 4, "title": "Building a Bird Recognition App and Large Scale Dataset With Citizen...", "date": "", "ddg_snippet": "4) Annotation Throughput: The average number of annotations obtainable per second, this scales with the total size of the pool of annotators.", "subpage_snippet": "", "source": "www.cv-foundation.org", "link": "https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Horn_Building_a_Bird_2015_CVPR_paper.pdf", "content": "4) Annotation Throughput: The average number of annotations obtainable per second, this scales with the total size of the pool of annotators."}
+{"idx": 5, "title": "alakxender/dhivehi-layout-syn-b1-florence · Datasets at Hugging Face", "date": "", "ddg_snippet": "Number of Images: 1,356 images. Image Format: .jpg, .png. Resolution: 300dpi (example). Number of Annotations : 1,800 bounding box annotations (example). Document Type: Synthetic, Dhivehi-language documents. Dataset Splits", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/alakxender/dhivehi-layout-syn-b1-florence", "content": "Number of Images: 1,356 images. Image Format: .jpg, .png. Resolution: 300dpi (example). Number of Annotations : 1,800 bounding box annotations (example). Document Type: Synthetic, Dhivehi-language documents. Dataset Splits"}
+{"idx": 6, "title": "LHRS-Bot: Empowering Remote Sensing with VGI ...", "date": "", "ddg_snippet": "LHRS - Bench includes 690 single-choice questions, spanning 5 top-level evaluation dimensions including 11 fine-grained categories to facilitate a comprehensive, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.02544v4", "content": "LHRS - Bench includes 690 single-choice questions, spanning 5 top-level evaluation dimensions including 11 fine-grained categories to facilitate a comprehensive, ..."}
+{"idx": 7, "title": "HKUST-LongGroup/Awesome-MLLM-Benchmarks", "date": "", "ddg_snippet": "A list of papers and open-sources on multimodal benchmarks, designed to keep pace with the expected surge in research in the coming months.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HKUST-LongGroup/Awesome-MLLM-Benchmarks", "content": "A list of papers and open-sources on multimodal benchmarks, designed to keep pace with the expected surge in research in the coming months."}
+{"idx": 8, "title": "Crowdsourced and automatic speech prominence...", "date": "", "ddg_snippet": "We investigate design decisions for neural prominence estimation as well as how neural prominence estimation improves as a function of two key factors of annotation cost: dataset size and the number of annotations per utterance.", "subpage_snippet": "", "source": "www.scholars.northwestern.edu", "link": "https://www.scholars.northwestern.edu/en/publications/crowdsourced-and-automatic-speech-prominence-estimation", "content": "We investigate design decisions for neural prominence estimation as well as how neural prominence estimation improves as a function of two key factors of annotation cost: dataset size and the number of annotations per utterance."}
+{"idx": 9, "title": "DEEPred: Automated Protein Function Prediction with Multi-task...", "date": "", "ddg_snippet": "*NoA: Number of Annotations , ME: Manual-Experimental Evidence, AE: All Evidence.In most of the protein function prediction methods, training was performed using only the annotations with experimental and/or manual evidence codes.", "subpage_snippet": "", "source": "open.metu.edu.tr", "link": "https://open.metu.edu.tr/bitstream/handle/11511/30761/s41598-019-43708-3.pdf", "content": "*NoA: Number of Annotations , ME: Manual-Experimental Evidence, AE: All Evidence.In most of the protein function prediction methods, training was performed using only the annotations with experimental and/or manual evidence codes."}
diff --git a/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_VGI_Enhanced_Muhtar_arxiv_abstract_year_2024.jsonl b/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_VGI_Enhanced_Muhtar_arxiv_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d2772efb477beac37d40ba9046c88960ef2e740d
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_VGI_Enhanced_Muhtar_arxiv_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2402.02544] LHRS - Bot : Empowering Remote Sensing with...", "date": "", "ddg_snippet": "View a PDF of the paper titled LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model, by Dilxat Muhtar and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.02544", "content": "View a PDF of the paper titled LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model, by Dilxat Muhtar and 4 other authors."}
+{"idx": 1, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "Introduction We are excited to introduce LHRS-Bot , a multimodal large language model (MLLM) that leverages globally available volunteer geographic information ( VGI ) and remote sensing images (RS). LHRS-Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "Introduction We are excited to introduce LHRS-Bot , a multimodal large language model (MLLM) that leverages globally available volunteer geographic information ( VGI ) and remote sensing images (RS). LHRS-Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain."}
+{"idx": 2, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "Nov 21, 2024 · To bridge this gap, we construct a large-scale RS image-text dataset, LHRS ( LHRS stands for ‘Language Helps Remote Sensing ’.)-Align, and an informative RS-specific instruction dataset, LHRS -Instruct, leveraging the extensive volunteered geographic information ( VGI ) and globally available RS images.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-72904-1_26", "content": "Nov 21, 2024 · To bridge this gap, we construct a large-scale RS image-text dataset, LHRS ( LHRS stands for ‘Language Helps Remote Sensing ’.)-Align, and an informative RS-specific instruction dataset, LHRS -Instruct, leveraging the extensive volunteered geographic information ( VGI ) and globally available RS images."}
+{"idx": 3, "title": "LHRS-Bot-Nova: Improved multimodal large language model for ...", "date": "", "ddg_snippet": "Sep 1, 2025 · LHRS-Bot -Nova incorporates an enhanced vision encoder and a new vision perceiver with an MoE structure for improved vision-language alignment. In this section, we delve into each component of LHRS-Bot -Nova, detailing how the enhanced architecture achieves better vision-language alignment.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0924271625002230", "content": "Sep 1, 2025 · LHRS-Bot -Nova incorporates an enhanced vision encoder and a new vision perceiver with an MoE structure for improved vision-language alignment. In this section, we delve into each component of LHRS-Bot -Nova, detailing how the enhanced architecture achieves better vision-language alignment."}
+{"idx": 4, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains. In the remote sensing (RS) field, however, the diverse geographical landscapes and varied objects in RS imagery are not adequately considered in recent MLLM endeavors. To bridge this gap, we ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240202544M/abstract", "content": "The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains. In the remote sensing (RS) field, however, the diverse geographical landscapes and varied objects in RS imagery are not adequately considered in recent MLLM endeavors. To bridge this gap, we ..."}
+{"idx": 5, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "LHRS-Bot achieves state-of-the-art performance across a variety of RS image understanding tasks (Fig. 1) and demonstrates remarkable abilities to detect intricate objects, engage with human conversation, and provide insights from visual information within RS images (Fig. 5).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.02544v4", "content": "LHRS-Bot achieves state-of-the-art performance across a variety of RS image understanding tasks (Fig. 1) and demonstrates remarkable abilities to detect intricate objects, engage with human conversation, and provide insights from visual information within RS images (Fig. 5)."}
+{"idx": 6, "title": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large...", "date": "", "ddg_snippet": "We present LHRS - Bot to harness the full potential of LLMs and general visual encoders for enhanced RS images understanding and reasoning. This section delves into each components of LHRS - Bot , detailing how we achieve fully alignment between vision and language models across...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.02544v4/", "content": "We present LHRS - Bot to harness the full potential of LLMs and general visual encoders for enhanced RS images understanding and reasoning. This section delves into each components of LHRS - Bot , detailing how we achieve fully alignment between vision and language models across..."}
+{"idx": 7, "title": "Dilxat Muhtar - Google Akademik", "date": "", "ddg_snippet": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model.G He, Z Dong, P Feng, D Muhtar , X Zhang. IEEE Geoscience and Remote Sensing Letters 20, 1-5, 2023.", "subpage_snippet": "", "source": "scholar.google.com.au", "link": "https://scholar.google.com.au/citations?user=WrBJQ9IAAAAJ&hl=tr", "content": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model.G He, Z Dong, P Feng, D Muhtar , X Zhang. IEEE Geoscience and Remote Sensing Letters 20, 1-5, 2023."}
+{"idx": 8, "title": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large...", "date": "", "ddg_snippet": "LHRS - Bot [36] focuses on high-resolution remote sensing tasks with an RS-specific image-text dataset but lacks multi-temporal diversity. EarthVQA [52] supports complex relational reasoning with a multi-modal dataset, though its emphasis on...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386016876_LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model", "content": "LHRS - Bot [36] focuses on high-resolution remote sensing tasks with an RS-specific image-text dataset but lacks multi-temporal diversity. EarthVQA [52] supports complex relational reasoning with a multi-modal dataset, though its emphasis on..."}
+{"idx": 9, "title": "Articles by Dilxat Muhtar | Synthical", "date": "", "ddg_snippet": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/5dc125a2-de1d-49d6-9145-46bb527b5b95/articles", "content": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model."}
diff --git a/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model_dataset_size.jsonl b/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model_dataset_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ac364e083634a2b6c6ebc4c1bcd6c84c06285e8b
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model_dataset_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced ...", "date": "", "ddg_snippet": "Multimodal Large language model Remote sensing . 22footnotetext: Equal contribution, listed in random order.11footnotetext: Corresponding author. 1 Introduction. Report issue for preceding element.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.02544v4", "content": "Multimodal Large language model Remote sensing . 22footnotetext: Equal contribution, listed in random order.11footnotetext: Corresponding author. 1 Introduction. Report issue for preceding element."}
+{"idx": 1, "title": "NJU-LHRS/ LHRS - Bot : VGI - Enhanced multimodal large language ...", "date": "", "ddg_snippet": "VGI - Enhanced multimodal large language model for remote sensing images. License.We are excited to introduce LHRS - Bot , a multimodal large language model (MLLM) that leverages globally available volunteer geographic information (VGI) and remote sensing images (RS).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "VGI - Enhanced multimodal large language model for remote sensing images. License.We are excited to introduce LHRS - Bot , a multimodal large language model (MLLM) that leverages globally available volunteer geographic information (VGI) and remote sensing images (RS)."}
+{"idx": 2, "title": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced ...", "date": "", "ddg_snippet": "LHRS - Bot -Nova: Improved Multimodal Large Language Model for Remote Sensing Vision- Language Interpretation. LHRS - Bot [36] focuses on high-resolution remote sensing tasks with an RS-specific image-text dataset but lacks multi-temporal diversity.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386016876_LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model", "content": "LHRS - Bot -Nova: Improved Multimodal Large Language Model for Remote Sensing Vision- Language Interpretation. LHRS - Bot [36] focuses on high-resolution remote sensing tasks with an RS-specific image-text dataset but lacks multi-temporal diversity."}
+{"idx": 3, "title": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced ...", "date": "", "ddg_snippet": "The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/LHRS-Bot%3A-Empowering-Remote-Sensing-with-VGI-Enhanced-Large-Multimodal-Language-Model-6f64619b-c8b1-4e89-9b8e-01d7ac6e4107", "content": "The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains."}
+{"idx": 4, "title": "Multimodal Language Model for Remote Sensing", "date": "", "ddg_snippet": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model .", "subpage_snippet": "", "source": "linnk.ai", "link": "https://linnk.ai/topic/multimodal-language-model-for-remote-sensing/", "content": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model ."}
+{"idx": 5, "title": "Quality-Driven Curation of Remote Sensing Vision- Language Data via...", "date": "", "ddg_snippet": "LHRS - Bot : Empowering remote sensing with vgi - enhanced large multimodal language model . In European Conference on Computer Vision, pages 440–457.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.00743v1", "content": "LHRS - Bot : Empowering remote sensing with vgi - enhanced large multimodal language model . In European Conference on Computer Vision, pages 440–457."}
+{"idx": 6, "title": "A Survey on Vision- Language Geo-Foundation Models (VLGFMs)", "date": "", "ddg_snippet": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model - LHRS/ LHRS - Bot )|.TEOChat: A Large Vision- Language Assistant for Temporal Earth Observation Data .", "subpage_snippet": "", "source": "awesome.ecosyste.ms", "link": "https://awesome.ecosyste.ms/lists/zytx121/awesome-vlgfm", "content": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model - LHRS/ LHRS - Bot )|.TEOChat: A Large Vision- Language Assistant for Temporal Earth Observation Data ."}
+{"idx": 7, "title": "How to Bridge the Gap Between Modalities: Survey on Multimodal ...", "date": "", "ddg_snippet": "[129] D. Muhtar et al., “ LHRS - bot : Empowering remote sensing with VGI - enhanced large multimodal language model ,” 2024, arXiv:2402. 02544.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tk/2025/09/10841938/23wAGxzB0U8", "content": "[129] D. Muhtar et al., “ LHRS - bot : Empowering remote sensing with VGI - enhanced large multimodal language model ,” 2024, arXiv:2402. 02544."}
+{"idx": 8, "title": "GeoPixel:: Pixel Grounding Large Multimodal Model in Remote ...", "date": "", "ddg_snippet": "GeoPixel, a large multimodal model , is designed to enhance the fine-grained spatial understanding of high-resolution remote sensing (RS) imagery.Chen, G., Shen, L., Shao, R., Deng, X., and Nie, L. Lion: Empowering multimodal large language model with dual-level visual knowledge.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=nF8NxPUd0q", "content": "GeoPixel, a large multimodal model , is designed to enhance the fine-grained spatial understanding of high-resolution remote sensing (RS) imagery.Chen, G., Shen, L., Shao, R., Deng, X., and Nie, L. Lion: Empowering multimodal large language model with dual-level visual knowledge."}
+{"idx": 9, "title": "SkySense-O: Towards Open-World Remote Sensing Interpretation...", "date": "", "ddg_snippet": "Lhrs - bot : Empowering remote sensing with vgi - enhanced large multimodal language model . arXiv preprint arXiv:2402.02544, 2024.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_SkySense-O_Towards_Open-World_Remote_Sensing_Interpretation_with_Vision-Centric_Visual-Language_Modeling_CVPR_2025_paper.pdf", "content": "Lhrs - bot : Empowering remote sensing with vgi - enhanced large multimodal language model . arXiv preprint arXiv:2402.02544, 2024."}
diff --git a/data/sampled_jsons/LLM_generate_GUI_automation_scripts_from_natural_language_2023-2024_year_2023-2024.jsonl b/data/sampled_jsons/LLM_generate_GUI_automation_scripts_from_natural_language_2023-2024_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..45f5ec6fea73113d725837ff89c8fddfe132bd1b
--- /dev/null
+++ b/data/sampled_jsons/LLM_generate_GUI_automation_scripts_from_natural_language_2023-2024_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LearnAct: Few-Shot Mobile GUI Agent with a Unified ...", "date": "", "ddg_snippet": "18 Apr 2025 — These agents leverage Large Language Models (LLMs) to autonomously complete human tasks through environmental interaction (Wen et al., 2023, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.13805v1", "content": "18 Apr 2025 — These agents leverage Large Language Models (LLMs) to autonomously complete human tasks through environmental interaction (Wen et al., 2023, ..."}
+{"idx": 1, "title": "LLM-Powered GUI Agents in Phone Automation", "date": "", "ddg_snippet": "We delve into the fundamen- tal reasons behind LLMs' capacity to enhance phone automation . By detailing their advancements in natural language comprehension, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/97488882edf61aec1f9d42514b1344eeb3a94e13.pdf", "content": "We delve into the fundamen- tal reasons behind LLMs' capacity to enhance phone automation . By detailing their advancements in natural language comprehension, ..."}
+{"idx": 2, "title": "Universal Visual Grounding for GUI Agents", "date": "", "ddg_snippet": "7 Oct 2024 — We find that a major bottleneck is grounding, i.e., mapping textual plans generated by an (M) LLM to the precise locations on the GUI . There are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05243v1", "content": "7 Oct 2024 — We find that a major bottleneck is grounding, i.e., mapping textual plans generated by an (M) LLM to the precise locations on the GUI . There are ..."}
+{"idx": 3, "title": "codefuse-ai/Awesome-Code-LLM", "date": "", "ddg_snippet": "A comprehensive review of LLM researches for code. Works in each category are ordered chronologically.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/codefuse-ai/Awesome-Code-LLM", "content": "A comprehensive review of LLM researches for code. Works in each category are ordered chronologically."}
+{"idx": 4, "title": "Large Language Model-Brained GUI Agents: A Survey", "date": "", "ddg_snippet": "The LLM -powered GUI agents survey paper presents a comprehensive overview of automated GUI interaction through large language models. Problem.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2411.18279v1", "content": "The LLM -powered GUI agents survey paper presents a comprehensive overview of automated GUI interaction through large language models. Problem."}
+{"idx": 5, "title": "How Do Your Code LLMs Perform? Empowering ...", "date": "", "ddg_snippet": "by Y Wang · 2024 · Cited by 7 — During appli- cation, we prompt the Unit Test Model to generate . 12 test cases for each training sample, and execute the unit testing program.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.777.pdf", "content": "by Y Wang · 2024 · Cited by 7 — During appli- cation, we prompt the Unit Test Model to generate . 12 test cases for each training sample, and execute the unit testing program."}
+{"idx": 6, "title": "A POWERFUL PIPELINE FOR LLM INSTRUCTION TUNING", "date": "", "ddg_snippet": "by S Kaur · Cited by 14 — Here, a strong LLM is prompted using a small set of human-created examples to generate a large number of (query, answer) examples on a variety of topics. Open ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=44z7HL4mfX", "content": "by S Kaur · Cited by 14 — Here, a strong LLM is prompted using a small set of human-created examples to generate a large number of (query, answer) examples on a variety of topics. Open ..."}
+{"idx": 7, "title": "Next-Generation Software Testing: AI-Powered ...", "date": "", "ddg_snippet": "by F Ricca · 2025 · Cited by 2 — GenAI relies on large language models (LLMs ) such as BERT, GPT, PaLM, and LLaMA, powering diverse natural language processing (NLP) ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/52/11024032/11024091.pdf", "content": "by F Ricca · 2025 · Cited by 2 — GenAI relies on large language models (LLMs ) such as BERT, GPT, PaLM, and LLaMA, powering diverse natural language processing (NLP) ..."}
+{"idx": 8, "title": "Next-Generation Software Testing: AI-Powered Test ...", "date": "", "ddg_snippet": "by F Ricca · 2025 · Cited by 2 — GenAI relies on large language models (LLMs) such as BERT, GPT, PaLM, and LLaMA, powering diverse natural language processing (NLP) applications. ... generate ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/magazine/so/2025/04/11024091/27gSQcKD6jC", "content": "by F Ricca · 2025 · Cited by 2 — GenAI relies on large language models (LLMs) such as BERT, GPT, PaLM, and LLaMA, powering diverse natural language processing (NLP) applications. ... generate ..."}
+{"idx": 9, "title": "AI in Project Management — How Generative and Agentic ...", "date": "", "ddg_snippet": "AI is reshaping project management by automating routine chores, analyzing complex data, and providing predictive insights.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@adnanmasood/ai-in-project-management-how-generative-and-agentic-ai-are-redefining-strategy-execution-and-ccfd45229e7b", "content": "AI is reshaping project management by automating routine chores, analyzing complex data, and providing predictive insights."}
diff --git a/data/sampled_jsons/LLaVA-Next_dynamic_high_resolution_image_tiling_architecture_LLaVA-1.5.jsonl b/data/sampled_jsons/LLaVA-Next_dynamic_high_resolution_image_tiling_architecture_LLaVA-1.5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..71b12f97709a8c9c5ed890971f5c2d1f17b38c1f
--- /dev/null
+++ b/data/sampled_jsons/LLaVA-Next_dynamic_high_resolution_image_tiling_architecture_LLaVA-1.5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LLaVA-RE: Binary Image-Text Relevancy Evaluation with", "date": "", "ddg_snippet": "... image -text R elevancy E ... To the best of our knowledge, LLaVA -RE is the first work to build MLLM for binary image -text relevancy evaluation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05602v1", "content": "... image -text R elevancy E ... To the best of our knowledge, LLaVA -RE is the first work to build MLLM for binary image -text relevancy evaluation."}
+{"idx": 1, "title": "When Large Vision-Language Model Meets Large Remote Sensing", "date": "", "ddg_snippet": "... 39 , 45 , 10 , 19 , 90 ] traverse pre-defined partition grids and obtain the most suitable grid, to divide the high - resolution image into image tiles ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.07588v3", "content": "... 39 , 45 , 10 , 19 , 90 ] traverse pre-defined partition grids and obtain the most suitable grid, to divide the high - resolution image into image tiles ..."}
+{"idx": 2, "title": "NVLM: Open Frontier-Class Multimodal LLMs", "date": "", "ddg_snippet": "Furthermore, we introduce a 1 -D tile -tagging design for tile -based dynamic high - resolution images , which significantly boosts performance on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.11402v1", "content": "Furthermore, we introduce a 1 -D tile -tagging design for tile -based dynamic high - resolution images , which significantly boosts performance on ..."}
+{"idx": 3, "title": "How Far Are We to GPT-4V? Closing the Gap to Commercial", "date": "", "ddg_snippet": "2) Dynamic High - Resolution : we divide images into tiles ranging from 1 to 40 of 448 × \\times × 448 pixels according to the aspect ratio and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.16821v2", "content": "2) Dynamic High - Resolution : we divide images into tiles ranging from 1 to 40 of 448 × \\times × 448 pixels according to the aspect ratio and ..."}
+{"idx": 4, "title": "StreamChat: Chatting with Streaming Video", "date": "", "ddg_snippet": "... notable progress in video understanding of LMMs [ 52 , 81 , 76 , 11 , 68 ] , existing models often overlook the crucial need to capture dynamic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.08646v2", "content": "... notable progress in video understanding of LMMs [ 52 , 81 , 76 , 11 , 68 ] , existing models often overlook the crucial need to capture dynamic ..."}
+{"idx": 5, "title": "Teaching AI to See: A Technical Deep-Dive on Vision Language", "date": "", "ddg_snippet": "... to see whether the vanilla transformer architecture (without any of the inductive biases of a CNN) could learn to extract the features from an image ...", "subpage_snippet": "", "source": "www.cognitiverevolution.ai", "link": "https://www.cognitiverevolution.ai/teaching-ai-to-see-a-technical-deep-dive-on-vision-language-models-with-will-hardman-data-sci/", "content": "... to see whether the vanilla transformer architecture (without any of the inductive biases of a CNN) could learn to extract the features from an image ..."}
+{"idx": 6, "title": "Introduction of InternVL 1.5 Series — InternVL", "date": "", "ddg_snippet": "Dynamic High - Resolution : we divide images into tiles ranging from 1 to 40 of 448 × 448 pixels according to the aspect ratio and resolution of the ...", "subpage_snippet": "", "source": "internvl.readthedocs.io", "link": "https://internvl.readthedocs.io/en/latest/internvl1.5/introduction.html", "content": "Dynamic High - Resolution : we divide images into tiles ranging from 1 to 40 of 448 × 448 pixels according to the aspect ratio and resolution of the ..."}
+{"idx": 7, "title": "Emu3", "date": "", "ddg_snippet": "... and perception tasks, surpassing flagship models such as SDXL and LLaVA - 1 .6, while eliminating the need for diffusion or compositional architectures ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/transformers/model_doc/emu3", "content": "... and perception tasks, surpassing flagship models such as SDXL and LLaVA - 1 .6, while eliminating the need for diffusion or compositional architectures ..."}
+{"idx": 8, "title": "OpenVINO™ Blog", "date": "", "ddg_snippet": "When dynamic quantization is enabled (i.e., dynamic _quantization_group_size != 0), a dynamic _quantize node is inserted before the target matrix ...", "subpage_snippet": "", "source": "blog.openvino.ai", "link": "https://blog.openvino.ai/", "content": "When dynamic quantization is enabled (i.e., dynamic _quantization_group_size != 0), a dynamic _quantize node is inserted before the target matrix ..."}
+{"idx": 9, "title": "NVLM: Open Frontier-Class Multimodal LLMs - NVIDIA ADLR", "date": "", "ddg_snippet": "Furthermore, we introduce a 1 -D tile -tagging design for tile -based dynamic high - resolution images , which significantly boosts performance on ...", "subpage_snippet": "", "source": "research.nvidia.com", "link": "https://research.nvidia.com/labs/adlr/NVLM-1/", "content": "Furthermore, we introduce a 1 -D tile -tagging design for tile -based dynamic high - resolution images , which significantly boosts performance on ..."}
diff --git a/data/sampled_jsons/Langevin_unlearning_A_new_perspective_of_noisy_gradient_descent_for_machine_unlearning_Chien_et_al._.jsonl b/data/sampled_jsons/Langevin_unlearning_A_new_perspective_of_noisy_gradient_descent_for_machine_unlearning_Chien_et_al._.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85dfffa8a740b4ecf5c9c2566dc6e5cae0dd9c5e
--- /dev/null
+++ b/data/sampled_jsons/Langevin_unlearning_A_new_perspective_of_noisy_gradient_descent_for_machine_unlearning_Chien_et_al._.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Certified Unlearning for Neural Networks", "date": "", "ddg_snippet": "Recent works aim to achieve certified unlearning for non-convex tasks (Golatkar et al ., 2020 ; Chourasia & Shah, 2023 ; Chien et al ., 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06985v2", "content": "Recent works aim to achieve certified unlearning for non-convex tasks (Golatkar et al ., 2020 ; Chourasia & Shah, 2023 ; Chien et al ., 2024 ..."}
+{"idx": 1, "title": "Towards Reliable Forgetting: A Survey on Machine Unlearning", "date": "", "ddg_snippet": "... for privacy protection, security, and legal compliance ( e .g., GDPR), machine unlearning has emerged as a critical technique for ensuring the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15115v1", "content": "... for privacy protection, security, and legal compliance ( e .g., GDPR), machine unlearning has emerged as a critical technique for ensuring the ..."}
+{"idx": 2, "title": "Distributional Unlearning: Forgetting Distributions, Not Just", "date": "", "ddg_snippet": "While these lines of work tackle important aspects of unlearning or robustness, they do not provide a unified, distribution‑level forgetting ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15112v1", "content": "While these lines of work tackle important aspects of unlearning or robustness, they do not provide a unified, distribution‑level forgetting ..."}
+{"idx": 3, "title": "Distributional Unlearning: Forgetting Distributions, Not Just", "date": "", "ddg_snippet": "While these lines of work tackle important aspects of unlearning or robustness, they do not provide a unified, distribution‑level forgetting ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15112v2", "content": "While these lines of work tackle important aspects of unlearning or robustness, they do not provide a unified, distribution‑level forgetting ..."}
+{"idx": 4, "title": "NeurIPS 2024 – Research Impact & Leadership", "date": "", "ddg_snippet": "Trustworthy machine learning aims to create AI systems people can rely on, delivering fair, transparent, secure and accurate results aligned with ...", "subpage_snippet": "", "source": "sites.gatech.edu", "link": "https://sites.gatech.edu/research/neurips-2024/", "content": "Trustworthy machine learning aims to create AI systems people can rely on, delivering fair, transparent, secure and accurate results aligned with ..."}
+{"idx": 5, "title": "Train the Trainer Courses, Workshops & Certifications | Langevin", "date": "", "ddg_snippet": "Become a Certified Trainer! More than 20,000 trainers have achieved a Professional Certification with Langevin . You can gain the highest credentials available in the training industry too. It’s as easy as 1-2-3!", "subpage_snippet": "", "source": "langevin.com", "link": "https://langevin.com/", "content": "Become a Certified Trainer! More than 20,000 trainers have achieved a Professional Certification with Langevin . You can gain the highest credentials available in the training industry too. It’s as easy as 1-2-3!"}
+{"idx": 6, "title": "Train the Trainer Certifications for Trainers | Langevin", "date": "", "ddg_snippet": "Join 20,000+ trainers certified with Langevin . Earn top industry credentials through step-by-step certification starting with your chosen area of expertise.", "subpage_snippet": "", "source": "langevin.com", "link": "https://langevin.com/certifications/become-a-certified-trainer/", "content": "Join 20,000+ trainers certified with Langevin . Earn top industry credentials through step-by-step certification starting with your chosen area of expertise."}
+{"idx": 7, "title": "Certified Instructional Designer/Developer | Langevin", "date": "", "ddg_snippet": "Learn how to confidently apply Langevin ’s 12-Step Design Cycle, as well as time-saving shortcuts that produce better courses faster. Receive simple, yet powerful tools and resources designed to ensure your courses are highly interactive, engaging, and lively.", "subpage_snippet": "", "source": "langevin.com", "link": "https://langevin.com/certified-instructional-designer-developer/", "content": "Learn how to confidently apply Langevin ’s 12-Step Design Cycle, as well as time-saving shortcuts that produce better courses faster. Receive simple, yet powerful tools and resources designed to ensure your courses are highly interactive, engaging, and lively."}
+{"idx": 8, "title": "Your Path to Professional Certification | Langevin.com", "date": "", "ddg_snippet": "Your starter kit includes a solid introduction to instructional design,with an overview of the12-Step Langevin Design Cycle. As an added bonus,we’ve even snuck in a few of our design secrets!", "subpage_snippet": "", "source": "langevin.com", "link": "https://langevin.com/your-path-to-professional-certification/", "content": "Your starter kit includes a solid introduction to instructional design,with an overview of the12-Step Langevin Design Cycle. As an added bonus,we’ve even snuck in a few of our design secrets!"}
+{"idx": 9, "title": "Instructional Design for New Designers | Langevin.com", "date": "", "ddg_snippet": "Learn how to confidently apply Langevin ’s 12-Step Design Cycle. Our methodology provides you with time-saving shortcuts that produce better courses faster. You’ll also receive simple, yet powerful tools and resources designed to ensure your courses are highly interactive, engaging, and lively.", "subpage_snippet": "", "source": "langevin.com", "link": "https://langevin.com/workshops/instructional-design-for-new-designers/", "content": "Learn how to confidently apply Langevin ’s 12-Step Design Cycle. Our methodology provides you with time-saving shortcuts that produce better courses faster. You’ll also receive simple, yet powerful tools and resources designed to ensure your courses are highly interactive, engaging, and lively."}
diff --git a/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_filetypepdf.jsonl b/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..092cd92f084caf46c437dd6765f88f896702682a
--- /dev/null
+++ b/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LAST-I CONVERGENCE PROPERTIES OF R -M ALGORITHMS IN GAMES - OpenReview", "date": "", "ddg_snippet": "ABSTRACT Algorithms based on regret matching , specifically regret matching+ (RM+), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice. Unlike algorithms such as optimistic gradient de-scent ascent, which have strong last-iterate and ergodic convergence properties for zero-sum games , virtually nothing is known about the last-iterate ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fWk5Qx0exc", "content": "ABSTRACT Algorithms based on regret matching , specifically regret matching+ (RM+), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice. Unlike algorithms such as optimistic gradient de-scent ascent, which have strong last-iterate and ergodic convergence properties for zero-sum games , virtually nothing is known about the last-iterate ..."}
+{"idx": 1, "title": "PDF Regret Matching+: - Instability, average- and last-iterate convergence ...", "date": "", "ddg_snippet": "Farina, G.-C., Kroer, Lee and Luo, NeurIPS 2023. Last-iterate convergence of regret matching -based algorithms in", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Farina, G.-C., Kroer, Lee and Luo, NeurIPS 2023. Last-iterate convergence of regret matching -based algorithms in"}
+{"idx": 2, "title": "PDF Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
+{"idx": 3, "title": "PDF Last-iterate Convergence in Extensive-Form Games", "date": "", "ddg_snippet": "Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games . However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence . Inspired by recent advances on last-iterate con - vergence of optimistic algorithms in zero-sum normal-form ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2021/file/77bb14f6132ea06dea456584b7d5581e-Paper.pdf", "content": "Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games . However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence . Inspired by recent advances on last-iterate con - vergence of optimistic algorithms in zero-sum normal-form ..."}
+{"idx": 4, "title": "PDF Adaptive Learning in Continuous Games: Optimal Regret Bounds and ...", "date": "", "ddg_snippet": "If all players follow one of these algorithms , the induced trajectory of play converges to Nash equilibrium and the individual regret of each player is bounded as O(1) in all variationally stable games - a large class of games that contains as special cases all convex-concave zero-sum games and monotone / diagonally convex games .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v134/hsieh21a/hsieh21a.pdf", "content": "If all players follow one of these algorithms , the induced trajectory of play converges to Nash equilibrium and the individual regret of each player is bounded as O(1) in all variationally stable games - a large class of games that contains as special cases all convex-concave zero-sum games and monotone / diagonally convex games ."}
+{"idx": 5, "title": "Last-iterate Convergence of Smooth Regret Matching Variants in Learning ...", "date": "", "ddg_snippet": "084 Compared to traditional no- regret algorithms , e.g., FTRL/OMD based algorithms , the primary 085 challenge in proving the last-iterate convergence of RM+ variants is that their feedback is not the 086 loss gradients of the vanilla games . This deviation results in the absence of crucial properties , e.g., 087 monotonicity or weak MVI, which are pivotal for establishing the last-iterate ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TU3wJQfKz8", "content": "084 Compared to traditional no- regret algorithms , e.g., FTRL/OMD based algorithms , the primary 085 challenge in proving the last-iterate convergence of RM+ variants is that their feedback is not the 086 loss gradients of the vanilla games . This deviation results in the absence of crucial properties , e.g., 087 monotonicity or weak MVI, which are pivotal for establishing the last-iterate ..."}
+{"idx": 6, "title": "Last-Iterate Convergence in Adaptive Regret Minimization for ...", "date": "", "ddg_snippet": "Developing an eficient adaptive per-turbation framework that dynamically adjusts perturbations based on the state of NE convergence remains an open challenge. In this paper, we propose an eficient last-iterate convergence algo-rithm for computing EFPE in two-player zero-sum Extensive-Form Games (EFGs) with imperfect information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.07699", "content": "Developing an eficient adaptive per-turbation framework that dynamically adjusts perturbations based on the state of NE convergence remains an open challenge. In this paper, we propose an eficient last-iterate convergence algo-rithm for computing EFPE in two-player zero-sum Extensive-Form Games (EFGs) with imperfect information."}
+{"idx": 7, "title": "PDF Fast Last-Iterate Convergence of Learning in Games Requires Forgetful ...", "date": "", "ddg_snippet": "Slow Convergence due to Lack of Forgetfulness Our work shows that various OFTRL-type algorithms do not have fast last-iterate convergence rates for learning in games .", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2024/neurips24_bad_last_iterate/neurips24_bad_last_iterate.pdf", "content": "Slow Convergence due to Lack of Forgetfulness Our work shows that various OFTRL-type algorithms do not have fast last-iterate convergence rates for learning in games ."}
+{"idx": 8, "title": "Efficient Last-Iterate Convergence in Solving Games - arXiv.org", "date": "", "ddg_snippet": "However, most of regret minimization algorithms , including CFR algorithms , typically only achieve average- iterate convergence and their strategy profile may diverge or cycle, even in normal-form games (NFGs) [Bailey and Piliouras, 2018, Mertikopoulos et al., 2018], a special form of EFGs where each player has only one information set (infoset). Average- iterate convergence implies that the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2308.11256v2", "content": "However, most of regret minimization algorithms , including CFR algorithms , typically only achieve average- iterate convergence and their strategy profile may diverge or cycle, even in normal-form games (NFGs) [Bailey and Piliouras, 2018, Mertikopoulos et al., 2018], a special form of EFGs where each player has only one information set (infoset). Average- iterate convergence implies that the ..."}
+{"idx": 9, "title": "arXiv:2311.00676v2 [cs.GT] 4 Mar 2025", "date": "", "ddg_snippet": "ABSTRACT We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last-iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.00676", "content": "ABSTRACT We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last-iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by ..."}
diff --git a/data/sampled_jsons/Lattimore_Szepesvari_optimism_partial_monitoring_bandits_exploration.jsonl b/data/sampled_jsons/Lattimore_Szepesvari_optimism_partial_monitoring_bandits_exploration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..487bdc1bb411c2563aec986c52b4a294e3bc1ae4
--- /dev/null
+++ b/data/sampled_jsons/Lattimore_Szepesvari_optimism_partial_monitoring_bandits_exploration.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear Partial Monitoring for Sequential Decision Making ...", "date": "", "ddg_snippet": "by J Kirschner · 2023 · Cited by 7 — Example 4 (Graph-Structured Dueling Bandits ). We propose a novel variant of the dueling bandit that extends Example 2 with a feedback graph structure. 45 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume24/22-1248/22-1248.pdf", "content": "by J Kirschner · 2023 · Cited by 7 — Example 4 (Graph-Structured Dueling Bandits ). We propose a novel variant of the dueling bandit that extends Example 2 with a feedback graph structure. 45 pages"}
+{"idx": 1, "title": "The End of Optimism? An Asymptotic Analysis of Finite-Armed ...", "date": "", "ddg_snippet": "by T Lattimore · 2017 · Cited by 154 — Although related, the partial monitoring framework is more general than the bandit setting because the learner may not observe the reward even for the action ... 10 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v54/lattimore17a/lattimore17a.pdf", "content": "by T Lattimore · 2017 · Cited by 154 — Although related, the partial monitoring framework is more general than the bandit setting because the learner may not observe the reward even for the action ... 10 pages"}
+{"idx": 2, "title": "Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "by S Parisi · 2024 · Cited by 4 — Note that Lattimore and Szepesvari [36] already argued against optimism in partial monitoring [8], a generalization of the bandit framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.13909?", "content": "by S Parisi · 2024 · Cited by 4 — Note that Lattimore and Szepesvari [36] already argued against optimism in partial monitoring [8], a generalization of the bandit framework ..."}
+{"idx": 3, "title": "Tor Lattimore", "date": "", "ddg_snippet": "The end of optimism ? an asymptotic analysis of finite-armed linear bandits . T Lattimore , C Szepesvari . Artificial Intelligence and Statistics, 728-737, 2017.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=fkDxJxcAAAAJ&hl=en", "content": "The end of optimism ? an asymptotic analysis of finite-armed linear bandits . T Lattimore , C Szepesvari . Artificial Intelligence and Statistics, 728-737, 2017."}
+{"idx": 4, "title": "Linear partial monitoring for sequential decision making", "date": "", "ddg_snippet": "We survey and extend recent results on the linear formulation of partial monitoring that naturally generalizes the standard linear bandit setting. The main ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3648699.3649045", "content": "We survey and extend recent results on the linear formulation of partial monitoring that naturally generalizes the standard linear bandit setting. The main ..."}
+{"idx": 5, "title": "Beyond Optimism: Exploration With Partially Observable ...", "date": "", "ddg_snippet": "But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=k6ZHvF1vkg&referrer=[the+profile+of+Michael+Bowling](/profile?id=~Michael_Bowling1)", "content": "But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 6, "title": "High-Dimensional Sparse Linear Bandits", "date": "", "ddg_snippet": "by B Hao · 2020 · Cited by 87 — Botao Hao, Tor Lattimore , and Csaba Szepesvari . Adaptive exploration in linear contextual bandit . AISTATS,. 2020. Adel Javanmard and Andrea Montanari. 11 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/7a006957be65e608e863301eb98e1808-Paper.pdf", "content": "by B Hao · 2020 · Cited by 87 — Botao Hao, Tor Lattimore , and Csaba Szepesvari . Adaptive exploration in linear contextual bandit . AISTATS,. 2020. Adel Javanmard and Andrea Montanari. 11 pages"}
+{"idx": 7, "title": "An exploration-by-optimization approach to best of both ...", "date": "", "ddg_snippet": "by S Ito · 2023 · Cited by 4 — In this paper, we consider how to construct best-of-both-worlds linear bandit algorithms that achieve nearly optimal performance for both ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3669256", "content": "by S Ito · 2023 · Cited by 4 — In this paper, we consider how to construct best-of-both-worlds linear bandit algorithms that achieve nearly optimal performance for both ..."}
+{"idx": 8, "title": "arXiv:2503.04010v2 [cs.LG] 18 May 2025", "date": "", "ddg_snippet": "by A Slivkins · 2025 — However, beyond such examples, the broader picture remains murky, especially for the widely- studied structured bandits — bandit problems with a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.04010", "content": "by A Slivkins · 2025 — However, beyond such examples, the broader picture remains murky, especially for the widely- studied structured bandits — bandit problems with a ..."}
+{"idx": 9, "title": "Asymptotically Optimal Information-Directed Sampling", "date": "", "ddg_snippet": "by J Kirschner · 2021 · Cited by 50 — Tor Lattimore and Csaba Szepesvári . The end of optimism ? an asymptotic analysis of finite-armed linear bandits . In Artificial Intelligence and Statistics, pages ... 45 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v134/kirschner21a/kirschner21a.pdf", "content": "by J Kirschner · 2021 · Cited by 50 — Tor Lattimore and Csaba Szepesvári . The end of optimism ? an asymptotic analysis of finite-armed linear bandits . In Artificial Intelligence and Statistics, pages ... 45 pages"}
diff --git a/data/sampled_jsons/Learned_Augmented_Residual_Layer_Table_1_params_added_vs_baseline.jsonl b/data/sampled_jsons/Learned_Augmented_Residual_Layer_Table_1_params_added_vs_baseline.jsonl
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index 0000000000000000000000000000000000000000..df4f81e1faad9c7f1b942777e86fbda045ec170e
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "Nov 12, 2024 · 2 Learned Augmented Residual Layer In this section we describe the main idea behind LAuReL. In its most general form, we reformulate the residual connection to be the following: ... Here α is a learned scalar parameter, and g () is a learned linear function with x i, x i 1 ,, x 0 as inputs, where x j is the output of the j th residual connection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v1", "content": "Nov 12, 2024 · 2 Learned Augmented Residual Layer In this section we describe the main idea behind LAuReL. In its most general form, we reformulate the residual connection to be the following: ... Here α is a learned scalar parameter, and g () is a learned linear function with x i, x i 1 ,, x 0 as inputs, where x j is the output of the j th residual connection."}
+{"idx": 1, "title": "LAuReL: Learned Augmented Residual Layer - OpenReview", "date": "", "ddg_snippet": "May 1 , 2025 · The paper introduces Learned Augmented Residual Layer (LAUREL), a novel enhancement to residual connections in CNNs and Transformers. LAUREL enriches the residual stream by incorporating learned scalar parameters and low-rank transformations, improving efficiency and expressivity.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rUDRWP9WvZ", "content": "May 1 , 2025 · The paper introduces Learned Augmented Residual Layer (LAUREL), a novel enhancement to residual connections in CNNs and Transformers. LAUREL enriches the residual stream by incorporating learned scalar parameters and low-rank transformations, improving efficiency and expressivity."}
+{"idx": 2, "title": "BAW2501/LAuReL-Learned-Augmented-Residual-Layer - GitHub", "date": "", "ddg_snippet": "Nov 20, 2024 · This repository contains my independent implementations of the three LAuReL variants described in the article titled \"LAuReL: Learned Augmented Residual Layer \". These implementations aim to explore the concepts presented in the paper and evaluate their effectiveness on image datasets. Unofficial ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/LAuReL-Learned-Augmented-Residual-Layer", "content": "Nov 20, 2024 · This repository contains my independent implementations of the three LAuReL variants described in the article titled \"LAuReL: Learned Augmented Residual Layer \". These implementations aim to explore the concepts presented in the paper and evaluate their effectiveness on image datasets. Unofficial ..."}
+{"idx": 3, "title": "LAUREL: Learned Augmented Residual Layer——学习增强残差层-CSDN博客", "date": "", "ddg_snippet": "Dec 18, 2024 · 文章浏览阅读852次，点赞22次，收藏26次。LAUREL: Learned Augmented Residual Layer ——学习增强残差层_laurel: learned augmented residual layer", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/Together_CZ/article/details/144331301", "content": "Dec 18, 2024 · 文章浏览阅读852次，点赞22次，收藏26次。LAUREL: Learned Augmented Residual Layer ——学习增强残差层_laurel: learned augmented residual layer"}
+{"idx": 4, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer (LAuReL) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.07501", "content": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer (LAuReL) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 5, "title": "ICML Poster LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper, we introduce Learned Augmented Residual Layer (LAuReL), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43889", "content": "In this paper, we introduce Learned Augmented Residual Layer (LAuReL), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation."}
+{"idx": 6, "title": "LAuReL: Learned Augmented Residual Layer - OpenReview", "date": "", "ddg_snippet": "Jun 20, 2024 · TL;DR: We introduce a novel alternative to the residual connection called 'LAuReL', which augments the residual connection with learnable light-weight improvements, outperforming the baseline on non-trivial vision and language tasks..", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=honBJOVRn5", "content": "Jun 20, 2024 · TL;DR: We introduce a novel alternative to the residual connection called 'LAuReL', which augments the residual connection with learnable light-weight improvements, outperforming the baseline on non-trivial vision and language tasks.."}
+{"idx": 7, "title": "LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "9 Jul 2025 — In terms of parameters, as seen in Table 1 LAuReL has +0.1% more parameters than the baseline , and +2.42% more step-time latency (forward + backward pass) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rUDRWP9WvZ&noteId=wS55prNog2", "content": "9 Jul 2025 — In terms of parameters, as seen in Table 1 LAuReL has +0.1% more parameters than the baseline , and +2.42% more step-time latency (forward + backward pass) ..."}
+{"idx": 8, "title": "LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "24 Jun 2025 — LAuReL variant outperforms the baseline on all but one dataset while adding only 0.012% extra parameters. ... one or two extra parameters per ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v4", "content": "24 Jun 2025 — LAuReL variant outperforms the baseline on all but one dataset while adding only 0.012% extra parameters. ... one or two extra parameters per ..."}
+{"idx": 9, "title": "LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "11 Nov 2024 — LAUREL, a novel residual layer , boosts performance in vision & language models with less parameter usage.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-LAuReL-Learned-Augmented-cm3hsv976bjks016fio3mu37r", "content": "11 Nov 2024 — LAUREL, a novel residual layer , boosts performance in vision & language models with less parameter usage."}
diff --git a/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016.jsonl b/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..93bfae6047a72638a7b425415fe4c2b6e6a0a9fb
--- /dev/null
+++ b/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Abstract page for arXiv paper 1606.09282: Learning without Forgetting", "date": "", "ddg_snippet": "Title: Learning without Forgetting . Authors:Zhizhong Li , Derek Hoiem .Conference version appears in ECCV 2016 ; updated with journal version. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1606.09282", "content": "Title: Learning without Forgetting . Authors:Zhizhong Li , Derek Hoiem .Conference version appears in ECCV 2016 ; updated with journal version. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML)."}
+{"idx": 1, "title": "Learning without forgetting - Illinois Experts", "date": "", "ddg_snippet": "Li , Z & Hoiem , D 2016 , Learning without forgetting . in B Leibe, J Matas, N Sebe & M Welling (eds), Computer Vision - 14th European Conference, ECCV 2016 , Proceedings.", "subpage_snippet": "", "source": "experts.illinois.edu", "link": "https://experts.illinois.edu/en/publications/learning-without-forgetting", "content": "Li , Z & Hoiem , D 2016 , Learning without forgetting . in B Leibe, J Matas, N Sebe & M Welling (eds), Computer Vision - 14th European Conference, ECCV 2016 , Proceedings."}
+{"idx": 2, "title": "(PDF) Learning without forgetting ( 2016 ) | Zhizhong Li | 642 Citations", "date": "", "ddg_snippet": "We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/learning-without-forgetting-34a4300f5h", "content": "We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities."}
+{"idx": 3, "title": "Learning Without Forgetting | springerprofessional.de", "date": "", "ddg_snippet": "Learning Without Forgetting . Authors : Zhizhong Li , Derek Hoiem . Published in: Computer Vision – ECCV 2016 .In: Proceedings of the International Conference on Learning Representations (ICLR) ( 2016 , to appear).", "subpage_snippet": "", "source": "www.springerprofessional.de", "link": "https://www.springerprofessional.de/en/learning-without-forgetting/10709316", "content": "Learning Without Forgetting . Authors : Zhizhong Li , Derek Hoiem . Published in: Computer Vision – ECCV 2016 .In: Proceedings of the International Conference on Learning Representations (ICLR) ( 2016 , to appear)."}
+{"idx": 4, "title": "[PDF] Learning without Forgetting | Semantic Scholar", "date": "", "ddg_snippet": "This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Learning-without-Forgetting-Li-Hoiem/8f3b80ddc0dd62e6c3369fabb1715990c29e9b9a", "content": "This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques."}
+{"idx": 5, "title": "Paper page - Learning without Forgetting", "date": "", "ddg_snippet": "Learning without Forgetting . Published on Jun 29, 2016 . Upvote. - Authors: Zhizhong Li . , Derek Hoiem . Abstract. When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/1606.09282", "content": "Learning without Forgetting . Published on Jun 29, 2016 . Upvote. - Authors: Zhizhong Li . , Derek Hoiem . Abstract. When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available."}
+{"idx": 6, "title": "Learning without Forgetting", "date": "", "ddg_snippet": "Zhizhong Li , Derek Hoiem .A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/29990101/", "content": "Zhizhong Li , Derek Hoiem .A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance."}
+{"idx": 7, "title": "Learning without Forgetting", "date": "", "ddg_snippet": "Learning without Forgetting . Zhizhong Li , Derek Hoiem , Member, IEEE. Abstract—When building a unied vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available.", "subpage_snippet": "", "source": "www.ivana.work", "link": "http://www.ivana.work/files/public/Learning+without+Forgetting.pdf", "content": "Learning without Forgetting . Zhizhong Li , Derek Hoiem , Member, IEEE. Abstract—When building a unied vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available."}
+{"idx": 8, "title": "Continual Learning and", "date": "", "ddg_snippet": "This section describes the approach called Learning without Forgetting given in Li and Hoiem [ 2016 ].", "subpage_snippet": "", "source": "www.cs.uic.edu", "link": "https://www.cs.uic.edu/~liub/lifelong-learning/continual-learning.pdf", "content": "This section describes the approach called Learning without Forgetting given in Li and Hoiem [ 2016 ]."}
+{"idx": 9, "title": "Learning without Forgetting | Zhizhong Li", "date": "", "ddg_snippet": "Zhizhong Li . Derek Hoiem . Learning without Forgetting spotlight video. Abstract. When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available.", "subpage_snippet": "", "source": "zhizhongli.vision", "link": "https://zhizhongli.vision/projects/learning-without-forgetting/", "content": "Zhizhong Li . Derek Hoiem . Learning without Forgetting spotlight video. Abstract. When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available."}
diff --git a/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016_abstract.jsonl b/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016_abstract.jsonl
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index 0000000000000000000000000000000000000000..24fb83e429e29ddc0195a379cc73718839a0703c
--- /dev/null
+++ b/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1606.09282] Learning without Forgetting", "date": "", "ddg_snippet": "View a PDF of the paper titled Learning without Forgetting , by Zhizhong Li and 1 other authors ... We propose our Learning without Forgetting method ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1606.09282", "content": "View a PDF of the paper titled Learning without Forgetting , by Zhizhong Li and 1 other authors ... We propose our Learning without Forgetting method ..."}
+{"idx": 1, "title": "Black-Box Forgetting", "date": "", "ddg_snippet": "... Learning with Selective Forgetting (LSF) (Shibata et al., 2021 ) , which has been proposed in the context of continual learning (Kirkpatrick ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.00409v1", "content": "... Learning with Selective Forgetting (LSF) (Shibata et al., 2021 ) , which has been proposed in the context of continual learning (Kirkpatrick ..."}
+{"idx": 2, "title": "Similarity-based Context Aware Continual Learning for Spiking", "date": "", "ddg_snippet": "Lifelong learning is the prominent capability of biological intelligence and the significant challenge of artificial intelligence.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.05802v1", "content": "Lifelong learning is the prominent capability of biological intelligence and the significant challenge of artificial intelligence."}
+{"idx": 3, "title": "Boosting Domain Incremental Learning: Selecting the Optimal", "date": "", "ddg_snippet": "... and learning new knowledge, where improvements in one domain often lead to reduced accuracy in another, ultimately limiting overall performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23744v1", "content": "... and learning new knowledge, where improvements in one domain often lead to reduced accuracy in another, ultimately limiting overall performance."}
+{"idx": 4, "title": "GitHub - lizhitwo/LearningWithoutForgetting: Repository for the", "date": "", "ddg_snippet": "... li2016learning, title={ Learning Without Forgetting }, author={ Li , Zhizhong and Hoiem , Derek}, booktitle={European Conference on Computer Vision}, ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lizhitwo/LearningWithoutForgetting", "content": "... li2016learning, title={ Learning Without Forgetting }, author={ Li , Zhizhong and Hoiem , Derek}, booktitle={European Conference on Computer Vision}, ..."}
+{"idx": 5, "title": "Research on a class-incremental learning method based on sonar", "date": "", "ddg_snippet": "... full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.", "subpage_snippet": "", "source": "www.jnwpu.org", "link": "https://www.jnwpu.org/articles/jnwpu/ref/2023/02/jnwpu2023412p303/jnwpu2023412p303.html", "content": "... full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform."}
+{"idx": 6, "title": "How Our Disentangled Learning Framework Tackles Lifelong", "date": "", "ddg_snippet": "... learning equivariant representations without catastrophic forgetting and explicitly updating class-specific information without harming information ...", "subpage_snippet": "", "source": "fewshot.tech", "link": "https://fewshot.tech/how-our-disentangled-learning-framework-tackles-lifelong-learning-challenges", "content": "... learning equivariant representations without catastrophic forgetting and explicitly updating class-specific information without harming information ..."}
+{"idx": 7, "title": "Federated Continual Learning based on Central Memory Rehearsal", "date": "", "ddg_snippet": "... obstacle encountered in practical implementations of federated learning revolves around the need to address the issue of catastrophic forgetting ...", "subpage_snippet": "", "source": "iecscience.org", "link": "https://iecscience.org/jpapers/181", "content": "... obstacle encountered in practical implementations of federated learning revolves around the need to address the issue of catastrophic forgetting ..."}
+{"idx": 8, "title": "ML4H 2025 Template: Proceedings Track", "date": "", "ddg_snippet": "Machine learning (ML) models in healthcare face significant operational friction when moving from research prototypes to clinical practice.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.16839v2", "content": "Machine learning (ML) models in healthcare face significant operational friction when moving from research prototypes to clinical practice."}
+{"idx": 9, "title": "Reawakening Knowledge:Anticipatory Recovery from Catastrophic", "date": "", "ddg_snippet": "Compared to standard task-incremental and class-incremental continual learning settings Chen and Liu ( 2018 ) which experience each task only once ...", "subpage_snippet": "", "source": "mengyeren.com", "link": "https://mengyeren.com/research/2024/reawakening-knowledge-anticipatory-recovery-from-catastrophic-interference-via-structured-training/", "content": "Compared to standard task-incremental and class-incremental continual learning settings Chen and Liu ( 2018 ) which experience each task only once ..."}
diff --git a/data/sampled_jsons/Learning_without_Forgetting_performs_favorably_compared_to_commonly_used_feature_extraction_and_fine.jsonl b/data/sampled_jsons/Learning_without_Forgetting_performs_favorably_compared_to_commonly_used_feature_extraction_and_fine.jsonl
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index 0000000000000000000000000000000000000000..c0aa6770a225186cd780e3b68cf2d50492bc9cfe
--- /dev/null
+++ b/data/sampled_jsons/Learning_without_Forgetting_performs_favorably_compared_to_commonly_used_feature_extraction_and_fine.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1606.09282] Learning without Forgetting - arXiv.org", "date": "", "ddg_snippet": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1606.09282", "content": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable."}
+{"idx": 1, "title": "Learning without Forgetting | IEEE Journals & Magazine | IEEE Xplore", "date": "", "ddg_snippet": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/8107520", "content": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable."}
+{"idx": 2, "title": "[PDF] Learning without Forgetting | Semantic Scholar", "date": "", "ddg_snippet": "This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques. When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Learning-without-Forgetting-Li-Hoiem/8f3b80ddc0dd62e6c3369fabb1715990c29e9b9a", "content": "This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques. When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all ..."}
+{"idx": 3, "title": "Learning without Forgetting - PubMed", "date": "", "ddg_snippet": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/29990101/", "content": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable."}
+{"idx": 4, "title": "Learning without Forgetting | IEEE Transactions on Pattern Analysis and ...", "date": "", "ddg_snippet": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/TPAMI.2017.2773081", "content": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable."}
+{"idx": 5, "title": "Learning without forgetting - Illinois Experts", "date": "", "ddg_snippet": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable.", "subpage_snippet": "", "source": "experts.illinois.edu", "link": "https://experts.illinois.edu/en/publications/learning-without-forgetting", "content": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable."}
+{"idx": 6, "title": "(Open Access) Learning without Forgetting (2016) | Zhizhong Li | 2558 ...", "date": "", "ddg_snippet": "TL;DR: This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/learning-without-forgetting-phr5pbe48k", "content": "TL;DR: This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques."}
+{"idx": 7, "title": "1 Learning without Forgetting - arXiv.org", "date": "", "ddg_snippet": "capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserv ng the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1606.09282", "content": "capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserv ng the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses ..."}
+{"idx": 8, "title": "Learning without Forgetting | alphaXiv", "date": "", "ddg_snippet": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/1606.09282v3", "content": "Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable."}
+{"idx": 9, "title": "LibContinual/reproduce/lwf/README.md at master - GitHub", "date": "", "ddg_snippet": "We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/RL-VIG/LibContinual/blob/master/reproduce/lwf/README.md", "content": "We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume ..."}
diff --git a/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Mohammadi_Sepahvand_Thudi_arXiv.jsonl b/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Mohammadi_Sepahvand_Thudi_arXiv.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LEVERAGING | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "LEVERAGING definition: 1. present participle of leverage 2. to use something that you already have in order to achieve…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/leveraging", "content": "LEVERAGING definition: 1. present participle of leverage 2. to use something that you already have in order to achieve…. Learn more."}
+{"idx": 1, "title": "Leveraging - definition of leveraging by The Free Dictionary", "date": "", "ddg_snippet": "Define leveraging . leveraging synonyms, leveraging pronunciation, leveraging translation, English dictionary definition of leveraging . n. 1. a. The action of a lever. b. The mechanical advantage of a lever. 2. Positional advantage; power to act effectively: \"started his ... career with far...", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/leveraging", "content": "Define leveraging . leveraging synonyms, leveraging pronunciation, leveraging translation, English dictionary definition of leveraging . n. 1. a. The action of a lever. b. The mechanical advantage of a lever. 2. Positional advantage; power to act effectively: \"started his ... career with far..."}
+{"idx": 2, "title": "What is another word for leveraging ? | Leveraging Synonyms ...", "date": "", "ddg_snippet": "Synonyms for leveraging include using, taking advantage of, exploiting, manipulating, abusing, beguiling, controlling, deceiving, defrauding and fleecing. Find more ...", "subpage_snippet": "", "source": "www.wordhippo.com", "link": "https://www.wordhippo.com/what-is/another-word-for/leveraging.html", "content": "Synonyms for leveraging include using, taking advantage of, exploiting, manipulating, abusing, beguiling, controlling, deceiving, defrauding and fleecing. Find more ..."}
+{"idx": 3, "title": "LEVERAGING - Definition & Meaning - Reverso English Dictionary", "date": "", "ddg_snippet": "Leveraging definition : use of something to maximum advantage. Check meanings, examples, usage tips, pronunciation, domains, related words.", "subpage_snippet": "", "source": "mobile-dictionary.reverso.net", "link": "https://mobile-dictionary.reverso.net/english-definition/leveraging", "content": "Leveraging definition : use of something to maximum advantage. Check meanings, examples, usage tips, pronunciation, domains, related words."}
+{"idx": 4, "title": "Leveraging - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "investing with borrowed money as a way to amplify potential gains (at the risk of greater losses)", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/leveraging", "content": "investing with borrowed money as a way to amplify potential gains (at the risk of greater losses)"}
+{"idx": 5, "title": "LEVERAGE Definition & Meaning | Dictionary.com", "date": "", "ddg_snippet": "Leverage definition: the action of a lever, a rigid bar that pivots about one point and that is used to move an object at a second point by a force applied at a third.. See examples of LEVERAGE used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/leverage", "content": "Leverage definition: the action of a lever, a rigid bar that pivots about one point and that is used to move an object at a second point by a force applied at a third.. See examples of LEVERAGE used in a sentence."}
+{"idx": 6, "title": "LEVERAGE definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "6 meanings: 1. the action of a lever 2. the mechanical advantage gained by employing a lever 3. power to accomplish something;.... Click for more definitions.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/leverage", "content": "6 meanings: 1. the action of a lever 2. the mechanical advantage gained by employing a lever 3. power to accomplish something;.... Click for more definitions."}
+{"idx": 7, "title": "leverage noun - Definition, pictures, pronunciation and usage ...", "date": "", "ddg_snippet": "Definition of leverage noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/leverage_1", "content": "Definition of leverage noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 8, "title": "LEVERAGE Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of LEVERAGE is the action of a lever or the mechanical advantage gained by it. How to use leverage in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/leverage", "content": "The meaning of LEVERAGE is the action of a lever or the mechanical advantage gained by it. How to use leverage in a sentence."}
+{"idx": 9, "title": "Leverage: Definition, Meaning, and Examples", "date": "", "ddg_snippet": "Jun 24, 2025 · What does it mean to \"leverage\" a situation? Discover the history, meaning, and real-world applications of this influential word.", "subpage_snippet": "", "source": "usdictionary.com", "link": "https://usdictionary.com/definitions/leverage/", "content": "Jun 24, 2025 · What does it mean to \"leverage\" a situation? Discover the history, meaning, and real-world applications of this influential word."}
diff --git a/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_SGLD_equation.jsonl b/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_SGLD_equation.jsonl
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index 0000000000000000000000000000000000000000..d1f56a189317deda3653c9d610d378b19ac9101c
--- /dev/null
+++ b/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_SGLD_equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "In this work, we have introduced a principled approach to quantifying machine unlearning difficulty at the level of individual data points in terms of per - instance privacy losses, which bound the Rényi divergence between training with and without a datapoint.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "In this work, we have introduced a principled approach to quantifying machine unlearning difficulty at the level of individual data points in terms of per - instance privacy losses, which bound the Rényi divergence between training with and without a datapoint."}
+{"idx": 1, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "May 24, 2025 · To demonstrate the practical applicability of our theory, we present empirical results showing that our theoretical predictions are born out both for Stochastic Gradient Langevin Dynamics ( SGLD ) as well as for standard fine-tuning without explicit noise.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "May 24, 2025 · To demonstrate the practical applicability of our theory, we present empirical results showing that our theoretical predictions are born out both for Stochastic Gradient Langevin Dynamics ( SGLD ) as well as for standard fine-tuning without explicit noise."}
+{"idx": 2, "title": "Leveraging Per-Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Our results show that per - instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and practical way to assess unlearning performance.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "Our results show that per - instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and practical way to assess unlearning performance."}
+{"idx": 3, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Armed with per - instance privacy losses, we revisit Chien et al.’s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin unlearning ” a.k.a “noisy fine-tuning”), based on training without the forget set.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.18786", "content": "Armed with per - instance privacy losses, we revisit Chien et al.’s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin unlearning ” a.k.a “noisy fine-tuning”), based on training without the forget set."}
+{"idx": 4, "title": "Forget to Flourish: Leveraging Machine-Unlearning on ...", "date": "", "ddg_snippet": "This scenario creates a privacy threat, as pre-trained models can be intentionally crafted to compromise the privacy of fine-tuning datasets. In this study, we introduce a novel poisoning technique that uses model- unlearning as an attack tool.", "subpage_snippet": "", "source": "shadow.merl.com", "link": "https://shadow.merl.com/publications/docs/TR2025-017.pdf", "content": "This scenario creates a privacy threat, as pre-trained models can be intentionally crafted to compromise the privacy of fine-tuning datasets. In this study, we introduce a novel poisoning technique that uses model- unlearning as an attack tool."}
+{"idx": 5, "title": "Pseudo-Labeling for Enhanced User Privacy in Approximate ...", "date": "", "ddg_snippet": "This paper presents an effective pseudo-labeling method for machine unlearning , focusing on protecting user privacy . Recent research predominantly focused on mo", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10890795", "content": "This paper presents an effective pseudo-labeling method for machine unlearning , focusing on protecting user privacy . Recent research predominantly focused on mo"}
+{"idx": 6, "title": "Enhancing Privacy in Machine Unlearning: Posterior ...", "date": "", "ddg_snippet": "Feb 17, 2025 · In this paper, we propose the posterior perturbation-based method to effectively defend against unlearned models against MIAs. This method randomizes the unlearned model’s output instead of modifying the model, thereby mitigating the privacy leakage risks caused by machine unlearning .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-1551-3_16", "content": "Feb 17, 2025 · In this paper, we propose the posterior perturbation-based method to effectively defend against unlearned models against MIAs. This method randomizes the unlearned model’s output instead of modifying the model, thereby mitigating the privacy leakage risks caused by machine unlearning ."}
+{"idx": 7, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "by NM Sepahvand — We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine- tuning. We begin by sharpening an analysis of.", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2025/pdf/sepahvand.pdf", "content": "by NM Sepahvand — We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine- tuning. We begin by sharpening an analysis of."}
+{"idx": 8, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/141960", "content": "We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}
+{"idx": 9, "title": "A Certified Unlearning Approach without Access to Source ...", "date": "", "ddg_snippet": "by UY Basaran — This paper proposes a novel certified unlearning framework that enables the unlearning process without requiring access to the original dataset.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8lt5776GLB", "content": "by UY Basaran — This paper proposes a novel certified unlearning framework that enables the unlearning process without requiring access to the original dataset."}
diff --git a/data/sampled_jsons/Li_et_al._2024_Parameter-free_scalable_incomplete_multiview_clustering_prototype_graph_year_2024.jsonl b/data/sampled_jsons/Li_et_al._2024_Parameter-free_scalable_incomplete_multiview_clustering_prototype_graph_year_2024.jsonl
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index 0000000000000000000000000000000000000000..4f074252aa829e630bc620c319e7283f9e0b26ef
--- /dev/null
+++ b/data/sampled_jsons/Li_et_al._2024_Parameter-free_scalable_incomplete_multiview_clustering_prototype_graph_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "One-step Multi-view Clustering With Adaptive Low-rank", "date": "", "ddg_snippet": "... the anchor graph for getting clustering indicators, which not only increases computational complexity but also leads to additional information loss.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14724v1", "content": "... the anchor graph for getting clustering indicators, which not only increases computational complexity but also leads to additional information loss."}
+{"idx": 1, "title": "Parameter - Free and Scalable Incomplete Multiview Clustering With...", "date": "", "ddg_snippet": "Inspired by recent unsupervised multiview prototype progress, we propose a novel parameter - free and scalable incomplete multiview clustering framework with the prototype graph termed PSIMVC-PG to solve the aforementioned issues.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9777866", "content": "Inspired by recent unsupervised multiview prototype progress, we propose a novel parameter - free and scalable incomplete multiview clustering framework with the prototype graph termed PSIMVC-PG to solve the aforementioned issues."}
+{"idx": 2, "title": "Incomplete multi - view clustering based on enhanced view-feature...", "date": "", "ddg_snippet": "Li M, Wang S, Liu X et al ( 2024 ) Parameter - free and scalable incomplete multiview clustering with prototype graph . IEEE Trans Neural Netw Learn Syst 35(1):300–310.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10489-025-06767-w", "content": "Li M, Wang S, Liu X et al ( 2024 ) Parameter - free and scalable incomplete multiview clustering with prototype graph . IEEE Trans Neural Netw Learn Syst 35(1):300–310."}
+{"idx": 3, "title": "Highly Efficient Rotation-Invariant Spectral Embedding for Scalable ...", "date": "", "ddg_snippet": "Parameter - free and scalable incomplete multiview clustering with prototype graph . Scalable Incomplete Multi - View Clustering with Structure Alignment. In Proceedings of the 31st ACM International Conference on Multimedia, 3031–3040.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.11898v1", "content": "Parameter - free and scalable incomplete multiview clustering with prototype graph . Scalable Incomplete Multi - View Clustering with Structure Alignment. In Proceedings of the 31st ACM International Conference on Multimedia, 3031–3040."}
+{"idx": 4, "title": "Scalable Incomplete Multi - View Clustering with Structure Alignment", "date": "", "ddg_snippet": "2022. Parameter - Free and Scalable Incomplete Multiview Clustering With Prototype Graph .2022. Refining graph structure for incomplete multi - view clustering . IEEE Transactions on Neural Networks and Learning Systems (2022). [25] Yingming Li , Ming Yang, and Zhongfei Zhang.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=l2GIKGydQQ", "content": "2022. Parameter - Free and Scalable Incomplete Multiview Clustering With Prototype Graph .2022. Refining graph structure for incomplete multi - view clustering . IEEE Transactions on Neural Networks and Learning Systems (2022). [25] Yingming Li , Ming Yang, and Zhongfei Zhang."}
+{"idx": 5, "title": "Self- Completed Bipartite Graph Learning for Fast Incomplete ...", "date": "", "ddg_snippet": "Index Terms— Incomplete multi - view clustering , bipartite graph learning, graph self-completion.[40] S. Wang et al ., “Fast parameter - free multi - view subspace clustering with consensus anchor guidance,” IEEE Trans.", "subpage_snippet": "", "source": "www.fst.um.edu.mo", "link": "https://www.fst.um.edu.mo/personal/wp-content/uploads/2024/04/SCBGL.pdf", "content": "Index Terms— Incomplete multi - view clustering , bipartite graph learning, graph self-completion.[40] S. Wang et al ., “Fast parameter - free multi - view subspace clustering with consensus anchor guidance,” IEEE Trans."}
+{"idx": 6, "title": "Hubness-Enabled Clustering and Recovery for Large-Scale...", "date": "", "ddg_snippet": "Incomplete multi - view clustering has gained considerable attention in recent years due to the prevalence of incomplete multi - view data in real-world applications.2022. Parameter - Free and Scalable Incomplete Multiview Clustering with Prototype Graph . IEEE Trans.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3694689", "content": "Incomplete multi - view clustering has gained considerable attention in recent years due to the prevalence of incomplete multi - view data in real-world applications.2022. Parameter - Free and Scalable Incomplete Multiview Clustering with Prototype Graph . IEEE Trans."}
+{"idx": 7, "title": "Scalable Incomplete Multi - View Clustering with Structure... | DeepAI", "date": "", "ddg_snippet": "The success of existing multi - view clustering (MVC) relies on the assumption that all views are complete . However, samples are usually partially available due to data corruption or sensor malfunction, which raises the research of incomplete multi - view clustering (IMVC).", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/scalable-incomplete-multi-view-clustering-with-structure-alignment", "content": "The success of existing multi - view clustering (MVC) relies on the assumption that all views are complete . However, samples are usually partially available due to data corruption or sensor malfunction, which raises the research of incomplete multi - view clustering (IMVC)."}
+{"idx": 8, "title": "Parameter - Free Auto-Weighted Multiple Graph Learning...", "date": "", "ddg_snippet": "When accessing to multi - view data, some researches based on multiple graph learning have been developed. [Chaud-huri et al ., 2009] projected the data into a lower dimensional subspace and clustered multiview data via canonical correla-tion analysis.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/Proceedings/16/Papers/269.pdf", "content": "When accessing to multi - view data, some researches based on multiple graph learning have been developed. [Chaud-huri et al ., 2009] projected the data into a lower dimensional subspace and clustered multiview data via canonical correla-tion analysis."}
+{"idx": 9, "title": "ICLR Poster Simple yet Effective Incomplete Multi - view Clustering ...", "date": "", "ddg_snippet": "Abstract: Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/30038", "content": "Abstract: Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity."}
diff --git a/data/sampled_jsons/Li_et_al_2024_episodic_reinforcement_learning.jsonl b/data/sampled_jsons/Li_et_al_2024_episodic_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05fe79d49e1981c2087c7705caa26342f0c92052
--- /dev/null
+++ b/data/sampled_jsons/Li_et_al_2024_episodic_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT LEARNING - arXiv.org", "date": "", "ddg_snippet": "a et al ., 2018a) where an action is sampled in each time step. The action selection concept in ERL is promising as sh wn in recent works in RL (Otto et al ., 2022; Li et al ., 2024 ). Similar insights have been made in the field of Imitation Learning , where predicting action sequences instead of single ac-tions ha", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536v2", "content": "a et al ., 2018a) where an action is sampled in each time step. The action selection concept in ERL is promising as sh wn in recent works in RL (Otto et al ., 2022; Li et al ., 2024 ). Similar insights have been made in the field of Imitation Learning , where predicting action sequences instead of single ac-tions ha"}
+{"idx": 1, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "The paper claims to be \"the first off-policy episodic reinforcement learning algorithm,\" but this seems to be an overclaim. To my knowledge, there are already several related works, such as: Liang D, Zhang Y, Liu Y. \" Episodic Reinforcement Learning with Expanded State-reward Space.\" arXiv preprint arXiv:2401.10516, 2024 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=N4NhVN30ph", "content": "The paper claims to be \"the first off-policy episodic reinforcement learning algorithm,\" but this seems to be an overclaim. To my knowledge, there are already several related works, such as: Liang D, Zhang Y, Liu Y. \" Episodic Reinforcement Learning with Expanded State-reward Space.\" arXiv preprint arXiv:2401.10516, 2024 ."}
+{"idx": 2, "title": "dblp: TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement ...", "date": "", "ddg_snippet": "Ge Li , Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann: TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning . ICLR 2025", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/LiTZJLN25", "content": "Ge Li , Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann: TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning . ICLR 2025"}
+{"idx": 3, "title": "PDF Episodic Reinforcement Learning with Expanded State-reward Space", "date": "", "ddg_snippet": "On the other hand, Li et al . [16] and Liang et al . [19] improved the ineficient episodic memory container via diferentiable neural net-works and K-means clusters, respectively.", "subpage_snippet": "", "source": "ifaamas.csc.liv.ac.uk", "link": "https://ifaamas.csc.liv.ac.uk/Proceedings/aamas2024/pdfs/p1192.pdf", "content": "On the other hand, Li et al . [16] and Liang et al . [19] improved the ineficient episodic memory container via diferentiable neural net-works and K-means clusters, respectively."}
+{"idx": 4, "title": "Episodic Reinforcement Learning with Expanded State-reward Space", "date": "", "ddg_snippet": "Empowered by deep neural networks, deep reinforcement learning (DRL) has demonstrated tremendous empirical successes in various domains, including games, health care, and autonomous driving. Despite these advancements, DRL is still identified as data-inefficient as effective policies demand vast numbers of environmental samples. Recently, episodic control (EC)-based model-free DRL methods ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/epdf/10.5555/3635637.3662976", "content": "Empowered by deep neural networks, deep reinforcement learning (DRL) has demonstrated tremendous empirical successes in various domains, including games, health care, and autonomous driving. Despite these advancements, DRL is still identified as data-inefficient as effective policies demand vast numbers of environmental samples. Recently, episodic control (EC)-based model-free DRL methods ..."}
+{"idx": 5, "title": "TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT LEARNING - OpenReview", "date": "", "ddg_snippet": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typ-ically parameterized by trajectory generators such as Movement ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fPHT0z4cS5", "content": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typ-ically parameterized by trajectory generators such as Movement ..."}
+{"idx": 6, "title": "Traffic navigation via reinforcement learning with episodic-guided ...", "date": "", "ddg_snippet": "In RL settings, the agent not only learns and represents its environment but also sequentially executes optimal actions to accomplish a given task. As a result, RL, particularly deep reinforcement learning (DRL), has garnered significant attention for decision-making in AVs (Aradi, 2022, Wang et al ., 2024 , Hou et al ., 2023).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197624013058", "content": "In RL settings, the agent not only learns and represents its environment but also sequentially executes optimal actions to accomplish a given task. As a result, RL, particularly deep reinforcement learning (DRL), has garnered significant attention for decision-making in AVs (Aradi, 2022, Wang et al ., 2024 , Hou et al ., 2023)."}
+{"idx": 7, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "Abstract and Figures This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929465_TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning", "content": "Abstract and Figures This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework."}
+{"idx": 8, "title": "A arXiv:2401.10516v1 [cs.LG] 19 Jan 2024 EPISODIC REINFOR", "date": "", "ddg_snippet": "sian random projection Lin et al . (2018), differentiable neural network Li et al . (2022) or K-means cluster Liang et al . (2023b) to compress the state-action pair into a low-dimensional form for the convenience of retrieval. However, once the past experiences have been retrieved, the exploitations of the retrieval experiences, especially the information-rich historical states, are not well ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.10516v1", "content": "sian random projection Lin et al . (2018), differentiable neural network Li et al . (2022) or K-means cluster Liang et al . (2023b) to compress the state-action pair into a low-dimensional form for the convenience of retrieval. However, once the past experiences have been retrieved, the exploitations of the retrieval experiences, especially the information-rich historical states, are not well ..."}
+{"idx": 9, "title": "dblp: Open the Black Box: Step-based Policy Updates for Temporally ...", "date": "", "ddg_snippet": "Ge Li , Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann: Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/LiZRTOLN24", "content": "Ge Li , Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann: Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning ."}
diff --git "a/data/sampled_jsons/Linear_convergence_Sinkhorn_algorithm_generalized_static_Schr\303\266dinger_bridge_conclusion_Luo_Tseng.jsonl" "b/data/sampled_jsons/Linear_convergence_Sinkhorn_algorithm_generalized_static_Schr\303\266dinger_bridge_conclusion_Luo_Tseng.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..e892dba553c4c59721083fb020c215a50a7bb74b
--- /dev/null
+++ "b/data/sampled_jsons/Linear_convergence_Sinkhorn_algorithm_generalized_static_Schr\303\266dinger_bridge_conclusion_Luo_Tseng.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear Convergence of Sinkhorn’s Algorithm for Generalized ...", "date": "", "ddg_snippet": "In this paper, we present the generalized static Schrödinger bridge problem, establish its Kantorovich dual, and show that the associated generalized Sinkhorn algorithm con-verges linearly in a dimension independent manner under mild assumptions on the general divergence functional f , weight matrix W and margin (r,c).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "In this paper, we present the generalized static Schrödinger bridge problem, establish its Kantorovich dual, and show that the associated generalized Sinkhorn algorithm con-verges linearly in a dimension independent manner under mild assumptions on the general divergence functional f , weight matrix W and margin (r,c)."}
+{"idx": 1, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "Jul 16, 2025 · We establish Kantorovich duality and linear convergence of Sinkhorn 's algorithm for the generalized SSB problem under mild conditions. Our results provide a new rigorous foundation for understanding Sinkhorn -type iterative methods in the context of large-scale generalized Schrödinger bridges. more »", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10625991-linear-convergence-sinkhorn-algorithm-generalized-static-schrodinger-bridge", "content": "Jul 16, 2025 · We establish Kantorovich duality and linear convergence of Sinkhorn 's algorithm for the generalized SSB problem under mild conditions. Our results provide a new rigorous foundation for understanding Sinkhorn -type iterative methods in the context of large-scale generalized Schrödinger bridges. more »"}
+{"idx": 2, "title": "On the Convergence Rate of Sinkhorn's Algorithm - arXiv.org", "date": "", "ddg_snippet": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.06000", "content": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates."}
+{"idx": 3, "title": "Convergence of the Sinkhorn algorithm when the Schrödinger ...", "date": "", "ddg_snippet": "The Sinkhorn algorithm is one of the most popular methods for solving the Schrödinger problem: it is known to converge as soon as the latter has a solution, and with a linear rate when the solution has the same support as the reference coupling. Motivated by recent applications of the Schrödinger problem where structured stochastic processes lead to degenerate situations with possibly no ...", "subpage_snippet": "", "source": "afst.centre-mersenne.org", "link": "https://afst.centre-mersenne.org/articles/10.5802/afst.1800/", "content": "The Sinkhorn algorithm is one of the most popular methods for solving the Schrödinger problem: it is known to converge as soon as the latter has a solution, and with a linear rate when the solution has the same support as the reference coupling. Motivated by recent applications of the Schrödinger problem where structured stochastic processes lead to degenerate situations with possibly no ..."}
+{"idx": 4, "title": "On the Linear Convergence of the Multimarginal Sinkhorn Algorithm", "date": "", "ddg_snippet": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/21M1410634", "content": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ..."}
+{"idx": 5, "title": "An Optimal Transport Approach for the Schrödinger Bridge ...", "date": "", "ddg_snippet": "In this section we generalize the results obtain previously for the Schrödinger problem with more than two marginals, including a proof of convergence of the Sinkhorn algorithm in the several marginals case.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10915-020-01325-7.pdf", "content": "In this section we generalize the results obtain previously for the Schrödinger problem with more than two marginals, including a proof of convergence of the Sinkhorn algorithm in the several marginals case."}
+{"idx": 6, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "16 Jul 2025 — Conclusion. In this paper, we present the generalized static Schrödinger bridge problem , establish its Kantorovich dual, and show that the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46671", "content": "16 Jul 2025 — Conclusion. In this paper, we present the generalized static Schrödinger bridge problem , establish its Kantorovich dual, and show that the ..."}
+{"idx": 7, "title": "Linear Convergence of Sinkhorn's Algorithm for Generalized ...", "date": "", "ddg_snippet": "Abstract. The classical static Schrödinger Bridge (SSB) problem, which seeks the most likely stochastic evolution between two marginal probability mea-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/a644c39247d5073d97fae0bbd2c5b921800516ea.pdf", "content": "Abstract. The classical static Schrödinger Bridge (SSB) problem, which seeks the most likely stochastic evolution between two marginal probability mea-."}
+{"idx": 8, "title": "Dequantified Diffusion-Schrödinger Bridge for Density ...", "date": "", "ddg_snippet": "This formulation ensures convexity and allows scalable computation via Sinkhorn's algorithm (Cuturi, 2013). Entropic regularization connects OT with the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43448", "content": "This formulation ensures convexity and allows scalable computation via Sinkhorn's algorithm (Cuturi, 2013). Entropic regularization connects OT with the ..."}
+{"idx": 9, "title": "Dequantified Diffusion-Schrödinger Bridge for Density ...", "date": "", "ddg_snippet": "13 Aug 2025 — Our proposed DDBI and DSBI build upon diffusive interpolants and DDBMs by incorporating Brownian bridge into the interpolation strategy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.05034v4", "content": "13 Aug 2025 — Our proposed DDBI and DSBI build upon diffusive interpolants and DDBMs by incorporating Brownian bridge into the interpolation strategy ..."}
diff --git a/data/sampled_jsons/Llama-2_7B_Mistral_7B_RL_synthetic_data_math_reasoning_NeurIPS_2024.jsonl b/data/sampled_jsons/Llama-2_7B_Mistral_7B_RL_synthetic_data_math_reasoning_NeurIPS_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..94418f73941e4ca175203156343637fb7c4f657b
--- /dev/null
+++ b/data/sampled_jsons/Llama-2_7B_Mistral_7B_RL_synthetic_data_math_reasoning_NeurIPS_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DART-Math-Uniform Dataset | Papers With Code", "date": "", "ddg_snippet": "... Mistral - 7B ](https://huggingface.co/mistralai/ Mistral - 7B -v0.1), except for Xwin- Math -V1.1 based on [Llama2- 7B ](https://huggingface.co/meta- llama / ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/dataset/dart-math-uniform", "content": "... Mistral - 7B ](https://huggingface.co/mistralai/ Mistral - 7B -v0.1), except for Xwin- Math -V1.1 based on [Llama2- 7B ](https://huggingface.co/meta- llama / ..."}
+{"idx": 1, "title": "Chain-of-Reasoning: Towards Unified Mathematical Reasoning", "date": "", "ddg_snippet": "... mathematical tasks (Feigenbaum et al., 1963 ; Hosseini et al., 2014 ) , advanced open-source reasoners still struggle with solving comprehensive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.11110v4", "content": "... mathematical tasks (Feigenbaum et al., 1963 ; Hosseini et al., 2014 ) , advanced open-source reasoners still struggle with solving comprehensive ..."}
+{"idx": 2, "title": "NeurIPS 2024 Datasets Benchmarks 2024", "date": "", "ddg_snippet": "... dataset which maps the sociodemographics and stated preferences of 1,500 diverse participants from 75 countries, to their contextual preferences and ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/events/datasets-benchmarks-2024", "content": "... dataset which maps the sociodemographics and stated preferences of 1,500 diverse participants from 75 countries, to their contextual preferences and ..."}
+{"idx": 3, "title": "Finding the Sweet Spot: Preference Data Construction for", "date": "", "ddg_snippet": "RLHF involves first training a reward model, which then provides feedback signals to optimize a policy model through reinforcement learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.16825v3", "content": "RLHF involves first training a reward model, which then provides feedback signals to optimize a policy model through reinforcement learning ..."}
+{"idx": 4, "title": "The 2025 AI Engineering Reading List - Latent.Space", "date": "", "ddg_snippet": "You can also view Mistral 7B , Mixtral and Pixtral as a branch on the Llama family tree. ... 2025, the frontier (o1, o3 , R1 , QwQ / QVQ , f1 ) will ...", "subpage_snippet": "", "source": "www.latent.space", "link": "https://www.latent.space/p/2025-papers", "content": "You can also view Mistral 7B , Mixtral and Pixtral as a branch on the Llama family tree. ... 2025, the frontier (o1, o3 , R1 , QwQ / QVQ , f1 ) will ..."}
+{"idx": 5, "title": "Han Zhao's homepage", "date": "", "ddg_snippet": "We propose a novel scaling law for general-purpose decoder-only language models (LMs) trained on multilingual data , tackling the problem of balancing ...", "subpage_snippet": "", "source": "hanzhaoml.github.io", "link": "https://hanzhaoml.github.io/", "content": "We propose a novel scaling law for general-purpose decoder-only language models (LMs) trained on multilingual data , tackling the problem of balancing ..."}
+{"idx": 6, "title": "Text2SQL Leaderboard | OpenLM.ai", "date": "", "ddg_snippet": "A more academic definition is to convert natural language problems in the database field into structured query languages that can be executed ...", "subpage_snippet": "", "source": "openlm.ai", "link": "https://openlm.ai/text2sql-leaderboard/", "content": "A more academic definition is to convert natural language problems in the database field into structured query languages that can be executed ..."}
+{"idx": 7, "title": "[AINews] Cerebras Inference: Faster, Better, AND Cheaper •", "date": "", "ddg_snippet": "Groq dominated the news cycle in Feb (lots of scattered discussion here ) by achieving ~450 tok/s for Mixtral 8x7B ( 240 tok/s for Llama 2 70b ).", "subpage_snippet": "", "source": "buttondown.com", "link": "https://buttondown.com/ainews/archive/ainews-cerebras-inference-faster-better-and/", "content": "Groq dominated the news cycle in Feb (lots of scattered discussion here ) by achieving ~450 tok/s for Mixtral 8x7B ( 240 tok/s for Llama 2 70b )."}
+{"idx": 8, "title": "CMU – Vedere AI", "date": "", "ddg_snippet": "... and with reports that high-quality text data for training maybe exhausted by 2028 , particularly for more difficult tasks, like solving reasoning ...", "subpage_snippet": "", "source": "www.vedereai.com", "link": "https://www.vedereai.com/category/cmu/", "content": "... and with reports that high-quality text data for training maybe exhausted by 2028 , particularly for more difficult tasks, like solving reasoning ..."}
+{"idx": 9, "title": "lw2.issarice.com/posts/8SjnKxjLniCAmcjnG/openai-deepmind-anthropic-etc-should-shut-down", "date": "", "ddg_snippet": "... reason the lone innovator model could be false is if invention doesn't really happen from inspiration, but when the baseline level of technology or ...", "subpage_snippet": "", "source": "lw2.issarice.com", "link": "https://lw2.issarice.com/posts/8SjnKxjLniCAmcjnG/openai-deepmind-anthropic-etc-should-shut-down", "content": "... reason the lone innovator model could be false is if invention doesn't really happen from inspiration, but when the baseline level of technology or ..."}
diff --git a/data/sampled_jsons/Llama_OR_Mistral_OR_GPT_sitearxiv.orghtml2406.14532v1_year_2024.jsonl b/data/sampled_jsons/Llama_OR_Mistral_OR_GPT_sitearxiv.orghtml2406.14532v1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..106fb0f643f734ef2f01e9203e608020b12067be
--- /dev/null
+++ b/data/sampled_jsons/Llama_OR_Mistral_OR_GPT_sitearxiv.orghtml2406.14532v1_year_2024.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "Comparing GPT, Claude, Llama, and Mistral: Which Large Language Model ...", "date": "", "ddg_snippet": "Comparing GPT , Claude, Llama , and Mistral : Which Large Language Model (LLM) is Right for Your Needs? Lost in the world of AI? Let's break down four of the biggest players: GPT , Claude, Llama , and Mistral . These powerful models, underpinned by deep learning techniques and trained on vast datasets, can perform a variety of natural language processing tasks, including text summarization, machine ...", "subpage_snippet": "", "source": "www.epista.com", "link": "https://www.epista.com/knowledge/break-down-four-of-the-biggest-players-in-ai-gpt-claude-llama-and-mistral", "content": "Comparing GPT , Claude, Llama , and Mistral : Which Large Language Model (LLM) is Right for Your Needs? Lost in the world of AI? Let's break down four of the biggest players: GPT , Claude, Llama , and Mistral . These powerful models, underpinned by deep learning techniques and trained on vast datasets, can perform a variety of natural language processing tasks, including text summarization, machine ..."}
+{"idx": 1, "title": "Top Large Language Models (LLMs): GPT-4, LLaMA 2, Mistral 7B ... - Vectara", "date": "", "ddg_snippet": "Top Large Language Models (LLMs): GPT -4, LLaMA 2, Mistral 7B, ChatGPT, and More The top large language models along with recommendations for when to use each based upon needs like API, tunable, or fully hosted. 10-minute read time", "subpage_snippet": "", "source": "www.vectara.com", "link": "https://www.vectara.com/blog/top-large-language-models-llms-gpt-4-llama-gato-bloom-and-when-to-choose-one-over-the-other", "content": "Top Large Language Models (LLMs): GPT -4, LLaMA 2, Mistral 7B, ChatGPT, and More The top large language models along with recommendations for when to use each based upon needs like API, tunable, or fully hosted. 10-minute read time"}
+{"idx": 2, "title": "DeepSeek v3 vs. GPT 4 vs. Llama 3 vs. Mistral 7B vs. Cohere", "date": "", "ddg_snippet": "Looking for the best AI model? Explore a side-by-side comparison of GPT -4, Llama 3, Mistral 7B, Cohere, and DeepSeek v3 to make an informed choice.", "subpage_snippet": "", "source": "www.aubergine.co", "link": "https://www.aubergine.co/insights/deepseek-v3-vs-gpt-4-vs-llama-3-vs-mistral-7b-vs-cohere", "content": "Looking for the best AI model? Explore a side-by-side comparison of GPT -4, Llama 3, Mistral 7B, Cohere, and DeepSeek v3 to make an informed choice."}
+{"idx": 3, "title": "Testing Mistral Nemo for Large and Complex Prompts: A ... - Medium", "date": "", "ddg_snippet": "Mistral Nemo offers an efficient solution for moderately complex tasks, Llama caters to a wide range of applications with its scalable models, and GPT -4 Turbo is the go-to model for handling the ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@researchgraph/testing-mistral-nemo-for-large-and-complex-prompts-a-comparison-with-llama-and-gpt-fc789177c845", "content": "Mistral Nemo offers an efficient solution for moderately complex tasks, Llama caters to a wide range of applications with its scalable models, and GPT -4 Turbo is the go-to model for handling the ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — In our work, we study and compare the performance scaling with positive synthetic data from bigger models like GPT -4 and Gemini 1.5 Pro with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — In our work, we study and compare the performance scaling with positive synthetic data from bigger models like GPT -4 and Gemini 1.5 Pro with ..."}
diff --git a/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_ResearchGate_abstract.jsonl b/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_ResearchGate_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6969042a90f34a0efb38eb8e82676e40a9f72b78
--- /dev/null
+++ b/data/sampled_jsons/Locally_Private_Graph_Neural_Networks_ResearchGate_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Locally Private Graph Neural Networks - ResearchGate", "date": "", "ddg_snippet": "Abstract and Figures Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348678319_Locally_Private_Graph_Neural_Networks", "content": "Abstract and Figures Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 1, "title": "[2006.05535] Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ..."}
+{"idx": 2, "title": "Locally Private Graph Neural Networks - ACM Digital Library", "date": "", "ddg_snippet": "Abstract Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460120.3484565", "content": "Abstract Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information."}
+{"idx": 3, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Abstract Graph Neural Networks (GNNs) have demon-strated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal infor-mation or variables related to individuals, learn-ing from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2aKHuXdr7Q", "content": "Abstract Graph Neural Networks (GNNs) have demon-strated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal infor-mation or variables related to individuals, learn-ing from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these ..."}
+{"idx": 4, "title": "PDF LOCALLY PRIVATE GRAPH NEURAL NETWORKS - Semantic Scholar", "date": "", "ddg_snippet": "LOCALLY PRIVATE GRAPH NEURAL NETWORKS Sina Sajadmanesh Daniel Gatica-Perez IDIAP RESEARCH INSTITUTE SWISS FEDERAL INSTITUTE OF TECHNOLOGY (EPFL) Twitter Machine Learning Seminar Jan 7, 2021", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/a509/e0d10dfc6ac84710d9249142ee25eba454f9.pdf", "content": "LOCALLY PRIVATE GRAPH NEURAL NETWORKS Sina Sajadmanesh Daniel Gatica-Perez IDIAP RESEARCH INSTITUTE SWISS FEDERAL INSTITUTE OF TECHNOLOGY (EPFL) Twitter Machine Learning Seminar Jan 7, 2021"}
+{"idx": 5, "title": "Locally Private Graph Neural Networks - NASA/ADS", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv200605535S/abstract", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ..."}
+{"idx": 6, "title": "Locally Private Graph Neural Networks (ACM CCS 2021) - GitHub", "date": "", "ddg_snippet": "Abstract Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/lpgnn", "content": "Abstract Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information."}
+{"idx": 7, "title": "PDF Locally Private Graph Neural Networks - Idiap Research Institute", "date": "", "ddg_snippet": "ABSTRACT Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g ...", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/attachments/papers/2021/Sajadmanesh_CCS2021_2021.pdf", "content": "ABSTRACT Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g ..."}
+{"idx": 8, "title": "Calibrating Privacy Budgets for Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Abstract : Graph neural networks have shown excellent performance in learning graph representations. In many cases, the graph structured data are crowd-sourced and may contain sensitive information, thus causing privacy issues. Therefore, privacy-preserving graph neural networks have spurred ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9634934", "content": "Abstract : Graph neural networks have shown excellent performance in learning graph representations. In many cases, the graph structured data are crowd-sourced and may contain sensitive information, thus causing privacy issues. Therefore, privacy-preserving graph neural networks have spurred ..."}
+{"idx": 9, "title": "Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05535", "content": "ABSTRACT Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information."}
diff --git a/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_Experimental_Setup_5_training_sequence_durat.jsonl b/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_Experimental_Setup_5_training_sequence_durat.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2bbeb5709672dd476f6d29303b842bf52f3bf3ba
--- /dev/null
+++ b/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_Experimental_Setup_5_training_sequence_durat.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long - Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "However, textless spoken language models struggle to generate plausible speech past tens of seconds , due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long - sequence training or extrapolation, and memory costs at inference time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.18603", "content": "However, textless spoken language models struggle to generate plausible speech past tens of seconds , due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long - sequence training or extrapolation, and memory costs at inference time."}
+{"idx": 1, "title": "(PDF) Long - Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387382437_Long-Form_Speech_Generation_with_Spoken_Language_Models", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants."}
+{"idx": 2, "title": "Long - Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "We introduce SpeechSSM, the first spoken language model for long - form speech . Our 2B and 9B models We release LibriSpeech-Long, a 3-4min reformat of LibriSpeech dev+test sets for future long - form speech processing research.", "subpage_snippet": "", "source": "google.github.io", "link": "https://google.github.io/tacotron/publications/speechssm/", "content": "We introduce SpeechSSM, the first spoken language model for long - form speech . Our 2B and 9B models We release LibriSpeech-Long, a 3-4min reformat of LibriSpeech dev+test sets for future long - form speech processing research."}
+{"idx": 3, "title": "GitHub - ga642381/ speech -trident: Awesome speech /audio LLMs...", "date": "", "ddg_snippet": "3. Speech Large Language Models : These models are trained on top of speech and acoustic tokens in a language modeling approach. They demonstrate proficiency in tasks on speech understanding and speech generation .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ga642381/speech-trident", "content": "3. Speech Large Language Models : These models are trained on top of speech and acoustic tokens in a language modeling approach. They demonstrate proficiency in tasks on speech understanding and speech generation ."}
+{"idx": 4, "title": "KAIST researcher Se Jin Park develops 'SpeechSSM... | EurekAlert!", "date": "", "ddg_snippet": "Paper Title: Long - Form Speech Generation with Spoken Language Models . DOI: 10.48550/arXiv.2412.18603. Ph.D. candidate Se Jin Park has demonstrated outstanding research capabilities as a member of Professor Yong Man Ro's MLLM...", "subpage_snippet": "", "source": "www.eurekalert.org", "link": "https://www.eurekalert.org/news-releases/1090061", "content": "Paper Title: Long - Form Speech Generation with Spoken Language Models . DOI: 10.48550/arXiv.2412.18603. Ph.D. candidate Se Jin Park has demonstrated outstanding research capabilities as a member of Professor Yong Man Ro's MLLM..."}
+{"idx": 5, "title": "Spoken Language Modeling from Raw Audio", "date": "", "ddg_snippet": "The so-called Spoken Language Models ( Speech Language Models , or SpeechLMs) have been shown to be working and offer new possibilities for speech processing compared to cascade systems. The objective of this thesis is thus to explore and improve this newly- formed do-main.", "subpage_snippet": "", "source": "theses.hal.science", "link": "https://theses.hal.science/tel-04646644/document", "content": "The so-called Spoken Language Models ( Speech Language Models , or SpeechLMs) have been shown to be working and offer new possibilities for speech processing compared to cascade systems. The objective of this thesis is thus to explore and improve this newly- formed do-main."}
+{"idx": 6, "title": "Textually Pretrained Speech Language Models", "date": "", "ddg_snippet": "Experimental Setup . Speech Language Models (SpeechLMs) are trained on the extracted discrete speech tokens, z, using a speech tokenizer. When operating on z, SpeechLMs enable directly modeling spoken data without accessing textual transcriptions.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/c859b99b5d717c9035e79d43dfd69435-Paper-Conference.pdf", "content": "Experimental Setup . Speech Language Models (SpeechLMs) are trained on the extracted discrete speech tokens, z, using a speech tokenizer. When operating on z, SpeechLMs enable directly modeling spoken data without accessing textual transcriptions."}
+{"idx": 7, "title": "Slamming: Training a Speech Language Model on One GPU in a Day", "date": "", "ddg_snippet": "We introduce Slam, a recipe for training high-quality Speech Language Models (SLMs) on a single academic GPU in 24 hours.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2502.15814/paper", "content": "We introduce Slam, a recipe for training high-quality Speech Language Models (SLMs) on a single academic GPU in 24 hours."}
+{"idx": 8, "title": "Generative Spoken Dialogue Language Modeling", "date": "", "ddg_snippet": "Experimental Setup . Training Set .For conditional generation , we select 50 10- second long prompts in the validation set . For each model and tem-perature, we generate 50 samples and limit the transcribed turn-based text sequences to 50 words.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.tacl-1.15.pdf", "content": "Experimental Setup . Training Set .For conditional generation , we select 50 10- second long prompts in the validation set . For each model and tem-perature, we generate 50 samples and limit the transcribed turn-based text sequences to 50 words."}
+{"idx": 9, "title": "Spoken Question Answering", "date": "", "ddg_snippet": "Generative Spoken Language Modeling (GSLM) (Lakhotia et al., 2021) offers a baseline system that operate on units quantized from pre- trained audio representations, such as HuBERT (Hsu et al., 2021). These quantized units are modeled by a Transformer decoder.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/lms-with-a-voice-spoken-language-modeling-beyond-speech-1t377iki.pdf", "content": "Generative Spoken Language Modeling (GSLM) (Lakhotia et al., 2021) offers a baseline system that operate on units quantized from pre- trained audio representations, such as HuBERT (Hsu et al., 2021). These quantized units are modeled by a Transformer decoder."}
diff --git a/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_Experimental_Setup_default_training_sequence.jsonl b/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_Experimental_Setup_default_training_sequence.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d0d3bea11344965677d7d554d496398dd818e480
--- /dev/null
+++ b/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_Experimental_Setup_default_training_sequence.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "by SJ Park · Cited by 9 — TL;DR: We introduce the first long - form spoken language model (16 min. of audio at once), discuss key design choices (e.g. state-space modeling), and propose ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4AmFA0qNQ2", "content": "by SJ Park · Cited by 9 — TL;DR: We introduce the first long - form spoken language model (16 min. of audio at once), discuss key design choices (e.g. state-space modeling), and propose ..."}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "24 Dec 2024 — We propose SpeechSSM , the first speech language model to learn from and sample long - form spoken audio (eg, 16 minutes of read or extemporaneous speech) in a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v1", "content": "24 Dec 2024 — We propose SpeechSSM , the first speech language model to learn from and sample long - form spoken audio (eg, 16 minutes of read or extemporaneous speech) in a ..."}
+{"idx": 2, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46499", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants."}
+{"idx": 3, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "10 Jul 2025 — We derive SpeechSSM , the first speech language model family to learn from and sample long - form spoken audio (eg, 16 minutes of read or extemporaneous speech)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v2", "content": "10 Jul 2025 — We derive SpeechSSM , the first speech language model family to learn from and sample long - form spoken audio (eg, 16 minutes of read or extemporaneous speech)"}
+{"idx": 4, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "by SJ Park · Cited by 9 — We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4AmFA0qNQ2", "content": "by SJ Park · Cited by 9 — We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants."}
+{"idx": 5, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "24 Dec 2024 — Training and Generation Setup Models are trained on segmented audiobooks with varying sequence lengths (30 seconds, 4 minutes, and 16 minutes) ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/94450", "content": "24 Dec 2024 — Training and Generation Setup Models are trained on segmented audiobooks with varying sequence lengths (30 seconds, 4 minutes, and 16 minutes) ..."}
+{"idx": 6, "title": "Generative Pre-trained Speech Language Model with ...", "date": "", "ddg_snippet": "by Y Zhu · 2024 · Cited by 9 — We add special tokens at the first position and the segment boundary of the sequence to inform the model to switch the generation space.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.97.pdf", "content": "by Y Zhu · 2024 · Cited by 9 — We add special tokens at the first position and the segment boundary of the sequence to inform the model to switch the generation space."}
+{"idx": 7, "title": "Textually Pretrained Speech Language Models", "date": "", "ddg_snippet": "In this work, we propose TW IST , a method for training SpeechLMs using a warm-start from a pretrained textual language models.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/71490", "content": "In this work, we propose TW IST , a method for training SpeechLMs using a warm-start from a pretrained textual language models."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Long-Form Speech Generation with Spoken Language Models · We consider the generative modeling of speech over multiple minutes, a requirement for long-form ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Freesound+platform", "content": "Long-Form Speech Generation with Spoken Language Models · We consider the generative modeling of speech over multiple minutes, a requirement for long-form ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "Long-Form Speech Generation with Spoken Language Models · We consider the generative modeling of speech over multiple minutes, a requirement for long-form ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=longform+generation", "content": "Long-Form Speech Generation with Spoken Language Models · We consider the generative modeling of speech over multiple minutes, a requirement for long-form ..."}
diff --git a/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_Entity-based_knowledge_conflicts_in_question_answering.jsonl b/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_Entity-based_knowledge_conflicts_in_question_answering.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..78fab0878f1af0c8fc4bbf0f295fa49e46534111
--- /dev/null
+++ b/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_Entity-based_knowledge_conflicts_in_question_answering.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.05052", "content": "Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over ..."}
+{"idx": 1, "title": "PDF Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Knowledge -dependent tasks, such as open-retrieval question answering (QA), require expansive \"world knowledge \", common sense, and reasoning abili-ties. State-of-the-art approaches typically follow a retrieve-and-read setup (Chen et al ., 2017), where the retriever sources relevant documents, and the reader produces an answer from these.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.emnlp-main.565.pdf", "content": "Knowledge -dependent tasks, such as open-retrieval question answering (QA), require expansive \"world knowledge \", common sense, and reasoning abili-ties. State-of-the-art approaches typically follow a retrieve-and-read setup (Chen et al ., 2017), where the retriever sources relevant documents, and the reader produces an answer from these."}
+{"idx": 2, "title": "Entity-Based Knowledge Conflicts in Question Answering - Shayne Longpre", "date": "", "ddg_snippet": "EMNLP 2021 .Abstract Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric, learned at training time, and (2) contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular ...", "subpage_snippet": "", "source": "www.shaynelongpre.com", "link": "https://www.shaynelongpre.com/publication/kcqa-emnlp2021/", "content": "EMNLP 2021 .Abstract Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric, learned at training time, and (2) contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular ..."}
+{"idx": 3, "title": "Entity-Based Knowledge Conflicts in Question Answeri", "date": "", "ddg_snippet": "1 Introduction Knowledge -dependent tasks, such as open-retrieval question answering (QA), require expansive \"world knowledge \", common sense, and reasoning abili-ties. State-of-the-art approaches typically follow a retrieve-and-read setup (Chen et al ., 2017), where the retriever sources relevant documents, and the reader produces an answer from these. In this sense, there are two sources of ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10462823", "content": "1 Introduction Knowledge -dependent tasks, such as open-retrieval question answering (QA), require expansive \"world knowledge \", common sense, and reasoning abili-ties. State-of-the-art approaches typically follow a retrieve-and-read setup (Chen et al ., 2017), where the retriever sources relevant documents, and the reader produces an answer from these. In this sense, there are two sources of ..."}
+{"idx": 4, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/354575176_Entity-Based_Knowledge_Conflicts_in_Question_Answering", "content": "Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these ..."}
+{"idx": 5, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Run Instructions | Paper | Citation | License This repository provides the Substitution Framework described in Section 2 of our paper Entity-Based Knowledge Conflicts in Question Answering . Given a quesion answering dataset, we derive a new dataset where the context passages have been modified to have new answers to their question . By training on the original examples and evaluating on the ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/apple/ml-knowledge-conflicts/blob/master/README.md", "content": "Run Instructions | Paper | Citation | License This repository provides the Substitution Framework described in Section 2 of our paper Entity-Based Knowledge Conflicts in Question Answering . Given a quesion answering dataset, we derive a new dataset where the context passages have been modified to have new answers to their question . By training on the original examples and evaluating on the ..."}
+{"idx": 6, "title": "PDF Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Entity-Based Knowledge Conflicts in Question Answering Shayne Longpre , Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, Sameer Singh", "subpage_snippet": "", "source": "anthonywchen.github.io", "link": "https://anthonywchen.github.io/Papers/knowledge_conflicts/poster.pdf", "content": "Entity-Based Knowledge Conflicts in Question Answering Shayne Longpre , Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, Sameer Singh"}
+{"idx": 7, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "( 2021 ) Longpre et al. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time.", "subpage_snippet": "", "source": "www.mendeley.com", "link": "https://www.mendeley.com/catalogue/746fdfc7-46a9-3f17-a0d6-6fb3af14de1b/", "content": "( 2021 ) Longpre et al. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time."}
+{"idx": 8, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Shayne Longpre , Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh. 2021 . Entity-Based Knowledge Conflicts in Question Answering . In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7052-7063, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Cite (Informal): Entity-Based Knowledge ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.emnlp-main.565/", "content": "Shayne Longpre , Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh. 2021 . Entity-Based Knowledge Conflicts in Question Answering . In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7052-7063, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Cite (Informal): Entity-Based Knowledge ..."}
+{"idx": 9, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "1 Introduction Knowledge -dependent tasks, such as open-retrieval question answering (QA), require expansive \"world knowledge \", common sense, and reasoning abilities. State-of-the-art approaches typically follow a retrieve-and-read setup (Chen et al ., 2017), where the retriever sources relevant documents, and the reader produces an answer from these. In this sense, there are two sources of ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2109.05052", "content": "1 Introduction Knowledge -dependent tasks, such as open-retrieval question answering (QA), require expansive \"world knowledge \", common sense, and reasoning abilities. State-of-the-art approaches typically follow a retrieve-and-read setup (Chen et al ., 2017), where the retriever sources relevant documents, and the reader produces an answer from these. In this sense, there are two sources of ..."}
diff --git a/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_dataset_abstract_year_2021.jsonl b/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_dataset_abstract_year_2021.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "younanna/NQ-Swap · Datasets at Hugging Face", "date": "", "ddg_snippet": "Reference This dataset is the reproduced version of \"Entity-Based Knowledge Conflicts in Question Answering\" dataset . @inproceedings{longpre- etal - 2021 -entity, title = \"Entity-Based Knowledge Conflicts in Question Answering\", author = \" Longpre , Shayne and Perisetla, Kartik and Chen, Anthony and Ramesh, Nikhil and DuBois, Chris and Singh, Sameer\", booktitle = \"Proceedings of the 2021 Conference ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/younanna/NQ-Swap", "content": "Reference This dataset is the reproduced version of \"Entity-Based Knowledge Conflicts in Question Answering\" dataset . @inproceedings{longpre- etal - 2021 -entity, title = \"Entity-Based Knowledge Conflicts in Question Answering\", author = \" Longpre , Shayne and Perisetla, Kartik and Chen, Anthony and Ramesh, Nikhil and DuBois, Chris and Singh, Sameer\", booktitle = \"Proceedings of the 2021 Conference ..."}
+{"idx": 1, "title": "README.md · younanna/NQ-Swap at main - Hugging Face", "date": "", "ddg_snippet": "like 0 Modalities: Text Formats: parquet Size: 1K - 10K ArXiv: arxiv:2109.05052 Libraries: Datasets pandas Croissant + 1 License: apple Dataset card Viewer Files and versions Community 3 main NQ-Swap /README.md younanna Update README.md bc50bb1 verified3 months ago preview code | raw Copy download link history blame contribute delete Safe 2.83 kB metadata license:otherlicense_name:applelicense ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/younanna/NQ-Swap/blob/main/README.md", "content": "like 0 Modalities: Text Formats: parquet Size: 1K - 10K ArXiv: arxiv:2109.05052 Libraries: Datasets pandas Croissant + 1 License: apple Dataset card Viewer Files and versions Community 3 main NQ-Swap /README.md younanna Update README.md bc50bb1 verified3 months ago preview code | raw Copy download link history blame contribute delete Safe 2.83 kB metadata license:otherlicense_name:applelicense ..."}
+{"idx": 2, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "nstance. Additionally, we evaluate on an existing knowledge conflict dataset , NQ-SWAP ( Longpre et al ., 2021 ), which is based on the NQ dataset and consists of synthetic conflict", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.07394", "content": "nstance. Additionally, we evaluate on an existing knowledge conflict dataset , NQ-SWAP ( Longpre et al ., 2021 ), which is based on the NQ dataset and consists of synthetic conflict"}
+{"idx": 3, "title": "pminervini/NQ-Swap · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/pminervini/NQ-Swap", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 4, "title": "Understanding and Leveraging the Expert Specialization of Context ...", "date": "", "ddg_snippet": "Figure 1: A case from NQ-Swap Longpre et al. ( 2021 ) where MoE experts exhibit different tendencies of context faithfulness (answer is underlined in green).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19594v1", "content": "Figure 1: A case from NQ-Swap Longpre et al. ( 2021 ) where MoE experts exhibit different tendencies of context faithfulness (answer is underlined in green)."}
+{"idx": 5, "title": "wjdghks950/Discern-and-Answer - GitHub", "date": "", "ddg_snippet": "GPT_in-context_learning_nq_longpre.ipynb : A script for generating answers using the GPT-3.5 model on the NQ dev/test set perturbed following the method by Longpre et al. ( 2021 ).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wjdghks950/Discern-and-Answer", "content": "GPT_in-context_learning_nq_longpre.ipynb : A script for generating answers using the GPT-3.5 model on the NQ dev/test set perturbed following the method by Longpre et al. ( 2021 )."}
+{"idx": 6, "title": "D C R : D C R H MITIGATE H - OpenReview", "date": "", "ddg_snippet": "ibed by Liu et al. (2024). NQ-Swap ( Longpre et al ., 2021 ) is a version of NQ where the answer entity in the context was replaced with another entity and is used to evaluate 194 the faithfulness of the mod", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tkqNDbukWW", "content": "ibed by Liu et al. (2024). NQ-Swap ( Longpre et al ., 2021 ) is a version of NQ where the answer entity in the context was replaced with another entity and is used to evaluate 194 the faithfulness of the mod"}
+{"idx": 7, "title": "arXiv:2305.14739v1 [cs.CL] 24 May 2023", "date": "", "ddg_snippet": "cts QA dataset ( Longpre et al ., 2021 ). Furthermore, we observe that this gain brought by CAD increases as the model s ze grows in knowledge conflicts tasks. These results demonstrate the potential of CAD in mitigating hallucinations in text generation and overriding prior knowledge", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.14739", "content": "cts QA dataset ( Longpre et al ., 2021 ). Furthermore, we observe that this gain brought by CAD increases as the model s ze grows in knowledge conflicts tasks. These results demonstrate the potential of CAD in mitigating hallucinations in text generation and overriding prior knowledge"}
+{"idx": 8, "title": "[2305.14739] Trusting Your Evidence: Hallucinate Less with ... - ar5iv", "date": "", "ddg_snippet": "CAD brings a 2.9x improvement to LLaMA-30B on a knowledge conflicts QA dataset Longpre et al. ( 2021 ). Furthermore, we observe that this gain brought by CAD increases as the model size grows in knowledge conflicts tasks.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2305.14739", "content": "CAD brings a 2.9x improvement to LLaMA-30B on a knowledge conflicts QA dataset Longpre et al. ( 2021 ). Furthermore, we observe that this gain brought by CAD increases as the model size grows in knowledge conflicts tasks."}
+{"idx": 9, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Request PDF | On Jan 1, 2021 , Shayne Longpre and others published Entity-Based Knowledge Conflicts in Question Answering | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/357122873_Entity-Based_Knowledge_Conflicts_in_Question_Answering", "content": "Request PDF | On Jan 1, 2021 , Shayne Longpre and others published Entity-Based Knowledge Conflicts in Question Answering | Find, read and cite all the research you need on ResearchGate"}
diff --git a/data/sampled_jsons/Longpre_et_al_2021_NQ-Swap_dataset_abstract_year_2021.jsonl b/data/sampled_jsons/Longpre_et_al_2021_NQ-Swap_dataset_abstract_year_2021.jsonl
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index 0000000000000000000000000000000000000000..391e79d719e49fd14cd619a48d8f5ac4a0accef9
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+++ b/data/sampled_jsons/Longpre_et_al_2021_NQ-Swap_dataset_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "younanna/NQ-Swap · Datasets at Hugging Face", "date": "", "ddg_snippet": "Reference This dataset is the reproduced version of \"Entity-Based Knowledge Conflicts in Question Answering\" dataset . @inproceedings{longpre- etal - 2021 -entity, title = \"Entity-Based Knowledge Conflicts in Question Answering\", author = \" Longpre , Shayne and Perisetla, Kartik and Chen, Anthony and Ramesh, Nikhil and DuBois, Chris and Singh, Sameer\", booktitle = \"Proceedings of the 2021 Conference ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/younanna/NQ-Swap", "content": "Reference This dataset is the reproduced version of \"Entity-Based Knowledge Conflicts in Question Answering\" dataset . @inproceedings{longpre- etal - 2021 -entity, title = \"Entity-Based Knowledge Conflicts in Question Answering\", author = \" Longpre , Shayne and Perisetla, Kartik and Chen, Anthony and Ramesh, Nikhil and DuBois, Chris and Singh, Sameer\", booktitle = \"Proceedings of the 2021 Conference ..."}
+{"idx": 1, "title": "README.md · younanna/NQ-Swap at main - Hugging Face", "date": "", "ddg_snippet": "like 0 Modalities: Text Formats: parquet Size: 1K - 10K ArXiv: arxiv:2109.05052 Libraries: Datasets pandas Croissant + 1 License: apple Dataset card Viewer Files and versions Community 3 main NQ-Swap /README.md younanna Update README.md bc50bb1 verified3 months ago preview code | raw Copy download link history blame contribute delete Safe 2.83 kB metadata license:otherlicense_name:applelicense ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/younanna/NQ-Swap/blob/main/README.md", "content": "like 0 Modalities: Text Formats: parquet Size: 1K - 10K ArXiv: arxiv:2109.05052 Libraries: Datasets pandas Croissant + 1 License: apple Dataset card Viewer Files and versions Community 3 main NQ-Swap /README.md younanna Update README.md bc50bb1 verified3 months ago preview code | raw Copy download link history blame contribute delete Safe 2.83 kB metadata license:otherlicense_name:applelicense ..."}
+{"idx": 2, "title": "EM accuracy on NQ-swap with contexts replacing the gold ... - ResearchGate", "date": "", "ddg_snippet": "Download scientific diagram | EM accuracy on NQ-swap with contexts replacing the gold answer with a random entity span. from publication: Adaptive Contrastive Decoding in Retrieval-Augmented ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/EM-accuracy-on-NQ-swap-with-contexts-replacing-the-gold-answer-with-a-random-entity-span_fig2_382867599", "content": "Download scientific diagram | EM accuracy on NQ-swap with contexts replacing the gold answer with a random entity span. from publication: Adaptive Contrastive Decoding in Retrieval-Augmented ..."}
+{"idx": 3, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "nstance. Additionally, we evaluate on an existing knowledge conflict dataset , NQ-SWAP ( Longpre et al ., 2021 ), which is based on the NQ dataset and consists of synthetic conflict", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.07394", "content": "nstance. Additionally, we evaluate on an existing knowledge conflict dataset , NQ-SWAP ( Longpre et al ., 2021 ), which is based on the NQ dataset and consists of synthetic conflict"}
+{"idx": 4, "title": "pminervini/NQ-Swap · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/pminervini/NQ-Swap", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 5, "title": "Understanding and Leveraging the Expert Specialization of", "date": "", "ddg_snippet": "Figure 1: A case from NQ - Swap Longpre et al . ... q q but also on an additional context c c , which provides essential supporting information Kazi et ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19594v2", "content": "Figure 1: A case from NQ - Swap Longpre et al . ... q q but also on an additional context c c , which provides essential supporting information Kazi et ..."}
+{"idx": 6, "title": "Why So Gullible? Enhancing the Robustness of", "date": "", "ddg_snippet": "Our empirical results show existing models such as FiD Izacard and Grave ( 2021 ) and GPT-3.5 ( text-davinci-003 ) Brown et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.01579v3", "content": "Our empirical results show existing models such as FiD Izacard and Grave ( 2021 ) and GPT-3.5 ( text-davinci-003 ) Brown et al ."}
+{"idx": 7, "title": "DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate", "date": "", "ddg_snippet": "... et al ., 2018 ) , MemoTrap (Liu & Liu, 2023 ) , Open Book Natural Questions ( NQ ; Kwiatkowski et al ., 2019 ) , and NQ - Swap ( Longpre et al ., ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.18860v1", "content": "... et al ., 2018 ) , MemoTrap (Liu & Liu, 2023 ) , Open Book Natural Questions ( NQ ; Kwiatkowski et al ., 2019 ) , and NQ - Swap ( Longpre et al ., ..."}
+{"idx": 8, "title": "Sparse Autoencoder Features for Classifications and", "date": "", "ddg_snippet": "... in scenarios with noisy or limited data with closed datasets Anthropic Interpretability Team ( 2024 ) or simplified tasks Kantamneni et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.11367v1", "content": "... in scenarios with noisy or limited data with closed datasets Anthropic Interpretability Team ( 2024 ) or simplified tasks Kantamneni et al ."}
+{"idx": 9, "title": "In-Context Pretraining: Language Modeling Beyond Document", "date": "", "ddg_snippet": "... we pretrain language models from 0.3 to 7 billion parameters on 300 billion tokens from the CommonCrawl dataset (Wenzek et al ., 2020 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.10638v6", "content": "... we pretrain language models from 0.3 to 7 billion parameters on 300 billion tokens from the CommonCrawl dataset (Wenzek et al ., 2020 ) ."}
diff --git a/data/sampled_jsons/Luo_&_Tseng_1992_optimization_problem_formulation.jsonl b/data/sampled_jsons/Luo_&_Tseng_1992_optimization_problem_formulation.jsonl
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index 0000000000000000000000000000000000000000..a5816329f630170c862f615cde58c3babed6ee68
--- /dev/null
+++ b/data/sampled_jsons/Luo_&_Tseng_1992_optimization_problem_formulation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Optimization Models and Formulations I - Stanford University", "date": "", "ddg_snippet": "3 Main Categories Covered in this Course Linear Optimization (Programming) Problem Formulation , Optimality Conditions Search Algorithms, e.g., Simplex and Interior-Point Algorithms Unconstrained Nonlinear Optimization Problem Formulation , Optimality Conditions 1st order methods, gradient method; 2nd order methods, Newton", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/msande211x/Lecture012023.pdf", "content": "3 Main Categories Covered in this Course Linear Optimization (Programming) Problem Formulation , Optimality Conditions Search Algorithms, e.g., Simplex and Interior-Point Algorithms Unconstrained Nonlinear Optimization Problem Formulation , Optimality Conditions 1st order methods, gradient method; 2nd order methods, Newton"}
+{"idx": 1, "title": "PDF Efficient Large-Scale Internet Media Selection Optimization for Online ...", "date": "", "ddg_snippet": "converge to a global optimum provided the optimization function in Equation 7 is convex ( Luo and Tseng 1992 ). We prove in Web Appendix B that a suf ficient condition for convexity is that each sjðwjÞ is concave.", "subpage_snippet": "", "source": "faculty.marshall.usc.edu", "link": "https://faculty.marshall.usc.edu/lan-Luo/ELMSO_Final.pdf", "content": "converge to a global optimum provided the optimization function in Equation 7 is convex ( Luo and Tseng 1992 ). We prove in Web Appendix B that a suf ficient condition for convexity is that each sjðwjÞ is concave."}
+{"idx": 2, "title": "PDF Iteration Complexity of Feasible Descent Methods for Convex Optimization", "date": "", "ddg_snippet": "Subsequently, Luo and Tseng (1993) considered a class of feasible descent methods that broadly covers coordinate descent and gradient projection methods. For problems including (1), they proved the asymptotic linear convergence.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume15/wang14a/wang14a.pdf", "content": "Subsequently, Luo and Tseng (1993) considered a class of feasible descent methods that broadly covers coordinate descent and gradient projection methods. For problems including (1), they proved the asymptotic linear convergence."}
+{"idx": 3, "title": "On the Linear Convergence of Descent Methods for - ProQuest", "date": "", "ddg_snippet": "On the Linear Convergence of Descent Methods for Convex Essentially Smooth Minimization Luo , Zhi-Quan; Tseng , Paul. SIAM Journal on Control and Optimization ; Philadelphia Vol. 30, Iss. 2, (Mar 1992 ): 18. DOI:10.1137/0330025 Copy Link CiteAll Options", "subpage_snippet": "", "source": "www.proquest.com", "link": "https://www.proquest.com/docview/925910878", "content": "On the Linear Convergence of Descent Methods for Convex Essentially Smooth Minimization Luo , Zhi-Quan; Tseng , Paul. SIAM Journal on Control and Optimization ; Philadelphia Vol. 30, Iss. 2, (Mar 1992 ): 18. DOI:10.1137/0330025 Copy Link CiteAll Options"}
+{"idx": 4, "title": "On the convergence of the coordinate descent method for convex ...", "date": "", "ddg_snippet": "The coordinate descent method enjoys a long history in convex differentiable minimization. Surprisingly, very little is known about the convergence of the iterates generated by this method. Convergence typically requires restrictive assumptions such as that the cost function has bounded level sets and is in some sense strictly convex. In a recent work, Luo and Tseng showed that the iterates ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/BF00939948", "content": "The coordinate descent method enjoys a long history in convex differentiable minimization. Surprisingly, very little is known about the convergence of the iterates generated by this method. Convergence typically requires restrictive assumptions such as that the cost function has bounded level sets and is in some sense strictly convex. In a recent work, Luo and Tseng showed that the iterates ..."}
+{"idx": 5, "title": "On the convergence of the coordinate descent method for convex ...", "date": "", "ddg_snippet": "On the convergence of the coordinate descent method for convex differentiable minimization Author (s) Luo , Zhi-Quan.; Tseng , Paul.; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/handle/1721.1/3164", "content": "On the convergence of the coordinate descent method for convex differentiable minimization Author (s) Luo , Zhi-Quan.; Tseng , Paul.; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems."}
+{"idx": 6, "title": "On the Linear Convergence of Descent Methods for Convex Essentially ...", "date": "", "ddg_snippet": "Z.-Q. Luo , P. Tseng , On the convergence of the coordinate descent method for convex differentiable minimization, J. Optim. Theory Appl., 72 ( 1992 ), 7-35, Laboratory for Information and Decision Systems Report No. P-1924, Massachusetts Institute of Technology, Cambridge, MA (1989; revised 1990)", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/0330025", "content": "Z.-Q. Luo , P. Tseng , On the convergence of the coordinate descent method for convex differentiable minimization, J. Optim. Theory Appl., 72 ( 1992 ), 7-35, Laboratory for Information and Decision Systems Report No. P-1924, Massachusetts Institute of Technology, Cambridge, MA (1989; revised 1990)"}
+{"idx": 7, "title": "On the linear convergence of descent methods for convex essentially ...", "date": "", "ddg_snippet": "SIAM Journal on Control and Optimization . 1992;30 (2):408-425. doi: 10.1137/0330025 Luo , Zhi Quan ; Tseng , Paul. / On the linear convergence of descent methods for convex essentially smooth minimization. In: SIAM Journal on Control and Optimization . 1992 ; Vol. 30, No. 2. pp. 408-425.", "subpage_snippet": "", "source": "experts.umn.edu", "link": "https://experts.umn.edu/en/publications/on-the-linear-convergence-of-descent-methods-for-convex-essential", "content": "SIAM Journal on Control and Optimization . 1992;30 (2):408-425. doi: 10.1137/0330025 Luo , Zhi Quan ; Tseng , Paul. / On the linear convergence of descent methods for convex essentially smooth minimization. In: SIAM Journal on Control and Optimization . 1992 ; Vol. 30, No. 2. pp. 408-425."}
+{"idx": 8, "title": "PDF On the Convergence Rate of Dual Ascent Methods for Linearly ... - JSTOR", "date": "", "ddg_snippet": "Special cases of the exact BCR method include a number of dual coordinate ascent algorithms for quadratic programming and for entropy optimization (see ?5 and Lin and Pang (1987), Tseng (1990) for discussions).", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/pdf/3690126.pdf", "content": "Special cases of the exact BCR method include a number of dual coordinate ascent algorithms for quadratic programming and for entropy optimization (see ?5 and Lin and Pang (1987), Tseng (1990) for discussions)."}
+{"idx": 9, "title": "New error bounds and their applications to convergence analysis of ...", "date": "", "ddg_snippet": "Luo , Z.-Q., Tseng , P. ( 1992 ): On the linear convergence of descent methods for convex essentially smooth minimization. SIAM J. Control Optim. 30, 408-425.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1007/s101070050020", "content": "Luo , Z.-Q., Tseng , P. ( 1992 ): On the linear convergence of descent methods for convex essentially smooth minimization. SIAM J. Control Optim. 30, 408-425."}
diff --git a/data/sampled_jsons/METransformer_BLEU-4_score_MIMIC-CXR_results_table.jsonl b/data/sampled_jsons/METransformer_BLEU-4_score_MIMIC-CXR_results_table.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..77947b48b9eba1ffb76cf7fd49ee4d83f9ee73f4
--- /dev/null
+++ b/data/sampled_jsons/METransformer_BLEU-4_score_MIMIC-CXR_results_table.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Supplementary Material METransformer: Radiology Report Generation by ...", "date": "", "ddg_snippet": "The experi-mental results for IU-Xray and MIMIC-CXR are shown in Table . I and Table . II, respectively. We also adopt an overall score to evaluate the performance of the model by consid-ering all metrics by the following literature [5], which is calculated by the Eqn. 1: C", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_METransformer_Radiology_Report_CVPR_2023_supplemental.pdf", "content": "The experi-mental results for IU-Xray and MIMIC-CXR are shown in Table . I and Table . II, respectively. We also adopt an overall score to evaluate the performance of the model by consid-ering all metrics by the following literature [5], which is calculated by the Eqn. 1: C"}
+{"idx": 1, "title": "[2304.02211] METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "Figure 2: Bleu_4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. Figure 3: An example of the generated reports and their attention-mapping visualization of three key medical terms from BASELINE and ours METransformer .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2304.02211", "content": "Figure 2: Bleu_4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. Figure 3: An example of the generated reports and their attention-mapping visualization of three key medical terms from BASELINE and ours METransformer ."}
+{"idx": 2, "title": "mimic-iv-website/content/cxr/cxr-record-list.md at master - GitHub", "date": "", "ddg_snippet": "This table lists all records in the MIMIC-CXR database. Each DICOM file, corresponding to a single chest x-ray, is assigned a unique dicom_id. This table links those IDs to a study_id for the radiology report and a subject_id for the patient. Table source: Hospital database. Table purpose: Provides a link between subject_id, study_id, and dicom_id. Number of rows: Links to: CORE.PATIENTS on ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MIT-LCP/mimic-iv-website/blob/master/content/cxr/cxr-record-list.md", "content": "This table lists all records in the MIMIC-CXR database. Each DICOM file, corresponding to a single chest x-ray, is assigned a unique dicom_id. This table links those IDs to a study_id for the radiology report and a subject_id for the patient. Table source: Hospital database. Table purpose: Provides a link between subject_id, study_id, and dicom_id. Number of rows: Links to: CORE.PATIENTS on ..."}
+{"idx": 3, "title": "METransformer: Radiology Report Generation by ... - Semantic Scholar", "date": "", "ddg_snippet": "Figure 2. Bleu 4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. - \" METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens\"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/METransformer:-Radiology-Report-Generation-by-with-Wang-Liu/4d90382730eefbe8639e64f6855f206657758cc7/figure/3", "content": "Figure 2. Bleu 4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. - \" METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens\""}
+{"idx": 4, "title": "(PDF) METransformer: Radiology Report Generation by Transformer with ...", "date": "", "ddg_snippet": "The experiments on the public IU-Xray and MIMIC-CXR datasets show that the Align-Transformer can achieve results competitive with state-of-the-art methods on the two datasets.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/369823367_METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens", "content": "The experiments on the public IU-Xray and MIMIC-CXR datasets show that the Align-Transformer can achieve results competitive with state-of-the-art methods on the two datasets."}
+{"idx": 5, "title": "MIMIC-CXR: Chest X-ray Image Classification and Report Generation - GitHub", "date": "", "ddg_snippet": "This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yuanditang/MIMIC-CXR", "content": "This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports."}
+{"idx": 6, "title": "PDF METransformer: Radiology Report Generation by Transformer with Multiple ...", "date": "", "ddg_snippet": "MIMIC-CXR The recently released MIMIC-CXR [13] is the largest public dataset containing both chest radio-graphs and free-text reports. In total, it consists of 377110 chest x-ray images and 227835 reports from 64588 patients of the Beth Israel Deaconess Medical Center examined be-tween 2011 and 2016.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "MIMIC-CXR The recently released MIMIC-CXR [13] is the largest public dataset containing both chest radio-graphs and free-text reports. In total, it consists of 377110 chest x-ray images and 227835 reports from 64588 patients of the Beth Israel Deaconess Medical Center examined be-tween 2011 and 2016."}
+{"idx": 7, "title": "Table 2 from METransformer: Radiology Report ... - Semantic Scholar", "date": "", "ddg_snippet": "Table 2. Comparison of clinical efficacy metrics on the test set of the MIMIC-CXR dataset for measuring the accuracy of the description of clinical abnormalities. - \" METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens\"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/METransformer:-Radiology-Report-Generation-by-with-Wang-Liu/4d90382730eefbe8639e64f6855f206657758cc7/figure/2", "content": "Table 2. Comparison of clinical efficacy metrics on the test set of the MIMIC-CXR dataset for measuring the accuracy of the description of clinical abnormalities. - \" METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens\""}
+{"idx": 8, "title": "CheXReport: A transformer-based architecture to generate chest X-ray ...", "date": "", "ddg_snippet": "We evaluate the CheXReport on the publicly available MIMIC-CXR dataset comprising 377,110 images and corresponding free-text reports. Specifically, CheXReport achieves state-of-the-art performance on the MIMIC-CXR dataset, outperforming other leading models on BLEU-4 and ROUGE metrics.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424015112", "content": "We evaluate the CheXReport on the publicly available MIMIC-CXR dataset comprising 377,110 images and corresponding free-text reports. Specifically, CheXReport achieves state-of-the-art performance on the MIMIC-CXR dataset, outperforming other leading models on BLEU-4 and ROUGE metrics."}
+{"idx": 9, "title": "arXiv:2408.09743v1 [cs.CV] 19 Aug 2024", "date": "", "ddg_snippet": "on the large-scale MIMIC-CXR dataset. Our method achieved a BLEU-1 score of 0.420, a BLEU-4 score of 0.136, and a ROUGE-L score of 0.291, in-dicating its ability to generate precise and", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.09743", "content": "on the large-scale MIMIC-CXR dataset. Our method achieved a BLEU-1 score of 0.420, a BLEU-4 score of 0.136, and a ROUGE-L score of 0.291, in-dicating its ability to generate precise and"}
diff --git a/data/sampled_jsons/METransformer_MIMIC-CXR_BLEU-4.jsonl b/data/sampled_jsons/METransformer_MIMIC-CXR_BLEU-4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..99132521f2f24143f3429a48eccb990fb9e54393
--- /dev/null
+++ b/data/sampled_jsons/METransformer_MIMIC-CXR_BLEU-4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2304.02211] METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "Figure 2: Bleu_4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. Figure 3: An example of the generated reports and their attention-mapping visualization of three key medical terms from BASELINE and ours METransformer .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2304.02211", "content": "Figure 2: Bleu_4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. Figure 3: An example of the generated reports and their attention-mapping visualization of three key medical terms from BASELINE and ours METransformer ."}
+{"idx": 1, "title": "PDF METransformer: Radiology Report Generation by Transformer with Multiple ...", "date": "", "ddg_snippet": "All evaluations are done on the test set. MIMIC-CXR The recently released MIMIC-CXR [13] is the largest public dataset containing both chest radio-graphs and free-text reports. In total, it consists of 377110 chest x-ray images and 227835 reports from 64588 patients of the Beth Israel Deaconess Medical Center examined be-tween 2011 and 2016.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "All evaluations are done on the test set. MIMIC-CXR The recently released MIMIC-CXR [13] is the largest public dataset containing both chest radio-graphs and free-text reports. In total, it consists of 377110 chest x-ray images and 227835 reports from 64588 patients of the Beth Israel Deaconess Medical Center examined be-tween 2011 and 2016."}
+{"idx": 2, "title": "MIMIC-CXR: Chest X-ray Image Classification and Report Generation - GitHub", "date": "", "ddg_snippet": "This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports. The goal is to develop an automated system capable of both classifying chest abnormalities and generating radiologist-style ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yuanditang/MIMIC-CXR", "content": "This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports. The goal is to develop an automated system capable of both classifying chest abnormalities and generating radiologist-style ..."}
+{"idx": 3, "title": "METransformer: Radiology Report Generation by Transformer with Multiple ...", "date": "", "ddg_snippet": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10203079", "content": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ..."}
+{"idx": 4, "title": "(PDF) METransformer: Radiology Report Generation by Transformer with ...", "date": "", "ddg_snippet": "The experiments on the public IU-Xray and MIMIC-CXR datasets show that the Align-Transformer can achieve results competitive with state-of-the-art methods on the two datasets.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/369823367_METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens", "content": "The experiments on the public IU-Xray and MIMIC-CXR datasets show that the Align-Transformer can achieve results competitive with state-of-the-art methods on the two datasets."}
+{"idx": 5, "title": "MIMIC-CXR Database v2.0.0 - PhysioNet", "date": "", "ddg_snippet": "The MIMIC Chest X-ray ( MIMIC-CXR ) Database v2.0.0 is a large publicly available dataset of chest radiographs in DICOM format with free-text radiology reports. The dataset contains 377,110 images corresponding to 227,835 radiographic studies performed at the Beth Israel Deaconess Medical Center in Boston, MA.", "subpage_snippet": "", "source": "physionet.org", "link": "https://physionet.org/content/mimic-cxr/2.0.0/", "content": "The MIMIC Chest X-ray ( MIMIC-CXR ) Database v2.0.0 is a large publicly available dataset of chest radiographs in DICOM format with free-text radiology reports. The dataset contains 377,110 images corresponding to 227,835 radiographic studies performed at the Beth Israel Deaconess Medical Center in Boston, MA."}
+{"idx": 6, "title": "CheXReport: A transformer-based architecture to generate chest X-ray ...", "date": "", "ddg_snippet": "We evaluate the CheXReport on the publicly available MIMIC-CXR dataset comprising 377,110 images and corresponding free-text reports. Specifically, CheXReport achieves state-of-the-art performance on the MIMIC-CXR dataset, outperforming other leading models on BLEU-4 and ROUGE metrics.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424015112", "content": "We evaluate the CheXReport on the publicly available MIMIC-CXR dataset comprising 377,110 images and corresponding free-text reports. Specifically, CheXReport achieves state-of-the-art performance on the MIMIC-CXR dataset, outperforming other leading models on BLEU-4 and ROUGE metrics."}
+{"idx": 7, "title": "Abnormal-region-aware Multi-modal Feature Fusion for medical report ...", "date": "", "ddg_snippet": "For MIMIC-CXR dataset, our method achieves the highest scores on BLEU-1, BLEU-2, BLEU-3 and achieves the second-best result on BLEU-4 , CIDEr, METEOR. The M2KT approach uses a knowledge base to extract information from medical reports and a multi-modal alignment to keep consistency among visual features, textual features and disease labels.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0950705125005842", "content": "For MIMIC-CXR dataset, our method achieves the highest scores on BLEU-1, BLEU-2, BLEU-3 and achieves the second-best result on BLEU-4 , CIDEr, METEOR. The M2KT approach uses a knowledge base to extract information from medical reports and a multi-modal alignment to keep consistency among visual features, textual features and disease labels."}
+{"idx": 8, "title": "arXiv:2408.09743v1 [cs.CV] 19 Aug 2024", "date": "", "ddg_snippet": "rts compared to current SOTA on the large-scale MIMIC-CXR dataset. Our method achieved a BLEU-1 score of 0.420, a BLEU-4 score of 0.136, and a ROUGE-L score of 0.291, in-dicating its ability to generate precise and", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.09743", "content": "rts compared to current SOTA on the large-scale MIMIC-CXR dataset. Our method achieved a BLEU-1 score of 0.420, a BLEU-4 score of 0.136, and a ROUGE-L score of 0.291, in-dicating its ability to generate precise and"}
+{"idx": 9, "title": "Comparison of proposed technique with current state of the art ...", "date": "", "ddg_snippet": "Comparison of proposed technique with current state of the art techniques in terms of BLEU-4 for the medical report generated on the MIMIC-CXR dataset.", "subpage_snippet": "", "source": "figshare.com", "link": "https://figshare.com/articles/dataset/Comparison_of_proposed_technique_with_current_state_of_the_art_techniques_in_terms_of_BLEU-4_for_the_medical_report_generated_on_the_MIMIC-CXR_dataset_/17952192/1", "content": "Comparison of proposed technique with current state of the art techniques in terms of BLEU-4 for the medical report generated on the MIMIC-CXR dataset."}
diff --git a/data/sampled_jsons/METransformer_Semantic_Scholar_Table_1_BLEU-4_MIMIC-CXR.jsonl b/data/sampled_jsons/METransformer_Semantic_Scholar_Table_1_BLEU-4_MIMIC-CXR.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1fa40f5741d0bb5b7431dd3978db635e88960eec
--- /dev/null
+++ b/data/sampled_jsons/METransformer_Semantic_Scholar_Table_1_BLEU-4_MIMIC-CXR.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Semantic Scholar - Wikipedia", "date": "", "ddg_snippet": "Semantic Scholar is a research tool for scientific literature. It is developed at the Allen Institute for AI and was publicly released in November 2015. Semantic Scholar uses modern techniques in natural language processing to support the research pr...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Semantic_Scholar", "content": "Semantic Scholar is a research tool for scientific literature. It is developed at the Allen Institute for AI and was publicly released in November 2015. Semantic Scholar uses modern techniques in natural language processing to support the research pr..."}
+{"idx": 1, "title": "Semantic Scholar — Википедия", "date": "", "ddg_snippet": "Semantic Scholar . Материал из Википедии — свободной энциклопедии. Semantic Scholar (S2) — поисковая интернет-платформа, разработанная в Институте искусственного интеллекта Аллена[англ.].", "subpage_snippet": "", "source": "ru.wikipedia.org", "link": "https://ru.wikipedia.org/wiki/Semantic_Scholar", "content": "Semantic Scholar . Материал из Википедии — свободной энциклопедии. Semantic Scholar (S2) — поисковая интернет-платформа, разработанная в Институте искусственного интеллекта Аллена[англ.]."}
+{"idx": 2, "title": "METransformer : Radiology Report Generation by Transformer With...", "date": "", "ddg_snippet": "MIMIC - CXR . Methods. BLEU- 1 BLEU -2 BLEU-3 BLEU - 4 ROUGE METEOR. Show-Tell [32]. Table 2. Comparison of clinical efficacy metrics on the test set of the MIMIC - CXR dataset for measuring the accuracy of the de-scription of clinical abnormalities.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "MIMIC - CXR . Methods. BLEU- 1 BLEU -2 BLEU-3 BLEU - 4 ROUGE METEOR. Show-Tell [32]. Table 2. Comparison of clinical efficacy metrics on the test set of the MIMIC - CXR dataset for measuring the accuracy of the de-scription of clinical abnormalities."}
+{"idx": 3, "title": "(PDF) METransformer : Radiology Report Generation by Transformer...", "date": "", "ddg_snippet": "MIMIC - CXR The recently released MIMIC - CXR [13] is the. largest public dataset containin g both chest radiographs. and free-text reports.Training Time. Bleu 4 . Rouge. Meteor. CIDEr. METransformer (num expert= 1 ). 152M.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371253180_METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens", "content": "MIMIC - CXR The recently released MIMIC - CXR [13] is the. largest public dataset containin g both chest radiographs. and free-text reports.Training Time. Bleu 4 . Rouge. Meteor. CIDEr. METransformer (num expert= 1 ). 152M."}
+{"idx": 4, "title": "GitHub - MIT-LCP/ mimic -code: MIMIC Code Repository: Code shared...", "date": "", "ddg_snippet": "mimic-iv-ed - build scripts for MIMIC-IV-ED.cite the dataset(s) you use as described in the PhysioNet project page: MIMIC-III, MIMIC- IV , MIMIC -IV-ED , and/or MIMIC - CXR . cite the Zenodo repository directly as it contains a static copy of the code.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MIT-LCP/mimic-code", "content": "mimic-iv-ed - build scripts for MIMIC-IV-ED.cite the dataset(s) you use as described in the PhysioNet project page: MIMIC-III, MIMIC- IV , MIMIC -IV-ED , and/or MIMIC - CXR . cite the Zenodo repository directly as it contains a static copy of the code."}
+{"idx": 5, "title": "Semantic Scholar | AI-Powered Research Tool", "date": "", "ddg_snippet": "Semantic Scholar uses groundbreaking AI and engineering to understand the semantics of scientific literature to help Scholars discover relevant research. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at Ai2.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/", "content": "Semantic Scholar uses groundbreaking AI and engineering to understand the semantics of scientific literature to help Scholars discover relevant research. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at Ai2."}
+{"idx": 6, "title": "Semantic Scholar - YouTube", "date": "", "ddg_snippet": "Semantic Scholar . @ semanticscholar 7151. • 1 ,29 тыс. подписчиков • 9 видео. Semantic Scholar is an AI-powered scientific exploration and discovery tool.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/channel/UCilmLgmmuiYL6uCb8bC5eUg", "content": "Semantic Scholar . @ semanticscholar 7151. • 1 ,29 тыс. подписчиков • 9 видео. Semantic Scholar is an AI-powered scientific exploration and discovery tool."}
+{"idx": 7, "title": "BLEU - a Hugging Face Space by evaluate-metric", "date": "", "ddg_snippet": "BLEU (Bilingual Evaluation Understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/evaluate-metric/bleu", "content": "BLEU (Bilingual Evaluation Understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another."}
+{"idx": 8, "title": "Enhanced Contrastive Learning with Multi-view Longitudinal Data for...", "date": "", "ddg_snippet": "Table 2. Comparison with SOTA methods on MIMIC - CXR (M-CXR), MIMIC-ABN (M-ABN), and Two-view CXR (T-CXR) datasets. ∆ denotes the performance difference between MLRG and the best peer methods. signifies results reproduced using official codes...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20056", "content": "Table 2. Comparison with SOTA methods on MIMIC - CXR (M-CXR), MIMIC-ABN (M-ABN), and Two-view CXR (T-CXR) datasets. ∆ denotes the performance difference between MLRG and the best peer methods. signifies results reproduced using official codes..."}
+{"idx": 9, "title": "Structural Entities Extraction and Patient", "date": "", "ddg_snippet": "Table 1 . Comparison of our SEI with SOTA approaches on MIMIC - CXR . † means quoted results from the published literature, excluding RG and CX5, as these were not calculated in the literature. The remaining results are reproduced using the official code and checkpoints.", "subpage_snippet": "", "source": "papers.miccai.org", "link": "https://papers.miccai.org/miccai-2024/paper/1768_paper.pdf", "content": "Table 1 . Comparison of our SEI with SOTA approaches on MIMIC - CXR . † means quoted results from the published literature, excluding RG and CX5, as these were not calculated in the literature. The remaining results are reproduced using the official code and checkpoints."}
diff --git a/data/sampled_jsons/METransformer_radiology_report_generation_BLEU-4_MIMIC-CXR_Wang_et_al_2023_year_2023.jsonl b/data/sampled_jsons/METransformer_radiology_report_generation_BLEU-4_MIMIC-CXR_Wang_et_al_2023_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dc30c6dff4de0d8aae6cec51df9106431d72f12b
--- /dev/null
+++ b/data/sampled_jsons/METransformer_radiology_report_generation_BLEU-4_MIMIC-CXR_Wang_et_al_2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "First, we propose a new diagnostic captioning frame-work, METransformer , which is conceptually “multi-expert joint diagnosis” for radiology report generation , by intro-ducing learnable expert tokens and encouraging them to learn complementary representations using both linear and non-linear attentions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "First, we propose a new diagnostic captioning frame-work, METransformer , which is conceptually “multi-expert joint diagnosis” for radiology report generation , by intro-ducing learnable expert tokens and encouraging them to learn complementary representations using both linear and non-linear attentions."}
+{"idx": 1, "title": "METransformer: Radiology Report Generation by Transformer ... R2GenGPT: Radiology Report Generation with frozen LLMs METransformer: Radiology Report Generation by Transformer ... METransformer: Radiology Report Generation by Transformer ... METransformer: Radiology Report Generation by Transformer ... METransformer: Radiology Report Generation by Transformer with Mu… METransformer: Radiology Report Generation by Transformer with Mu… R2GenGPT: Radiology Report Generation with frozen LLMs METransformer: Radiology Report Generation by Transformer with Mu… R2GenGPT: Radiology Report Generation with frozen LLMs METransformer: Radiology Report Generation by Transformer with Mu… Label knowledge guided transformer for automatic radiology ...", "date": "", "ddg_snippet": "Apr 5, 2023 · In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based backbone ... Nov 1, 2023 · In 42, Wang et al . introduced an image-text matching branch to facilitate report generation , utilizing report features to augment image characteristics and consequently minimize the impact of data bias. In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ... In addition, Wang et al [35] introduced an image- report matching network to bridge the domain gap between image and text for reducing the difficulty of report generation . Apr 5, 2023 · The proposed METransformer , a method to realize a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature, enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. In clinical scenarios, multi ... What is a'multi-expert joint diagnosis' for radiology report generation? First, we propose a new diagnostic captioning frame-work, METransformer , which is conceptually “multi-expert joint diagnosis” for radiology report generation, by intro-ducing learnable expert tokens and encouraging them to learn complementary representations using both linear and non-linear attentions. What is metransformer? Based on this motivation, we propose a new diagnostic captioning framework , METransformer, to mimic the “multi-expert joint diagnosis” process. Built upon a transformer backbone, METransformer introduces multiple “expert tokens”, representing multiple experts, into both the transformer encoder and decoder. Is mimic-CXR better than metransformer40? In the MIMIC-CXR dataset, apart from CIDEr, we significantly outperform the latest METransformer40 method across all metrics. For instance, our BLEU_4 score is improved from 0.124 to 0.134, marking an 8.1 % increase. However, we achieved a CIDEr score of 0.269, which is lower than METransformer's 0.362. Does re-port generation improve disease classification in chest X-rays? Improved disease classifica-tion in chest x-rays with transferred features from report gen-eration. In IPMI 2019. 2 Yuan Xue, Tao Xu, L. Rodney Long, Zhiyun Xue, Sameer Antani, George R. Thoma, and Xiaolei Huang. Multimodal recurrent model with attention for automated radiology re-port generation . Can large language models be used for radiology report generation (r2gen)? Large Language Models (LLMs) have consistently showcased remarkable generalization capa-bilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology Report Generation (R2Gen) still presents a challenge , stemming from the inherent disparity in modality between LLMs and the R2Gen task. Can multimodal recurrent model be used for automated radiology re-port generation? Multimodal recurrent model with attention for automated radiology re-port generation . In MICCAI, 2018. 2 S. Yang, X. Wu, S. Ge, X. Wu, S. K. Zhou, and L. Xiao. Radiology report generation with a learned knowledge base and multi-modal alignment. Sep 1, 2025 · Hou et al . [16] proposed an observation-guided radiology report generation model that can generate an observation template, which is then input into the report generation module along with the image, thereby enhancing contextual features by capturing the format information of each observation template.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.02211", "content": "Apr 5, 2023 · In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based backbone ... Nov 1, 2023 · In 42, Wang et al . introduced an image-text matching branch to facilitate report generation , utilizing report features to augment image characteristics and consequently minimize the impact of data bias. In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ... In addition, Wang et al [35] introduced an image- report matching network to bridge the domain gap between image and text for reducing the difficulty of report generation . Apr 5, 2023 · The proposed METransformer , a method to realize a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature, enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. In clinical scenarios, multi ... What is a'multi-expert joint diagnosis' for radiology report generation? First, we propose a new diagnostic captioning frame-work, METransformer , which is conceptually “multi-expert joint diagnosis” for radiology report generation, by intro-ducing learnable expert tokens and encouraging them to learn complementary representations using both linear and non-linear attentions. What is metransformer? Based on this motivation, we propose a new diagnostic captioning framework , METransformer, to mimic the “multi-expert joint diagnosis” process. Built upon a transformer backbone, METransformer introduces multiple “expert tokens”, representing multiple experts, into both the transformer encoder and decoder. Is mimic-CXR better than metransformer40? In the MIMIC-CXR dataset, apart from CIDEr, we significantly outperform the latest METransformer40 method across all metrics. For instance, our BLEU_4 score is improved from 0.124 to 0.134, marking an 8.1 % increase. However, we achieved a CIDEr score of 0.269, which is lower than METransformer's 0.362. Does re-port generation improve disease classification in chest X-rays? Improved disease classifica-tion in chest x-rays with transferred features from report gen-eration. In IPMI 2019. 2 Yuan Xue, Tao Xu, L. Rodney Long, Zhiyun Xue, Sameer Antani, George R. Thoma, and Xiaolei Huang. Multimodal recurrent model with attention for automated radiology re-port generation . Can large language models be used for radiology report generation (r2gen)? Large Language Models (LLMs) have consistently showcased remarkable generalization capa-bilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology Report Generation (R2Gen) still presents a challenge , stemming from the inherent disparity in modality between LLMs and the R2Gen task. Can multimodal recurrent model be used for automated radiology re-port generation? Multimodal recurrent model with attention for automated radiology re-port generation . In MICCAI, 2018. 2 S. Yang, X. Wu, S. Ge, X. Wu, S. K. Zhou, and L. Xiao. Radiology report generation with a learned knowledge base and multi-modal alignment. Sep 1, 2025 · Hou et al . [16] proposed an observation-guided radiology report generation model that can generate an observation template, which is then input into the report generation module along with the image, thereby enhancing contextual features by capturing the format information of each observation template."}
+{"idx": 2, "title": "R2GenGPT: Radiology Report Generation with frozen LLMs", "date": "", "ddg_snippet": "Nov 1, 2023 · In 42, Wang et al . introduced an image-text matching branch to facilitate report generation , utilizing report features to augment image characteristics and consequently minimize the impact of data bias.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2950162823000334", "content": "Nov 1, 2023 · In 42, Wang et al . introduced an image-text matching branch to facilitate report generation , utilizing report features to augment image characteristics and consequently minimize the impact of data bias."}
+{"idx": 3, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10203079", "content": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ..."}
+{"idx": 4, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "In addition, Wang et al [35] introduced an image- report matching network to bridge the domain gap between image and text for reducing the difficulty of report generation .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Sofia-Nathan/publication/371253180_METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens/links/647aa000b3dfd73b775ddc99/METransformer-Radiology-Report-Generation-by-Transformer-with-Multiple-Learnable-Expert-Tokens.pdf", "content": "In addition, Wang et al [35] introduced an image- report matching network to bridge the domain gap between image and text for reducing the difficulty of report generation ."}
+{"idx": 5, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "Apr 5, 2023 · The proposed METransformer , a method to realize a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature, enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. In clinical scenarios, multi ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/METransformer:-Radiology-Report-Generation-by-with-Wang-Liu/4d90382730eefbe8639e64f6855f206657758cc7", "content": "Apr 5, 2023 · The proposed METransformer , a method to realize a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature, enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. In clinical scenarios, multi ..."}
+{"idx": 6, "title": "Label knowledge guided transformer for automatic radiology ...", "date": "", "ddg_snippet": "Sep 1, 2025 · Hou et al . [16] proposed an observation-guided radiology report generation model that can generate an observation template, which is then input into the report generation module along with the image, thereby enhancing contextual features by capturing the format information of each observation template.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0169260725002949", "content": "Sep 1, 2025 · Hou et al . [16] proposed an observation-guided radiology report generation model that can generate an observation template, which is then input into the report generation module along with the image, thereby enhancing contextual features by capturing the format information of each observation template."}
+{"idx": 7, "title": "CVPR Poster METransformer: Radiology Report Generation ...", "date": "", "ddg_snippet": "In addition, Wang et al [35] introduced an image- report matching network to bridge the domain gap between image and text for reducing the difficulty of report ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/poster/21217", "content": "In addition, Wang et al [35] introduced an image- report matching network to bridge the domain gap between image and text for reducing the difficulty of report ..."}
+{"idx": 8, "title": "A survey of deep-learning-based radiology report ...", "date": "", "ddg_snippet": "by X Wang · 2025 · Cited by 4 — This survey summarizes the key techniques developed in the most recent works and proposes a general workflow for deep-learning-based report generation .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1361841525001744", "content": "by X Wang · 2025 · Cited by 4 — This survey summarizes the key techniques developed in the most recent works and proposes a general workflow for deep-learning-based report generation ."}
+{"idx": 9, "title": "Advancements in Radiology Report Generation", "date": "", "ddg_snippet": "by D Mamdouh · 2025 · Cited by 2 — The METransformer, proposed by Wang et al., adopts a Vision Transformer (ViT) in its architecture to encode radiographic images using several ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12292164/", "content": "by D Mamdouh · 2025 · Cited by 2 — The METransformer, proposed by Wang et al., adopts a Vision Transformer (ViT) in its architecture to encode radiographic images using several ..."}
diff --git a/data/sampled_jsons/MLD_CVPR_2023_Table_4_HumanAct12_results_FID.jsonl b/data/sampled_jsons/MLD_CVPR_2023_Table_4_HumanAct12_results_FID.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - ChenFengYe/motion-latent-diffusion: [CVPR 2023] Executing your ...", "date": "", "ddg_snippet": "Motion Latent Diffusion ( MLD ) is a text-to-motion and action-to-motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion", "content": "Motion Latent Diffusion ( MLD ) is a text-to-motion and action-to-motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data."}
+{"idx": 1, "title": "motion-latent-diffusion arXiv:2212.04048v3 [cs.CV] 19 May 2023", "date": "", "ddg_snippet": "he comparison on two datasets, UESTC [26] and HumanAct12 [19]. MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12 , indicating that diffusion models in moti", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.04048", "content": "he comparison on two datasets, UESTC [26] and HumanAct12 [19]. MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12 , indicating that diffusion models in moti"}
+{"idx": 2, "title": "motion-latent-diffusion/configs/config_mld_humanact12.yaml at main ...", "date": "", "ddg_snippet": "[ CVPR 2023 ] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model - ChenFengYe/motion-latent-diffusion", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/configs/config_mld_humanact12.yaml", "content": "[ CVPR 2023 ] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model - ChenFengYe/motion-latent-diffusion"}
+{"idx": 3, "title": "CVPR 2023 Accepted Papers", "date": "", "ddg_snippet": "CVPR 2023 Statistics: Submissions: 9155 papers Accepted: 2359 papers (25.8% acceptance rate) Highlights: 235 papers (10% of accepted papers, 2.6% of submitted papers) Award candidates: 12 papers (0.51% of accepted papers, 0.13% of submitted papers)", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2023/AcceptedPapers", "content": "CVPR 2023 Statistics: Submissions: 9155 papers Accepted: 2359 papers (25.8% acceptance rate) Highlights: 235 papers (10% of accepted papers, 2.6% of submitted papers) Award candidates: 12 papers (0.51% of accepted papers, 0.13% of submitted papers)"}
+{"idx": 4, "title": "PDF arXiv:2312.02256v1 [cs.CV] 4 Dec 2023", "date": "", "ddg_snippet": "EMDM achieves competitive results on HumanAct12 while achieving superior run-time perfor- mance. Notably, although MLD also uses less time for motion sampling, it is a two-stage method.", "subpage_snippet": "", "source": "storage.prod.researchhub.com", "link": "https://storage.prod.researchhub.com/uploads/papers/2023/12/06/2312.02256v1.pdf", "content": "EMDM achieves competitive results on HumanAct12 while achieving superior run-time perfor- mance. Notably, although MLD also uses less time for motion sampling, it is a two-stage method."}
+{"idx": 5, "title": "GitHub - shunlinlu/MLD-chenxin", "date": "", "ddg_snippet": "[ 2023 /06/20] MotionGPT is released! A unified motion-language model. Do all your motion tasks in MotionGPT [ 2023 /03/08] add the script for latent space visualization and the script for the floating point operations (FLOPs) [ 2023 /02/28] MLD got accepted by CVPR 2023 ! [ 2023 /02/02] release action-to-motion task, please refer to the config and the pre-train model [ 2023 /01/18] add a detailed readme ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shunlinlu/MLD-chenxin", "content": "[ 2023 /06/20] MotionGPT is released! A unified motion-language model. Do all your motion tasks in MotionGPT [ 2023 /03/08] add the script for latent space visualization and the script for the floating point operations (FLOPs) [ 2023 /02/28] MLD got accepted by CVPR 2023 ! [ 2023 /02/02] release action-to-motion task, please refer to the config and the pre-train model [ 2023 /01/18] add a detailed readme ..."}
+{"idx": 6, "title": "CVPR Poster Executing Your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks, with two orders of magnitude faster than previous diffusion models on raw motion sequences.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/poster/23272", "content": "Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks, with two orders of magnitude faster than previous diffusion models on raw motion sequences."}
+{"idx": 7, "title": "PDF Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12 , indicating that diffusion models in motion la-tent can also benefit action-conditioned generation task.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf", "content": "MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12 , indicating that diffusion models in motion la-tent can also benefit action-conditioned generation task."}
+{"idx": 8, "title": "PDF Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "In addition, Table 4 shows the comparison results for Action-to-Motion task on HumanML3D dataset. The results under the same metrics are consistent with the results on the HumanAct12 dataset, which further demonstrates the effectiveness of our method.", "subpage_snippet": "", "source": "52.152.142.11", "link": "https://52.152.142.11/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "In addition, Table 4 shows the comparison results for Action-to-Motion task on HumanML3D dataset. The results under the same metrics are consistent with the results on the HumanAct12 dataset, which further demonstrates the effectiveness of our method."}
+{"idx": 9, "title": "HumanML3D: 3D Human Motion-Language Dataset - GitHub", "date": "", "ddg_snippet": "HumanML3D is a 3D human motion-language dataset that originates from a combination of HumanAct12 and Amass dataset. It covers a broad range of human actions such as daily activities (e.g., 'walking', 'jumping'), sports (e.g., 'swimming', 'playing golf'), acrobatics (e.g., 'cartwheel') and artistry (e.g., 'dancing').", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/EricGuo5513/HumanML3D", "content": "HumanML3D is a 3D human motion-language dataset that originates from a combination of HumanAct12 and Amass dataset. It covers a broad range of human actions such as daily activities (e.g., 'walking', 'jumping'), sports (e.g., 'swimming', 'playing golf'), acrobatics (e.g., 'cartwheel') and artistry (e.g., 'dancing')."}
diff --git a/data/sampled_jsons/MLD_Chen_et_al._2023_FID_HumanAct12_table_results.jsonl b/data/sampled_jsons/MLD_Chen_et_al._2023_FID_HumanAct12_table_results.jsonl
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+{"idx": 0, "title": "GitHub - ChenFengYe/motion-latent-diffusion: [CVPR 2023 ... arXiv:2312.12227v1 [cs.CV] 19 Dec 2023 NERM: L NEURAL REPRESENTATIONS FOR H -F HUMAN MOTION ... arXiv:2312.02256v1 [cs.CV] 4 Dec 2023 MOTION FLOW MATCHING FOR EFFICIENT HUMAN MOTION SYNTHESIS AND ... Executing your Commands via Motion Diffusion in Latent Space ChenFengYe/ motion -latent- diffusion - GitHub Executing your Commands via Motion Diffusion in Latent Space GitHub - EricGuo5513/action-to-motion: Official ...", "date": "", "ddg_snippet": "Executing your Commands via Motion Diffusion in Latent Space Project Page | Arxiv - CVPR 2023 Motion Latent Diffusion ( MLD ) is a text-to-motion and action-to-motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data. See full list on github.com •[ 2023 /06/20] MotionGPT is released! A unified motion-language model. Do all your motion tasks in MotionGPT •[ 2023 /03/08] add the script for latent space visualization and the script for the floating point operations (FLOPs) •[ 2023 /02/28] MLD got accepted by CVPR 2023 ! •[ 2023 /02/02] release action-to-motion task, please refer to the config and the pre-train model •[ 2023 /01/18] add a detailed readme of the configuration •[ 2023 /01/09] release no VAE config and pre-train model, you can use MLD framework to train diffusion on raw motion like MDM. See full list on github.com Setup and download1. Conda environment Install the packages in requirements.txt and install PyTorch 1.12.1We test our code on Python 3.9.12 and PyTorch 1.12.1. 2. Dependencies Run the script to download dependencies materials:For Text to Motion Evaluation 3. Pre-train model Run the script to download the pre-train model See full list on github.com Text-to-motion We support text file or keyboard input, the generated motions are npy files. Please check the configs/asset.yaml for path config, TEST.FOLDER as output folder.Then, run the following script:Some parameters:•--example=./demo/example.txt: input file as text prompts•--task=text_motion: generate from the test set of dataset•--task=random_sampling: random motion sampling from noise•--replication: generate motions for same input texts multiple times•--allinone: store all generated motions in a single npy file with the shape of [num_samples, num_ replication, num_frames, num_joints, xyz]The outputs:•npy file: the generated motions with the shape of (nframe, 22, 3)•text file: the input text prompt See full list on github.com Training guidance1. Prepare the datasets Please refer to HumanML3D for text-to-motion dataset setup. We will provide instructions for other datasets soon. 2.1. Ready to train VAE model Please first check the parameters in configs/config_vae_humanml3d.yaml, e.g. NAME,DEBUG.Then, run the following command: 2.2. Ready to train MLD model Please update the parameters in configs/config_mld_humanml3d.yaml, e.g. NAME,DEBUG,PRETRAINED_VAE (change to your latest ckpt model path in previous step)Then, run the following command: See full list on github.com Render SMPL1. Set up blender - WIP Refer to TEMOS-Rendering motions for blender setup, then install the following dependencies. 2. (Optional) Render rigged cylinders Run the following command using blender: 2. Create SMPL meshes with: This outputs:•mesh npy file: the generate SMPL vertices with the shape of (nframe, 6893, 3)•ply files: the ply mesh file for blender or meshlab See full list on github.com Solve foot sliding issue If your demo results have a severe issue on foot sliding, please take a look to the below. It could happen when self.feats2joints (use mean and std for de-normalization) is broken.motion-latent-diffusion/ mld /models/modeltype/ mld .pyLine 264 in af507c4motion-latent-diffusion/ mld /data/get_data.pyLines 26 to 41 in 5c264c3 Details of training 1.GPUs. You can indicate the IDs to use all your GPUs.motion-latent-diffusion/configs/config_vae_humanml3d.yamlLine 4 in 6643f172.Epoch Nums. 1500~3000 epoch is enough for VAE or MLD . I suggest you use wandb(prefer) or tensorborad to check FID curve of your training.3.Training Speed. 2000 epoch could cost 1 day for a single GPU, and around 12 hours for 8 GPUs. Training speed also depends on VAL_EVERY_STEPS (Validation Frequency), DataIO Speed. Your training is a little slow.motion-latent-diffusion/configs/config_vae_humanml3d.yamlLine 77 in 6643f174.Data Log. Only loss print by default. After validation, more metrics of val will print. More details in wandb (prefer) or tensorborad.5.Debug or not. Please use --nodebug for all your training.6.VAE loading. Please load your pre-train VAE correctly for the MLD diffusion training.7. FID . FID of validation will drop to 0.5~1 after 1500 epochs for both VAE and MLD training. By default, validation is on test split...motion-latent-diffusion/configs/config_vae_humanml3d.yamlLine 30 in 6643f17 Details of motion lengths Our model is capable of generating motions with arbitrary lengths. To handle different lengths of motions in the same batch, padding and masking are utilized in our motion encoder and decoder. After latent vector z is obtained by diffusion process, motion length L represented as a sequence of positional encodings in the form of sinusoidal functions are also provided to the motion decoder, so our motion decoder is able to generate output with variable target lengths. See full list on github.com If you find our code or paper helps, please consider citing: See full list on github.com Thanks to TEMOS, ACTOR, HumanML3D and joints2smpl, our code is partially borrowing from them. See full list on github.com This code is distributed under an MIT LICENSE. Note that our code depends on other libraries, including SMPL, SMPL-X, PyTorch3D, and uses datasets which each have their own respective licenses that must also be followed. See full list on github.com These results showcase the supe- rior capability of our method in producing motions that are both natural and semantically accurate, surpassing the per- formance of MDM (Tevet et al . 2022) and MLD ( Chen et al . 2023 ). 3), PhysDiff (Yuan et al ., 2023 ), and MLD ( Chen et al ., 2023 ). In Table 1, we show our performance of 20 fps generation on HumanML3D and 12.5 fps on KIT which are aligned with the generations of existing baselines. Our method outperforms these baselines in terms of FID , R-Precision, Multimodal Dist and Diversity on both datasets, which proves ... Dec 6, 2023 · Following MDM [55], we report the FID , ACC, DIV, MM and Running Time of the afore- mentioned methods. The comparison on HumanAct12 [11] is shown in Tab. 1. EMDM achieves competitive results on HumanAct12 while achieving superior run-time perfor- mance. Notably, although MLD also uses less time for motion sampling, it is a two-stage method. Our method achieves auto-regressive approaches grounded in diffu- a better FID with fewer sampling steps in compari-sion models ( Chen et al ., 2023 ; Tevet et al ., son with baselines. Note the Y-axis is log-scaled. 2023 ; Zhang et al ., 2022). How many action-to-motion sequences does humanact12 provide? Thanks to , after the pro-cessing, HumanAct12 provides 1,191 raw motion se-quences and 12 action categories, and UESTC pro-vides 24K sequences and 40 action categories. We rely on these two datasets for action-to-motion evaluation. Evaluation Metrics summarize in four parts. Is humanml3d better than mld-7? HumanML3D only includes 15k motion sequences, much smaller than billions of images in image generation. MLD -7 could work better when the motion data amount reaches the million level. We provide a detailed ablation study with DDIM below. What is the most important variable of MLD? The most important variable of MLD, the latent vector z , is a bridge between V and diffusion models . We lock the pose xi (one frame of motion) embedding dimensionality to 256, which is the same as , and ex-plore z 2 Ri 256, giving MLD-i. Download HumanAct12 Dataset If you'd like to use HumanAct12 dataset, download the data folder here, and place it in dataset/", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion", "content": "Executing your Commands via Motion Diffusion in Latent Space Project Page | Arxiv - CVPR 2023 Motion Latent Diffusion ( MLD ) is a text-to-motion and action-to-motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data. See full list on github.com •[ 2023 /06/20] MotionGPT is released! A unified motion-language model. Do all your motion tasks in MotionGPT •[ 2023 /03/08] add the script for latent space visualization and the script for the floating point operations (FLOPs) •[ 2023 /02/28] MLD got accepted by CVPR 2023 ! •[ 2023 /02/02] release action-to-motion task, please refer to the config and the pre-train model •[ 2023 /01/18] add a detailed readme of the configuration •[ 2023 /01/09] release no VAE config and pre-train model, you can use MLD framework to train diffusion on raw motion like MDM. See full list on github.com Setup and download1. Conda environment Install the packages in requirements.txt and install PyTorch 1.12.1We test our code on Python 3.9.12 and PyTorch 1.12.1. 2. Dependencies Run the script to download dependencies materials:For Text to Motion Evaluation 3. Pre-train model Run the script to download the pre-train model See full list on github.com Text-to-motion We support text file or keyboard input, the generated motions are npy files. Please check the configs/asset.yaml for path config, TEST.FOLDER as output folder.Then, run the following script:Some parameters:•--example=./demo/example.txt: input file as text prompts•--task=text_motion: generate from the test set of dataset•--task=random_sampling: random motion sampling from noise•--replication: generate motions for same input texts multiple times•--allinone: store all generated motions in a single npy file with the shape of [num_samples, num_ replication, num_frames, num_joints, xyz]The outputs:•npy file: the generated motions with the shape of (nframe, 22, 3)•text file: the input text prompt See full list on github.com Training guidance1. Prepare the datasets Please refer to HumanML3D for text-to-motion dataset setup. We will provide instructions for other datasets soon. 2.1. Ready to train VAE model Please first check the parameters in configs/config_vae_humanml3d.yaml, e.g. NAME,DEBUG.Then, run the following command: 2.2. Ready to train MLD model Please update the parameters in configs/config_mld_humanml3d.yaml, e.g. NAME,DEBUG,PRETRAINED_VAE (change to your latest ckpt model path in previous step)Then, run the following command: See full list on github.com Render SMPL1. Set up blender - WIP Refer to TEMOS-Rendering motions for blender setup, then install the following dependencies. 2. (Optional) Render rigged cylinders Run the following command using blender: 2. Create SMPL meshes with: This outputs:•mesh npy file: the generate SMPL vertices with the shape of (nframe, 6893, 3)•ply files: the ply mesh file for blender or meshlab See full list on github.com Solve foot sliding issue If your demo results have a severe issue on foot sliding, please take a look to the below. It could happen when self.feats2joints (use mean and std for de-normalization) is broken.motion-latent-diffusion/ mld /models/modeltype/ mld .pyLine 264 in af507c4motion-latent-diffusion/ mld /data/get_data.pyLines 26 to 41 in 5c264c3 Details of training 1.GPUs. You can indicate the IDs to use all your GPUs.motion-latent-diffusion/configs/config_vae_humanml3d.yamlLine 4 in 6643f172.Epoch Nums. 1500~3000 epoch is enough for VAE or MLD . I suggest you use wandb(prefer) or tensorborad to check FID curve of your training.3.Training Speed. 2000 epoch could cost 1 day for a single GPU, and around 12 hours for 8 GPUs. Training speed also depends on VAL_EVERY_STEPS (Validation Frequency), DataIO Speed. Your training is a little slow.motion-latent-diffusion/configs/config_vae_humanml3d.yamlLine 77 in 6643f174.Data Log. Only loss print by default. After validation, more metrics of val will print. More details in wandb (prefer) or tensorborad.5.Debug or not. Please use --nodebug for all your training.6.VAE loading. Please load your pre-train VAE correctly for the MLD diffusion training.7. FID . FID of validation will drop to 0.5~1 after 1500 epochs for both VAE and MLD training. By default, validation is on test split...motion-latent-diffusion/configs/config_vae_humanml3d.yamlLine 30 in 6643f17 Details of motion lengths Our model is capable of generating motions with arbitrary lengths. To handle different lengths of motions in the same batch, padding and masking are utilized in our motion encoder and decoder. After latent vector z is obtained by diffusion process, motion length L represented as a sequence of positional encodings in the form of sinusoidal functions are also provided to the motion decoder, so our motion decoder is able to generate output with variable target lengths. See full list on github.com If you find our code or paper helps, please consider citing: See full list on github.com Thanks to TEMOS, ACTOR, HumanML3D and joints2smpl, our code is partially borrowing from them. See full list on github.com This code is distributed under an MIT LICENSE. Note that our code depends on other libraries, including SMPL, SMPL-X, PyTorch3D, and uses datasets which each have their own respective licenses that must also be followed. See full list on github.com These results showcase the supe- rior capability of our method in producing motions that are both natural and semantically accurate, surpassing the per- formance of MDM (Tevet et al . 2022) and MLD ( Chen et al . 2023 ). 3), PhysDiff (Yuan et al ., 2023 ), and MLD ( Chen et al ., 2023 ). In Table 1, we show our performance of 20 fps generation on HumanML3D and 12.5 fps on KIT which are aligned with the generations of existing baselines. Our method outperforms these baselines in terms of FID , R-Precision, Multimodal Dist and Diversity on both datasets, which proves ... Dec 6, 2023 · Following MDM [55], we report the FID , ACC, DIV, MM and Running Time of the afore- mentioned methods. The comparison on HumanAct12 [11] is shown in Tab. 1. EMDM achieves competitive results on HumanAct12 while achieving superior run-time perfor- mance. Notably, although MLD also uses less time for motion sampling, it is a two-stage method. Our method achieves auto-regressive approaches grounded in diffu- a better FID with fewer sampling steps in compari-sion models ( Chen et al ., 2023 ; Tevet et al ., son with baselines. Note the Y-axis is log-scaled. 2023 ; Zhang et al ., 2022). How many action-to-motion sequences does humanact12 provide? Thanks to , after the pro-cessing, HumanAct12 provides 1,191 raw motion se-quences and 12 action categories, and UESTC pro-vides 24K sequences and 40 action categories. We rely on these two datasets for action-to-motion evaluation. Evaluation Metrics summarize in four parts. Is humanml3d better than mld-7? HumanML3D only includes 15k motion sequences, much smaller than billions of images in image generation. MLD -7 could work better when the motion data amount reaches the million level. We provide a detailed ablation study with DDIM below. What is the most important variable of MLD? The most important variable of MLD, the latent vector z , is a bridge between V and diffusion models . We lock the pose xi (one frame of motion) embedding dimensionality to 256, which is the same as , and ex-plore z 2 Ri 256, giving MLD-i. Download HumanAct12 Dataset If you'd like to use HumanAct12 dataset, download the data folder here, and place it in dataset/"}
+{"idx": 1, "title": "arXiv:2312.12227v1 [cs.CV] 19 Dec 2023", "date": "", "ddg_snippet": "These results showcase the supe- rior capability of our method in producing motions that are both natural and semantically accurate, surpassing the per- formance of MDM (Tevet et al . 2022) and MLD ( Chen et al . 2023 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.12227", "content": "These results showcase the supe- rior capability of our method in producing motions that are both natural and semantically accurate, surpassing the per- formance of MDM (Tevet et al . 2022) and MLD ( Chen et al . 2023 )."}
+{"idx": 2, "title": "arXiv:2312.02256v1 [cs.CV] 4 Dec 2023", "date": "", "ddg_snippet": "Dec 6, 2023 · Following MDM [55], we report the FID , ACC, DIV, MM and Running Time of the afore- mentioned methods. The comparison on HumanAct12 [11] is shown in Tab. 1. EMDM achieves competitive results on HumanAct12 while achieving superior run-time perfor- mance. Notably, although MLD also uses less time for motion sampling, it is a two-stage method.", "subpage_snippet": "", "source": "storage.prod.researchhub.com", "link": "https://storage.prod.researchhub.com/uploads/papers/2023/12/06/2312.02256v1.pdf", "content": "Dec 6, 2023 · Following MDM [55], we report the FID , ACC, DIV, MM and Running Time of the afore- mentioned methods. The comparison on HumanAct12 [11] is shown in Tab. 1. EMDM achieves competitive results on HumanAct12 while achieving superior run-time perfor- mance. Notably, although MLD also uses less time for motion sampling, it is a two-stage method."}
+{"idx": 3, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12 , indicating that diffusion models in motion la-tent can also benefit action-conditioned generation task.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf", "content": "MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12 , indicating that diffusion models in motion la-tent can also benefit action-conditioned generation task."}
+{"idx": 4, "title": "NERM: L NEURAL REPRESENTATIONS FOR H -F HUMAN MOTION ...", "date": "", "ddg_snippet": "3), PhysDiff (Yuan et al ., 2023 ), and MLD ( Chen et al ., 2023 ). In Table 1, we show our performance of 20 fps generation on HumanML3D and 12.5 fps on KIT which are aligned with the generations of existing baselines. Our method outperforms these baselines in terms of FID , R-Precision, Multimodal Dist and Diversity on both datasets, which proves ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/9e3b203e72c4e058de26d02a92a81844-Paper-Conference.pdf", "content": "3), PhysDiff (Yuan et al ., 2023 ), and MLD ( Chen et al ., 2023 ). In Table 1, we show our performance of 20 fps generation on HumanML3D and 12.5 fps on KIT which are aligned with the generations of existing baselines. Our method outperforms these baselines in terms of FID , R-Precision, Multimodal Dist and Diversity on both datasets, which proves ..."}
+{"idx": 5, "title": "MOTION FLOW MATCHING FOR EFFICIENT HUMAN MOTION SYNTHESIS AND ...", "date": "", "ddg_snippet": "Our method achieves auto-regressive approaches grounded in diffu- a better FID with fewer sampling steps in compari-sion models ( Chen et al ., 2023 ; Tevet et al ., son with baselines. Note the Y-axis is log-scaled. 2023 ; Zhang et al ., 2022).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ikdB0VXPlw", "content": "Our method achieves auto-regressive approaches grounded in diffu- a better FID with fewer sampling steps in compari-sion models ( Chen et al ., 2023 ; Tevet et al ., son with baselines. Note the Y-axis is log-scaled. 2023 ; Zhang et al ., 2022)."}
+{"idx": 6, "title": "GitHub - EricGuo5513/action-to-motion: Official ...", "date": "", "ddg_snippet": "Download HumanAct12 Dataset If you'd like to use HumanAct12 dataset, download the data folder here, and place it in dataset/", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/EricGuo5513/action-to-motion", "content": "Download HumanAct12 Dataset If you'd like to use HumanAct12 dataset, download the data folder here, and place it in dataset/"}
+{"idx": 7, "title": "MotionGPT3: Human Motion as a Second Modality", "date": "", "ddg_snippet": "While prior work focuses mainly on text-driven motion synthesis mdm2022human ; shafir2023priormdm ; chen2023mld ; chuan2022tm2t ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.24086v1", "content": "While prior work focuses mainly on text-driven motion synthesis mdm2022human ; shafir2023priormdm ; chen2023mld ; chuan2022tm2t ..."}
+{"idx": 8, "title": "FlowMotion: Target-Predictive Conditional Flow Matching for", "date": "", "ddg_snippet": "... resulting in enhanced generation fidelity ... This approach is conceptually aligned with a recent formulation introduced by Lipman et al . [ 40 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.01338v3", "content": "... resulting in enhanced generation fidelity ... This approach is conceptually aligned with a recent formulation introduced by Lipman et al . [ 40 ] ."}
+{"idx": 9, "title": "Rethinking Diffusion for Text-Driven Human Motion Generation", "date": "", "ddg_snippet": "In 2023 , the exploration of Vector Quantization (VQ) techniques for human motion representation becomes increasingly dominant, marked a noticeable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.16575v1", "content": "In 2023 , the exploration of Vector Quantization (VQ) techniques for human motion representation becomes increasingly dominant, marked a noticeable ..."}
diff --git a/data/sampled_jsons/MLD_Table_4_HumanAct12_FID_0.077.jsonl b/data/sampled_jsons/MLD_Table_4_HumanAct12_FID_0.077.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..909326a8262bd0b420a2ef6e41e801bbe4ae59d1
--- /dev/null
+++ b/data/sampled_jsons/MLD_Table_4_HumanAct12_FID_0.077.jsonl
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+{"idx": 0, "title": "PDF Beef Cattle Nutrition Series Part 3: Nutrient Requirement Tables - MP 391", "date": "", "ddg_snippet": "Updates Seventh Revised to this report were determined Edition based previous on tables , the need modify to correct tables known to better issues date animal classes and adjust values according accommo- with to model changes associated with the Nutrient Requirements of Beef Cattle, Eighth Revised Edition (2016, a.k.a. NRC).", "subpage_snippet": "", "source": "www.uaex.uada.edu", "link": "https://www.uaex.uada.edu/publications/pdf/MP391.pdf", "content": "Updates Seventh Revised to this report were determined Edition based previous on tables , the need modify to correct tables known to better issues date animal classes and adjust values according accommo- with to model changes associated with the Nutrient Requirements of Beef Cattle, Eighth Revised Edition (2016, a.k.a. NRC)."}
+{"idx": 1, "title": "Motion Synthesis On Humanact12 | SOTA | HyperAI", "date": "", "ddg_snippet": "Build the Future of Artificial Intelligence", "subpage_snippet": "", "source": "hyper.ai", "link": "https://hyper.ai/en/sota/tasks/motion-synthesis/benchmark/motion-synthesis-on-humanact12", "content": "Build the Future of Artificial Intelligence"}
+{"idx": 2, "title": "LS-GAN: Human Motion Synthesis with Latent-space GANs", "date": "", "ddg_snippet": "Our experiment results on HumanML3D [14] benchmark suggest that a simple GAN architecture achieves an FID of 0.482 with percent 91 91\\% 91 % in FLOPS reduction compared to MLD . In addition, our method shows competitive performance on action-to-motion HumanAct12 [16] benchmark.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.01449v1", "content": "Our experiment results on HumanML3D [14] benchmark suggest that a simple GAN architecture achieves an FID of 0.482 with percent 91 91\\% 91 % in FLOPS reduction compared to MLD . In addition, our method shows competitive performance on action-to-motion HumanAct12 [16] benchmark."}
+{"idx": 3, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "The diffusion step is set to 100. The balanced parameter λ c l for J C L loss is set to 0.3. Table 1: The comparison results of unconditional human motion synthesis between our method and state-of-the-art methods on HumanAct12 dataset. Bold and underline indicate the best and the second best result.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00998", "content": "The diffusion step is set to 100. The balanced parameter λ c l for J C L loss is set to 0.3. Table 1: The comparison results of unconditional human motion synthesis between our method and state-of-the-art methods on HumanAct12 dataset. Bold and underline indicate the best and the second best result."}
+{"idx": 4, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Besides, some settings like 0.25 7.5 achieve the best FID of 0.229, but we still suggest 0.1 7.5 as dropout and scale 𝑝 𝑠 for MLD models (Sec. 4 ) overall metrics.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2212.04048", "content": "Besides, some settings like 0.25 7.5 achieve the best FID of 0.229, but we still suggest 0.1 7.5 as dropout and scale 𝑝 𝑠 for MLD models (Sec. 4 ) overall metrics."}
+{"idx": 5, "title": "EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion ...", "date": "", "ddg_snippet": "Table 3: Comparison of action-to-motion task on HumanAct12 [21]: subscript FID train \\text { FID }_ {\\text {train}} FID start_POSTSUBSCRIPT train end_POSTSUBSCRIPT indicating the evaluated splits.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.02256", "content": "Table 3: Comparison of action-to-motion task on HumanAct12 [21]: subscript FID train \\text { FID }_ {\\text {train}} FID start_POSTSUBSCRIPT train end_POSTSUBSCRIPT indicating the evaluated splits."}
+{"idx": 6, "title": "Ficoll-Paque™ PREMIUM density gradient med - Cytiva", "date": "", "ddg_snippet": "CELL PREPARATION Ficoll-PaqueTM PREMIUM products are a range of sterile, ready-to-use density gradient media* for the preparation of mononuclear cells. All Ficoll-PaqueTM PREMIUM products have low endotoxin levels (< 0.12 EU/mL) and are manufactured under a Quality Management System certified to ISO 13485. Ficoll-PaqueTM PREMIUM products are available in densities of 1.073, 1.077, and 1.084 g ...", "subpage_snippet": "", "source": "cdn.cytivalifesciences.com", "link": "https://cdn.cytivalifesciences.com/api/public/content/digi-46695-pdf", "content": "CELL PREPARATION Ficoll-PaqueTM PREMIUM products are a range of sterile, ready-to-use density gradient media* for the preparation of mononuclear cells. All Ficoll-PaqueTM PREMIUM products have low endotoxin levels (< 0.12 EU/mL) and are manufactured under a Quality Management System certified to ISO 13485. Ficoll-PaqueTM PREMIUM products are available in densities of 1.073, 1.077, and 1.084 g ..."}
+{"idx": 7, "title": "PDF Manual for SOA Exam MLC. - Binghamton University", "date": "", "ddg_snippet": "(A) 8.0 (B) 8.1 (C) 8.2 (D) 8.3 (E) 8.4 (#26, Exam MLC, Spring 2006) Oil wells produce until they run dry. The survival function for a well is given by: An oil company owns 10 wells age 3. It insures them for 1 million each against failure for two years where the loss is payable at the end of the year of failure. You are given:", "subpage_snippet": "", "source": "people.math.binghamton.edu", "link": "https://people.math.binghamton.edu/arcones/exam-mlc/chap-3-act.pdf", "content": "(A) 8.0 (B) 8.1 (C) 8.2 (D) 8.3 (E) 8.4 (#26, Exam MLC, Spring 2006) Oil wells produce until they run dry. The survival function for a well is given by: An oil company owns 10 wells age 3. It insures them for 1 million each against failure for two years where the loss is payable at the end of the year of failure. You are given:"}
+{"idx": 8, "title": "Motion Flow Matching for Human Motion Synthesis and Editing", "date": "", "ddg_snippet": "Human motion generation [15, 69] constitutes a foundational task in computer animation with diverse applications spanning computer graphics, human-computer interaction, and robotics. In contrast to unconditional motion generation [43, 47], recent endeavors have focused on introducing different conditions for enhanced controllability, such as action name [58], text [9, 21], audio [61], and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08895v1", "content": "Human motion generation [15, 69] constitutes a foundational task in computer animation with diverse applications spanning computer graphics, human-computer interaction, and robotics. In contrast to unconditional motion generation [43, 47], recent endeavors have focused on introducing different conditions for enhanced controllability, such as action name [58], text [9, 21], audio [61], and ..."}
+{"idx": 9, "title": "OM Final Flashcards | Quizlet", "date": "", "ddg_snippet": "Study with Quizlet and memorize flashcards containing terms like 16% Overall process yield = 1 × 0.80 × 0.20 = 0.16., flow unit types, resource types, amount of work and more.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/592088862/om-final-flash-cards/", "content": "Study with Quizlet and memorize flashcards containing terms like 16% Overall process yield = 1 × 0.80 × 0.20 = 0.16., flow unit types, resource types, amount of work and more."}
diff --git a/data/sampled_jsons/MLflow_model_dependency_tracking_automated_verification.jsonl b/data/sampled_jsons/MLflow_model_dependency_tracking_automated_verification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..efdb59b8da81a23242d1df5710aae553970a82a8
--- /dev/null
+++ b/data/sampled_jsons/MLflow_model_dependency_tracking_automated_verification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Managing Dependencies in MLflow Models | MLflow", "date": "", "ddg_snippet": "When you create an MLflow model using the MLflow Tracking APIs, for instance, mlflow .pytorch.log_model (), MLflow automatically infers the required dependencies for the model flavor you're using and records them as a part of Model metadata.", "subpage_snippet": "", "source": "mlflow.org", "link": "https://mlflow.org/docs/2.21.3/model/dependencies/", "content": "When you create an MLflow model using the MLflow Tracking APIs, for instance, mlflow .pytorch.log_model (), MLflow automatically infers the required dependencies for the model flavor you're using and records them as a part of Model metadata."}
+{"idx": 1, "title": "Log model dependencies - Azure Databricks | Microsoft Learn", "date": "", "ddg_snippet": "Learn how to log a model and its dependencies alongside the model artifact to ensure they are available and reproducible in your production environment; for example when deploying a model from the MLflow Tracking Server or Model Registry.", "subpage_snippet": "", "source": "learn.microsoft.com", "link": "https://learn.microsoft.com/en-us/azure/databricks/mlflow/log-model-dependencies", "content": "Learn how to log a model and its dependencies alongside the model artifact to ensure they are available and reproducible in your production environment; for example when deploying a model from the MLflow Tracking Server or Model Registry."}
+{"idx": 2, "title": "Track and compare models using MLflow Logged Models", "date": "", "ddg_snippet": "Logged Model tracking lets you compare models against each other, find the most performant model , and track down information during debugging. Logged Models can also be registered to the Unity Catalog model registry, making information about the model from all MLflow experiments and workspaces available in a single location.", "subpage_snippet": "", "source": "docs.databricks.com", "link": "https://docs.databricks.com/gcp/en/mlflow/logged-model", "content": "Logged Model tracking lets you compare models against each other, find the most performant model , and track down information during debugging. Logged Models can also be registered to the Unity Catalog model registry, making information about the model from all MLflow experiments and workspaces available in a single location."}
+{"idx": 3, "title": "Track and Version Your ML Models Like a Pro — With MLflow and Poetry", "date": "", "ddg_snippet": "Why this matters Managing machine learning workflows isn't just about writing good models — it's also about reproducibility, transparency, and clean project structure. In this article, I'll walk you through how to: Structure a clean ML project using Poetry Log metrics, parameters, and artifacts using MLflow Register and version your models Reload a model version from the Model Registry ...", "subpage_snippet": "", "source": "python.plainenglish.io", "link": "https://python.plainenglish.io/track-and-version-your-ml-models-like-a-pro-with-mlflow-and-poetry-b3e624d6d4b5", "content": "Why this matters Managing machine learning workflows isn't just about writing good models — it's also about reproducibility, transparency, and clean project structure. In this article, I'll walk you through how to: Structure a clean ML project using Poetry Log metrics, parameters, and artifacts using MLflow Register and version your models Reload a model version from the Model Registry ..."}
+{"idx": 4, "title": "Version Tracking Data Model - MLflow", "date": "", "ddg_snippet": "MLflow's version tracking data model provides a structured approach to managing and analyzing different versions of your GenAI applications across their entire lifecycle. By organizing version metadata within MLflow's core entities, you can systematically track performance, debug regressions, and validate deployments across development, staging, and production environments.", "subpage_snippet": "", "source": "mlflow.org", "link": "https://mlflow.org/docs/3.1.3/genai/data-model/app-versions/", "content": "MLflow's version tracking data model provides a structured approach to managing and analyzing different versions of your GenAI applications across their entire lifecycle. By organizing version metadata within MLflow's core entities, you can systematically track performance, debug regressions, and validate deployments across development, staging, and production environments."}
+{"idx": 5, "title": "MLflow Tracking for models - Azure Machine Learning", "date": "", "ddg_snippet": "MLflow Tracking is a component of MLflow that logs and tracks your training run metrics and model artifacts, no matter your experiment's environment--locally on your computer, on a remote compute target, a virtual machine, or an Azure Databricks cluster.", "subpage_snippet": "", "source": "learn.microsoft.com", "link": "https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow?view=azureml-api-1", "content": "MLflow Tracking is a component of MLflow that logs and tracks your training run metrics and model artifacts, no matter your experiment's environment--locally on your computer, on a remote compute target, a virtual machine, or an Azure Databricks cluster."}
+{"idx": 6, "title": "MLflow Integration: Track LLM Experiments and Model Versioning for ...", "date": "", "ddg_snippet": "Learn MLflow integration for LLM experiment tracking and model versioning. Step-by-step guide with code examples to streamline AI deployment workflows.", "subpage_snippet": "", "source": "markaicode.com", "link": "https://markaicode.com/mlflow-llm-experiment-tracking-model-versioning/", "content": "Learn MLflow integration for LLM experiment tracking and model versioning. Step-by-step guide with code examples to streamline AI deployment workflows."}
+{"idx": 7, "title": "MLflow Model Registry: Guide to Managing the ML Lifecycle", "date": "", "ddg_snippet": "The MLflow Model Registry simplifies this process by offering a centralized platform where teams can register, track, deploy, and manage model lifecycles. This guide covers everything you need to know about the MLflow Model Registry, from its features to setting up a robust CI/CD pipeline for machine learning. What is the MLflow Model Registry?", "subpage_snippet": "", "source": "mljourney.com", "link": "https://mljourney.com/mlflow-model-registry-guide-to-managing-the-ml-lifecycle/", "content": "The MLflow Model Registry simplifies this process by offering a centralized platform where teams can register, track, deploy, and manage model lifecycles. This guide covers everything you need to know about the MLflow Model Registry, from its features to setting up a robust CI/CD pipeline for machine learning. What is the MLflow Model Registry?"}
+{"idx": 8, "title": "Log model dependencies - Databricks on AWS", "date": "", "ddg_snippet": "Learn how to log a model and its dependencies alongside the model artifact to ensure they are available and reproducible in your production environment; for example when deploying a model from the MLflow Tracking Server or Model Registry.", "subpage_snippet": "", "source": "docs.databricks.com", "link": "https://docs.databricks.com/aws/en/mlflow/log-model-dependencies", "content": "Learn how to log a model and its dependencies alongside the model artifact to ensure they are available and reproducible in your production environment; for example when deploying a model from the MLflow Tracking Server or Model Registry."}
+{"idx": 9, "title": "MLflow Model Versioning: Effective Strategies for MLOps", "date": "", "ddg_snippet": "Learn effective strategies for model versioning with MLflow . Discover tools and techniques to track and manage machine learning models efficiently.", "subpage_snippet": "", "source": "codezup.com", "link": "https://codezup.com/mlflow-model-versioning-strategies/", "content": "Learn effective strategies for model versioning with MLflow . Discover tools and techniques to track and manage machine learning models efficiently."}
diff --git a/data/sampled_jsons/MSVD_dataset_frame_sampling_video_retrieval.jsonl b/data/sampled_jsons/MSVD_dataset_frame_sampling_video_retrieval.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2124448da6bc614577f84c977d99caad53d1f569
--- /dev/null
+++ b/data/sampled_jsons/MSVD_dataset_frame_sampling_video_retrieval.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Video Retrieval | fundamentalvision/Uni-Perceiver | DeepWiki", "date": "", "ddg_snippet": "The video retrieval task uses the MSVD dataset , which contains video clips paired with text descriptions. Uni-Perceiver processes these videos using a sampling strategy that extracts frames for efficient representation learning.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/fundamentalvision/Uni-Perceiver/3.4-video-retrieval", "content": "The video retrieval task uses the MSVD dataset , which contains video clips paired with text descriptions. Uni-Perceiver processes these videos using a sampling strategy that extracts frames for efficient representation learning."}
+{"idx": 1, "title": "mmSampler: Efficient Frame Sampler for Multimodal Video Retrieval", "date": "", "ddg_snippet": "To this end, we propose mmSampler, a learning-based sampler, to adaptively select salient frames to represent the videos for multimodal video retrieval . mmSampler can greatly reduce the computational overhead for video representation without affecting the retrieval performance.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SamsungLabs/video-retrieval-sampler", "content": "To this end, we propose mmSampler, a learning-based sampler, to adaptively select salient frames to represent the videos for multimodal video retrieval . mmSampler can greatly reduce the computational overhead for video representation without affecting the retrieval performance."}
+{"idx": 2, "title": "Narrating the Video: Boosting Text-Video Retrieval via Comprehensive ...", "date": "", "ddg_snippet": "Qualitative Results To analyze the effectiveness of using narration in text- video retrieval , we provide examples of retrieval results and generated frame -level captions for three datasets : MSVD 1, VATEX 2, and DiDeMo 3. Additionally, we include examples of incorrect results due to short and general queries.", "subpage_snippet": "", "source": "multimodal-understanding-group.github.io", "link": "https://multimodal-understanding-group.github.io/NarVid/", "content": "Qualitative Results To analyze the effectiveness of using narration in text- video retrieval , we provide examples of retrieval results and generated frame -level captions for three datasets : MSVD 1, VATEX 2, and DiDeMo 3. Additionally, we include examples of incorrect results due to short and general queries."}
+{"idx": 3, "title": "Example video frames and captions from MSVD dataset", "date": "", "ddg_snippet": "Download scientific diagram | Example video frames and captions from MSVD dataset from publication: Video Description: Datasets & Evaluation Metrics | Rapid expansion and the novel phenomenon of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Example-video-frames-and-captions-from-MSVD-dataset_fig2_354175109", "content": "Download scientific diagram | Example video frames and captions from MSVD dataset from publication: Video Description: Datasets & Evaluation Metrics | Rapid expansion and the novel phenomenon of ..."}
+{"idx": 4, "title": "MSVD - Dataset - LDM", "date": "", "ddg_snippet": "MSVD Text- Video Retrieval (TVR) aims to align relevant video content with natural language queries. To date, most state-of-the-art TVR methods learn image-to- video transfer learning based on large-scale pre-trained vision-language models (e.g., CLIP).", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/msvd", "content": "MSVD Text- Video Retrieval (TVR) aims to align relevant video content with natural language queries. To date, most state-of-the-art TVR methods learn image-to- video transfer learning based on large-scale pre-trained vision-language models (e.g., CLIP)."}
+{"idx": 5, "title": "MSVD Dataset Corpus - Kaggle", "date": "", "ddg_snippet": "The Microsoft Research Video Description Corpus ( MSVD ) dataset consists of about 120K sentences collected during the summer of 2010. Workers on Mechanical Turk were paid to watch a short video snippet and then summarize the action in a single sentence. The result is a set of roughly parallel descriptions of 1800 video snippets. Because the workers were urged to complete the task in the ...", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/vtrnanh/msvd-dataset-corpus", "content": "The Microsoft Research Video Description Corpus ( MSVD ) dataset consists of about 120K sentences collected during the summer of 2010. Workers on Mechanical Turk were paid to watch a short video snippet and then summarize the action in a single sentence. The result is a set of roughly parallel descriptions of 1800 video snippets. Because the workers were urged to complete the task in the ..."}
+{"idx": 6, "title": "collaborative-experts/misc/datasets/msvd/README.md at master - GitHub", "date": "", "ddg_snippet": "This folder contains a collection of features, extracted from the MSVD [2] dataset as part of the paper: Use what you have: Video retrieval using representations from collaborative experts.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/albanie/collaborative-experts/blob/master/misc/datasets/msvd/README.md", "content": "This folder contains a collection of features, extracted from the MSVD [2] dataset as part of the paper: Use what you have: Video retrieval using representations from collaborative experts."}
+{"idx": 7, "title": "Narrating the Video: Boosting Text-Video Retrieval via Comprehensive ...", "date": "", "ddg_snippet": "To qualitatively analyze and demonstrate the effectiveness of employing narration in text- video retrieval , we provide additional examples of retrieval results and generated frame -level captions for three datasets : Fig. 8 for MSVD , Fig. 9 for VATEX, and Fig. 10 for DiDeMo.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05186v1", "content": "To qualitatively analyze and demonstrate the effectiveness of employing narration in text- video retrieval , we provide additional examples of retrieval results and generated frame -level captions for three datasets : Fig. 8 for MSVD , Fig. 9 for VATEX, and Fig. 10 for DiDeMo."}
+{"idx": 8, "title": "PDF Enhancing Text-Video Retrieval Performance With Low-Salient but ...", "date": "", "ddg_snippet": "For simpler datasets like MSVD and DiDeMo, employing a single frame sufices for obtaining accurate LSDO information, considering additional frames may interfere with the final retrieval results.", "subpage_snippet": "", "source": "ivyzheng.github.io", "link": "https://ivyzheng.github.io/2025-1-LSDO.pdf", "content": "For simpler datasets like MSVD and DiDeMo, employing a single frame sufices for obtaining accurate LSDO information, considering additional frames may interfere with the final retrieval results."}
+{"idx": 9, "title": "friedrichor/MSVD · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/friedrichor/MSVD", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
diff --git a/data/sampled_jsons/Ma_et_al._online_matching_markets_fairness_offline_abstract.jsonl b/data/sampled_jsons/Ma_et_al._online_matching_markets_fairness_offline_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..be7ff16b80ec7462e4d85e8af9bf9ae468e9f70d
--- /dev/null
+++ b/data/sampled_jsons/Ma_et_al._online_matching_markets_fairness_offline_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fairness Maximization among Offline Agents in Online-Matching Markets", "date": "", "ddg_snippet": "In this paper, we propose online matching algorithms which optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve online competitive ratios at least 0.725 for individual fairness maximization (IFM) and 0.719 for group fairness ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.08934", "content": "In this paper, we propose online matching algorithms which optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve online competitive ratios at least 0.725 for individual fairness maximization (IFM) and 0.719 for group fairness ..."}
+{"idx": 1, "title": "Fairness Maximization among Offline Agents in Online-Matching Markets", "date": "", "ddg_snippet": "Nanda et al. [43] proposed a bi-objective online - matching -based model to study the tradeofbetween the system eficiency (profit) and the fairness among rideshare riders during high-demand hours.", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10420152", "content": "Nanda et al. [43] proposed a bi-objective online - matching -based model to study the tradeofbetween the system eficiency (profit) and the fairness among rideshare riders during high-demand hours."}
+{"idx": 2, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets ...", "date": "", "ddg_snippet": "Authors Will Ma , Pan Xu Abstract Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served. We instead study the objective of *maximizing the minimum matching rate across all online types*, which is referred to as long-run (individual) fairness . For Online Matching ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/959f70ee50044bed305e48e3484005a7-Abstract-Conference.html", "content": "Authors Will Ma , Pan Xu Abstract Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served. We instead study the objective of *maximizing the minimum matching rate across all online types*, which is referred to as long-run (individual) fairness . For Online Matching ..."}
+{"idx": 3, "title": "A report on \"Fairness Maximization among Offline Agents in Online ...", "date": "", "ddg_snippet": "Abstract and Figures Implementation of some parts of the paper \" Fairness Maximization among Offline Agents in Online-Matching Markets \" and some suggestions on it.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/357888689_A_report_on_Fairness_Maximization_among_Offline_Agents_in_Online-Matching_Markets", "content": "Abstract and Figures Implementation of some parts of the paper \" Fairness Maximization among Offline Agents in Online-Matching Markets \" and some suggestions on it."}
+{"idx": 4, "title": "Fairness Maximization among Offline Agents in Online-Matching Markets", "date": "", "ddg_snippet": "This article proposes online matching algorithms that optimize for either individual or group-level fairness among offline agents in OMMs, and presents two linear-programming (LP) based sampling algorithms, which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 for group fairness maximized. Online matching markets (OMMs) are commonly used in today's ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Fairness-Maximization-among-Offline-Agents-in-Ma-Xu/36c62e639e5c693a71868d8b7b1e302f93f8fb41", "content": "This article proposes online matching algorithms that optimize for either individual or group-level fairness among offline agents in OMMs, and presents two linear-programming (LP) based sampling algorithms, which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 for group fairness maximized. Online matching markets (OMMs) are commonly used in today's ..."}
+{"idx": 5, "title": "NeurIPS Poster Promoting Fairness Among Dynamic Agents in Online ...", "date": "", "ddg_snippet": "We instead study the objective of *maximizing the minimum matching rate across all online types*, which is referred to as long-run (individual) fairness . For Online Matching under long-run Fairness (OM-LF) with a single offline agent, we show that the first-come-first-serve (FCFS) policy is 1 -competitive, i.e., matching any optimal clairvoyant.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96945", "content": "We instead study the objective of *maximizing the minimum matching rate across all online types*, which is referred to as long-run (individual) fairness . For Online Matching under long-run Fairness (OM-LF) with a single offline agent, we show that the first-come-first-serve (FCFS) policy is 1 -competitive, i.e., matching any optimal clairvoyant."}
+{"idx": 6, "title": "Fairness Maximization among Offline Agents in Online-Matching Markets ...", "date": "", "ddg_snippet": "Abstract Online matching markets (OMMs) are commonly used in today's world to pair agents from two parties (whom we will call offline and online agents) for mutual benefit. However, studies have shown that the algorithms making decisions in these OMMs often leave disparities in matching rates, especially for offline agents.", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/abs/10.1145/3569705", "content": "Abstract Online matching markets (OMMs) are commonly used in today's world to pair agents from two parties (whom we will call offline and online agents) for mutual benefit. However, studies have shown that the algorithms making decisions in these OMMs often leave disparities in matching rates, especially for offline agents."}
+{"idx": 7, "title": "\"Fairness Maximization among Offline Agents in Online-Matching Markets ...", "date": "", "ddg_snippet": "In this article, we propose online matching algorithms that optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 for group fairness maximization.", "subpage_snippet": "", "source": "digitalcommons.njit.edu", "link": "https://digitalcommons.njit.edu/fac_pubs/1789/", "content": "In this article, we propose online matching algorithms that optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 for group fairness maximization."}
+{"idx": 8, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets ...", "date": "", "ddg_snippet": "(1) Ma et al. [28] considered both long-run individual and group-level fairness maximization, but their focus was on fairness among offline agents. This is in contrast to the emphasis on fairness among online types.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0C3bLHwjsY", "content": "(1) Ma et al. [28] considered both long-run individual and group-level fairness maximization, but their focus was on fairness among offline agents. This is in contrast to the emphasis on fairness among online types."}
+{"idx": 9, "title": "FairnessMaximizationamong OfflineAgentsinOnline-MatchingMarke", "date": "", "ddg_snippet": "Abstract . Matching markets involve heterogeneous agents (typically from two parties) who are paired for mutual benefit. During the last decade, matching markets have emerged and grown rapidly through the medium of the Internet. They have evolved into a new format, called Online Matching Markets (OMMs), with examples ranging from crowdsourcing to online recommendations to ridesharing. There are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.08934v2", "content": "Abstract . Matching markets involve heterogeneous agents (typically from two parties) who are paired for mutual benefit. During the last decade, matching markets have emerged and grown rapidly through the medium of the Internet. They have evolved into a new format, called Online Matching Markets (OMMs), with examples ranging from crowdsourcing to online recommendations to ridesharing. There are ..."}
diff --git "a/data/sampled_jsons/Magnani_Kr\303\244mer_Eschenhagen_Rosasco_Hennig_2022_Approximate_Bayesian_Neural_Operators_abstract.jsonl" "b/data/sampled_jsons/Magnani_Kr\303\244mer_Eschenhagen_Rosasco_Hennig_2022_Approximate_Bayesian_Neural_Operators_abstract.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..2c3bd0728c0f7c452e1a48bdeec55f6d06983fe7
--- /dev/null
+++ "b/data/sampled_jsons/Magnani_Kr\303\244mer_Eschenhagen_Rosasco_Hennig_2022_Approximate_Bayesian_Neural_Operators_abstract.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2208.01565] Approximate Bayesian Neural Operators : Uncertainty...", "date": "", "ddg_snippet": "Authors:Emilia Magnani , Nicholas Krämer , Runa Eschenhagen , Lorenzo Rosasco , Philipp Hennig . View a PDF of the paper titled Approximate Bayesian Neural Operators : Uncertainty Quantification for Parametric PDEs, by Emilia Magnani and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2208.01565", "content": "Authors:Emilia Magnani , Nicholas Krämer , Runa Eschenhagen , Lorenzo Rosasco , Philipp Hennig . View a PDF of the paper titled Approximate Bayesian Neural Operators : Uncertainty Quantification for Parametric PDEs, by Emilia Magnani and 4 other authors."}
+{"idx": 1, "title": "Approximate Bayesian Neural Operators ... | Papers With Code", "date": "", "ddg_snippet": "2 Aug 2022 · Emilia Magnani , Nicholas Krämer , Runa Eschenhagen , Lorenzo Rosasco , Philipp Hennig ·. Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs).", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/approximate-bayesian-neural-operators", "content": "2 Aug 2022 · Emilia Magnani , Nicholas Krämer , Runa Eschenhagen , Lorenzo Rosasco , Philipp Hennig ·. Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs)."}
+{"idx": 2, "title": "Emilia Magnani - Google Scholar", "date": "", "ddg_snippet": "Approximate Bayesian neural operators : Uncertainty quantification for parametric PDEs. E Magnani , N Krämer , R Eschenhagen , L Rosasco , P Hennig .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=_zPcNdEAAAAJ&hl=en", "content": "Approximate Bayesian neural operators : Uncertainty quantification for parametric PDEs. E Magnani , N Krämer , R Eschenhagen , L Rosasco , P Hennig ."}
+{"idx": 3, "title": "Approximate Bayesian Neural Operators : Uncertainty... | OpenReview", "date": "", "ddg_snippet": "Emilia Magnani , Nicholas Krämer , Runa Eschenhagen , Lorenzo Rosasco , Philipp Hennig . Abstract : Neural operators are a type of deep architecture that learns to solve (i.e.~learns the nonlinear solution operator of) partial differential equations (PDEs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=jHS-esD-2pe", "content": "Emilia Magnani , Nicholas Krämer , Runa Eschenhagen , Lorenzo Rosasco , Philipp Hennig . Abstract : Neural operators are a type of deep architecture that learns to solve (i.e.~learns the nonlinear solution operator of) partial differential equations (PDEs)."}
+{"idx": 4, "title": "Approximate Bayesian Neural Operators : Uncertainty Quantification...", "date": "", "ddg_snippet": "Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Approximate-Bayesian-Neural-Operators:-Uncertainty-Quantification-for-Parametric-PDEs-25fdb85e-ffbd-11ed-9b54-72eb57fa10b3", "content": "Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification."}
+{"idx": 5, "title": "Neural PDE operator learning — The Dan MacKinlay stable of...", "date": "", "ddg_snippet": "Neural operator architectures employ neural networks to approximate operators mapping between Banach spaces of functions; they may be used to accelerate model evaluations via emulation, or to discover models from data. Magnani , Krämer , Eschenhagen , et al.", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/ml_pde_operator", "content": "Neural operator architectures employ neural networks to approximate operators mapping between Banach spaces of functions; they may be used to accelerate model evaluations via emulation, or to discover models from data. Magnani , Krämer , Eschenhagen , et al."}
+{"idx": 6, "title": "Approximate Bayesian neural Doppler imaging | Astronomy...", "date": "", "ddg_snippet": "The posterior distribution is approximated with conditional normalizing flows, which are flexible, tractable, and easy-to-sample approximations to arbitrary distributions.", "subpage_snippet": "", "source": "www.aanda.org", "link": "https://www.aanda.org/articles/aa/abs/2022/02/aa42027-21/aa42027-21.html", "content": "The posterior distribution is approximated with conditional normalizing flows, which are flexible, tractable, and easy-to-sample approximations to arbitrary distributions."}
+{"idx": 7, "title": "dblp: List of computer science publications by Emilia Magnani", "date": "", "ddg_snippet": "Emilia Magnani , Nicholas Krämer , Runa Eschenhagen , Lorenzo Rosasco , Philipp Hennig : Approximate Bayesian Neural Operators : Uncertainty Quantification for Parametric PDEs. CoRR abs/2208.01565 ( 2022 ).", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/206/6101.html", "content": "Emilia Magnani , Nicholas Krämer , Runa Eschenhagen , Lorenzo Rosasco , Philipp Hennig : Approximate Bayesian Neural Operators : Uncertainty Quantification for Parametric PDEs. CoRR abs/2208.01565 ( 2022 )."}
+{"idx": 8, "title": "An Approximate Bayesian Approach to Optimal Input... | Preprints.org", "date": "", "ddg_snippet": "This paper develops a Bayesian approach that uses the mutual information (MI) between observations and parameters as the utility function.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202509.1390/v1", "content": "This paper develops a Bayesian approach that uses the mutual information (MI) between observations and parameters as the utility function."}
+{"idx": 9, "title": "Being a Bit Frequentist Improves Bayesian Neural Networks", "date": "", "ddg_snippet": "theoretical properties, Bayesian neural networks (BNNs) tend to perform worse than frequentist methods in classification-based uncertainty quantification (UQ) tasks such as out-of-distribution (OOD) detection.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v151/kristiadi22a.html", "content": "theoretical properties, Bayesian neural networks (BNNs) tend to perform worse than frequentist methods in classification-based uncertainty quantification (UQ) tasks such as out-of-distribution (OOD) detection."}
diff --git a/data/sampled_jsons/Marvin_Li_Blink_of_an_eye_feature_localization_generative_models_GitHub.jsonl b/data/sampled_jsons/Marvin_Li_Blink_of_an_eye_feature_localization_generative_models_GitHub.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b364f7aa0af0c4b8d44751dd8b7f35f50340b86
--- /dev/null
+++ b/data/sampled_jsons/Marvin_Li_Blink_of_an_eye_feature_localization_generative_models_GitHub.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00921v1] Blink of an eye : a simple theory for feature ...", "date": "", "ddg_snippet": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow ``critical windows'' of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921v1", "content": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow ``critical windows'' of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon."}
+{"idx": 1, "title": "(PDF) Blink of an eye : a simple theory for feature localization in...", "date": "", "ddg_snippet": "generative models . Marvin Li *. generative model given by a time-reversal of a Markovian degradation process which takes a sample. from the target distribution and generates progressively less informative “observations” of it. In diffusion.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "generative models . Marvin Li *. generative model given by a time-reversal of a Markovian degradation process which takes a sample. from the target distribution and generates progressively less informative “observations” of it. In diffusion."}
+{"idx": 2, "title": "ICML Poster Blink of an eye : a simple theory for feature localization ...", "date": "", "ddg_snippet": "Marvin Li · Aayush Karan · Sitan Chen.In this work we develop a simple, unifying theory to explain this phenomenon. We show that it emerges generically as the generation process localizes to a subpopulation of the distribution it models .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45312", "content": "Marvin Li · Aayush Karan · Sitan Chen.In this work we develop a simple, unifying theory to explain this phenomenon. We show that it emerges generically as the generation process localizes to a subpopulation of the distribution it models ."}
+{"idx": 3, "title": "Blink of an eye : a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "OverviewResearch explores how generative AI models learn specific features during trainingIntroduces \" blink of an eye \" theory to explain rapid feature emergence", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/blink-eye-simple-theory-feature-localization-generative", "content": "OverviewResearch explores how generative AI models learn specific features during trainingIntroduces \" blink of an eye \" theory to explain rapid feature emergence"}
+{"idx": 4, "title": "gen_l10n/app_ localizations .dart` is not recognized · Issue #120561...", "date": "", "ddg_snippet": "GitHub Models New. Manage and compare prompts. GitHub Advanced Security.I got app_ localizations .dart generated , but import of gen_l10n/app_ localizations .dart is not recognized.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/flutter/flutter/issues/120561", "content": "GitHub Models New. Manage and compare prompts. GitHub Advanced Security.I got app_ localizations .dart generated , but import of gen_l10n/app_ localizations .dart is not recognized."}
+{"idx": 5, "title": "Flutter gen-l10n localization . Can't import 'package... - Stack Overfl...", "date": "", "ddg_snippet": "I am writing flutter application with localization support. It worked locally, I generated code using flutter gen-l10n and was able to use import 'package:flutter_gen/l10n/app_ localizations .dart'; . Then I decided to automate code compilation using github actions...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/76905937/flutter-gen-l10n-localization-cant-import-packageflutter-gen-l10n-app-locali", "content": "I am writing flutter application with localization support. It worked locally, I generated code using flutter gen-l10n and was able to use import 'package:flutter_gen/l10n/app_ localizations .dart'; . Then I decided to automate code compilation using github actions..."}
+{"idx": 6, "title": "Marvin Li (@ marvin _ li 03) on X", "date": "", "ddg_snippet": "Marvin Li (@ marvin _ li 03) on X Harvard '25 | Building theory for generative models .", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/marvin_li03", "content": "Marvin Li (@ marvin _ li 03) on X Harvard '25 | Building theory for generative models ."}
+{"idx": 7, "title": "Face Swap Online Free", "date": "", "ddg_snippet": "Join the latest social media craze by participating in the TikTok, Snapchat, and Instagram gender swap trend. This fun and creative trend allows you to explore a different side of yourself by swapping genders in your photos, offering a fresh and eye -catching way to update your online persona.", "subpage_snippet": "", "source": "remaker.ai", "link": "https://remaker.ai/face-swap-free/", "content": "Join the latest social media craze by participating in the TikTok, Snapchat, and Instagram gender swap trend. This fun and creative trend allows you to explore a different side of yourself by swapping genders in your photos, offering a fresh and eye -catching way to update your online persona."}
+{"idx": 8, "title": "An open platform for evaluating AI through human preference", "date": "", "ddg_snippet": "Compare answers across top AI models , share your feedback and power our public leaderboard. Inputs are processed by third-party AI and responses may be inaccurate.", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/", "content": "Compare answers across top AI models , share your feedback and power our public leaderboard. Inputs are processed by third-party AI and responses may be inaccurate."}
+{"idx": 9, "title": "QR Code Scanner | #1 Super Accurate QR Reader", "date": "", "ddg_snippet": "QR Code Scanner Online. Choose image from media or capture from your camera and get scanning results in an eye blink . Just Try Now It's Free!", "subpage_snippet": "", "source": "www.imgocr.com", "link": "https://www.imgocr.com/en/qr-code-scanner", "content": "QR Code Scanner Online. Choose image from media or capture from your camera and get scanning results in an eye blink . Just Try Now It's Free!"}
diff --git a/data/sampled_jsons/Masked_Autoregressive_Flow_MAF_MADE_MLP_architecture_details.jsonl b/data/sampled_jsons/Masked_Autoregressive_Flow_MAF_MADE_MLP_architecture_details.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4eaf7b19757d2815cdea3a6bc6901a56c8dfae0a
--- /dev/null
+++ b/data/sampled_jsons/Masked_Autoregressive_Flow_MAF_MADE_MLP_architecture_details.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Understand & Implement Masked AutoRegressive Flow with TensorFlow", "date": "", "ddg_snippet": "What can you expect from this post - Why Triangular Matrices are crucial for Autoregressive Flows ? Basic constructions of Autoregressive Flow -based models — Masked Autoregressive Flow ( MAF ) — Inverse Autoregressive Flow (IAF) How to implement MAF in TensorFlow and train them for density estimation tasks? Without any delay, let's begin!", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/understand-implement-masked-autoregressive-flow-with-tensorflow-9c361cd1354c/", "content": "What can you expect from this post - Why Triangular Matrices are crucial for Autoregressive Flows ? Basic constructions of Autoregressive Flow -based models — Masked Autoregressive Flow ( MAF ) — Inverse Autoregressive Flow (IAF) How to implement MAF in TensorFlow and train them for density estimation tasks? Without any delay, let's begin!"}
+{"idx": 1, "title": "tfp.bijectors.MaskedAutoregressiveFlow | TensorFlow Probability", "date": "", "ddg_snippet": "It is possible that this architecture is suboptimal for your task. To build alternative networks, either change the arguments to tfp.bijectors.AutoregressiveNetwork or use some other architecture , e.g., using tf.keras.layers. Warning: no attempt is made to validate that the shift_and_log_scale_fn enforces the 'autoregressive property'.", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/probability/api_docs/python/tfp/bijectors/MaskedAutoregressiveFlow", "content": "It is possible that this architecture is suboptimal for your task. To build alternative networks, either change the arguments to tfp.bijectors.AutoregressiveNetwork or use some other architecture , e.g., using tf.keras.layers. Warning: no attempt is made to validate that the shift_and_log_scale_fn enforces the 'autoregressive property'."}
+{"idx": 2, "title": "Masked Autoregressive Flow with PyTorch - GitHub", "date": "", "ddg_snippet": "🎄 Masked Autoregressive Flow with PyTorch This is a PyTorch implementation of the masked autoregressive flow ( MAF ) by Papamakarios et al. [1]. The Gaussian MADE that makes up each layer in the MAF is found in MADE .py, while the MAF itself is found in maf .py.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/e-hulten/maf", "content": "🎄 Masked Autoregressive Flow with PyTorch This is a PyTorch implementation of the masked autoregressive flow ( MAF ) by Papamakarios et al. [1]. The Gaussian MADE that makes up each layer in the MAF is found in MADE .py, while the MAF itself is found in maf .py."}
+{"idx": 3, "title": "MAF — pythae documentation", "date": "", "ddg_snippet": "class pythae.models.normalizing_flows.MAF(model_config) [source] ¶ Masked Autoregressive Flow . Parameters model_config (MAFConfig) - The MAF model configuration setting the main parameters of the model. forward(x, **kwargs) [source] ¶ The input data is transformed toward the prior Parameters inputs (torch.Tensor) - An input tensor Returns", "subpage_snippet": "", "source": "pythae.readthedocs.io", "link": "https://pythae.readthedocs.io/en/latest/models/normalizing_flows/maf.html", "content": "class pythae.models.normalizing_flows.MAF(model_config) [source] ¶ Masked Autoregressive Flow . Parameters model_config (MAFConfig) - The MAF model configuration setting the main parameters of the model. forward(x, **kwargs) [source] ¶ The input data is transformed toward the prior Parameters inputs (torch.Tensor) - An input tensor Returns"}
+{"idx": 4, "title": "PDF Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "In this paper we present Masked Autoregressive Flow ( MAF ), which is a particular implementation of the above normalizing flow that uses the Masked Autoencoder for Distribution Estimation ( MADE ) [6] as a building block.", "subpage_snippet": "", "source": "homepages.inf.ed.ac.uk", "link": "https://homepages.inf.ed.ac.uk/imurray2/pub/17maf/maf.pdf", "content": "In this paper we present Masked Autoregressive Flow ( MAF ), which is a particular implementation of the above normalizing flow that uses the Masked Autoencoder for Distribution Estimation ( MADE ) [6] as a building block."}
+{"idx": 5, "title": "Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "This type of flow is closely related to Inverse Autoregressive Flow and is a generalization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1705.07057", "content": "This type of flow is closely related to Inverse Autoregressive Flow and is a generalization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks."}
+{"idx": 6, "title": "Masked Autoregressive Flow in PyTorch: A Comprehensive Guide", "date": "", "ddg_snippet": "Masked Autoregressive Flow ( MAF ) is a powerful generative model that combines the autoregressive property with normalizing flows . PyTorch, a popular deep learning framework, provides a flexible and efficient platform to implement MAF .", "subpage_snippet": "", "source": "www.codegenes.net", "link": "https://www.codegenes.net/blog/masked-autoregressive-flow-pytorch/", "content": "Masked Autoregressive Flow ( MAF ) is a powerful generative model that combines the autoregressive property with normalizing flows . PyTorch, a popular deep learning framework, provides a flexible and efficient platform to implement MAF ."}
+{"idx": 7, "title": "Autoregressive Flows with TensorFlow | TDS Archive - Medium", "date": "", "ddg_snippet": "What are Normalizing Flows ? What is a Masked Autoregressive Flow & how to implement it using TensorFlow are discussed in this post in detail .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/understand-implement-masked-autoregressive-flow-with-tensorflow-9c361cd1354c", "content": "What are Normalizing Flows ? What is a Masked Autoregressive Flow & how to implement it using TensorFlow are discussed in this post in detail ."}
+{"idx": 8, "title": "Autoregressive Flow - Wei Deng", "date": "", "ddg_snippet": "Masked Autogressive Flow ( MAF ) MAF (Papamakarios et al., 2017) propose to optimize the reverse KL objective (3), which is efficient in the conditional density estimation while the sampling stage is slow in high dimensions because we need to iterate the dimension sequentially. To help understand the masked autoregressive flow , I simplified (Karpathy, 2019) 's code template and added some ...", "subpage_snippet": "", "source": "www.weideng.org", "link": "https://www.weideng.org/posts/autoregressive_flow/", "content": "Masked Autogressive Flow ( MAF ) MAF (Papamakarios et al., 2017) propose to optimize the reverse KL objective (3), which is efficient in the conditional density estimation while the sampling stage is slow in high dimensions because we need to iterate the dimension sequentially. To help understand the masked autoregressive flow , I simplified (Karpathy, 2019) 's code template and added some ..."}
+{"idx": 9, "title": "2021-09-08-01-AutoRegressive-flows-and-RealNVP.ipynb - Colab", "date": "", "ddg_snippet": "If the flow has autogressive property, then its log det jacobian calculation may be easy since log det jacobian matrix is lower triangular. Tensorflow bijector has AutogressiveNetwork class for this usage. Actually, it is not a bijector, though. Example case is an implementation of MADE architecture , which stands for Masked Autoencoder for Distribution Estimation. If you have interests about ...", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/goodboychan/goodboychan.github.io/blob/main/_notebooks/2021-09-08-01-AutoRegressive-flows-and-RealNVP.ipynb", "content": "If the flow has autogressive property, then its log det jacobian calculation may be easy since log det jacobian matrix is lower triangular. Tensorflow bijector has AutogressiveNetwork class for this usage. Actually, it is not a bijector, though. Example case is an implementation of MADE architecture , which stands for Masked Autoencoder for Distribution Estimation. If you have interests about ..."}
diff --git a/data/sampled_jsons/Masked_Autoregressive_Flow_MLP.jsonl b/data/sampled_jsons/Masked_Autoregressive_Flow_MLP.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8c9d2a6098a26f4e7b48d82477d7f85f2b1d3554
--- /dev/null
+++ b/data/sampled_jsons/Masked_Autoregressive_Flow_MLP.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Eric Jang: Normalizing Flows Tutorial, Part 2: Modern ...", "date": "", "ddg_snippet": "Jan 17, 2018 · The content and outline of this blog post was heavily influenced by the Masked Autoregressive Flow for Density Estimation paper, which is very well-written and is more or less my primary source for understanding this topic.", "subpage_snippet": "", "source": "blog.evjang.com", "link": "https://blog.evjang.com/2018/01/nf2.html", "content": "Jan 17, 2018 · The content and outline of this blog post was heavily influenced by the Masked Autoregressive Flow for Density Estimation paper, which is very well-written and is more or less my primary source for understanding this topic."}
+{"idx": 1, "title": "Understand & Implement Masked AutoRegressive Flow with ...", "date": "", "ddg_snippet": "Feb 21, 2023 · Finally, we went through the basics of state of the art autoregressive flow models and why triangular matrices are important in this regard. Finally, we implemented MAF using TensorFlow and saw an example of training chained MAFs to transform normal to a bit more complex distribution.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/understand-implement-masked-autoregressive-flow-with-tensorflow-9c361cd1354c/", "content": "Feb 21, 2023 · Finally, we went through the basics of state of the art autoregressive flow models and why triangular matrices are important in this regard. Finally, we implemented MAF using TensorFlow and saw an example of training chained MAFs to transform normal to a bit more complex distribution."}
+{"idx": 2, "title": "Masked Autoregressive Flow for Density Estimation tfp.bijectors.MaskedAutoregressiveFlow | TensorFlow Probability Masked Autoregressive Flow for Density Estimation - NeurIPS Autoregressive Flow - Wei Deng Eric Jang: Normalizing Flows Tutorial, Part 2: Modern ... [1705.07057] Masked Autoregressive Flow for Density Estimation - arXi… tfp.bijectors.MaskedAutoregressiveFlow | TensorFlow Probability Masked Autoregressive Flow for Density Estimation - NeurIPS tfp.bijectors.MaskedAutoregressiveFlow | TensorFlow Probability Masked Autoregressive Flow with PyTorch - GitHub", "date": "", "ddg_snippet": "May 19, 2017 · By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow . MADE is a feed-forward network that computes a shift and log(scale) using masked dense layers in a deep neural network. Weights are masked to ensure the autoregressive property. By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow . Mar 16, 2024 · To help understand the masked autoregressive flow , I simplified (Karpathy, 2019) ’s code template and added some edits and comments. Jan 17, 2018 · The content and outline of this blog post was heavily influenced by the Masked Autoregressive Flow for Density Estimation paper, which is very well-written and is more or less my primary source for understanding this topic. What is masked autoregressive flow? By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation , which we call Masked Autoregressive Flow. This type of flow is closely related to Inverse Autoregressive Flow and is a generalization of Real NVP. What is affine masked autoregressive flow bijector? Affine MaskedAutoregressiveFlow bijector. Inherits From: Bijector The affine autoregressive flow [ (Papamakarios et al., 2016)] provides a relatively simple framework for user-specified (deep) architectures to learn a distribution over continuous events. Regarding terminology, Does batch normalization improve performance in real NVP and MAF? We found that batch normalization in Real NVP and MAF reduces training time, increases stability during training and improves performance (as observed by Dinh et al. for Real NVP). Section B of the supplementary material discusses our implementation of batch normalization and its use in normalizing flows. How do I get TFB maskedautoregressiveflow to track variables? To get tfb.MaskedAutoregressiveFlow to track such variables, either: Replace the Python function with a tf.Module , tf.keras.Layer, or other callable object through which tf.Module can find variables. Or, add a reference to the variables to the tfb.MaskedAutoregressiveFlow instance by setting an attribute -- for example: This is a PyTorch implementation of the masked autoregressive flow ( MAF ) by Papamakarios et al. [1]. The Gaussian MADE that makes up each layer in the MAF is found in MADE.py, while the MAF itself is found in maf.py.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1705.07057", "content": "May 19, 2017 · By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow . MADE is a feed-forward network that computes a shift and log(scale) using masked dense layers in a deep neural network. Weights are masked to ensure the autoregressive property. By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow . Mar 16, 2024 · To help understand the masked autoregressive flow , I simplified (Karpathy, 2019) ’s code template and added some edits and comments. Jan 17, 2018 · The content and outline of this blog post was heavily influenced by the Masked Autoregressive Flow for Density Estimation paper, which is very well-written and is more or less my primary source for understanding this topic. What is masked autoregressive flow? By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation , which we call Masked Autoregressive Flow. This type of flow is closely related to Inverse Autoregressive Flow and is a generalization of Real NVP. What is affine masked autoregressive flow bijector? Affine MaskedAutoregressiveFlow bijector. Inherits From: Bijector The affine autoregressive flow [ (Papamakarios et al., 2016)] provides a relatively simple framework for user-specified (deep) architectures to learn a distribution over continuous events. Regarding terminology, Does batch normalization improve performance in real NVP and MAF? We found that batch normalization in Real NVP and MAF reduces training time, increases stability during training and improves performance (as observed by Dinh et al. for Real NVP). Section B of the supplementary material discusses our implementation of batch normalization and its use in normalizing flows. How do I get TFB maskedautoregressiveflow to track variables? To get tfb.MaskedAutoregressiveFlow to track such variables, either: Replace the Python function with a tf.Module , tf.keras.Layer, or other callable object through which tf.Module can find variables. Or, add a reference to the variables to the tfb.MaskedAutoregressiveFlow instance by setting an attribute -- for example: This is a PyTorch implementation of the masked autoregressive flow ( MAF ) by Papamakarios et al. [1]. The Gaussian MADE that makes up each layer in the MAF is found in MADE.py, while the MAF itself is found in maf.py."}
+{"idx": 3, "title": "tfp.bijectors.MaskedAutoregressiveFlow | TensorFlow Probability", "date": "", "ddg_snippet": "MADE is a feed-forward network that computes a shift and log(scale) using masked dense layers in a deep neural network. Weights are masked to ensure the autoregressive property.", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/probability/api_docs/python/tfp/bijectors/MaskedAutoregressiveFlow", "content": "MADE is a feed-forward network that computes a shift and log(scale) using masked dense layers in a deep neural network. Weights are masked to ensure the autoregressive property."}
+{"idx": 4, "title": "Masked Autoregressive Flow for Density Estimation - NeurIPS", "date": "", "ddg_snippet": "By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2017/file/6c1da886822c67822bcf3679d04369fa-Paper.pdf", "content": "By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow ."}
+{"idx": 5, "title": "Autoregressive Flow - Wei Deng", "date": "", "ddg_snippet": "Mar 16, 2024 · To help understand the masked autoregressive flow , I simplified (Karpathy, 2019) ’s code template and added some edits and comments.", "subpage_snippet": "", "source": "www.weideng.org", "link": "https://www.weideng.org/posts/autoregressive_flow/", "content": "Mar 16, 2024 · To help understand the masked autoregressive flow , I simplified (Karpathy, 2019) ’s code template and added some edits and comments."}
+{"idx": 6, "title": "Masked Autoregressive Flow with PyTorch - GitHub", "date": "", "ddg_snippet": "This is a PyTorch implementation of the masked autoregressive flow ( MAF ) by Papamakarios et al. [1]. The Gaussian MADE that makes up each layer in the MAF is found in MADE.py, while the MAF itself is found in maf.py.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/e-hulten/maf", "content": "This is a PyTorch implementation of the masked autoregressive flow ( MAF ) by Papamakarios et al. [1]. The Gaussian MADE that makes up each layer in the MAF is found in MADE.py, while the MAF itself is found in maf.py."}
+{"idx": 7, "title": "Normalizing Flows . I have been learning about Normalizing | Medium", "date": "", "ddg_snippet": "...the nodes are connected to nodes in the subsequent hidden layer such that node numbered m is connected to all nodes that are numbered greater than or equal to m. A masked autoregressive flow (MAF) is similar, except it can be a normal MLP (multi-layer perceptron).", "subpage_snippet": "", "source": "grishmaprs.medium.com", "link": "https://grishmaprs.medium.com/normalizing-flows-5b5a713e45e2", "content": "...the nodes are connected to nodes in the subsequent hidden layer such that node numbered m is connected to all nodes that are numbered greater than or equal to m. A masked autoregressive flow (MAF) is similar, except it can be a normal MLP (multi-layer perceptron)."}
+{"idx": 8, "title": "Normalizing Flows for Fragmentation and", "date": "", "ddg_snippet": "We use masked autoregressive flows as a generator for the kinematic distributions in the hadronization pipeline. We condition normalizing flows (NFs) on different hadron masses and initial configuration energies, which allows for the emission of hadrons with arbitrary masses.", "subpage_snippet": "", "source": "ml4physicalsciences.github.io", "link": "https://ml4physicalsciences.github.io/2022/files/NeurIPS_ML4PS_2022_125.pdf", "content": "We use masked autoregressive flows as a generator for the kinematic distributions in the hadronization pipeline. We condition normalizing flows (NFs) on different hadron masses and initial configuration energies, which allows for the emission of hadrons with arbitrary masses."}
+{"idx": 9, "title": "Masked Autoregressive Flow for Density Estimation with George...", "date": "", "ddg_snippet": "George walks us through the idea of Masked Autoregressive Flow , which uses neural networks to produce estimates of probability densities from a set of input examples. We discuss some of the related work that’s laid the groundwork for his research, including Inverse Autoregressive Flow ...", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/35235dec50424cce3c2bd922997188ec/", "content": "George walks us through the idea of Masked Autoregressive Flow , which uses neural networks to produce estimates of probability densities from a set of input examples. We discuss some of the related work that’s laid the groundwork for his research, including Inverse Autoregressive Flow ..."}
diff --git a/data/sampled_jsons/Medusa_arxiv_2401.10774_abstract_multiple_decoding_heads_year_2024.jsonl b/data/sampled_jsons/Medusa_arxiv_2401.10774_abstract_multiple_decoding_heads_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bdbe2e0247c77f30362430f33cff4b6a23681b84
--- /dev/null
+++ b/data/sampled_jsons/Medusa_arxiv_2401.10774_abstract_multiple_decoding_heads_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Medusa: Simple LLM Inference Acceleration Framework with Multiple ...", "date": "", "ddg_snippet": "In this paper, we present Medusa , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. Using a tree-based attention mechanism, Medusa constructs multiple candidate continuations and verifies them simultaneously in each decoding step.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.10774", "content": "In this paper, we present Medusa , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. Using a tree-based attention mechanism, Medusa constructs multiple candidate continuations and verifies them simultaneously in each decoding step."}
+{"idx": 1, "title": "Medusa: Simple LLM Inference Acceleration Framework with Multiple ...", "date": "", "ddg_snippet": "Medusa : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads ... Tianle Cai , Yuhong Li , Zhengyang Geng ,", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2401.10774", "content": "Medusa : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads ... Tianle Cai , Yuhong Li , Zhengyang Geng ,"}
+{"idx": 2, "title": "Medusa: Multiple Decoding Heads for Faster LLM Inference", "date": "", "ddg_snippet": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation, with each step reliant on the previous one's output. This creates a bottleneck… we present Medusa , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. Using a tree-based attention mechanism, Medusa constructs multiple ...", "subpage_snippet": "", "source": "mlscrapbook.substack.com", "link": "https://mlscrapbook.substack.com/p/medusa-multiple-decoding-heads-for", "content": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation, with each step reliant on the previous one's output. This creates a bottleneck… we present Medusa , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. Using a tree-based attention mechanism, Medusa constructs multiple ..."}
+{"idx": 3, "title": "Medusa: Simple Framework for Accelerating LLM Generation with Multiple ...", "date": "", "ddg_snippet": "Medusa adds extra \" heads \" to LLMs to predict multiple future tokens simultaneously. When augmenting a model with Medusa , the original model stays untouched, and only the new heads are fine-tuned during training. During generation, these heads each produce multiple likely words for the corresponding position.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FasterDecoding/Medusa", "content": "Medusa adds extra \" heads \" to LLMs to predict multiple future tokens simultaneously. When augmenting a model with Medusa , the original model stays untouched, and only the new heads are fine-tuned during training. During generation, these heads each produce multiple likely words for the corresponding position."}
+{"idx": 4, "title": "MEDUSA | Proceedings of the 41st International Conference on Machine ...", "date": "", "ddg_snippet": "In this paper, we present MEDUSA , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. Using a tree-based attention mechanism, MEDUSA constructs multiple candidate continuations and verifies them simultaneously in each decoding step.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3692273", "content": "In this paper, we present MEDUSA , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. Using a tree-based attention mechanism, MEDUSA constructs multiple candidate continuations and verifies them simultaneously in each decoding step."}
+{"idx": 5, "title": "Medusa: Simple LLM Inference Acceleration Framework with Multiple ...", "date": "", "ddg_snippet": "In this pa-per, we present MEDUSA , an eficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. Using a tree-based attention mechanism, MEDUSA constructs multiple can-didate continuations and verifies them simulta-neously in each decoding step.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.10774v2", "content": "In this pa-per, we present MEDUSA , an eficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. Using a tree-based attention mechanism, MEDUSA constructs multiple can-didate continuations and verifies them simulta-neously in each decoding step."}
+{"idx": 6, "title": "Medusa: Simple Framework for Accelerating LLM Generation with Multiple ...", "date": "", "ddg_snippet": "Instead of using an additional draft model like speculative decoding , Medusa merely introduces a few additional decoding heads , following the idea of [Stern et al. 2018] with some other ingredients. Despite its simple design, Medusa can improve the generation efficiency of LLMs by about 2x.", "subpage_snippet": "", "source": "www.predictiveanalyticsworld.com", "link": "https://www.predictiveanalyticsworld.com/machinelearningtimes/medusa-simple-framework-for-accelerating-llm-generation-with-multiple-decoding-heads/13190/", "content": "Instead of using an additional draft model like speculative decoding , Medusa merely introduces a few additional decoding heads , following the idea of [Stern et al. 2018] with some other ingredients. Despite its simple design, Medusa can improve the generation efficiency of LLMs by about 2x."}
+{"idx": 7, "title": "MEDUSA: Simple LLM Inference Acceleration Framework with Multiple ...", "date": "", "ddg_snippet": "Summary This paper introduces MEDUSA , a framework designed to accelerate inference in Large Language Models (LLMs) by utilizing multiple decoding heads that allow for concurrent token prediction. Traditional autoregressive decoding is limited by sequential computation, which MEDUSA seeks to overcome through a tree-based attention mechanism that enables parallel evaluations of multiple ...", "subpage_snippet": "", "source": "scoutml.com", "link": "https://scoutml.com/wiki/2401.10774/", "content": "Summary This paper introduces MEDUSA , a framework designed to accelerate inference in Large Language Models (LLMs) by utilizing multiple decoding heads that allow for concurrent token prediction. Traditional autoregressive decoding is limited by sequential computation, which MEDUSA seeks to overcome through a tree-based attention mechanism that enables parallel evaluations of multiple ..."}
+{"idx": 8, "title": "Medusa: LLM Inference Acceleration", "date": "", "ddg_snippet": "The paper introduces Medusa , a framework that adds multiple decoding heads to enable parallel token prediction and accelerate LLM inference. Medusa-1 and Medusa-2 offer distinct strategies—freezing or jointly fine-tuning the backbone—ensuring preserved model quality while improving speed.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2401.10774", "content": "The paper introduces Medusa , a framework that adds multiple decoding heads to enable parallel token prediction and accelerate LLM inference. Medusa-1 and Medusa-2 offer distinct strategies—freezing or jointly fine-tuning the backbone—ensuring preserved model quality while improving speed."}
+{"idx": 9, "title": "Medusa: Simple LLM Inference Acceleration Framework with Multiple ...", "date": "", "ddg_snippet": "@article{cai2024medusa, title = { Medusa : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads }, author = {Tianle Cai and Yuhong Li and Zhengyang Geng and Hongwu Peng and Jason D. Lee and Deming Chen and Tri Dao}, year = {2024}, journal = { arXiv preprint arXiv : 2401.10774 } } Why is it hard to run inference for large transformer models? Besides the increasing size of SoTA ...", "subpage_snippet": "", "source": "ssawant.github.io", "link": "https://ssawant.github.io/posts/Medusa/Medusa.html", "content": "@article{cai2024medusa, title = { Medusa : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads }, author = {Tianle Cai and Yuhong Li and Zhengyang Geng and Hongwu Peng and Jason D. Lee and Deming Chen and Tri Dao}, year = {2024}, journal = { arXiv preprint arXiv : 2401.10774 } } Why is it hard to run inference for large transformer models? Besides the increasing size of SoTA ..."}
diff --git a/data/sampled_jsons/Meeting_Room_storage_efficiency_Ours-s_3DGStream_MB.jsonl b/data/sampled_jsons/Meeting_Room_storage_efficiency_Ours-s_3DGStream_MB.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..18fcede2cc339667ab7156f422938759a45dd9df
--- /dev/null
+++ b/data/sampled_jsons/Meeting_Room_storage_efficiency_Ours-s_3DGStream_MB.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "Fast and Generalizable Streaming of Dynamic Scene ...", "date": "", "ddg_snippet": "by J Yan · 2025 · Cited by 6 — Our method outperforms 3DGStream in rendering quality, train time, and storage efficiency , achieving stream- ing with just 2.77s of per-frame reconstruction ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "by J Yan · 2025 · Cited by 6 — Our method outperforms 3DGStream in rendering quality, train time, and storage efficiency , achieving stream- ing with just 2.77s of per-frame reconstruction ..."}
+{"idx": 1, "title": "Fast and Generalizable Streaming of Dynamic Scene ...", "date": "", "ddg_snippet": "14 Mar 2025 — Meeting Room Datasets [29] includes 3 dynamic ... Our method outperforms 3DGStream in rendering quality, train time, and storage efficiency ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "14 Mar 2025 — Meeting Room Datasets [29] includes 3 dynamic ... Our method outperforms 3DGStream in rendering quality, train time, and storage efficiency ..."}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "Ours 3 DGstream (a)High Quality Result.Train time: 2.67 s /frame Storage : 7.9 MB /frame.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "Ours 3 DGstream (a)High Quality Result.Train time: 2.67 s /frame Storage : 7.9 MB /frame."}
+{"idx": 3, "title": "ImViD: Immersive Volumetric Videos for Enhanced VR Engagement", "date": "", "ddg_snippet": "... speed, spatiotemporal consistency, and storage efficiency in dynamic scenes; In the realm of sound field reconstruction, it is typically necessary to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.14359v1", "content": "... speed, spatiotemporal consistency, and storage efficiency in dynamic scenes; In the realm of sound field reconstruction, it is typically necessary to ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 5, "title": "MAPo : Motion-Aware Partitioning of Deformable 3D Gaussian", "date": "", "ddg_snippet": "Instead of relying on a single, unified model, our strategy enables multiple sets of networks and their corresponding 3DGs to model the dynamic scene ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19786v1", "content": "Instead of relying on a single, unified model, our strategy enables multiple sets of networks and their corresponding 3DGs to model the dynamic scene ..."}
diff --git a/data/sampled_jsons/Merrill_et_al._2024_abstract_year_2024.jsonl b/data/sampled_jsons/Merrill_et_al._2024_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0ff991bf70de950eceabc013b7c4a81430eb85d6
--- /dev/null
+++ b/data/sampled_jsons/Merrill_et_al._2024_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "American College of Rheumatology (ACR), 2024 | UCBCOMPASS", "date": "", "ddg_snippet": "Megan E. B. Clowse, David A. Isenberg, Joan T. Merrill , et al. Oral Presentation Patient Diagnostic Journey and Time to Diagnosis in Axial Spondyloarthritis: A Retrospective Cohort Study Using US Claims Data Dubreuil Maureen, Magrey Marina, Haeffs Kathrin, et al.", "subpage_snippet": "", "source": "www.ucbcompass.com", "link": "https://www.ucbcompass.com/congress-materials/acr-2024", "content": "Megan E. B. Clowse, David A. Isenberg, Joan T. Merrill , et al. Oral Presentation Patient Diagnostic Journey and Time to Diagnosis in Axial Spondyloarthritis: A Retrospective Cohort Study Using US Claims Data Dubreuil Maureen, Magrey Marina, Haeffs Kathrin, et al."}
+{"idx": 1, "title": "FlyBase Reference Report: Merrill et al., 2024, Sci. Adv. 10 (13): eadi4393", "date": "", "ddg_snippet": "The Drosophila brain contains tens of thousands of distinct cell types. Thousands of different transgenic lines reproducibly target specific neuron subsets, yet most still express in several cell types. Furthermore, most lines were developed without a priori knowledge of where the transgenes would be expressed. To aid in the development of cell type-specific tools for neuronal identification ...", "subpage_snippet": "", "source": "flybase.org", "link": "https://flybase.org/reports/FBrf0259124", "content": "The Drosophila brain contains tens of thousands of distinct cell types. Thousands of different transgenic lines reproducibly target specific neuron subsets, yet most still express in several cell types. Furthermore, most lines were developed without a priori knowledge of where the transgenes would be expressed. To aid in the development of cell type-specific tools for neuronal identification ..."}
+{"idx": 2, "title": "Waning Greenhouse Gas Emissions From U.S. Federal Lease Coal Production ...", "date": "", "ddg_snippet": "Abstract This study presents estimates of future years ( 2024 -2051) United States Federal lease coal production and the resulting greenhouse gas (GHG) emissions from the combustion, transport, and m...", "subpage_snippet": "", "source": "agupubs.onlinelibrary.wiley.com", "link": "https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024EF005735", "content": "Abstract This study presents estimates of future years ( 2024 -2051) United States Federal lease coal production and the resulting greenhouse gas (GHG) emissions from the combustion, transport, and m..."}
+{"idx": 3, "title": "[2404.08819] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 4, "title": "Dapirolizumab Pegol Demonstrated Significant Improvement in Systemic ...", "date": "", "ddg_snippet": "To cite this abstract in AMA style: Clowse M, Isenberg D, Merrill J, Dörner T, Petri M, Vital E, Morand E, Jimenez T, Brookes S, Gaiha-Rohrbach J, Martin C, Nelde A, Stach C. Dapirolizumab Pegol Demonstrated Significant Improvement in Systemic Lupus Erythematosus Disease Activity: Efficacy and Safety Results of a Phase 3 Trial [ abstract ].", "subpage_snippet": "", "source": "acrabstracts.org", "link": "https://acrabstracts.org/abstract/dapirolizumab-pegol-demonstrated-significant-improvement-in-systemic-lupus-erythematosus-disease-activity-efficacy-and-safety-results-of-a-phase-3-trial/", "content": "To cite this abstract in AMA style: Clowse M, Isenberg D, Merrill J, Dörner T, Petri M, Vital E, Morand E, Jimenez T, Brookes S, Gaiha-Rohrbach J, Martin C, Nelde A, Stach C. Dapirolizumab Pegol Demonstrated Significant Improvement in Systemic Lupus Erythematosus Disease Activity: Efficacy and Safety Results of a Phase 3 Trial [ abstract ]."}
+{"idx": 5, "title": "Evaluating n-Gram Novelty of Language Models Using Rusty-DAWG", "date": "", "ddg_snippet": "William Merrill , Noah A. Smith, Yanai Elazar. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024 .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.800/", "content": "William Merrill , Noah A. Smith, Yanai Elazar. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024 ."}
+{"idx": 6, "title": "Can You Learn Semantics Through Next-Word Prediction? The Case of ...", "date": "", "ddg_snippet": "Do LMs infer the semantics of text from co-occurrence patterns in their training data? Merrill et al. (2022) argue that, in theory, probabilities predicted by an optimal LM encode semantic information about entailment relations, but it is unclear whether neural LMs trained on corpora learn entailment in this way because of strong idealizing assumptions made by Merrill et al. In this work, we ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240213956M/abstract", "content": "Do LMs infer the semantics of text from co-occurrence patterns in their training data? Merrill et al. (2022) argue that, in theory, probabilities predicted by an optimal LM encode semantic information about entailment relations, but it is unclear whether neural LMs trained on corpora learn entailment in this way because of strong idealizing assumptions made by Merrill et al. In this work, we ..."}
+{"idx": 7, "title": "The Illusion of State in State-Space Models - arXiv.org", "date": "", "ddg_snippet": "5We use TC0 to mean L-uniform TC0, meaning the circuit family is constructible by a Turing machine that runs in space log-arithmic in the size of the input (cf. Merrill & Sabharwal, 2023a; Strobl et al ., 2024 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.08819", "content": "5We use TC0 to mean L-uniform TC0, meaning the circuit family is constructible by a Turing machine that runs in space log-arithmic in the size of the input (cf. Merrill & Sabharwal, 2023a; Strobl et al ., 2024 )."}
+{"idx": 8, "title": "Language Models Still Struggle to Zero-shot Reason about Time Series", "date": "", "ddg_snippet": "Mike A Merrill , Mingtian Tan, Vinayak Gupta, Thomas Hartvigsen, Tim Althoff. Findings of the Association for Computational Linguistics: EMNLP 2024 . 2024 .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.201/", "content": "Mike A Merrill , Mingtian Tan, Vinayak Gupta, Thomas Hartvigsen, Tim Althoff. Findings of the Association for Computational Linguistics: EMNLP 2024 . 2024 ."}
+{"idx": 9, "title": "Consensus on the key characteristics of metabolism disruptors", "date": "", "ddg_snippet": "Metabolism-disrupting agents (MDAs) are chemical, infectious or physical agents that increase the risk of metabolic disorders. Examples include pharmaceuticals, such as antidepressants, and environmental agents, such as bisphenol A. Various types of studies can provide evidence to identify MDAs, yet …", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/39613954/", "content": "Metabolism-disrupting agents (MDAs) are chemical, infectious or physical agents that increase the risk of metabolic disorders. Examples include pharmaceuticals, such as antidepressants, and environmental agents, such as bisphenol A. Various types of studies can provide evidence to identify MDAs, yet …"}
diff --git a/data/sampled_jsons/Mind2Web_Deng_et_al._2024_task_construction_methodology_sitearxiv.org_year_2023,2024.jsonl b/data/sampled_jsons/Mind2Web_Deng_et_al._2024_task_construction_methodology_sitearxiv.org_year_2023,2024.jsonl
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index 0000000000000000000000000000000000000000..4a2b32e5f7f441030191ef1797ea2808e8fbe7f0
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_Deng_et_al._2024_task_construction_methodology_sitearxiv.org_year_2023,2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Breaking the Data Barrier – Building GUI Agents Through Task ...", "date": "", "ddg_snippet": "MM- Mind 2 Web : MM- Mind 2 Web (Zheng et al ., 2024 a) is the multi-modal extension of the Mind 2 Web dataset ( Deng et al ., 2023) , designed for developing and evaluating generalist web agents capable of following natural language instructions to complete complex tasks on arbitrary websites.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.10127v2", "content": "MM- Mind 2 Web : MM- Mind 2 Web (Zheng et al ., 2024 a) is the multi-modal extension of the Mind 2 Web dataset ( Deng et al ., 2023) , designed for developing and evaluating generalist web agents capable of following natural language instructions to complete complex tasks on arbitrary websites."}
+{"idx": 1, "title": "arXiv:2402.15057v1 [cs.CL] 23 Feb 2024", "date": "", "ddg_snippet": "by Y Deng · 2024 · Cited by 38 — Mind2Web (MT- Mind2Web ). MT- Mind2Web is constructed by using the single-turn interactions from Mind2Web ( Deng et al ., 2023), an expert-.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.15057", "content": "by Y Deng · 2024 · Cited by 38 — Mind2Web (MT- Mind2Web ). MT- Mind2Web is constructed by using the single-turn interactions from Mind2Web ( Deng et al ., 2023), an expert-."}
+{"idx": 2, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "by X Deng · 2023 · Cited by 635 — We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.06070", "content": "by X Deng · 2023 · Cited by 635 — We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ..."}
+{"idx": 3, "title": "Navi-plus: Managing Ambiguous GUI Navigation Tasks ...", "date": "", "ddg_snippet": "The data construction process consists of three key steps: (1) Low-level Instruction Completion, (2) Informative Step Decision, (2) Formation of Ambiguous Tasks ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.24180v2", "content": "The data construction process consists of three key steps: (1) Low-level Instruction Completion, (2) Informative Step Decision, (2) Formation of Ambiguous Tasks ..."}
+{"idx": 4, "title": "arXiv:2503.24180v1 [cs.CV] 31 Mar 2025", "date": "", "ddg_snippet": "by Z Cheng · 2025 · Cited by 4 — MT- Mind2Web ( Deng et al ., 2024b ) proposed synthesizing conversational navigation data with existing GUI trajectory datasets by break- ing down ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.24180", "content": "by Z Cheng · 2025 · Cited by 4 — MT- Mind2Web ( Deng et al ., 2024b ) proposed synthesizing conversational navigation data with existing GUI trajectory datasets by break- ing down ..."}
+{"idx": 5, "title": "Mind2Web: Towards a Generalist Agent for the Web - ar5iv", "date": "", "ddg_snippet": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2306.06070", "content": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ..."}
+{"idx": 6, "title": "On the Multi-turn Instruction Following for Conversational Web Agents", "date": "", "ddg_snippet": "( 2024 ); Deng et al . (2023) in advanced settings, such as Mind 2 Web . In this work, we propose a new task , namely conversational web navigation, which requires multi-turn interaction capabilities with both users and the environment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.15057v1", "content": "( 2024 ); Deng et al . (2023) in advanced settings, such as Mind 2 Web . In this work, we propose a new task , namely conversational web navigation, which requires multi-turn interaction capabilities with both users and the environment."}
+{"idx": 7, "title": "An Illusion of Progress? Assessing the Current State of Web", "date": "", "ddg_snippet": "Rule-based methods such as Mind 2 Web -Live (Pan et al ., 2024 b) define key nodes (e.g., specific URLs or elements) per task , but are limited by annotation quality, sensitivity to webpage updates, small scale, and poor scalability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.01382", "content": "Rule-based methods such as Mind 2 Web -Live (Pan et al ., 2024 b) define key nodes (e.g., specific URLs or elements) per task , but are limited by annotation quality, sensitivity to webpage updates, small scale, and poor scalability."}
+{"idx": 8, "title": "Navigating the Digital World as Humans Do: Universal Visual...", "date": "", "ddg_snippet": "We use Multimodal- Mind 2 Web (Zheng et al ., 2024 ) , the multimodal extension of Mind 2 Web ( Deng et al ., 2023) , for our evaluation on realistic web tasks .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05243v1", "content": "We use Multimodal- Mind 2 Web (Zheng et al ., 2024 ) , the multimodal extension of Mind 2 Web ( Deng et al ., 2023) , for our evaluation on realistic web tasks ."}
+{"idx": 9, "title": "PAFFA: Premeditated Actions For Fast Agents", "date": "", "ddg_snippet": "Mind 2 Web -Live (Pan et al ., 2024 b) introduces progress-aware evaluation allowing multiple valid paths to task completion. While frameworks have attempted to create reusable components, most focus on low-level actions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.07958v2", "content": "Mind 2 Web -Live (Pan et al ., 2024 b) introduces progress-aware evaluation allowing multiple valid paths to task completion. While frameworks have attempted to create reusable components, most focus on low-level actions."}
diff --git a/data/sampled_jsons/Mind2Web_Deng_et_al._2024_year_2024.jsonl b/data/sampled_jsons/Mind2Web_Deng_et_al._2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a55ef5840c9ed74566b33aab71025f4bcf225f42
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_Deng_et_al._2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FinResearchBench: A Logic Tree based Agent-as-a-Judge", "date": "", "ddg_snippet": "... to be bounded by the training data the model has seen during the pre-training and alignment stages (Bai et al ... 2024 ) , Mind2Web ( Deng et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16248v1", "content": "... to be bounded by the training data the model has seen during the pre-training and alignment stages (Bai et al ... 2024 ) , Mind2Web ( Deng et al ."}
+{"idx": 1, "title": "GUI-ReWalk: Massive Data Generation for GUI Agent via", "date": "", "ddg_snippet": "The emergence of modular agent frameworks—integrating foundation models ( e .g., GPT- 4o (OpenAI, 2024 ) ), memory systems ( e .g., Cradle (Tan et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15738v1", "content": "The emergence of modular agent frameworks—integrating foundation models ( e .g., GPT- 4o (OpenAI, 2024 ) ), memory systems ( e .g., Cradle (Tan et al ..."}
+{"idx": 2, "title": "Turbocharging Web Automation: The Impact of Compressed History", "date": "", "ddg_snippet": "Experiments are carried out on the challenging Mind2Web Deng et al . ... Mind2Web and WebLINX datasets across different evaluation metrics compared to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21369v1", "content": "Experiments are carried out on the challenging Mind2Web Deng et al . ... Mind2Web and WebLINX datasets across different evaluation metrics compared to ..."}
+{"idx": 3, "title": "Detecting Pipeline Failures through Fine-Grained Analysis of", "date": "", "ddg_snippet": "Yet, most existing benchmarks and studies, such as those based on the Mind2Web dataset Zheng et al . ... action selection difficult Deng et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14382v1", "content": "Yet, most existing benchmarks and studies, such as those based on the Mind2Web dataset Zheng et al . ... action selection difficult Deng et al ..."}
+{"idx": 4, "title": "VisualWebBench: How Far Have Multimodal LLMs Evolved in Web", "date": "", "ddg_snippet": "On the other hand, web-agent benchmarks, like WebShop (Yao et al ., 2022 ) , Mind2Web ( Deng et al ., 2024 ) , and (Visual)WebArena (Zhou et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.05955v1", "content": "On the other hand, web-agent benchmarks, like WebShop (Yao et al ., 2022 ) , Mind2Web ( Deng et al ., 2024 ) , and (Visual)WebArena (Zhou et al ..."}
+{"idx": 5, "title": "Researchers are developing AI to make the internet more", "date": "", "ddg_snippet": "... tasks and all domains featured in MIND2WEB ... More information: Xiang Deng et al , Mind2Web : Towards a Generalist Agent for the Web, arXiv (2023).", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2024-01-ai-internet-accessible.html", "content": "... tasks and all domains featured in MIND2WEB ... More information: Xiang Deng et al , Mind2Web : Towards a Generalist Agent for the Web, arXiv (2023)."}
+{"idx": 6, "title": "GitHub - MiuLab/PersonaLLM-Survey", "date": "", "ddg_snippet": "... is the official repository of the paper \" Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization \" , EMNLP 2024 ... Deng et al .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MiuLab/PersonaLLM-Survey", "content": "... is the official repository of the paper \" Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization \" , EMNLP 2024 ... Deng et al ."}
+{"idx": 7, "title": "Synapse: Trajectory-as-Exemplar Prompting with Memory for", "date": "", "ddg_snippet": "... et al ., 2023) prompts the LLM with a step-by-step plan combined with the current state and the previous actions to generate each action, while MindAct ...", "subpage_snippet": "", "source": "ltzheng.github.io", "link": "https://ltzheng.github.io/Synapse/", "content": "... et al ., 2023) prompts the LLM with a step-by-step plan combined with the current state and the previous actions to generate each action, while MindAct ..."}
+{"idx": 8, "title": "AI Agents | AI Policy", "date": "", "ddg_snippet": "A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent ), expands this concept by proactively pursuing ...", "subpage_snippet": "", "source": "aipolicy.onair.cc", "link": "https://aipolicy.onair.cc/ai-agents/", "content": "A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent ), expands this concept by proactively pursuing ..."}
+{"idx": 9, "title": "Lectures | 11-711 ANLP", "date": "", "ddg_snippet": "Reference: Attention is All You Need (Vaswani et al . ... Reference: Are All Languages Equally Hard to Language-Model? (Cotterell et al .", "subpage_snippet": "", "source": "phontron.com", "link": "https://phontron.com/class/anlp2024/lectures/", "content": "Reference: Attention is All You Need (Vaswani et al . ... Reference: Are All Languages Equally Hard to Language-Model? (Cotterell et al ."}
diff --git a/data/sampled_jsons/Mind2Web_dataset_manually_annotated_action_sequences_construction.jsonl b/data/sampled_jsons/Mind2Web_dataset_manually_annotated_action_sequences_construction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..660645a78e608b91953f7f251c2734ddb84f82b0
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_dataset_manually_annotated_action_sequences_construction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dataset items", "date": "", "ddg_snippet": "Mind 2 Web dataset . We leverage the cached candidate generation results and direct QA formulation by following Deng et al.We adopt 4096 input sequence length and 910 output sequence length during the pre-training. The batch size for training is set to 128.", "subpage_snippet": "", "source": "blog.apify.com", "link": "https://blog.apify.com/content/files/2024/03/Dataset-from-PDF-Text-Extractor.html", "content": "Mind 2 Web dataset . We leverage the cached candidate generation results and direct QA formulation by following Deng et al.We adopt 4096 input sequence length and 910 output sequence length during the pre-training. The batch size for training is set to 128."}
+{"idx": 1, "title": "lumos Understanding processing of Mind 2 Web dataset for Lumos...", "date": "", "ddg_snippet": "Code and data for \"Lumos: Learning Agents with Unified Data , Modular Design, and Open-Source LLMs\".I am trying to map the Lumos WebAgent grounding dataset onto the original Mind 2 Web dataset . Unfortunetly the ids ( annotation _id, action _uid) were removed in the Lumos...", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/allenai/lumos/issues/5", "content": "Code and data for \"Lumos: Learning Agents with Unified Data , Modular Design, and Open-Source LLMs\".I am trying to map the Lumos WebAgent grounding dataset onto the original Mind 2 Web dataset . Unfortunetly the ids ( annotation _id, action _uid) were removed in the Lumos..."}
+{"idx": 2, "title": "Learning Realistic Human Actions from Movies", "date": "", "ddg_snippet": "We construct two video training sets , a manual and an automatic one, as well as a video test set .For the clean, manual dataset as well as the test set we manu - ally select visually correct samples from the set of manu - ally text- annotated actions in scripts.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/inria-00548659/document", "content": "We construct two video training sets , a manual and an automatic one, as well as a video test set .For the clean, manual dataset as well as the test set we manu - ally select visually correct samples from the set of manu - ally text- annotated actions in scripts."}
+{"idx": 3, "title": "(PDF) COCO (Creating Common Object in Context) Dataset for...", "date": "", "ddg_snippet": "a set of datasets that annotate manually . Average time to manually annotate per objects is three and a half minutes. 4 Silvia Rostianingsih / Procedia Computer Science 00 (2019) 000–000.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/341903636_COCO_Creating_Common_Object_in_Context_Dataset_for_Chemistry_Apparatus", "content": "a set of datasets that annotate manually . Average time to manually annotate per objects is three and a half minutes. 4 Silvia Rostianingsih / Procedia Computer Science 00 (2019) 000–000."}
+{"idx": 4, "title": "M IND 2W EB : Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "In light of this, we present MIND 2 WEB , a new dataset with natural language tasks and manually annotated action sequences for developing and evaluating generalist agents for the web. It offers the following unique features: 1. Diverse coverage of domains, websites, and tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kiYqbO3wqw", "content": "In light of this, we present MIND 2 WEB , a new dataset with natural language tasks and manually annotated action sequences for developing and evaluating generalist agents for the web. It offers the following unique features: 1. Diverse coverage of domains, websites, and tasks."}
+{"idx": 5, "title": "A Real-World WebAgent with Planning, Long Context Understanding...", "date": "", "ddg_snippet": "Mind 2 Web (Deng et al., 2023) is an action - annotated real-world dataset with over 2K instructions collected from 137 websites. It provides action prediction tasks that measure the generalization of LLMs across the tasks, websites, and their domains (e.g. travel, shopping).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.12856v4", "content": "Mind 2 Web (Deng et al., 2023) is an action - annotated real-world dataset with over 2K instructions collected from 137 websites. It provides action prediction tasks that measure the generalization of LLMs across the tasks, websites, and their domains (e.g. travel, shopping)."}
+{"idx": 6, "title": "Inquiry Regarding Bounding Box Annotations in... - Githubissues", "date": "", "ddg_snippet": "Could you please confirm whether the webpage snapshots in the Mind 2 Web dataset include bounding box annotations for the UI elements?", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/OSU-NLP-Group/Mind2Web/31", "content": "Could you please confirm whether the webpage snapshots in the Mind 2 Web dataset include bounding box annotations for the UI elements?"}
+{"idx": 7, "title": "Mind 2 Web : Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "Dataset . Mind 2 Web includes over 2,000 open-ended tasks collected from 137 websites spanning 31 domains, with manually annotated action sequences .", "subpage_snippet": "", "source": "ukgovernmentbeis.github.io", "link": "https://ukgovernmentbeis.github.io/inspect_evals/evals/assistants/mind2web/", "content": "Dataset . Mind 2 Web includes over 2,000 open-ended tasks collected from 137 websites spanning 31 domains, with manually annotated action sequences ."}
+{"idx": 8, "title": "Microsoft Word - PROCS_CoCoNet_2019_Templates.docm", "date": "", "ddg_snippet": "Keywords: COCO dataset ; synthetic dataset , annotate , chemistry apparatus.11. We had a set of datasets that annotate manually . Average time to manually annotate per objects is three and a half minutes.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/565317034.pdf", "content": "Keywords: COCO dataset ; synthetic dataset , annotate , chemistry apparatus.11. We had a set of datasets that annotate manually . Average time to manually annotate per objects is three and a half minutes."}
+{"idx": 9, "title": "Continuous or Discrete, That Is the Question: A Survey... | Preprints.org", "date": "", "ddg_snippet": "“ Manual Annotation \" indicates whether the dataset is annotated by humans, and \"LLM / LMM Synthesis\" indicates whether the annotations in the dataset are synthesized by LLMs or LMMs.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202411.0685/v1", "content": "“ Manual Annotation \" indicates whether the dataset is annotated by humans, and \"LLM / LMM Synthesis\" indicates whether the annotations in the dataset are synthesized by LLMs or LMMs."}
diff --git a/data/sampled_jsons/Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them_Shen_Lee_2019_Picard_itera.jsonl b/data/sampled_jsons/Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them_Shen_Lee_2019_Picard_itera.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3c25b3b77f7214a2984bbeb2df5f4e9a82d05061
--- /dev/null
+++ b/data/sampled_jsons/Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them_Shen_Lee_2019_Picard_itera.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "matrix - How to parallelize and minimize the time of a simple", "date": "", "ddg_snippet": "... is clearly meant as a toy problem, but anyways, just to think out of the box, you could get a tremendous speedup by using a normal distribution as ...", "subpage_snippet": "", "source": "mathematica.stackexchange.com", "link": "https://mathematica.stackexchange.com/questions/303376/how-to-parallelize-and-minimize-the-time-of-a-simple-code-using-paralleltable", "content": "... is clearly meant as a toy problem, but anyways, just to think out of the box, you could get a tremendous speedup by using a normal distribution as ..."}
+{"idx": 1, "title": "java - How do I uncheck a JCheckBox that I am using as one of", "date": "", "ddg_snippet": "I ve tried many ways and found one close solution so far which is to set the header back to the default renderer then back to my checkbox renderer ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/52845609/how-do-i-uncheck-a-jcheckbox-that-i-am-using-as-one-of-my-header-columns", "content": "I ve tried many ways and found one close solution so far which is to set the header back to the default renderer then back to my checkbox renderer ..."}
+{"idx": 2, "title": "or tools - Minimizing the sum of an array as exponent to the", "date": "", "ddg_snippet": "Is there a nice and efficient way to ... For example, we could say that they have to be between 0 and 10, and the model will function as expected.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/75727326/minimizing-the-sum-of-an-array-as-exponent-to-the-base-2", "content": "Is there a nice and efficient way to ... For example, we could say that they have to be between 0 and 10, and the model will function as expected."}
+{"idx": 3, "title": "Waste as a Critique of the Concept of the Economy | Waste as a", "date": "", "ddg_snippet": "Conservation of the Environment (Social Science) ... Theory, Methods, and Historiography ... Literary Studies (History of the Book)", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/book/59620/chapter/503316134", "content": "Conservation of the Environment (Social Science) ... Theory, Methods, and Historiography ... Literary Studies (History of the Book)"}
+{"idx": 4, "title": "Untilting the tilted loss | Alex Shtoff", "date": "", "ddg_snippet": "The LogSumExp function \\(\\mathbf{x} \\to \\ln(\\ sum _{ i = 1 }^n x_ i )\\) serves as the “aggregator” of losses over individual samples, instead of just the ...", "subpage_snippet": "", "source": "alexshtf.github.io", "link": "https://alexshtf.github.io/2024/06/14/Untilting.html", "content": "The LogSumExp function \\(\\mathbf{x} \\to \\ln(\\ sum _{ i = 1 }^n x_ i )\\) serves as the “aggregator” of losses over individual samples, instead of just the ..."}
+{"idx": 5, "title": "likelihood - why minimize loss function instead of maximizing", "date": "", "ddg_snippet": "For a linear model with a multivariate normal likelihood function , the log of the likelihood function is minus the sum of squares.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/94783/why-minimize-loss-function-instead-of-maximizing-reward-function", "content": "For a linear model with a multivariate normal likelihood function , the log of the likelihood function is minus the sum of squares."}
+{"idx": 6, "title": "algorithms - Concatenate multiple integer arrays such that", "date": "", "ddg_snippet": "I tried to concatenate the arrays on the basis of sum , such that the one with minimal sum goes first and one with maximal sum goes last.", "subpage_snippet": "", "source": "cs.stackexchange.com", "link": "https://cs.stackexchange.com/questions/115037/concatenate-multiple-integer-arrays-such-that-number-of-inversions-in-resulting", "content": "I tried to concatenate the arrays on the basis of sum , such that the one with minimal sum goes first and one with maximal sum goes last."}
+{"idx": 7, "title": "database design - Finding all possible minimal covers -", "date": "", "ddg_snippet": "In general, there are different canonical covers of a set of functional dependencies, and a canonical cover is called minimal if it has less ...", "subpage_snippet": "", "source": "dba.stackexchange.com", "link": "https://dba.stackexchange.com/questions/250724/finding-all-possible-minimal-covers", "content": "In general, there are different canonical covers of a set of functional dependencies, and a canonical cover is called minimal if it has less ..."}
+{"idx": 8, "title": "Extractives for Development: Introduction and Ten Main Messages", "date": "", "ddg_snippet": "Extractive Industries: The Management of Resources as a Driver of Sustainable Development ... Literary Studies (History of the Book)", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/book/27405/chapter/197215985", "content": "Extractive Industries: The Management of Resources as a Driver of Sustainable Development ... Literary Studies (History of the Book)"}
+{"idx": 9, "title": "mixed integer programming - Comparing L1 vs L2 norm objectives", "date": "", "ddg_snippet": "The due date objective functions are frequently represented as the minimization of the maximum Lateness or the minimization of total tardiness.", "subpage_snippet": "", "source": "or.stackexchange.com", "link": "https://or.stackexchange.com/questions/13340/comparing-l1-vs-l2-norm-objectives-for-minimizing-deviation-from-due-dates-in-pa", "content": "The due date objective functions are frequently represented as the minimization of the maximum Lateness or the minimization of total tardiness."}
diff --git a/data/sampled_jsons/Mistral-7B_60.1_vs_Pythia-7B_30.5_MMLU_comparison.jsonl b/data/sampled_jsons/Mistral-7B_60.1_vs_Pythia-7B_30.5_MMLU_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bce2d97bdcd5426064215b37cee9f1eaf105dc84
--- /dev/null
+++ b/data/sampled_jsons/Mistral-7B_60.1_vs_Pythia-7B_30.5_MMLU_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mistral 7B - arXiv.org", "date": "", "ddg_snippet": "Figure 5: Results on MMLU , commonsense reasoning, world knowledge and reading comprehension for Mistral 7B and Llama 2 ( 7B /13B/70B). Mistral 7B largely outperforms Llama 2 13B on all evaluations, except on knowledge benchmarks, where it is on par (this is likely due to its limited parameter count, which limits the amount of knowledge it can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.06825v1", "content": "Figure 5: Results on MMLU , commonsense reasoning, world knowledge and reading comprehension for Mistral 7B and Llama 2 ( 7B /13B/70B). Mistral 7B largely outperforms Llama 2 13B on all evaluations, except on knowledge benchmarks, where it is on par (this is likely due to its limited parameter count, which limits the amount of knowledge it can ..."}
+{"idx": 1, "title": "[2310.06825] Mistral 7B - ar5iv", "date": "", "ddg_snippet": "Feb 28, 2024 · Mistral 7B outperforms Llama 2 13B on all metrics, and approaches the code performance of Code-Llama 7B without sacrificing performance on non-code benchmarks. Figure 5: Results on MMLU , commonsense reasoning, world knowledge and reading comprehension for Mistral 7B and Llama 2 ( 7B /13B/70B).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2310.06825", "content": "Feb 28, 2024 · Mistral 7B outperforms Llama 2 13B on all metrics, and approaches the code performance of Code-Llama 7B without sacrificing performance on non-code benchmarks. Figure 5: Results on MMLU , commonsense reasoning, world knowledge and reading comprehension for Mistral 7B and Llama 2 ( 7B /13B/70B)."}
+{"idx": 2, "title": "Europe's largest seeded startup Mistral AI releases first ...", "date": "", "ddg_snippet": "Sep 27, 2023 · The Meta model delivered an accuracy of 31.1% and 52.5% in 0-shot Humaneval and 3-shot MBPP (hand-verified subset) tests, while Mistral 7B sat closely behind with an accuracy of 30.5 % and 47.5% ...", "subpage_snippet": "", "source": "venturebeat.com", "link": "https://venturebeat.com/ai/mistral-ai-europe-startup-releases-mistral-7b-model/", "content": "Sep 27, 2023 · The Meta model delivered an accuracy of 31.1% and 52.5% in 0-shot Humaneval and 3-shot MBPP (hand-verified subset) tests, while Mistral 7B sat closely behind with an accuracy of 30.5 % and 47.5% ..."}
+{"idx": 3, "title": "Benchmarks for Gemma 7B seem to be in the ballpark of Mistral ...", "date": "", "ddg_snippet": "Feb 22, 2024 · According to their paper, average of standard task of Mistral is 54.0 and for Gemma it's 56.4, so 4.4% relative better. Not as big as you would expect for the company which invented transformers and probably has 2-3 order more compute for training it vs few month old French startup. Also for note on their human evaluations, Gemma 7B IT has a 51.7% win rate against Mistral v0.2 7B Instruct.", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=39453780", "content": "Feb 22, 2024 · According to their paper, average of standard task of Mistral is 54.0 and for Gemma it's 56.4, so 4.4% relative better. Not as big as you would expect for the company which invented transformers and probably has 2-3 order more compute for training it vs few month old French startup. Also for note on their human evaluations, Gemma 7B IT has a 51.7% win rate against Mistral v0.2 7B Instruct."}
+{"idx": 4, "title": "Mistral 7 B | Mistral AI", "date": "", "ddg_snippet": "Performance in details. We compared Mistral 7 B to the Llama 2 family, and re-run all model evaluations ourselves for fair comparison . histograms Performance of Mistral 7 B and different Llama models on a wide range of benchmarks.", "subpage_snippet": "", "source": "mistral.ai", "link": "https://mistral.ai/news/announcing-mistral-7b", "content": "Performance in details. We compared Mistral 7 B to the Llama 2 family, and re-run all model evaluations ourselves for fair comparison . histograms Performance of Mistral 7 B and different Llama models on a wide range of benchmarks."}
+{"idx": 5, "title": "mistralai/ Mistral - 7 B -v0.1 · Hugging Face", "date": "", "ddg_snippet": "The Mistral - 7 B -v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral - 7 B -v0.1 outperforms Llama 2 13B on all benchmarks we tested. For full details of this model please read our paper and release blog post.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/mistralai/Mistral-7B-v0.1", "content": "The Mistral - 7 B -v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral - 7 B -v0.1 outperforms Llama 2 13B on all benchmarks we tested. For full details of this model please read our paper and release blog post."}
+{"idx": 6, "title": "LLM Leaderboard - Comparison of over 100 AI... | Artificial Analysis", "date": "", "ddg_snippet": "Comparison and ranking the performance of over 100 AI models (LLMs) across key metrics including intelligence, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others.", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/leaderboards/models", "content": "Comparison and ranking the performance of over 100 AI models (LLMs) across key metrics including intelligence, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others."}
+{"idx": 7, "title": "Mistral 7 B vs DeepSeek R1 Performance: Which LLM is the Better...", "date": "", "ddg_snippet": "Mistral 7 B vs DeepSeek R1 Performance compared —Which LLM offers better efficiency, inference speed, and cost-effectiveness?Listen to the audio version, crafted with Gemini 2.0. TL;DR: Quick Comparison Table. Feature. Mistral 7 B . DeepSeek R1.", "subpage_snippet": "", "source": "blog.adyog.com", "link": "https://blog.adyog.com/2025/01/31/mistral-7b-vs-deepseek-r1-performance-which-llm-is-the-better-choice/", "content": "Mistral 7 B vs DeepSeek R1 Performance compared —Which LLM offers better efficiency, inference speed, and cost-effectiveness?Listen to the audio version, crafted with Gemini 2.0. TL;DR: Quick Comparison Table. Feature. Mistral 7 B . DeepSeek R1."}
+{"idx": 8, "title": "Open-Orca/ Mistral - 7 B -OpenOrca · Hugging Face", "date": "", "ddg_snippet": "We compare our results to the base Mistral - 7 B model (using LM Evaluation Harness). We find 129% of the base model's performance on AGI Eval, averaging 0.397.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/Open-Orca/Mistral-7B-OpenOrca", "content": "We compare our results to the base Mistral - 7 B model (using LM Evaluation Harness). We find 129% of the base model's performance on AGI Eval, averaging 0.397."}
+{"idx": 9, "title": "AI大模型评测榜单 | MMLU Pro, GPQA... - 数据学习(DataLearner)", "date": "", "ddg_snippet": "Mistral - 7 B -Instruct-v0.3. MMLU Pro (知识问答) 85.60GPQA Diamond (常识推理) 未公布", "subpage_snippet": "", "source": "www.datalearner.com", "link": "https://www.datalearner.com/leaderboards", "content": "Mistral - 7 B -Instruct-v0.3. MMLU Pro (知识问答) 85.60GPQA Diamond (常识推理) 未公布"}
diff --git a/data/sampled_jsons/Mistral-7B_vs_Pythia-7B_MMLU_performance.jsonl b/data/sampled_jsons/Mistral-7B_vs_Pythia-7B_MMLU_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0838fc8abfa9ffdfc0c6b806537cda60465c256a
--- /dev/null
+++ b/data/sampled_jsons/Mistral-7B_vs_Pythia-7B_MMLU_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Zamba: A Compact 7B SSM Hybrid Model", "date": "", "ddg_snippet": "In this technical report, we present Zamba, a novel 7B SSM-transformer hybrid model which achieves competitive performance against leading open ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.16712v1", "content": "In this technical report, we present Zamba, a novel 7B SSM-transformer hybrid model which achieves competitive performance against leading open ..."}
+{"idx": 1, "title": "\\emojicroissantCroissantLLM: A Truly Bilingual French-English", "date": "", "ddg_snippet": "... as Llama (Touvron et al., 2023a ; b ) , Qwen (Bai et al., 2023a ) or Mistral (Jiang et al., 2023 ; 2024 ) are rapidly bridging the performance gap.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.00786v5", "content": "... as Llama (Touvron et al., 2023a ; b ) , Qwen (Bai et al., 2023a ) or Mistral (Jiang et al., 2023 ; 2024 ) are rapidly bridging the performance gap."}
+{"idx": 2, "title": "Open Language Models (OLMos) and the LLM landscape", "date": "", "ddg_snippet": "... data to the end of the pretraining mix? Rumors are that Mistral did this, but more than boosting base model performance on reasoning tasks like MMLU ...", "subpage_snippet": "", "source": "www.interconnects.ai", "link": "https://www.interconnects.ai/p/olmo", "content": "... data to the end of the pretraining mix? Rumors are that Mistral did this, but more than boosting base model performance on reasoning tasks like MMLU ..."}
+{"idx": 3, "title": "LLM Benchmarks — Klu", "date": "", "ddg_snippet": "The above models are evaluated based on their performance on the Chatbot Arena Elo, MT-bench , AlpacaEval 2 , and MMLU benchmarks.", "subpage_snippet": "", "source": "klu.ai", "link": "https://klu.ai/glossary/llm-benchmarks", "content": "The above models are evaluated based on their performance on the Chatbot Arena Elo, MT-bench , AlpacaEval 2 , and MMLU benchmarks."}
+{"idx": 4, "title": "jamescalam/ai-arxiv2 · Datasets at Hugging Face", "date": "", "ddg_snippet": "Mixtral has the same architecture as Mistral 7B , with the difference that each layer is composed of 8 feedforward blocks (i.e.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/jamescalam/ai-arxiv2", "content": "Mixtral has the same architecture as Mistral 7B , with the difference that each layer is composed of 8 feedforward blocks (i.e."}
+{"idx": 5, "title": "Uncategorized | Import AI | Page 2", "date": "", "ddg_snippet": "Do you test your models on MMLU ? Want to know how they perform in other languages? Use Global MMLU ! …Translated benchmark gives us a better sense ...", "subpage_snippet": "", "source": "jack-clark.net", "link": "https://jack-clark.net/category/uncategorized/page/2/", "content": "Do you test your models on MMLU ? Want to know how they perform in other languages? Use Global MMLU ! …Translated benchmark gives us a better sense ..."}
+{"idx": 6, "title": "Best 44 Large Language Models (LLMs) in 2025", "date": "", "ddg_snippet": "Large language models are pre-trained on large datasets and use natural language processing to perform linguistic tasks such as text generation, code ...", "subpage_snippet": "", "source": "explodingtopics.com", "link": "https://explodingtopics.com/blog/list-of-llms", "content": "Large language models are pre-trained on large datasets and use natural language processing to perform linguistic tasks such as text generation, code ..."}
+{"idx": 7, "title": "LLM360, A true Open Source LLM", "date": "", "ddg_snippet": "They released two foundational models: Amber (A 7B parameter model for generating texts in the English language) and CrystalCoder (Another 7B ...", "subpage_snippet": "", "source": "blog.premai.io", "link": "https://blog.premai.io/llama-360-a-true-open-source-llm/", "content": "They released two foundational models: Amber (A 7B parameter model for generating texts in the English language) and CrystalCoder (Another 7B ..."}
+{"idx": 8, "title": "Aman's AI Journal • Primers • Overview of Large Language", "date": "", "ddg_snippet": "For example, given the analogy task “man is to woman as king is to what?”, we can find the answer (queen) by performing the following operation ...", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/LLM/", "content": "For example, given the analogy task “man is to woman as king is to what?”, we can find the answer (queen) by performing the following operation ..."}
+{"idx": 9, "title": "Vinija's Notes • Primers • Overview of Large Language Models", "date": "", "ddg_snippet": "For example, given the analogy task “man is to woman as king is to what?”, we can find the answer (queen) by performing the following operation ...", "subpage_snippet": "", "source": "vinija.ai", "link": "https://vinija.ai/models/LLM/", "content": "For example, given the analogy task “man is to woman as king is to what?”, we can find the answer (queen) by performing the following operation ..."}
diff --git a/data/sampled_jsons/Mistral-7B_vs_Pythia-7B_performance_comparison_benchmarks_year_2023.jsonl b/data/sampled_jsons/Mistral-7B_vs_Pythia-7B_performance_comparison_benchmarks_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..93fbcae7dc61e562b6e9f66065a0f198babe2ef3
--- /dev/null
+++ b/data/sampled_jsons/Mistral-7B_vs_Pythia-7B_performance_comparison_benchmarks_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improving Reasoning Performance in Large Language ...", "date": "", "ddg_snippet": "3.4 Results. In this section we report results for Pythia-1.4B, Pythia-2.8B and Mistral-7B-Instruct with the PCA derived control vectors applied.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.19483v1", "content": "3.4 Results. In this section we report results for Pythia-1.4B, Pythia-2.8B and Mistral-7B-Instruct with the PCA derived control vectors applied."}
+{"idx": 1, "title": "RWKV 7B is appears to be approaching Mistral ...", "date": "", "ddg_snippet": "RWKV 7B is appears to be approaching Mistral 7B performance, but with multilingual support and and linear runtime. 86% trained, 1T tokens, ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/19essc5/rwkv_7b_is_appears_to_be_approaching_mistral_7b/", "content": "RWKV 7B is appears to be approaching Mistral 7B performance, but with multilingual support and and linear runtime. 86% trained, 1T tokens, ..."}
+{"idx": 2, "title": "Improving Reasoning Performance in Large Language ...", "date": "", "ddg_snippet": "by B Højer · Cited by 5 — Models such as Mistral - 7B outperform larger models such as pythia -12b that are trained on the exact same amount of data in the exact same order as the smaller ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=IssPhpUsKt", "content": "by B Højer · Cited by 5 — Models such as Mistral - 7B outperform larger models such as pythia -12b that are trained on the exact same amount of data in the exact same order as the smaller ..."}
+{"idx": 3, "title": "GPT-J vs. Mistral: Which LLM is Better?", "date": "", "ddg_snippet": "Side-by-side comparison of GPT-J and Mistral with feature breakdowns and pros/cons of each large language model.", "subpage_snippet": "", "source": "sapling.ai", "link": "https://sapling.ai/llm/gpt-j-vs-mistral", "content": "Side-by-side comparison of GPT-J and Mistral with feature breakdowns and pros/cons of each large language model."}
+{"idx": 4, "title": "SLM-Bench: A Comprehensive Benchmark of Small ...", "date": "", "ddg_snippet": "21 Aug 2025 — Notably, Mistral - 7B outperforms Phi -1.5B and Zephyr- 7B across all three evaluation metrics, indicating that it provides a more balanced trade- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15478v1", "content": "21 Aug 2025 — Notably, Mistral - 7B outperforms Phi -1.5B and Zephyr- 7B across all three evaluation metrics, indicating that it provides a more balanced trade- ..."}
+{"idx": 5, "title": "Top 15 Small Language Models for 2025", "date": "", "ddg_snippet": "14 Nov 2024 — It competes with models like LLaMA 2 ( 7B ) and Mistral 7B , performing just as well in tasks like commonsense reasoning and logical deduction.", "subpage_snippet": "", "source": "www.datacamp.com", "link": "https://www.datacamp.com/blog/top-small-language-models", "content": "14 Nov 2024 — It competes with models like LLaMA 2 ( 7B ) and Mistral 7B , performing just as well in tasks like commonsense reasoning and logical deduction."}
+{"idx": 6, "title": "Best Open Source LLMs of 2025", "date": "", "ddg_snippet": "The table below presents a ranking of open source LLMs, detailing their competitive performance through the Arena Elo rating, translation quality via MT-bench ...", "subpage_snippet": "", "source": "klu.ai", "link": "https://klu.ai/blog/open-source-llm-models", "content": "The table below presents a ranking of open source LLMs, detailing their competitive performance through the Arena Elo rating, translation quality via MT-bench ..."}
+{"idx": 7, "title": "The Top 10 Open Source Large Language Models of 2023", "date": "", "ddg_snippet": "2 Nov 2023 — Mistral 7B surpasses the performance of Llama 2 13B on all benchmark tasks and excels on many benchmarks compared to Llama 34B. It also ...", "subpage_snippet": "", "source": "www.clarifai.com", "link": "https://www.clarifai.com/blog/top-10-open-source-large-language-models-in-2023", "content": "2 Nov 2023 — Mistral 7B surpasses the performance of Llama 2 13B on all benchmark tasks and excels on many benchmarks compared to Llama 34B. It also ..."}
+{"idx": 8, "title": "You are missing out this top Open-Source LLMs | Medium", "date": "", "ddg_snippet": "The Mistral 7B model is simple to fine-tune for any task, and when fine-tuned for chat, it performs better than the Llama 2 13B chat model. It ...", "subpage_snippet": "", "source": "nlp4everyone.medium.com", "link": "https://nlp4everyone.medium.com/you-are-missing-out-this-top-open-source-llms-8c6799b2a551", "content": "The Mistral 7B model is simple to fine-tune for any task, and when fine-tuned for chat, it performs better than the Llama 2 13B chat model. It ..."}
+{"idx": 9, "title": "Best Small Language Models for Accuracy and Enterprise ...", "date": "", "ddg_snippet": "Best Small Language Models for Accuracy and Enterprise Use Cases — Benchmark Results What are the most accurate small language models?", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@darrenoberst/best-small-language-models-for-accuracy-and-enterprise-use-cases-benchmark-results-cf71964759c8", "content": "Best Small Language Models for Accuracy and Enterprise Use Cases — Benchmark Results What are the most accurate small language models?"}
diff --git a/data/sampled_jsons/ModelGo_Licenses_MGL_features_dependency_tracking_model_lineage.jsonl b/data/sampled_jsons/ModelGo_Licenses_MGL_features_dependency_tracking_model_lineage.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf1e8e2c16150d54c1648b28227837063061615e
--- /dev/null
+++ b/data/sampled_jsons/ModelGo_Licenses_MGL_features_dependency_tracking_model_lineage.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Майстерня Steam::sfm models", "date": "", "ddg_snippet": "Dark Souls 2 Old Dragon Slayer/Ornstein pack. Автор Fuzz. Old Dragon Slayer/Ornstein models from ©Dark Souls 1/2. Models Included. .Old Dragon Slayer ...", "subpage_snippet": "", "source": "steamcommunity.com", "link": "https://steamcommunity.com/sharedfiles/filedetails/?l=ukrainian&id=1725666675", "content": "Dark Souls 2 Old Dragon Slayer/Ornstein pack. Автор Fuzz. Old Dragon Slayer/Ornstein models from ©Dark Souls 1/2. Models Included. .Old Dragon Slayer ..."}
+{"idx": 1, "title": "Mathematical Modeling In The Social And Life Sciences ...", "date": "", "ddg_snippet": "Olinick's Mathematical Models in the Social and Life Sciences concentrates not on physical models , but on models found in biology, social science, and daily ...", "subpage_snippet": "", "source": "vdoc.pub", "link": "https://vdoc.pub/documents/mathematical-modeling-in-the-social-and-life-sciences-1hkp6l56jdq8", "content": "Olinick's Mathematical Models in the Social and Life Sciences concentrates not on physical models , but on models found in biology, social science, and daily ..."}
+{"idx": 2, "title": "A Standard Way for Model Publishing | ModelGo Licenses", "date": "", "ddg_snippet": "ModelGo licenses provide CreativeCommons-style licensing solutions to meet your specific needs in publishing AI models . The goal of ModelGo is to facilitate managed sharing of models while protecting Intellectual Property, striking a balance between openness and control.", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/", "content": "ModelGo licenses provide CreativeCommons-style licensing solutions to meet your specific needs in publishing AI models . The goal of ModelGo is to facilitate managed sharing of models while protecting Intellectual Property, striking a balance between openness and control."}
+{"idx": 3, "title": "GitHub - Xtra-Computing/ModelGo", "date": "", "ddg_snippet": "Please visit our website for the full text of the ModelGo Licenses Set and more information. 💡 Note: The ModelGo Licenses Set is a set of licenses (Terms & Conditions) designed for ML models for the purpose of standardized model licensing (We just reuses the name ModelGo ).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xtra-Computing/ModelGo", "content": "Please visit our website for the full text of the ModelGo Licenses Set and more information. 💡 Note: The ModelGo Licenses Set is a set of licenses (Terms & Conditions) designed for ML models for the purpose of standardized model licensing (We just reuses the name ModelGo )."}
+{"idx": 4, "title": "ModelGo: A Pratical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "ML project licenseing exhibit the following 211 characteristics: 1) Ambiguous, unaccredited and over- 212 permissive license declarations; 2) Emerging RAIL options 213 for model licensing; 3) Unique license dependency struc- 214 tures in ML-specific components reusing.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Et9rHdWGAZ", "content": "ML project licenseing exhibit the following 211 characteristics: 1) Ambiguous, unaccredited and over- 212 permissive license declarations; 2) Emerging RAIL options 213 for model licensing; 3) Unique license dependency struc- 214 tures in ML-specific components reusing."}
+{"idx": 5, "title": "Best Tools for ML Model Governance, Provenance, and Lineage", "date": "", "ddg_snippet": "Explore tools for ML model governance, provenance, and lineage , including guidance on how to choose the right one.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/tools-for-ml-model-governance-provenance-lineage", "content": "Explore tools for ML model governance, provenance, and lineage , including guidance on how to choose the right one."}
+{"idx": 6, "title": "ModelGo: A Practical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "In this paper, we introduce ModelGo , a practical tool for auditing potential legal risks in machine learning projects to enhance compliance and fairness. With ModelGo , we present license assessment reports based on five use cases with diverse model -reusing scenarios, rendered by real-world machine learning components.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3589334.3645520", "content": "In this paper, we introduce ModelGo , a practical tool for auditing potential legal risks in machine learning projects to enhance compliance and fairness. With ModelGo , we present license assessment reports based on five use cases with diverse model -reusing scenarios, rendered by real-world machine learning components."}
+{"idx": 7, "title": "Using ModelGo Licenses | ModelGo Licenses", "date": "", "ddg_snippet": "ModelGo Licenses include eight variants, grouped into Permissive Licenses , Conditional Permissive Licenses , and Stringent Licenses . Please select the one that best suits your needs.", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/get-started/using-modelgo-licenses", "content": "ModelGo Licenses include eight variants, grouped into Permissive Licenses , Conditional Permissive Licenses , and Stringent Licenses . Please select the one that best suits your needs."}
+{"idx": 8, "title": "What is Model Lineage, Model Registry & Artifact Tracking", "date": "", "ddg_snippet": "Explore the concepts of model lineage , model registry, and artifact tracking in the context of machine learning. Learn how these tools can help you keep track of your machine learning models , their versions, and their dependencies, making it easier to reproduce and maintain your work. Discover best practices for implementing these tools in your machine learning workflow.", "subpage_snippet": "", "source": "makemeanalyst.com", "link": "https://makemeanalyst.com/what-is-model-lineage-artifact-tracking/", "content": "Explore the concepts of model lineage , model registry, and artifact tracking in the context of machine learning. Learn how these tools can help you keep track of your machine learning models , their versions, and their dependencies, making it easier to reproduce and maintain your work. Discover best practices for implementing these tools in your machine learning workflow."}
+{"idx": 9, "title": "Advanced Model Versioning and Lineage Tracking", "date": "", "ddg_snippet": "Implementing lineage tracking typically involves: Metadata Association: The core principle is linking the versions of code, data, model , and environment together. Model registries and MLOps platforms often provide mechanisms to store these relationships as metadata associated with model versions or pipeline runs.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/monitoring-managing-ml-models-production/chapter-6-ml-governance-compliance/advanced-model-versioning", "content": "Implementing lineage tracking typically involves: Metadata Association: The core principle is linking the versions of code, data, model , and environment together. Model registries and MLOps platforms often provide mechanisms to store these relationships as metadata associated with model versions or pipeline runs."}
diff --git a/data/sampled_jsons/ModelGo_Licenses_vs_RAIL_OpenRAIL_differences_behavioral_use_restrictions_year_2024.jsonl b/data/sampled_jsons/ModelGo_Licenses_vs_RAIL_OpenRAIL_differences_behavioral_use_restrictions_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..106c95601685bbd6cb46320d951fd6c1d9c243c0
--- /dev/null
+++ b/data/sampled_jsons/ModelGo_Licenses_vs_RAIL_OpenRAIL_differences_behavioral_use_restrictions_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FAQ | ModelGo Licenses", "date": "", "ddg_snippet": "Q: What is the difference between ModelGo and OpenRAILs ? From the compositional perspective, OpenRAILs (-M) is built upon Apache-2.0 with additional terms tailored for ML fields. Their main alterations include adding a Use Restrictions attachment and use -based behaviour restriction terms in the license text. To provide more comprehensive licensing control, ModelGo draws inspiration from ...", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/learn-more/faq", "content": "Q: What is the difference between ModelGo and OpenRAILs ? From the compositional perspective, OpenRAILs (-M) is built upon Apache-2.0 with additional terms tailored for ML fields. Their main alterations include adding a Use Restrictions attachment and use -based behaviour restriction terms in the license text. To provide more comprehensive licensing control, ModelGo draws inspiration from ..."}
+{"idx": 1, "title": "FAQ — Responsible AI Licenses (RAIL)", "date": "", "ddg_snippet": "What is RAIL and OpenRAIL ? Responsible AI Licenses ( RAIL ) are a class of licenses designed to encourage the responsible use of an AI artifact being licensed by including a set of use restrictions applied to AI artifact. RAILs can be more or less restrictive depending on the aims of the licensor. For instance, a license can be RAIL while being a proprietary license , or a license just allowing ...", "subpage_snippet": "", "source": "www.licenses.ai", "link": "https://www.licenses.ai/faq-2", "content": "What is RAIL and OpenRAIL ? Responsible AI Licenses ( RAIL ) are a class of licenses designed to encourage the responsible use of an AI artifact being licensed by including a set of use restrictions applied to AI artifact. RAILs can be more or less restrictive depending on the aims of the licensor. For instance, a license can be RAIL while being a proprietary license , or a license just allowing ..."}
+{"idx": 2, "title": "From RAIL to Open RAIL: Topologies of RAIL Licenses — Responsible AI", "date": "", "ddg_snippet": "In essence, we could consider licenses associated with AI related artifacts to be RAIL Licenses if: they include behavioral-use restrictions which disallow/restrict certain applications by the licensee; and they require downstream use , including re-distribution, to include, at minimum, those same behavioral-use restrictions Collectively, we refer to these as the \" Use Restrictions \".", "subpage_snippet": "", "source": "stream.cloudnexa.com", "link": "https://stream.cloudnexa.com/category/machine-learning/6724033/10/22/2023/from-rail-to-open-rail-topologies-of-rail-licenses-responsible-ai-licenses-rail/", "content": "In essence, we could consider licenses associated with AI related artifacts to be RAIL Licenses if: they include behavioral-use restrictions which disallow/restrict certain applications by the licensee; and they require downstream use , including re-distribution, to include, at minimum, those same behavioral-use restrictions Collectively, we refer to these as the \" Use Restrictions \"."}
+{"idx": 3, "title": "ModelGo: A Pratical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "The most popular 200 license is Open Responsible AI License ( OpenRAIL ) [9], which is a 201 permissive license but includes copyleft-style use -based restrictions 202 governing the use of the model and its derivatives.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Et9rHdWGAZ", "content": "The most popular 200 license is Open Responsible AI License ( OpenRAIL ) [9], which is a 201 permissive license but includes copyleft-style use -based restrictions 202 governing the use of the model and its derivatives."}
+{"idx": 4, "title": "AI-model-licensing/open-rail-m-license-variants.md at main · Open ...", "date": "", "ddg_snippet": "Open RAIL-M license variations This document analysues the differences between the different variants of the Open RAIL-M license family that we have encountered during our analysis of licenses used on huggingface.ai. It is not a complete overview of all veriants of the Open RAIL-M licenses , since other variants may be in use on other platforms.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Open-Future-Foundation/AI-model-licensing/blob/main/open-rail-m-license-variants.md", "content": "Open RAIL-M license variations This document analysues the differences between the different variants of the Open RAIL-M license family that we have encountered during our analysis of licenses used on huggingface.ai. It is not a complete overview of all veriants of the Open RAIL-M licenses , since other variants may be in use on other platforms."}
+{"idx": 5, "title": "PDF On the Standardization of Behavioral Use Clauses and Their Adoption for ...", "date": "", "ddg_snippet": "Currently, the \" Open-RAIL \" moniker is given to license terms that (1) incorporate behavioral-use clauses, and (2) allow for the otherwise un-limited use , modification, and distribution of applicable artifacts, as long as the behavioral-use restrictions are in-cluded.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.05979.pdf", "content": "Currently, the \" Open-RAIL \" moniker is given to license terms that (1) incorporate behavioral-use clauses, and (2) allow for the otherwise un-limited use , modification, and distribution of applicable artifacts, as long as the behavioral-use restrictions are in-cluded."}
+{"idx": 6, "title": "Responsible AI Licenses (RAIL): Here's What You Need to Know", "date": "", "ddg_snippet": "The RAIL Initiative-governed RAIL-A license comes in two parts, an end-user agreement and a source code license . Both reiterate each other's use restrictions . Some (like the creators and users of the licenses ), certainly call it responsible, while others might call it paternalistic.", "subpage_snippet": "", "source": "www.mend.io", "link": "https://www.mend.io/blog/responsible-ai-licenses-rail-heres-what-you-need-to-know/", "content": "The RAIL Initiative-governed RAIL-A license comes in two parts, an end-user agreement and a source code license . Both reiterate each other's use restrictions . Some (like the creators and users of the licenses ), certainly call it responsible, while others might call it paternalistic."}
+{"idx": 7, "title": "From RAIL to Open RAIL: Topologies of RAIL Licenses", "date": "", "ddg_snippet": "Open RAIL Licenses Does a RAIL License include open-access/free- use terms, akin to what is used with open source software? If it does, it would be helpful for the community to know upfront that the license promotes free use and re-distribution of the applicable artifact, albeit subject to Use Restrictions . We suggest the use of the prefix \"Open\" to each RAIL license to clarify, on its face ...", "subpage_snippet": "", "source": "www.licenses.ai", "link": "https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses", "content": "Open RAIL Licenses Does a RAIL License include open-access/free- use terms, akin to what is used with open source software? If it does, it would be helpful for the community to know upfront that the license promotes free use and re-distribution of the applicable artifact, albeit subject to Use Restrictions . We suggest the use of the prefix \"Open\" to each RAIL license to clarify, on its face ..."}
+{"idx": 8, "title": "Understanding ModelGo | ModelGo Licenses", "date": "", "ddg_snippet": "ModelGo licenses consist of six sections. Section 2, \" License Rights,\" is the primary provision that grants rights licenses and states the restrictions of use and distribution. ModelGo licenses include a Disclaimer and Limitation of Liability (Section3, 4). Additionally, our licenses include terms, as stated in Section 6, that allow you to modify the license text, provided you furnish a ...", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/learn-more/understanding-modelgo", "content": "ModelGo licenses consist of six sections. Section 2, \" License Rights,\" is the primary provision that grants rights licenses and states the restrictions of use and distribution. ModelGo licenses include a Disclaimer and Limitation of Liability (Section3, 4). Additionally, our licenses include terms, as stated in Section 6, that allow you to modify the license text, provided you furnish a ..."}
+{"idx": 9, "title": "The BigScience OpenRAIL-M License - Hugging Face", "date": "", "ddg_snippet": "Open RAIL licenses promote free use and re-distribution of the applicable artifact, while maintaining behavioral Use Restrictions . The BigScience OpenRAIL-M License", "subpage_snippet": "", "source": "bigscience.huggingface.co", "link": "https://bigscience.huggingface.co/blog/bigscience-openrail-m", "content": "Open RAIL licenses promote free use and re-distribution of the applicable artifact, while maintaining behavioral Use Restrictions . The BigScience OpenRAIL-M License"}
diff --git a/data/sampled_jsons/Motion_Flow_Matching_Table_4_MLD_FID_HumanAct12.jsonl b/data/sampled_jsons/Motion_Flow_Matching_Table_4_MLD_FID_HumanAct12.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..11a8bfa4330c7f0e041a6ef2b03e833f928ff56b
--- /dev/null
+++ b/data/sampled_jsons/Motion_Flow_Matching_Table_4_MLD_FID_HumanAct12.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Motion Flow Matching for Human Motion Synthesis and Editing", "date": "", "ddg_snippet": "HumanAct 12 [14] offers approximately 1200 motion clips, organized into 12 action categories, with 47 to 218 samples per label. We adhere to the cross-subject testing protocol used by current works, with 225-345 samples per action class.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08895v1", "content": "HumanAct 12 [14] offers approximately 1200 motion clips, organized into 12 action categories, with 47 to 218 samples per label. We adhere to the cross-subject testing protocol used by current works, with 225-345 samples per action class."}
+{"idx": 1, "title": "#2 best model for Motion Synthesis on HumanAct 12 (Accuracy metric)", "date": "", "ddg_snippet": "REMOVE. Motion Synthesis. HumanAct 12 . MLD . FID . 0.077. Motion -X. MLD . TMR- Matching Score. 0.883.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/executing-your-commands-via-motion-diffusion?ref=taskswithcode.ghost.io", "content": "REMOVE. Motion Synthesis. HumanAct 12 . MLD . FID . 0.077. Motion -X. MLD . TMR- Matching Score. 0.883."}
+{"idx": 2, "title": "PoseGPT: Quantization-based 3D Human Motion", "date": "", "ddg_snippet": "Table 5: State-of-the-art comparison. On HumanAct 12 (left), PoseGPT obtains better. FID and comparable classication accuracy. On BABEL (center) and on GRAB (right), PoseGPT obtains substantial gains for all metrics. ˚ means trained by us based on of-cial code.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136660409.pdf", "content": "Table 5: State-of-the-art comparison. On HumanAct 12 (left), PoseGPT obtains better. FID and comparable classication accuracy. On BABEL (center) and on GRAB (right), PoseGPT obtains substantial gains for all metrics. ˚ means trained by us based on of-cial code."}
+{"idx": 3, "title": "[PDF] MotionDiffuse: Text-Driven Human Motion ... | Semantic Scholar", "date": "", "ddg_snippet": "Motion Flow Matching for Human Motion Synthesis and Editing.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/MotionDiffuse:-Text-Driven-Human-Motion-Generation-Zhang-Cai/1bbf99b5bfe9869876ac3bdd2999e16b2632c283", "content": "Motion Flow Matching for Human Motion Synthesis and Editing."}
+{"idx": 4, "title": "FLAME: Free-Form Language-Based Motion Synthesis & Editing", "date": "", "ddg_snippet": "We perform experiments on the HumanML3D, HumanAct 12 benchmarks and demonstrate that a remarkably simple GAN in the latent space achieves a FID of 0.482 with more than 91% in FLOPs reduction compared to latent diffusion model.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371919603_FLAME_Free-Form_Language-Based_Motion_Synthesis_Editing", "content": "We perform experiments on the HumanML3D, HumanAct 12 benchmarks and demonstrate that a remarkably simple GAN in the latent space achieves a FID of 0.482 with more than 91% in FLOPs reduction compared to latent diffusion model."}
+{"idx": 5, "title": "Action2 Motion : Conditioned Generation of 3D Human Motions", "date": "", "ddg_snippet": "A new 3D human motion dataset, HumanAct 12 , is also constructed.The detailed statistics of HumanAct 12 could be referred to our paper or documents in the dataset link.", "subpage_snippet": "", "source": "ericguo5513.github.io", "link": "https://ericguo5513.github.io/action-to-motion/", "content": "A new 3D human motion dataset, HumanAct 12 , is also constructed.The detailed statistics of HumanAct 12 could be referred to our paper or documents in the dataset link."}
+{"idx": 6, "title": "GitHub - EricGuo5513/action-to- motion : Official implementations for...", "date": "", "ddg_snippet": "Download HumanAct 12 Dataset. If you'd like to use HumanAct 12 dataset, download the data folder here, and place it in dataset/.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/EricGuo5513/action-to-motion", "content": "Download HumanAct 12 Dataset. If you'd like to use HumanAct 12 dataset, download the data folder here, and place it in dataset/."}
+{"idx": 7, "title": "Evaluation on HumanAct 12 unclear - Githubissues", "date": "", "ddg_snippet": "In table 3 of the respective Paper you report evaluation metrics on the HumanAct 12 dataset.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/GuyTevet/motion-diffusion-model/167", "content": "In table 3 of the respective Paper you report evaluation metrics on the HumanAct 12 dataset."}
+{"idx": 8, "title": "WAN 2.2 VACE: Object Swap with an Image in ComfyUI (GGUF Q5)", "date": "", "ddg_snippet": "Stable face. Natural motion . Compare FP8 vs Q5 GGUF, fix small issues, and upscale inside one simple ComfyUI workflow.", "subpage_snippet": "", "source": "aistudynow.com", "link": "https://aistudynow.com/wan-2-2-vace-object-swap-with-an-image-in-comfyui-gguf-q5/", "content": "Stable face. Natural motion . Compare FP8 vs Q5 GGUF, fix small issues, and upscale inside one simple ComfyUI workflow."}
+{"idx": 9, "title": "Inches to centimeters (in to cm) converter and how to convert.", "date": "", "ddg_snippet": "12 inches to cm conversion.RAPID TABLES . Recommend Site.", "subpage_snippet": "", "source": "www.rapidtables.com", "link": "https://www.rapidtables.com/convert/length/inch-to-cm.html", "content": "12 inches to cm conversion.RAPID TABLES . Recommend Site."}
diff --git a/data/sampled_jsons/MultiPDENet_MaNN_Block_description_function.jsonl b/data/sampled_jsons/MultiPDENet_MaNN_Block_description_function.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5207ebc196e10cac451a661229db6a9bc206f6e2
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_MaNN_Block_description_function.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping", "date": "", "ddg_snippet": "MultiPDENet consists of a multi-scale temporal learning architecture, a learnable Physics Block for solution prediction at the fine time scale, where trainable symmetric filters are designed for improved derivative approximation on coarse spatial grids.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "MultiPDENet consists of a multi-scale temporal learning architecture, a learnable Physics Block for solution prediction at the fine time scale, where trainable symmetric filters are designed for improved derivative approximation on coarse spatial grids."}
+{"idx": 1, "title": "[2501.15987] MultiPDENet: PDE-embedded Learning with Multi ... PDE-EMBEDDED LEARNING WITH MULTI TIME STEPPING FOR ... Synergistic learning with multi-task DeepONet for efficient ... [PDF] MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "Jan 27, 2025 · To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows. Unlike PINNs, our MultiPDENet directly embedsPDEsinto its architecture, resulting in a loss function that exclusively comprises data loss, given by: J(λ) =1 BN Apr 1, 2025 · Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization per… Jan 27, 2025 · A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations (PDEs) by numerical methods meet ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.15987", "content": "Jan 27, 2025 · To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows. Unlike PINNs, our MultiPDENet directly embedsPDEsinto its architecture, resulting in a loss function that exclusively comprises data loss, given by: J(λ) =1 BN Apr 1, 2025 · Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization per… Jan 27, 2025 · A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations (PDEs) by numerical methods meet ..."}
+{"idx": 2, "title": "[PDF] MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "Jan 27, 2025 · A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations (PDEs) by numerical methods meet ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/MultiPDENet:-PDE-embedded-Learning-with-for-Flow-Wang-Mi/6aee4adf8e7489f251995859a5f0432a2c60bb82", "content": "Jan 27, 2025 · A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations (PDEs) by numerical methods meet ..."}
+{"idx": 3, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "15 Jul 2025 — To alleviate the error accumulation during long-term predictions on coarse grids, we introduce the MiNN and MaNN Blocks , operating at micro- and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46029", "content": "15 Jul 2025 — To alleviate the error accumulation during long-term predictions on coarse grids, we introduce the MiNN and MaNN Blocks , operating at micro- and ..."}
+{"idx": 4, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "The MaNN Block (see Section 3.2.4) refines these incremental updates generated by the Physics Block on coarse grids, yielding the final update for the macro ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/046ccccd77df13df48f47ff1081b58969771121a.pdf", "content": "The MaNN Block (see Section 3.2.4) refines these incremental updates generated by the Physics Block on coarse grids, yielding the final update for the macro ..."}
+{"idx": 5, "title": "[Literature Review] MultiPDENet: PDE-embedded Learning with ...", "date": "", "ddg_snippet": "MaNN Block : This block operates at the macro-scale step, refining the outputs produced by the Physics Block and adjusting them based on the larger temporal ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/multipdenet-pde-embedded-learning-with-multi-time-stepping-for-accelerated-flow-simulation", "content": "MaNN Block : This block operates at the macro-scale step, refining the outputs produced by the Physics Block and adjusting them based on the larger temporal ..."}
+{"idx": 6, "title": "PDE-constrained Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "Sep 27, 2024 · To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of fluid flows.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "Sep 27, 2024 · To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of fluid flows."}
+{"idx": 7, "title": "PDE-EMBEDDED LEARNING WITH MULTI TIME STEPPING FOR ...", "date": "", "ddg_snippet": "Unlike PINNs, our MultiPDENet directly embedsPDEsinto its architecture, resulting in a loss function that exclusively comprises data loss, given by: J(λ) =1 BN", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=stcN89QGfL", "content": "Unlike PINNs, our MultiPDENet directly embedsPDEsinto its architecture, resulting in a loss function that exclusively comprises data loss, given by: J(λ) =1 BN"}
+{"idx": 8, "title": "Pivoting Factorization: A Compact Meta Low-Rank ...", "date": "", "ddg_snippet": "This research presents a new method called MultiPDENet , which combines numerical math techniques and machine learning to solve complex equations that describe ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46433/paper", "content": "This research presents a new method called MultiPDENet , which combines numerical math techniques and machine learning to solve complex equations that describe ..."}
+{"idx": 9, "title": "Microstructures and Accuracy of Graph Recall by Large La...", "date": "", "ddg_snippet": "This research presents a new method called MultiPDENet , which combines numerical math techniques and machine learning to solve complex equations that describe ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/93339/paper", "content": "This research presents a new method called MultiPDENet , which combines numerical math techniques and machine learning to solve complex equations that describe ..."}
diff --git a/data/sampled_jsons/MultiPDENet_paper_Table_2_Burgers_dataset_RMSE_PINO_MultiPDENet_year_2023.jsonl b/data/sampled_jsons/MultiPDENet_paper_Table_2_Burgers_dataset_RMSE_PINO_MultiPDENet_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..409d36885fbd08458426d00e20d895545ab1848c
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_paper_Table_2_Burgers_dataset_RMSE_PINO_MultiPDENet_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Root mean square deviation - Wikipedia", "date": "", "ddg_snippet": "The root mean square deviation (RMSD) or root mean square error ( RMSE ) is either one of two closely related and frequently used measures of the differences between true or predicted values on the one hand and observed values or an estimator on the other.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Root_mean_square_deviation", "content": "The root mean square deviation (RMSD) or root mean square error ( RMSE ) is either one of two closely related and frequently used measures of the differences between true or predicted values on the one hand and observed values or an estimator on the other."}
+{"idx": 1, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "Table 2 . Results of MultiPDENet and baselines. For KdV, Burgers , and GS, we inferred upper time limits of 50 s, 1.4 s, and 1200 s, for the test set as the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/046ccccd77df13df48f47ff1081b58969771121a.pdf", "content": "Table 2 . Results of MultiPDENet and baselines. For KdV, Burgers , and GS, we inferred upper time limits of 50 s, 1.4 s, and 1200 s, for the test set as the ..."}
+{"idx": 2, "title": "MultiPDENet : PDE-embedded Learning with Multi-time-stepping for...", "date": "", "ddg_snippet": "Table 2 : Results of MultiPDENet and baselines. For KdV, Burgers , and GS, we inferred upper time limits of 50 s, 1.4 s, and 1200 s, for the test set as the system dynamics stabilized within these trajectories.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "Table 2 : Results of MultiPDENet and baselines. For KdV, Burgers , and GS, we inferred upper time limits of 50 s, 1.4 s, and 1200 s, for the test set as the system dynamics stabilized within these trajectories."}
+{"idx": 3, "title": "3 Regression Metrics You Must Know: MAE, MSE, and RMSE", "date": "", "ddg_snippet": "Root Mean Squared Error ( RMSE ). MAE vs. RMSE . Practice using Python & Scikit-Learn.", "subpage_snippet": "", "source": "proclusacademy.com", "link": "https://proclusacademy.com/blog/explainer/regression-metrics-you-must-know/", "content": "Root Mean Squared Error ( RMSE ). MAE vs. RMSE . Practice using Python & Scikit-Learn."}
+{"idx": 4, "title": "Formatting tables and figures in your research paper - YouTube", "date": "", "ddg_snippet": "This video covers how to format tables and figures properly in your research paper .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=axjUhtr6Sz8", "content": "This video covers how to format tables and figures properly in your research paper ."}
+{"idx": 5, "title": "EA Sports FC 26 Tactics Codes for the best formations | VG247", "date": "", "ddg_snippet": "Needs fast defenders for the high line, front 3 set to attack with width coming from balanced wingbacks. Ovvy FC.You’d better believe we’ll add Jose Mourinho once his feet are under the table at Benfica. Arsenal/ Mikel Arteta - 4-3-3 Holding - #BhMps6gy#eq.", "subpage_snippet": "", "source": "www.vg247.com", "link": "https://www.vg247.com/ea-sports-fc-26-tactics-codes-best-formations", "content": "Needs fast defenders for the high line, front 3 set to attack with width coming from balanced wingbacks. Ovvy FC.You’d better believe we’ll add Jose Mourinho once his feet are under the table at Benfica. Arsenal/ Mikel Arteta - 4-3-3 Holding - #BhMps6gy#eq."}
+{"idx": 6, "title": "Semantic Scholar | AI-Powered Research Tool", "date": "", "ddg_snippet": "Introducing Semantic Reader in Beta. Semantic Reader is an augmented reader with the potential to revolutionize scientific reading by making it more accessible and richly contextual. Try it for select papers .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/", "content": "Introducing Semantic Reader in Beta. Semantic Reader is an augmented reader with the potential to revolutionize scientific reading by making it more accessible and richly contextual. Try it for select papers ."}
+{"idx": 7, "title": "machinelearningmastery.com/smote-oversampling-for-imbalanced...", "date": "", "ddg_snippet": "SMOTE for Imbalanced Dataset with Python.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/smote-oversampling-for-imbalanced-classification/", "content": "SMOTE for Imbalanced Dataset with Python."}
+{"idx": 8, "title": "What’s New in DeepSeek-V3.1-Terminus: Improved Language...", "date": "", "ddg_snippet": "Vendor and partner benchmark tables complement the paper with practical, deployment-oriented metrics. Q6: Is V3.1 a full model upgrade or a refinement?", "subpage_snippet": "", "source": "www.remio.ai", "link": "https://www.remio.ai/post/what-s-new-in-deepseek-v3-1-terminus-improved-language-consistency-and-code-search-agents-upgrade", "content": "Vendor and partner benchmark tables complement the paper with practical, deployment-oriented metrics. Q6: Is V3.1 a full model upgrade or a refinement?"}
+{"idx": 9, "title": "Football Live Scores, Latest Football Results | Flashscore.com.ng", "date": "", "ddg_snippet": "Rugby Union. Snooker. Table tennis.Football. Premier League table 2025/26.", "subpage_snippet": "", "source": "www.flashscore.com.ng", "link": "https://www.flashscore.com.ng/", "content": "Rugby Union. Snooker. Table tennis.Football. Premier League table 2025/26."}
diff --git a/data/sampled_jsons/N-H_H-N_architecture_NFR_followed_by_HOA_HOA_followed_by_NFR_privacy_budget_low.jsonl b/data/sampled_jsons/N-H_H-N_architecture_NFR_followed_by_HOA_HOA_followed_by_NFR_privacy_budget_low.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b7e19ad4a2711d1a74a0867dde6cf577c5f7cbfb
--- /dev/null
+++ b/data/sampled_jsons/N-H_H-N_architecture_NFR_followed_by_HOA_HOA_followed_by_NFR_privacy_budget_low.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Neferneferuaten - Wikipedia", "date": "", "ddg_snippet": "Aidan Dodson proposes that: Smenkhkare did not have an independent reign; that Neferneferuaten must have come after him; that Smenkhkare's reign was ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neferneferuaten", "content": "Aidan Dodson proposes that: Smenkhkare did not have an independent reign; that Neferneferuaten must have come after him; that Smenkhkare's reign was ..."}
+{"idx": 1, "title": "Going Deeper into Locally Differentially Private Graph Neural ...", "date": "", "ddg_snippet": "UPGNET comprises two architectures: H-N ( HOA followed by NFR ) and N-H ( NFR followed by HOA ), both of which can be independently integrated with any GNN architecture . We evaluate overall performance of UPGNET and the contribu-tions of each component under different parameters through theoretical analysis and extensive experiments.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2aKHuXdr7Q", "content": "UPGNET comprises two architectures: H-N ( HOA followed by NFR ) and N-H ( NFR followed by HOA ), both of which can be independently integrated with any GNN architecture . We evaluate overall performance of UPGNET and the contribu-tions of each component under different parameters through theoretical analysis and extensive experiments."}
+{"idx": 2, "title": "Going Deeper into Locally Differentially Private Graph Neural ...", "date": "", "ddg_snippet": "comprises two architectures : H-N ( HOA followed by NFR ) and N-H ( NFR followed by HOA ) ... privacy ... for the Cora, the N-H architecture outperforms the H-N .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/7efbd36f792e0a32a0c290ac11c17eaa63d6680d.pdf", "content": "comprises two architectures : H-N ( HOA followed by NFR ) and N-H ( NFR followed by HOA ) ... privacy ... for the Cora, the N-H architecture outperforms the H-N ."}
diff --git a/data/sampled_jsons/NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis_Mildenhall_et_al._abstract.jsonl b/data/sampled_jsons/NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis_Mildenhall_et_al._abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..55b144ca90718a01b827840b93194ca38cf176ce
--- /dev/null
+++ b/data/sampled_jsons/NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis_Mildenhall_et_al._abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NeRF: Representing Scenes as Neural Radiance Fields for View ...", "date": "", "ddg_snippet": "Mar 19, 2020 · We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2003.08934", "content": "Mar 19, 2020 · We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis ."}
+{"idx": 1, "title": "NeRF: Representing Scenes - Department of Computer Science", "date": "", "ddg_snippet": "We describe how to effectively optimize neu-ral radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis .", "subpage_snippet": "", "source": "www.cs.jhu.edu", "link": "https://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Mildenhall21.pdf", "content": "We describe how to effectively optimize neu-ral radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis ."}
+{"idx": 2, "title": "NeRF: Representing Scenes as Neural Radiance Fields for View ...", "date": "", "ddg_snippet": "Nov 1, 2020 · Request PDF | NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis | We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/346022000_NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis", "content": "Nov 1, 2020 · Request PDF | NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis | We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by ..."}
+{"idx": 3, "title": "NeRF: Neural Radiance Fields - Matthew Tancik", "date": "", "ddg_snippet": "We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis .", "subpage_snippet": "", "source": "www.matthewtancik.com", "link": "https://www.matthewtancik.com/nerf", "content": "We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis ."}
+{"idx": 4, "title": "NeRF: representing scenes as neural radiance fields for view ...", "date": "", "ddg_snippet": "NeRF: representing scenes as neural radiance fields for view synthesis By Ben Mildenhall , Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng", "subpage_snippet": "", "source": "www.idi.ntnu.no", "link": "https://www.idi.ntnu.no/emner/tdt03/Presentations2023/NeRF-Telsto.pdf", "content": "NeRF: representing scenes as neural radiance fields for view synthesis By Ben Mildenhall , Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng"}
+{"idx": 5, "title": "Neural Radiance Fields (NeRFs)", "date": "", "ddg_snippet": "Dec 5, 2024 · NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis", "subpage_snippet": "", "source": "slazebni.cs.illinois.edu", "link": "https://slazebni.cs.illinois.edu/fall24/lec21_nerf.pdf", "content": "Dec 5, 2024 · NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis"}
+{"idx": 6, "title": "NeRF : Representing Scenes as Neural Radiance Fields for View ...", "date": "", "ddg_snippet": "Neural Radiance Fields ( NeRF ) introduce a method for synthesizing photorealistic novel views by representing a scene as a continuous 5D function within a neural network. NeRF represents a paradigm shift in 3D scene representation and view synthesis for several reasons", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2003.08934v1", "content": "Neural Radiance Fields ( NeRF ) introduce a method for synthesizing photorealistic novel views by representing a scene as a continuous 5D function within a neural network. NeRF represents a paradigm shift in 3D scene representation and view synthesis for several reasons"}
+{"idx": 7, "title": "NeRF : Representing Scenes as Neural Radiance Fields for View ...", "date": "", "ddg_snippet": "Mildenhall , B., et al .: Local light field fusion: practical view synthesis with prescriptive sampling guidelines. ACM Trans. Graph.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-58452-8_24", "content": "Mildenhall , B., et al .: Local light field fusion: practical view synthesis with prescriptive sampling guidelines. ACM Trans. Graph."}
+{"idx": 8, "title": "NeRF : representing scenes as neural radiance fields for view ...", "date": "", "ddg_snippet": "Neural Radiance Fields ( NeRF ) ( Mildenhall et al . 2021) enabled high-fidelity novel view synthesis through implicit volumetric representation , with accelerations like Instant-NGP .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/357498745_NeRF_representing_scenes_as_neural_radiance_fields_for_view_synthesis", "content": "Neural Radiance Fields ( NeRF ) ( Mildenhall et al . 2021) enabled high-fidelity novel view synthesis through implicit volumetric representation , with accelerations like Instant-NGP ."}
+{"idx": 9, "title": "Notes on NeRF : Representing Scenes as Neural Radiance Fields ...", "date": "", "ddg_snippet": "Mildenhall et al built a fully connected network without using convolutional layers. It uses a single 5D coordinate (x,y,z,θ, Φ) that compresses spatial and viewing /ray direction information to output a single volume density σ and RGB color radiance . It requires 3 steps to render a NeRF .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@chengmu/notes-on-nerf-representing-scenes-as-neural-radiance-fields-for-view-synthesis-by-mildenhall-et-al-fd88f715fe77", "content": "Mildenhall et al built a fully connected network without using convolutional layers. It uses a single 5D coordinate (x,y,z,θ, Φ) that compresses spatial and viewing /ray direction information to output a single volume density σ and RGB color radiance . It requires 3 steps to render a NeRF ."}
diff --git a/data/sampled_jsons/Near-Optimal_Online_Learning_Multi-Agent_Submodular_Coordination_Figure_1_sparse_network_vs_fully_co_year_2023.jsonl b/data/sampled_jsons/Near-Optimal_Online_Learning_Multi-Agent_Submodular_Coordination_Figure_1_sparse_network_vs_fully_co_year_2023.jsonl
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+{"idx": 1, "title": "Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "7 Feb 2025 — 1 . Multi - agent submodular maximization (MA-SM) problem involves coordinating multiple agents to collaboratively maximize a submodular utility ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05028v1", "content": "7 Feb 2025 — 1 . Multi - agent submodular maximization (MA-SM) problem involves coordinating multiple agents to collaboratively maximize a submodular utility ..."}
+{"idx": 2, "title": "Near-Optimal Online Learning for Multi-Agent Submodular", "date": "", "ddg_snippet": "Figure 1 : Left: Multi -target tracking with 4 mobile sensors over a complete directed acyclic communication network . Right: Multi -target tracking with 4 sensors ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2502.05028/paper", "content": "Figure 1 : Left: Multi -target tracking with 4 mobile sensors over a complete directed acyclic communication network . Right: Multi -target tracking with 4 sensors ..."}
+{"idx": 3, "title": "AGENT SUBMODULAR COORDINATION: TIGHT AP", "date": "", "ddg_snippet": "by Q Zhang · Cited by 4 — Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i8dYPGdB1C", "content": "by Q Zhang · Cited by 4 — Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in."}
+{"idx": 4, "title": "Track: Poster Session 6", "date": "", "ddg_snippet": "26 Apr 2025 — Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency. Qixin ZHANG · Zongqi ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31976", "content": "26 Apr 2025 — Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency. Qixin ZHANG · Zongqi ..."}
+{"idx": 5, "title": "ICLR 2025 Spotlights", "date": "", "ddg_snippet": "Improving the Sparse Structure Learning ... Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/events/spotlight-posters", "content": "Improving the Sparse Structure Learning ... Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency."}
+{"idx": 6, "title": "Computer Science Feb 2023", "date": "", "ddg_snippet": "Title: Constrained Online Two-stage Stochastic Optimization: Near Optimal Algorithms via Adversarial Learning . Jiashuo Jiang. Comments: This version is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2023-02?skip=115&show=2000", "content": "Title: Constrained Online Two-stage Stochastic Optimization: Near Optimal Algorithms via Adversarial Learning . Jiashuo Jiang. Comments: This version is ..."}
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+{"idx": 8, "title": "A Complete Bibliography of Publications in Algorithmica", "date": "", "ddg_snippet": "by NHF Beebe · 2025 — Page 1 . A Complete Bibliography of Publications in. Algorithmica. Nelson H. F. ... -Competitive. [2814, 1873, 1451, 2004, 346, 2377]. -computer [476]. - Connected . 645 pages", "subpage_snippet": "", "source": "ftp.math.utah.edu", "link": "https://ftp.math.utah.edu/pub/tex/bib/algorithmica.pdf", "content": "by NHF Beebe · 2025 — Page 1 . A Complete Bibliography of Publications in. Algorithmica. Nelson H. F. ... -Competitive. [2814, 1873, 1451, 2004, 346, 2377]. -computer [476]. - Connected . 645 pages"}
+{"idx": 9, "title": "arXiv daily: Robotics (cs.RO)", "date": "", "ddg_snippet": "The two-stage approach consists of a high-level behavioral planning step and a low-level optimization step.", "subpage_snippet": "", "source": "sciencecast.org", "link": "https://sciencecast.org/podcasts/arxiv_daily/robotics", "content": "The two-stage approach consists of a high-level behavioral planning step and a low-level optimization step."}
diff --git a/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_Tight_Approximation_and_Communi_year_2023.jsonl b/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_Tight_Approximation_and_Communi_year_2023.jsonl
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+{"idx": 1, "title": "Performance-Aware Self-Configurable Multi-Agent Networks: A", "date": "", "ddg_snippet": "... approach that enables multi - agent networks to self-configure their communication topology to balance the trade-off between scalability and optimality ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.01411v1", "content": "... approach that enables multi - agent networks to self-configure their communication topology to balance the trade-off between scalability and optimality ..."}
+{"idx": 2, "title": "Multiagent Systems Feb 2025", "date": "", "ddg_snippet": "Title: Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.MA/2025-02", "content": "Title: Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency"}
+{"idx": 3, "title": "Recent Advances in Data-driven Intelligent Control for Wireless", "date": "", "ddg_snippet": "... optimizing beamforming and beam management techniques to enhance signal quality, reduce interference, and improve the efficiency of millimeter-wave ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.02943v1", "content": "... optimizing beamforming and beam management techniques to enhance signal quality, reduce interference, and improve the efficiency of millimeter-wave ..."}
+{"idx": 4, "title": "Diffusion Models for Influence Maximization on Temporal", "date": "", "ddg_snippet": "... for diverse applications, including enhancing public health outreach, mitigating the spread of harmful rumors, and optimizing marketing campaigns.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22589v1", "content": "... for diverse applications, including enhancing public health outreach, mitigating the spread of harmful rumors, and optimizing marketing campaigns."}
+{"idx": 5, "title": "Convergent Experience Courses - Udemy™ Online Courses Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Find the right instructor for you. Choose from many topics, skill levels, and languages. Join millions of learners from around the world already learning on Udemy.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=EovLaPxvbnOrKfI0u3yb_hJEG_SqHEaNQaemkjTyN96fs35i_sO-sHUJsqVgyzsJP2B6DxkULf95oe06UrdmwCO1cBHnkig2Q1LFjBS3_ZTNs41wic2MhrmY9aMsZu8C.U5bswjpOCe4vBHbLcQbAig&rut=e50645636fff5b4ad9e78710a98cd3c0308dde6d3f49147b2deb27bdca61f58e&u3=https://www.bing.com/aclick?ld=e8MGSQUD6K-LbX4GMfe60o0jVUCUyaNOQCarepcWW9NCH6N0ARtgM4Kcsfe6QTNm2hDHwZsrpcaPJCu4IVIo7emLJbJireR7Nf4qn7DZFRSmzpPuYq0RJEQ9ihbZkWzA3LFWzRSpsrxepmb3w9lAp21x-hGvFzl1PKod_KpGt14sHRhIlHJK4tDCQHqndESmv_9Q5OGg&u=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&rlid=2ed5d1f73b9d1f89ae8cb925a74353ad&vqd=4-64089913152077381742274936125961356375&iurl={1}IG=8CF6AE8E5FF540A4852BFFB168E130A7&CID=222603A4719767D308C015D4706A6615&ID=DevEx,5045.1", "content": "Find the right instructor for you. Choose from many topics, skill levels, and languages. Join millions of learners from around the world already learning on Udemy."}
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+{"idx": 9, "title": "Leveraging Untrustworthy Commands for Multi-Robot Coordination in ...", "date": "", "ddg_snippet": "The optimization problem in eq. (1) is generally NP-hard [5] but near-optimal approximation algorithms are possible in polynomial time when ft is submodular [6] — submodularity is a diminishing returns property capturing the potential information overlap among the robots.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2309.16161", "content": "The optimization problem in eq. (1) is generally NP-hard [5] but near-optimal approximation algorithms are possible in polynomial time when ft is submodular [6] — submodularity is a diminishing returns property capturing the potential information overlap among the robots."}
diff --git a/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_random_graph_complete_graph_com.jsonl b/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_random_graph_complete_graph_com.jsonl
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+{"idx": 0, "title": "Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Near - optimal multi - agent learning for safe coverage control.Then, when considering the random graph , we set the weight matrix. 𝐖𝐖\\mathbf{W}bold_W. as follow: if the edge.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05028v1", "content": "Near - optimal multi - agent learning for safe coverage control.Then, when considering the random graph , we set the weight matrix. 𝐖𝐖\\mathbf{W}bold_W. as follow: if the edge."}
+{"idx": 1, "title": "(PDF) Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Near - optimal online learning for multi -. Agent submodular coordination : tight ap-. Proximation and communication efficiency . Qixin Zhang1Zongqi Wan4Yu Yang2Li Shen3Dacheng Tao1.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388847678_Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_Tight_Approximation_and_Communication_Efficiency", "content": "Near - optimal online learning for multi -. Agent submodular coordination : tight ap-. Proximation and communication efficiency . Qixin Zhang1Zongqi Wan4Yu Yang2Li Shen3Dacheng Tao1."}
+{"idx": 2, "title": "Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=i8dYPGdB1C", "content": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control."}
+{"idx": 3, "title": "Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/near-optimal-online-learning-for-multi-agent", "content": "algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques."}
+{"idx": 4, "title": "Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Abstract: Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2502.05028", "content": "Abstract: Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control."}
+{"idx": 5, "title": "[Literature Review] Near - Optimal Online Learning for Multi - Agent ...", "date": "", "ddg_snippet": "Keywords. multi - agent . online learning . submodular maximization. coordination . approximation guarantee. communication efficiency . algorithms.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/near-optimal-online-learning-for-multi-agent-submodular-coordination-tight-approximation-and-communication-efficiency", "content": "Keywords. multi - agent . online learning . submodular maximization. coordination . approximation guarantee. communication efficiency . algorithms."}
+{"idx": 6, "title": "GitHub - Aaron617/ICLR-2025-Submissions- Agent : ICLR 2025...", "date": "", "ddg_snippet": "Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency (Rating: 6.00). Efficient Active Imitation Learning with Random Network Distillation (Rating: 5.25).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Aaron617/ICLR-2025-Submissions-Agent", "content": "Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency (Rating: 6.00). Efficient Active Imitation Learning with Random Network Distillation (Rating: 5.25)."}
+{"idx": 7, "title": "On Submodular Set Cover Problems for Near - Optimal Online Kernel...", "date": "", "ddg_snippet": "This allows us to design a constructive variant of a greedy subspace projections in [1], [2] according to a submodular set cover (SSC) problem, which provably picks at most logarithmically more elements than the optimal one.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/on-submodular-set-cover-problems-for-near-optimal-online-19xxnyef", "content": "This allows us to design a constructive variant of a greedy subspace projections in [1], [2] according to a submodular set cover (SSC) problem, which provably picks at most logarithmically more elements than the optimal one."}
+{"idx": 8, "title": "Communication - and Computation- Efficient Distributed...", "date": "", "ddg_snippet": "This algorithm enables agents to self-configure their communication topology efficiently for real-time and near - optimal coordination in multi -robot networks.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-Communication--and-Computation-Efficient-clyowh6qu22tn019jq6fo3u0u", "content": "This algorithm enables agents to self-configure their communication topology efficiently for real-time and near - optimal coordination in multi -robot networks."}
+{"idx": 9, "title": "Near - Optimal Multi - Agent Learning for", "date": "", "ddg_snippet": "Near - Optimal Multi - Agent Learning for Safe Coverage Control. Manish Prajapat ETH Zurich. manishp@ai.ethz.ch.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2022/file/60dc26558762425a465cb0409fc3dc52-Paper-Conference.pdf", "content": "Near - Optimal Multi - Agent Learning for Safe Coverage Control. Manish Prajapat ETH Zurich. manishp@ai.ethz.ch."}
diff --git a/data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization_filetypepdf.jsonl b/data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization_filetypepdf.jsonl
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+{"idx": 0, "title": "PDF Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization", "date": "", "ddg_snippet": "[1] Ma Z et al. MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning. NeurIPS 2023. [2] Ma Z et al. Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization. arXiv preprint arXiv:2411.00625, 2024.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/30417.pdf", "content": "[1] Ma Z et al. MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning. NeurIPS 2023. [2] Ma Z et al. Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization. arXiv preprint arXiv:2411.00625, 2024."}
+{"idx": 1, "title": "[2408.10672] Neural Exploratory Landscape Analysis for ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization , by Zeyuan Ma and 3 other authors. View PDF HTML (experimental).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.10672", "content": "View a PDF of the paper titled Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization , by Zeyuan Ma and 3 other authors. View PDF HTML (experimental)."}
+{"idx": 2, "title": "MetaEvo/Neur-ELA: Official code for Neural Exploratory Landscape ...", "date": "", "ddg_snippet": "Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization . Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper.The PDF version of the paper is available here.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MetaEvo/Neur-ELA", "content": "Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization . Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper.The PDF version of the paper is available here."}
+{"idx": 3, "title": "Neural Exploratory Landscape Analysis for ...", "date": "", "ddg_snippet": "Recent research in Meta - Black - Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black - box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Neural-Exploratory-Landscape-Analysis-for-Meta-Black-Box-Optimization-e0549d78-9d78-4162-ab06-b0a02f417612", "content": "Recent research in Meta - Black - Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black - box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions."}
+{"idx": 4, "title": "( PDF ) MetaBox-v2: A Unified Benchmark Platform for Meta - Black - Box ...", "date": "", "ddg_snippet": "Neural exploratory landscape . analysis for meta - black - box - optimization .Rudolph. Exploratory landscape analysis . In Proceedings of the 13th Annual Conference on. Genetic and Evolutionary Computation, 2011.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392015793_MetaBox-v2_A_Unified_Benchmark_Platform_for_Meta-Black-Box_Optimization", "content": "Neural exploratory landscape . analysis for meta - black - box - optimization .Rudolph. Exploratory landscape analysis . In Proceedings of the 13th Annual Conference on. Genetic and Evolutionary Computation, 2011."}
+{"idx": 5, "title": "NEURAL EXPLORATORY LANDSCAPE ANALYSIS FOR ...", "date": "", "ddg_snippet": "by Z Ma · Cited by 1 — Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box op-.", "subpage_snippet": "", "source": "human-competitive.org", "link": "https://human-competitive.org/sites/default/files/neural_exploratory_landscape_analysis_for_meta-black-box-optimization.pdf", "content": "by Z Ma · Cited by 1 — Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box op-."}
+{"idx": 6, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "Neural exploratory landscape analysis for meta - black - box - optimization .Automated algorithm selection on continuous black - box problems by combining exploratory landscape analysis and machine learning. Evolutionary Computation, 2019a.", "subpage_snippet": "", "source": "yuejiaogong.github.io", "link": "https://yuejiaogong.github.io/papers/NeurELA.pdf", "content": "Neural exploratory landscape analysis for meta - black - box - optimization .Automated algorithm selection on continuous black - box problems by combining exploratory landscape analysis and machine learning. Evolutionary Computation, 2019a."}
+{"idx": 7, "title": "Zeyuan Ma - Google Scholar", "date": "", "ddg_snippet": "2024. Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization .Accurate Peak Detection in Multimodal Optimization via Approximated Landscape Learning.", "subpage_snippet": "", "source": "scholar.google.nl", "link": "https://scholar.google.nl/citations?user=Jcy8wPgAAAAJ&hl=en", "content": "2024. Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization .Accurate Peak Detection in Multimodal Optimization via Approximated Landscape Learning."}
+{"idx": 8, "title": "dblp: List of computer science publications by Yue-Jiao Gong", "date": "", "ddg_snippet": "Zeyuan Ma, Jiacheng Chen, Hongshu Guo, Yue-Jiao Gong: Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/65/7184.html", "content": "Zeyuan Ma, Jiacheng Chen, Hongshu Guo, Yue-Jiao Gong: Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization ."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Neural_Persistence_Dynamics_20_structure_elements_persistence_diagram_60-dimensional_vector.jsonl b/data/sampled_jsons/Neural_Persistence_Dynamics_20_structure_elements_persistence_diagram_60-dimensional_vector.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0ead3c5984bd050bf4bc7b80c14b7d1f15600bdb
--- /dev/null
+++ b/data/sampled_jsons/Neural_Persistence_Dynamics_20_structure_elements_persistence_diagram_60-dimensional_vector.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Temporal network analysis using zigzag persistence | EPJ Data", "date": "", "ddg_snippet": "Zigzag persistence tracks the formation and disappearance of homological structures through a persistence diagram as a two- dimensional summary ...", "subpage_snippet": "", "source": "epjdatascience.springeropen.com", "link": "https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-023-00379-5", "content": "Zigzag persistence tracks the formation and disappearance of homological structures through a persistence diagram as a two- dimensional summary ..."}
+{"idx": 1, "title": "U.S. Patent for Generative adversarial neural network assisted", "date": "", "ddg_snippet": "The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image.", "subpage_snippet": "", "source": "patents.justia.com", "link": "https://patents.justia.com/patent/11775829", "content": "The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image."}
+{"idx": 2, "title": "US11127167B2 - Efficient matrix format suitable for neural", "date": "", "ddg_snippet": "... also relates to graphics processing units (GPUs) generating, storing and/or using compressed and decompressed sparse matrix data in deep neural ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11127167B2/en", "content": "... also relates to graphics processing units (GPUs) generating, storing and/or using compressed and decompressed sparse matrix data in deep neural ..."}
+{"idx": 3, "title": "US12307342B1 - Iterative attention-based neural network", "date": "", "ddg_snippet": "the user 200 may also represent the adaptive system 100 itself as a means of representing interactions with itself (or among its constituent elements ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US12307342B1/en", "content": "the user 200 may also represent the adaptive system 100 itself as a means of representing interactions with itself (or among its constituent elements ..."}
+{"idx": 4, "title": "Time Series Classification via Topological Data Analysis", "date": "", "ddg_snippet": "2016) and combined with quantization and pooling methods to address non rigid shape analysis problems; Betti curves extracted from persistence ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/316604237_Time_Series_Classification_via_Topological_Data_Analysis", "content": "2016) and combined with quantization and pooling methods to address non rigid shape analysis problems; Betti curves extracted from persistence ..."}
+{"idx": 5, "title": "Dynamical System Parameter Path Optimization using Persistent", "date": "", "ddg_snippet": "... persistence optimization followed by a dictionary of cost function terms to promote different persistence diagrams that map to dynamical system ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00782v1", "content": "... persistence optimization followed by a dictionary of cost function terms to promote different persistence diagrams that map to dynamical system ..."}
+{"idx": 6, "title": "Tags | SoftwarePatternsLexicon.com", "date": "", "ddg_snippet": "Relational, dimensional , time-series. ... Akka Persistence (2) ... Algebraic Structures (3)", "subpage_snippet": "", "source": "softwarepatternslexicon.com", "link": "https://softwarepatternslexicon.com/tags/", "content": "Relational, dimensional , time-series. ... Akka Persistence (2) ... Algebraic Structures (3)"}
+{"idx": 7, "title": "Generative modelling in latent space – Sander Dieleman", "date": "", "ddg_snippet": "... 13 14 ) would typically compress an entire image into a single latent vector , resulting in a representation without any kind of topological structure ...", "subpage_snippet": "", "source": "sander.ai", "link": "https://sander.ai/2025/04/15/latents.html", "content": "... 13 14 ) would typically compress an entire image into a single latent vector , resulting in a representation without any kind of topological structure ..."}
+{"idx": 8, "title": "Topological Embedding of Human Brain Networks with Applications", "date": "", "ddg_snippet": "... persistent homology to static networks or summarized time-varying networks in a static manner, the exploration of dynamic patterns in persistent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07835v1", "content": "... persistent homology to static networks or summarized time-varying networks in a static manner, the exploration of dynamic patterns in persistent ..."}
+{"idx": 9, "title": "Fuzzy based binary feature profiling for modus operandi", "date": "", "ddg_snippet": "Until 2014, grave crimes were classified under 21 crime categories and in 2015 another 5 new crime categories were introduced, making it 26 ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-65/", "content": "Until 2014, grave crimes were classified under 21 crime categories and in 2015 another 5 new crime categories were introduced, making it 26 ..."}
diff --git a/data/sampled_jsons/Neural_Persistence_Dynamics_arXiv_PDF_Section_2_scalability_issues_crocker_plots_dimensionality_numb_year_2024.jsonl b/data/sampled_jsons/Neural_Persistence_Dynamics_arXiv_PDF_Section_2_scalability_issues_crocker_plots_dimensionality_numb_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..18cd987298a73f834ffc7b7f6e7856f10a952f21
--- /dev/null
+++ b/data/sampled_jsons/Neural_Persistence_Dynamics_arXiv_PDF_Section_2_scalability_issues_crocker_plots_dimensionality_numb_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "Despite remarkable success in distinguishing different configurations of models for collective behav-ior, all approaches suffer scalability issues , either in terms of the dimensionality of the vectorized persistence diagrams (as with the PSK approach of [23]), or in terms of the number of observation sequences (as is the case for crocker plots ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15732v1", "content": "Despite remarkable success in distinguishing different configurations of models for collective behav-ior, all approaches suffer scalability issues , either in terms of the dimensionality of the vectorized persistence diagrams (as with the PSK approach of [23]), or in terms of the number of observation sequences (as is the case for crocker plots ..."}
+{"idx": 1, "title": "(PDF) Neural Persistence Dynamics - ResearchGate", "date": "", "ddg_snippet": "Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380895040_Neural_Persistence_Dynamics", "content": "Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science ..."}
+{"idx": 2, "title": "Neural Persistence Dynamics - papers.nips.cc", "date": "", "ddg_snippet": "Abstract We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/3a509449a73fd0aab8c0cf5705827036-Abstract-Conference.html", "content": "Abstract We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities."}
+{"idx": 3, "title": "Neural Persistence Dynamics · NeurIPS 2024", "date": "", "ddg_snippet": "This table compares the performance of the proposed Neural Persistence Dynamics model against two state-of-the-art methods (Path Signature Kernel and Crocker stacks) for parameter regression tasks on four different datasets simulating collective behavior.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/rcnzrfikx6/", "content": "This table compares the performance of the proposed Neural Persistence Dynamics model against two state-of-the-art methods (Path Signature Kernel and Crocker stacks) for parameter regression tasks on four different datasets simulating collective behavior."}
+{"idx": 4, "title": "Neural Persistence Dynamics - Paris-Lodron-University Salzburg", "date": "", "ddg_snippet": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model -- Neural Persistence Dynamics -- substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks.", "subpage_snippet": "", "source": "uni-salzburg.elsevierpure.com", "link": "https://uni-salzburg.elsevierpure.com/en/publications/neural-persistence-dynamics", "content": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model -- Neural Persistence Dynamics -- substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks."}
+{"idx": 5, "title": "(PDF) Neural Persistence Dynamics | Sebastia Zeng - Academia.edu", "date": "", "ddg_snippet": "The use of topological descriptors in modern machine learning applications, such as Persistence Diagrams (PDs) arising from Topological Data Analysis (TDA), has shown great potential in various domains. However, their practical use in applications is often hindered by two major limitations: the computational complexity required to compute such descriptors exactly, and their sensitivity to even ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/122585963/Neural_Persistence_Dynamics", "content": "The use of topological descriptors in modern machine learning applications, such as Persistence Diagrams (PDs) arising from Topological Data Analysis (TDA), has shown great potential in various domains. However, their practical use in applications is often hindered by two major limitations: the computational complexity required to compute such descriptors exactly, and their sensitivity to even ..."}
+{"idx": 6, "title": "plus-rkwitt/neural_persistence_dynamics - GitHub", "date": "", "ddg_snippet": "The Crocker stacks baseline comparison is implemented in crocker_stacks.py. To execute this script, you must first prepare the data using compute_cs.py. Additionally, you need to install the teaspoon library with the appropriate version for computing the Crocker stacks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plus-rkwitt/neural_persistence_dynamics", "content": "The Crocker stacks baseline comparison is implemented in crocker_stacks.py. To execute this script, you must first prepare the data using compute_cs.py. Additionally, you need to install the teaspoon library with the appropriate version for computing the Crocker stacks."}
+{"idx": 7, "title": "[2405.15732] Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.15732", "content": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ..."}
+{"idx": 8, "title": "Neural Persistence Dynamics - OpenReview", "date": "", "ddg_snippet": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=163DugGjNn", "content": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ..."}
+{"idx": 9, "title": "Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "Despite their remarkable performance in distinguishing different configurations of models for collective behavior, both approaches suffer scalability issues : either (i) in terms of unfavorable scalability with respect to the dimensionality of vectorized persistence diagrams (as with the PSK approach of [23]), or (ii) in terms of unfavorable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15732v2", "content": "Despite their remarkable performance in distinguishing different configurations of models for collective behavior, both approaches suffer scalability issues : either (i) in terms of unfavorable scalability with respect to the dimensionality of vectorized persistence diagrams (as with the PSK approach of [23]), or (ii) in terms of unfavorable ..."}
diff --git a/data/sampled_jsons/Neural_Persistence_Dynamics_crocker_plots_scalability_issues.jsonl b/data/sampled_jsons/Neural_Persistence_Dynamics_crocker_plots_scalability_issues.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..31f9ebbacfae1a7e2ca1327768b38650d19701af
--- /dev/null
+++ b/data/sampled_jsons/Neural_Persistence_Dynamics_crocker_plots_scalability_issues.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "Report issue for preceding element. Neural Persistence Dynamics .Report issue for preceding element. The crocker plot is computed as follows: for each time step, the persistence diagrams are computed up to a scale parameter. ε𝜀\\varepsilonitalic_ε.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15732v1/", "content": "Report issue for preceding element. Neural Persistence Dynamics .Report issue for preceding element. The crocker plot is computed as follows: for each time step, the persistence diagrams are computed up to a scale parameter. ε𝜀\\varepsilonitalic_ε."}
+{"idx": 1, "title": "(PDF) Neural Persistence Dynamics", "date": "", "ddg_snippet": "neural persistence dynamics – substantially outperforms the state-of-the-art across.Crocker stacks are an extension to crocker plots [49] and constitute a. topological summary for time-varying persistence diagrams.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380895040_Neural_Persistence_Dynamics", "content": "neural persistence dynamics – substantially outperforms the state-of-the-art across.Crocker stacks are an extension to crocker plots [49] and constitute a. topological summary for time-varying persistence diagrams."}
+{"idx": 2, "title": "Detecting bifurcations in dynamical systems with CROCKER plots", "date": "", "ddg_snippet": "Therefore, we describe an alternative method based on persistent homology—a tool from topological data analysis—that utilizes Betti numbers and CROCKER plots .", "subpage_snippet": "", "source": "aip.scitation.org", "link": "https://aip.scitation.org/doi/figure/10.1063/5.0102421", "content": "Therefore, we describe an alternative method based on persistent homology—a tool from topological data analysis—that utilizes Betti numbers and CROCKER plots ."}
+{"idx": 3, "title": "[PDF] Detecting bifurcations in dynamical systems with CROCKER ...", "date": "", "ddg_snippet": "Therefore, we describe an alternative method based on persistent homology-a tool from topological data analysis-that utilizes Betti numbers and CROCKER plots .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Detecting-bifurcations-in-dynamical-systems-with-Guzel-Munch/83b6f9562185b73872a66b9a29a21b1c563185f5", "content": "Therefore, we describe an alternative method based on persistent homology-a tool from topological data analysis-that utilizes Betti numbers and CROCKER plots ."}
+{"idx": 4, "title": "lxiancode/tda- crocker : replication code for \"Capturing dynamics of...\"", "date": "", "ddg_snippet": "Creating crocker plots . crocker-plot-functions.R.crocker-stack-analysis.R (as an example, this file does clustering analysis based on crocker stacks for Experiment 2). bottleneck distance. About. replication code for \"Capturing dynamics of time-varying data via topology\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lxiancode/tda-crocker", "content": "Creating crocker plots . crocker-plot-functions.R.crocker-stack-analysis.R (as an example, this file does clustering analysis based on crocker stacks for Experiment 2). bottleneck distance. About. replication code for \"Capturing dynamics of time-varying data via topology\"."}
+{"idx": 5, "title": "Detecting bifurcations in dynamical systems with CROCKER plots", "date": "", "ddg_snippet": "Therefore, we describe an alternative method based on persistent homology-a tool from topological data analysis-that utilizes Betti numbers and CROCKER plots .", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/36182371/", "content": "Therefore, we describe an alternative method based on persistent homology-a tool from topological data analysis-that utilizes Betti numbers and CROCKER plots ."}
+{"idx": 6, "title": "Capturing dynamics of time-varying data via topology", "date": "", "ddg_snippet": "computational persistent homology, dynamics , mathematical models, machine learning.(Top) A persistence diagram, the corresponding persistence intervals (drawn vertically), and one column of a crocker plot matrix.", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/doi/10.3934/fods.2021033?viewType=HTML", "content": "computational persistent homology, dynamics , mathematical models, machine learning.(Top) A persistence diagram, the corresponding persistence intervals (drawn vertically), and one column of a crocker plot matrix."}
+{"idx": 7, "title": "Move schedules: fast persistence computations in coarse dynamic ...", "date": "", "ddg_snippet": "Computing persistence dynamically can be reduced to maintaining a valid decomposition under adjacent transpositions in the filtration order.4.2 Crocker stacks. There are many challenges to characterizing topological behavior in dynamic settings.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s41468-023-00156-3", "content": "Computing persistence dynamically can be reduced to maintaining a valid decomposition under adjacent transpositions in the filtration order.4.2 Crocker stacks. There are many challenges to characterizing topological behavior in dynamic settings."}
+{"idx": 8, "title": "Temporal network analysis using zigzag persistence | EPJ Data Science", "date": "", "ddg_snippet": "This work presents a framework for studying temporal networks using zigzag persistence , a tool from the field of Topological Data Analysis (TDA).Güzel I, Munch E, Khasawneh FA (2022) Detecting bifurcations in dynamical systems with CROCKER plots .", "subpage_snippet": "", "source": "epjdatascience.springeropen.com", "link": "https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-023-00379-5", "content": "This work presents a framework for studying temporal networks using zigzag persistence , a tool from the field of Topological Data Analysis (TDA).Güzel I, Munch E, Khasawneh FA (2022) Detecting bifurcations in dynamical systems with CROCKER plots ."}
+{"idx": 9, "title": "(Open Access) Erosion distance for generalized persistence modules...", "date": "", "ddg_snippet": "Patel also introduced a distance for persistence diagrams, the erosion distance.This extension of the erosion distance also gives, as a special case, a distance for multidimensional persistent homology groups with torsion introduced by Frosini.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/erosion-distance-for-generalized-persistence-modules-1wyn9q4gzh", "content": "Patel also introduced a distance for persistence diagrams, the erosion distance.This extension of the erosion distance also gives, as a special case, a distance for multidimensional persistent homology groups with torsion introduced by Frosini."}
diff --git a/data/sampled_jsons/No_Free_Delivery_Service_Epistemic_limits_passive_data_collection_Wolpert_NFL_theorems.jsonl b/data/sampled_jsons/No_Free_Delivery_Service_Epistemic_limits_passive_data_collection_Wolpert_NFL_theorems.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a1e96b9d05b078f33ab3031b6d4515babd08dd84
--- /dev/null
+++ b/data/sampled_jsons/No_Free_Delivery_Service_Epistemic_limits_passive_data_collection_Wolpert_NFL_theorems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No free delivery service Epistemic limits of passive data ...", "date": "", "ddg_snippet": "by M Nickel · 2024 · Cited by 1 — While the NFL theorems show the impossibility of an assumption- free general purpose learning algorithm, a common criticism is that they need to assume an ... 28 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/b97fc02c9e536d68300d82be05c23aa2-Paper-Conference.pdf", "content": "by M Nickel · 2024 · Cited by 1 — While the NFL theorems show the impossibility of an assumption- free general purpose learning algorithm, a common criticism is that they need to assume an ... 28 pages"}
+{"idx": 1, "title": "No Free Delivery Service: Epistemic Limits of Passive Data ...", "date": "", "ddg_snippet": "While the NFL theorems show the impossibility of an assumption- free general purpose learning algorithm, a common criticism is that they need to assume an ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/795949995/2411-13653v1", "content": "While the NFL theorems show the impossibility of an assumption- free general purpose learning algorithm, a common criticism is that they need to assume an ..."}
+{"idx": 2, "title": "2506.10130v2 - Artificial Intelligence", "date": "", "ddg_snippet": "the No Free Lunch ( NFL ) theorems in machine learning ... No Free Delivery Service : Epistemic Limits of Passive Data Collection in Complex Social Systems.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/910019017/2506-10130v2", "content": "the No Free Lunch ( NFL ) theorems in machine learning ... No Free Delivery Service : Epistemic Limits of Passive Data Collection in Complex Social Systems."}
+{"idx": 3, "title": "The Compatibility of Evolution and Design ...", "date": "", "ddg_snippet": "For example, the “ No Free Lunch” ( NFL ) theorem , first stated by Wolpert and Macready (1997), claims that there is no universal search strategy that would ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/the-compatibility-of-evolution-and-design-9783030696825-9783030696832.html", "content": "For example, the “ No Free Lunch” ( NFL ) theorem , first stated by Wolpert and Macready (1997), claims that there is no universal search strategy that would ..."}
+{"idx": 4, "title": "Download book PDF", "date": "", "ddg_snippet": "Dembski often cites the main “ no free lunch” ( NFL ) theorem for optimiza- ... When sufficient amounts of noise- free data were given as observed data , the ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-540-74111-4.pdf", "content": "Dembski often cites the main “ no free lunch” ( NFL ) theorem for optimiza- ... When sufficient amounts of noise- free data were given as observed data , the ..."}
+{"idx": 5, "title": "Why Machines Will Never Rule the World: Artificial ...", "date": "", "ddg_snippet": "This new edition also brings up to date the arguments relating to the limits of AI, showing why AI systems are best viewed as pieces of mathematics, which ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/download/why-machines-will-never-rule-the-world-artificial-intelligence-without-fear-9781032946917-9781040346822-9781032941400-9781003581253.html", "content": "This new edition also brings up to date the arguments relating to the limits of AI, showing why AI systems are best viewed as pieces of mathematics, which ..."}
+{"idx": 6, "title": "History Of Machine Learning", "date": "", "ddg_snippet": "Focus on collecting comprehensive and accurate data , ensuring it is free from artifacts and homogeneous, to effectively train AI algorithms and reduce inter ...", "subpage_snippet": "", "source": "rexdouglass.com", "link": "https://rexdouglass.com/Machine+Learning.html", "content": "Focus on collecting comprehensive and accurate data , ensuring it is free from artifacts and homogeneous, to effectively train AI algorithms and reduce inter ..."}
+{"idx": 7, "title": "Evolutionary Scheduling", "date": "", "ddg_snippet": "two important sciences - artificial intelligence and operational research. Scheduling in its wide variety of forms is a critical problem in today's pro-.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-540-48584-1.pdf", "content": "two important sciences - artificial intelligence and operational research. Scheduling in its wide variety of forms is a critical problem in today's pro-."}
+{"idx": 8, "title": "Symmetry in Mechanical Engineering", "date": "", "ddg_snippet": "This is a reprint of articles from the Special Issue published online in the open access journal. Symmetry (ISSN 2073-8994) (available at: https://www.mdpi.com/ ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/2351/Symmetry_in_Mechanical_Engineering.pdf", "content": "This is a reprint of articles from the Special Issue published online in the open access journal. Symmetry (ISSN 2073-8994) (available at: https://www.mdpi.com/ ..."}
+{"idx": 9, "title": "the annals - applied statistics", "date": "", "ddg_snippet": "by CW CLARK · Cited by 5973 — A copula model for marked point process with a terminal event: An application in dynamic prediction of insurance claims . 148 pages", "subpage_snippet": "", "source": "www.imstat.org", "link": "https://www.imstat.org/publications/aoas/aoas_18_4/aoas_18_4.pdf", "content": "by CW CLARK · Cited by 5973 — A copula model for marked point process with a terminal event: An application in dynamic prediction of insurance claims . 148 pages"}
diff --git a/data/sampled_jsons/No_Free_Delivery_Service_Epistemic_limits_passive_data_collection_complex_social_systems.jsonl b/data/sampled_jsons/No_Free_Delivery_Service_Epistemic_limits_passive_data_collection_complex_social_systems.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ce7d1a213fe88d260a529f40938390ded45aa721
--- /dev/null
+++ b/data/sampled_jsons/No_Free_Delivery_Service_Epistemic_limits_passive_data_collection_complex_social_systems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2411.13653] No Free Delivery Service : Epistemic limits of passive ...", "date": "", "ddg_snippet": "These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices. Importantly, this includes variants of both recommender systems and reasoning via large...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.13653", "content": "These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices. Importantly, this includes variants of both recommender systems and reasoning via large..."}
+{"idx": 1, "title": "No Free Delivery Service : Epistemic limits of passive data ...", "date": "", "ddg_snippet": "Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems , including positive social impact, nor continue to advance AI research in a scientifically sound way. In this paper, I will show that for widely considered inference settings in complex ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386021650_No_Free_Delivery_Service_Epistemic_limits_of_passive_data_collection_in_complex_social_systems", "content": "Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems , including positive social impact, nor continue to advance AI research in a scientifically sound way. In this paper, I will show that for widely considered inference settings in complex ..."}
+{"idx": 2, "title": "No Free Delivery Service : Epistemic limits of passive data ...", "date": "", "ddg_snippet": "In complex social systems , passive data collection for AI model validation is flawed due to inherent biases and heavy-tailed distributions. This invalidates the train-test paradigm under ontological parsimony, affecting tasks like recommendation systems and question answering.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-No-Free-Delivery-cm3t89fot54t201a7hdglyl3v", "content": "In complex social systems , passive data collection for AI model validation is flawed due to inherent biases and heavy-tailed distributions. This invalidates the train-test paradigm under ontological parsimony, affecting tasks like recommendation systems and question answering."}
+{"idx": 3, "title": "No Free Delivery Service : Epistemic limits of passive data ...", "date": "", "ddg_snippet": "Passive data collection in complex social systems invalidates standard AI model validation; new methods are needed.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/xz0fpoakeb/", "content": "Passive data collection in complex social systems invalidates standard AI model validation; new methods are needed."}
+{"idx": 4, "title": "Over-simplified models, complex social systems - The Hindu", "date": "", "ddg_snippet": "Along with fundamental epistemic limitations , western scientific methods are showing up ethical weaknesses too.", "subpage_snippet": "", "source": "www.thehindu.com", "link": "https://www.thehindu.com/opinion/lead/over-simplified-models-complex-social-systems/article37061493.ece", "content": "Along with fundamental epistemic limitations , western scientific methods are showing up ethical weaknesses too."}
+{"idx": 5, "title": "World Complexity", "date": "", "ddg_snippet": "A Complex Evolutionary Social System Appro-ach to Global Governance. Data Availability Statement: All rel-evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest.", "subpage_snippet": "", "source": "oajournals.fupress.net", "link": "https://oajournals.fupress.net/index.php/smp/issue/download/585/227", "content": "A Complex Evolutionary Social System Appro-ach to Global Governance. Data Availability Statement: All rel-evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest."}
+{"idx": 6, "title": "On Epistemic Limit , Dynamic of Complex (Financial) System ...", "date": "", "ddg_snippet": "Financial market is nonlinear (and thus has this characteristic of “geometric”). The use of mathematical logarithmic function, that is the inverse of exponentiation function, serves as the “toolkit” for a better and intuitive analysis. So, in order to be able to “directly” add the drawdown with its...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/epistemic-limit-dynamic-complex-financial-system-bima-deniansha-dvaoc", "content": "Financial market is nonlinear (and thus has this characteristic of “geometric”). The use of mathematical logarithmic function, that is the inverse of exponentiation function, serves as the “toolkit” for a better and intuitive analysis. So, in order to be able to “directly” add the drawdown with its..."}
+{"idx": 7, "title": "dblp: List of computer science publications by Maximilian Nickel", "date": "", "ddg_snippet": "Maximilian Nickel: No Free Delivery Service : Epistemic limits of passive data collection in complex social systems . NeurIPS 2024.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/83/10622.html", "content": "Maximilian Nickel: No Free Delivery Service : Epistemic limits of passive data collection in complex social systems . NeurIPS 2024."}
+{"idx": 8, "title": "Maximilian Nickel · CSAuthors", "date": "", "ddg_snippet": "No Free Delivery Service : Epistemic limits of passive data collection in complex social systems . Maximilian Nickel.Group fairness without demographics using social networks.", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/maximilian-nickel/", "content": "No Free Delivery Service : Epistemic limits of passive data collection in complex social systems . Maximilian Nickel.Group fairness without demographics using social networks."}
+{"idx": 9, "title": "aipaper.dev | Daily", "date": "", "ddg_snippet": "No Free Delivery Service : Epistemic limits of passive data collection in complex social systems . DL. NLP.", "subpage_snippet": "", "source": "aipaper.dev", "link": "https://aipaper.dev/daily", "content": "No Free Delivery Service : Epistemic limits of passive data collection in complex social systems . DL. NLP."}
diff --git a/data/sampled_jsons/No_Free_Delivery_Service_Nickel_strengthen_No_Free_Lunch_Wolpert_epistemic_limits_year_2024.jsonl b/data/sampled_jsons/No_Free_Delivery_Service_Nickel_strengthen_No_Free_Lunch_Wolpert_epistemic_limits_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8ce02956e6e26d4772a55c1ec979a00e6b168457
--- /dev/null
+++ b/data/sampled_jsons/No_Free_Delivery_Service_Nickel_strengthen_No_Free_Lunch_Wolpert_epistemic_limits_year_2024.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "No free delivery service Epistemic limits of passive data ...", "date": "", "ddg_snippet": "by M Nickel · 2024 · Cited by 1 — The no-free-lunch theorems for machine learning [68, 60] share important ... In particular, I have shown that there exists no free delivery service of ... 28 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/b97fc02c9e536d68300d82be05c23aa2-Paper-Conference.pdf", "content": "by M Nickel · 2024 · Cited by 1 — The no-free-lunch theorems for machine learning [68, 60] share important ... In particular, I have shown that there exists no free delivery service of ... 28 pages"}
+{"idx": 1, "title": "No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design ...", "date": "", "ddg_snippet": "The table highlights the trade-offs between watermark robustness, utility, and usability. Full paper ← No Free Lunch Theorem and Black-Box Complexity Analysis for Adversarial Optimisation 26 September 2024 No Free Delivery Service : Epistemic limits of passive data collection in complex social systems 26 September 2024 →", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/riol7kbskv/", "content": "The table highlights the trade-offs between watermark robustness, utility, and usability. Full paper ← No Free Lunch Theorem and Black-Box Complexity Analysis for Adversarial Optimisation 26 September 2024 No Free Delivery Service : Epistemic limits of passive data collection in complex social systems 26 September 2024 →"}
+{"idx": 2, "title": "No Free Delivery Service : Epistemic limits of passive data collection...", "date": "", "ddg_snippet": "# ← → No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices 26 September 2024 No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models 26 September 2024 → ←.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/xz0fpoakeb/", "content": "# ← → No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices 26 September 2024 No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models 26 September 2024 → ←."}
+{"idx": 3, "title": "No Free Delivery Service : Epistemic limits of passive data collection...", "date": "", "ddg_snippet": "The no - free - lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others?David H. Wolpert David H. Wolpert .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386021650_No_Free_Delivery_Service_Epistemic_limits_of_passive_data_collection_in_complex_social_systems", "content": "The no - free - lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others?David H. Wolpert David H. Wolpert ."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/No_Free_Delivery_Service_Theorem_1_power_law_distribution_epistemic_limits.jsonl b/data/sampled_jsons/No_Free_Delivery_Service_Theorem_1_power_law_distribution_epistemic_limits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c462c9764fa4abe2137ddc5b38e0d5860192ff56
--- /dev/null
+++ b/data/sampled_jsons/No_Free_Delivery_Service_Theorem_1_power_law_distribution_epistemic_limits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No free delivery service Epistemic limits of passive data ...", "date": "", "ddg_snippet": "by M Nickel · 2024 · Cited by 1 — Clearly, sampling from S is highly inefficient to overcome the issues raised by theorem 1 since (i) it is extremely difficult to get successful samples from ... 28 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/b97fc02c9e536d68300d82be05c23aa2-Paper-Conference.pdf", "content": "by M Nickel · 2024 · Cited by 1 — Clearly, sampling from S is highly inefficient to overcome the issues raised by theorem 1 since (i) it is extremely difficult to get successful samples from ... 28 pages"}
+{"idx": 1, "title": "No free delivery service: epistemic limits of passive data ...", "date": "", "ddg_snippet": "[1] ... \"Matrix Completion from Power-Law Distributed Samples\". ... No free delivery service: epistemic limits of passive data collection in complex social systems .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3741164", "content": "[1] ... \"Matrix Completion from Power-Law Distributed Samples\". ... No free delivery service: epistemic limits of passive data collection in complex social systems ."}
+{"idx": 2, "title": "No Free Delivery Service: Epistemic Limits of Passive Data ...", "date": "", "ddg_snippet": "power - law distribution . Based on this observation, passive data in complex social systems will then refer to the following: Definition 2 (Passive data in ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/795949995/2411-13653v1", "content": "power - law distribution . Based on this observation, passive data in complex social systems will then refer to the following: Definition 2 (Passive data in ..."}
+{"idx": 3, "title": "ai #machinelearning #training #inference #data #epistemic ...", "date": "", "ddg_snippet": "Hmm, no free delivery service . Epistemic limits of passive data collection in complex social systems. And you know, I know you, you live and ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/igor-halperin-092175a_ai-machinelearning-training-activity-7268248015700725762-Wn1p", "content": "Hmm, no free delivery service . Epistemic limits of passive data collection in complex social systems. And you know, I know you, you live and ..."}
+{"idx": 4, "title": "Epistemic clashes in network science: Mapping the ...", "date": "", "ddg_snippet": "by M Jacomy · 2020 · Cited by 24 — This article maps a controversy in network science over the last 15 years, dividing the field about the epistemic status of a central notion ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/2053951720949577", "content": "by M Jacomy · 2020 · Cited by 24 — This article maps a controversy in network science over the last 15 years, dividing the field about the epistemic status of a central notion ..."}
+{"idx": 5, "title": "Epistemic (in)justice, social identity and the Black Box ...", "date": "", "ddg_snippet": "by M Khan · 2024 · Cited by 4 — In that case, power should be distributed to all, not merely by representation of marginalized groups in the room but by including the marginalized in ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11076305/", "content": "by M Khan · 2024 · Cited by 4 — In that case, power should be distributed to all, not merely by representation of marginalized groups in the room but by including the marginalized in ..."}
+{"idx": 6, "title": "Power-Law Distributions in Empirical Data | SIAM Review", "date": "", "ddg_snippet": "by A Clauset · 2009 · Cited by 12283 — Here we present a principled statistical framework for discerning and quantifying power - law behavior in empirical data.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/070710111", "content": "by A Clauset · 2009 · Cited by 12283 — Here we present a principled statistical framework for discerning and quantifying power - law behavior in empirical data."}
+{"idx": 7, "title": "Epistemic Alignment: A Mediating Framework for User-LLM ...", "date": "", "ddg_snippet": "1 Apr 2025 — We propose the Epistemic Alignment Framework, a set of ten challenges in knowledge transmission derived from the philosophical literature of epistemology.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.01205v1", "content": "1 Apr 2025 — We propose the Epistemic Alignment Framework, a set of ten challenges in knowledge transmission derived from the philosophical literature of epistemology."}
+{"idx": 8, "title": "No Free Delivery Service: Epistemic limits of passive data ...", "date": "", "ddg_snippet": "The paper by Maximilian Nickel explores the limitations of the train-test paradigm in validating AI models within complex social systems.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-No-Free-Delivery-cm3t89fot54t201a7hdglyl3v", "content": "The paper by Maximilian Nickel explores the limitations of the train-test paradigm in validating AI models within complex social systems."}
+{"idx": 9, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "No Free Delivery Service: Epistemic limits of passive data collection in complex social systems · Retrieval & Fine-Tuning for In-Context Tabular Models ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "No Free Delivery Service: Epistemic limits of passive data collection in complex social systems · Retrieval & Fine-Tuning for In-Context Tabular Models ..."}
diff --git a/data/sampled_jsons/Node_Feature_Regularization_NFR_layer_feature_selection_mechanism_UPGNET.jsonl b/data/sampled_jsons/Node_Feature_Regularization_NFR_layer_feature_selection_mechanism_UPGNET.jsonl
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+{"idx": 1, "title": "Download Node.js®", "date": "", "ddg_snippet": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts.", "subpage_snippet": "", "source": "nodejs.org", "link": "https://nodejs.org/en/download", "content": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts."}
+{"idx": 2, "title": "About Node.js®", "date": "", "ddg_snippet": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts.", "subpage_snippet": "", "source": "nodejs.org", "link": "https://nodejs.org/en/about", "content": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts."}
+{"idx": 3, "title": "Introduction to Node.js", "date": "", "ddg_snippet": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts.", "subpage_snippet": "", "source": "nodejs.org", "link": "https://nodejs.org/en/learn/getting-started/introduction-to-nodejs", "content": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts."}
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+{"idx": 6, "title": "Node.js — Installing Node.js via package manager", "date": "", "ddg_snippet": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts.", "subpage_snippet": "", "source": "nodejs.org", "link": "https://nodejs.org/en/download/package-manager/all", "content": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts."}
+{"idx": 7, "title": "Node.js", "date": "", "ddg_snippet": "Apr 23, 2025 · Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts.", "subpage_snippet": "", "source": "nodejs.org", "link": "https://nodejs.org/en/blog/release/v22.15.0", "content": "Apr 23, 2025 · Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts."}
+{"idx": 8, "title": "Node.js Releases", "date": "", "ddg_snippet": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts.", "subpage_snippet": "", "source": "nodejs.org", "link": "https://nodejs.org/en/about/previous-releases", "content": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts."}
+{"idx": 9, "title": "下載 Node.js®", "date": "", "ddg_snippet": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts.", "subpage_snippet": "", "source": "nodejs.org", "link": "https://nodejs.org/zh-tw/download", "content": "Node .js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts."}
diff --git a/data/sampled_jsons/Nonparametric_Conditional_Density_Estimation_using_Generative_Adversarial_Networks_Zhou.jsonl b/data/sampled_jsons/Nonparametric_Conditional_Density_Estimation_using_Generative_Adversarial_Networks_Zhou.jsonl
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+++ b/data/sampled_jsons/Nonparametric_Conditional_Density_Estimation_using_Generative_Adversarial_Networks_Zhou.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Conditional Density Estimation with Neural Networks : Best", "date": "", "ddg_snippet": "• Conditional Kernel Density Estimation (CKDE): Non - parametric approach introduced in II.B using bandwidth selection via the rule-of-thumb (Silverman, 1982).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.00954", "content": "• Conditional Kernel Density Estimation (CKDE): Non - parametric approach introduced in II.B using bandwidth selection via the rule-of-thumb (Silverman, 1982)."}
+{"idx": 1, "title": "(PDF) Conditional Density Estimation with Neural Networks : Best...", "date": "", "ddg_snippet": "networks for estimating conditional densities . densities in a non - parametric manner. Originally introduced in Rosenblatt (1956); Parzen (1962), KDE uses kernel functions to estimate the probability density at a query point, based on the.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/331519536_Conditional_Density_Estimation_with_Neural_Networks_Best_Practices_and_Benchmarks", "content": "networks for estimating conditional densities . densities in a non - parametric manner. Originally introduced in Rosenblatt (1956); Parzen (1962), KDE uses kernel functions to estimate the probability density at a query point, based on the."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "(2021), who investigated conditional density estimation using Generative Adversarial Networks (GANs) and Wasserstein-GANs, respectively .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "(2021), who investigated conditional density estimation using Generative Adversarial Networks (GANs) and Wasserstein-GANs, respectively ."}
+{"idx": 3, "title": "Nonparametric Conditional Density Estimation ... | Papers With Code", "date": "", "ddg_snippet": "Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting.We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/nonparametric-conditional-density-estimation", "content": "Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting.We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated."}
+{"idx": 4, "title": "Conditional Density Estimation with Normalizing Flows", "date": "", "ddg_snippet": "Nonparametric density estimation , on the other hand, does not explicitly restrict the family of distri-butions. Rather, it imposes implicit smoothness assumptions onto the density estimate .", "subpage_snippet": "", "source": "siboehm.com", "link": "https://siboehm.com/assets/img/nfn/Bachelorarbeit_Simon_Boehm.pdf", "content": "Nonparametric density estimation , on the other hand, does not explicitly restrict the family of distri-butions. Rather, it imposes implicit smoothness assumptions onto the density estimate ."}
+{"idx": 5, "title": "Tools to use for conditional density estimation in... - Stack Overflow", "date": "", "ddg_snippet": "Nonparametric ( conditional & neighborhood kernel density estimation ). parametric neural network -based methods (mixture density networks , kernel density estimation ).", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/26558576/tools-to-use-for-conditional-density-estimation-in-python", "content": "Nonparametric ( conditional & neighborhood kernel density estimation ). parametric neural network -based methods (mixture density networks , kernel density estimation )."}
+{"idx": 6, "title": "Bottleneck Conditional Density Estimation", "date": "", "ddg_snippet": "Conditional density estimation (CDE) refers to the problem of estimating a conditional density p(y|x) for the input vector x and target vector y. In contrast to classication where the target y is simply a discrete class label, y is typically continuous or high-dimensional in CDE.", "subpage_snippet": "", "source": "bayesiandeeplearning.org", "link": "http://bayesiandeeplearning.org/2016/papers/BDL_36.pdf", "content": "Conditional density estimation (CDE) refers to the problem of estimating a conditional density p(y|x) for the input vector x and target vector y. In contrast to classication where the target y is simply a discrete class label, y is typically continuous or high-dimensional in CDE."}
+{"idx": 7, "title": "Semi-supervised Conditional Density Estimation with Wasserstein...", "date": "", "ddg_snippet": "Wasserstein Generative Adversarial Networks . Nonparametric regression using deep neural networks with ReLU activation function. The Annals of Statistics, 48(4): 1875–1897. Shu, R.; Bui, H. H.; and Ghavamzadeh, M. 2017.", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/20630/20630-13-24643-1-2-20220628.pdf", "content": "Wasserstein Generative Adversarial Networks . Nonparametric regression using deep neural networks with ReLU activation function. The Annals of Statistics, 48(4): 1875–1897. Shu, R.; Bui, H. H.; and Ghavamzadeh, M. 2017."}
+{"idx": 8, "title": "Conditional independence testing, two sample comparison and...", "date": "", "ddg_snippet": "comparison and density estimation using neural networks / Marco Inacio; orientador Rafael Izbicki. Keywords: artificial neural networks , conditional density estimation , conditional inde-pendence testing, two-sample comparison, machine learning. List of figures.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/conditional-independence-testing-two-sample-comparison-and-4hpw3dhnmd.pdf", "content": "comparison and density estimation using neural networks / Marco Inacio; orientador Rafael Izbicki. Keywords: artificial neural networks , conditional density estimation , conditional inde-pendence testing, two-sample comparison, machine learning. List of figures."}
+{"idx": 9, "title": "Nonparametric Conditional Density Estimation Using ... - Peeref", "date": "", "ddg_snippet": "Peeref uses cookies to improve your experience. Please read our Privacy Policy for more details. Accept.", "subpage_snippet": "", "source": "www.peeref.com", "link": "https://www.peeref.com/works/13851506", "content": "Peeref uses cookies to improve your experience. Please read our Privacy Policy for more details. Accept."}
diff --git a/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_Appendix_B.2_SDXL_feature_selection_year_2024.jsonl b/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_Appendix_B.2_SDXL_feature_selection_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8060be5eafa294ec70195b847e006861e09cafce
--- /dev/null
+++ b/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_Appendix_B.2_SDXL_feature_selection_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Not All Diffusion Model Activations Have Been Evaluated as...", "date": "", "ddg_snippet": "B . 2 Feature Selection Solution for SDXL .On top of this, concrete feature selection solutions are proposed for two popular diffusion models , i.e., SDv1.5 and SDXL . Finally, extensive experiments on three discriminative tasks validate the effectiveness of our method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03558v3", "content": "B . 2 Feature Selection Solution for SDXL .On top of this, concrete feature selection solutions are proposed for two popular diffusion models , i.e., SDv1.5 and SDXL . Finally, extensive experiments on three discriminative tasks validate the effectiveness of our method."}
+{"idx": 1, "title": "(PDF) Not All Diffusion Model Activations Have Been Evaluated as...", "date": "", "ddg_snippet": "PDF | Diffusion models are initially designed for image generation.if the input. image is 512 ×512), suggesting inferiority, as supported by the quantitative comparison. B . 2 Feature Selection Solution for SDXL .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384680516_Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features", "content": "PDF | Diffusion models are initially designed for image generation.if the input. image is 512 ×512), suggesting inferiority, as supported by the quantitative comparison. B . 2 Feature Selection Solution for SDXL ."}
+{"idx": 2, "title": "Not All Diffusion Model Activations Have Been Evaluated as...", "date": "", "ddg_snippet": "Diffusion models are initially designed for image generation.Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/633780c1344d0c95e4d2dd3431fe08d9-Abstract-Conference.html", "content": "Diffusion models are initially designed for image generation.Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated ."}
+{"idx": 3, "title": "Not All Diffusion Model Activations Have Been Evaluated as...", "date": "", "ddg_snippet": "Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7uqVfZW6Mo", "content": "Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation."}
+{"idx": 4, "title": "generic- diffusion - feature /README.md at main...", "date": "", "ddg_snippet": "Diffusion feature is a quite popular way to utilize generative diffusion models for discrimination. It's very simple: just extract some internal activations from a diffusion model , and then use these 2 D features to replace image inputs of any discriminative model .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Darkbblue/generic-diffusion-feature/blob/main/README.md", "content": "Diffusion feature is a quite popular way to utilize generative diffusion models for discrimination. It's very simple: just extract some internal activations from a diffusion model , and then use these 2 D features to replace image inputs of any discriminative model ."}
+{"idx": 5, "title": "Diffusion features for image", "date": "", "ddg_snippet": "Features of stable diffusion models . Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features , NeurIPS24.", "subpage_snippet": "", "source": "cs294-43-fall2024.pages.dev", "link": "https://cs294-43-fall2024.pages.dev/assets/presentations/prompt_to_prompt.pdf", "content": "Features of stable diffusion models . Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features , NeurIPS24."}
+{"idx": 6, "title": "Available Projects Fall 2025 ‒ IVRL ‐ EPFL", "date": "", "ddg_snippet": "“ Not all diffusion model activations have been evaluated as discriminative features .” Advances in Neural Information Processing Systems 37 (2024): 55141-55177.", "subpage_snippet": "", "source": "www.epfl.ch", "link": "https://www.epfl.ch/labs/ivrl/available-projects/", "content": "“ Not all diffusion model activations have been evaluated as discriminative features .” Advances in Neural Information Processing Systems 37 (2024): 55141-55177."}
+{"idx": 7, "title": "Not All Diffusion Model Activations Have Been Evaluated as...", "date": "", "ddg_snippet": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features .", "subpage_snippet": "", "source": "darkbblue.github.io", "link": "https://darkbblue.github.io/publications/2024-10-10-sdxl-feature/", "content": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features ."}
+{"idx": 8, "title": "200+ Best Stable Diffusion Negative Prompts with Examples", "date": "", "ddg_snippet": "A negative prompt is to specify what you don't want to see in the generated images. Here are some best Stable Diffusion negative prompts to help you get better outputs.", "subpage_snippet": "", "source": "www.aiarty.com", "link": "https://www.aiarty.com/stable-diffusion-prompts/stable-diffusion-negative-prompt.htm", "content": "A negative prompt is to specify what you don't want to see in the generated images. Here are some best Stable Diffusion negative prompts to help you get better outputs."}
+{"idx": 9, "title": "Wan 2 .1: Install & Generate Videos locally with lower VRAM", "date": "", "ddg_snippet": "Again the new diffusion based video generation model released by AlibabaCloud. Wan 2 .1 an open-source suite of video foundation models licensed under Apache 2 .0.", "subpage_snippet": "", "source": "www.stablediffusiontutorials.com", "link": "https://www.stablediffusiontutorials.com/2025/02/wan-video-generation.html", "content": "Again the new diffusion based video generation model released by AlibabaCloud. Wan 2 .1 an open-source suite of video foundation models licensed under Apache 2 .0."}
diff --git a/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features_sitearxiv.org.jsonl b/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e4b8beeb297d5619168ed73eb4666d015b97d098
--- /dev/null
+++ b/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Not All Diffusion Model Activations Have Been Evaluated as ... Not All Diffusion Model Activations Have Been Evaluated as ... [2305.10722] Discffusion: Discriminative Diffusion Models as ... Discffusion: Discriminative Diffusion Models as Few-shot ... [2504.17253] DIVE: Inverting Conditional Diffusion Models for ... [2505.10999] DDAE++: Enhancing Diffusion Models Towards ... DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks Not All Diffusion Model Activations Have Been Evaluated as Discffusion: Discriminative Diffusion Models - arXiv . org Discffusion: Discriminative Diffusion Models - arXiv . org Not All Diffusion Model Activations Have Been Evaluated as [2305.10722] Discffusion: Discriminative Diffusion Models as Few-sh… Aligning Text to Image in Diffusion Models is Easier Than You ...", "date": "", "ddg_snippet": "Oct 4, 2024 · Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations , selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field ... To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. May 18, 2023 · Diffusion models, such as Stable Diffusion , have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trained diffusion models for discriminative tasks such as image-text matching ... The results show that the Discriminative Stable Diffusion (Discffusion) model , when fine-tuned in a few-shot setting for discriminative tasks on the ComVG dataset does not compromise the generative capability of the model , which remains on par with the performance of the original Stable Diffusion model . Apr 24, 2025 · Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. Specifically, we extend the discriminative capability of pretrained frozen generative diffusion models from the classification task to the more complex object detection task ... May 16, 2025 · While diffusion models have gained prominence in image synthesis, their generative pre-training has been shown to yield discriminative representations, paving the way towards unified visual generation and understanding. However, two key questions remain: 1) Can these representations be leveraged to improve the training of diffusion models themselves, rather than solely benefiting downstream ... Can diffusion models be used to perform discriminative tasks? Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. What is a diffusion model? Diffusion models are initially designed for image generation . Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. What is discriminative stable diffusion (discffusion)? The Discriminative Stable Diffusion (Discffusion) approach proposed in this paper is dependent on a pre-trained Stable Diffusion model , which may be challenging to obtain in certain scenarios where the model has yet to be publicly released or where the computational resources required for training are not available. Is discffusion a good model? It is evident that Discffusion surpasses both the fine-tuned CLIP and the CLIP with prompt learning baselines by a large margin. These outcomes suggest that the Discffusion model can adeptly harness the benefits of larger input image resolutions. The training efficiency of the Discriminative Stable Diffusion (Discffusion) model is noteworthy. Are activations discriminative? To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. Can pre-trained text-to-Image Diffusion models be discriminative learners? To answer this question, we propose a novel approach, Discriminative Stable Diffusion (DSD), which turns pre-trained text-to-image diffusion models into few-shot discriminative learners. Not all diffusion model activations have been evaluated as discriminative features . Advances in Neural Information Processing Systems, 37:55141–55177, 2025. [27]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.03558", "content": "Oct 4, 2024 · Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations , selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field ... To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. May 18, 2023 · Diffusion models, such as Stable Diffusion , have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trained diffusion models for discriminative tasks such as image-text matching ... The results show that the Discriminative Stable Diffusion (Discffusion) model , when fine-tuned in a few-shot setting for discriminative tasks on the ComVG dataset does not compromise the generative capability of the model , which remains on par with the performance of the original Stable Diffusion model . Apr 24, 2025 · Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. Specifically, we extend the discriminative capability of pretrained frozen generative diffusion models from the classification task to the more complex object detection task ... May 16, 2025 · While diffusion models have gained prominence in image synthesis, their generative pre-training has been shown to yield discriminative representations, paving the way towards unified visual generation and understanding. However, two key questions remain: 1) Can these representations be leveraged to improve the training of diffusion models themselves, rather than solely benefiting downstream ... Can diffusion models be used to perform discriminative tasks? Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. What is a diffusion model? Diffusion models are initially designed for image generation . Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. What is discriminative stable diffusion (discffusion)? The Discriminative Stable Diffusion (Discffusion) approach proposed in this paper is dependent on a pre-trained Stable Diffusion model , which may be challenging to obtain in certain scenarios where the model has yet to be publicly released or where the computational resources required for training are not available. Is discffusion a good model? It is evident that Discffusion surpasses both the fine-tuned CLIP and the CLIP with prompt learning baselines by a large margin. These outcomes suggest that the Discffusion model can adeptly harness the benefits of larger input image resolutions. The training efficiency of the Discriminative Stable Diffusion (Discffusion) model is noteworthy. Are activations discriminative? To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. Can pre-trained text-to-Image Diffusion models be discriminative learners? To answer this question, we propose a novel approach, Discriminative Stable Diffusion (DSD), which turns pre-trained text-to-image diffusion models into few-shot discriminative learners. Not all diffusion model activations have been evaluated as discriminative features . Advances in Neural Information Processing Systems, 37:55141–55177, 2025. [27]"}
+{"idx": 1, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03558v1", "content": "To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores."}
+{"idx": 2, "title": "[2305.10722] Discffusion: Discriminative Diffusion Models as ... Discffusion: Discriminative Diffusion Models as Few-shot ... [2504.17253] DIVE: Inverting Conditional Diffusion Models for ... [2505.10999] DDAE++: Enhancing Diffusion Models Towards ... DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks Not All Diffusion Model Activations Have Been Evaluated as Discffusion: Discriminative Diffusion Models - arXiv . org Discffusion: Discriminative Diffusion Models - arXiv . org Not All Diffusion Model Activations Have Been Evaluated as [2305.10722] Discffusion: Discriminative Diffusion Models as Few-sh… Aligning Text to Image in Diffusion Models is Easier Than You ...", "date": "", "ddg_snippet": "May 18, 2023 · Diffusion models, such as Stable Diffusion , have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trained diffusion models for discriminative tasks such as image-text matching ... The results show that the Discriminative Stable Diffusion (Discffusion) model , when fine-tuned in a few-shot setting for discriminative tasks on the ComVG dataset does not compromise the generative capability of the model , which remains on par with the performance of the original Stable Diffusion model . Apr 24, 2025 · Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. Specifically, we extend the discriminative capability of pretrained frozen generative diffusion models from the classification task to the more complex object detection task ... May 16, 2025 · While diffusion models have gained prominence in image synthesis, their generative pre-training has been shown to yield discriminative representations, paving the way towards unified visual generation and understanding. However, two key questions remain: 1) Can these representations be leveraged to improve the training of diffusion models themselves, rather than solely benefiting downstream ... Can diffusion models be used to perform discriminative tasks? Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. What is a diffusion model? Diffusion models are initially designed for image generation . Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. What is discriminative stable diffusion (discffusion)? The Discriminative Stable Diffusion (Discffusion) approach proposed in this paper is dependent on a pre-trained Stable Diffusion model , which may be challenging to obtain in certain scenarios where the model has yet to be publicly released or where the computational resources required for training are not available. Is discffusion a good model? It is evident that Discffusion surpasses both the fine-tuned CLIP and the CLIP with prompt learning baselines by a large margin. These outcomes suggest that the Discffusion model can adeptly harness the benefits of larger input image resolutions. The training efficiency of the Discriminative Stable Diffusion (Discffusion) model is noteworthy. Are activations discriminative? To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. Can pre-trained text-to-Image Diffusion models be discriminative learners? To answer this question, we propose a novel approach, Discriminative Stable Diffusion (DSD), which turns pre-trained text-to-image diffusion models into few-shot discriminative learners. Not all diffusion model activations have been evaluated as discriminative features . Advances in Neural Information Processing Systems, 37:55141–55177, 2025. [27]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.10722", "content": "May 18, 2023 · Diffusion models, such as Stable Diffusion , have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trained diffusion models for discriminative tasks such as image-text matching ... The results show that the Discriminative Stable Diffusion (Discffusion) model , when fine-tuned in a few-shot setting for discriminative tasks on the ComVG dataset does not compromise the generative capability of the model , which remains on par with the performance of the original Stable Diffusion model . Apr 24, 2025 · Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. Specifically, we extend the discriminative capability of pretrained frozen generative diffusion models from the classification task to the more complex object detection task ... May 16, 2025 · While diffusion models have gained prominence in image synthesis, their generative pre-training has been shown to yield discriminative representations, paving the way towards unified visual generation and understanding. However, two key questions remain: 1) Can these representations be leveraged to improve the training of diffusion models themselves, rather than solely benefiting downstream ... Can diffusion models be used to perform discriminative tasks? Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. What is a diffusion model? Diffusion models are initially designed for image generation . Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. What is discriminative stable diffusion (discffusion)? The Discriminative Stable Diffusion (Discffusion) approach proposed in this paper is dependent on a pre-trained Stable Diffusion model , which may be challenging to obtain in certain scenarios where the model has yet to be publicly released or where the computational resources required for training are not available. Is discffusion a good model? It is evident that Discffusion surpasses both the fine-tuned CLIP and the CLIP with prompt learning baselines by a large margin. These outcomes suggest that the Discffusion model can adeptly harness the benefits of larger input image resolutions. The training efficiency of the Discriminative Stable Diffusion (Discffusion) model is noteworthy. Are activations discriminative? To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. Can pre-trained text-to-Image Diffusion models be discriminative learners? To answer this question, we propose a novel approach, Discriminative Stable Diffusion (DSD), which turns pre-trained text-to-image diffusion models into few-shot discriminative learners. Not all diffusion model activations have been evaluated as discriminative features . Advances in Neural Information Processing Systems, 37:55141–55177, 2025. [27]"}
+{"idx": 3, "title": "Discffusion: Discriminative Diffusion Models as Few-shot ...", "date": "", "ddg_snippet": "The results show that the Discriminative Stable Diffusion (Discffusion) model , when fine-tuned in a few-shot setting for discriminative tasks on the ComVG dataset does not compromise the generative capability of the model , which remains on par with the performance of the original Stable Diffusion model .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.10722v3", "content": "The results show that the Discriminative Stable Diffusion (Discffusion) model , when fine-tuned in a few-shot setting for discriminative tasks on the ComVG dataset does not compromise the generative capability of the model , which remains on par with the performance of the original Stable Diffusion model ."}
+{"idx": 4, "title": "[2504.17253] DIVE: Inverting Conditional Diffusion Models for ... [2505.10999] DDAE++: Enhancing Diffusion Models Towards ... DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks Not All Diffusion Model Activations Have Been Evaluated as Discffusion: Discriminative Diffusion Models - arXiv . org Discffusion: Discriminative Diffusion Models - arXiv . org Not All Diffusion Model Activations Have Been Evaluated as [2305.10722] Discffusion: Discriminative Diffusion Models as Few-sh… Aligning Text to Image in Diffusion Models is Easier Than You ...", "date": "", "ddg_snippet": "Apr 24, 2025 · Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. Specifically, we extend the discriminative capability of pretrained frozen generative diffusion models from the classification task to the more complex object detection task ... May 16, 2025 · While diffusion models have gained prominence in image synthesis, their generative pre-training has been shown to yield discriminative representations, paving the way towards unified visual generation and understanding. However, two key questions remain: 1) Can these representations be leveraged to improve the training of diffusion models themselves, rather than solely benefiting downstream ... Can diffusion models be used to perform discriminative tasks? Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. What is a diffusion model? Diffusion models are initially designed for image generation . Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. What is discriminative stable diffusion (discffusion)? The Discriminative Stable Diffusion (Discffusion) approach proposed in this paper is dependent on a pre-trained Stable Diffusion model , which may be challenging to obtain in certain scenarios where the model has yet to be publicly released or where the computational resources required for training are not available. Is discffusion a good model? It is evident that Discffusion surpasses both the fine-tuned CLIP and the CLIP with prompt learning baselines by a large margin. These outcomes suggest that the Discffusion model can adeptly harness the benefits of larger input image resolutions. The training efficiency of the Discriminative Stable Diffusion (Discffusion) model is noteworthy. Are activations discriminative? To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. Can pre-trained text-to-Image Diffusion models be discriminative learners? To answer this question, we propose a novel approach, Discriminative Stable Diffusion (DSD), which turns pre-trained text-to-image diffusion models into few-shot discriminative learners. Not all diffusion model activations have been evaluated as discriminative features . Advances in Neural Information Processing Systems, 37:55141–55177, 2025. [27]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.17253", "content": "Apr 24, 2025 · Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. Specifically, we extend the discriminative capability of pretrained frozen generative diffusion models from the classification task to the more complex object detection task ... May 16, 2025 · While diffusion models have gained prominence in image synthesis, their generative pre-training has been shown to yield discriminative representations, paving the way towards unified visual generation and understanding. However, two key questions remain: 1) Can these representations be leveraged to improve the training of diffusion models themselves, rather than solely benefiting downstream ... Can diffusion models be used to perform discriminative tasks? Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. What is a diffusion model? Diffusion models are initially designed for image generation . Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. What is discriminative stable diffusion (discffusion)? The Discriminative Stable Diffusion (Discffusion) approach proposed in this paper is dependent on a pre-trained Stable Diffusion model , which may be challenging to obtain in certain scenarios where the model has yet to be publicly released or where the computational resources required for training are not available. Is discffusion a good model? It is evident that Discffusion surpasses both the fine-tuned CLIP and the CLIP with prompt learning baselines by a large margin. These outcomes suggest that the Discffusion model can adeptly harness the benefits of larger input image resolutions. The training efficiency of the Discriminative Stable Diffusion (Discffusion) model is noteworthy. Are activations discriminative? To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. Can pre-trained text-to-Image Diffusion models be discriminative learners? To answer this question, we propose a novel approach, Discriminative Stable Diffusion (DSD), which turns pre-trained text-to-image diffusion models into few-shot discriminative learners. Not all diffusion model activations have been evaluated as discriminative features . Advances in Neural Information Processing Systems, 37:55141–55177, 2025. [27]"}
+{"idx": 5, "title": "[2505.10999] DDAE++: Enhancing Diffusion Models Towards ...", "date": "", "ddg_snippet": "May 16, 2025 · While diffusion models have gained prominence in image synthesis, their generative pre-training has been shown to yield discriminative representations, paving the way towards unified visual generation and understanding. However, two key questions remain: 1) Can these representations be leveraged to improve the training of diffusion models themselves, rather than solely benefiting downstream ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.10999", "content": "May 16, 2025 · While diffusion models have gained prominence in image synthesis, their generative pre-training has been shown to yield discriminative representations, paving the way towards unified visual generation and understanding. However, two key questions remain: 1) Can these representations be leveraged to improve the training of diffusion models themselves, rather than solely benefiting downstream ..."}
+{"idx": 6, "title": "Aligning Text to Image in Diffusion Models is Easier Than You ...", "date": "", "ddg_snippet": "Not all diffusion model activations have been evaluated as discriminative features . Advances in Neural Information Processing Systems, 37:55141–55177, 2025. [27]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.08250", "content": "Not all diffusion model activations have been evaluated as discriminative features . Advances in Neural Information Processing Systems, 37:55141–55177, 2025. [27]"}
+{"idx": 7, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Diffusion models are initially designed for image generation.Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03558v2", "content": "Diffusion models are initially designed for image generation.Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated ."}
+{"idx": 8, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Diffusion models are initially designed for image generation.In this direction, diffusion feature is one simple yet effective approach, where the intermediate signals, named activations , are extracted from the pre-trained diffusion U-Net as dense features [2, 58, 56, 29, 53, 13, 14] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03558v3", "content": "Diffusion models are initially designed for image generation.In this direction, diffusion feature is one simple yet effective approach, where the intermediate signals, named activations , are extracted from the pre-trained diffusion U-Net as dense features [2, 58, 56, 29, 53, 13, 14] ."}
+{"idx": 9, "title": "DDAE++: Enhancing Diffusion Models Towards Unified Generative...", "date": "", "ddg_snippet": "Meng et al. [2024] Benyuan Meng, Qianqian Xu, Zitai Wang, Xiaochun Cao, and Qingming Huang. Not all diffusion model activations have been evaluated as discriminative features .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10999v1", "content": "Meng et al. [2024] Benyuan Meng, Qianqian Xu, Zitai Wang, Xiaochun Cao, and Qingming Huang. Not all diffusion model activations have been evaluated as discriminative features ."}
diff --git a/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features_three_properties_.jsonl b/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features_three_properties_.jsonl
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index 0000000000000000000000000000000000000000..20cb0486b8df07d869c7fceab30d83a27e3d41cb
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+++ b/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features_three_properties_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MDDM: A Multi-view Discriminative Enhanced Diffusion-based", "date": "", "ddg_snippet": "To further improve the performance, we introduce a diffusion model using the discriminative intermediate feature with multi-view information as a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13029v3", "content": "To further improve the performance, we introduce a diffusion model using the discriminative intermediate feature with multi-view information as a ..."}
+{"idx": 1, "title": "Denoising Diffusion Implicit Models", "date": "", "ddg_snippet": "... diffusion models , including score-based generative models , gained popularity as a powerful class of generative models , that can rival even ...", "subpage_snippet": "", "source": "keras.io", "link": "https://keras.io/examples/generative/ddim/", "content": "... diffusion models , including score-based generative models , gained popularity as a powerful class of generative models , that can rival even ..."}
+{"idx": 2, "title": "Vinija's Notes • Primers • Diffusion Models", "date": "", "ddg_snippet": "Diffusion probabilistic models (also simply called diffusion models ) are generative models , meaning that they are used to generate data similar to ...", "subpage_snippet": "", "source": "vinija.ai", "link": "https://vinija.ai/models/diffusion-models/", "content": "Diffusion probabilistic models (also simply called diffusion models ) are generative models , meaning that they are used to generate data similar to ..."}
+{"idx": 3, "title": "Estimating Welfare Effects in a Nonparametric Choice Model: The", "date": "", "ddg_snippet": "C32 - Time-Series Models ; Dynamic Quantile Regressions; Dynamic Treatment Effect Models ; Diffusion Processes; State Space Models", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdaf064/8219920", "content": "C32 - Time-Series Models ; Dynamic Quantile Regressions; Dynamic Treatment Effect Models ; Diffusion Processes; State Space Models"}
+{"idx": 4, "title": "Aman's AI Journal • Primers • Diffusion Models", "date": "", "ddg_snippet": "Diffusion probabilistic models (also simply called diffusion models ) are generative models , meaning that they are used to generate data similar to ...", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/diffusion-models/", "content": "Diffusion probabilistic models (also simply called diffusion models ) are generative models , meaning that they are used to generate data similar to ..."}
+{"idx": 5, "title": "CVPR 2024 Papers", "date": "", "ddg_snippet": "... Uncertainty: Identifying Unreliable ... Draw Step by Step: Reconstructing CAD Construction Sequences from Point Clouds via Multimodal Diffusion .", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/papers.html", "content": "... Uncertainty: Identifying Unreliable ... Draw Step by Step: Reconstructing CAD Construction Sequences from Point Clouds via Multimodal Diffusion ."}
+{"idx": 6, "title": "Enhancing EEG Signal Generation through a Hybrid Approach", "date": "", "ddg_snippet": "... uniqueness of our approach lies in its capacity to concurrently model time-domain characteristics, such as waveform morphology, and frequency-domain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00013v1", "content": "... uniqueness of our approach lies in its capacity to concurrently model time-domain characteristics, such as waveform morphology, and frequency-domain ..."}
+{"idx": 7, "title": "Keywords - Idiap Publications", "date": "", "ddg_snippet": "Cookie Consent by Free Privacy Policy Generator Update cookies preferences ... All -pass filter based bilinear transformations", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/keywords", "content": "Cookie Consent by Free Privacy Policy Generator Update cookies preferences ... All -pass filter based bilinear transformations"}
+{"idx": 8, "title": "Global Race for Talent: Brain Drain, Knowledge Transfer, and", "date": "", "ddg_snippet": "C32 - Time-Series Models ; Dynamic Quantile Regressions; Dynamic Treatment Effect Models ; Diffusion Processes; State Space Models", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/qje/article-abstract/140/1/165/7912563", "content": "C32 - Time-Series Models ; Dynamic Quantile Regressions; Dynamic Treatment Effect Models ; Diffusion Processes; State Space Models"}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "3DOS: Towards 3D Open Set Learning - Benchmarking and ... Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2022", "content": "3DOS: Towards 3D Open Set Learning - Benchmarking and ... Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation"}
diff --git a/data/sampled_jsons/OFUL_algorithm_implementation_challenge.jsonl b/data/sampled_jsons/OFUL_algorithm_implementation_challenge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..471b9018d7422aff91af7692b2ef233ac4550734
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_implementation_challenge.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "optimal_bandit/oful.py at master · yang0110/optimal_bandit ...", "date": "", "ddg_snippet": "To design a new index algorithm for linear bandit to minimize cumulative regret - optimal_bandit/ oful .py at master · yang0110/optimal_bandit", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yang0110/optimal_bandit/blob/master/oful.py", "content": "To design a new index algorithm for linear bandit to minimize cumulative regret - optimal_bandit/ oful .py at master · yang0110/optimal_bandit"}
+{"idx": 1, "title": "Improved Algorithms for Linear Stochastic Bandits - NeurIPS", "date": "", "ddg_snippet": "We fixed the number of times the algorithm is allowed to update its action in OFUL . For larger values of C, the algorithm changes action less frequently, hence, will play for a longer time period.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2011/file/e1d5be1c7f2f456670de3d53c7b54f4a-Paper.pdf", "content": "We fixed the number of times the algorithm is allowed to update its action in OFUL . For larger values of C, the algorithm changes action less frequently, hence, will play for a longer time period."}
+{"idx": 2, "title": "GitHub - vineet0814/contextual-bandits-OFUL", "date": "", "ddg_snippet": "contextual-bandits Currently the main.py contains implementation of OFUL algorithm by Abbasi-Yadkori 2011 from paper titled 'Improved Algorithms for Linear Stochastic Bandits'.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vineet0814/contextual-bandits-OFUL", "content": "contextual-bandits Currently the main.py contains implementation of OFUL algorithm by Abbasi-Yadkori 2011 from paper titled 'Improved Algorithms for Linear Stochastic Bandits'."}
+{"idx": 3, "title": "Bandits with Mean Bounds - OpenReview", "date": "", "ddg_snippet": "We study a variant of the bandit problem where side information in the form of bounds on the mean of each arm is provided. We prove that these translate to tighter estimates of subgaussian factors and develop novel algorithms that exploit these estimates.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4TZ4DE24fX", "content": "We study a variant of the bandit problem where side information in the form of bounds on the mean of each arm is provided. We prove that these translate to tighter estimates of subgaussian factors and develop novel algorithms that exploit these estimates."}
+{"idx": 4, "title": "Istituto Italiano di Tecnologia and University College London ...", "date": "", "ddg_snippet": "Abstract which works well on average over a class of bandits tasks, that are sampled from a task-distribution. Inspired by recent work on learning-to-learn linear regression, we consider a class of bandit algorithms that implement a regularized version of t e well-known OFUL algorithm , where the regularization is a square euclidean distance to", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2005.08531.pdf", "content": "Abstract which works well on average over a class of bandits tasks, that are sampled from a task-distribution. Inspired by recent work on learning-to-learn linear regression, we consider a class of bandit algorithms that implement a regularized version of t e well-known OFUL algorithm , where the regularization is a square euclidean distance to"}
+{"idx": 5, "title": "Meta-learning with Stochastic Linear Bandits", "date": "", "ddg_snippet": "Inspired by recent work on learning-to-learn linear regres-sion, we consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm , where the regularization is a square euclidean distance to a bias vector.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/cella20a/cella20a.pdf", "content": "Inspired by recent work on learning-to-learn linear regres-sion, we consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm , where the regularization is a square euclidean distance to a bias vector."}
+{"idx": 6, "title": "Misspecified Linear Bandits | Proceedings of the AAAI ...", "date": "", "ddg_snippet": "We argue that the OFUL algorithm can fail to achieve sublinear regret even under models that have non-sparse deviation. We finally develop a novel bandit algorithm, comprising a hypothesis test for linearity followed by a decision to use either the OFUL or Upper Confidence Bound (UCB) algorithm.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/11052", "content": "We argue that the OFUL algorithm can fail to achieve sublinear regret even under models that have non-sparse deviation. We finally develop a novel bandit algorithm, comprising a hypothesis test for linearity followed by a decision to use either the OFUL or Upper Confidence Bound (UCB) algorithm."}
+{"idx": 7, "title": "Improved Regret of Linear Ensemble Sampling", "date": "", "ddg_snippet": "... upon the regret bound but also simplifies the algorithm by avoiding the use of symmetrized perturbations, making it more practical for implementation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.03932v2", "content": "... upon the regret bound but also simplifies the algorithm by avoiding the use of symmetrized perturbations, making it more practical for implementation ..."}
+{"idx": 8, "title": "Generalized Linear Bandits: Almost Optimal Regret with One-Pass", "date": "", "ddg_snippet": "This paper proposes a jointly efficient algorithm for GLBs that achieves an improved regret bound in terms of κ 𝜅 \\kappa italic_κ with constant ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11847v1", "content": "This paper proposes a jointly efficient algorithm for GLBs that achieves an improved regret bound in terms of κ 𝜅 \\kappa italic_κ with constant ..."}
+{"idx": 9, "title": "DAL: A Practical Prior-Free Black-Box Framework for", "date": "", "ddg_snippet": "NS bandit algorithms are typically either adaptive —adjusting actions continuously—or restarting , choosing to unlearn and kickstart the learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.19401v2", "content": "NS bandit algorithms are typically either adaptive —adjusting actions continuously—or restarting , choosing to unlearn and kickstart the learning ..."}
diff --git a/data/sampled_jsons/OFUL_algorithm_implementation_challenge_function_dependent_theta.jsonl b/data/sampled_jsons/OFUL_algorithm_implementation_challenge_function_dependent_theta.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2c994044d8e5696182414a2bf1ad3e6bf83bda2d
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_implementation_challenge_function_dependent_theta.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "bandit_algorithms/oful.py at master · zackbh/bandit ... - GitHub", "date": "", "ddg_snippet": "Collection of bandit algorithms . Contribute to zackbh/bandit_ algorithms development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zackbh/bandit_algorithms/blob/master/oful.py", "content": "Collection of bandit algorithms . Contribute to zackbh/bandit_ algorithms development by creating an account on GitHub ."}
+{"idx": 1, "title": "Improved Algorithms for Linear Stochastic Bandits - NeurIPS", "date": "", "ddg_snippet": "We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem. In particular, we show that a simple modification of Auer’s UCB algorithm (Auer, 2002) achieves with high probability constant regret. More importantly, we modify and, consequently, improve the analysis of the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2011/file/e1d5be1c7f2f456670de3d53c7b54f4a-Paper.pdf", "content": "We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem. In particular, we show that a simple modification of Auer’s UCB algorithm (Auer, 2002) achieves with high probability constant regret. More importantly, we modify and, consequently, improve the analysis of the ..."}
+{"idx": 2, "title": "LinUCB/OFUL算法的Regret分析——上篇 - 知乎 optimal_bandit/oful.py at master · yang0110/optimal_bandit ... ICML 2021 Near-Optimal Representation Learning for Linear ...", "date": "", "ddg_snippet": "关于LinUCB/OFUL算法的Regret分析涉及到的定理和引理较多，本文为上篇，内容包括除定理1-自规范化的鞅尾不等式（Self-normalized Martingale Tail Inequality）之外的其他主要证明，这里先利用其结论完成遗憾分析。下篇将回顾一些通用的数学证明工具，主要是一些测度论和集中不等式的相关内容，包括sigma代数、尾概率、次高斯性以及鞅不等式等，并对定理1给出证明。 LinUCB算法，也称为OFUL (Optimism in the Face of Uncertainty Linear Bandit Algorithm)算法： 算法1. OFUL算法 类似于UCB1算法，OFUL也是基于面对不确定性的乐观主义（Optimism in the Face of Uncertainty）原则的算法，但这里的环境不再是多臂老虎机问题（Multi-armed Bandit，MAB），而是上下文老虎机问题（Contextual Bandit）问题。具体而言，Agent在做选择时需要考虑arm的上下文特征，这里假设每个arm的奖励与其上下文特征是线性相关的，即 Y_t = \\langle X_t, \\theta_{*} \\rangle+\\eta_t ，其中 X_t 是在 t 时刻所选择arm的特征向量， \\theta_* 是一个固定的未知参数， \\eta_t 是满足某些约束的随机噪声。 显然，如果 \\theta_* 是已知的，那么每轮的最优决策就是选择能够使得上述内积最大的arm，从而取得最优的累积奖励。然而，我们在与环境的交互过程中并不知晓真实的参数值，算法取得的实际累积奖励往往会低于理想情况下的最优累积奖励，故将二者的差值定义为遗憾。在理论分析时，由于随机噪声难以控制，计算遗憾时会忽略掉噪声项，并将对应的遗憾称为伪遗憾。值得注意的是，得益于对随机噪声的假设，伪遗憾与遗憾具有相同的期望，但由于没有考虑噪声，因此具有较低的方差。在后文讨论中，我们实际上是在针对伪遗憾进行上界分析。 不同于UCB1算法直接对奖励期望进行估计，OFUL的核心思想是对参数 \\theta_* 进行区间估计，并为其构造一个置信域（Confidence Region/Set，即置信区间在多维数据下的推广）。如算法1所示，在每轮交互中，算法从置信域和动作集合中选择能够使得内积最大的参数估计 \\tilde{\\theta}_t 和动作 X_t ，也就是一对组合，并利用与环境交互的历史经验（即历史动作和奖励）不断地优化该置信域。通常来说，构造的置信域越窄，所设计算法的遗憾上界就越低，相应地，其在实际应用中的表现也就越好。 See full list on zhuanlan.zhihu.com 定理2. 置信椭圆（Confidence Ellipsoid） 对于线性Bandit问题: Y_t = \\langle X_t, \\theta_*\\rangle+\\eta_t \\\\我们采用L2正则化的最小二乘法对未知总体参数 \\theta_* 进行估计，得到点估计值 \\hat{\\theta_t} : \\hat\\theta_t = (\\mathbf{X}_{1:t}^\\top\\mathbf{X}_{1:t}+\\lambda I)^{-1}\\mathbf{X}_{1:t}^\\top\\mathbf{Y}_{1:t} \\\\其中 \\lambda > 0 为正则化系数， \\mathbf{X}_{1:t}= \\begin{equation*} \\begin{bmatrix} X_1^{\\top} \\\\ X_2^{\\top} \\\\ \\vdots \\\\ X_t^{\\top} \\end{bmatrix} \\in \\mathbb{R}^{t \\times d} \\end{equation*} ， \\mathbf{Y}_{1:t}= \\begin{equation*} \\begin{bmatrix} \\langle X_1, \\theta_*\\rangle \\\\ \\langle X_2, \\theta_*\\rangle \\\\ \\vdots \\\\ \\langle X_t, \\theta_*\\rangle \\end{bmatrix} + \\eta \\end{equation*} ， \\eta = \\begin{bmatrix} \\eta_1 \\\\ \\eta_2 \\\\ \\vdots \\\\ \\eta_t \\end{bmatrix} \\in \\mathbb{R}^{t} 。 由于点估计值无法给出估计的可靠性度量，也无法说明点估计值与总体参数的接近程度。因此，以该点估计值 \\hat\\theta_t 为中心，对 \\theta_* 进行区间估计，构造 1-\\delta 置信域 C_t 。具体而言，当 \\delta=0.05 时， C_t 即为95%置信度的置信域。多维样本的置信域往往被表示为一个椭圆，因此本文中构造的置信域也称为置信椭圆。 接下来为便于书写，我们在证明过程中将样本记为 \\mathbf{X} = \\mathbf{X}_{1:t}, \\; \\mathbf{Y} = \\mathbf{Y}_{1:t}。 \\begin{align} \\hat\\theta_t &= (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\mathbf{Y} \\\\ & = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top(\\mathbf{X}\\theta_*+\\eta) \\\\ & = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\eta + (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\mathbf{X}\\theta_* \\\\ & = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\eta + (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}(\\mathbf{X}^\\top\\mathbf{X} + \\color{red}{\\lambda I -\\lambda I})\\theta_* \\\\ & = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\eta + (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}(\\mathbf{X}^\\top\\mathbf{X} + \\lambda I)\\theta_* -\\lambda (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\theta_* \\\\ & = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\eta + \\theta_* - \\lambda (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\theta_* \\end{align} \\\\因此可得 \\hat\\theta_t - \\theta_* = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\eta - \\lambda (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\theta_* \\\\令 \\bar{V}_{t} = \\lambda I + \\mathbf{X}^\\top\\mathbf{X} ，则有 \\begin{align} \\Vert \\hat\\theta_t-\\theta_*\\Vert_{\\bar{V}_{t}} &= \\Vert (\\bar{V}_{t}^{-1} \\mathbf{X}^\\top\\eta) + ( -\\lambda \\bar{V}_{t}^{-1}\\theta_*)\\Vert_{\\bar{V}_{t}} \\\\ &\\leq \\Vert \\bar{V}_{t}^{-1}\\mathbf{X}^\\top\\eta\\Vert_{\\bar{V}_{t}} + \\Vert \\lambda \\bar{V}_{t}^{-1}\\theta_*\\Vert_{\\bar{V}_{t}} \\\\ & = \\sqrt{(\\bar{V}_{t}^{-1}\\mathbf{X}^\\top\\eta)^\\top \\cdot \\bar{V}_{t}\\cdot (\\bar{V}_{t}^{-1}\\mathbf{X}^\\top\\eta)} + \\sqrt{(\\lambda \\bar{V}_{t}^{-1}\\theta_*)^\\top \\cdot \\bar{V}_{t} \\cdot (\\lambda \\bar{V}_{t}^{-1}\\theta_*)} \\\\ & = \\sqrt{(\\mathbf{X}^\\top\\eta)^\\top \\color{red}{\\bar{V}_{t}^{-1}\\cdot \\bar{V}_{t}} \\cdot (\\bar{V}_{t}^{-1}\\mathbf{X}^\\top\\eta)} + \\sqrt{\\lambda\\theta_*^\\top \\color{red}{\\bar{V}_{t}^{-1} \\cdot \\bar{V}_{t}} \\cdot(\\lambda \\bar{V}_{t}^{-1}\\theta_*)}\\\\ & = \\sqrt{(\\mathbf{X}^\\top\\eta)^\\top\\cdot \\bar{V}_{t}^{-1} \\cdot (\\mathbf{X}^\\top\\eta)} + \\lambda\\sqrt{\\theta_*^\\top \\bar{V}_{t}^{-1}\\theta_*} \\\\ & = \\Vert \\mathbf{X}^\\top\\eta\\Vert_{\\bar{V}_{t}^{-1}} + \\lambda\\Vert\\theta_*\\Vert_{\\bar{V}_{t}^{-1}} \\end{align} \\\\再次利用瑞利-里茨定理（Rayleigh-Ritz Theorem）：\\Vert \\theta_*\\Vert^2_{\\bar{V}_{t}^{-1}} \\leq \\lambda_\\max(\\bar{V}_{t}^{-1})\\Vert \\theta_*\\Vert_2^2 = \\lambda^{-1}_\\min(\\bar{V}_{t})\\Vert \\theta_*\\Vert_2^2 \\leq \\lambda^{-1}_\\min(V)\\Vert \\theta_*\\Vert_2^2\\\\因为 \\Vert \\theta_*\\Vert_2 \\leq S, \\; V = \\lambda I ，所以\\Vert \\theta_*\\Vert_{\\bar{V}_{t}^{-1}} \\leq \\sqrt{\\lambda^{-1}_\\min(\\bar{V}_{t})\\Vert \\theta_*\\Vert_2^2} \\leq \\sqrt\\frac{1}{\\lambda_\\min(V)}\\Vert \\theta_*\\Vert_2= \\lambda^{-\\frac12}S\\\\因此有\\Vert \\hat\\theta_t-\\theta_*\\Vert_{\\bar{V}_{t}} \\leq \\Vert \\mathbf{X}^\\top\\eta\\Vert_{\\bar{V}_{t}^{-1}} + \\lambda\\Vert\\theta_*\\Vert_{\\bar{V}_{t}^{-1}} \\leq \\Vert \\mathbf{X}^\\top\\eta\\Vert_{\\bar{V}_{t}^{-1}}+\\lambda\\cdot\\lambda^{-\\frac12}S = \\Vert \\mathbf{X}^\\top\\eta\\Vert_{\\bar{V}_{t}^{-1}} + \\lambda^{\\frac12}S \\\\又由定理1（具体证明见LinUCB/OFUL算法的Regret分析——下篇）： See full list on zhuanlan.zhihu.com To design a new index algorithm for linear bandit to minimize cumulative regret - yang0110/optimal_bandit Abstract: This paper studies representation learning for multi-task linear bandits and multi-task episodic RL with linear value function approximation. We first consider the setting where we play M M linear bandits with dimension d d concurrently, and these bandits share a common k k -dimensional linear representation so that k≪d k ≪ d and ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/589299916", "content": "关于LinUCB/OFUL算法的Regret分析涉及到的定理和引理较多，本文为上篇，内容包括除定理1-自规范化的鞅尾不等式（Self-normalized Martingale Tail Inequality）之外的其他主要证明，这里先利用其结论完成遗憾分析。下篇将回顾一些通用的数学证明工具，主要是一些测度论和集中不等式的相关内容，包括sigma代数、尾概率、次高斯性以及鞅不等式等，并对定理1给出证明。 LinUCB算法，也称为OFUL (Optimism in the Face of Uncertainty Linear Bandit Algorithm)算法： 算法1. OFUL算法 类似于UCB1算法，OFUL也是基于面对不确定性的乐观主义（Optimism in the Face of Uncertainty）原则的算法，但这里的环境不再是多臂老虎机问题（Multi-armed Bandit，MAB），而是上下文老虎机问题（Contextual Bandit）问题。具体而言，Agent在做选择时需要考虑arm的上下文特征，这里假设每个arm的奖励与其上下文特征是线性相关的，即 Y_t = \\langle X_t, \\theta_{*} \\rangle+\\eta_t ，其中 X_t 是在 t 时刻所选择arm的特征向量， \\theta_* 是一个固定的未知参数， \\eta_t 是满足某些约束的随机噪声。 显然，如果 \\theta_* 是已知的，那么每轮的最优决策就是选择能够使得上述内积最大的arm，从而取得最优的累积奖励。然而，我们在与环境的交互过程中并不知晓真实的参数值，算法取得的实际累积奖励往往会低于理想情况下的最优累积奖励，故将二者的差值定义为遗憾。在理论分析时，由于随机噪声难以控制，计算遗憾时会忽略掉噪声项，并将对应的遗憾称为伪遗憾。值得注意的是，得益于对随机噪声的假设，伪遗憾与遗憾具有相同的期望，但由于没有考虑噪声，因此具有较低的方差。在后文讨论中，我们实际上是在针对伪遗憾进行上界分析。 不同于UCB1算法直接对奖励期望进行估计，OFUL的核心思想是对参数 \\theta_* 进行区间估计，并为其构造一个置信域（Confidence Region/Set，即置信区间在多维数据下的推广）。如算法1所示，在每轮交互中，算法从置信域和动作集合中选择能够使得内积最大的参数估计 \\tilde{\\theta}_t 和动作 X_t ，也就是一对组合，并利用与环境交互的历史经验（即历史动作和奖励）不断地优化该置信域。通常来说，构造的置信域越窄，所设计算法的遗憾上界就越低，相应地，其在实际应用中的表现也就越好。 See full list on zhuanlan.zhihu.com 定理2. 置信椭圆（Confidence Ellipsoid） 对于线性Bandit问题: Y_t = \\langle X_t, \\theta_*\\rangle+\\eta_t \\\\我们采用L2正则化的最小二乘法对未知总体参数 \\theta_* 进行估计，得到点估计值 \\hat{\\theta_t} : \\hat\\theta_t = (\\mathbf{X}_{1:t}^\\top\\mathbf{X}_{1:t}+\\lambda I)^{-1}\\mathbf{X}_{1:t}^\\top\\mathbf{Y}_{1:t} \\\\其中 \\lambda > 0 为正则化系数， \\mathbf{X}_{1:t}= \\begin{equation*} \\begin{bmatrix} X_1^{\\top} \\\\ X_2^{\\top} \\\\ \\vdots \\\\ X_t^{\\top} \\end{bmatrix} \\in \\mathbb{R}^{t \\times d} \\end{equation*} ， \\mathbf{Y}_{1:t}= \\begin{equation*} \\begin{bmatrix} \\langle X_1, \\theta_*\\rangle \\\\ \\langle X_2, \\theta_*\\rangle \\\\ \\vdots \\\\ \\langle X_t, \\theta_*\\rangle \\end{bmatrix} + \\eta \\end{equation*} ， \\eta = \\begin{bmatrix} \\eta_1 \\\\ \\eta_2 \\\\ \\vdots \\\\ \\eta_t \\end{bmatrix} \\in \\mathbb{R}^{t} 。 由于点估计值无法给出估计的可靠性度量，也无法说明点估计值与总体参数的接近程度。因此，以该点估计值 \\hat\\theta_t 为中心，对 \\theta_* 进行区间估计，构造 1-\\delta 置信域 C_t 。具体而言，当 \\delta=0.05 时， C_t 即为95%置信度的置信域。多维样本的置信域往往被表示为一个椭圆，因此本文中构造的置信域也称为置信椭圆。 接下来为便于书写，我们在证明过程中将样本记为 \\mathbf{X} = \\mathbf{X}_{1:t}, \\; \\mathbf{Y} = \\mathbf{Y}_{1:t}。 \\begin{align} \\hat\\theta_t &= (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\mathbf{Y} \\\\ & = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top(\\mathbf{X}\\theta_*+\\eta) \\\\ & = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\eta + (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\mathbf{X}\\theta_* \\\\ & = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\eta + (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}(\\mathbf{X}^\\top\\mathbf{X} + \\color{red}{\\lambda I -\\lambda I})\\theta_* \\\\ & = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\eta + (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}(\\mathbf{X}^\\top\\mathbf{X} + \\lambda I)\\theta_* -\\lambda (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\theta_* \\\\ & = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\eta + \\theta_* - \\lambda (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\theta_* \\end{align} \\\\因此可得 \\hat\\theta_t - \\theta_* = (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\mathbf{X}^\\top\\eta - \\lambda (\\mathbf{X}^\\top\\mathbf{X}+\\lambda I)^{-1}\\theta_* \\\\令 \\bar{V}_{t} = \\lambda I + \\mathbf{X}^\\top\\mathbf{X} ，则有 \\begin{align} \\Vert \\hat\\theta_t-\\theta_*\\Vert_{\\bar{V}_{t}} &= \\Vert (\\bar{V}_{t}^{-1} \\mathbf{X}^\\top\\eta) + ( -\\lambda \\bar{V}_{t}^{-1}\\theta_*)\\Vert_{\\bar{V}_{t}} \\\\ &\\leq \\Vert \\bar{V}_{t}^{-1}\\mathbf{X}^\\top\\eta\\Vert_{\\bar{V}_{t}} + \\Vert \\lambda \\bar{V}_{t}^{-1}\\theta_*\\Vert_{\\bar{V}_{t}} \\\\ & = \\sqrt{(\\bar{V}_{t}^{-1}\\mathbf{X}^\\top\\eta)^\\top \\cdot \\bar{V}_{t}\\cdot (\\bar{V}_{t}^{-1}\\mathbf{X}^\\top\\eta)} + \\sqrt{(\\lambda \\bar{V}_{t}^{-1}\\theta_*)^\\top \\cdot \\bar{V}_{t} \\cdot (\\lambda \\bar{V}_{t}^{-1}\\theta_*)} \\\\ & = \\sqrt{(\\mathbf{X}^\\top\\eta)^\\top \\color{red}{\\bar{V}_{t}^{-1}\\cdot \\bar{V}_{t}} \\cdot (\\bar{V}_{t}^{-1}\\mathbf{X}^\\top\\eta)} + \\sqrt{\\lambda\\theta_*^\\top \\color{red}{\\bar{V}_{t}^{-1} \\cdot \\bar{V}_{t}} \\cdot(\\lambda \\bar{V}_{t}^{-1}\\theta_*)}\\\\ & = \\sqrt{(\\mathbf{X}^\\top\\eta)^\\top\\cdot \\bar{V}_{t}^{-1} \\cdot (\\mathbf{X}^\\top\\eta)} + \\lambda\\sqrt{\\theta_*^\\top \\bar{V}_{t}^{-1}\\theta_*} \\\\ & = \\Vert \\mathbf{X}^\\top\\eta\\Vert_{\\bar{V}_{t}^{-1}} + \\lambda\\Vert\\theta_*\\Vert_{\\bar{V}_{t}^{-1}} \\end{align} \\\\再次利用瑞利-里茨定理（Rayleigh-Ritz Theorem）：\\Vert \\theta_*\\Vert^2_{\\bar{V}_{t}^{-1}} \\leq \\lambda_\\max(\\bar{V}_{t}^{-1})\\Vert \\theta_*\\Vert_2^2 = \\lambda^{-1}_\\min(\\bar{V}_{t})\\Vert \\theta_*\\Vert_2^2 \\leq \\lambda^{-1}_\\min(V)\\Vert \\theta_*\\Vert_2^2\\\\因为 \\Vert \\theta_*\\Vert_2 \\leq S, \\; V = \\lambda I ，所以\\Vert \\theta_*\\Vert_{\\bar{V}_{t}^{-1}} \\leq \\sqrt{\\lambda^{-1}_\\min(\\bar{V}_{t})\\Vert \\theta_*\\Vert_2^2} \\leq \\sqrt\\frac{1}{\\lambda_\\min(V)}\\Vert \\theta_*\\Vert_2= \\lambda^{-\\frac12}S\\\\因此有\\Vert \\hat\\theta_t-\\theta_*\\Vert_{\\bar{V}_{t}} \\leq \\Vert \\mathbf{X}^\\top\\eta\\Vert_{\\bar{V}_{t}^{-1}} + \\lambda\\Vert\\theta_*\\Vert_{\\bar{V}_{t}^{-1}} \\leq \\Vert \\mathbf{X}^\\top\\eta\\Vert_{\\bar{V}_{t}^{-1}}+\\lambda\\cdot\\lambda^{-\\frac12}S = \\Vert \\mathbf{X}^\\top\\eta\\Vert_{\\bar{V}_{t}^{-1}} + \\lambda^{\\frac12}S \\\\又由定理1（具体证明见LinUCB/OFUL算法的Regret分析——下篇）： See full list on zhuanlan.zhihu.com To design a new index algorithm for linear bandit to minimize cumulative regret - yang0110/optimal_bandit Abstract: This paper studies representation learning for multi-task linear bandits and multi-task episodic RL with linear value function approximation. We first consider the setting where we play M M linear bandits with dimension d d concurrently, and these bandits share a common k k -dimensional linear representation so that k≪d k ≪ d and ..."}
+{"idx": 3, "title": "optimal_bandit/oful.py at master · yang0110/optimal_bandit ...", "date": "", "ddg_snippet": "To design a new index algorithm for linear bandit to minimize cumulative regret - yang0110/optimal_bandit", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yang0110/optimal_bandit/blob/master/oful.py", "content": "To design a new index algorithm for linear bandit to minimize cumulative regret - yang0110/optimal_bandit"}
+{"idx": 4, "title": "Bandit Algorithms for Recommender Systems", "date": "", "ddg_snippet": "by ZM Korkut · 2022 — We propose an algorithm that recovers the underlying lower-dimensional subspace by solving a convex relaxation of a novel rank-minimization ... 106 pages", "subpage_snippet": "", "source": "www.cmu.edu", "link": "https://www.cmu.edu/tepper/programs/phd/program/assets/dissertations/2022-operations-research-korkut-dissertation.pdf", "content": "by ZM Korkut · 2022 — We propose an algorithm that recovers the underlying lower-dimensional subspace by solving a convex relaxation of a novel rank-minimization ... 106 pages"}
+{"idx": 5, "title": "Bandit Algorithms - Li Zhuohua @ Xidian University", "date": "", "ddg_snippet": "8 Dec 2021 — Using this algorithm on the online classification problem , we can prove that the regret is sublinear. Proof. We only consider the case when \\(\\ ...", "subpage_snippet": "", "source": "zhuohua.me", "link": "https://zhuohua.me/notes/20211208101747-bandit_algorithms/", "content": "8 Dec 2021 — Using this algorithm on the online classification problem , we can prove that the regret is sublinear. Proof. We only consider the case when \\(\\ ..."}
+{"idx": 6, "title": "Bandits with Mean Bounds - OpenReview", "date": "", "ddg_snippet": "The authors consider the MAB problem, where additional side information on the rewards distribution is available. This feature makes the mean estimate tighter and improves the method's overall convergence. Based on this approach, the authors propose a Restricted-set OFUL algorithm for the linear bandits setting and a Global Under-Explore algorithm for the stochastic setting. The corresponding ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4TZ4DE24fX", "content": "The authors consider the MAB problem, where additional side information on the rewards distribution is available. This feature makes the mean estimate tighter and improves the method's overall convergence. Based on this approach, the authors propose a Restricted-set OFUL algorithm for the linear bandits setting and a Global Under-Explore algorithm for the stochastic setting. The corresponding ..."}
+{"idx": 7, "title": "Tackling Heavy-Tailed Rewards in Reinforcement Learning with ...", "date": "", "ddg_snippet": "In this work, we address the challenge of such rewards in RL with linear function approximation. We first design an algorithm , HEAVY- OFUL , for heavy-tailed linear bandits, achieving an instance- dependent T-round regret of 1−ε q 1−ε eO dT", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.06836", "content": "In this work, we address the challenge of such rewards in RL with linear function approximation. We first design an algorithm , HEAVY- OFUL , for heavy-tailed linear bandits, achieving an instance- dependent T-round regret of 1−ε q 1−ε eO dT"}
+{"idx": 8, "title": "ICML 2021 Near-Optimal Representation Learning for Linear ...", "date": "", "ddg_snippet": "Abstract: This paper studies representation learning for multi-task linear bandits and multi-task episodic RL with linear value function approximation. We first consider the setting where we play M M linear bandits with dimension d d concurrently, and these bandits share a common k k -dimensional linear representation so that k≪d k ≪ d and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/spotlight/8470", "content": "Abstract: This paper studies representation learning for multi-task linear bandits and multi-task episodic RL with linear value function approximation. We first consider the setting where we play M M linear bandits with dimension d d concurrently, and these bandits share a common k k -dimensional linear representation so that k≪d k ≪ d and ..."}
+{"idx": 9, "title": "Achieving Limited Adaptivity for Multinomial Logistic Bandits", "date": "", "ddg_snippet": "5 Aug 2025 — To address these challenges , we present two algorithms , B-MNL-CB and RS-MNL, that operate in the batched and rarely-switching paradigms, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03072v1", "content": "5 Aug 2025 — To address these challenges , we present two algorithms , B-MNL-CB and RS-MNL, that operate in the batched and rarely-switching paradigms, ..."}
diff --git a/data/sampled_jsons/OFUL_algorithm_main_implementation_challenge_computational_complexity_year_2023.jsonl b/data/sampled_jsons/OFUL_algorithm_main_implementation_challenge_computational_complexity_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b2df0b258372641703bb31a15f3fc98d9e4ba64
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_main_implementation_challenge_computational_complexity_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithm - Wikipedia", "date": "", "ddg_snippet": "Most algorithms are intended to be implemented as computer programs.Natural language expressions of algorithms tend to be verbose and ambiguous and are rarely used for complex or technical algorithms .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Algorithm", "content": "Most algorithms are intended to be implemented as computer programs.Natural language expressions of algorithms tend to be verbose and ambiguous and are rarely used for complex or technical algorithms ."}
+{"idx": 1, "title": "Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "We call the resulting algorithm the OFUL ALGORITHM for “optimism in the face of uncertainty linear bandit algorithm ”.Just to mention a few examples, the new inequality could be used to improve the computational complexity of the HOO algorithm Bubeck et al.", "subpage_snippet": "", "source": "david.palenica.com", "link": "https://david.palenica.com/papers/linear-bandit/linear-bandits-NIPS2011-camera-ready.pdf", "content": "We call the resulting algorithm the OFUL ALGORITHM for “optimism in the face of uncertainty linear bandit algorithm ”.Just to mention a few examples, the new inequality could be used to improve the computational complexity of the HOO algorithm Bubeck et al."}
+{"idx": 2, "title": "DSA Tutorial - Learn Data Structures and Algorithms - GeeksforGeeks", "date": "", "ddg_snippet": "2. Learn about Complexities . To analyze algorithms , we mainly measure order of growth of time or space taken in terms of input size. We do this in the worst case scenario in most of the cases.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/dsa-tutorial-learn-data-structures-and-algorithms/", "content": "2. Learn about Complexities . To analyze algorithms , we mainly measure order of growth of time or space taken in terms of input size. We do this in the worst case scenario in most of the cases."}
+{"idx": 3, "title": "Ant Colony Optimization: Nature-Inspired Algorithm for Solving...", "date": "", "ddg_snippet": "Implementing Ant Colony Optimization. Let’s look at a basic implementation of ACO in Python to solve the Traveling Salesman Problem (TSP), a classic optimization challenge", "subpage_snippet": "", "source": "algocademy.com", "link": "https://algocademy.com/blog/ant-colony-optimization-nature-inspired-algorithm-for-solving-complex-problems/", "content": "Implementing Ant Colony Optimization. Let’s look at a basic implementation of ACO in Python to solve the Traveling Salesman Problem (TSP), a classic optimization challenge"}
+{"idx": 4, "title": "Computational Complexity Of Berry Phase Estimation In Topological...", "date": "", "ddg_snippet": "The accurate determination of Berry phase, a crucial property for classifying different states of matter, presents a significant computational challenge , and researchers are continually seeking more efficient methods for its estimation.", "subpage_snippet": "", "source": "quantumzeitgeist.com", "link": "https://quantumzeitgeist.com/algorithm-berry-phase-estimation-achieves-completeness-ground-state-energy/", "content": "The accurate determination of Berry phase, a crucial property for classifying different states of matter, presents a significant computational challenge , and researchers are continually seeking more efficient methods for its estimation."}
+{"idx": 5, "title": "Reinforcement Learning with Trajectory Feedback", "date": "", "ddg_snippet": "Although the OFUL -based algorithm gives better performance than the TS-based algorithm , its update rule is computationally in-tractable, as it requires solving a convex maximization prob-lem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2008.06036", "content": "Although the OFUL -based algorithm gives better performance than the TS-based algorithm , its update rule is computationally in-tractable, as it requires solving a convex maximization prob-lem."}
+{"idx": 6, "title": "On Worst-Case Regret of Linear Thompson Sampling", "date": "", "ddg_snippet": "Algorithm 2 OFUL algorithm . Computation eciency: when At is a polytope · · · LinTS solves an LP problem, OFUL becomes an NP-hard problem!", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~hamidi/talk/2020-02-purdue/purdue-linear-ts.pdf", "content": "Algorithm 2 OFUL algorithm . Computation eciency: when At is a polytope · · · LinTS solves an LP problem, OFUL becomes an NP-hard problem!"}
+{"idx": 7, "title": "Computer science | Definition, Types, & Facts | Britannica", "date": "", "ddg_snippet": "Algorithms and complexity . Architecture and organization. Computational science.The ( computational ) complexity of an algorithm is a measure of the amount of computing resources (time and space) that a particular algorithm consumes when it runs.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/science/computer-science", "content": "Algorithms and complexity . Architecture and organization. Computational science.The ( computational ) complexity of an algorithm is a measure of the amount of computing resources (time and space) that a particular algorithm consumes when it runs."}
+{"idx": 8, "title": "Online Ordinal Optimization under Model Misspecification | Request PDF", "date": "", "ddg_snippet": "Regret guarantees for state-of-the-art linear bandit algorithms such as Optimism in the Face of Uncertainty Linear bandit ( OFUL ) hold under the assumption that the arms expected rewards are perfectly linear in their features.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/352337220_Online_Ordinal_Optimization_under_Model_Misspecification", "content": "Regret guarantees for state-of-the-art linear bandit algorithms such as Optimism in the Face of Uncertainty Linear bandit ( OFUL ) hold under the assumption that the arms expected rewards are perfectly linear in their features."}
+{"idx": 9, "title": "Link Prediction For GNNs Made Simple & 6 Powerful Tools", "date": "", "ddg_snippet": "Challenge : GNNs often face computational challenges when applied to large-scale networks, impacting efficiency.", "subpage_snippet": "", "source": "spotintelligence.com", "link": "https://spotintelligence.com/2024/01/23/link-prediction-gnn/", "content": "Challenge : GNNs often face computational challenges when applied to large-scale networks, impacting efficiency."}
diff --git a/data/sampled_jsons/OFUL_algorithm_main_implementation_challenge_year_2023.jsonl b/data/sampled_jsons/OFUL_algorithm_main_implementation_challenge_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..190a0e504a3a994c210ed0242fe274cb6bc3e5b0
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_main_implementation_challenge_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bandit algorithms to emulate human decision making using ...", "date": "", "ddg_snippet": "by RK Kolla · 2016 · Cited by 1 — The regret analysis of the resulting W- OFUL algorithm poses novel challenges compared to that in the linear bandit problem , primarily because ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1611.10283", "content": "by RK Kolla · 2016 · Cited by 1 — The regret analysis of the resulting W- OFUL algorithm poses novel challenges compared to that in the linear bandit problem , primarily because ..."}
+{"idx": 1, "title": "Improved Algorithms for Stochastic Linear Bandits Using ...", "date": "", "ddg_snippet": "(2011) proposed the OFUL algorithm for linear bandit problems with a ... The main challenge in this setting is that the regret bound must now depend on ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/71551", "content": "(2011) proposed the OFUL algorithm for linear bandit problems with a ... The main challenge in this setting is that the regret bound must now depend on ..."}
+{"idx": 2, "title": "Optimal and Practical Batched Linear Bandit Algorithm", "date": "", "ddg_snippet": "by S Yu — The comparison with E4 and RS- OFUL shows that BLAE consistently achieves lower regret and fewer updates per batch. Develop batch-wise ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=WcFLasjwXs", "content": "by S Yu — The comparison with E4 and RS- OFUL shows that BLAE consistently achieves lower regret and fewer updates per batch. Develop batch-wise ..."}
+{"idx": 3, "title": "Improved Algorithms for Linear Stochastic Bandits - Dávid Pál", "date": "", "ddg_snippet": "by Y Abbasi-Yadkori · Cited by 2412 — We call the resulting algorithm the OFUL ALGORITHM for. “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo-code of the algorithm is given in ... 19 pages", "subpage_snippet": "", "source": "david.palenica.com", "link": "https://david.palenica.com/papers/linear-bandit/linear-bandits-NIPS2011-camera-ready.pdf", "content": "by Y Abbasi-Yadkori · Cited by 2412 — We call the resulting algorithm the OFUL ALGORITHM for. “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo-code of the algorithm is given in ... 19 pages"}
+{"idx": 4, "title": "Regret minimization in Linear Bandits with offline data via ...", "date": "", "ddg_snippet": "11 Aug 2025 — For the same problem , Abbasi-Yadkori et al. (2011) developed the OFUL algorithm which again achieves O ~ ( d T ) \\tilde{O}(d\\sqrt{T}) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08420v1", "content": "11 Aug 2025 — For the same problem , Abbasi-Yadkori et al. (2011) developed the OFUL algorithm which again achieves O ~ ( d T ) \\tilde{O}(d\\sqrt{T}) ..."}
+{"idx": 5, "title": "Lecture 9: Linear Bandits and Thompson Sampling", "date": "", "ddg_snippet": "In this section we will give a bound on the regret of the OFUL algorithm using the confidence sets constructed in Theorem 2. We will assume that the expected ... 20 pages", "subpage_snippet": "", "source": "courses.cs.washington.edu", "link": "https://courses.cs.washington.edu/courses/cse599i/18wi/resources/lecture9/lecture9.pdf", "content": "In this section we will give a bound on the regret of the OFUL algorithm using the confidence sets constructed in Theorem 2. We will assume that the expected ... 20 pages"}
+{"idx": 6, "title": "Norm-Agnostic Linear Bandits", "date": "", "ddg_snippet": "by SB Gales · 2022 · Cited by 15 — This is not just a theoretical issue as we show the failure of OFUL numerically in our experiments; see Figure 1(c). Such weakness of existing linear bandits ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v151/gales22a/gales22a.pdf", "content": "by SB Gales · 2022 · Cited by 15 — This is not just a theoretical issue as we show the failure of OFUL numerically in our experiments; see Figure 1(c). Such weakness of existing linear bandits ..."}
+{"idx": 7, "title": "Linear Bandits with Partially Observable Features", "date": "", "ddg_snippet": "by W Kim · Cited by 3 — ... challenge , we propose a novel theoretical framework and an algorithm ... Baseline methods (e.g., OFUL , LinTS, DRLasso) only use x a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Ojuu0ewXC1", "content": "by W Kim · Cited by 3 — ... challenge , we propose a novel theoretical framework and an algorithm ... Baseline methods (e.g., OFUL , LinTS, DRLasso) only use x a ..."}
+{"idx": 8, "title": "Model Selection for Contextual Bandits and Reinforcement ...", "date": "", "ddg_snippet": "by A Pacchiano · 2021 — As an application, we consider the problem of choosing between UCB and OFUL . ... OFUL Algorithm . We now recall the relevant components of the OFUL ...", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-164.pdf", "content": "by A Pacchiano · 2021 — As an application, we consider the problem of choosing between UCB and OFUL . ... OFUL Algorithm . We now recall the relevant components of the OFUL ..."}
+{"idx": 9, "title": "Bilinear Bandits with Low-rank Structure", "date": "", "ddg_snippet": "by KS Jun · 2019 · Cited by 81 — However, the subspace mismatch invalidates the upper confidence bound used in OFUL ; i.e., the confidence bound does not actually bound the mean reward. Attempts ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v97/jun19a/jun19a.pdf", "content": "by KS Jun · 2019 · Cited by 81 — However, the subspace mismatch invalidates the upper confidence bound used in OFUL ; i.e., the confidence bound does not actually bound the mean reward. Attempts ..."}
diff --git a/data/sampled_jsons/OS-ATLAS_A_Foundation_Action_Model_for_Generalist_GUI_arXiv_implementation_details_year_2024.jsonl b/data/sampled_jsons/OS-ATLAS_A_Foundation_Action_Model_for_Generalist_GUI_arXiv_implementation_details_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c514f2e039ffafa5aedd0a044b7a8ac3dabbb58
--- /dev/null
+++ b/data/sampled_jsons/OS-ATLAS_A_Foundation_Action_Model_for_Generalist_GUI_arXiv_implementation_details_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OS - ATLAS : A Foundation Action Model for Generalist GUI Agents", "date": "", "ddg_snippet": "In this paper, we present OS - Atlas , a foundation action model for GUI agents. OS - Atlas demonstrates exceptional performance in tackling open-environment GUI tasks across six complex benchmarks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23218v1", "content": "In this paper, we present OS - Atlas , a foundation action model for GUI agents. OS - Atlas demonstrates exceptional performance in tackling open-environment GUI tasks across six complex benchmarks."}
+{"idx": 1, "title": "OS-Copilot/ OS - Atlas : OS - ATLAS : A Foundation Action Model For ...", "date": "", "ddg_snippet": "journal={ arXiv preprint arXiv :2410.23218}, year={2024} }. About. OS - ATLAS : A Foundation Action Model For Generalist GUI Agents. Resources.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OS-Copilot/OS-Atlas", "content": "journal={ arXiv preprint arXiv :2410.23218}, year={2024} }. About. OS - ATLAS : A Foundation Action Model For Generalist GUI Agents. Resources."}
+{"idx": 2, "title": "OS-Copilot/ OS - Atlas -Pro-7B · Hugging Face", "date": "", "ddg_snippet": "OS - Atlas : A Foundation Action Model For Generalist GUI Agents. Overview. OS - Atlas -Pro-7B is a GUI action model finetuned from OS - Atlas -Base-7B.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/OS-Copilot/OS-Atlas-Pro-7B", "content": "OS - Atlas : A Foundation Action Model For Generalist GUI Agents. Overview. OS - Atlas -Pro-7B is a GUI action model finetuned from OS - Atlas -Base-7B."}
+{"idx": 3, "title": "OS - ATLAS : A Foundation Action Model for Generalist GUI Agents", "date": "", "ddg_snippet": "The paper introduces OS - ATLAS , a foundational GUI action model for generalist GUI agents that excels in GUI grounding and Out-Of-Distribution (OOD) scenarios.", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/paper/a23e0e34-c74c-41a3-a880-22decbbdc032", "content": "The paper introduces OS - ATLAS , a foundational GUI action model for generalist GUI agents that excels in GUI grounding and Out-Of-Distribution (OOD) scenarios."}
+{"idx": 4, "title": "(PDF) OS - ATLAS : A Foundation Action Model for Generalist GUI ...", "date": "", "ddg_snippet": "OS - Atlas , the first foundation action model specifically designed for GUI agents.We denote the pre-trained model as OS - Atlas -Base. 3. OS - ATLAS : A Foundation Action Model for Generalist GUI Agents.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385386867_OS-ATLAS_A_Foundation_Action_Model_for_Generalist_GUI_Agents", "content": "OS - Atlas , the first foundation action model specifically designed for GUI agents.We denote the pre-trained model as OS - Atlas -Base. 3. OS - ATLAS : A Foundation Action Model for Generalist GUI Agents."}
+{"idx": 5, "title": "OS - ATLAS : A Foundation Action Model for Generalist GUI Agents", "date": "", "ddg_snippet": "OverviewPresents OS - ATLAS , a foundational action model for generalist GUI agentsAims to enable AI agents to interact with graphical user interfaces ( GUIs ) in a human-like way", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/os-atlas-foundation-action-model-generalist-gui", "content": "OverviewPresents OS - ATLAS , a foundational action model for generalist GUI agentsAims to enable AI agents to interact with graphical user interfaces ( GUIs ) in a human-like way"}
+{"idx": 6, "title": "GUI -R1 : A Generalist R1-Style Vision-Language Action Model For ...", "date": "", "ddg_snippet": "This citation introduces OS - Atlas , a large action model for GUI agents, and serves as a primary comparison point for GUI -R1, highlighting the limitations of supervised fine-tuning that GUI -R1 addresses.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2504.10458v1", "content": "This citation introduces OS - Atlas , a large action model for GUI agents, and serves as a primary comparison point for GUI -R1, highlighting the limitations of supervised fine-tuning that GUI -R1 addresses."}
+{"idx": 7, "title": "HF Papers – AI Native Foundation", "date": "", "ddg_snippet": "OS - ATLAS : A Foundation Action Model for Generalist GUI Agents.", "subpage_snippet": "", "source": "ainativefoundation.org", "link": "https://ainativefoundation.org/ai-papers/?current_page=103", "content": "OS - ATLAS : A Foundation Action Model for Generalist GUI Agents."}
+{"idx": 8, "title": "OS - Atlas Homepage", "date": "", "ddg_snippet": "OS - ATLAS : A Foundation Action Model For Generalist GUI Agents.", "subpage_snippet": "", "source": "osatlas.github.io", "link": "https://osatlas.github.io/", "content": "OS - ATLAS : A Foundation Action Model For Generalist GUI Agents."}
+{"idx": 9, "title": "OS - ATLAS from AI Hub Admin, Genuine Reviews, Ratings and...", "date": "", "ddg_snippet": "OS - ATLAS . Category. Ai agent - ai agent. Publisher. pub- os - atlas .Prompts. Reviews. Tags. Write Your Review. Detailed Ratings. ALL. Correctness.", "subpage_snippet": "", "source": "deepnlp.org", "link": "https://deepnlp.org/store/ai-agent/ai-agent/pub-os-atlas/os-atlas", "content": "OS - ATLAS . Category. Ai agent - ai agent. Publisher. pub- os - atlas .Prompts. Reviews. Tags. Write Your Review. Detailed Ratings. ALL. Correctness."}
diff --git a/data/sampled_jsons/Observation_4_safety_fine-tuning_reduces_local_Lipschitzness_unsafe_samples_year_2024.jsonl b/data/sampled_jsons/Observation_4_safety_fine-tuning_reduces_local_Lipschitzness_unsafe_samples_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06b96e3de90f8cc552706a794581b09562108e46
--- /dev/null
+++ b/data/sampled_jsons/Observation_4_safety_fine-tuning_reduces_local_Lipschitzness_unsafe_samples_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Makes and Breaks Safety Fine-tuning? Mechanistic Study", "date": "", "ddg_snippet": "by minimally transforming MLP weights to specifically project unsafe samples into the null space of its weights, and the inductive biases of safety fine-tuning which sub-stantially reduce the local Lipschitzness of a model for unsafe samples .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BS2CbUkJpy", "content": "by minimally transforming MLP weights to specifically project unsafe samples into the null space of its weights, and the inductive biases of safety fine-tuning which sub-stantially reduce the local Lipschitzness of a model for unsafe samples ."}
+{"idx": 1, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "We provide comprehensive analyses on the mechanisms learned by safety fine-tuning showing that these methods (i) encourage separate cluster formations for safe and unsafe samples by minimally transforming MLP weights to specifically project unsafe samples into the null space of model’s weights, and (ii) substantially reduce the local ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JEflV4nRlH", "content": "We provide comprehensive analyses on the mechanisms learned by safety fine-tuning showing that these methods (i) encourage separate cluster formations for safe and unsafe samples by minimally transforming MLP weights to specifically project unsafe samples into the null space of model’s weights, and (ii) substantially reduce the local ..."}
+{"idx": 2, "title": "Picky LLMs and Unreliable RMs: An Empirical Study on Safety ...", "date": "", "ddg_snippet": "From an-other perspective, Zhao et al. (2024) find that aligned LLMs tend to forget unsafe examples existing in the instruction- tuning dataset after an additional safety fine-tuning proce-dure.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01116", "content": "From an-other perspective, Zhao et al. (2024) find that aligned LLMs tend to forget unsafe examples existing in the instruction- tuning dataset after an additional safety fine-tuning proce-dure."}
+{"idx": 3, "title": "arXiv:2407.10264v3 [cs.LG] 21 Aug 2024", "date": "", "ddg_snippet": "Safety fine-tuning methods yield specialized transformations that primarily activate for unsafe inputs. We provide comprehensive analyses on the mechanisms learned by safety fine-tuning , showing that it encourages separate cluster formations for safe and unsafe samples by minimally transforming MLP weights to specifically project unsafe samples into the null space of its weights, and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.10264", "content": "Safety fine-tuning methods yield specialized transformations that primarily activate for unsafe inputs. We provide comprehensive analyses on the mechanisms learned by safety fine-tuning , showing that it encourages separate cluster formations for safe and unsafe samples by minimally transforming MLP weights to specifically project unsafe samples into the null space of its weights, and the ..."}
+{"idx": 4, "title": "arXiv:2407.10264v3 [cs.LG] 21 Aug 2024 - ResearchGate", "date": "", "ddg_snippet": "d Unlearning are used for fine-tuning . This makes sense as, for unsafe samples , the variation in the preferred output strings in safety fine-tuning dataset is much less compared to that of safe ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Kemal-Oksuz/publication/382271359_What_Makes_and_Breaks_Safety_Fine-tuning_Mechanistic_Study/links/6746f2c43d17281c7de2d06f/What-Makes-and-Breaks-Safety-Fine-tuning-A-Mechanistic-Study.pdf", "content": "d Unlearning are used for fine-tuning . This makes sense as, for unsafe samples , the variation in the preferred output strings in safety fine-tuning dataset is much less compared to that of safe ..."}
+{"idx": 5, "title": "NeurIPS 2022 Oral-Equivalent Papers", "date": "", "ddg_snippet": "... large pre-trained models is changing the landscape of Machine Learning research and practice, moving from a \"training from scratch\" to a \" fine - tuning ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2022/events/highlighted", "content": "... large pre-trained models is changing the landscape of Machine Learning research and practice, moving from a \"training from scratch\" to a \" fine - tuning ..."}
+{"idx": 6, "title": "Targeted Forgetting of Image Subgroups in CLIP Models", "date": "", "ddg_snippet": "In the forgetting stage, we identify layers essential for representing the forgetting samples but less critical to other data, fine - tuning the model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03117v1", "content": "In the forgetting stage, we identify layers essential for representing the forgetting samples but less critical to other data, fine - tuning the model ..."}
+{"idx": 7, "title": "Semi-Supervised Safe Visuomotor Policy Synthesis using Barrier", "date": "", "ddg_snippet": "... optimal-control methods based on the Hamilton-Jacobi reachability analysis framework [ 3 , 4 , 5 , 6 ] have been instrumental in addressing safety ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.12616v1", "content": "... optimal-control methods based on the Hamilton-Jacobi reachability analysis framework [ 3 , 4 , 5 , 6 ] have been instrumental in addressing safety ..."}
+{"idx": 8, "title": "Nathan Kallus", "date": "", "ddg_snippet": "Compared to existing process reward models (PRMs), our method does not require a fine -grained notion of \"step,\" which is difficult to define for long ...", "subpage_snippet": "", "source": "nathankallus.com", "link": "https://nathankallus.com/", "content": "Compared to existing process reward models (PRMs), our method does not require a fine -grained notion of \"step,\" which is difficult to define for long ..."}
+{"idx": 9, "title": "Yang Liu", "date": "", "ddg_snippet": "... Erdős and the average Erdős number among them is 4 .67. ... Comprehensive Fine - Tuning Large Language Models of Code for Automated Program Repair.", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/yang-liu-003/", "content": "... Erdős and the average Erdős number among them is 4 .67. ... Comprehensive Fine - Tuning Large Language Models of Code for Automated Program Repair."}
diff --git a/data/sampled_jsons/Offline_RL_with_Preference_Data_Chen_et_al._2022_Theorem_4.5.jsonl b/data/sampled_jsons/Offline_RL_with_Preference_Data_Chen_et_al._2022_Theorem_4.5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a34bc2a4c1647a113b6ad12e5a1d42ae779fe701
--- /dev/null
+++ b/data/sampled_jsons/Offline_RL_with_Preference_Data_Chen_et_al._2022_Theorem_4.5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Adaptive Scaling of Policy Constraints for Offline", "date": "", "ddg_snippet": "Offline reinforcement learning ( RL ) learns a policy exclusively from a fixed, pre-collected dataset without further interactions with the environment ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19900v1", "content": "Offline reinforcement learning ( RL ) learns a policy exclusively from a fixed, pre-collected dataset without further interactions with the environment ..."}
+{"idx": 1, "title": "Regret minimization in Linear Bandits with offline data via", "date": "", "ddg_snippet": "... the Offline -Online Phased Elimination ( OOPE ) algorithm for regret minimization in linear bandits in the online phase with access to offline data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08420v1", "content": "... the Offline -Online Phased Elimination ( OOPE ) algorithm for regret minimization in linear bandits in the online phase with access to offline data ..."}
+{"idx": 2, "title": "What Matters in Data for DPO?", "date": "", "ddg_snippet": "... for achieving this alignment are Reinforcement Learning from Human Feedback (RLHF) (Bai et al ., 2022 ; Ouyang et al ., 2022 ) and Direct Preference ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18312v1", "content": "... for achieving this alignment are Reinforcement Learning from Human Feedback (RLHF) (Bai et al ., 2022 ; Ouyang et al ., 2022 ) and Direct Preference ..."}
+{"idx": 3, "title": "Policy-labeled Preference Learning: Is Preference Enough for", "date": "", "ddg_snippet": "Early RLHF research (Lee et al ., 2021 ; Park et al ., 2022 ) assumed humans prefer trajectories with higher cumulative rewards, leading to a two ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06273v2", "content": "Early RLHF research (Lee et al ., 2021 ; Park et al ., 2022 ) assumed humans prefer trajectories with higher cumulative rewards, leading to a two ..."}
+{"idx": 4, "title": "Reconstructing physics-informed machine learning for traffic ...", "date": "", "ddg_snippet": "A good example is Ji et al . [ 2022 ], who present the Spatio-Temporal Differential Equation Network (STDEN). Rather than predicting traffic states outright, STDEN assumes an underlying potential-energy field that steers traffic evolution across the road network.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0968090X25003481", "content": "A good example is Ji et al . [ 2022 ], who present the Spatio-Temporal Differential Equation Network (STDEN). Rather than predicting traffic states outright, STDEN assumes an underlying potential-energy field that steers traffic evolution across the road network."}
+{"idx": 5, "title": "DYNAMICS OF A FRACTIONAL-ORDER MODEL FOR - Korea Science", "date": "", "ddg_snippet": "Abstract. This study develops a fractional-order model for Marburg virus disease (MVD), incorporating mutation dynamics, public health education, and quarantine measures. Using the Caputo fractional derivative (C − FD), the model captures memory effects in disease progression. We establish positivity, boundedness, existence, and uniqueness of solutions via fixed-point theorems and analyze ...", "subpage_snippet": "", "source": "koreascience.kr", "link": "https://koreascience.kr/article/JAKO202525232435494.pdf", "content": "Abstract. This study develops a fractional-order model for Marburg virus disease (MVD), incorporating mutation dynamics, public health education, and quarantine measures. Using the Caputo fractional derivative (C − FD), the model captures memory effects in disease progression. We establish positivity, boundedness, existence, and uniqueness of solutions via fixed-point theorems and analyze ..."}
+{"idx": 6, "title": "Quantile-Optimal Policy Learning under Unmeasured Confounding", "date": "", "ddg_snippet": "... offline dataset often lacks full coverage, meaning that the distribution of the collected data might have insufficient overlap with that induced by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07140v1", "content": "... offline dataset often lacks full coverage, meaning that the distribution of the collected data might have insufficient overlap with that induced by ..."}
+{"idx": 7, "title": "‘RL exploration’ directory · Gwern.net", "date": "", "ddg_snippet": "Don’t Change the Algorithm, Change the Data : Exploratory Data for Offline Reinforcement Learning (ExORL) ”, Yarats et al 2022", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/exploration/index", "content": "Don’t Change the Algorithm, Change the Data : Exploratory Data for Offline Reinforcement Learning (ExORL) ”, Yarats et al 2022"}
+{"idx": 8, "title": "‘model-based RL’ directory · Gwern.net", "date": "", "ddg_snippet": "PI-ARS: Accelerating Evolution-Learned Visual-Locomotion With Predictive Information Representations ”, Lee et al 2022", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/model/index", "content": "PI-ARS: Accelerating Evolution-Learned Visual-Locomotion With Predictive Information Representations ”, Lee et al 2022"}
+{"idx": 9, "title": "Support vector machines for optimal channel decoding | EURASIP", "date": "", "ddg_snippet": "... aims at handling complex and time-consuming communication problems in a data -based approach, as opposed to the traditional model-based approach [ 4 ...", "subpage_snippet": "", "source": "jwcn-eurasipjournals.springeropen.com", "link": "https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-025-02493-6", "content": "... aims at handling complex and time-consuming communication problems in a data -based approach, as opposed to the traditional model-based approach [ 4 ..."}
diff --git a/data/sampled_jsons/Olah_2020_circuit_analysis_neural_networks_interpretability_year_2020.jsonl b/data/sampled_jsons/Olah_2020_circuit_analysis_neural_networks_interpretability_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..61de3c1d3a3bd07a4578c2e98212d124492672a5
--- /dev/null
+++ b/data/sampled_jsons/Olah_2020_circuit_analysis_neural_networks_interpretability_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Zoom In: An Introduction to Circuits - Distill", "date": "", "ddg_snippet": "Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/zoom-in/", "content": "Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks ."}
+{"idx": 1, "title": "Zoom In: An Introduction to Circuits — AI Alignment Forum", "date": "", "ddg_snippet": "I, for one, am very excited about circuits as a direction for building up an understanding-focused interpretability field and want to congratulate Chris and the rest of OpenAI Clarity for putting in the hard work of doing the foundational work necessary to start building a real field around neural network interpretability .", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/MG4ZjWQDrdpgeu8wG/zoom-in-an-introduction-to-circuits", "content": "I, for one, am very excited about circuits as a direction for building up an understanding-focused interpretability field and want to congratulate Chris and the rest of OpenAI Clarity for putting in the hard work of doing the foundational work necessary to start building a real field around neural network interpretability ."}
+{"idx": 2, "title": "Zoom In: An Introduction to Circuits | Nick's Notes", "date": "", "ddg_snippet": "Makes three speculative claims about the understandability of neural networks : Features - Features are the fundamental unit of neural networks (e.g. curve detectors). Circuits - Features are connected by weights, forming circuits . A \" circuit \" is a computational subgraph of a neural network . It consists of a set of features, and the weighted edges that go between them in the original ...", "subpage_snippet": "", "source": "www.nickjalbert.com", "link": "http://www.nickjalbert.com/reading/2020/03/27/zoom-in-an-introduction-to-circuits.html", "content": "Makes three speculative claims about the understandability of neural networks : Features - Features are the fundamental unit of neural networks (e.g. curve detectors). Circuits - Features are connected by weights, forming circuits . A \" circuit \" is a computational subgraph of a neural network . It consists of a set of features, and the weighted edges that go between them in the original ..."}
+{"idx": 3, "title": "Distill: Zoom in on Circuits - Dynamically Typed", "date": "", "ddg_snippet": "From DT #35: \"By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .\" Chris Olah et al. wrote a fascinating new Distill article about \" circuits \" in convolutional neural networks . The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ...", "subpage_snippet": "", "source": "dynamicallytyped.com", "link": "https://dynamicallytyped.com/stories/2020/distill-zoom-in-on-circuits/", "content": "From DT #35: \"By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .\" Chris Olah et al. wrote a fascinating new Distill article about \" circuits \" in convolutional neural networks . The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ..."}
+{"idx": 4, "title": "PDF An Introduction to Circuits in CNNs - GitHub Pages", "date": "", "ddg_snippet": "To what extent are there abstract recurring patterns -- \" circuit motifs\" -- in neural networks ? Traditional study of circuit motifs relies on highly sparse graphs to systematically analyze them.", "subpage_snippet": "", "source": "interpretablevision.github.io", "link": "https://interpretablevision.github.io/slide/cvpr20_chris.pdf", "content": "To what extent are there abstract recurring patterns -- \" circuit motifs\" -- in neural networks ? Traditional study of circuit motifs relies on highly sparse graphs to systematically analyze them."}
+{"idx": 5, "title": "Zoom In: An Introduction to Circuits - ResearchGate", "date": "", "ddg_snippet": "A multitude of studies have later focused on interpreting weights and intermediate representations in neural networks ( Olah et al., 2017 ( Olah et al., , 2018 ( Olah et al., , 2020 Voss et al., 2021 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/339841165_Zoom_In_An_Introduction_to_Circuits", "content": "A multitude of studies have later focused on interpreting weights and intermediate representations in neural networks ( Olah et al., 2017 ( Olah et al., , 2018 ( Olah et al., , 2020 Voss et al., 2021 ..."}
+{"idx": 6, "title": "Transformer Circuit Faithfulness Metrics Are Not Robust", "date": "", "ddg_snippet": "1 Introduction Mechanistic interpretability (MI) is a form of post-hoc interpretability that attempts to reverse engineer neural networks to provide faithful low-level explanations of model behaviour ( Olah et al., 2020 ). One focus of interpretability work on transformer language models is identifying 'circuits' - subgraphs of the entire model's computational graph that are primarily ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zSf8PJyQb2", "content": "1 Introduction Mechanistic interpretability (MI) is a form of post-hoc interpretability that attempts to reverse engineer neural networks to provide faithful low-level explanations of model behaviour ( Olah et al., 2020 ). One focus of interpretability work on transformer language models is identifying 'circuits' - subgraphs of the entire model's computational graph that are primarily ..."}
+{"idx": 7, "title": "Aligning AI Through Internal Understanding: The Role of Interpretability", "date": "", "ddg_snippet": "Others, especially in mechanistic interpretability , attempt to look inside the model's architecture—identifying which neurons, attention heads, or circuits contribute to specific behaviors ( Olah et al., 2020 ; Nanda et al., 2023; Elhage et al., 2021a). These approaches are promising, but far from perfect.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08592v1", "content": "Others, especially in mechanistic interpretability , attempt to look inside the model's architecture—identifying which neurons, attention heads, or circuits contribute to specific behaviors ( Olah et al., 2020 ; Nanda et al., 2023; Elhage et al., 2021a). These approaches are promising, but far from perfect."}
+{"idx": 8, "title": "Exploring Circuit-Based Interpretability - Medium", "date": "", "ddg_snippet": "Circuit -based interpretability focuses on analyzing the internal mechanisms of neural networks by identifying and understanding the specific circuits (collections of neurons and connections ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@aruna.kolluru/exploring-circuit-based-interpretability-b0421a717872", "content": "Circuit -based interpretability focuses on analyzing the internal mechanisms of neural networks by identifying and understanding the specific circuits (collections of neurons and connections ..."}
+{"idx": 9, "title": "Circuits Updates - July 2024", "date": "", "ddg_snippet": "In thinking about circuits , we often think about attention heads as having a role similar to \"relative position\" in a convolutional neural network . Consider the car detection circuit at the top of Zoom In, which looks for windows above the car, and wheels below.", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2024/july-update/index.html", "content": "In thinking about circuits , we often think about attention heads as having a role similar to \"relative position\" in a convolutional neural network . Consider the car detection circuit at the top of Zoom In, which looks for windows above the car, and wheels below."}
diff --git a/data/sampled_jsons/Olah_et_al._2020_mechanistic_interpretability_circuits_transformer_abstract_year_2020.jsonl b/data/sampled_jsons/Olah_et_al._2020_mechanistic_interpretability_circuits_transformer_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f08b1e7f73dd8d2d90f754f0063de5272d9f2b0
--- /dev/null
+++ b/data/sampled_jsons/Olah_et_al._2020_mechanistic_interpretability_circuits_transformer_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mechanistic Unveiling of Transformer Circuits : Self-Influence as...", "date": "", "ddg_snippet": "A key method in this field is circuit analysis (Conmy et al ., 2023; Olah et al ., 2020 ) . In this approach, neural networks are conceptualized as computational graphs, where circuits represent sub-graphs composed of interconnected features and the weights that link them.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.09022v2", "content": "A key method in this field is circuit analysis (Conmy et al ., 2023; Olah et al ., 2020 ) . In this approach, neural networks are conceptualized as computational graphs, where circuits represent sub-graphs composed of interconnected features and the weights that link them."}
+{"idx": 1, "title": "(PDF) Mechanistic Unveiling of Transformer Circuits : Self-Influence...", "date": "", "ddg_snippet": "(Michaud et al .,2024; Olah et al ., 2020 ). Repre-. senting the most critical components for complet2023. Towards automated circuit discovery. for mechanistic interpretability . Advances in Neural. Information Processing Systems, 36:16318–16352.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388963747_Mechanistic_Unveiling_of_Transformer_Circuits_Self-Influence_as_a_Key_to_Model_Reasoning", "content": "(Michaud et al .,2024; Olah et al ., 2020 ). Repre-. senting the most critical components for complet2023. Towards automated circuit discovery. for mechanistic interpretability . Advances in Neural. Information Processing Systems, 36:16318–16352."}
+{"idx": 2, "title": "Mechanistic ?", "date": "", "ddg_snippet": "The beginnings of mechanistic interpretability .The term mechanistic interpretability was coined by Chris Olah and first publicly used in the Dis-till.pub Circuits thread, a series of blogposts by OpenAI researchers between March 2020 –April 2021. The first post ( Olah et al ., 2020 ) set out to...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=schAf4BPtD", "content": "The beginnings of mechanistic interpretability .The term mechanistic interpretability was coined by Chris Olah and first publicly used in the Dis-till.pub Circuits thread, a series of blogposts by OpenAI researchers between March 2020 –April 2021. The first post ( Olah et al ., 2020 ) set out to..."}
+{"idx": 3, "title": "Interpretability Dreams", "date": "", "ddg_snippet": "Transformer Circuits Thread. Interpretability Dreams. An informal note on future goals for mechanistic interpretability by Chris Olah . Published May 24th, 2023.", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2023/interpretability-dreams", "content": "Transformer Circuits Thread. Interpretability Dreams. An informal note on future goals for mechanistic interpretability by Chris Olah . Published May 24th, 2023."}
+{"idx": 4, "title": "EIS VI: Critiques of Mechanistic Interpretability Work in AI Safety", "date": "", "ddg_snippet": "Olah et al . ( 2020 ) pick a number of these examples, do not do any correction for testing many hypotheses, present select ones as coherent circuits , and pontificate about how they work.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/wt7HXaCWzuKQipqz3/eis-vi-critiques-of-mechanistic-interpretability-work-in-ai", "content": "Olah et al . ( 2020 ) pick a number of these examples, do not do any correction for testing many hypotheses, present select ones as coherent circuits , and pontificate about how they work."}
+{"idx": 5, "title": "Explaining AI through mechanistic interpretability | European Journal...", "date": "", "ddg_snippet": "Olah and colleagues (Cammarata et al ., 2021; Olah et al ., 2020 a) discover and analyse a curve detector circuit within InceptionV1. Circuits are sub-graphs of neural networks which, crucially, are not specified as distinct parts of the ANN’s architecture.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s13194-024-00614-4", "content": "Olah and colleagues (Cammarata et al ., 2021; Olah et al ., 2020 a) discover and analyse a curve detector circuit within InceptionV1. Circuits are sub-graphs of neural networks which, crucially, are not specified as distinct parts of the ANN’s architecture."}
+{"idx": 6, "title": "A Practical Review of Mechanistic Interpretability for T...", "date": "", "ddg_snippet": "between features ( Olah et al ., 2020 ), subsequent studies have generalized them as connections between the activation outputs of transformer components (Olsson et al ., 2022; Wang et al ., 2022a), where interpreting individual transformer components becomes part of the circuit ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2407.02646/paper", "content": "between features ( Olah et al ., 2020 ), subsequent studies have generalized them as connections between the activation outputs of transformer components (Olsson et al ., 2022; Wang et al ., 2022a), where interpreting individual transformer components becomes part of the circuit ..."}
+{"idx": 7, "title": "A Walkthrough of A Mathematical Framework for Transformer Circuits", "date": "", "ddg_snippet": "Mechanistic Interpretability .You can watch it here. Sadly, it turns out I have a lot of things to say about Transformer Circuits and this turned into a 3 hour monologue, but I hope it's still useful!", "subpage_snippet": "", "source": "www.neelnanda.io", "link": "https://www.neelnanda.io/mechanistic-interpretability/a-walkthrough-of-a-mathematical-framework-for-transformer-circuits", "content": "Mechanistic Interpretability .You can watch it here. Sadly, it turns out I have a lot of things to say about Transformer Circuits and this turned into a 3 hour monologue, but I hope it's still useful!"}
+{"idx": 8, "title": "Mechanistic Interpretability Needs Philosophy", "date": "", "ddg_snippet": "Abstract . Mechanistic interpretability (MI) aims to explain how neural networks work by un-covering their underlying causal mechanisms .", "subpage_snippet": "", "source": "philarchive.org", "link": "https://philarchive.org/archive/WILMIN-2", "content": "Abstract . Mechanistic interpretability (MI) aims to explain how neural networks work by un-covering their underlying causal mechanisms ."}
+{"idx": 9, "title": "Understanding Mechanistic Interpretability in AI Models | IntuitionLabs", "date": "", "ddg_snippet": "Learn about mechanistic interpretability , a method to reverse-engineer AI models. This article explains how it uncovers causal mechanisms within neural networks.", "subpage_snippet": "", "source": "intuitionlabs.ai", "link": "https://intuitionlabs.ai/articles/mechanistic-interpretability-ai-llms", "content": "Learn about mechanistic interpretability , a method to reverse-engineer AI models. This article explains how it uncovers causal mechanisms within neural networks."}
diff --git a/data/sampled_jsons/Olah_et_al_2020_circuits_self-attention_matrices_analysis.jsonl b/data/sampled_jsons/Olah_et_al_2020_circuits_self-attention_matrices_analysis.jsonl
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index 0000000000000000000000000000000000000000..1ee7e61186a39892f4ef40978b7fa016cef162e8
--- /dev/null
+++ b/data/sampled_jsons/Olah_et_al_2020_circuits_self-attention_matrices_analysis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Oláh (surname) - Wikipedia", "date": "", "ddg_snippet": "Oláh is an old Hungarian family name meaning Romanian. [1] The name is descended from the word Vlach (see also Walhaz), and was used to designate the Romanians in the Kingdom of Hungary.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Oláh_(surname)", "content": "Oláh is an old Hungarian family name meaning Romanian. [1] The name is descended from the word Vlach (see also Walhaz), and was used to designate the Romanians in the Kingdom of Hungary."}
+{"idx": 1, "title": "Olah , A Verisma Company – Archive Legacy Healthcare Applications", "date": "", "ddg_snippet": "Olah allows you to archive any legacy application in a fraction of the time and with a fraction of the effort, all while keeping your original information safe and secure.", "subpage_snippet": "", "source": "olahht.com", "link": "https://olahht.com/", "content": "Olah allows you to archive any legacy application in a fraction of the time and with a fraction of the effort, all while keeping your original information safe and secure."}
+{"idx": 2, "title": "What does olah mean? - Definitions.net", "date": "", "ddg_snippet": "Olah is a surname of Hungarian origin. It may also refer to a village or geographical location in various countries such as Indonesia, Nigeria, and Iran. According to the U.S. Census Bureau, Olah is ranked #16265 in terms of the most common surnames in America.", "subpage_snippet": "", "source": "www.definitions.net", "link": "https://www.definitions.net/definition/olah", "content": "Olah is a surname of Hungarian origin. It may also refer to a village or geographical location in various countries such as Indonesia, Nigeria, and Iran. According to the U.S. Census Bureau, Olah is ranked #16265 in terms of the most common surnames in America."}
+{"idx": 3, "title": "Olah - Name Meaning and Origin", "date": "", "ddg_snippet": "The surname Olah is of Hungarian origin and is derived from the Hungarian word \" oláh ,\" meaning \"shepherd\" or \"Romanian.\" It is a common surname among people of Hungarian descent and may have been originally used to denote someone who worked as a shepherd or had Romanian ancestry.", "subpage_snippet": "", "source": "namediscoveries.com", "link": "https://namediscoveries.com/surnames/olah", "content": "The surname Olah is of Hungarian origin and is derived from the Hungarian word \" oláh ,\" meaning \"shepherd\" or \"Romanian.\" It is a common surname among people of Hungarian descent and may have been originally used to denote someone who worked as a shepherd or had Romanian ancestry."}
+{"idx": 4, "title": "Verisma Acquires Olah , Streamlining Patient Data Management ...", "date": "", "ddg_snippet": "Nov 20, 2024 · Alpharetta, Ga., Nov. 20, 2024 – Verisma, a leading provider of intelligent health data management solutions, announces today the company is acquiring Olah Healthcare Technology.", "subpage_snippet": "", "source": "verisma.com", "link": "https://verisma.com/blog/verisma-acquires-olah-streamlining-patient-data-management-for-hospitals-and-healthcare-facilities-nationwide/", "content": "Nov 20, 2024 · Alpharetta, Ga., Nov. 20, 2024 – Verisma, a leading provider of intelligent health data management solutions, announces today the company is acquiring Olah Healthcare Technology."}
+{"idx": 5, "title": "Olah - HebrewNamer", "date": "", "ddg_snippet": "Olah is a Hebrew name that means “burnt offering”. This name is often given to baby girls in Jewish culture, and it has a rich cultural significance within the religion.", "subpage_snippet": "", "source": "hebrewnamer.com", "link": "https://hebrewnamer.com/names/olah/", "content": "Olah is a Hebrew name that means “burnt offering”. This name is often given to baby girls in Jewish culture, and it has a rich cultural significance within the religion."}
+{"idx": 6, "title": "About Us - Olah , a Verisma Company", "date": "", "ddg_snippet": "While traditional application archiving can get messy, Olah is modern application archiving that doesn’t mess with your information or your workflow.", "subpage_snippet": "", "source": "olahht.com", "link": "https://olahht.com/about-us/", "content": "While traditional application archiving can get messy, Olah is modern application archiving that doesn’t mess with your information or your workflow."}
+{"idx": 7, "title": "Is Random Attention Sufficient for Sequence Modeling?", "date": "", "ddg_snippet": "Frozen-QK , preserves the conventional attention structure but freezes the query and key weight matrices , allowing only the value weight matrix to be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.01115v3", "content": "Frozen-QK , preserves the conventional attention structure but freezes the query and key weight matrices , allowing only the value weight matrix to be ..."}
+{"idx": 8, "title": "A Theoretical Study of (Hyper) Self-Attention through the Lens", "date": "", "ddg_snippet": "... et al ., 2023 ) , attention is the building block for many domains, spanning natural language processing (Brown et al ., 2020 ; Devlin et al ., 2019 ) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06179v1", "content": "... et al ., 2023 ) , attention is the building block for many domains, spanning natural language processing (Brown et al ., 2020 ; Devlin et al ., 2019 ) ..."}
+{"idx": 9, "title": "Back Attention: Understanding and Enhancing Multi-Hop Reasoning", "date": "", "ddg_snippet": "Second, several studies have shown that the higher attention and feed-forward network (FFN) layers also store knowledge Geva et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10835v1", "content": "Second, several studies have shown that the higher attention and feed-forward network (FFN) layers also store knowledge Geva et al ."}
diff --git a/data/sampled_jsons/Olsson_et_al._2022_induction_heads_in-context_learning_attention.jsonl b/data/sampled_jsons/Olsson_et_al._2022_induction_heads_in-context_learning_attention.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..163b2ae99d02686db4c82b08d609ed6b1ef2cbcb
--- /dev/null
+++ b/data/sampled_jsons/Olsson_et_al._2022_induction_heads_in-context_learning_attention.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2209.11895] In-context Learning and Induction Heads - arXiv.org", "date": "", "ddg_snippet": "Sep 24, 2022 · \" Induction heads \" are attention heads that implement a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. In this work, we present preliminary and indirect evidence for a hypothesis that induction heads might constitute the mechanism for the majority of all \" in-context learning \" in large transformer models (i.e. decreasing loss at increasing token indices). We find that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2209.11895", "content": "Sep 24, 2022 · \" Induction heads \" are attention heads that implement a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. In this work, we present preliminary and indirect evidence for a hypothesis that induction heads might constitute the mechanism for the majority of all \" in-context learning \" in large transformer models (i.e. decreasing loss at increasing token indices). We find that ..."}
+{"idx": 1, "title": "In-context Learning and Induction Heads", "date": "", "ddg_snippet": "Mar 8, 2022 · During this phase change, the majority of in-context learning ability (as measured by difference in loss between tokens early and late in the sequence) is acquired, and simultaneously induction heads form within the model that are capable of implementing fairly abstract and fuzzy versions of pattern completion.", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html", "content": "Mar 8, 2022 · During this phase change, the majority of in-context learning ability (as measured by difference in loss between tokens early and late in the sequence) is acquired, and simultaneously induction heads form within the model that are capable of implementing fairly abstract and fuzzy versions of pattern completion."}
+{"idx": 2, "title": "Induction Heads as an Essential Mechanism for Pattern ...", "date": "", "ddg_snippet": "Building on this foundation, Olsson et al .( 2022 ) hypothesised that induction heads are capable of abstract pattern matching and conducted a qualita- tive analysis of attention patterns observed in an example from an abstract classication task.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.283.pdf", "content": "Building on this foundation, Olsson et al .( 2022 ) hypothesised that induction heads are capable of abstract pattern matching and conducted a qualita- tive analysis of attention patterns observed in an example from an abstract classication task."}
+{"idx": 3, "title": "Talk on In-Context Learning and Induction Heads", "date": "", "ddg_snippet": "Apr 6, 2023 · In this talk we discuss the paper Olsson et al ( 2022 ) In-context Learning and Induction Heads . The paper discusses induction heads , which are a proposed emergent structure in transformers which implements a particular algorithm.", "subpage_snippet": "", "source": "rohanhitchcock.com", "link": "https://rohanhitchcock.com/notes/2023-4-6-slt-seminar-induction-heads.html", "content": "Apr 6, 2023 · In this talk we discuss the paper Olsson et al ( 2022 ) In-context Learning and Induction Heads . The paper discusses induction heads , which are a proposed emergent structure in transformers which implements a particular algorithm."}
+{"idx": 4, "title": "In-context Learning and Induction Heads | OpenReview", "date": "", "ddg_snippet": "Dec 31, 2021 · We find that induction heads develop at precisely the same point as a sudden sharp increase in in-context learning ability, visible as a bump in the training loss. We present six complementary lines of evidence, arguing that induction heads may be the mechanistic source of general in-context learning in transformer models of any size.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nJ10GgImU0", "content": "Dec 31, 2021 · We find that induction heads develop at precisely the same point as a sudden sharp increase in in-context learning ability, visible as a bump in the training loss. We present six complementary lines of evidence, arguing that induction heads may be the mechanistic source of general in-context learning in transformer models of any size."}
+{"idx": 5, "title": "Paper page - In-context Learning and Induction Heads", "date": "", "ddg_snippet": "Abstract Induction heads in large transformer models may be the mechanism enabling in-context learning , with evidence from both small attention -only models and larger models with MLPs.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2209.11895", "content": "Abstract Induction heads in large transformer models may be the mechanism enabling in-context learning , with evidence from both small attention -only models and larger models with MLPs."}
+{"idx": 6, "title": "(PDF) In-context Learning and Induction Heads - ResearchGate", "date": "", "ddg_snippet": "Sep 23, 2022 · We find that induction heads develop at precisely the same point as a sudden sharp increase in in-context learning ability, visible as a bump in the training loss.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/363859214_In-context_Learning_and_Induction_Heads", "content": "Sep 23, 2022 · We find that induction heads develop at precisely the same point as a sudden sharp increase in in-context learning ability, visible as a bump in the training loss."}
+{"idx": 7, "title": "Which Attention Heads Matter for In-Context Learning?", "date": "", "ddg_snippet": "by K Yin · Cited by 16 — We compare the role of induction heads and function vector (FV) heads, we find that FV heads drive most of in-context learning (ICL).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KadOFOsUpQ", "content": "by K Yin · Cited by 16 — We compare the role of induction heads and function vector (FV) heads, we find that FV heads drive most of in-context learning (ICL)."}
+{"idx": 8, "title": "Which Attention Heads Matter for In-Context Learning?", "date": "", "ddg_snippet": "19 Feb 2025 — The initial evidence for induction heads ' role in ICL came from Olsson et al . ( 2022 ) , who studied small attention -only models (1-3 layers).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.14010v1", "content": "19 Feb 2025 — The initial evidence for induction heads ' role in ICL came from Olsson et al . ( 2022 ) , who studied small attention -only models (1-3 layers)."}
+{"idx": 9, "title": "Exploring Induction Heads | Cynthia Chen", "date": "", "ddg_snippet": "by C Chen — We focus here on behavioral induction heads (rather than mechanistic ones), defined as attention heads that exhibit prefix matching or copying behaviors.", "subpage_snippet": "", "source": "chenxcynthia.github.io", "link": "https://chenxcynthia.github.io/projects/induction/", "content": "by C Chen — We focus here on behavioral induction heads (rather than mechanistic ones), defined as attention heads that exhibit prefix matching or copying behaviors."}
diff --git a/data/sampled_jsons/OmniBench_Coverage_Rate_formula_page_6.jsonl b/data/sampled_jsons/OmniBench_Coverage_Rate_formula_page_6.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..540e1a7f4f87510f76c2b1a9045f96f09e9c3c42
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_Coverage_Rate_formula_page_6.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "Coverage Rate (CR). It evaluates an agent's progress on a task graph by weighting subtasks based on their depth, where deeper subtasks are assigned higher ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d5d3691187b9f32f92e8e287e5005d40aa429f80.pdf", "content": "Coverage Rate (CR). It evaluates an agent's progress on a task graph by weighting subtasks based on their depth, where deeper subtasks are assigned higher ..."}
+{"idx": 1, "title": "Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "by LI Yizhi — OmniBench is specifically designed to evaluate large-scale multimodal models with broad knowledge coverage, not human performance. The challenging questions ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Rc8z5wLzBF", "content": "by LI Yizhi — OmniBench is specifically designed to evaluate large-scale multimodal models with broad knowledge coverage, not human performance. The challenging questions ..."}
+{"idx": 2, "title": "Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "27 Mar 2025 — We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.15272v4", "content": "27 Mar 2025 — We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs ..."}
+{"idx": 3, "title": "Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "3 Oct 2024 — The main focus of OmniBench is to evaluate how well could the MLLMs understand and reconstruct the context given information from image ( I 𝐼 I ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.15272v3", "content": "3 Oct 2024 — The main focus of OmniBench is to evaluate how well could the MLLMs understand and reconstruct the context given information from image ( I 𝐼 I ..."}
+{"idx": 4, "title": "Towards Reliable Large Audio Language Model", "date": "", "ddg_snippet": "by Z Ma · 2025 — The Rejection Rate for the main results is provided in Appendix F.2. The model undergoes SFT for reliability on the speech modality and is ... 15 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.56.pdf", "content": "by Z Ma · 2025 — The Rejection Rate for the main results is provided in Appendix F.2. The model undergoes SFT for reliability on the speech modality and is ... 15 pages"}
+{"idx": 5, "title": "Track: Poster Session 6 West", "date": "", "ddg_snippet": "17 Jul 2025 — Click-Through Rate (CTR) prediction models estimate the probability of users clicking on items based on feature interactions, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50262", "content": "17 Jul 2025 — Click-Through Rate (CTR) prediction models estimate the probability of users clicking on items based on feature interactions, ..."}
+{"idx": 6, "title": "A Survey on MLLMs Aligned with Multi-modalities", "date": "", "ddg_snippet": "by S Jiang · 2025 · Cited by 2 — Omni-MLLMs aim for omni-modal understanding and generation, mapping various non-linguistic modalities into the LLM embedding space, expanding ... 36 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.453.pdf", "content": "by S Jiang · 2025 · Cited by 2 — Omni-MLLMs aim for omni-modal understanding and generation, mapping various non-linguistic modalities into the LLM embedding space, expanding ... 36 pages"}
+{"idx": 7, "title": "CVPR Poster Evaluating Vision-Language Models as ...", "date": "", "ddg_snippet": "We introduce PathEval, a novel benchmark evaluating VLMs as plan evaluators in complex path-planning scenarios.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32727", "content": "We introduce PathEval, a novel benchmark evaluating VLMs as plan evaluators in complex path-planning scenarios."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "3 days ago — In this work, we propose OREO (Offline Reasoning Optimization), an offline RL method for enhancing LLM multi-step reasoning. Building on ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=credit+assignment+strategy", "content": "3 days ago — In this work, we propose OREO (Offline Reasoning Optimization), an offline RL method for enhancing LLM multi-step reasoning. Building on ..."}
+{"idx": 9, "title": "dair-ai/ML-Papers-of-the-Week", "date": "", "ddg_snippet": "Highlighting the top ML papers every week. Contribute to dair-ai/ML-Papers-of-the-Week development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dair-ai/ML-Papers-of-the-Week?search=1", "content": "Highlighting the top ML papers every week. Contribute to dair-ai/ML-Papers-of-the-Week development by creating an account on GitHub."}
diff --git a/data/sampled_jsons/OmniBench_Section_3.3_Cross-Verification_module_description.jsonl b/data/sampled_jsons/OmniBench_Section_3.3_Cross-Verification_module_description.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c613eca7fe3b439c5cb176b2fecc9d62acc4d2f2
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_Section_3.3_Cross-Verification_module_description.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OmniBench - m-a-p.ai", "date": "", "ddg_snippet": "The OmniBench aims to create the first comprehensive benchmark for evaluating multimodal large language models that support simultaneous image, audio, and text inputs. While OmniBench is designed to evaluate the understanding capability of MLLMs on cross -modality complementary information, the models are required to interpret the multimodal input and provide accurate text answer. The problem ...", "subpage_snippet": "", "source": "m-a-p.ai", "link": "https://m-a-p.ai/OmniBench/", "content": "The OmniBench aims to create the first comprehensive benchmark for evaluating multimodal large language models that support simultaneous image, audio, and text inputs. While OmniBench is designed to evaluate the understanding capability of MLLMs on cross -modality complementary information, the models are required to interpret the multimodal input and provide accurate text answer. The problem ..."}
+{"idx": 1, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable ...", "date": "", "ddg_snippet": "The cross-verification mechanism iter-atively optimizes the demonstration trajectories and evaluation functions of subtasks, the intent extraction module ensures that the tasks have coherent goals, and the consistency validator aligns the semantics of the task graph and task instructions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "The cross-verification mechanism iter-atively optimizes the demonstration trajectories and evaluation functions of subtasks, the intent extraction module ensures that the tasks have coherent goals, and the consistency validator aligns the semantics of the task graph and task instructions."}
+{"idx": 2, "title": "GitHub - antgroup/OmniBench: [ICML 2025 Oral] This is the ...", "date": "", "ddg_snippet": "OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench", "content": "OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition."}
+{"idx": 3, "title": "OmniBench", "date": "", "ddg_snippet": "Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs. It evaluates ten virtual agent capabilities using high-quality graph-based data, ensuring scalable and realistic task assessments.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs. It evaluates ten virtual agent capabilities using high-quality graph-based data, ensuring scalable and realistic task assessments."}
+{"idx": 4, "title": "OmniBench: Towards The Future of Universal Omni-Language ...", "date": "", "ddg_snippet": "Sep 27, 2024 · Applicability in Industrial Contexts OmniBench ’s primary focus is to benchmark tri-modal understanding and reasoning abilities, targeting advancements in academic research. While industrial applications often involve task-specific tuning, OmniBench provides a robust framework for assessing the foundational quality of base models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Rc8z5wLzBF", "content": "Sep 27, 2024 · Applicability in Industrial Contexts OmniBench ’s primary focus is to benchmark tri-modal understanding and reasoning abilities, targeting advancements in academic research. While industrial applications often involve task-specific tuning, OmniBench provides a robust framework for assessing the foundational quality of base models."}
+{"idx": 5, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "May 1, 2025 · This paper introduces OmniBench , a scalable, graph-based benchmark designed to evaluate multimodal large language model (MLLM)-based virtual agents across multiple dimensions. OmniBench employs a bottom-up subtask composition pipeline to generate 36k tasks with controllable complexity across 20 scenarios.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "May 1, 2025 · This paper introduces OmniBench , a scalable, graph-based benchmark designed to evaluate multimodal large language model (MLLM)-based virtual agents across multiple dimensions. OmniBench employs a bottom-up subtask composition pipeline to generate 36k tasks with controllable complexity across 20 scenarios."}
+{"idx": 6, "title": "OmniBench/README.md at main · antgroup/OmniBench · GitHub", "date": "", "ddg_snippet": "OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench/blob/main/README.md", "content": "OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition."}
+{"idx": 7, "title": "omnibench · PyPI", "date": "", "ddg_snippet": "OmniBench follows a clean, modular architecture that makes it easy to understand and extendBuilt Distribution. omnibench -0.1.2-py3-none-any.whl (65.2 kB view details).", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/omnibench/", "content": "OmniBench follows a clean, modular architecture that makes it easy to understand and extendBuilt Distribution. omnibench -0.1.2-py3-none-any.whl (65.2 kB view details)."}
+{"idx": 8, "title": "OmniBench : Towards The Future of Universal Omni -Language Models", "date": "", "ddg_snippet": "We introduce OmniBench , a novel benchmark designed to rigorously evaluate models’ ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni -language models (OLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.15272v1", "content": "We introduce OmniBench , a novel benchmark designed to rigorously evaluate models’ ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni -language models (OLMs)."}
+{"idx": 9, "title": "GitHub - multimodal-art-projection/ OmniBench : A project for tri-modal...", "date": "", "ddg_snippet": "The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/multimodal-art-projection/OmniBench", "content": "The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously."}
diff --git a/data/sampled_jsons/OmniBench_github.io_Cross-Verification.jsonl b/data/sampled_jsons/OmniBench_github.io_Cross-Verification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..506c77cce764a251d7ccfc012da15613079d14d6
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_github.io_Cross-Verification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A ...", "date": "", "ddg_snippet": "by W Bu · Cited by 1 — ... omni-bench . github . io ... The authors' Cross - Verification method cleverly integrates subtask trajectory data with evaluation functions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "by W Bu · Cited by 1 — ... omni-bench . github . io ... The authors' Cross - Verification method cleverly integrates subtask trajectory data with evaluation functions."}
+{"idx": 1, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "Our project is available at https:// omni-bench . github . io /. 1. Introduction. With the development of MLLMs (Fei et al., 2024c; Wu et al., 2024a), recent MLLM ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d5d3691187b9f32f92e8e287e5005d40aa429f80.pdf", "content": "Our project is available at https:// omni-bench . github . io /. 1. Introduction. With the development of MLLMs (Fei et al., 2024c; Wu et al., 2024a), recent MLLM ..."}
+{"idx": 2, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "Our project is available at https://omni-bench.github.io/. first image ... Cross-verification mechanism ; Intent extraction module; Consistency validator.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/148286", "content": "Our project is available at https://omni-bench.github.io/. first image ... Cross-verification mechanism ; Intent extraction module; Consistency validator."}
+{"idx": 3, "title": "Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "23 Sept 2024 — We introduce OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.15272v1", "content": "23 Sept 2024 — We introduce OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and ..."}
+{"idx": 4, "title": "Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "27 Mar 2025 — We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.15272v4", "content": "27 Mar 2025 — We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs ..."}
+{"idx": 5, "title": "[Literature Review] What Limits Virtual Agent Application? ...", "date": "", "ddg_snippet": "9 Jun 2025 — A novel cross - verification algorithm iteratively refines trajectories and evaluation functions based on detailed feedback from evaluations.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/what-limits-virtual-agent-application-omnibench-a-scalable-multi-dimensional-benchmark-for-essential-virtual-agent-capabilities", "content": "9 Jun 2025 — A novel cross - verification algorithm iteratively refines trajectories and evaluation functions based on detailed feedback from evaluations."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "8 Aug 2025 — OmniBench : Towards The Future of Universal Omni-Language Models ... Project Page: https://vita-home.github.io. 15 authors. ·. Aug 9, 2024 ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=omni-MLLM", "content": "8 Aug 2025 — OmniBench : Towards The Future of Universal Omni-Language Models ... Project Page: https://vita-home.github.io. 15 authors. ·. Aug 9, 2024 ..."}
+{"idx": 7, "title": "Tavish9/awesome-daily-AI-arxiv", "date": "", "ddg_snippet": "Our designed agent architecture includes three core tools: precise web search tool, source credibility assessment tool and numerical claim verification tool.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Tavish9/awesome-daily-AI-arxiv", "content": "Our designed agent architecture includes three core tools: precise web search tool, source credibility assessment tool and numerical claim verification tool."}
+{"idx": 8, "title": "yaotingwangofficial/Awesome-MCoT: Multimodal Chain-of- ...", "date": "", "ddg_snippet": "We present the first systematic survey of MCoT reasoning, elucidating the foundational concepts and definitions pertinent to this area.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yaotingwangofficial/Awesome-MCoT", "content": "We present the first systematic survey of MCoT reasoning, elucidating the foundational concepts and definitions pertinent to this area."}
+{"idx": 9, "title": "Boosting Virtual Agent Learning and Reasoning: A Step- ...", "date": "", "ddg_snippet": "Poster boosting virtual agent learning and reasoning: A step-wise, multi-dimensional, and generalist reward model with benchmark.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45451", "content": "Poster boosting virtual agent learning and reasoning: A step-wise, multi-dimensional, and generalist reward model with benchmark."}
diff --git a/data/sampled_jsons/OmniBench_paper_2506.08933_Section_5.1_experimental_setup_GPU_model.jsonl b/data/sampled_jsons/OmniBench_paper_2506.08933_Section_5.1_experimental_setup_GPU_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c84640b2db47aa9801fc7a19d3b378b24f79020f
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_paper_2506.08933_Section_5.1_experimental_setup_GPU_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench : A Scalable...", "date": "", "ddg_snippet": "arXiv: 2506 . 08933 v 1 [cs.CV] 10 Jun 2025.In this section , we first introduce the experimental setup ( Sec - tion 5 . 1 ). Then, we comprehensively compare the differences in capabilities across various models on OmniBench , along with several key findings ( Section 5 .2).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "arXiv: 2506 . 08933 v 1 [cs.CV] 10 Jun 2025.In this section , we first introduce the experimental setup ( Sec - tion 5 . 1 ). Then, we comprehensively compare the differences in capabilities across various models on OmniBench , along with several key findings ( Section 5 .2)."}
+{"idx": 1, "title": "[ 2506 . 08933 ] What Limits Virtual Agent Application? OmniBench ...", "date": "", "ddg_snippet": "(or arXiv: 2506 . 08933 v 1 [cs.CV] for this version).View a PDF of the paper titled What Limits Virtual Agent Application? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities, by Wendong Bu and 12 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.08933", "content": "(or arXiv: 2506 . 08933 v 1 [cs.CV] for this version).View a PDF of the paper titled What Limits Virtual Agent Application? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities, by Wendong Bu and 12 other authors."}
+{"idx": 2, "title": "GPU System Requirements for Running DeepSeek-R1", "date": "", "ddg_snippet": "Distributed GPU Setup Required for Larger Models : DeepSeek-R1-Zero and DeepSeek-R1 require significant VRAM, making distributed GPU setups (e.g., NVIDIA A100 or H100 in multi- GPU configurations) mandatory for efficient operation.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/posts/gpu-requirements-deepseek-r1", "content": "Distributed GPU Setup Required for Larger Models : DeepSeek-R1-Zero and DeepSeek-R1 require significant VRAM, making distributed GPU setups (e.g., NVIDIA A100 or H100 in multi- GPU configurations) mandatory for efficient operation."}
+{"idx": 3, "title": "Bottleneck Calculator | CPU GPU Performance Fix", "date": "", "ddg_snippet": "Find and fix performance bottlenecks in your gaming PC. Our free tool helps you identify if your CPU is bottlenecking your GPU or vice versa, with personalized upgrade recommendations.", "subpage_snippet": "", "source": "bottleneckcalculator.to", "link": "https://bottleneckcalculator.to/", "content": "Find and fix performance bottlenecks in your gaming PC. Our free tool helps you identify if your CPU is bottlenecking your GPU or vice versa, with personalized upgrade recommendations."}
+{"idx": 4, "title": "How to Setup GPU for Deep Learning [Full Guide from...] - GeekChamp", "date": "", "ddg_snippet": "Learn step-by-step to set up your GPU for deep learning success.Before diving into setup , identify what you need from your GPU : Model Complexity: Larger models require more VRAM. Batch Sizes: Bigger batch sizes demand more memory.", "subpage_snippet": "", "source": "geekchamp.com", "link": "https://geekchamp.com/how-to-setup-gpu-for-deep-learning-full-guide-from-scratch/", "content": "Learn step-by-step to set up your GPU for deep learning success.Before diving into setup , identify what you need from your GPU : Model Complexity: Larger models require more VRAM. Batch Sizes: Bigger batch sizes demand more memory."}
+{"idx": 5, "title": "Shut Up Experimental Settings ! 1.16.2 Forge Mod Overview - YouTube", "date": "", "ddg_snippet": "A mod that that removes the experimental settings displays.Mod Link:https://www.curseforge.com/minecraft/mc-mods/shutup- experimental - settings .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=Dld4sSFwE4M", "content": "A mod that that removes the experimental settings displays.Mod Link:https://www.curseforge.com/minecraft/mc-mods/shutup- experimental - settings ."}
+{"idx": 6, "title": "3 Quick Ways to Check Your Graphics Card Model on Windows 11", "date": "", "ddg_snippet": "Knowing your GPU model is important for checking system requirements or getting driver updates. Here's how to find out on Windows 11.", "subpage_snippet": "", "source": "www.makeuseof.com", "link": "https://www.makeuseof.com/check-graphics-card-model-windows-11/", "content": "Knowing your GPU model is important for checking system requirements or getting driver updates. Here's how to find out on Windows 11."}
+{"idx": 7, "title": "How to Check Your GPU and Why It Matters | Microsoft Windows", "date": "", "ddg_snippet": "Check GPU from Settings . Select Settings > System . Select Display and scroll down to Related settings .", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/windows/learning-center/how-to-check-gpu", "content": "Check GPU from Settings . Select Settings > System . Select Display and scroll down to Related settings ."}
+{"idx": 8, "title": "OmniBench", "date": "", "ddg_snippet": "Paper Code. OmniBench spans five fundamental types of task complexity to construct 10 evaluation dimensions (see the main figure). Test tasks across these dimensions are categorized based on combinations of complexity types.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "Paper Code. OmniBench spans five fundamental types of task complexity to construct 10 evaluation dimensions (see the main figure). Test tasks across these dimensions are categorized based on combinations of complexity types."}
+{"idx": 9, "title": "How to Check What Graphics Card ( GPU ) Is in Your PC", "date": "", "ddg_snippet": "GPU is the most critical component for playing PC games, and a powerful GPU is necessary for newer games or higher graphical settings .Navigate to Video Adapter > (Your GPU ). You'll see the model , manufacturer, and other information about your GPU displayed on the right-hand side.", "subpage_snippet": "", "source": "www.howtogeek.com", "link": "https://www.howtogeek.com/414201/how-to-check-what-graphics-card-gpu-is-in-your-pc/", "content": "GPU is the most critical component for playing PC games, and a powerful GPU is necessary for newer games or higher graphical settings .Navigate to Video Adapter > (Your GPU ). You'll see the model , manufacturer, and other information about your GPU displayed on the right-hand side."}
diff --git a/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_A100.jsonl b/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_A100.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d907a5de1b1ee24de759561151d922916b0e17c
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_A100.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable ...", "date": "", "ddg_snippet": "In this section , we first introduce the experimental setup ( Sec-tion 5.1 ). Then, we comprehensively compare the differences in capabilities across various models on OmniBench , along with several key findings ( Section 5.2).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "In this section , we first introduce the experimental setup ( Sec-tion 5.1 ). Then, we comprehensively compare the differences in capabilities across various models on OmniBench , along with several key findings ( Section 5.2)."}
+{"idx": 1, "title": "NVIDIA DGX A100 User Guide", "date": "", "ddg_snippet": "NVIDIADGXA100UserGuide 1.1.1.2 ComponentDescription Table2:ComponentDescription Component Description GPU NVIDIAA100GPU CPU 2xAMDEPYC7742CPUw/64cores NVSwitch 600GB/sGPU-to-GPUbandwidth Storage(OS) 1.92TBNVMeM.2SSD(ea)inRAID1array Storage(DataCache) 3.84TBNVMeU.2SED(ea)inRAID0array (Optional7.68TBNVMeU.2.SEDs) Network(Cluster)card ...", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/dgx/dgxa100-user-guide/dgxa100-user-guide.pdf", "content": "NVIDIADGXA100UserGuide 1.1.1.2 ComponentDescription Table2:ComponentDescription Component Description GPU NVIDIAA100GPU CPU 2xAMDEPYC7742CPUw/64cores NVSwitch 600GB/sGPU-to-GPUbandwidth Storage(OS) 1.92TBNVMeM.2SSD(ea)inRAID1array Storage(DataCache) 3.84TBNVMeU.2SED(ea)inRAID0array (Optional7.68TBNVMeU.2.SEDs) Network(Cluster)card ..."}
+{"idx": 2, "title": "GitHub - multimodal-art-projection/OmniBench: A project for ...", "date": "", "ddg_snippet": "🌐 Homepage | 🏆 Leaderboard | 📖 Arxiv Paper | 🤗 Paper | 🤗 OmniBench Dataset | 🤗 OmniInstruct_V1 Dataset | 🦜 Tweets The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/multimodal-art-projection/OmniBench", "content": "🌐 Homepage | 🏆 Leaderboard | 📖 Arxiv Paper | 🤗 Paper | 🤗 OmniBench Dataset | 🤗 OmniInstruct_V1 Dataset | 🦜 Tweets The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni ..."}
+{"idx": 3, "title": "OmniBench", "date": "", "ddg_snippet": "OmniBench consists of 36k high-quality graph-structured tasks across 20 distinct scenarios (e.g. image editing, video editing) derived from its self-generating framework, with the task scale being 40x larger than most environment-based benchmarks, as shown in the comparison table.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "OmniBench consists of 36k high-quality graph-structured tasks across 20 distinct scenarios (e.g. image editing, video editing) derived from its self-generating framework, with the task scale being 40x larger than most environment-based benchmarks, as shown in the comparison table."}
+{"idx": 4, "title": "GPU Performance Background User's Guide - NVIDIA Docs", "date": "", "ddg_snippet": "Feb 1 , 2023 · As an example, an NVIDIA A100 GPU contains 108 SMs, a 40 MB L2 cache, and up to 2039 GB/s bandwidth from 80 GB of HBM2 memory. Figure 1. Simplified view of the GPU architecture Each SM has its own instruction schedulers and various instruction execution pipelines.", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/deeplearning/performance/dl-performance-gpu-background/index.html", "content": "Feb 1 , 2023 · As an example, an NVIDIA A100 GPU contains 108 SMs, a 40 MB L2 cache, and up to 2039 GB/s bandwidth from 80 GB of HBM2 memory. Figure 1. Simplified view of the GPU architecture Each SM has its own instruction schedulers and various instruction execution pipelines."}
+{"idx": 5, "title": "OmniBench: Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "These results underscore the importance of OmniBench as a tool for identifying areas of improvement and guiding research in multimodal systems. In the following sections, we detail the data collection protocol of OmniBench , present our evaluation results on current state-of-the-art MLLMs, and discuss the implications of our findings for the future of research and development. Through OmniBench ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.15272v1", "content": "These results underscore the importance of OmniBench as a tool for identifying areas of improvement and guiding research in multimodal systems. In the following sections, we detail the data collection protocol of OmniBench , present our evaluation results on current state-of-the-art MLLMs, and discuss the implications of our findings for the future of research and development. Through OmniBench ..."}
+{"idx": 6, "title": "OmniBench - m-a-p.ai", "date": "", "ddg_snippet": "OmniBench comprises 1142 question-answer pairs, with task type distribution, text length, and image and audio characteristics. The dataset's audio content falls into three categories: speech (human vocal communication), sound events (non-speech natural, environmental and mechanical sounds), and music (various compositions and performances).", "subpage_snippet": "", "source": "m-a-p.ai", "link": "https://m-a-p.ai/OmniBench/", "content": "OmniBench comprises 1142 question-answer pairs, with task type distribution, text length, and image and audio characteristics. The dataset's audio content falls into three categories: speech (human vocal communication), sound events (non-speech natural, environmental and mechanical sounds), and music (various compositions and performances)."}
+{"idx": 7, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "Experiments. In this section , we first introduce the experimental setup (Sec- tion 5.1 ). Then, we comprehensively compare the differences in capabilities ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d5d3691187b9f32f92e8e287e5005d40aa429f80.pdf", "content": "Experiments. In this section , we first introduce the experimental setup (Sec- tion 5.1 ). Then, we comprehensively compare the differences in capabilities ..."}
+{"idx": 8, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "... OmniBench ... Experiments. In this section , we first introduce the experimental setup ( Section 5.1 ). ... All experiments are conducted with NVIDIA A100 80G GPUs .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46463/paper", "content": "... OmniBench ... Experiments. In this section , we first introduce the experimental setup ( Section 5.1 ). ... All experiments are conducted with NVIDIA A100 80G GPUs ."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "In this work, we propose MCP-MedSAM, a powerful and lightweight medical SAM model designed to be trainable on a single A100 GPU with 40GB of memory within one ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=intra-modality+separation", "content": "In this work, we propose MCP-MedSAM, a powerful and lightweight medical SAM model designed to be trainable on a single A100 GPU with 40GB of memory within one ..."}
diff --git a/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_used_for_Omni-UGround-V1-7B_year_2024.jsonl b/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_used_for_Omni-UGround-V1-7B_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5024f28b3083ba12b0f4d9ff4e6c7b59ebe38f38
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_paper_Section_5.1_experimental_setup_GPU_used_for_Omni-UGround-V1-7B_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable Multi ...", "date": "", "ddg_snippet": "We follow the training details provided in the OS-Atlas paper and adopt the same experimental setup to train our backbone mod-els: OS-Atlas-4B and UGround - 7B - V1 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "We follow the training details provided in the OS-Atlas paper and adopt the same experimental setup to train our backbone mod-els: OS-Atlas-4B and UGround - 7B - V1 ."}
+{"idx": 1, "title": "multimodal-art-projection/OmniBench - GitHub", "date": "", "ddg_snippet": "🌐 Homepage | 🏆 Leaderboard | 📖 Arxiv Paper | 🤗 Paper | 🤗 OmniBench Dataset | 🤗 OmniInstruct_V1 Dataset | 🦜 Tweets The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/multimodal-art-projection/OmniBench", "content": "🌐 Homepage | 🏆 Leaderboard | 📖 Arxiv Paper | 🤗 Paper | 🤗 OmniBench Dataset | 🤗 OmniInstruct_V1 Dataset | 🦜 Tweets The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni ..."}
+{"idx": 2, "title": "OmniBench", "date": "", "ddg_snippet": "OmniBench consists of 36k high-quality graph-structured tasks across 20 distinct scenarios (e.g. image editing, video editing) derived from its self-generating framework, with the task scale being 40x larger than most environment-based benchmarks, as shown in the comparison table.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "OmniBench consists of 36k high-quality graph-structured tasks across 20 distinct scenarios (e.g. image editing, video editing) derived from its self-generating framework, with the task scale being 40x larger than most environment-based benchmarks, as shown in the comparison table."}
+{"idx": 3, "title": "OSU-NLP-Group/UGround - GitHub", "date": "", "ddg_snippet": "The performance of Qwen2-VL-based UGround-V1 on several benchmarks are also updated on the homepage (e.g., AndroidWorld: 33->44). 2025/01/05: Qwen2-VL-based UGround-V1 acheives SOTA results on a new and comprehensive GUI grounding benchmark ScreenSpot-Pro, substaintially outperforms prior models (18.9->31.1).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/UGround", "content": "The performance of Qwen2-VL-based UGround-V1 on several benchmarks are also updated on the homepage (e.g., AndroidWorld: 33->44). 2025/01/05: Qwen2-VL-based UGround-V1 acheives SOTA results on a new and comprehensive GUI grounding benchmark ScreenSpot-Pro, substaintially outperforms prior models (18.9->31.1)."}
+{"idx": 4, "title": "OmniBench - m-a-p.ai", "date": "", "ddg_snippet": "OmniBench comprises 1142 question-answer pairs, with task type distribution, text length, and image and audio characteristics. The dataset's audio content falls into three categories: speech (human vocal communication), sound events (non-speech natural, environmental and mechanical sounds), and music (various compositions and performances).", "subpage_snippet": "", "source": "m-a-p.ai", "link": "https://m-a-p.ai/OmniBench/", "content": "OmniBench comprises 1142 question-answer pairs, with task type distribution, text length, and image and audio characteristics. The dataset's audio content falls into three categories: speech (human vocal communication), sound events (non-speech natural, environmental and mechanical sounds), and music (various compositions and performances)."}
+{"idx": 5, "title": "(PDF) Baichuan-Omni Technical Report - ResearchGate", "date": "", "ddg_snippet": "In this paper , we introduce Baichuan- Omni , the first open-source 7B Multimodal Large Language Model (MLLM) adept at concurrently processing and analyzing modalities of image, video, audio, and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384887170_Baichuan-Omni_Technical_Report", "content": "In this paper , we introduce Baichuan- Omni , the first open-source 7B Multimodal Large Language Model (MLLM) adept at concurrently processing and analyzing modalities of image, video, audio, and ..."}
+{"idx": 6, "title": "OmniBench: Towards The Future of Universal Omni-Language Models", "date": "", "ddg_snippet": "Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.15272", "content": "Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We ..."}
+{"idx": 7, "title": "How-to guides - omnibenchmark", "date": "", "ddg_snippet": "How-to guides Install Omnibenchmark Omnibenchmark is a pip-installable python package (PyPI, source code). Supported Platforms", "subpage_snippet": "", "source": "docs.omnibenchmark.org", "link": "https://docs.omnibenchmark.org/latest/howto/", "content": "How-to guides Install Omnibenchmark Omnibenchmark is a pip-installable python package (PyPI, source code). Supported Platforms"}
+{"idx": 8, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank models with human preferences. However, traditional Arena methods, while established, require an excessive number of comparisons for ranking to converge and are vulnerable to preference noise in voting, suggesting the ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=WISE+benchmark", "content": "The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank models with human preferences. However, traditional Arena methods, while established, require an excessive number of comparisons for ranking to converge and are vulnerable to preference noise in voting, suggesting the ..."}
+{"idx": 9, "title": "arXiv:2409.15272v4 [cs.CL] 27 Mar 2025", "date": "", "ddg_snippet": "ssing as omni -language mod-els (OLMs). OmniBench features high-quality human annotations that require integrate under-standing across all modalities. Our evaluation re-veals that: i) open-source OLMs show significant limitations in instruction-following and reasoning in tri-modal contexts; and ii) most baseline mod-els perform poorly (around 50 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.15272", "content": "ssing as omni -language mod-els (OLMs). OmniBench features high-quality human annotations that require integrate under-standing across all modalities. Our evaluation re-veals that: i) open-source OLMs show significant limitations in instruction-following and reasoning in tri-modal contexts; and ii) most baseline mod-els perform poorly (around 50 ..."}
diff --git a/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Bregman_divergence_density_ratio.jsonl b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Bregman_divergence_density_ratio.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4a6e884ead380fc4f64d56e38b2f619f710e5e0b
--- /dev/null
+++ b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Bregman_divergence_density_ratio.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF | alphaXiv", "date": "", "ddg_snippet": "The connection between RLHF and imitation learning established in this paper has several important implicationsExploring different density ratio estimation techniques beyond Bregman divergences .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.05079v1", "content": "The connection between RLHF and imitation learning established in this paper has several important implicationsExploring different density ratio estimation techniques beyond Bregman divergences ."}
+{"idx": 2, "title": "[Literature Review] On a Connection Between Imitation Learning ...", "date": "", "ddg_snippet": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-a-connection-between-imitation-learning-and-rlhf", "content": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning ."}
+{"idx": 3, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2QdsjiNXgj", "content": "We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 4, "title": "tengxiao1/DIL: On a Connection Between Imitation Learning and ...", "date": "", "ddg_snippet": "This repository contains the code for our ICLR 2025 paper On a Connection Between Imitation Learning and RLHF .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL", "content": "This repository contains the code for our ICLR 2025 paper On a Connection Between Imitation Learning and RLHF ."}
+{"idx": 5, "title": "(PDF) ALIGN: Word Association Learning for Cross-Cultural...", "date": "", "ddg_snippet": "2025. On a connection between imitation learning and RLHF . In The Thirteenth International Conference on Learning Representations.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/143590844/ALIGN_Word_Association_Learning_for_Cross_Cultural_Generalization_in_Large_Language_Models", "content": "2025. On a connection between imitation learning and RLHF . In The Thirteenth International Conference on Learning Representations."}
+{"idx": 6, "title": "Yige Yuan - Google Scholar", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF .MITA: Bridging the Gap between Model and Data for Test-time Adaptation.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=lf6GtCIAAAAJ&hl=en", "content": "On a Connection Between Imitation Learning and RLHF .MITA: Bridging the Gap between Model and Data for Test-time Adaptation."}
+{"idx": 7, "title": "Vasant Honavar | The Huck Institutes (en-US)", "date": "", "ddg_snippet": "On a connection between imitation learning and rlhf . Teng Xiao, Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G. Honavar, 2025, on p. 4670-4685. Dspo: direct score preference optimization for diffusion model alignment.", "subpage_snippet": "", "source": "www.huck.psu.edu", "link": "https://www.huck.psu.edu/people/vasant-hanavar", "content": "On a connection between imitation learning and rlhf . Teng Xiao, Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G. Honavar, 2025, on p. 4670-4685. Dspo: direct score preference optimization for diffusion model alignment."}
+{"idx": 8, "title": "Zhengyu CHEN | Zhejiang University, Hangzhou | ZJU | College of...", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF .This work studies the alignment of large language models with preference data from an imitation learning perspective.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Zhengyu-Chen-8", "content": "On a Connection Between Imitation Learning and RLHF .This work studies the alignment of large language models with preference data from an imitation learning perspective."}
+{"idx": 9, "title": "dblp: List of computer science publications by Vasant G. Honavar", "date": "", "ddg_snippet": "Neil Ashtekar, Jingxi Zhu, Vasant G. Honavar: Class Incremental Learning from First Principles: A Review.Teng Xiao, Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G. Honavar: On a Connection Between Imitation Learning and RLHF .", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/h/VasantHonavar.html", "content": "Neil Ashtekar, Jingxi Zhu, Vasant G. Honavar: Class Incremental Learning from First Principles: A Review.Teng Xiao, Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G. Honavar: On a Connection Between Imitation Learning and RLHF ."}
diff --git a/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_equation_21_LSIF_loss_function.jsonl b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_equation_21_LSIF_loss_function.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2fad97f8cfe8b9b3b19cc95b002520a017ec151f
--- /dev/null
+++ b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_equation_21_LSIF_loss_function.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2QdsjiNXgj", "content": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 1, "title": "ON A CONNECTION BETWEEN IMITATION LEARNING ...", "date": "", "ddg_snippet": "by T Xiao · Cited by 14 — We establish a close theoretical connection be- tween reinforcement learning from human feedback ( RLHF ) and imitation learning . (IL), revealing that RLHF ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2QdsjiNXgj", "content": "by T Xiao · Cited by 14 — We establish a close theoretical connection be- tween reinforcement learning from human feedback ( RLHF ) and imitation learning . (IL), revealing that RLHF ..."}
+{"idx": 2, "title": "learning and alignment with human preferences and values", "date": "", "ddg_snippet": "by T Xiao · 2025 — Recent work proposes simplifying RLHF by directly optimizing language models with contrastive learning on preference data, resulting in contrastive preference ...", "subpage_snippet": "", "source": "etda.libraries.psu.edu", "link": "https://etda.libraries.psu.edu/files/final_submissions/32968", "content": "by T Xiao · 2025 — Recent work proposes simplifying RLHF by directly optimizing language models with contrastive learning on preference data, resulting in contrastive preference ..."}
+{"idx": 3, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose DIL, a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.05079", "content": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose DIL, a ..."}
+{"idx": 4, "title": "On a Connection Between Imitation Learningand RLHF", "date": "", "ddg_snippet": "RLHF Step 2: Reinforcement Learning Reinforcement learning objective: KL-regularized reward maximization Reward model KL regularization (proxy human preference) (closed to reference model) Complexity and poor stability due to the high variance in optimization", "subpage_snippet": "", "source": "aitime-lundao.oss-cn-beijing.aliyuncs.com", "link": "https://aitime-lundao.oss-cn-beijing.aliyuncs.com/AitimeReport/20250312/1741781794566", "content": "RLHF Step 2: Reinforcement Learning Reinforcement learning objective: KL-regularized reward maximization Reward model KL regularization (proxy human preference) (closed to reference model) Complexity and poor stability due to the high variance in optimization"}
+{"idx": 5, "title": "PDF Lecture 8: Imitation Learning and RLHF - Stanford University", "date": "", "ddg_snippet": "Imitation Learning Summary Imitation learning can greatly reduce the amount of data need to learn a good policy Challenges remain and one exciting area is combining inverse RL / learning from demonstration and online reinforcement learning For a look into some of the theory between imitation learning and RL, see Sun, Venkatraman, Gordon, Boots, Bagnell (ICML 2017)", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs234/slides/lecture8post.pdf", "content": "Imitation Learning Summary Imitation learning can greatly reduce the amount of data need to learn a good policy Challenges remain and one exciting area is combining inverse RL / learning from demonstration and online reinforcement learning For a look into some of the theory between imitation learning and RL, see Sun, Venkatraman, Gordon, Boots, Bagnell (ICML 2017)"}
+{"idx": 6, "title": "PDF On a Connection Between Imitation Learning and Rlhf", "date": "", "ddg_snippet": "One can note that the above imitation learning loss over energy-based policy is exactly the same as the reward loss based on BT assumption in Equation (3) in RLHF .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/acf4a08f67724e9d2de34099f57a9c25-Paper-Conference.pdf", "content": "One can note that the above imitation learning loss over energy-based policy is exactly the same as the reward loss based on BT assumption in Equation (3) in RLHF ."}
+{"idx": 7, "title": "Connection Between Imitation Learning and RLHF | Cong's Log", "date": "", "ddg_snippet": "It can be noted that the imitation learning loss based on the energy strategy is identical to the reward loss of RLHF based on the BT assumption ( Equation (3)).", "subpage_snippet": "", "source": "congchan.github.io", "link": "https://congchan.github.io/posts/connection-between-imitation-learning-and-rlhf/", "content": "It can be noted that the imitation learning loss based on the energy strategy is identical to the reward loss of RLHF based on the BT assumption ( Equation (3))."}
+{"idx": 8, "title": "【深度论文解读】On a Connection Between Imitation Learning and RLHF (7 Mar 2025)", "date": "", "ddg_snippet": "1 此文动机 这篇文章做了一篇类似 DPO (Direct Preference Optimization) 却高于它的工作 (通过理论分析将 reward 融进 loss 中，再付诸实验)，将 IL ( Imitation Learning ) 与 RLHF (Reinforcement Learning for Human Feedback) 归一为 DIL (Direct Imitation Learning )，其中涉及大量概念和数学推导以及丰富的实验，直接阅读难以短时间理解 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1910382777079165403", "content": "1 此文动机 这篇文章做了一篇类似 DPO (Direct Preference Optimization) 却高于它的工作 (通过理论分析将 reward 融进 loss 中，再付诸实验)，将 IL ( Imitation Learning ) 与 RLHF (Reinforcement Learning for Human Feedback) 归一为 DIL (Direct Imitation Learning )，其中涉及大量概念和数学推导以及丰富的实验，直接阅读难以短时间理解 ..."}
+{"idx": 9, "title": "dblp: On a Connection Between Imitation Learning and RLHF.", "date": "", "ddg_snippet": "Bibliographic details on On a Connection Between Imitation Learning and RLHF .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/XiaoYLCH25", "content": "Bibliographic details on On a Connection Between Imitation Learning and RLHF ."}
diff --git a/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Algorithm_1_Sahara_computational_compl.jsonl b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Algorithm_1_Sahara_computational_compl.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..29277a7d44c3b8c8b70495223d9563d8412bc690
--- /dev/null
+++ b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Algorithm_1_Sahara_computational_compl.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.13708", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model ."}
+{"idx": 1, "title": "PDF On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Based on this, we gen-eralize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads in -side the model . Our findings show that the special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/d0bcff6425bbf850ec87d5327a965db9-Paper-Conference.pdf", "content": "Based on this, we gen-eralize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads in -side the model . Our findings show that the special attention head has a significant impact on safety ."}
+{"idx": 2, "title": "GitHub - ydyjya/SafetyHeadAttribution", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ydyjya/SafetyHeadAttribution", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety ."}
+{"idx": 3, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "The Safety Attention Head Attribution Algorithm ( Sahara ) is presented to identify groups of heads whose ablation weakens safety capabilities. Findings include the importance of certain attention heads for safety , overlap of safety heads in fine - tuned models , and minimal impact of ablating these heads on helpfulness, providing a basis for ...", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper/arxiv/2410.13708", "content": "The Safety Attention Head Attribution Algorithm ( Sahara ) is presented to identify groups of heads whose ablation weakens safety capabilities. Findings include the importance of certain attention heads for safety , overlap of safety heads in fine - tuned models , and minimal impact of ablating these heads on helpfulness, providing a basis for ..."}
+{"idx": 4, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "This paper proposes a novel metric which tailored for multi- head attention , the Safety Head ImPortant Score (Ships), to assess the individual heads' contributions to model safety and introduces the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Large language models (LLMs) achieve state- of - the -art performance on multiple ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-the-Role-of-Attention-Heads-in-Large-Language-Zhou-Yu/c2bb84fbd2e9ec75e275f049353ed5d867ad5748", "content": "This paper proposes a novel metric which tailored for multi- head attention , the Safety Head ImPortant Score (Ships), to assess the individual heads' contributions to model safety and introduces the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Large language models (LLMs) achieve state- of - the -art performance on multiple ..."}
+{"idx": 5, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=h0Ak8A5yqw", "content": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that special attention head has a significant impact on safety ."}
+{"idx": 6, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety ."}
+{"idx": 7, "title": "SafetyHeadAttribution/Readme.md at main · ydyjya ... - GitHub", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ydyjya/SafetyHeadAttribution/blob/main/Readme.md", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety ."}
+{"idx": 8, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the ..."}
+{"idx": 9, "title": "PDF arXiv:2410.13708v1 [cs.CL] 17 Oct 2024 - ResearchGate", "date": "", "ddg_snippet": "-v1.5, underscoring its effectiveness. This work also presents the Safety Attention Head Attribution Algorithm ( Sahara ), a generalized version of Ships that identifies groups of heads whos", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety/fulltext/6711f20e069cb92a811a75e8/On-the-Role-of-Attention-Heads-in-Large-Language-Model-Safety.pdf", "content": "-v1.5, underscoring its effectiveness. This work also presents the Safety Attention Head Attribution Algorithm ( Sahara ), a generalized version of Ships that identifies groups of heads whos"}
diff --git a/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Figure_2a_ASR_Advbench_year_2023-2024.jsonl b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Figure_2a_ASR_Advbench_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8e3260d196e276355b6f236169ed7e260b7e6a86
--- /dev/null
+++ b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Figure_2a_ASR_Advbench_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "October 17, 2024 - Therefore, when a head is ablated, ... responses, indicating that the ablated head is most likely safety parameter. ... Figure 2: Attack success rate (ASR) for harmful queries after ablating the most important safety attention head (bars with x-axis labels ‘Greedy’ and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v1", "content": "October 17, 2024 - Therefore, when a head is ablated, ... responses, indicating that the ablated head is most likely safety parameter. ... Figure 2: Attack success rate (ASR) for harmful queries after ablating the most important safety attention head (bars with x-axis labels ‘Greedy’ and ..."}
+{"idx": 1, "title": "arXiv [2410.13708] On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "February 24, 2025 - Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that when safety representations or component ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.13708", "content": "February 24, 2025 - Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that when safety representations or component ..."}
+{"idx": 2, "title": "OpenReview On the Role of Attention Heads in Large Language Model Safety | OpenReview", "date": "", "ddg_snippet": "October 4, 2024 - Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm (Sahara) to attribute the critical safety attention heads inside the model. Our findings show that special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=h0Ak8A5yqw", "content": "October 4, 2024 - Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm (Sahara) to attribute the critical safety attention heads inside the model. Our findings show that special attention head has a significant impact on safety ."}
+{"idx": 3, "title": "ResearchGate (PDF) On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "October 17, 2024 - attention head (bars with x-axis labels ‘Greedy’ and ‘Top-5’), calculated using Ships. And ‘tem- plate’ means using chat template as input, ‘direct’ means direct input (refer to Appendix B.2 for · detailed introduce). Figure 2a shows results with undifferentiated attention , while ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety", "content": "October 17, 2024 - attention head (bars with x-axis labels ‘Greedy’ and ‘Top-5’), calculated using Ships. And ‘tem- plate’ means using chat template as input, ‘direct’ means direct input (refer to Appendix B.2 for · detailed introduce). Figure 2a shows results with undifferentiated attention , while ..."}
+{"idx": 4, "title": "arXiv Safety Alignment Should Be Made More Than Just A Few Attention Heads", "date": "", "ddg_snippet": "1 month ago - Current safety alignment for large language models (LLMs) continues to present vulnerabilities, given that adversarial prompting can effectively bypass their safety measures. Our investigation shows that these safety mechanisms predominantly depend on a limited subset of attention heads : removing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19697", "content": "1 month ago - Current safety alignment for large language models (LLMs) continues to present vulnerabilities, given that adversarial prompting can effectively bypass their safety measures. Our investigation shows that these safety mechanisms predominantly depend on a limited subset of attention heads : removing ..."}
+{"idx": 5, "title": "ICLR ICLR 2025 On the Role of Attention Heads in Large Language Model Safety Oral", "date": "", "ddg_snippet": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm (Sahara) to attribute the critical safety attention heads inside the model. Our findings show that special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/oral/31798", "content": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm (Sahara) to attribute the critical safety attention heads inside the model. Our findings show that special attention head has a significant impact on safety ."}
+{"idx": 6, "title": "GitHub GitHub - IAAR-Shanghai/Awesome-Attention-Heads: An awesome repository & A comprehensive survey on interpretability of LLM attention heads.", "date": "", "ddg_snippet": "An awesome repository & A comprehensive survey on interpretability of LLM attention heads . - IAAR-Shanghai/Awesome- Attention - Heads", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IAAR-Shanghai/Awesome-Attention-Heads", "content": "An awesome repository & A comprehensive survey on interpretability of LLM attention heads . - IAAR-Shanghai/Awesome- Attention - Heads"}
+{"idx": 7, "title": "The Moonlight [Literature Review] On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "The findings suggest fine-tuned ... properties derived from initial training. The findings validate that certain attention heads are instrumental in maintaining the safety capabilities of LLMs , providing insight into their function and suggesting pathways for improving ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-the-role-of-attention-heads-in-large-language-model-safety", "content": "The findings suggest fine-tuned ... properties derived from initial training. The findings validate that certain attention heads are instrumental in maintaining the safety capabilities of LLMs , providing insight into their function and suggesting pathways for improving ..."}
+{"idx": 8, "title": "ICLR On the Role of Attention Heads in Large Language Model ...", "date": "", "ddg_snippet": "The Fourteenth International Conference on Learning Representations · Rio de Janeiro, Brazil", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/31798_cDe2uvw.pdf", "content": "The Fourteenth International Conference on Learning Representations · Rio de Janeiro, Brazil"}
+{"idx": 9, "title": "AI Models On the Role of Attention Heads in Large Language Model Safety | AI Research Paper Details", "date": "", "ddg_snippet": "The paper introduces a novel technique called \"Safety Head\" to enhance the safety and alignment of large language models. Attention heads are a key component of these models, which learn to focus on different parts of the input text when generating output .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/role-attention-heads-large-language-model-safety", "content": "The paper introduces a novel technique called \"Safety Head\" to enhance the safety and alignment of large language models. Attention heads are a key component of these models, which learn to focus on different parts of the input text when generating output ."}
diff --git a/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Section_5.1_mechanistic_explanation_un.jsonl b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Section_5.1_mechanistic_explanation_un.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..894729bb044af46a1132f5d820bd85b84af826a8
--- /dev/null
+++ b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Section_5.1_mechanistic_explanation_un.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Interpreting Attention Layer Outputs with Sparse Autoencoders", "date": "", "ddg_snippet": "... to systematically inspect every attention head in GPT-2 Small ( Section 4. 1 ), and extend this analysis to make progress on the open question of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.17759v1", "content": "... to systematically inspect every attention head in GPT-2 Small ( Section 4. 1 ), and extend this analysis to make progress on the open question of ..."}
+{"idx": 1, "title": "Causal Head Gating: A Framework for Interpreting Roles of", "date": "", "ddg_snippet": "We present causal head gating (CHG), a scalable method for interpreting the functional roles of attention heads in transformer models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13737v1", "content": "We present causal head gating (CHG), a scalable method for interpreting the functional roles of attention heads in transformer models ."}
+{"idx": 2, "title": "Explaining AI through mechanistic interpretability | European", "date": "", "ddg_snippet": "However, the picture of XAI research that is typically presented is one in which computational methods deliver explanations for specific stakeholders ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s13194-024-00614-4", "content": "However, the picture of XAI research that is typically presented is one in which computational methods deliver explanations for specific stakeholders ..."}
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+{"idx": 4, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Orecchia , Lorenzo; Ameranis , Konstantinos; Tsourakakis , Charalampos; Talwar , Kunal Date Published: 2022-07-01 Journal Name: Proceedings of the 39th International Conference on Machine Learning (ICML 2022) in Proceedings of Machine Learning Research Volume: 162 Page Range / eLocation ID: 17071-17093 Format (s): Medium: X Sponsoring Org:", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10349734-practical-almost-linear-time-approximation-algorithms-hybrid-overlapping-graph-clustering", "content": "Orecchia , Lorenzo; Ameranis , Konstantinos; Tsourakakis , Charalampos; Talwar , Kunal Date Published: 2022-07-01 Journal Name: Proceedings of the 39th International Conference on Machine Learning (ICML 2022) in Proceedings of Machine Learning Research Volume: 162 Page Range / eLocation ID: 17071-17093 Format (s): Medium: X Sponsoring Org:"}
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+{"idx": 0, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — I did not check the proofs in the supplementary material in detail . ... ( Orecchia et al., 2022 ) whose operating environment includes a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "by Y Pan — I did not check the proofs in the supplementary material in detail . ... ( Orecchia et al., 2022 ) whose operating environment includes a ..."}
+{"idx": 1, "title": "Harnessing Heterogeneous Statistical Strength for ...", "date": "", "ddg_snippet": "... Orecchia , 2018) to optimize the objective functions. The optimization process consists of two alternating steps: first on the server side, we optimize the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44831", "content": "... Orecchia , 2018) to optimize the objective functions. The optimization process consists of two alternating steps: first on the server side, we optimize the ..."}
+{"idx": 2, "title": "Stacey: Promoting Stochastic Steepest Descent via ...", "date": "", "ddg_snippet": "18 Jun 2025 — The supplementary material includes code, additional proofs, and extra experimental results. ... Orecchia , 2017; Nemirovskii & Nesterov, 1985).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=TaqwI9qF5Q&noteId=8PBwvla0hL", "content": "18 Jun 2025 — The supplementary material includes code, additional proofs, and extra experimental results. ... Orecchia , 2017; Nemirovskii & Nesterov, 1985)."}
+{"idx": 3, "title": "Cut-matching Games for Generalized Hypergraph Ratio Cuts", "date": "", "ddg_snippet": "by N Veldt · 2023 · Cited by 6 — In International Conference on Machine Learning,. ICML '22, pages 17071–17093, 2022 . [40] Lorenzo Orecchia , Leonard J Schulman, Umesh V Vazirani ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2301.12274", "content": "by N Veldt · 2023 · Cited by 6 — In International Conference on Machine Learning,. ICML '22, pages 17071–17093, 2022 . [40] Lorenzo Orecchia , Leonard J Schulman, Umesh V Vazirani ..."}
+{"idx": 4, "title": "Stacey: Promoting Stochastic Steepest Descent via ...", "date": "", "ddg_snippet": "(Cited on page 16.) Kelner, J. A., Lee, Y. T., Orecchia , L., and Sidford, A. An almost-linear-time algorithm for approximate max flow in undirected graphs, and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45168", "content": "(Cited on page 16.) Kelner, J. A., Lee, Y. T., Orecchia , L., and Sidford, A. An almost-linear-time algorithm for approximate max flow in undirected graphs, and ..."}
+{"idx": 5, "title": "Effects of Dropout on Performance in Long-range Graph ...", "date": "", "ddg_snippet": "by J Singh · 2025 — 1, and the experimental details are in Appendix E.2. 5.1 ... [1] Zeyuan Allen-Zhu, Aditya Bhaskara, Silvio Lattanzi, Vahab Mirrokni, and Lorenzo Orecchia .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.07364", "content": "by J Singh · 2025 — 1, and the experimental details are in Appendix E.2. 5.1 ... [1] Zeyuan Allen-Zhu, Aditya Bhaskara, Silvio Lattanzi, Vahab Mirrokni, and Lorenzo Orecchia ."}
+{"idx": 6, "title": "EXTRA-NEWTON: A First Approach to Noise-Adaptive ...", "date": "", "ddg_snippet": "by K Antonakopoulos · Cited by 14 — the International. Conference on Machine Learning ( ICML ), June 2019. [19] Jelena Diakonikolas and Lorenzo Orecchia . Accelerated extra -gradient descent: A ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2022/file/c10804702be5a0cca89331315413f1a2-Paper-Conference.pdf", "content": "by K Antonakopoulos · Cited by 14 — the International. Conference on Machine Learning ( ICML ), June 2019. [19] Jelena Diakonikolas and Lorenzo Orecchia . Accelerated extra -gradient descent: A ..."}
+{"idx": 7, "title": "Faster Local Solvers for Graph Diffusion Equations", "date": "", "ddg_snippet": "by J Bai · 2024 — [61] Lorenzo Orecchia , Sushant Sachdeva, and Nisheeth K Vishnoi. Approximating the exponential, the lanczos method and an o(m)-time spectral algorithm for ... 39 pages", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/0506ad3d1bcc8398a920db9340f27fe4-Paper-Conference.pdf", "content": "by J Bai · 2024 — [61] Lorenzo Orecchia , Sushant Sachdeva, and Nisheeth K Vishnoi. Approximating the exponential, the lanczos method and an o(m)-time spectral algorithm for ... 39 pages"}
+{"idx": 8, "title": "A Tighter Analysis of Spectral Clustering, and Beyond", "date": "", "ddg_snippet": "by P Macgregor · 2022 · Cited by 21 — Orecchia , L. and Zhu, Z. A. Flow-based algorithms for lo- cal graph ... In this section we provide additional details on our experimental setup and provide some ... 26 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/macgregor22a/macgregor22a.pdf", "content": "by P Macgregor · 2022 · Cited by 21 — Orecchia , L. and Zhu, Z. A. Flow-based algorithms for lo- cal graph ... In this section we provide additional details on our experimental setup and provide some ... 26 pages"}
+{"idx": 9, "title": "Training Deep Learning Models with Norm-Constrained ...", "date": "", "ddg_snippet": "Kelner, J. A., Lee, Y. T., Orecchia , L., and Sidford, A. An almost-linear-time algorithm for approximate max flow in undirected graphs, and its ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46586", "content": "Kelner, J. A., Lee, Y. T., Orecchia , L., and Sidford, A. An almost-linear-time algorithm for approximate max flow in undirected graphs, and its ..."}
diff --git a/data/sampled_jsons/Orecchia_Practical_Almost-Linear-Time_machine_server_computing_environment_hardware.jsonl b/data/sampled_jsons/Orecchia_Practical_Almost-Linear-Time_machine_server_computing_environment_hardware.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..26500cc550465b6a5be15a8fe3e82b23f5005ebd
--- /dev/null
+++ b/data/sampled_jsons/Orecchia_Practical_Almost-Linear-Time_machine_server_computing_environment_hardware.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Practical Almost - Linear - Time Approximation Algorithms for Hybrid...", "date": "", "ddg_snippet": "While almost - linear - time approximation algorithms are known for edge-boundary-based graph partitioning, little progress has been made on fast algorithms for HGP, even in the special case of vertex-boundary-based graph partitioning.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a.html", "content": "While almost - linear - time approximation algorithms are known for edge-boundary-based graph partitioning, little progress has been made on fast algorithms for HGP, even in the special case of vertex-boundary-based graph partitioning."}
+{"idx": 1, "title": "ICML Poster Practical Almost - Linear - Time Approximation Algorithms...", "date": "", "ddg_snippet": "While almost - linear - time approximation algorithms are known for edge-boundary-based graph partitioning, little progress has been made on fast algorithms for HGP, even in the special case of vertex-boundary-based graph partitioning.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/poster/16879", "content": "While almost - linear - time approximation algorithms are known for edge-boundary-based graph partitioning, little progress has been made on fast algorithms for HGP, even in the special case of vertex-boundary-based graph partitioning."}
+{"idx": 2, "title": "dblp: List of computer science publications by Lorenzo Orecchia", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar: Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML 2022: 17071-17093. 2020.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/32/4340.html", "content": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar: Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML 2022: 17071-17093. 2020."}
+{"idx": 3, "title": "Practical Almost - Linear - Time ... | Connected Papers Search", "date": "", "ddg_snippet": "Showing paper suggestions for \" Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering\". Tip: hold ctrl while clicking a paper to build it in the background.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/search?q=Practical+Almost-Linear-Time+Approximation+Algorithms+for+Hybrid+and+Overlapping+Graph+Clustering", "content": "Showing paper suggestions for \" Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering\". Tip: hold ctrl while clicking a paper to build it in the background."}
+{"idx": 4, "title": "Proceedings of the International Conference on Machine Learning 2022", "date": "", "ddg_snippet": "• nternational Conference on Machine Learning (ICML 2022). Proceedings of Machine Learning Research Volume 162. Baltimore, Maryland, USA 17-23 July 2022. Part 1 of 33.Lorenzo Orecchia , Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar.", "subpage_snippet": "", "source": "www.proceedings.com", "link": "https://www.proceedings.com/content/068/068317webtoc.pdf", "content": "• nternational Conference on Machine Learning (ICML 2022). Proceedings of Machine Learning Research Volume 162. Baltimore, Maryland, USA 17-23 July 2022. Part 1 of 33.Lorenzo Orecchia , Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar."}
+{"idx": 5, "title": "Department of Computer Science > Academic Catalog | The", "date": "", "ddg_snippet": "... Computer Science, that includes the whole spectrum of computing , from relevant mathematics and statistics to building hardware devices, networks, ...", "subpage_snippet": "", "source": "graduateannouncements.uchicago.edu", "link": "http://graduateannouncements.uchicago.edu/graduate/departmentofcomputerscience/", "content": "... Computer Science, that includes the whole spectrum of computing , from relevant mathematics and statistics to building hardware devices, networks, ..."}
+{"idx": 6, "title": "Milwaukee Archives - nathaniel stern", "date": "", "ddg_snippet": "At times the exhibition felt overly familiar, reminiscent of other art and literature describing the world emerging from the tide of mechanical ...", "subpage_snippet": "", "source": "nathanielstern.com", "link": "https://nathanielstern.com/text-tag/milwaukee/", "content": "At times the exhibition felt overly familiar, reminiscent of other art and literature describing the world emerging from the tide of mechanical ..."}
+{"idx": 7, "title": "Κonstantinos Ameranis - Google Scholar", "date": "", "ddg_snippet": "Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering.K Ameranis, AF DePavia, L Orecchia , E Tani. Forty-first International Conference on Machine Learning, 2024.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=eajqSs4AAAAJ&hl=en", "content": "Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering.K Ameranis, AF DePavia, L Orecchia , E Tani. Forty-first International Conference on Machine Learning, 2024."}
+{"idx": 8, "title": "Publications", "date": "", "ddg_snippet": "Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Co-authors: Kostas Ameranis, Lorenzo Orecchia , Kunal Talwar International Conference on Machine Learning (ICML 2022).", "subpage_snippet": "", "source": "tsourakakis.com", "link": "https://tsourakakis.com/publications/", "content": "Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Co-authors: Kostas Ameranis, Lorenzo Orecchia , Kunal Talwar International Conference on Machine Learning (ICML 2022)."}
+{"idx": 9, "title": "Fast algorithms for hypergraph pagerank with applications to...", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos Tsourakakis, and Kunal Talwar. Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3692125", "content": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos Tsourakakis, and Kunal Talwar. Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering."}
diff --git a/data/sampled_jsons/Orecchia_overlapping_graph_clustering_ICML_hardware_experiment_setup_year_2022.jsonl b/data/sampled_jsons/Orecchia_overlapping_graph_clustering_ICML_hardware_experiment_setup_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..60745d1655fe4e14d339f9564cf48267cd766a4f
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+++ b/data/sampled_jsons/Orecchia_overlapping_graph_clustering_ICML_hardware_experiment_setup_year_2022.jsonl
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+{"idx": 0, "title": "Cluster analysis - Wikipedia", "date": "", "ddg_snippet": "The result of a cluster analysis shown as the coloring of the squares into three clusters . Cluster analysis, or clustering , is a data analysis technique aimed at partitioning a set of objects into groups such that objects within the same group exhibi...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cluster_analysis", "content": "The result of a cluster analysis shown as the coloring of the squares into three clusters . Cluster analysis, or clustering , is a data analysis technique aimed at partitioning a set of objects into groups such that objects within the same group exhibi..."}
+{"idx": 1, "title": "dblp: List of computer science publications by Lorenzo Orecchia", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar: Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering . ICML 2022: 17071-17093.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/32/4340.html", "content": "Lorenzo Orecchia , Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar: Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering . ICML 2022: 17071-17093."}
+{"idx": 2, "title": "Vilhelm Agdur: Community Structure in Graphs : Algorithms and...", "date": "", "ddg_snippet": "In Paper I, we study the problem of finding overlapping clusterings of hypergraphs, continuing the line of research started by Carlsson and Mémoli (2013) of classifying clustering schemes as functors.", "subpage_snippet": "", "source": "www.uu.se", "link": "https://www.uu.se/kalendarium/disputationer/2025-09-19-vilhelm-agdur-community-structure-in-graphs-algorithms-and-theoretical-guarantees", "content": "In Paper I, we study the problem of finding overlapping clusterings of hypergraphs, continuing the line of research started by Carlsson and Mémoli (2013) of classifying clustering schemes as functors."}
+{"idx": 3, "title": "Adaptive k-means algorithm for overlapped graph ...", "date": "", "ddg_snippet": "Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters . In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph .", "subpage_snippet": "", "source": "www.peeref.com", "link": "https://www.peeref.com/works/10766118", "content": "Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters . In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph ."}
+{"idx": 4, "title": "Adaptive k-means algorithm for overlapped graph ...", "date": "", "ddg_snippet": "↓ скачать. Bello-orgaz, g., menéndez, h. D., & camacho, D. (2012). Adaptive k-means algorithm for overlapped graph clustering .", "subpage_snippet": "", "source": "sci-hub.se", "link": "https://sci-hub.se/10.1142/S0129065712500189", "content": "↓ скачать. Bello-orgaz, g., menéndez, h. D., & camacho, D. (2012). Adaptive k-means algorithm for overlapped graph clustering ."}
+{"idx": 5, "title": "Attention Beyond Neighborhoods: Reviving Transformer for Graph ...", "date": "", "ddg_snippet": "While GNNs have proven effective for graph clustering due to their ability to model graph structures, they face well-known challenges associated with message-passing, particularly when capturing long-range dependencies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15024", "content": "While GNNs have proven effective for graph clustering due to their ability to model graph structures, they face well-known challenges associated with message-passing, particularly when capturing long-range dependencies."}
+{"idx": 6, "title": "Structural Network Properties of Niche- Overlap Graphs", "date": "", "ddg_snippet": "Keywords: Food-webs, Niche- Overlap Graphs , Structure of Networks, Clustering Coefcient, Betweenness Centrality, Assor-tativity, Modularity, Chordless Cycles.", "subpage_snippet": "", "source": "richard.baltensp.home.hefr.ch", "link": "http://richard.baltensp.home.hefr.ch/Publications/22.pdf", "content": "Keywords: Food-webs, Niche- Overlap Graphs , Structure of Networks, Clustering Coefcient, Betweenness Centrality, Assor-tativity, Modularity, Chordless Cycles."}
+{"idx": 7, "title": "Weighted Flow Diffusion for Local Graph Clustering with Node...", "date": "", "ddg_snippet": "In International Conference on Machine Learning ( ICML ), 2013. Andersen, R., Chung, F., and Lang, K. Local graph par-titioning using pagerank vectors.to node s such that, the path lengths are at most 2 and the paths are mutually non- overlapping , i.e., an edge appears in at.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6rlGbYv4bT", "content": "In International Conference on Machine Learning ( ICML ), 2013. Andersen, R., Chung, F., and Lang, K. Local graph par-titioning using pagerank vectors.to node s such that, the path lengths are at most 2 and the paths are mutually non- overlapping , i.e., an edge appears in at."}
+{"idx": 8, "title": "Metaheuristic Based Clustering Algorithms for Biological Hypergraphs", "date": "", "ddg_snippet": "As commonly known, graph - clustering problem is NP - Hard [2] and [5], meta-heuristic is a suitable candidate for providing possible satisfactory solutions.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/metaheuristic-based-clustering-algorithms-for-biological-2oyltftwom.pdf", "content": "As commonly known, graph - clustering problem is NP - Hard [2] and [5], meta-heuristic is a suitable candidate for providing possible satisfactory solutions."}
+{"idx": 9, "title": "Lorenzo Orecchia , Institute Junior Fellow, Gives Jan 27, 2016 Meet...", "date": "", "ddg_snippet": "In this talk, Professor Orecchia will describe how techniques from continuous optimization are becoming an essential tool in the design and analysis of algorithms for discrete optimization problems, highlighting his work on nearly-linear-time algorithms for fundamental graph problems...", "subpage_snippet": "", "source": "www.bu.edu", "link": "https://www.bu.edu/hic/2016/01/15/lorenzo-orecchia-institute-junior-fellow-gives-jan-27-2016-meet-our-fellows-talk/", "content": "In this talk, Professor Orecchia will describe how techniques from continuous optimization are becoming an essential tool in the design and analysis of algorithms for discrete optimization problems, highlighting his work on nearly-linear-time algorithms for fundamental graph problems..."}
diff --git a/data/sampled_jsons/Origin_Identification_Text-Guided_Image-to-Image_Diffusion_overfitting_generalization_Section_5.5.jsonl b/data/sampled_jsons/Origin_Identification_Text-Guided_Image-to-Image_Diffusion_overfitting_generalization_Section_5.5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1bdef44f787bba729813f4161c74f499df719668
--- /dev/null
+++ b/data/sampled_jsons/Origin_Identification_Text-Guided_Image-to-Image_Diffusion_overfitting_generalization_Section_5.5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diffusion Augmented Retrieval: A Training-Free Approach to", "date": "", "ddg_snippet": "Interactive Text - to - Image Retrieval (I-TIR) seeks to identify relevant images for a user through a turn-by-turn dialogue with a conversational agent.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15379v2", "content": "Interactive Text - to - Image Retrieval (I-TIR) seeks to identify relevant images for a user through a turn-by-turn dialogue with a conversational agent."}
+{"idx": 1, "title": "DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete", "date": "", "ddg_snippet": "For instance, in the case of images , even within a class we find images with vastly different styles and color patterns, which corresponds to large ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.03300v1", "content": "For instance, in the case of images , even within a class we find images with vastly different styles and color patterns, which corresponds to large ..."}
+{"idx": 2, "title": "OTTER: A Vision-Language-Action Model with Text-Aware Visual", "date": "", "ddg_snippet": "... to improve the performance of downstream tasks that involve both vision and language by training models on extensive datasets of image - text pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.03734v3", "content": "... to improve the performance of downstream tasks that involve both vision and language by training models on extensive datasets of image - text pairs."}
+{"idx": 3, "title": "CN117095083B - Text-image generation method, system, device and", "date": "", "ddg_snippet": "... to further fine tune the text - to - image generation model by utilizing feedback of human preferences to ensure that images generated from text conform ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/CN117095083B/en", "content": "... to further fine tune the text - to - image generation model by utilizing feedback of human preferences to ensure that images generated from text conform ..."}
+{"idx": 4, "title": "Temporal Scene Generation w/ Stable Diffusion", "date": "", "ddg_snippet": "... Diffusion 1. 5 model ( runwayml/stable- diffusion -v1- 5 ), We performed DreamBooth on specific characters and combined it with textual inversion to train ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/Bilal326/stable-diffusion-project", "content": "... Diffusion 1. 5 model ( runwayml/stable- diffusion -v1- 5 ), We performed DreamBooth on specific characters and combined it with textual inversion to train ..."}
+{"idx": 5, "title": "DrugDiff: small molecule diffusion model with flexible guidance", "date": "", "ddg_snippet": "Specifically, we trained a latent diffusion model— DrugDiff —paired with predictor guidance to generate novel compounds with a variety of desired ...", "subpage_snippet": "", "source": "jcheminf.biomedcentral.com", "link": "https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-00965-x", "content": "Specifically, we trained a latent diffusion model— DrugDiff —paired with predictor guidance to generate novel compounds with a variety of desired ..."}
+{"idx": 6, "title": "To Appear in the Network and Distributed System Security (NDSS)", "date": "", "ddg_snippet": "... divides images into patches, constructs image and text graphs, and integrates them for analysis using Graph Neural Networks (GNNs) to identify ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.18031v1", "content": "... divides images into patches, constructs image and text graphs, and integrates them for analysis using Graph Neural Networks (GNNs) to identify ..."}
+{"idx": 7, "title": "Spurious reconstruction from brain activity", "date": "", "ddg_snippet": "... text - guided reconstruction methods, which leverage a large- scale dataset (Natural Scene Dataset, NSD) and text - to - image diffusion models, reveals ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.10078v5", "content": "... text - guided reconstruction methods, which leverage a large- scale dataset (Natural Scene Dataset, NSD) and text - to - image diffusion models, reveals ..."}
+{"idx": 8, "title": "A novel low-rank reconstruction framework for... | F1000Research", "date": "", "ddg_snippet": "Our methodology aims to enhance the specificity of diffusion -weighted imaging for distinguishing malignant lesions from adipose tissue, thereby ...", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/13-919", "content": "Our methodology aims to enhance the specificity of diffusion -weighted imaging for distinguishing malignant lesions from adipose tissue, thereby ..."}
+{"idx": 9, "title": "Prediction of mine water quality by the Seq2Seq model based on", "date": "", "ddg_snippet": "The emergence of deep learning (DL) provides a solution to this challenge by leveraging massive data to train DL models and obtain reliable ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/heliyon/fulltext/S2405-8440(24)13947-3", "content": "The emergence of deep learning (DL) provides a solution to this challenge by leveraging massive data to train DL models and obtain reliable ..."}
diff --git a/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Wenhao_Wang_Yifan_Sun_Zongxin_.jsonl b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Wenhao_Wang_Yifan_Sun_Zongxin_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8e36a7cd50e4e23c341307624a8efc02989e30c9
--- /dev/null
+++ b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Wenhao_Wang_Yifan_Sun_Zongxin_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Generalizable Origin Identification for Text - Guided ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387767437_Generalizable_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications."}
+{"idx": 1, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/generalizable-origin-identification-for-text", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights..."}
+{"idx": 2, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/generalizable-origin-identification-text-guided-image-to", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights..."}
+{"idx": 3, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.Authors: Wenhao Wang , Yifan Sun , Zongxin Yang , Zhentao Tan , Zhengdong Hu , Yi Yang . cs.AI.", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/dashboard/paper/bab4dd04-edc9-4908-a029-7ed4e8b4ede8", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.Authors: Wenhao Wang , Yifan Sun , Zongxin Yang , Zhentao Tan , Zhengdong Hu , Yi Yang . cs.AI."}
+{"idx": 4, "title": "Origin Identification for Text - Guided Image - to - Image Diffusion ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.Subsequently, it is demonstrated that such a simple linear transformation can be generalized across different diffusion models .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Origin-Identification-for-Text-Guided-Image-to-Image-Diffusion-Models-cbcf4366-f59f-4199-b877-a2c10f4dc1ed", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.Subsequently, it is demonstrated that such a simple linear transformation can be generalized across different diffusion models ."}
+{"idx": 5, "title": "Zhentao Tan - Google Akademik", "date": "", "ddg_snippet": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models .Image Copy Detection for Diffusion Models . W Wang , Y Sun , Z Tan , Y Yang .", "subpage_snippet": "", "source": "scholar.google.com.pk", "link": "https://scholar.google.com.pk/citations?user=jDsfBUwAAAAJ&hl=tr", "content": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models .Image Copy Detection for Diffusion Models . W Wang , Y Sun , Z Tan , Y Yang ."}
+{"idx": 6, "title": "GitHub - wd1511/Awesome- Diffusion -for- Image -Translation...", "date": "", "ddg_snippet": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models Wenhao Wang , Yifan Sun , Zongxin Yang , Zhentao Tan , Zhengdong Hu , Yi Yang .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wd1511/Awesome-Diffusion-for-Image-Translation", "content": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models Wenhao Wang , Yifan Sun , Zongxin Yang , Zhentao Tan , Zhengdong Hu , Yi Yang ."}
+{"idx": 7, "title": "Image To Image Translation", "date": "", "ddg_snippet": "Authors: Wenhao Wang , Yifan Sun , Zongxin Yang , Zhentao Tan , Zhengdong Hu , Yi Yang .This motivates us to introduce the task of origin IDentification for text - guided Image - to - image Diffusion models (ID$^2$), aiming to retrieve the original image of a given translated query.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/search?query=Image+To+Image+Translation&page=12", "content": "Authors: Wenhao Wang , Yifan Sun , Zongxin Yang , Zhentao Tan , Zhengdong Hu , Yi Yang .This motivates us to introduce the task of origin IDentification for text - guided Image - to - image Diffusion models (ID$^2$), aiming to retrieve the original image of a given translated query."}
+{"idx": 8, "title": "Wenhao Wang - University of Technology Sydney | 人才画像 - AMiner", "date": "", "ddg_snippet": "Wenhao Wang , Yifan Sun , Zongxin Yang , Zhentao Tan , Zhengdong Hu , Yi Yang .VidProM: A Million-scale Real Prompt-Gallery Dataset for Text -to-Video Diffusion Models . Wenhao Wang , Yi Yang .", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/profile/wenhao-wang/644265e4ca4e0609eeda8964", "content": "Wenhao Wang , Yifan Sun , Zongxin Yang , Zhentao Tan , Zhengdong Hu , Yi Yang .VidProM: A Million-scale Real Prompt-Gallery Dataset for Text -to-Video Diffusion Models . Wenhao Wang , Yi Yang ."}
+{"idx": 9, "title": "(PDF) Text - image guided Diffusion Model for generating Deepfake...", "date": "", "ddg_snippet": "Wenhao Wang ,Yuna Sun , Zongxin Yang ,Zhe Hu ,Zhengquan Tan , Yi Yang +5 more.A Study on the Application of Using Hypernetwork and Low Rank Adaptation for Text - to - Image Generation Based on Diffusion Models .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/text-image-guided-diffusion-model-for-generating-deepfake-mkk27rbq24", "content": "Wenhao Wang ,Yuna Sun , Zongxin Yang ,Zhe Hu ,Zhengquan Tan , Yi Yang +5 more.A Study on the Application of Using Hypernetwork and Low Rank Adaptation for Text - to - Image Generation Based on Diffusion Models ."}
diff --git a/data/sampled_jsons/OwMatch+_CIFAR-100_Table_1_'All'_accuracy_Open-World_Semi-Supervised_Learning_year_2023.jsonl b/data/sampled_jsons/OwMatch+_CIFAR-100_Table_1_'All'_accuracy_Open-World_Semi-Supervised_Learning_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ccb3459a8b5d8babe9547a67808627c24de719fe
--- /dev/null
+++ b/data/sampled_jsons/OwMatch+_CIFAR-100_Table_1_'All'_accuracy_Open-World_Semi-Supervised_Learning_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "4 Nov 2024 — Table 1: Average accuracy on the CIFAR-10/100 and ... OpenLDN: learning to discover novel classes for open-world semi-supervised learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.01833v1", "content": "4 Nov 2024 — Table 1: Average accuracy on the CIFAR-10/100 and ... OpenLDN: learning to discover novel classes for open-world semi-supervised learning."}
+{"idx": 1, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "It includes experimental results and in-depth analysis, demonstrating the effectiveness of our approach. Table 1 : Average accuracy on the CIFAR -10/ 100 and ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93416", "content": "It includes experimental results and in-depth analysis, demonstrating the effectiveness of our approach. Table 1 : Average accuracy on the CIFAR -10/ 100 and ..."}
+{"idx": 2, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "This paper proposes OwMatch , a new approach for open - world semi - supervised learning (OwSSL). The key contributions are: ( 1 ) A conditional self-labeling method ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rle9X7DQuH&referrer=[the+profile+of+Jian+Huang](/profile?id=~Jian_Huang5)", "content": "This paper proposes OwMatch , a new approach for open - world semi - supervised learning (OwSSL). The key contributions are: ( 1 ) A conditional self-labeling method ..."}
+{"idx": 3, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "4 Nov 2024 — The results in Table 1 show OwMatch attains the highest average accuracy across CIFAR-10/100 and ImageNet-100, consistently outperforming ...", "subpage_snippet": "", "source": "liner.com", "link": "https://liner.com/review/owmatch-conditional-selflabeling-with-consistency-for-openworld-semisupervised-learning", "content": "4 Nov 2024 — The results in Table 1 show OwMatch attains the highest average accuracy across CIFAR-10/100 and ImageNet-100, consistently outperforming ..."}
+{"idx": 4, "title": "Conditional Self-Labeling with Consistency for Open-world ...", "date": "", "ddg_snippet": "by S Niu · 2024 · Cited by 4 — Table 1 : Average accuracy on the CIFAR -10/ 100 and ImageNet100 with 50% novel classes and. 50% labeled data within seen classes. Method. CIFAR -10.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/93416.pdf", "content": "by S Niu · 2024 · Cited by 4 — Table 1 : Average accuracy on the CIFAR -10/ 100 and ImageNet100 with 50% novel classes and. 50% labeled data within seen classes. Method. CIFAR -10."}
+{"idx": 5, "title": "Rethinking Semi-Supervised Learning and Pretrain- ...", "date": "", "ddg_snippet": "by SL Lv · 2025 — Table 1: Accuracy on CIFAR-10, CIFAR-100 , and STL-10. The best performance for each dataset is in bold, while the top scores in semi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.13317", "content": "by SL Lv · 2025 — Table 1: Accuracy on CIFAR-10, CIFAR-100 , and STL-10. The best performance for each dataset is in bold, while the top scores in semi ..."}
+{"idx": 6, "title": "Towards Realistic Long-tailed Semi - supervised Learning in an Open ...", "date": "", "ddg_snippet": "Open - world Semi - supervised Learning (OSSL): ORCA Cao et al. Table 2: Accuracy on the CIFAR-10, CIFAR - 100 , and ImageNet-100 datasets with 50% known and 50% novel classes under three different long-tailed conditions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.14516v1", "content": "Open - world Semi - supervised Learning (OSSL): ORCA Cao et al. Table 2: Accuracy on the CIFAR-10, CIFAR - 100 , and ImageNet-100 datasets with 50% known and 50% novel classes under three different long-tailed conditions."}
+{"idx": 7, "title": "Towards Realistic Semi - Supervised Learning", "date": "", "ddg_snippet": "Keywords: Semi - supervised learning , Open - world , Uncertainty. Table 1 : Average accuracy on the CIFAR-10, CIFAR - 100 , and ImageNet-100 datasets with 50% classes as seen and 50% classes as novel. The results are averaged over three independent runs.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136910423.pdf", "content": "Keywords: Semi - supervised learning , Open - world , Uncertainty. Table 1 : Average accuracy on the CIFAR-10, CIFAR - 100 , and ImageNet-100 datasets with 50% classes as seen and 50% classes as novel. The results are averaged over three independent runs."}
+{"idx": 8, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open - world ...", "date": "", "ddg_snippet": "# Open - world semi - supervised learning (OwSSL) faces challenges as unlabeled data might contain samples from unseen classes, leading to misclassification.This table compares the performance of OwMatch+ against several other state-of-the-art methods on the ImageNet- 100 dataset.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/rle9x7dquh/", "content": "# Open - world semi - supervised learning (OwSSL) faces challenges as unlabeled data might contain samples from unseen classes, leading to misclassification.This table compares the performance of OwMatch+ against several other state-of-the-art methods on the ImageNet- 100 dataset."}
+{"idx": 9, "title": "CIFAR-10 and CIFAR - 100 datasets", "date": "", "ddg_snippet": "CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.Here is a python2 routine which will open such a file and return a dictionary: def unpickle(file): import cPickle with open (file, 'rb') as fo: dict = cPickle.load(fo) return dict.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "http://www.cs.toronto.edu/~kriz/cifar.html", "content": "CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.Here is a python2 routine which will open such a file and return a dictionary: def unpickle(file): import cPickle with open (file, 'rb') as fo: dict = cPickle.load(fo) return dict."}
diff --git a/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_Table_1_CIFAR-10_accuracy_year_2024.jsonl b/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_Table_1_CIFAR-10_accuracy_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44c720f7a6c3ce928274b4ca5d45b876d4110840
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_Table_1_CIFAR-10_accuracy_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open ...", "date": "", "ddg_snippet": "Table 2: Ablation study on datasets with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self-labeling , PLCR refers to consistency regularization, and OwAT refers to an open-world hierarchical thresholding scheme.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/93416.pdf", "content": "Table 2: Ablation study on datasets with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self-labeling , PLCR refers to consistency regularization, and OwAT refers to an open-world hierarchical thresholding scheme."}
+{"idx": 1, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "4 Nov 2024 — Table 1: Average accuracy on the CIFAR-10/100 and ImageNet-100 with both novel class ratio and label ratio of 50%. We compare OwMatch with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.01833v1", "content": "4 Nov 2024 — Table 1: Average accuracy on the CIFAR-10/100 and ImageNet-100 with both novel class ratio and label ratio of 50%. We compare OwMatch with ..."}
+{"idx": 2, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "9 Dec 2024 — Table 1: Average accuracy on the CIFAR-10/100 and ImageNet-100 with both novel class ratio and label ratio of 50%. We compare OwMatch with ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93416", "content": "9 Dec 2024 — Table 1: Average accuracy on the CIFAR-10/100 and ImageNet-100 with both novel class ratio and label ratio of 50%. We compare OwMatch with ..."}
+{"idx": 3, "title": "Self-adaptive Thresholding for Semi-supervised Learning", "date": "", "ddg_snippet": "This work proposes FreeMatch to adjust the confidence threshold in a self -adaptive manner according to the model's learning status and introduces aSelf- ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/FreeMatch:-Self-adaptive-Thresholding-for-Learning-Wang-Chen/a4c99a1f69909443b3ca895cdd3dc78070c03377", "content": "This work proposes FreeMatch to adjust the confidence threshold in a self -adaptive manner according to the model's learning status and introduces aSelf- ..."}
+{"idx": 4, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open ...", "date": "", "ddg_snippet": "Nov 4, 2024 · To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency , tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.01833", "content": "Nov 4, 2024 · To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency , tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding."}
+{"idx": 5, "title": "GitHub - niusj03/OwMatch: [NeurIPS 2024] OwMatch", "date": "", "ddg_snippet": "This is the official repository for the paper OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/niusj03/OwMatch", "content": "This is the official repository for the paper OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch."}
+{"idx": 6, "title": "dblp: OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "Bibliographic details on OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2411-01833", "content": "Bibliographic details on OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning."}
+{"idx": 7, "title": "OwMatch | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency , tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3741083", "content": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency , tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding."}
+{"idx": 8, "title": "SelfMatch: Combining Contrastive Self-Supervision and ...", "date": "", "ddg_snippet": "SelfMatch consists of two stages: ( 1 ) self -supervised pre-training based on contrastive learning and (2) semi-supervised fine-tuning based on augmentation consistency regularization. We empirically demonstrate that SelfMatch achieves the state-of-the-art results on standard benchmark datasets such as CIFAR-10 and SVHN.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2101.06480", "content": "SelfMatch consists of two stages: ( 1 ) self -supervised pre-training based on contrastive learning and (2) semi-supervised fine-tuning based on augmentation consistency regularization. We empirically demonstrate that SelfMatch achieves the state-of-the-art results on standard benchmark datasets such as CIFAR-10 and SVHN."}
+{"idx": 9, "title": "Unlabeled Data or Pre-trained Model: Rethinking Semi- ...", "date": "", "ddg_snippet": "19 May 2025 — Table 1: Accuracy on CIFAR-10 , CIFAR-100, and STL-10. The best ... Owmatch: Conditional self-labeling with consistency for open-world ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2505.13317v1", "content": "19 May 2025 — Table 1: Accuracy on CIFAR-10 , CIFAR-100, and STL-10. The best ... Owmatch: Conditional self-labeling with consistency for open-world ..."}
diff --git a/data/sampled_jsons/OwMatch_GitHub_repository_Table_3_CIFAR-100_results_year_2024.jsonl b/data/sampled_jsons/OwMatch_GitHub_repository_Table_3_CIFAR-100_results_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f59371e510c3c464fc6b8c62a94f76197bfdf17
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_GitHub_repository_Table_3_CIFAR-100_results_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - niusj03/OwMatch: [NeurIPS 2024] OwMatch", "date": "", "ddg_snippet": "This is the official repository for the paper OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/niusj03/OwMatch", "content": "This is the official repository for the paper OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch."}
+{"idx": 1, "title": "OwMatch/open_world_cifar.py at main · niusj03/OwMatch · GitHub", "date": "", "ddg_snippet": "Contribute to niusj03/ OwMatch development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/niusj03/OwMatch/blob/main/open_world_cifar.py", "content": "Contribute to niusj03/ OwMatch development by creating an account on GitHub ."}
+{"idx": 2, "title": "Running Experiments on ImageNet 100 · Issue #2 · niusj03/OwMatch - GitHub", "date": "", "ddg_snippet": "Hi, Thanks for this awesome project. I noticed that the current code is set up for CIFAR and TinyImageNet only, but since your paper also mentions experiments on a ImageNet100. I was wondering if y...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/niusj03/OwMatch/issues/2", "content": "Hi, Thanks for this awesome project. I noticed that the current code is set up for CIFAR and TinyImageNet only, but since your paper also mentions experiments on a ImageNet100. I was wondering if y..."}
+{"idx": 3, "title": "cifar100 · GitHub Topics · GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/cifar100", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 4, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "It is noteworthy that the enhancement brought about by OwMatch is more pronounced on the CIFAR-100 dataset, which presents a greater challenge due to the increasing number of classes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.01833", "content": "It is noteworthy that the enhancement brought about by OwMatch is more pronounced on the CIFAR-100 dataset, which presents a greater challenge due to the increasing number of classes."}
+{"idx": 5, "title": "GitHub - nelson-chu/keras-cifar-100-example: This repository provides a ...", "date": "", "ddg_snippet": "This repository contains an implementation of a neural network model that combines SE-Residual Blocks, Dense Blocks, and Transition Layers to train on the CIFAR-100 dataset using Keras. The model achieves a top-1 accuracy of 0.70819 and a top-5 accuracy of 0.91829 on the test set.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nelson-chu/keras-cifar-100-example", "content": "This repository contains an implementation of a neural network model that combines SE-Residual Blocks, Dense Blocks, and Transition Layers to train on the CIFAR-100 dataset using Keras. The model achieves a top-1 accuracy of 0.70819 and a top-5 accuracy of 0.91829 on the test set."}
+{"idx": 6, "title": "CIFAR100 dataset extraction · GitHub", "date": "", "ddg_snippet": "CIFAR100 dataset extraction. GitHub Gist: instantly share code, notes, and snippets.", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/beeva-albertorincon/1ef96e071ac5adcb421663f3bbe7b1a6", "content": "CIFAR100 dataset extraction. GitHub Gist: instantly share code, notes, and snippets."}
+{"idx": 7, "title": "GitHub - AccidentalThief/CIFAR-100-GAN: An attempt at making a GAN ...", "date": "", "ddg_snippet": "This project is a modular PyTorch framework for both conditional GANs (cGANs) and image classifiers, supporting CIFAR-100 , CIFAR -10, and MNIST. The codebase is designed for easy experimentation, robust label conditioning, checkpointing, and extensibility.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AccidentalThief/CIFAR-100-GAN", "content": "This project is a modular PyTorch framework for both conditional GANs (cGANs) and image classifiers, supporting CIFAR-100 , CIFAR -10, and MNIST. The codebase is designed for easy experimentation, robust label conditioning, checkpointing, and extensibility."}
+{"idx": 8, "title": "CIFAR-100 on Benchmarks.AI", "date": "", "ddg_snippet": "Explore CIFAR-100 dataset benchmarks, pre-trained models and fine-tuning techniques to improve deep learning performance on vision tasks.", "subpage_snippet": "", "source": "benchmarks.ai", "link": "https://benchmarks.ai/cifar-100", "content": "Explore CIFAR-100 dataset benchmarks, pre-trained models and fine-tuning techniques to improve deep learning performance on vision tasks."}
+{"idx": 9, "title": "CIFAR-100 - GitHub Pages", "date": "", "ddg_snippet": "CIFAR-100 CIFAR-100 dataset The CIFAR-100 dataset Dataset Statistics Color: RGB Sample Size: 32x32 This dataset is just like the CIFAR -10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are roughly grouped into 20 superclasses.", "subpage_snippet": "", "source": "git-disl.github.io", "link": "https://git-disl.github.io/GTDLBench/datasets/cifar-100_datasets/", "content": "CIFAR-100 CIFAR-100 dataset The CIFAR-100 dataset Dataset Statistics Color: RGB Sample Size: 32x32 This dataset is just like the CIFAR -10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are roughly grouped into 20 superclasses."}
diff --git a/data/sampled_jsons/PCA_whitening_control_data_biological_context_feature_quality_microscopy_foundation_models_year_2024.jsonl b/data/sampled_jsons/PCA_whitening_control_data_biological_context_feature_quality_microscopy_foundation_models_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6f9b821b3c63f32a17a8c76a7ad06d857d7c8aa
--- /dev/null
+++ b/data/sampled_jsons/PCA_whitening_control_data_biological_context_feature_quality_microscopy_foundation_models_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Principal component analysis - Wikipedia", "date": "", "ddg_snippet": "Principal component analysis ( PCA ) is a linear dimensionality reduction technique with applications in exploratory data analysis , visualization ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Principal_component_analysis", "content": "Principal component analysis ( PCA ) is a linear dimensionality reduction technique with applications in exploratory data analysis , visualization ..."}
+{"idx": 1, "title": "Assessment of Slow Feature Analysis and Its Variants for Fault", "date": "", "ddg_snippet": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7080/12/12/237", "content": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ..."}
+{"idx": 2, "title": "WO2002103954A2 - Data mining platform for bioinformatics and", "date": "", "ddg_snippet": "It provides unified access to molecular biology databases, integration of analysis tools and advanced parsing tools for disseminating and ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2002103954A2/en", "content": "It provides unified access to molecular biology databases, integration of analysis tools and advanced parsing tools for disseminating and ..."}
+{"idx": 3, "title": "US7542947B2 - Data mining platform for bioinformatics and other", "date": "", "ddg_snippet": "G06V10/77 — Processing image or video features in feature spaces; using data integration or data reduction, e.g.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US7542947B2/en", "content": "G06V10/77 — Processing image or video features in feature spaces; using data integration or data reduction, e.g."}
+{"idx": 4, "title": "Reducing Dataset Size Without Losing Model Performance |", "date": "", "ddg_snippet": "... reduction is a fundamental technique in machine learning (ML) that simplifies datasets by reducing the number of input variables or features .", "subpage_snippet": "", "source": "encord.com", "link": "https://encord.com/blog/dimentionality-reduction-techniques-machine-learning/", "content": "... reduction is a fundamental technique in machine learning (ML) that simplifies datasets by reducing the number of input variables or features ."}
+{"idx": 5, "title": "Changes of the human skin microbiota upon chronic exposure to", "date": "", "ddg_snippet": "PM 10 and PM 2.5 data acquired from the WHO ( http://www.who.int/airpollution/ data /cities/en/ , accessed 5th November 2018)", "subpage_snippet": "", "source": "microbiomejournal.biomedcentral.com", "link": "https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-020-00874-1", "content": "PM 10 and PM 2.5 data acquired from the WHO ( http://www.who.int/airpollution/ data /cities/en/ , accessed 5th November 2018)"}
+{"idx": 6, "title": "openconstructionbuildingtechnologyjournal.com/VOLUME/19/ELOCATOR/e18748368389322/FULLTEXT", "date": "", "ddg_snippet": "The dataset, comprising 135 instances from prior experimental studies, underwent PCA for dimensionality reduction, retaining 99% of the variance.", "subpage_snippet": "", "source": "openconstructionbuildingtechnologyjournal.com", "link": "https://openconstructionbuildingtechnologyjournal.com/VOLUME/19/ELOCATOR/e18748368389322/FULLTEXT/", "content": "The dataset, comprising 135 instances from prior experimental studies, underwent PCA for dimensionality reduction, retaining 99% of the variance."}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2022", "content": "ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment"}
+{"idx": 8, "title": "ICML 2021 Papers", "date": "", "ddg_snippet": "Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification ... Which transformer architecture fits my data ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/papers.html", "content": "Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification ... Which transformer architecture fits my data ..."}
+{"idx": 9, "title": "Principal component analysis - HandWiki", "date": "", "ddg_snippet": "PCA can be thought of as fitting a p -dimensional ellipsoid to the data , where each axis of the ellipsoid represents a principal component.", "subpage_snippet": "", "source": "handwiki.org", "link": "https://handwiki.org/wiki/Principal_component_analysis", "content": "PCA can be thought of as fitting a p -dimensional ellipsoid to the data , where each axis of the ellipsoid represents a principal component."}
diff --git a/data/sampled_jsons/PINNs_physical_constraints_loss_function_Raissi_2019_abstract_year_2019.jsonl b/data/sampled_jsons/PINNs_physical_constraints_loss_function_Raissi_2019_abstract_year_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d4e3c0ec5592fcd265aad883fd3b63b4a14a4a2
--- /dev/null
+++ b/data/sampled_jsons/PINNs_physical_constraints_loss_function_Raissi_2019_abstract_year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Applying Physical Constraints in Physics-Informed Neural... - Freedium", "date": "", "ddg_snippet": "Methods for Embedding Physical Constraints in PINNs . 1. Loss Function Modification (Soft Constraints). The most common approach is adding physical constraints as penalty terms in the loss function . This penalizes violations of known laws during training.", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/715f7e778e47", "content": "Methods for Embedding Physical Constraints in PINNs . 1. Loss Function Modification (Soft Constraints). The most common approach is adding physical constraints as penalty terms in the loss function . This penalizes violations of known laws during training."}
+{"idx": 1, "title": "Application of Physical Information Neural Network... | SpringerLink", "date": "", "ddg_snippet": "In 2019 , Raissi et al. introduced Physics-Informed Neural Networks ( PINNs ) [8], which integrate physical constraints like PDEs and boundary conditions into the network's loss function . This reduces the dependence on large datasets and enhances physical interpretability.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-4856-6_6", "content": "In 2019 , Raissi et al. introduced Physics-Informed Neural Networks ( PINNs ) [8], which integrate physical constraints like PDEs and boundary conditions into the network's loss function . This reduces the dependence on large datasets and enhances physical interpretability."}
+{"idx": 2, "title": "Physics-Informed Neural Networks ( PINNs )", "date": "", "ddg_snippet": "For PINNs , the loss function includes additional terms derived from the physics of the problem.This technique enables PINNs to compute derivatives of the network output with respect to its inputs, which is necessary for enforcing physical constraints (e.g., PDEs) in the loss function .", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/physics-informed-neural-networks-pinns-2357aeec4fbc", "content": "For PINNs , the loss function includes additional terms derived from the physics of the problem.This technique enables PINNs to compute derivatives of the network output with respect to its inputs, which is necessary for enforcing physical constraints (e.g., PDEs) in the loss function ."}
+{"idx": 3, "title": "GitHub - nishant-ai/ PINNs -Burgers-Equation", "date": "", "ddg_snippet": "PINNs are neural networks that incorporate physical laws (described by PDEs) directly into the learning process. Instead of purely learning from data, they enforce physical constraints during training, making them", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nishant-ai/PINNs-Burgers-Equation", "content": "PINNs are neural networks that incorporate physical laws (described by PDEs) directly into the learning process. Instead of purely learning from data, they enforce physical constraints during training, making them"}
+{"idx": 4, "title": "DiffPINN: Generative diffusion-initialized physics-informed neural...", "date": "", "ddg_snippet": "Physics-informed neural networks ( PINNs ) ( Raissi et al., 2019 ) have gained considerable attention as a powerful framework for solving partial differential equations (PDEs) in various fields.by embedding physical constraints directly into its loss function ( Raissi et al., 2019 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00471v1", "content": "Physics-informed neural networks ( PINNs ) ( Raissi et al., 2019 ) have gained considerable attention as a powerful framework for solving partial differential equations (PDEs) in various fields.by embedding physical constraints directly into its loss function ( Raissi et al., 2019 ) ."}
+{"idx": 5, "title": "(PDF) Physics-informed neural networks for structural health...", "date": "", "ddg_snippet": "( PINNs ), which adjust their loss function to incorporate physics-based constraints , ensuring adherence to. Raissi M,Perdikaris P and Karniadakis GE ( 2019 ) Physics-informed neural networks: A deep learning framework for solving.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/378908420_Physics-informed_neural_networks_for_structural_health_monitoring_a_case_study_for_Kirchhoff-Love_plates", "content": "( PINNs ), which adjust their loss function to incorporate physics-based constraints , ensuring adherence to. Raissi M,Perdikaris P and Karniadakis GE ( 2019 ) Physics-informed neural networks: A deep learning framework for solving."}
+{"idx": 6, "title": "PINP: Physics-Informed Neural Predictor with latent... | alphaXiv", "date": "", "ddg_snippet": "The integration of physical constraints into the loss function guides the learning process toward solutions that are not only consistent with the observed data but also adhere to the underlying physical laws. Experimental Results.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2504.06070v1", "content": "The integration of physical constraints into the loss function guides the learning process toward solutions that are not only consistent with the observed data but also adhere to the underlying physical laws. Experimental Results."}
+{"idx": 7, "title": "P hysics -i nformed n eural n etworks", "date": "", "ddg_snippet": "PINNs , also called vanilla PINNs , typically utilize a mean squared error loss function and cannot quantify uncertainty.Two baselines are considered: PINN with a constant parameter for the standard deviation of the noise (Xiang et al., 2021) and vanilla PINN ( Raissi et al., 2017).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=T5NjaIOcNr", "content": "PINNs , also called vanilla PINNs , typically utilize a mean squared error loss function and cannot quantify uncertainty.Two baselines are considered: PINN with a constant parameter for the standard deviation of the noise (Xiang et al., 2021) and vanilla PINN ( Raissi et al., 2017)."}
+{"idx": 8, "title": "Ithy - Unlocking Precision: How Physics Meets AI to Revolutionize...", "date": "", "ddg_snippet": "Loss Function Formulation in Tribological PINNs . The loss function in a PINN for tribological applications typically consists of multiple components", "subpage_snippet": "", "source": "ithy.com", "link": "https://ithy.com/article/pinns-enhance-tribological-prediction-accuracy-dxhhnqa4", "content": "Loss Function Formulation in Tribological PINNs . The loss function in a PINN for tribological applications typically consists of multiple components"}
+{"idx": 9, "title": "GMD - A Python library for solving ice sheet modeling problems using...", "date": "", "ddg_snippet": "Abstract .The loss function of the PINN consists of two parts, data and physical loss, which are both constructed using these output variables. The data loss measures the misfit between the data and the corresponding output variable at the location and time of the data acquired.", "subpage_snippet": "", "source": "gmd.copernicus.org", "link": "https://gmd.copernicus.org/articles/18/5311/2025/", "content": "Abstract .The loss function of the PINN consists of two parts, data and physical loss, which are both constructed using these output variables. The data loss measures the misfit between the data and the corresponding output variable at the location and time of the data acquired."}
diff --git a/data/sampled_jsons/PPO_instability_moving_targets_gradient_variance_symmetric_reinforcement_learning.jsonl b/data/sampled_jsons/PPO_instability_moving_targets_gradient_variance_symmetric_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f214aabd1dde957060a089375ba55b3cb109500a
--- /dev/null
+++ b/data/sampled_jsons/PPO_instability_moving_targets_gradient_variance_symmetric_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Adaptability in Multi-Agent Reinforcement Learning: A Framework", "date": "", "ddg_snippet": "Multi-Agent Reinforcement Learning (MARL) has shown clear effectiveness in coordinating multiple agents across simulated benchmarks and constrained ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10142v1", "content": "Multi-Agent Reinforcement Learning (MARL) has shown clear effectiveness in coordinating multiple agents across simulated benchmarks and constrained ..."}
+{"idx": 1, "title": "Nuclear Microreactor Control with Deep Reinforcement Learning", "date": "", "ddg_snippet": "Outside the nuclear domain, while PID control and MPC continue to dominate in practical applications, reinforcement learning (RL) is starting to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.00156v1", "content": "Outside the nuclear domain, while PID control and MPC continue to dominate in practical applications, reinforcement learning (RL) is starting to ..."}
+{"idx": 2, "title": "A deep reinforcement learning control approach for", "date": "", "ddg_snippet": "Reinforcement learning (RL) is a branch of machine learning that offers an approach to intelligent control inspired by the way biological organisms ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11071-023-08725-y", "content": "Reinforcement learning (RL) is a branch of machine learning that offers an approach to intelligent control inspired by the way biological organisms ..."}
+{"idx": 3, "title": "Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle", "date": "", "ddg_snippet": "Leveraging PPO and WGAN-GP, our model addresses nonlinear interdependencies and training instability inherent in microscopic settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.17418v1", "content": "Leveraging PPO and WGAN-GP, our model addresses nonlinear interdependencies and training instability inherent in microscopic settings."}
+{"idx": 4, "title": "RAFT: Reward rAnked FineTuning for Generative Foundation Model", "date": "", "ddg_snippet": "... to fine-tune pre-trained models using policy-based deep reinforcement learning (DRL) algorithms, typically the Proximal Policy Optimization ( PPO ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2304.06767v4", "content": "... to fine-tune pre-trained models using policy-based deep reinforcement learning (DRL) algorithms, typically the Proximal Policy Optimization ( PPO ..."}
+{"idx": 5, "title": "Multiple-Frequencies Population-Based Training", "date": "", "ddg_snippet": "Reinforcement Learning ’s high sensitivity to hyperparameters is a source of instability and inefficiency, creating significant challenges for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03225v1", "content": "Reinforcement Learning ’s high sensitivity to hyperparameters is a source of instability and inefficiency, creating significant challenges for ..."}
+{"idx": 6, "title": "Jian Li's Publications (2024)", "date": "", "ddg_snippet": "... algorithm-dependent generalization error bounds for gradient -type optimization methods has attracted significant attention recently in learning ...", "subpage_snippet": "", "source": "hyboll.shop", "link": "https://hyboll.shop/article/jian-li-s-publications", "content": "... algorithm-dependent generalization error bounds for gradient -type optimization methods has attracted significant attention recently in learning ..."}
+{"idx": 7, "title": "Blog | Simulately", "date": "", "ddg_snippet": "... perceptions with ground truth segmentation masks, to spawning parallel environments with GPU for efficient data sampling in Reinforcement Learning ...", "subpage_snippet": "", "source": "simulately.wiki", "link": "https://simulately.wiki/blog/", "content": "... perceptions with ground truth segmentation masks, to spawning parallel environments with GPU for efficient data sampling in Reinforcement Learning ..."}
+{"idx": 8, "title": "PRATA: A Framework to Enable Predictive QoS in Vehicular", "date": "", "ddg_snippet": "A promising tool for PQoS is given by Reinforcement Learning (RL) , a methodology that enables the design of decision-making strategies for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14211v1", "content": "A promising tool for PQoS is given by Reinforcement Learning (RL) , a methodology that enables the design of decision-making strategies for ..."}
+{"idx": 9, "title": "Abstract", "date": "", "ddg_snippet": "This study provides a thorough background on reinforcement learning and multi-agent systems, along with a comprehensive literature review of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.00165v1", "content": "This study provides a thorough background on reinforcement learning and multi-agent systems, along with a comprehensive literature review of ..."}
diff --git a/data/sampled_jsons/PPO_robustness_RLHF_alternative_loss_functions_improvements_year_2024.jsonl b/data/sampled_jsons/PPO_robustness_RLHF_alternative_loss_functions_improvements_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fd248c3f2cc14cd302e2b2ae493dc01b600fe0ed
--- /dev/null
+++ b/data/sampled_jsons/PPO_robustness_RLHF_alternative_loss_functions_improvements_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · Cited by 1 — This paper shows that the symmetric PPO loss improves performance over the regular PPO loss , probably because it mitigates the effects of the small batch.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9oq0iY2Jxx", "content": "by JS Byun · Cited by 1 — This paper shows that the symmetric PPO loss improves performance over the regular PPO loss , probably because it mitigates the effects of the small batch."}
+{"idx": 1, "title": "An Efficient RLHF Algorithm with Robustness to Both ...", "date": "", "ddg_snippet": "3 Aug 2025 — Extensive experiments demonstrated that our algorithm achieves strong performance and improved computational efficiency across multiple RLHF ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.03262v8", "content": "3 Aug 2025 — Extensive experiments demonstrated that our algorithm achieves strong performance and improved computational efficiency across multiple RLHF ..."}
+{"idx": 2, "title": "Group Robust Preference Optimization in Reward-free RLHF", "date": "", "ddg_snippet": "9 Dec 2024 — To tackle the same, we introduced GRPO, a group robust formulation of reward-free RLHF , aiming to minimize worst-case loss among groups. We ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95314", "content": "9 Dec 2024 — To tackle the same, we introduced GRPO, a group robust formulation of reward-free RLHF , aiming to minimize worst-case loss among groups. We ..."}
+{"idx": 3, "title": "An Efficient RLHF Algorithm with Robustness to Both ...", "date": "", "ddg_snippet": "3 Apr 2025 — We propose REINFORCE ++, a novel REINFORCE-based method that eliminates the critic model from PPO and uses the mean reward of a global batch as the baseline.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.03262v2", "content": "3 Apr 2025 — We propose REINFORCE ++, a novel REINFORCE-based method that eliminates the critic model from PPO and uses the mean reward of a global batch as the baseline."}
+{"idx": 4, "title": "Secrets of RLHF in Large Language Models Part I: PPO", "date": "", "ddg_snippet": "by R Zheng · Cited by 201 — Preventing overly large policy updates ensures the learning process's robustness while maintaining more sample-efficient learning than vanilla policy gradient ... 32 pages", "subpage_snippet": "", "source": "openlmlab.github.io", "link": "https://openlmlab.github.io/MOSS-RLHF/paper/SecretsOfRLHFPart1.pdf", "content": "by R Zheng · Cited by 201 — Preventing overly large policy updates ensures the learning process's robustness while maintaining more sample-efficient learning than vanilla policy gradient ... 32 pages"}
+{"idx": 5, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "Therefore, we propose the reverse RL loss term, which can make PPO in existing RLHF methods more robust . 3. Preliminaries. 3.1. Reinforcement Learning.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44897", "content": "Therefore, we propose the reverse RL loss term, which can make PPO in existing RLHF methods more robust . 3. Preliminaries. 3.1. Reinforcement Learning."}
+{"idx": 6, "title": "Awesome RLHF (RL with Human Feedback)", "date": "", "ddg_snippet": "The idea of RLHF is to use methods from reinforcement learning to directly optimize a language model with human feedback. RLHF has enabled language models ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/opendilab/awesome-RLHF", "content": "The idea of RLHF is to use methods from reinforcement learning to directly optimize a language model with human feedback. RLHF has enabled language models ..."}
+{"idx": 7, "title": "Reinforcement Learning from Human Feedback (RLHF)", "date": "", "ddg_snippet": "RLHF is a machine-learning technique that leverages direct human feedback to train models, particularly when predefined reward functions are inadequate or too ...", "subpage_snippet": "", "source": "www.lakera.ai", "link": "https://www.lakera.ai/blog/reinforcement-learning-from-human-feedback", "content": "RLHF is a machine-learning technique that leverages direct human feedback to train models, particularly when predefined reward functions are inadequate or too ..."}
+{"idx": 8, "title": "Proximal Policy Optimization (PPO): The Key to LLM Alignment", "date": "", "ddg_snippet": "Notably, TRPO and PPO both have drastically improved data efficiency, allowing us to train an effective policy faster and with less data. Going ...", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/proximal-policy-optimization-ppo", "content": "Notably, TRPO and PPO both have drastically improved data efficiency, allowing us to train an effective policy faster and with less data. Going ..."}
+{"idx": 9, "title": "Reinforcement Learning for Large Language Models ...", "date": "", "ddg_snippet": "GRPO is a recent alternative that removes the need for a value function entirely, simplifying training and reducing computation. Motivation. PPO ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@hexiangnan/reinforcement-learning-for-large-language-models-llms-a-comprehensive-overview-of-ppo-grpo-and-26d7ec24c6a4", "content": "GRPO is a recent alternative that removes the need for a value function entirely, simplifying training and reducing computation. Motivation. PPO ..."}
diff --git a/data/sampled_jsons/PS-EIP_Figure_7_table_results_Glossy_EventPS-FCN_MAE_degrees.jsonl b/data/sampled_jsons/PS-EIP_Figure_7_table_results_Glossy_EventPS-FCN_MAE_degrees.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a7847669df092622128ff71359c961eff656055c
--- /dev/null
+++ b/data/sampled_jsons/PS-EIP_Figure_7_table_results_Glossy_EventPS-FCN_MAE_degrees.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "MAE : 3.30. Figure 7 . Quantitative evaluation with the sphere-based shapes. From left to right: photograph, event accumulation image from the. events, ground truth, normal map recovered by EventPS - FCN [67]. with its angular error map, and those by ours.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "MAE : 3.30. Figure 7 . Quantitative evaluation with the sphere-based shapes. From left to right: photograph, event accumulation image from the. events, ground truth, normal map recovered by EventPS - FCN [67]. with its angular error map, and those by ours."}
+{"idx": 1, "title": "Procedure of the proposed method: Events are recorded under moving...", "date": "", "ddg_snippet": "The experimental results are shown in Fig. 17. Glossy 2C is pained with two different colors and then given a glossy top coat. The MAE remains low, comparable to the case without the top coat, Colors.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Procedure-of-the-proposed-method-Events-are-recorded-under-moving-light-conditions-The_fig1_390142307", "content": "The experimental results are shown in Fig. 17. Glossy 2C is pained with two different colors and then given a glossy top coat. The MAE remains low, comparable to the case without the top coat, Colors."}
+{"idx": 2, "title": "CVPR 2025 collected by Wang", "date": "", "ddg_snippet": "We hypothesize PS results from the neuron bias effect, making neurons favor features of certain classes.NoR- MAE elegantly repels the semantic aliasing between patches and their closest neighboring patch (semantic centroid) with negligible training cost overhead.", "subpage_snippet": "", "source": "hongsong-wang.github.io", "link": "https://hongsong-wang.github.io/CVPR2025_ABSTRACT/", "content": "We hypothesize PS results from the neuron bias effect, making neurons favor features of certain classes.NoR- MAE elegantly repels the semantic aliasing between patches and their closest neighboring patch (semantic centroid) with negligible training cost overhead."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/PS-EIP_photometric_stereo_tau+_tau-_1(tau+(t)_-_tau-(t)).jsonl b/data/sampled_jsons/PS-EIP_photometric_stereo_tau+_tau-_1(tau+(t)_-_tau-(t)).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/PS-EIP_photometric_stereo_tau+_tau-_1(tau+(t)_-_tau-(t)).jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Papamakarios_et_al._2017_Masked_Autoregressive_Flows_abstract.jsonl b/data/sampled_jsons/Papamakarios_et_al._2017_Masked_Autoregressive_Flows_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b44284dca01464a44ff47d776a7854cd38232358
--- /dev/null
+++ b/data/sampled_jsons/Papamakarios_et_al._2017_Masked_Autoregressive_Flows_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "... autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density ...", "subpage_snippet": "", "source": "homepages.inf.ed.ac.uk", "link": "https://homepages.inf.ed.ac.uk/imurray2/pub/17maf/", "content": "... autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density ..."}
+{"idx": 1, "title": "Neural Autoregressive Flows", "date": "", "ddg_snippet": "... abstract = {Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v80/huang18d.html", "content": "... abstract = {Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via ..."}
+{"idx": 2, "title": "Distilling Normalizing Flows", "date": "", "ddg_snippet": "Normalizing flows have unique properties that allow for a non-traditional forms of knowledge transfer, where we can transfer that knowledge within ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21003v1", "content": "Normalizing flows have unique properties that allow for a non-traditional forms of knowledge transfer, where we can transfer that knowledge within ..."}
+{"idx": 3, "title": "NFAD: fixing anomaly detection using normalizing flows [PeerJ]", "date": "", "ddg_snippet": "... methods based on generative adversarial networks ( Schlegl et al ., 2017 ), variational autoencoders ( Xu et al ., 2018 ), and normalizing flows ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-757/", "content": "... methods based on generative adversarial networks ( Schlegl et al ., 2017 ), variational autoencoders ( Xu et al ., 2018 ), and normalizing flows ..."}
+{"idx": 4, "title": "Publications by Iain Murray", "date": "", "ddg_snippet": "Masked Autoregressive Flow for Density Estimation George Papamakarios , Theo Pavlakou , and Iain Murray Advances in Neural Information Processing ...", "subpage_snippet": "", "source": "homepages.inf.ed.ac.uk", "link": "https://homepages.inf.ed.ac.uk/imurray2/pub/", "content": "Masked Autoregressive Flow for Density Estimation George Papamakarios , Theo Pavlakou , and Iain Murray Advances in Neural Information Processing ..."}
+{"idx": 5, "title": "IN-Flow: Instance Normalization Flow for Non-stationary Time", "date": "", "ddg_snippet": "Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/393845692_IN-Flow_Instance_Normalization_Flow_for_Non-stationary_Time_Series_Forecasting", "content": "Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact."}
+{"idx": 6, "title": "μGUIDE: a framework for quantitative imaging via generalized", "date": "", "ddg_snippet": "The ‘Neural Posterior Estimator’ (NPE) module [ Papamakarios et al ., 2017 ] uses normalizing flows [ Papamakarios et al ., 2021 ] to approximate ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/101069", "content": "The ‘Neural Posterior Estimator’ (NPE) module [ Papamakarios et al ., 2017 ] uses normalizing flows [ Papamakarios et al ., 2021 ] to approximate ..."}
+{"idx": 7, "title": "μGUIDE: a framework for quantitative imaging via generalized", "date": "", "ddg_snippet": "The ‘Neural Posterior Estimator’ (NPE) module [ Papamakarios et al ., 2017 ] uses normalizing flows [ Papamakarios et al ., 2021 ] to approximate ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/101069v1", "content": "The ‘Neural Posterior Estimator’ (NPE) module [ Papamakarios et al ., 2017 ] uses normalizing flows [ Papamakarios et al ., 2021 ] to approximate ..."}
+{"idx": 8, "title": "Parameter inference of microlensed gravitational waves using", "date": "", "ddg_snippet": "... 93 93 93 93 GW events in its first three observing runs, which enables us to conduct various novel tests of general relativity Abbott et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.20996v1", "content": "... 93 93 93 93 GW events in its first three observing runs, which enables us to conduct various novel tests of general relativity Abbott et al ..."}
+{"idx": 9, "title": "Variational inference for acceleration of SN Ia photometric", "date": "", "ddg_snippet": "Branch & Tammann 1992 ; Sandage & Tammann 1993 ; Riess, Press & Kirshner 1996a , b ; Phillips et al .", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/mnras/article/535/3/2306/7875246", "content": "Branch & Tammann 1992 ; Sandage & Tammann 1993 ; Riess, Press & Kirshner 1996a , b ; Phillips et al ."}
diff --git a/data/sampled_jsons/PapersWithCode_Executing_your_Commands_via_Motion_Diffusion_FID_HumanAct12_table_value_year_2023.jsonl b/data/sampled_jsons/PapersWithCode_Executing_your_Commands_via_Motion_Diffusion_FID_HumanAct12_table_value_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5dcb0531f2f964e354f89b56aa6b4f8a56a4d44e
--- /dev/null
+++ b/data/sampled_jsons/PapersWithCode_Executing_your_Commands_via_Motion_Diffusion_FID_HumanAct12_table_value_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MotionLLM: Multimodal Motion-Language Learning with ...", "date": "", "ddg_snippet": "27 May 2024 — Executing your commands via motion diffusion in latent space . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17013v1", "content": "27 May 2024 — Executing your commands via motion diffusion in latent space . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ..."}
+{"idx": 1, "title": "Paper tables with annotated results for Executing your Commands ...", "date": "", "ddg_snippet": "Executing your Commands via Motion Diffusion in Latent Space. Table 3: Comparison of action-conditional motion synthesis on UESTC [ji2018large] and HumanAct 12 [guo2020action2 motion ] dataset: FIDtrain, FIDtrain indicate the evaluated splits.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/executing-your-commands-via-motion-diffusion/review/?hl=87197", "content": "Executing your Commands via Motion Diffusion in Latent Space. Table 3: Comparison of action-conditional motion synthesis on UESTC [ji2018large] and HumanAct 12 [guo2020action2 motion ] dataset: FIDtrain, FIDtrain indicate the evaluated splits."}
+{"idx": 2, "title": "Executing your Commands via Motion Diffusion ... | Papers With Code", "date": "", "ddg_snippet": "Metric value . Global rank. Remove. Motion Synthesis. HumanAct 12 . MLD. FID . 0.077.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/executing-your-commands-via-motion-diffusion?ref=taskswithcode.ghost.io", "content": "Metric value . Global rank. Remove. Motion Synthesis. HumanAct 12 . MLD. FID . 0.077."}
+{"idx": 3, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Table 3. Comparison of action-conditional motion synthesis on UESTC [26] and HumanAct 12 [19] dataset: FIDtrain, FIDtrain indi-cate the evaluated splits.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.04048", "content": "Table 3. Comparison of action-conditional motion synthesis on UESTC [26] and HumanAct 12 [19] dataset: FIDtrain, FIDtrain indi-cate the evaluated splits."}
+{"idx": 4, "title": "(PDF) Executing your Commands via Motion Diffusion in Latent...", "date": "", "ddg_snippet": "HumanAct 12 , indicating that diffusion models in motion la Table 9. Classifier-free Diffusion Guidance: We study the influence of its hyperparameters, dropout pand scale son text-to- motion . Models Batch Size R Precision FID ↓MM Dist↓Diversity→MModality↑.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366136169_Executing_your_Commands_via_Motion_Diffusion_in_Latent_Space", "content": "HumanAct 12 , indicating that diffusion models in motion la Table 9. Classifier-free Diffusion Guidance: We study the influence of its hyperparameters, dropout pand scale son text-to- motion . Models Batch Size R Precision FID ↓MM Dist↓Diversity→MModality↑."}
+{"idx": 5, "title": "Executing your Commands via Motion Diffusion in Latent... | DeepAI", "date": "", "ddg_snippet": "12/08/2022.Our proposed Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/executing-your-commands-via-motion-diffusion-in-latent-space", "content": "12/08/2022.Our proposed Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages."}
+{"idx": 6, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Our proposed Motion Latent Diffusion model (MLD) could produce vivid motion sequences (left) conforming to the given conditional inputs and substantially reduce the computational overhead (right) in both the training and inference stages.11. 12. Text-to- Motion comparisons.", "subpage_snippet": "", "source": "chenxin.tech", "link": "https://chenxin.tech/mld/", "content": "Our proposed Motion Latent Diffusion model (MLD) could produce vivid motion sequences (left) conforming to the given conditional inputs and substantially reduce the computational overhead (right) in both the training and inference stages.11. 12. Text-to- Motion comparisons."}
+{"idx": 7, "title": "ChenFengYe/ motion -latent- diffusion : [CVPR 2023] Executing your ...", "date": "", "ddg_snippet": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion", "content": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model."}
+{"idx": 8, "title": "ChenFengYe motion -latent- diffusion issues - Githubissues", "date": "", "ddg_snippet": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/ChenFengYe/motion-latent-diffusion", "content": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model."}
+{"idx": 9, "title": "Executing your Commands via Motion Diffusion in Latent... | alphaXiv", "date": "", "ddg_snippet": "Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Amit H Bermano, and Daniel Cohen-Or. Human motion diffusion model. arXiv preprint arXiv:2209.14916, 2022.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/ru/overview/2212.04048v3", "content": "Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Amit H Bermano, and Daniel Cohen-Or. Human motion diffusion model. arXiv preprint arXiv:2209.14916, 2022."}
diff --git a/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_all_grids_updated_simu.jsonl b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_all_grids_updated_simu.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f7e1142d10f65ebc3ebd0ffa21ae8eee2ececab
--- /dev/null
+++ b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_all_grids_updated_simu.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Log-Concavity and Score Regularity: Improved Convergence", "date": "", "ddg_snippet": "Beyond Log -Concavity and Score Regularity: Improved Convergence Bounds for Score - Based Generative Models in 𝒲 2 \\mathcal{W}_{2} -distance", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02298v4", "content": "Beyond Log -Concavity and Score Regularity: Improved Convergence Bounds for Score - Based Generative Models in 𝒲 2 \\mathcal{W}_{2} -distance"}
+{"idx": 1, "title": "Beyond Scores: Proximal Diffusion Models", "date": "", "ddg_snippet": "The reverse process, which allows for sampling data from noise, involves the so-called score function—the gradient of the log -density of the noisy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.08956v1", "content": "The reverse process, which allows for sampling data from noise, involves the so-called score function—the gradient of the log -density of the noisy ..."}
+{"idx": 2, "title": "Downloads", "date": "", "ddg_snippet": "A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model - Based Reinforcement Learning ... and Normalizing Flow Toward ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model - Based Reinforcement Learning ... and Normalizing Flow Toward ..."}
+{"idx": 3, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling ... between Simulation and Diffusion for ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling ... between Simulation and Diffusion for ..."}
+{"idx": 4, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "... for Fast High-Quality Sampling from Diffusion ... Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html", "content": "... for Fast High-Quality Sampling from Diffusion ... Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning"}
+{"idx": 5, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "... for Fast High-Quality Sampling from Diffusion ... Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html?filter=titles", "content": "... for Fast High-Quality Sampling from Diffusion ... Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning"}
+{"idx": 6, "title": "Downloads", "date": "", "ddg_snippet": "... Method for Smooth and Convex- Concave ... A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2022", "content": "... Method for Smooth and Convex- Concave ... A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions"}
+{"idx": 7, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "... for Fast High-Quality Sampling from Diffusion ... Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/papers.html?filter=titles", "content": "... for Fast High-Quality Sampling from Diffusion ... Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning"}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2021", "content": "Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously"}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2018", "content": "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models"}
diff --git a/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_arXiv_year_2024.jsonl b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_arXiv_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..65c38c0734d51cc4cddf4f9c7d0e00823072c59f
--- /dev/null
+++ b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_arXiv_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Faster Diffusion Sampling with Randomized Midpoints: Sequential", "date": "", "ddg_snippet": "... in the randomized midpoint method , originally introduced by Shen and Lee [ SL19 ] in the context of Langevin Monte Carlo for log - concave sampling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.00924v2", "content": "... in the randomized midpoint method , originally introduced by Shen and Lee [ SL19 ] in the context of Langevin Monte Carlo for log - concave sampling ..."}
+{"idx": 1, "title": "Beyond Scores: Proximal Diffusion Models", "date": "", "ddg_snippet": "Diffusion models combine a forward diffusion process that converts the data distribution we wish to sample into noise and a reverse-time process that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.08956v1", "content": "Diffusion models combine a forward diffusion process that converts the data distribution we wish to sample into noise and a reverse-time process that ..."}
+{"idx": 2, "title": "Beyond Log-Concavity and Score Regularity: Improved Convergence", "date": "", "ddg_snippet": "Beyond Log -Concavity and Score Regularity: Improved Convergence Bounds for Score - Based Generative Models in 𝒲 2 \\mathcal{W}_{2} -distance", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02298v4", "content": "Beyond Log -Concavity and Score Regularity: Improved Convergence Bounds for Score - Based Generative Models in 𝒲 2 \\mathcal{W}_{2} -distance"}
+{"idx": 3, "title": "Beyond Log-Concavity and Score Regularity: Improved Convergence", "date": "", "ddg_snippet": "Beyond Log -Concavity and Score Regularity: Improved Convergence Bounds for Score - Based Generative Models in 𝒲 2 subscript 𝒲 2 \\mathcal{W}_{2 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02298v1", "content": "Beyond Log -Concavity and Score Regularity: Improved Convergence Bounds for Score - Based Generative Models in 𝒲 2 subscript 𝒲 2 \\mathcal{W}_{2 ..."}
+{"idx": 4, "title": "Restricted Spectral Gap Decomposition for Simulated Tempering", "date": "", "ddg_snippet": "Additionally, denoising- diffusion - based samplers have been proposed for sampling from non- log - concave targets without relying on isoperimetric ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.15059v1", "content": "Additionally, denoising- diffusion - based samplers have been proposed for sampling from non- log - concave targets without relying on isoperimetric ..."}
+{"idx": 5, "title": "Methodology | arXiver", "date": "", "ddg_snippet": "... methodological advances to perform simulation - based inference (SBI) of a general class of Bayesian hierarchical models (BHMs), while checking for ...", "subpage_snippet": "", "source": "arxiver.moonhats.com", "link": "https://arxiver.moonhats.com/tag/methodology/", "content": "... methodological advances to perform simulation - based inference (SBI) of a general class of Bayesian hierarchical models (BHMs), while checking for ..."}
+{"idx": 6, "title": "Probabilistic image reconstruction for radio interferometers |", "date": "", "ddg_snippet": "... for deconvolving and denoizing images from gridded radio interferometric visibilities using Bayesian inference based on a Gaussian process model .", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/W2084590735/probabilistic-image-reconstruction-for-radio-interferometers", "content": "... for deconvolving and denoizing images from gridded radio interferometric visibilities using Bayesian inference based on a Gaussian process model ."}
+{"idx": 7, "title": "Markov Chain Monte Carlo methods — The Dan MacKinlay stable", "date": "", "ddg_snippet": "Implicit Langevin Algorithms for Sampling From Log - Concave Densities .” arXiv :1903.12322 [Cs, Stat] . ... for Latent Gaussian Models and Beyond .” ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/mcmc", "content": "Implicit Langevin Algorithms for Sampling From Log - Concave Densities .” arXiv :1903.12322 [Cs, Stat] . ... for Latent Gaussian Models and Beyond .” ..."}
+{"idx": 8, "title": "AI conference paper analysis 2024", "date": "", "ddg_snippet": "On this page I created embeddings for each paper using Qwen 2 and clustered them together using HDBSCAN recursively, then labeled these clusters ...", "subpage_snippet": "", "source": "arxiv-cat.eamag.me", "link": "https://arxiv-cat.eamag.me/icml_embeddings", "content": "On this page I created embeddings for each paper using Qwen 2 and clustered them together using HDBSCAN recursively, then labeled these clusters ..."}
+{"idx": 9, "title": "Shivam Gupta", "date": "", "ddg_snippet": "Abstract: The growing use of foundation models (FMs) in real-world applications demands adaptive, reliable, and efficient strategies for dynamic ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Shivam+Gupta", "content": "Abstract: The growing use of foundation models (FMs) in real-world applications demands adaptive, reliable, and efficient strategies for dynamic ..."}
diff --git a/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_biased_simulation.jsonl b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_biased_simulation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9cda40b820fdc254a230cb74759a5e88e39541cb
--- /dev/null
+++ b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_biased_simulation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Faster Diffusion Sampling with Randomized Midpoints: Sequential", "date": "", "ddg_snippet": "As a byproduct of our methods, for the well-studied problem of log - concave sampling in total variation distance, we give an algorithm and simple ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.00924v2", "content": "As a byproduct of our methods, for the well-studied problem of log - concave sampling in total variation distance, we give an algorithm and simple ..."}
+{"idx": 1, "title": "Beyond Scores: Proximal Diffusion Models", "date": "", "ddg_snippet": "Diffusion models combine a forward diffusion process that converts the data distribution we wish to sample into noise and a reverse-time process that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.08956v1", "content": "Diffusion models combine a forward diffusion process that converts the data distribution we wish to sample into noise and a reverse-time process that ..."}
+{"idx": 2, "title": "What's the score? – Review of latest Score Based Generative", "date": "", "ddg_snippet": "... of Score - based Diffusion Model for ... ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy- based Generative Models", "subpage_snippet": "", "source": "scorebasedgenerativemodeling.github.io", "link": "https://scorebasedgenerativemodeling.github.io/", "content": "... of Score - based Diffusion Model for ... ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy- based Generative Models"}
+{"idx": 3, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks ... Optimization: Models , Insights, and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks ... Optimization: Models , Insights, and ..."}
+{"idx": 4, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks ... Optimization: Models , Insights, and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks ... Optimization: Models , Insights, and ..."}
+{"idx": 5, "title": "Downloads", "date": "", "ddg_snippet": "A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model - Based Reinforcement Learning ... and Normalizing Flow Toward ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model - Based Reinforcement Learning ... and Normalizing Flow Toward ..."}
+{"idx": 6, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling ... between Simulation and Diffusion for ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling ... between Simulation and Diffusion for ..."}
+{"idx": 7, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "... for Fast High-Quality Sampling from Diffusion ... Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html", "content": "... for Fast High-Quality Sampling from Diffusion ... Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning"}
+{"idx": 8, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "... for Fast High-Quality Sampling from Diffusion ... Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html?filter=titles", "content": "... for Fast High-Quality Sampling from Diffusion ... Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning"}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "Accelerated Primal-Dual Gradient Method for Smooth and Convex- Concave Saddle-Point Problems with Bilinear Coupling ... sampling method with complexity ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2022", "content": "Accelerated Primal-Dual Gradient Method for Smooth and Convex- Concave Saddle-Point Problems with Bilinear Coupling ... sampling method with complexity ..."}
diff --git a/data/sampled_jsons/Pearl_structural_causal_model_two_main_components_DAG_structural_equations.jsonl b/data/sampled_jsons/Pearl_structural_causal_model_two_main_components_DAG_structural_equations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..59967988a876087c1a3921528ec1d693818d5c1e
--- /dev/null
+++ b/data/sampled_jsons/Pearl_structural_causal_model_two_main_components_DAG_structural_equations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Structural equation modeling - Wikipedia", "date": "", "ddg_snippet": "The boundary between what is and is not a structural equation model is not always clear but SE models often contain postulated causal connections ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Structural_equation_modeling", "content": "The boundary between what is and is not a structural equation model is not always clear but SE models often contain postulated causal connections ..."}
+{"idx": 1, "title": "Applications of DAGs in Causal Inference | Quasilinear Musings", "date": "", "ddg_snippet": "Two years ago I came across Pearl s work on using directed cyclical graphs ( DAGs ) to model the problem of causal inference and have read the debate ...", "subpage_snippet": "", "source": "www.timlrx.com", "link": "https://www.timlrx.com/blog/applications-of-dags-in-causal-inference", "content": "Two years ago I came across Pearl s work on using directed cyclical graphs ( DAGs ) to model the problem of causal inference and have read the debate ..."}
+{"idx": 2, "title": "causal DAGs – Andi Fugard (∧⇒)", "date": "", "ddg_snippet": "This paper provides a general introduction to the use of causal models in the metaphysics of causation, specifically structural equation models and ...", "subpage_snippet": "", "source": "andifugard.info", "link": "https://andifugard.info/tag/causal-dags/", "content": "This paper provides a general introduction to the use of causal models in the metaphysics of causation, specifically structural equation models and ..."}
+{"idx": 3, "title": "Cross-modal Causal Intervention for Alzheimer’s Disease", "date": "", "ddg_snippet": "Our ADPC employs large language model (LLM) to summarize clinical data under strict templates, maintaining structured text outputs even with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.13956v1", "content": "Our ADPC employs large language model (LLM) to summarize clinical data under strict templates, maintaining structured text outputs even with ..."}
+{"idx": 4, "title": "Structural Causal Models for Extremes: an Approach Based on", "date": "", "ddg_snippet": "The structural causal model (SCM) , also known as the structural equation model , is a widely used approach for modeling causal interactions among ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.00223v1", "content": "The structural causal model (SCM) , also known as the structural equation model , is a widely used approach for modeling causal interactions among ..."}
+{"idx": 5, "title": "Newest 'causal-diagram' Questions - Cross Validated", "date": "", "ddg_snippet": "This is a claim made by Professor Judea Pearl in his classical monograph Causality: Models , Reasoning and Inference (footnote 5 in Section 2 .3).", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/tagged/causal-diagram", "content": "This is a claim made by Professor Judea Pearl in his classical monograph Causality: Models , Reasoning and Inference (footnote 5 in Section 2 .3)."}
+{"idx": 6, "title": "Introduction to structural causal models in science studies –", "date": "", "ddg_snippet": "... Open Science Impact Indicator Handbook we introduce a particular view on causal inference, namely that of structural causal models ( Pearl 2009 ) .", "subpage_snippet": "", "source": "handbook.pathos-project.eu", "link": "https://handbook.pathos-project.eu/sections/0_causality/causal_intro/article/intro-causality.html", "content": "... Open Science Impact Indicator Handbook we introduce a particular view on causal inference, namely that of structural causal models ( Pearl 2009 ) ."}
+{"idx": 7, "title": "Writing Causal Models Like We Write Programs - LessWrong 2.0", "date": "", "ddg_snippet": "Hopefully the mapping from clunc to probabilistic causal models is obvious: any clunc with random variables in it is a typical Pearl -style causal DAG ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/Xd9FLs4geRAWxkQPE/writing-causal-models-like-we-write-programs", "content": "Hopefully the mapping from clunc to probabilistic causal models is obvious: any clunc with random variables in it is a typical Pearl -style causal DAG ..."}
+{"idx": 8, "title": "Bayesian identification of structural coefficients in causal", "date": "", "ddg_snippet": "Although the identification of structural causal models (SCM) and the calculation of structural coefficients has received much attention, a key ...", "subpage_snippet": "", "source": "bmcmedresmethodol.biomedcentral.com", "link": "https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01473-w", "content": "Although the identification of structural causal models (SCM) and the calculation of structural coefficients has received much attention, a key ..."}
+{"idx": 9, "title": "references - Competitions/datasets fit for exploring", "date": "", "ddg_snippet": "Are there any competitions/challenges/datasets fit for testing Pearl 's graphical causal inference methods? I do not necessarily mean live ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/617247/competitions-datasets-fit-for-exploring-pearls-graphical-causal-models", "content": "Are there any competitions/challenges/datasets fit for testing Pearl 's graphical causal inference methods? I do not necessarily mean live ..."}
diff --git a/data/sampled_jsons/Peebles_Xie_DiT_diffusion_model.jsonl b/data/sampled_jsons/Peebles_Xie_DiT_diffusion_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e148986d21f2d968b58722221cfdb52abe9f8ab
--- /dev/null
+++ b/data/sampled_jsons/Peebles_Xie_DiT_diffusion_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2212.09748] Scalable Diffusion Models with Transformers", "date": "", "ddg_snippet": "Authors:William Peebles , Saining Xie . View a PDF of the paper titled Scalable Diffusion Models with Transformers, by William Peebles and 1 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.09748", "content": "Authors:William Peebles , Saining Xie . View a PDF of the paper titled Scalable Diffusion Models with Transformers, by William Peebles and 1 other authors."}
+{"idx": 1, "title": "Scalable Diffusion Models with Transformers Scalable Diffusion Models with Transformers (DiT) - Hugging Face Scalable Diffusion Models with Transformers (DiT) - GitHub Scalable Diffusion Models with Transformers - Semantic Scholar Scalable Diffusion Models with Transformers (DiT) - Hugging Face Scalable Diffusion Models with Transformers Scalable Diffusion Models with Transformers - CVF Open Access Scalable Diffusion Models with Transformers [2212.09748] Scalable Diffusion Models with Transformers Scalable Diffusion Models with Transformers - CVF Open Access A Deep Dive into Diffusion Transformers: A Review - Medium", "date": "", "ddg_snippet": "Diffusion models have achieved amazing results in image generation over the past year. Almost all of these models use a convolutional U-Net as a backbone. This is sort of surprising! The big story of deep learning over the past couple of years has been the dominance of transformers across domains. Is there something special about the U-Net—or convo... See full list on wpeebles.com Transformers are known to scale well in a variety of domains. How about as diffusion models? We scale DiT along two axes in this paper: model size and number of input tokens. Scaling model size.We tried four configs that differ by model depth and width: DiT -S, DiT -B, DiT -L and DiT -XL. These model configs range from 33M to 675M parameters and 0.4 to... See full list on wpeebles.com We trained two versions of DiT -XL/2 at 256x256 and 512x512 resolution on ImageNet for 7M and 3M steps, respectively. When using classifier-free guidance, DiT -XL/2 outperforms all prior diffusion models, decreasing the previous best FID-50K of 3.60 achieved by LDM (256x256) to 2.27; this is state-of-the-art among all generative models. XL/2 again ou... See full list on wpeebles.com Finally, we show some latent walks for DiT -XL/2. We slerp through several different selections of initial noise, using the deterministic DDIM sampler to generate each intermediate image. We can also walk through the label embedding space of DiT . For example, we can linearly interpolate between the embeddings for many dog breeds as well as the \"tenn... See full list on wpeebles.com @article{Peebles2022DiT, title={Scalable Diffusion Models with Transformers}, author={William Peebles and Saining Xie }, year={2022}, journal={arXiv preprint arXiv:2212.09748}, } See full list on wpeebles.com Scalable Diffusion Models with Transformers ( DiT ) by William Peebles and Saining Xie . The abstract of the paper is the following: We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion ... Paper | Project Page | Run DiT -XL/2 This repo contains PyTorch model definitions, pre-trained weights and training/sampling code for our paper exploring diffusion models with transformers (DiTs). You can find more visualizations on our project page. Scalable Diffusion Models with Transformers William Peebles , Saining Xie UC Berkeley, New York ... Dec 19, 2022 · Scalable Interpolant Transformers is presented, a family of generative models built on the backbone of Diffusion Transformers that surpasses DiT uniformly across model sizes on the conditional ImageNet 256x256 and 512x512 benchmark using the exact same model structure, number of parameters, and GFLOPs. What is a scalable diffusion model with a transformer? Scalable Diffusion Models with Transformers ( DiT ) by William Peebles and Saining Xie. The abstract of the paper is the following: We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. What is a 512x512 dit-xl2 diffusion model? Walking through the latent space of our 512x512 DiT-XL/2 diffusion model. It's a class-conditional diffusion model that uses a transformer backbone. [Uncompressed] We explore a new class of diffusion models based on the transformer architecture. What are diffusion Transformers (dits)? We introduce Diffusion Transformers (DiTs), a new ar-chitecture for diffusion models . We aim to be as faithful to the standard transformer architecture as possible to retain its scaling properties. How do we replace the U-Net backbone in latent diffusion models? In this paper, we replace the U-Net backbone in latent diffusion models (LDMs) with a transformer . We call these models Diffusion Transformers, or DiTs for short. The DiT architecture is very similar to a standard Vision Transformer (ViT), with a few small, but important, tweaks. How do we train latent diffusion models? We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches . We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. Are dits scalable architectures for diffusion mod-Els? We further show that DiTs are scalable architectures for diffusion mod-els : there is a strong correlation between the network com-plexity (measured by Gflops) vs. sample quality (measured by FID). Mar 20, 2025 · But the paper “Scalable Diffusion Models with Transformers ( DiT )” by William Peebles and Saining Xie (ICCV 2023) proposes a game-changing shift: replacing the U-Net with a Transformer backbone.", "subpage_snippet": "", "source": "www.wpeebles.com", "link": "https://www.wpeebles.com/DiT", "content": "Diffusion models have achieved amazing results in image generation over the past year. Almost all of these models use a convolutional U-Net as a backbone. This is sort of surprising! The big story of deep learning over the past couple of years has been the dominance of transformers across domains. Is there something special about the U-Net—or convo... See full list on wpeebles.com Transformers are known to scale well in a variety of domains. How about as diffusion models? We scale DiT along two axes in this paper: model size and number of input tokens. Scaling model size.We tried four configs that differ by model depth and width: DiT -S, DiT -B, DiT -L and DiT -XL. These model configs range from 33M to 675M parameters and 0.4 to... See full list on wpeebles.com We trained two versions of DiT -XL/2 at 256x256 and 512x512 resolution on ImageNet for 7M and 3M steps, respectively. When using classifier-free guidance, DiT -XL/2 outperforms all prior diffusion models, decreasing the previous best FID-50K of 3.60 achieved by LDM (256x256) to 2.27; this is state-of-the-art among all generative models. XL/2 again ou... See full list on wpeebles.com Finally, we show some latent walks for DiT -XL/2. We slerp through several different selections of initial noise, using the deterministic DDIM sampler to generate each intermediate image. We can also walk through the label embedding space of DiT . For example, we can linearly interpolate between the embeddings for many dog breeds as well as the \"tenn... See full list on wpeebles.com @article{Peebles2022DiT, title={Scalable Diffusion Models with Transformers}, author={William Peebles and Saining Xie }, year={2022}, journal={arXiv preprint arXiv:2212.09748}, } See full list on wpeebles.com Scalable Diffusion Models with Transformers ( DiT ) by William Peebles and Saining Xie . The abstract of the paper is the following: We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion ... Paper | Project Page | Run DiT -XL/2 This repo contains PyTorch model definitions, pre-trained weights and training/sampling code for our paper exploring diffusion models with transformers (DiTs). You can find more visualizations on our project page. Scalable Diffusion Models with Transformers William Peebles , Saining Xie UC Berkeley, New York ... Dec 19, 2022 · Scalable Interpolant Transformers is presented, a family of generative models built on the backbone of Diffusion Transformers that surpasses DiT uniformly across model sizes on the conditional ImageNet 256x256 and 512x512 benchmark using the exact same model structure, number of parameters, and GFLOPs. What is a scalable diffusion model with a transformer? Scalable Diffusion Models with Transformers ( DiT ) by William Peebles and Saining Xie. The abstract of the paper is the following: We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. What is a 512x512 dit-xl2 diffusion model? Walking through the latent space of our 512x512 DiT-XL/2 diffusion model. It's a class-conditional diffusion model that uses a transformer backbone. [Uncompressed] We explore a new class of diffusion models based on the transformer architecture. What are diffusion Transformers (dits)? We introduce Diffusion Transformers (DiTs), a new ar-chitecture for diffusion models . We aim to be as faithful to the standard transformer architecture as possible to retain its scaling properties. How do we replace the U-Net backbone in latent diffusion models? In this paper, we replace the U-Net backbone in latent diffusion models (LDMs) with a transformer . We call these models Diffusion Transformers, or DiTs for short. The DiT architecture is very similar to a standard Vision Transformer (ViT), with a few small, but important, tweaks. How do we train latent diffusion models? We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches . We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. Are dits scalable architectures for diffusion mod-Els? We further show that DiTs are scalable architectures for diffusion mod-els : there is a strong correlation between the network com-plexity (measured by Gflops) vs. sample quality (measured by FID). Mar 20, 2025 · But the paper “Scalable Diffusion Models with Transformers ( DiT )” by William Peebles and Saining Xie (ICCV 2023) proposes a game-changing shift: replacing the U-Net with a Transformer backbone."}
+{"idx": 2, "title": "Scalable Diffusion Models with Transformers (DiT) - GitHub", "date": "", "ddg_snippet": "Paper | Project Page | Run DiT -XL/2 This repo contains PyTorch model definitions, pre-trained weights and training/sampling code for our paper exploring diffusion models with transformers (DiTs). You can find more visualizations on our project page. Scalable Diffusion Models with Transformers William Peebles , Saining Xie UC Berkeley, New York ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/DiT", "content": "Paper | Project Page | Run DiT -XL/2 This repo contains PyTorch model definitions, pre-trained weights and training/sampling code for our paper exploring diffusion models with transformers (DiTs). You can find more visualizations on our project page. Scalable Diffusion Models with Transformers William Peebles , Saining Xie UC Berkeley, New York ..."}
+{"idx": 3, "title": "Scalable Diffusion Models with Transformers - Semantic Scholar", "date": "", "ddg_snippet": "Dec 19, 2022 · Scalable Interpolant Transformers is presented, a family of generative models built on the backbone of Diffusion Transformers that surpasses DiT uniformly across model sizes on the conditional ImageNet 256x256 and 512x512 benchmark using the exact same model structure, number of parameters, and GFLOPs.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Scalable-Diffusion-Models-with-Transformers-Peebles-Xie/736973165f98105fec3729b7db414ae4d80fcbeb", "content": "Dec 19, 2022 · Scalable Interpolant Transformers is presented, a family of generative models built on the backbone of Diffusion Transformers that surpasses DiT uniformly across model sizes on the conditional ImageNet 256x256 and 512x512 benchmark using the exact same model structure, number of parameters, and GFLOPs."}
+{"idx": 4, "title": "Scalable Diffusion Models with Transformers - CVF Open Access", "date": "", "ddg_snippet": "William Peebles * Saining Xie UC Berkeley New York University Figure 1: Diffusion models with transformer backbones achieve state-of-the-art image quality. We show selected sam-ples from two of our class-conditional DiT -XL/2 models trained on ImageNet at 512⇥512 and 256⇥256 resolution.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Peebles_Scalable_Diffusion_Models_with_Transformers_ICCV_2023_paper.pdf", "content": "William Peebles * Saining Xie UC Berkeley New York University Figure 1: Diffusion models with transformer backbones achieve state-of-the-art image quality. We show selected sam-ples from two of our class-conditional DiT -XL/2 models trained on ImageNet at 512⇥512 and 256⇥256 resolution."}
+{"idx": 5, "title": "Scalable Diffusion Models with Transformers (DiT) - Hugging Face", "date": "", "ddg_snippet": "Scalable Diffusion Models with Transformers ( DiT ) by William Peebles and Saining Xie . The abstract of the paper is the following: We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/diffusers/v0.17.1/en/api/pipelines/dit", "content": "Scalable Diffusion Models with Transformers ( DiT ) by William Peebles and Saining Xie . The abstract of the paper is the following: We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion ..."}
+{"idx": 6, "title": "A Deep Dive into Diffusion Transformers: A Review - Medium", "date": "", "ddg_snippet": "Mar 20, 2025 · But the paper “Scalable Diffusion Models with Transformers ( DiT )” by William Peebles and Saining Xie (ICCV 2023) proposes a game-changing shift: replacing the U-Net with a Transformer backbone.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@supe.one.0/a-deep-dive-into-diffusion-transformers-dits-a-review-612b8f529a4e", "content": "Mar 20, 2025 · But the paper “Scalable Diffusion Models with Transformers ( DiT )” by William Peebles and Saining Xie (ICCV 2023) proposes a game-changing shift: replacing the U-Net with a Transformer backbone."}
+{"idx": 7, "title": "Scalability of Diffusion Models with Transformer Backbone | Encord", "date": "", "ddg_snippet": "Developed by William Peebles at UC Berkeley and Saining Xie at New York University, DiT aims to improve the performance of diffusion models by replacing the commonly used U-Net backbone with a transformer.", "subpage_snippet": "", "source": "encord.com", "link": "https://encord.com/blog/diffusion-models-with-transformers/", "content": "Developed by William Peebles at UC Berkeley and Saining Xie at New York University, DiT aims to improve the performance of diffusion models by replacing the commonly used U-Net backbone with a transformer."}
+{"idx": 8, "title": "The Evolution and Rise of Diffusion Models in AI | by LM Po | Medium", "date": "", "ddg_snippet": "Diffusion models operate through two complementary processes: forward diffusion (adding noise) and reverse diffusion (denoising).The Diffusion Transformer ( DiT ) architecture. (Image source: Peebles & Xie , 2023).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@lmpo/from-words-to-pixels-the-evolution-and-rise-of-diffusion-models-in-ai-1053a95deabd", "content": "Diffusion models operate through two complementary processes: forward diffusion (adding noise) and reverse diffusion (denoising).The Diffusion Transformer ( DiT ) architecture. (Image source: Peebles & Xie , 2023)."}
+{"idx": 9, "title": "Diffusion Transformers Explained: The Beginner’s Guide", "date": "", "ddg_snippet": "DiT -XL/2 refers to the largest Diffusion Transformer model configuration introduced by Peebles & Xie (2023). “XL/2” denotes an Extra-Large model using a patch size of 2 (meaning smaller patches, more tokens).", "subpage_snippet": "", "source": "www.lightly.ai", "link": "https://www.lightly.ai/blog/diffusion-transformers-dit", "content": "DiT -XL/2 refers to the largest Diffusion Transformer model configuration introduced by Peebles & Xie (2023). “XL/2” denotes an Extra-Large model using a patch size of 2 (meaning smaller patches, more tokens)."}
diff --git a/data/sampled_jsons/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter_ab_year_2015.jsonl b/data/sampled_jsons/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter_ab_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e0f984c14e584768650af9c78f95c4f0456d5ce7
--- /dev/null
+++ b/data/sampled_jsons/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter_ab_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) People as sensors : Mass media and local temperature ...", "date": "", "ddg_snippet": "local temperature to climate change and whether mass media influences the process. We used the volume of Twitter messages containing words “ climate change ” and “global warming” as the indicator of attention that public pays to the issue.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/29095713/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter", "content": "local temperature to climate change and whether mass media influences the process. We used the volume of Twitter messages containing words “ climate change ” and “global warming” as the indicator of attention that public pays to the issue."}
+{"idx": 1, "title": "People as sensors : Mass media and local temperature influence ...", "date": "", "ddg_snippet": "Keywords: Climate change Temperature Social media Twitter Mass media . ABSTRACT . This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media inuences the process.", "subpage_snippet": "", "source": "research.fit.edu", "link": "https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/climate-communications/youth-climate-amp-social-media/Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect-CC-Discussion-On-Twitter.pdf", "content": "Keywords: Climate change Temperature Social media Twitter Mass media . ABSTRACT . This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media inuences the process."}
+{"idx": 2, "title": "People as sensors : Mass media and local temperature influence ...", "date": "", "ddg_snippet": "... Furthermore, the influences of climate change can also be studied locally . For instance, in a study of local mass media , Kirilenko et al. (2015) compared the temperature of climate change and identified indicators in corresponding Twitter discussions .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/268221450_People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter", "content": "... Furthermore, the influences of climate change can also be studied locally . For instance, in a study of local mass media , Kirilenko et al. (2015) compared the temperature of climate change and identified indicators in corresponding Twitter discussions ."}
+{"idx": 3, "title": "People as sensors : Mass media and local temperature influence ...", "date": "", "ddg_snippet": "Climate change ; Temperature ; Social media ; Twitter ; Mass media .However, no convincing evidence was found that the media acts as a mediator in the relationship between local weather and climate change discourse.", "subpage_snippet": "", "source": "www.peeref.com", "link": "https://www.peeref.com/works/553482", "content": "Climate change ; Temperature ; Social media ; Twitter ; Mass media .However, no convincing evidence was found that the media acts as a mediator in the relationship between local weather and climate change discourse."}
+{"idx": 4, "title": "Yeo | The influence of temperature on # ClimateChange and...", "date": "", "ddg_snippet": "‘ People as sensors : Mass media and local temperature influence climate change discussion on Twitter ’.", "subpage_snippet": "", "source": "jcom.sissa.it", "link": "https://jcom.sissa.it/article/pubid/JCOM_1605_2017_A01/", "content": "‘ People as sensors : Mass media and local temperature influence climate change discussion on Twitter ’."}
+{"idx": 5, "title": "Andrei P. Kirilenko - Google Scholar", "date": "", "ddg_snippet": "2018. People as sensors : Mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=HqV8fnwAAAAJ&hl=en", "content": "2018. People as sensors : Mass media and local temperature influence climate change discussion on Twitter ."}
+{"idx": 6, "title": "Climate Change Disinformation and How to Combat It | Annual Reviews", "date": "", "ddg_snippet": "2015. People as sensors : mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "www.annualreviews.org", "link": "https://www.annualreviews.org/content/journals/10.1146/annurev-publhealth-090419-102409", "content": "2015. People as sensors : mass media and local temperature influence climate change discussion on Twitter ."}
+{"idx": 7, "title": "Climate Change Sentiment on Twitter : An Unsolicited Public Opinion...", "date": "", "ddg_snippet": "People as sensors : Mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4546368/", "content": "People as sensors : Mass media and local temperature influence climate change discussion on Twitter ."}
+{"idx": 8, "title": "Event attribution and partisanship shape local discussion of climate ...", "date": "", "ddg_snippet": "Kirilenko, A. P., Molodtsova, T. & Stepchenkova, S. O. People as sensors : mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41558-019-0641-3?error=cookies_not_supported&code=c0573718-100f-4df2-95c3-5ee00c3524a2", "content": "Kirilenko, A. P., Molodtsova, T. & Stepchenkova, S. O. People as sensors : mass media and local temperature influence climate change discussion on Twitter ."}
+{"idx": 9, "title": "Analyzing Tweets to Understand Factors Affecting Opinion on Climate ...", "date": "", "ddg_snippet": "Abstract . Climate change is a topic that is frequently debated on social media .Kirilenko, A.P., Molodtsova, T., Stepchenkova, S.O.: People as sensors : mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-69377-0_9", "content": "Abstract . Climate change is a topic that is frequently debated on social media .Kirilenko, A.P., Molodtsova, T., Stepchenkova, S.O.: People as sensors : mass media and local temperature influence climate change discussion on Twitter ."}
diff --git a/data/sampled_jsons/Perez_et_al._2022_large_language_model.jsonl b/data/sampled_jsons/Perez_et_al._2022_large_language_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..49f4003c962ac7e05d029ff52ed3dfa63ce9d447
--- /dev/null
+++ b/data/sampled_jsons/Perez_et_al._2022_large_language_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2202.03286] Red Teaming Language Models with ...", "date": "", "ddg_snippet": "by E Perez · 2022 · Cited by 869 — In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases (\"red teaming\") using another LM.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.03286", "content": "by E Perez · 2022 · Cited by 869 — In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases (\"red teaming\") using another LM."}
+{"idx": 1, "title": "Ignore Previous Prompt: Attack Techniques For Language ...", "date": "", "ddg_snippet": "by F Perez · 2022 · Cited by 546 — Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2211.09527", "content": "by F Perez · 2022 · Cited by 546 — Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing ..."}
+{"idx": 2, "title": "Red Teaming Language Models with ...", "date": "", "ddg_snippet": "by E Perez · 2022 · Cited by 861 — Language Models (LMs) often cannot be deployed because of their potential to harm users in ways that are hard to predict in advance.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.emnlp-main.225.pdf", "content": "by E Perez · 2022 · Cited by 861 — Language Models (LMs) often cannot be deployed because of their potential to harm users in ways that are hard to predict in advance."}
+{"idx": 3, "title": "[PDF] Red Teaming Language Models with ...", "date": "", "ddg_snippet": "This work focuses on Red Teaming Language Models with Language Models by Perez et al. (2022), developing a pipeline for automated test case generation via ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Red-Teaming-Language-Models-with-Language-Models-Perez-Huang/5d49c7401c5f2337c4cc88d243ae39ed659afe64", "content": "This work focuses on Red Teaming Language Models with Language Models by Perez et al. (2022), developing a pipeline for automated test case generation via ..."}
+{"idx": 4, "title": "Large Language Model - an overview", "date": "", "ddg_snippet": "A ' Large Language Model ' refers to a highly demanding and resource-intensive model used in computer science, which contains a significant number of ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/computer-science/large-language-model", "content": "A ' Large Language Model ' refers to a highly demanding and resource-intensive model used in computer science, which contains a significant number of ..."}
+{"idx": 5, "title": "Emergent Abilities of Large Language Models", "date": "", "ddg_snippet": "by J Wei · Cited by 3953 — Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=yzkSU5zdwD", "content": "by J Wei · Cited by 3953 — Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks."}
+{"idx": 6, "title": "Discovering Language Model Behaviors with ...", "date": "", "ddg_snippet": "20 Dec 2022 — “Discovering Language Model Behaviors with Model-Written Evaluations” is a new Anthropic paper by Ethan Perez et al . that I (Evan Hubinger) ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/yRAo2KEGWenKYZG9K/discovering-language-model-behaviors-with-model-written", "content": "20 Dec 2022 — “Discovering Language Model Behaviors with Model-Written Evaluations” is a new Anthropic paper by Ethan Perez et al . that I (Evan Hubinger) ..."}
+{"idx": 7, "title": "Language models don't always say what they think", "date": "", "ddg_snippet": "Large Language Models (LLMs ) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output, often referred ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3669397", "content": "Large Language Models (LLMs ) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output, often referred ..."}
+{"idx": 8, "title": "Whose Opinions Do Language Models Reflect?", "date": "", "ddg_snippet": "by S Santurkar · 2023 · Cited by 632 — Prior works hint at the types of human viewpoints that cur- rent LMs reflect. For instance, Perez et al . ( 2022b ) and Hart- mann et al . (2023) show that in ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/santurkar23a/santurkar23a.pdf", "content": "by S Santurkar · 2023 · Cited by 632 — Prior works hint at the types of human viewpoints that cur- rent LMs reflect. For instance, Perez et al . ( 2022b ) and Hart- mann et al . (2023) show that in ..."}
+{"idx": 9, "title": "Evaluating the Prompt Steerability of Large Language ...", "date": "", "ddg_snippet": "by E Miehling · 2025 · Cited by 6 — Building pluralistic AI requires designing mod- els that are able to be shaped to represent a wide range of value systems and cultures. 27 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.400.pdf", "content": "by E Miehling · 2025 · Cited by 6 — Building pluralistic AI requires designing mod- els that are able to be shaped to represent a wide range of value systems and cultures. 27 pages"}
diff --git a/data/sampled_jsons/Physics-informed_neural_networks_PINNs_loss_function_physical_constraints_PDE_residual.jsonl b/data/sampled_jsons/Physics-informed_neural_networks_PINNs_loss_function_physical_constraints_PDE_residual.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1200022b03943d0c899260b67514f2b18f6fcd2b
--- /dev/null
+++ b/data/sampled_jsons/Physics-informed_neural_networks_PINNs_loss_function_physical_constraints_PDE_residual.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Physics-informed neural networks", "date": "", "ddg_snippet": "Physics - informed PointNet (PIPN) is fundamentally the result of a combination of PINN's loss function with PointNet. In fact, instead of using a ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Physics-informed_neural_networks", "content": "Physics - informed PointNet (PIPN) is fundamentally the result of a combination of PINN's loss function with PointNet. In fact, instead of using a ..."}
+{"idx": 1, "title": "Applying Physical Constraints in Physics-Informed Neural ...", "date": "", "ddg_snippet": "In this article, we explore various methods to enforce physical laws in PINNs , along with examples from wind power forecasting and other domains.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@noorfatimaafzalbutt/applying-physical-constraints-in-physics-informed-neural-networks-pinns-methods-and-examples-715f7e778e47", "content": "In this article, we explore various methods to enforce physical laws in PINNs , along with examples from wind power forecasting and other domains."}
+{"idx": 2, "title": "Physics-informed neural networks for PDE problems", "date": "", "ddg_snippet": "by K Luo · 2025 · Cited by 8 — PINNs are a class of machine learning methods that integrate domain-specific knowledge, typically described by PDEs , into the neural network ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11322-7", "content": "by K Luo · 2025 · Cited by 8 — PINNs are a class of machine learning methods that integrate domain-specific knowledge, typically described by PDEs , into the neural network ..."}
+{"idx": 3, "title": "Dual-Balancing for Physics-Informed Neural Networks", "date": "", "ddg_snippet": "by C Zhou · 2025 — In this paper, we propose a novel Dual-Balanced PINN (DB- PINN ), which dynamically adjusts loss weights by integrating inter-balancing and intra-balancing.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.11117", "content": "by C Zhou · 2025 — In this paper, we propose a novel Dual-Balanced PINN (DB- PINN ), which dynamically adjusts loss weights by integrating inter-balancing and intra-balancing."}
+{"idx": 4, "title": "A physics-informed neural networks framework for model ...", "date": "", "ddg_snippet": "by RO Teloli · 2025 · Cited by 12 — This study introduces an innovative approach that employs Physics - Informed Neural Networks ( PINNs ) to address inverse problems in structural analysis.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0888327024010884", "content": "by RO Teloli · 2025 · Cited by 12 — This study introduces an innovative approach that employs Physics - Informed Neural Networks ( PINNs ) to address inverse problems in structural analysis."}
+{"idx": 5, "title": "What Are Physics-Informed Neural Networks (PINNs)?", "date": "", "ddg_snippet": "PINNs are neural networks that incorporate physical laws described by differential equations into their loss functions to guide the learning process.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/discovery/physics-informed-neural-networks.html", "content": "PINNs are neural networks that incorporate physical laws described by differential equations into their loss functions to guide the learning process."}
+{"idx": 6, "title": "Physics-Informed Neural Networks: Minimizing Residual ...", "date": "", "ddg_snippet": "by NH Dashtbayaz · Cited by 5 — The residual loss in Physics - Informed Neural Net- works ( PINNs ) alters the simple recursive relation of layers in a feed-forward neural network by ap-. 9 pages", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0647.pdf", "content": "by NH Dashtbayaz · Cited by 5 — The residual loss in Physics - Informed Neural Net- works ( PINNs ) alters the simple recursive relation of layers in a feed-forward neural network by ap-. 9 pages"}
+{"idx": 7, "title": "PDE-aware Optimizer for Physics-informed Neural Networks", "date": "", "ddg_snippet": "10 Jul 2025 — These loss functions encode the physical constraints as soft penalties, guiding the model to learn solutions that are not only data-driven but ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.08118v1", "content": "10 Jul 2025 — These loss functions encode the physical constraints as soft penalties, guiding the model to learn solutions that are not only data-driven but ..."}
+{"idx": 8, "title": "Deep fuzzy physics-informed neural networks for forward ...", "date": "", "ddg_snippet": "by W Wu · 2025 · Cited by 9 — ... PINNs (gPINNs), which extracts gradient data from PDE residuals and employs a novel loss function to enhance the precision and training efficacy of PINNs .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0893608024006749", "content": "by W Wu · 2025 · Cited by 9 — ... PINNs (gPINNs), which extracts gradient data from PDE residuals and employs a novel loss function to enhance the precision and training efficacy of PINNs ."}
+{"idx": 9, "title": "Optimal control of PDEs using physics-informed neural ...", "date": "", "ddg_snippet": "15 Nov 2022 — By incorporating the residual of the PDE into the loss function of a neural network -based surrogate model for the unknown state, PINNs can ... 29 pages", "subpage_snippet": "", "source": "www.merl.com", "link": "https://www.merl.com/publications/docs/TR2022-143.pdf", "content": "15 Nov 2022 — By incorporating the residual of the PDE into the loss function of a neural network -based surrogate model for the unknown state, PINNs can ... 29 pages"}
diff --git a/data/sampled_jsons/PiPT_ratio_broadband_price_affordability_target_formula.jsonl b/data/sampled_jsons/PiPT_ratio_broadband_price_affordability_target_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd7912a1bf8298f3b479f6b67569164aba5502a5
--- /dev/null
+++ b/data/sampled_jsons/PiPT_ratio_broadband_price_affordability_target_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Framework for Improving Web Affordability and ...", "date": "", "ddg_snippet": "by R Habib · 2023 · Cited by 10 — where PT is the average price target set by UN's Broadband Com- mission, which currently stands at 2%, Pi is the average mobile broadband price in ... 16 pages", "subpage_snippet": "", "source": "www.ietf.org", "link": "https://www.ietf.org/slides/slides-biasws-a-framework-for-improving-web-affordability-and-inclusiveness-00.pdf", "content": "by R Habib · 2023 · Cited by 10 — where PT is the average price target set by UN's Broadband Com- mission, which currently stands at 2%, Pi is the average mobile broadband price in ... 16 pages"}
+{"idx": 1, "title": "Time-Dependent Broadband Pricing: Feasibility and Benefits", "date": "", "ddg_snippet": "by C Joe-Wong · Cited by 89 — Abstract—Charging different prices for Internet access at different times induces users to spread out their bandwidth consumption across times of the day.", "subpage_snippet": "", "source": "www.andrew.cmu.edu", "link": "https://www.andrew.cmu.edu/user/cjoewong/TUBE_ICDCS.pdf", "content": "by C Joe-Wong · Cited by 89 — Abstract—Charging different prices for Internet access at different times induces users to spread out their bandwidth consumption across times of the day."}
+{"idx": 2, "title": "Producer Price Index Manual - IMF eLibrary", "date": "", "ddg_snippet": "This Producer Price Index Manual replaces the United Nations' Manual on Producers' Price Indices for Industrial Goods issued in 1979 (Series M, No. 66).", "subpage_snippet": "", "source": "www.elibrary.imf.org", "link": "https://www.elibrary.imf.org/downloadpdf/display/book/9781589063044/9781589063044.pdf", "content": "This Producer Price Index Manual replaces the United Nations' Manual on Producers' Price Indices for Industrial Goods issued in 1979 (Series M, No. 66)."}
+{"idx": 3, "title": "Revenue Maximization for Broadband Service Providers ...", "date": "", "ddg_snippet": "by H Mehmood · Cited by 4 — This paper proposes an algorithm to compute the revenue capacity for multiuser gaussian channels with staircase price functions, and to produce ...", "subpage_snippet": "", "source": "people.orie.cornell.edu", "link": "https://people.orie.cornell.edu/mru8/doc/MehmoodUdellCioffi2015_revenue_capacity.pdf", "content": "by H Mehmood · Cited by 4 — This paper proposes an algorithm to compute the revenue capacity for multiuser gaussian channels with staircase price functions, and to produce ..."}
+{"idx": 4, "title": "Why Has House Price Dispersion Gone Up? - Oxford Academic", "date": "", "ddg_snippet": "We sort the MSAs into five equally sized wage bins and calculate the ratio of the number of people in each quintile to the number of people in the economy ( ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/restud/article/77/4/1567/1645685?login=true", "content": "We sort the MSAs into five equally sized wage bins and calculate the ratio of the number of people in each quintile to the number of people in the economy ( ..."}
+{"idx": 5, "title": "Broadband Advocacy Target 2 - Broadband Commission", "date": "", "ddg_snippet": "Making broadband affordable is a critical step in achieving meaningful universal connectivity. The Broadband Commission’s affordability target was originally set to “less than 5% of average monthly income,” however in 2018, the Commission updated this target to specify that prices for entry-level broadband services should be below 2% of monthly GNI per capita in developing countries* by ...", "subpage_snippet": "", "source": "www.broadbandcommission.org", "link": "https://www.broadbandcommission.org/advocacy-targets/2-affordability/", "content": "Making broadband affordable is a critical step in achieving meaningful universal connectivity. The Broadband Commission’s affordability target was originally set to “less than 5% of average monthly income,” however in 2018, the Commission updated this target to specify that prices for entry-level broadband services should be below 2% of monthly GNI per capita in developing countries* by ..."}
+{"idx": 6, "title": "Analyzing Disparities in Broadband Plans - ADDRESS", "date": "", "ddg_snippet": "Analyzing Disparities in Broadband Plans Digital equity in Internet access is often measured along three axes: availability, affordability , and adoption. Most prior work focuses on availability; the other two aspects have received little attention. In this work, we study broadband affordability in the US.", "subpage_snippet": "", "source": "address.cs.ucsb.edu", "link": "https://address.cs.ucsb.edu/bqt/", "content": "Analyzing Disparities in Broadband Plans Digital equity in Internet access is often measured along three axes: availability, affordability , and adoption. Most prior work focuses on availability; the other two aspects have received little attention. In this work, we study broadband affordability in the US."}
+{"idx": 7, "title": "A Blueprint for Broadband Affordability | ITIF", "date": "", "ddg_snippet": "Jan 13, 2025 · The point of broadband affordability policy is to make broadband affordable as defined by 2 percent of household income. Broadband is just one of the components of a basket of goods that are necessities for a household alongside food, housing, electricity, water, etc.", "subpage_snippet": "", "source": "itif.org", "link": "https://itif.org/publications/2025/01/13/a-blueprint-for-broadband-affordability/", "content": "Jan 13, 2025 · The point of broadband affordability policy is to make broadband affordable as defined by 2 percent of household income. Broadband is just one of the components of a basket of goods that are necessities for a household alongside food, housing, electricity, water, etc."}
+{"idx": 8, "title": "ECONOMICS OF BROADBAND NETWORKS", "date": "", "ddg_snippet": "Deploying affordable broadband to unserved and underserved locations is often challenging for providers due to the economics. To optimally deploy incoming broadband funding, it is critical to understand the unique challenges of deploying broadband in these areas, the economics of broadband networks, and what considerations are important when designing networks to ensure broadband access for all.", "subpage_snippet": "", "source": "broadbandusa.ntia.gov", "link": "https://broadbandusa.ntia.gov/sites/default/files/2022-03/Economics+of+Broadband+Networks+PDF.pdf", "content": "Deploying affordable broadband to unserved and underserved locations is often challenging for providers due to the economics. To optimally deploy incoming broadband funding, it is critical to understand the unique challenges of deploying broadband in these areas, the economics of broadband networks, and what considerations are important when designing networks to ensure broadband access for all."}
+{"idx": 9, "title": "The affordability of ICT services 2023", "date": "", "ddg_snippet": "As of 2023, 114 economies had met the Broadband Commission affordability target , up from 103 in 2022. In an inflationary context, where the price of many goods and services has increased, ICT service prices actually dropped in real terms in 2023. The entry-level fixed- broadband basket is more affordable than it was before the pandemic.", "subpage_snippet": "", "source": "www.itu.int", "link": "https://www.itu.int/en/ITU-D/Statistics/Documents/publications/prices2023/ICTPriceBrief2023.pdf", "content": "As of 2023, 114 economies had met the Broadband Commission affordability target , up from 103 in 2022. In an inflationary context, where the price of many goods and services has increased, ICT service prices actually dropped in real terms in 2023. The entry-level fixed- broadband basket is more affordable than it was before the pandemic."}
diff --git a/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_PDF_arXiv.jsonl b/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_PDF_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..71d8ed28ff61f36b05364957c3670f4988d97520
--- /dev/null
+++ b/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_PDF_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "by H Wallach · 2025 · Cited by 12 — We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00561", "content": "by H Wallach · 2025 · Cited by 12 — We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and ..."}
+{"idx": 1, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · 2024 · Cited by 15 — View a PDF of the paper titled Evaluating Generative AI Systems is a Social Science Measurement Challenge , by Hanna Wallach and 19 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.10939", "content": "by H Wallach · 2024 · Cited by 15 — View a PDF of the paper titled Evaluating Generative AI Systems is a Social Science Measurement Challenge , by Hanna Wallach and 19 other authors."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "by H Wallach · 2025 · Cited by 10 — In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using mea-.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00561", "content": "by H Wallach · 2025 · Cited by 10 — In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using mea-."}
+{"idx": 3, "title": "Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · Cited by 12 — In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1ZC4RNjqzU", "content": "by H Wallach · Cited by 12 — In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and ..."}
+{"idx": 4, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · Cited by 12 — In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using mea-.", "subpage_snippet": "", "source": "afedercooper.info", "link": "https://afedercooper.info/paper/wallach2024measurement.pdf", "content": "by H Wallach · Cited by 12 — In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using mea-."}
+{"idx": 5, "title": "Revision History for Position: Evaluating Generative AI...", "date": "", "ddg_snippet": "23 Mar 2025 — Specifically, our position is that evaluating GenAI systems is a social science measurement challenge . We present a four-level framework, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/revisions?id=AJm6unbR3f", "content": "23 Mar 2025 — Specifically, our position is that evaluating GenAI systems is a social science measurement challenge . We present a four-level framework, ..."}
+{"idx": 6, "title": "Position: Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Key takeaway: ' Evaluating generative AI systems is a social science measurement challenge , and using a four-level framework based on social science theory ...", "subpage_snippet": "", "source": "k8s.consensus.app", "link": "https://k8s.consensus.app/papers/details/6c05b8e5c587597da2560bb61f2cb2f9/", "content": "Key takeaway: ' Evaluating generative AI systems is a social science measurement challenge , and using a four-level framework based on social science theory ..."}
+{"idx": 7, "title": "Toward an evaluation science for generative AI systems", "date": "", "ddg_snippet": "by L Weidinger · 2025 · Cited by 17 — In this piece, we advocate for maturing an evaluation science for generative . AI systems . While generative AI creates unique challenges for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.05336", "content": "by L Weidinger · 2025 · Cited by 17 — In this piece, we advocate for maturing an evaluation science for generative . AI systems . While generative AI creates unique challenges for ..."}
+{"idx": 8, "title": "Towards Interactive Evaluations for Interaction Harms in ...", "date": "", "ddg_snippet": "23 Jun 2025 — Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge . arXiv preprint arXiv:2502.00561. Wang, A.; Morgenstern ...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "23 Jun 2025 — Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge . arXiv preprint arXiv:2502.00561. Wang, A.; Morgenstern ..."}
+{"idx": 9, "title": "Hanna Wallach at Microsoft Research", "date": "", "ddg_snippet": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge ... Systems | March 2020. CHI 2020 Best Paper Award. PDF Project. 2019 ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/people/wallach/publications/", "content": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge ... Systems | March 2020. CHI 2020 Best Paper Award. PDF Project. 2019 ..."}
diff --git a/data/sampled_jsons/PowerPaint_avoid_generating_sundries_object_removal_negative_prompt_year_2024.jsonl b/data/sampled_jsons/PowerPaint_avoid_generating_sundries_object_removal_negative_prompt_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5ea3c2a1c7f290aa4fb5658271157c5518a465c3
--- /dev/null
+++ b/data/sampled_jsons/PowerPaint_avoid_generating_sundries_object_removal_negative_prompt_year_2024.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation...", "date": "", "ddg_snippet": "PowerPaint [52] integrates text-guided generation and object erasure within a single model, using different text prompts to represent various tasks. For the erasure task, it introduces classifier-free guidance by using the generation prompt as the negative prompt to reduce sun - dries .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "PowerPaint [52] integrates text-guided generation and object erasure within a single model, using different text prompts to represent various tasks. For the erasure task, it introduces classifier-free guidance by using the generation prompt as the negative prompt to reduce sun - dries ."}
+{"idx": 1, "title": "CVPR Poster EntityErasure: Erasing Entity Cleanly via Amodal ...", "date": "", "ddg_snippet": "... object generation/erasure, and introduces classifier-free-guidance to help avoid generating sundries . CLIPAway [8] leverages CLIP embeddings to make ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34016", "content": "... object generation/erasure, and introduces classifier-free-guidance to help avoid generating sundries . CLIPAway [8] leverages CLIP embeddings to make ..."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_machine_OR_CPU_OR_memory_OR_server_OR_computin.jsonl b/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_machine_OR_CPU_OR_memory_OR_server_OR_computin.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..084ded71e0367eca1de949f8c2f6eb71c212f457
--- /dev/null
+++ b/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_machine_OR_CPU_OR_memory_OR_server_OR_computin.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithmic efficiency - Wikipedia", "date": "", "ddg_snippet": "... which the data is arranged; for example, some sorting algorithms perform poorly on data which is already sorted, or which is sorted in reverse order.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Algorithmic_efficiency", "content": "... which the data is arranged; for example, some sorting algorithms perform poorly on data which is already sorted, or which is sorted in reverse order."}
+{"idx": 1, "title": "Sorting algorithm - Wikipedia", "date": "", "ddg_snippet": "... algorithm is used in both cases, the sort-by-class-section operation will not change the name order; with an unstable sort, it could be that sorting ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Sorting_algorithm", "content": "... algorithm is used in both cases, the sort-by-class-section operation will not change the name order; with an unstable sort, it could be that sorting ..."}
+{"idx": 2, "title": "Program optimization - Wikipedia", "date": "", "ddg_snippet": "... algorithms , this primarily consists of ensuring that algorithms are constant O(1), logarithmic O(log n ), linear O( n ), or in some cases log- linear ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Program_optimization", "content": "... algorithms , this primarily consists of ensuring that algorithms are constant O(1), logarithmic O(log n ), linear O( n ), or in some cases log- linear ..."}
+{"idx": 3, "title": "An Asymptotically Optimal Approximation Algorithm for", "date": "", "ddg_snippet": "All known polynomial- time approximation algorithms either obtain a weak approximation guarantee or rely on the evaluation of the multilinear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.09525v2", "content": "All known polynomial- time approximation algorithms either obtain a weak approximation guarantee or rely on the evaluation of the multilinear ..."}
+{"idx": 4, "title": "Efficient Preimage Approximation for Neural Network", "date": "", "ddg_snippet": "... concepts and provide an overview of the PREiMage APproximation (PREMAP) method [ 38 , 39 ] , focusing on key aspects needed for our algorithmic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22798v1", "content": "... concepts and provide an overview of the PREiMage APproximation (PREMAP) method [ 38 , 39 ] , focusing on key aspects needed for our algorithmic ..."}
+{"idx": 5, "title": "The Brain as a Universal Learning Machine - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "... is entirely based on building AI systems using simple universal learning algorithms (such as Stochastic Gradient Descent or other various approximate ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/9Yc7Pp7szcjPgPsjf/the-brain-as-a-universal-learning-machine)+correctly+many+years+in+advance,+contra+EY", "content": "... is entirely based on building AI systems using simple universal learning algorithms (such as Stochastic Gradient Descent or other various approximate ..."}
+{"idx": 6, "title": "A Polynomial Approximation Scheme for Scheduling on Uniform", "date": "", "ddg_snippet": "... algorithms ; the best performance ... Shmoys, Using dual approximation algorithms for scheduling problems: theoretical and practical results, J.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/0217033", "content": "... algorithms ; the best performance ... Shmoys, Using dual approximation algorithms for scheduling problems: theoretical and practical results, J."}
+{"idx": 7, "title": "A Polynomial Approximation Scheme for Scheduling on Uniform", "date": "", "ddg_snippet": "... algorithms ; the best performance ... Shmoys, Using dual approximation algorithms for scheduling problems: theoretical and practical results, J.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/0217033", "content": "... algorithms ; the best performance ... Shmoys, Using dual approximation algorithms for scheduling problems: theoretical and practical results, J."}
+{"idx": 8, "title": "Are algorithms with high time complexity ever used in the real", "date": "", "ddg_snippet": "For smaller projects focusing on saving developer time over machine time by implementing only one algorithm is often the better choice.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/75102854/are-algorithms-with-high-time-complexity-ever-used-in-the-real-world-for-small-i", "content": "For smaller projects focusing on saving developer time over machine time by implementing only one algorithm is often the better choice."}
+{"idx": 9, "title": "Calculating complexity of algorithm - Stack Overflow", "date": "", "ddg_snippet": "... predictions as the lower limit of the CPU time required to compute some dataset because in practice , other computing resources as memory, caches or ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/6516496/calculating-complexity-of-algorithm", "content": "... predictions as the lower limit of the CPU time required to compute some dataset because in practice , other computing resources as memory, caches or ..."}
diff --git a/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_supplementary_material_OR_technical_report_OR_.jsonl b/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_supplementary_material_OR_technical_report_OR_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05a4e684494a5d92f397db8290b81b941595df79
--- /dev/null
+++ b/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_supplementary_material_OR_technical_report_OR_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Approximation Algorithms", "date": "", "ddg_snippet": "by B HongKong · 2001 — This book presents the theory of ap- proximation algorithms as it stands today. It is reasonable to expect the picture to change with time . The book is divided ... 396 pages", "subpage_snippet": "", "source": "www.ics.uci.edu", "link": "https://www.ics.uci.edu/~vazirani/book.pdf", "content": "by B HongKong · 2001 — This book presents the theory of ap- proximation algorithms as it stands today. It is reasonable to expect the picture to change with time . The book is divided ... 396 pages"}
+{"idx": 1, "title": "Efficient Approximation Algorithms for Spanning Centrality", "date": "", "ddg_snippet": "by S Zhang · 2023 · Cited by 10 — An almost - linear - time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. In SODA. 217–226. [19] Richard B ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.16086", "content": "by S Zhang · 2023 · Cited by 10 — An almost - linear - time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. In SODA. 217–226. [19] Richard B ..."}
+{"idx": 2, "title": "Approximate computing, skeleton programming and run ...", "date": "", "ddg_snippet": "by N Vasilas · 2022 · Cited by 5 — Approximate computing, skeleton programming and run-time scheduling in an algorithm for process design and controllability in distributed and heterogeneous ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0098135422002125", "content": "by N Vasilas · 2022 · Cited by 5 — Approximate computing, skeleton programming and run-time scheduling in an algorithm for process design and controllability in distributed and heterogeneous ..."}
+{"idx": 3, "title": "Discretely Beyond 1/e: Guided Combinatorial Algorithms ...", "date": "", "ddg_snippet": "by Y Chen · 2024 · Cited by 3 — Next, we introduce the nearly linear - time deterministic algorithm in Section 3, with omitted analysis provided in Appendix E. Our empirical evaluation is ... 45 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/c4e40d310af1be55f2c0f32cb16a9a16-Paper-Conference.pdf", "content": "by Y Chen · 2024 · Cited by 3 — Next, we introduce the nearly linear - time deterministic algorithm in Section 3, with omitted analysis provided in Appendix E. Our empirical evaluation is ... 45 pages"}
+{"idx": 4, "title": "Settling Time vs. Accuracy Tradeoffs for Clustering Big Data", "date": "", "ddg_snippet": "by A Draganov · 2024 · Cited by 8 — Abstract. We study the theoretical and practical runtime limits of k-means and k-median clustering on large datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.01936", "content": "by A Draganov · 2024 · Cited by 8 — Abstract. We study the theoretical and practical runtime limits of k-means and k-median clustering on large datasets."}
+{"idx": 5, "title": "Approximate Computing Survey, Part II: Application-Specific ...", "date": "", "ddg_snippet": "by V Leon · 2025 · Cited by 19 — Part II of the survey classifies and presents the technical details of application-specific and architectural approximation techniques.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3711683", "content": "by V Leon · 2025 · Cited by 19 — Part II of the survey classifies and presents the technical details of application-specific and architectural approximation techniques."}
+{"idx": 6, "title": "APPROXIMATION ALGORITHMS FOR SCHEDULING ...", "date": "", "ddg_snippet": "by C Chekuri · 1998 · Cited by 37 — This thesis describes efficient approximation algorithms for some NP-Hard deterministic machine scheduling and related problems. An approximation algorithm ... 145 pages", "subpage_snippet": "", "source": "i.stanford.edu", "link": "http://i.stanford.edu/pub/cstr/reports/cs/tr/98/1611/CS-TR-98-1611.pdf", "content": "by C Chekuri · 1998 · Cited by 37 — This thesis describes efficient approximation algorithms for some NP-Hard deterministic machine scheduling and related problems. An approximation algorithm ... 145 pages"}
+{"idx": 7, "title": "Practical 0.385-Approximation for Submodular ...", "date": "", "ddg_snippet": "by M Tukan · 2024 · Cited by 4 — The best practical algorithms for the problem only guarantee 1/e- approximation . In this work, we present a novel algorithm for submodular maximization subject ... 31 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/5ba9ad2e7abefb68a7165396583038da-Paper-Conference.pdf", "content": "by M Tukan · 2024 · Cited by 4 — The best practical algorithms for the problem only guarantee 1/e- approximation . In this work, we present a novel algorithm for submodular maximization subject ... 31 pages"}
+{"idx": 8, "title": "Scalable and E cient Graph Algorithms and Analysis ...", "date": "", "ddg_snippet": "by QC Liu · 2021 · Cited by 2 — We conclude this part with a near - linear time scheduling algorithm for scheduling on identi- cal machines with communication delay where precedence constrained ... 420 pages", "subpage_snippet": "", "source": "erikdemaine.org", "link": "https://erikdemaine.org/theses/qliu.pdf", "content": "by QC Liu · 2021 · Cited by 2 — We conclude this part with a near - linear time scheduling algorithm for scheduling on identi- cal machines with communication delay where precedence constrained ... 420 pages"}
+{"idx": 9, "title": "Approximation Algorithms for the Minimum (Sliding Window ...", "date": "", "ddg_snippet": "by S Heck · 2023 — First, we summarize the general research on tem- poral graphs and temporal graph problems by describing the different notions of temporal. 82 pages", "subpage_snippet": "", "source": "schulzchristian.github.io", "link": "https://schulzchristian.github.io/thesis/ma_heck.pdf", "content": "by S Heck · 2023 — First, we summarize the general research on tem- poral graphs and temporal graph problems by describing the different notions of temporal. 82 pages"}
diff --git "a/data/sampled_jsons/ProDet_github_deepfake_detector_hyperparameters_\316\262_\316\263_configuration.jsonl" "b/data/sampled_jsons/ProDet_github_deepfake_detector_hyperparameters_\316\262_\316\263_configuration.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..3f0ad7f85be7b8432a6d360b301b9f678aea3786
--- /dev/null
+++ "b/data/sampled_jsons/ProDet_github_deepfake_detector_hyperparameters_\316\262_\316\263_configuration.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "9 Dec 2024 — Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93195", "content": "9 Dec 2024 — Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?"}
+{"idx": 1, "title": "Improving Adaptability of DeepFake Detection via Online ...", "date": "", "ddg_snippet": "A novel online test-time adaptation method that enhances the adaptability of detectors during inference without requiring access to source training data or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18787v2", "content": "A novel online test-time adaptation method that enhances the adaptability of detectors during inference without requiring access to source training data or ..."}
+{"idx": 2, "title": "Quantifying DeepFake Detection Accuracy for a Variety of ...", "date": "", "ddg_snippet": "by P Prajapati · 2020 — In this research, we explore ways to detect such fake content. Our goal is to quantify the accuracy of DeepFake detection for a variety of natural settings ...", "subpage_snippet": "", "source": "scholarworks.sjsu.edu", "link": "https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1962&context=etd_projects", "content": "by P Prajapati · 2020 — In this research, we explore ways to detect such fake content. Our goal is to quantify the accuracy of DeepFake detection for a variety of natural settings ..."}
+{"idx": 3, "title": "Visual Deepfake Detection: Review of Techniques, Tools, ...", "date": "", "ddg_snippet": "by NUR Ahmed · 2024 · Cited by 11 — A variety of tools are available which are easily accessible for the generation of deepfake ; the deepfake generated by these tools is quite ... 39 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/10816641.pdf", "content": "by NUR Ahmed · 2024 · Cited by 11 — A variety of tools are available which are easily accessible for the generation of deepfake ; the deepfake generated by these tools is quite ... 39 pages"}
+{"idx": 4, "title": "A Survey on Speech Deepfake Detection - ACM Digital Library", "date": "", "ddg_snippet": "8 Feb 2025 — The lack of details, such as loss functions and hyperparameter configurations , in some publications, is especially particularly concerning.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3714458", "content": "8 Feb 2025 — The lack of details, such as loss functions and hyperparameter configurations , in some publications, is especially particularly concerning."}
+{"idx": 5, "title": "High-compressed deepfake video detection with ...", "date": "", "ddg_snippet": "Our proposed approach addresses challenges associated with detecting deepfake videos in highly compressed settings , where confused artifacts and blurred details ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231223009955/pdf", "content": "Our proposed approach addresses challenges associated with detecting deepfake videos in highly compressed settings , where confused artifacts and blurred details ..."}
+{"idx": 6, "title": "Generalizable speech deepfake detection via meta ...", "date": "", "ddg_snippet": "15 Aug 2025 — The meta-test loss is computed on Θ ′ \\Theta^{\\prime} , and its gradient is scaled by a hyperparameter β = 0.5 \\ beta =0.5 before being ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10838v2", "content": "15 Aug 2025 — The meta-test loss is computed on Θ ′ \\Theta^{\\prime} , and its gradient is scaled by a hyperparameter β = 0.5 \\ beta =0.5 before being ..."}
+{"idx": 7, "title": "Generalization Limits of Deepfake Detectors in the Wild", "date": "", "ddg_snippet": "by A Garg — We sample this λi through Stochastic Gradient Langevin. Dynamics (SGLD), by setting a Gamma hyperparameter with α = 0.01, β = 100. Once we obtain λi's ...", "subpage_snippet": "", "source": "www.ischool.berkeley.edu", "link": "https://www.ischool.berkeley.edu/sites/default/files/bb_paper.pdf", "content": "by A Garg — We sample this λi through Stochastic Gradient Langevin. Dynamics (SGLD), by setting a Gamma hyperparameter with α = 0.01, β = 100. Once we obtain λi's ..."}
+{"idx": 8, "title": "Effectiveness and optimization of bidirectional long short ...", "date": "", "ddg_snippet": "by H Wang · 2025 — This study proposes a rapid detection method for deepfake face videos designed for real-time applications using bidirectional long short-term memory (BiLSTM) ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12192840/", "content": "by H Wang · 2025 — This study proposes a rapid detection method for deepfake face videos designed for real-time applications using bidirectional long short-term memory (BiLSTM) ..."}
+{"idx": 9, "title": "AI-Face: A Million-Scale Demographically Annotated ... - CVPR", "date": "", "ddg_snippet": "We introduce the AI-Face dataset, the first million-scale demographically annotated AI-generated face image dataset, including real faces, faces from deepfake ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34370", "content": "We introduce the AI-Face dataset, the first million-scale demographically annotated AI-generated face image dataset, including real faces, faces from deepfake ..."}
diff --git a/data/sampled_jsons/Progressive_Proximal_Transport_PPT_optimal_transport_class_prior_estimation_novel.jsonl b/data/sampled_jsons/Progressive_Proximal_Transport_PPT_optimal_transport_class_prior_estimation_novel.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ec2c3d76c2e1a51a3be918f630a5d71ec8f9488d
--- /dev/null
+++ b/data/sampled_jsons/Progressive_Proximal_Transport_PPT_optimal_transport_class_prior_estimation_novel.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Proximal optimal transport divergences", "date": "", "ddg_snippet": "We introduce proximal optimal transport divergence, a novel discrepancy measure that interpolates between information divergences and optimal transport distances via an infimal convolution formulation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/391878122_Proximal_optimal_transport_divergences", "content": "We introduce proximal optimal transport divergence, a novel discrepancy measure that interpolates between information divergences and optimal transport distances via an infimal convolution formulation."}
+{"idx": 1, "title": "Proximal Optimal Transport Divergences", "date": "", "ddg_snippet": "We introduce the proximal optimal transport divergence, a novel discrepancy measure that interpolates between information divergences and optimal transport distances via an infimal convolution formulation. This divergence provides a principled foundation for optimal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.12097v2", "content": "We introduce the proximal optimal transport divergence, a novel discrepancy measure that interpolates between information divergences and optimal transport distances via an infimal convolution formulation. This divergence provides a principled foundation for optimal ..."}
+{"idx": 2, "title": "Proximal optimal transport divergences-Bohrium", "date": "", "ddg_snippet": "We introduce proximal optimal transport divergence, a novel discrepancy measure that interpolates between information divergences and optimal transport distances via an infimal convolution formulation.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/proximal-optimal-transport-divergences/1131559229862182927-108552", "content": "We introduce proximal optimal transport divergence, a novel discrepancy measure that interpolates between information divergences and optimal transport distances via an infimal convolution formulation."}
+{"idx": 3, "title": "Transportation planning | pptx", "date": "", "ddg_snippet": "urban transportation planning introduction. ppt . krrish706. February21st esp179-traffic.Modal Split Traffic Assignment • to estimate the number • Intended to give a traffic of trips by different types direction to which road in of transport road/ transport network. •", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/transportation-planning-11880455/11880455", "content": "urban transportation planning introduction. ppt . krrish706. February21st esp179-traffic.Modal Split Traffic Assignment • to estimate the number • Intended to give a traffic of trips by different types direction to which road in of transport road/ transport network. •"}
+{"idx": 4, "title": "Optimal Transport using Helmholtz-Hodge Decomposition and...", "date": "", "ddg_snippet": "Index Terms— Convex optimization, optimal transport , proximal splitting, image processing, Helmholtz-Hodge de-composition.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-01134194v1/document", "content": "Index Terms— Convex optimization, optimal transport , proximal splitting, image processing, Helmholtz-Hodge de-composition."}
+{"idx": 5, "title": "Theoretical and computational aspects of robust optimal ... | DeepAI", "date": "", "ddg_snippet": "Optimal transport (OT) theory and the related p-Wasserstein distance (W_p, p≥ 1) are popular tools in statistics and machine learning. Recent studies have been remarking that inference based on OT and on W_p is sensitive to outliers.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/theoretical-and-computational-aspects-of-robust-optimal-transportation-with-applications-to-statistics-and-machine-learning", "content": "Optimal transport (OT) theory and the related p-Wasserstein distance (W_p, p≥ 1) are popular tools in statistics and machine learning. Recent studies have been remarking that inference based on OT and on W_p is sensitive to outliers."}
+{"idx": 6, "title": "PPT - Lecture 2/28: Optimal Transport PowerPoint Presentation, free...", "date": "", "ddg_snippet": "Introduction What is Optimal Transport ? • Transport one mass distribution to another optimally • Analogy: Move sand from piles to fill holes Sai Aditya, Xiao Sai, Chad Atalla Lecture 2/28", "subpage_snippet": "", "source": "www.slideserve.com", "link": "https://www.slideserve.com/ramelia/lecture-2-28-optimal-transport-powerpoint-ppt-presentation", "content": "Introduction What is Optimal Transport ? • Transport one mass distribution to another optimally • Analogy: Move sand from piles to fill holes Sai Aditya, Xiao Sai, Chad Atalla Lecture 2/28"}
+{"idx": 7, "title": "Automatic Outlier Rectification via Optimal Transport", "date": "", "ddg_snippet": "Our novel approach leverages the optimal transport distance with a concave cost function to construct a rectification set within the realm of probability distributions. Within this set, we identify the optimal distribution for conducting the estimation task.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/3e2d037e4e8e2c924d3c5e8cb4767150-Paper-Conference.pdf", "content": "Our novel approach leverages the optimal transport distance with a concave cost function to construct a rectification set within the realm of probability distributions. Within this set, we identify the optimal distribution for conducting the estimation task."}
+{"idx": 8, "title": "Local L^\\infty estimates for optimal transport problems", "date": "", "ddg_snippet": "I will explain how to obtain local L^\\infty estimates for optimal transport problems.", "subpage_snippet": "", "source": "www.maths.ox.ac.uk", "link": "https://www.maths.ox.ac.uk/node/74101", "content": "I will explain how to obtain local L^\\infty estimates for optimal transport problems."}
+{"idx": 9, "title": "AISTATS Poster Consistent Optimal Transport with Empirical...", "date": "", "ddg_snippet": "In such settings, standard OT variants cannot be employed, and novel estimation techniques are necessary.Under mild conditions, we prove that our estimated transport plans, as a function of the conditioned variable, are asymptotically optimal .", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2024/poster/6937", "content": "In such settings, standard OT variants cannot be employed, and novel estimation techniques are necessary.Under mild conditions, we prove that our estimated transport plans, as a function of the conditioned variable, are asymptotically optimal ."}
diff --git a/data/sampled_jsons/Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_technical_challenges_year_2023.jsonl b/data/sampled_jsons/Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_technical_challenges_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..14fa7296ee767969a987a9c4838a4796cbd82560
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RA-PbRL: Provably Eficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "To the best of our knowledge, our proposed RA-PbRL algorithm is the first provably eficient Preference-based Reinforcement Learning (PbRL) algorithm that incorporates both nested and static risk objectives in one algorithm.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "To the best of our knowledge, our proposed RA-PbRL algorithm is the first provably eficient Preference-based Reinforcement Learning (PbRL) algorithm that incorporates both nested and static risk objectives in one algorithm."}
+{"idx": 1, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Abstract Preference-based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v1", "content": "Abstract Preference-based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware ..."}
+{"idx": 2, "title": "Risk-Aware Preference-baser Reinforcement Learning (RA-PbRL)", "date": "", "ddg_snippet": "Code for paper \"RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning \" Code Setup Documentation Libraries (>= Python 3.12.4) For more information on the version specifics, see the environment. yaml file. To import the environment, execute the following command prompt commands:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "Code for paper \"RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning \" Code Setup Documentation Libraries (>= Python 3.12.4) For more information on the version specifics, see the environment. yaml file. To import the environment, execute the following command prompt commands:"}
+{"idx": 3, "title": "Efficient Preference-Based Reinforcement Learning Using ...", "date": "", "ddg_snippet": "May 29, 2023 · Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10161081", "content": "May 29, 2023 · Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ..."}
+{"idx": 4, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Oct 31, 2024 · Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Oct 31, 2024 · Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 5, "title": "P R -AGNOSTIC PREFERENCE-BASED REINFORCEMENT LEARNING", "date": "", "ddg_snippet": "ABSTRACT Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories, rather than explicit reward signals. While PbRL has demonstrated practical success in fine-tuning language models, existing theoretical work focuses on regret minimization and fails to capture most of the practical ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=yTBXeXdbMf", "content": "ABSTRACT Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories, rather than explicit reward signals. While PbRL has demonstrated practical success in fine-tuning language models, existing theoretical work focuses on regret minimization and fails to capture most of the practical ..."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning ... Our main technical challenges lie in estimating the confidence set and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf&referrer=[the+profile+of+Huazheng+Wang](/profile?id=~Huazheng_Wang1)", "content": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning ... Our main technical challenges lie in estimating the confidence set and ..."}
+{"idx": 7, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "9 Dec 2024 — Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/95716", "content": "9 Dec 2024 — Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each ..."}
+{"idx": 8, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "by Y Zhao · 2024 · Cited by 4 — We explore and prove the applicability of two risk - aware objectives to PbRL : nested and static quantile risk objectives. We also introduce Risk -AwarePbRL (RA- ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "by Y Zhao · 2024 · Cited by 4 — We explore and prove the applicability of two risk - aware objectives to PbRL : nested and static quantile risk objectives. We also introduce Risk -AwarePbRL (RA- ..."}
+{"idx": 9, "title": "[Literature Review] RA-PbRL: Provably Efficient Risk-Aware ...", "date": "", "ddg_snippet": "This page provides the most accurate and concise summary worldwide for the paper titled RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/ra-pbrl-provably-efficient-risk-aware-preference-based-reinforcement-learning", "content": "This page provides the most accurate and concise summary worldwide for the paper titled RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement ..."}
diff --git a/data/sampled_jsons/Proximal_Policy_Optimization_Schulman_2017_Atari_A2C_performance_table_year_2017.jsonl b/data/sampled_jsons/Proximal_Policy_Optimization_Schulman_2017_Atari_A2C_performance_table_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd557cfc9d0527575ff5263dfe7483d87d408db7
--- /dev/null
+++ b/data/sampled_jsons/Proximal_Policy_Optimization_Schulman_2017_Atari_A2C_performance_table_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Robust off-policy Reinforcement Learning via Soft Constrained", "date": "", "ddg_snippet": "First, the mutual dependency between the policy and its corresponding optimal adversary limits the development of off- policy RL algorithms; although ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.00418v1", "content": "First, the mutual dependency between the policy and its corresponding optimal adversary limits the development of off- policy RL algorithms; although ..."}
+{"idx": 1, "title": "Proximal Policy Optimization (PPO): The Key to LLM Alignment", "date": "", "ddg_snippet": "... into two RL algorithms that are more directly related to RLHF: Trust Region Policy Optimization (TRPO) [1] and Proximal Policy Optimization (PPO) [2].", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/proximal-policy-optimization-ppo", "content": "... into two RL algorithms that are more directly related to RLHF: Trust Region Policy Optimization (TRPO) [1] and Proximal Policy Optimization (PPO) [2]."}
+{"idx": 2, "title": "1 Introduction", "date": "", "ddg_snippet": "We propose an LLM-based generative optimization approach for developing Atari game-playing agents, where policies are represented as modular Python ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19506v1", "content": "We propose an LLM-based generative optimization approach for developing Atari game-playing agents, where policies are represented as modular Python ..."}
+{"idx": 3, "title": "CaRL: Learning Scalable Planning Policies with Simple Rewards", "date": "", "ddg_snippet": "... we observe that increasing the mini- batch size by a factor of 4 with a complex reward reduces the performance of Proximal Policy Optimization (PPO ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.17838v3", "content": "... we observe that increasing the mini- batch size by a factor of 4 with a complex reward reduces the performance of Proximal Policy Optimization (PPO ..."}
+{"idx": 4, "title": "Reinforcement Learning Framework for Quantitative Trading", "date": "", "ddg_snippet": "This limits the performance of the agent, but also fails to provided the agent with an observation space that is ideal to optimize decision making.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07585v1", "content": "This limits the performance of the agent, but also fails to provided the agent with an observation space that is ideal to optimize decision making."}
+{"idx": 5, "title": "GitHub - andri27-ts/Reinforcement-Learning: Learn Deep", "date": "", "ddg_snippet": "... the definition of the problem all the way through the estimation and optimization of the functions that are used to express the quality of a policy ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/andri27-ts/Reinforcement-Learning", "content": "... the definition of the problem all the way through the estimation and optimization of the functions that are used to express the quality of a policy ..."}
+{"idx": 6, "title": "Alternatives and detailed information of Reinforcement Learning", "date": "", "ddg_snippet": "PyTorch implementation of Advantage Actor Critic ( A2C ), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement ...", "subpage_snippet": "", "source": "www.gitplanet.com", "link": "https://www.gitplanet.com/project/reinforcement-learning1618783873", "content": "PyTorch implementation of Advantage Actor Critic ( A2C ), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement ..."}
+{"idx": 7, "title": "andri27-ts/Reinforcement-Learning: Learn Deep Reinforcement", "date": "", "ddg_snippet": "Vanilla PG and A2C applied to CartPole - The exercise of this week is to implement a policy gradient method or a more sophisticated actor-critic.", "subpage_snippet": "", "source": "www.gitmemories.com", "link": "https://www.gitmemories.com/andri27-ts/Reinforcement-Learning", "content": "Vanilla PG and A2C applied to CartPole - The exercise of this week is to implement a policy gradient method or a more sophisticated actor-critic."}
+{"idx": 8, "title": "Reinforcement-Learning | Learn Deep Reinforcement Learning in", "date": "", "ddg_snippet": "Vanilla PG and A2C applied to CartPole - The exercise of this week is to implement a policy gradient method or a more sophisticated actor-critic.", "subpage_snippet": "", "source": "andri27-ts.github.io", "link": "https://andri27-ts.github.io/Reinforcement-Learning/", "content": "Vanilla PG and A2C applied to CartPole - The exercise of this week is to implement a policy gradient method or a more sophisticated actor-critic."}
+{"idx": 9, "title": "Introduction to Deep Reinforcement Learning – Robotic Sea", "date": "", "ddg_snippet": "The thing is, most practical environments are not tabular, so we can ’ t represent value functions and/or policies as lookup tables .", "subpage_snippet": "", "source": "roboticseabass.com", "link": "https://roboticseabass.com/2020/08/15/introduction-to-deep-reinforcement-learning/", "content": "The thing is, most practical environments are not tabular, so we can ’ t represent value functions and/or policies as lookup tables ."}
diff --git a/data/sampled_jsons/Qi_et_al_2024_arxiv_critical_windows.jsonl b/data/sampled_jsons/Qi_et_al_2024_arxiv_critical_windows.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ad69f4470289a2f3630c46eff29b250f50837285
--- /dev/null
+++ b/data/sampled_jsons/Qi_et_al_2024_arxiv_critical_windows.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Indigenous peoples of Siberia - Wikipedia", "date": "", "ddg_snippet": "Massilani et al . (2020) model the Yana individuals as around one-third East Eurasian and two-thirds West Eurasian.Vallini et al .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Indigenous_peoples_of_Siberia", "content": "Massilani et al . (2020) model the Yana individuals as around one-third East Eurasian and two-thirds West Eurasian.Vallini et al ."}
+{"idx": 1, "title": "GitHub - lidq92/ arxiv -daily: [NOT UPDATED][To be updated with http...", "date": "", "ddg_snippet": "Jingwei Bao et . al . 2412.14449. null. 2024 -12-16. EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera. Zheng Fang et . al . 2024 -12-11. Unicorn: Unified Neural Image Compression with One Number Reconstruction. Qi Zheng et . al .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lidq92/arxiv-daily", "content": "Jingwei Bao et . al . 2412.14449. null. 2024 -12-16. EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera. Zheng Fang et . al . 2024 -12-11. Unicorn: Unified Neural Image Compression with One Number Reconstruction. Qi Zheng et . al ."}
+{"idx": 2, "title": "Обзоры препринтов научных статей «astro-ph/ arxiv .org» за... / Хабр", "date": "", "ddg_snippet": "arxiv :2506.02316 Быстрый популяционный синтез звезд и двойных систем с помощью COMPAS: методы, статья II (Rapid stellar and binary population synthesis with COMPAS: methods paper II)Authors: Ilya Mandel et al .Comments: 13 pages.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948996/", "content": "arxiv :2506.02316 Быстрый популяционный синтез звезд и двойных систем с помощью COMPAS: методы, статья II (Rapid stellar and binary population synthesis with COMPAS: methods paper II)Authors: Ilya Mandel et al .Comments: 13 pages."}
+{"idx": 3, "title": "MetaScientist: A Human-AI Synergistic Framework for Automated...", "date": "", "ddg_snippet": "Presenting at NeurIPS 2024 . New preprint using KG structure as inspiration for reasoning. Awarded the J.P. Morgan Chase AI PhD Fellowship. Presenting at AAAI 2024 . New preprint on LLM ownership protection.", "subpage_snippet": "", "source": "derek.ma", "link": "https://derek.ma/publication/qi-etal-2024-metascientist/", "content": "Presenting at NeurIPS 2024 . New preprint using KG structure as inspiration for reasoning. Awarded the J.P. Morgan Chase AI PhD Fellowship. Presenting at AAAI 2024 . New preprint on LLM ownership protection."}
+{"idx": 4, "title": "Benign Samples Matter! Fine-tuning On Outlier... | Read Paper on Bytez", "date": "", "ddg_snippet": "Lisa (Huang et al ., 2024 d) is a well-established fine-tuning mitigation strategy. It employs a bi-state optimization approach, alternating between fine-tuning user data and alignment data. The ratio of alignment steps to fine-tuning steps is critical to Lisa’s effectiveness.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/45842/paper", "content": "Lisa (Huang et al ., 2024 d) is a well-established fine-tuning mitigation strategy. It employs a bi-state optimization approach, alternating between fine-tuning user data and alignment data. The ratio of alignment steps to fine-tuning steps is critical to Lisa’s effectiveness."}
+{"idx": 5, "title": "Kimi K2: Open Agentic Intelligence", "date": "", "ddg_snippet": "[40] Haonan Li et al . CMMLU: Measuring massive multitask language understanding in Chinese. 2024 . arXiv : 2306.09212 [cs.CL] . qi · kj. (1). grows unboundedly during training.", "subpage_snippet": "", "source": "agenticai-learning.org", "link": "https://agenticai-learning.org/slides/d11.pdf", "content": "[40] Haonan Li et al . CMMLU: Measuring massive multitask language understanding in Chinese. 2024 . arXiv : 2306.09212 [cs.CL] . qi · kj. (1). grows unboundedly during training."}
+{"idx": 6, "title": "Semantics Outperforms Prosody in Emotional Speech... - ACL Anthology", "date": "", "ddg_snippet": "qi - etal - 2024 -semantics. Cite (ACL): Jing Qi , Kaile Zhang, and Gang Peng.@inproceedings{ qi - etal - 2024 -semantics, title = \"Semantics Outperforms Prosody in Emotional Speech Processing: Evidence from a Complex Stroop Experiment\", author = \" Qi , Jing and.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.paclic-1.118/", "content": "qi - etal - 2024 -semantics. Cite (ACL): Jing Qi , Kaile Zhang, and Gang Peng.@inproceedings{ qi - etal - 2024 -semantics, title = \"Semantics Outperforms Prosody in Emotional Speech Processing: Evidence from a Complex Stroop Experiment\", author = \" Qi , Jing and."}
+{"idx": 7, "title": "science.org/doi/10.1126/science.adk3705", "date": "", "ddg_snippet": "Judd et al (Science, 2024 ): A 485-million-year history of Earth’s surface...", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/science.adk3705", "content": "Judd et al (Science, 2024 ): A 485-million-year history of Earth’s surface..."}
+{"idx": 8, "title": "ChatGPT на русском | Chad AI", "date": "", "ddg_snippet": "Модель выпущена 7 августа 2025 года, имеет больший контекст диалога и обладает знаниями событий до октября 2024 года. Также доступна GPT-4o, o1 и o3 Mini: они выдают более качественные ответы, но при их использовании расходуется больше слов.", "subpage_snippet": "", "source": "chadgpt.ru", "link": "https://chadgpt.ru/", "content": "Модель выпущена 7 августа 2025 года, имеет больший контекст диалога и обладает знаниями событий до октября 2024 года. Также доступна GPT-4o, o1 и o3 Mini: они выдают более качественные ответы, но при их использовании расходуется больше слов."}
+{"idx": 9, "title": "Technological Impact on Data Ecology in Tropical Regions, with...", "date": "", "ddg_snippet": "For instance, the use of multi-source Earth Observation data has enabled the development of habitat suitability models for invasive species like the Fall Armyworm (Spodoptera frugiperda), which threatens agricultural productivity across the continent ( Qi et al ., 2024 ).", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/technological-impact-data-ecology-tropical-regions-focus-ali-vphif", "content": "For instance, the use of multi-source Earth Observation data has enabled the development of habitat suitability models for invasive species like the Fall Armyworm (Spodoptera frugiperda), which threatens agricultural productivity across the continent ( Qi et al ., 2024 )."}
diff --git a/data/sampled_jsons/R2GenGPT_Radiology_Report_Generation_with_frozen_LLMs_METransformer_Table_1.jsonl b/data/sampled_jsons/R2GenGPT_Radiology_Report_Generation_with_frozen_LLMs_METransformer_Table_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f56d0e4859ca61f0f4ebc18cc74c8fa84093bc4
--- /dev/null
+++ b/data/sampled_jsons/R2GenGPT_Radiology_Report_Generation_with_frozen_LLMs_METransformer_Table_1.jsonl
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+{"idx": 0, "title": "R2GenGPT: Radiology Report Generation with Frozen LLMs", "date": "", "ddg_snippet": "Large Language Models ( LLMs ) have consistently showcased remarkable generalization capabilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology Report Generation (R2Gen) still presents a challenge, stemming from the inherent disparity in modality between LLMs and the R2Gen task. To bridge this gap effectively, we propose R2GenGPT , which ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2309.09812", "content": "Large Language Models ( LLMs ) have consistently showcased remarkable generalization capabilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology Report Generation (R2Gen) still presents a challenge, stemming from the inherent disparity in modality between LLMs and the R2Gen task. To bridge this gap effectively, we propose R2GenGPT , which ..."}
+{"idx": 1, "title": "GitHub - CHARAN422176/R2GenGPT-main", "date": "", "ddg_snippet": "R2GenGPT : Radiology Report Generation with Frozen LLMs Introduction Getting Started Installation 1 . Prepare the code and the environment", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CHARAN422176/R2GenGPT-main", "content": "R2GenGPT : Radiology Report Generation with Frozen LLMs Introduction Getting Started Installation 1 . Prepare the code and the environment"}
+{"idx": 2, "title": "PDF METransformer: Radiology Report Generation by Transformer with Multiple ...", "date": "", "ddg_snippet": "First, we propose a new diagnostic captioning frame-work, METransformer , which is conceptually \"multi-expert joint diagnosis\" for radiology report generation , by intro-ducing learnable expert tokens and encouraging them to learn complementary representations using both linear and non-linear attentions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "First, we propose a new diagnostic captioning frame-work, METransformer , which is conceptually \"multi-expert joint diagnosis\" for radiology report generation , by intro-ducing learnable expert tokens and encouraging them to learn complementary representations using both linear and non-linear attentions."}
+{"idx": 3, "title": "R2GenGPT: Radiology Report Generation with frozen LLMs", "date": "", "ddg_snippet": "We propose a novel LLMs -based Radiology report generation (R2Gen) framework, dubbed R2GenGPT . This marks the first instance of harnessing pre-trained large language models ( LLMs ) for the R2Gen task with comprehensive comparisons conducted on two frequently employed benchmark datasets.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2950162823000334", "content": "We propose a novel LLMs -based Radiology report generation (R2Gen) framework, dubbed R2GenGPT . This marks the first instance of harnessing pre-trained large language models ( LLMs ) for the R2Gen task with comprehensive comparisons conducted on two frequently employed benchmark datasets."}
+{"idx": 4, "title": "Bidirectional Learning for the Visual Representation in Radiology ...", "date": "", "ddg_snippet": "The manuscript targets a critical part of radiology report generation by utilizing large language models ( LLMs ), emphasizing the importance of aligning the LLM with image embeddings. The use of a frozen LLM to generate visual embeddings for regularizing training is novel. The manuscript is well-written in fluent language.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gZue5gHQHp", "content": "The manuscript targets a critical part of radiology report generation by utilizing large language models ( LLMs ), emphasizing the importance of aligning the LLM with image embeddings. The use of a frozen LLM to generate visual embeddings for regularizing training is novel. The manuscript is well-written in fluent language."}
+{"idx": 5, "title": "R2GenGPT: Radiology Report Generation with Frozen LLMs", "date": "", "ddg_snippet": "As for long text generation , LLMs are equipped with an inherent understanding of grammar, syntax, and semantic coherence, making them well-suited for tasks requiring extended text generation , such as medical reporting. Furthermore, their proficiency in context modeling allows them to maintain consistency and relevance throughout a lengthy report .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2309.09812", "content": "As for long text generation , LLMs are equipped with an inherent understanding of grammar, syntax, and semantic coherence, making them well-suited for tasks requiring extended text generation , such as medical reporting. Furthermore, their proficiency in context modeling allows them to maintain consistency and relevance throughout a lengthy report ."}
+{"idx": 6, "title": "R2GenGPT: Radiology Report Generation with Frozen LLMs", "date": "", "ddg_snippet": "Download Citation | On Nov 1 , 2023, Zhanyu Wang and others published R2GenGPT : Radiology Report Generation with Frozen LLMs | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/375674807_R2GenGPT_Radiology_Report_Generation_with_Frozen_LLMs", "content": "Download Citation | On Nov 1 , 2023, Zhanyu Wang and others published R2GenGPT : Radiology Report Generation with Frozen LLMs | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 7, "title": "R2GenGPT: Radiology Report Generation with Frozen LLMs - GitHub", "date": "", "ddg_snippet": "Radiology Report Generation with Frozen LLMs . Contribute to sergiotasconmorales/ReportWizard development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sergiotasconmorales/ReportWizard", "content": "Radiology Report Generation with Frozen LLMs . Contribute to sergiotasconmorales/ReportWizard development by creating an account on GitHub."}
+{"idx": 8, "title": "【文献阅读002】R2GenGPT: Radiology Report Generation with frozen LLMs（静态LLM用于 ...", "date": "", "ddg_snippet": "文章浏览阅读2.4k次，点赞57次，收藏42次。大型语言模型 ( llm )在应用于各种语言任务时一直显示出卓越的泛化能力。尽管如此，挖掘大语言模型在放射学报告生成 (R2Gen)中的全部潜力仍然是一个挑战，这源于大语言模型和R2Gen任务之间固有的模式差异。为了有效地弥合这一差距，提出了R2GenGPT，使用 ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_39213607/article/details/140232325", "content": "文章浏览阅读2.4k次，点赞57次，收藏42次。大型语言模型 ( llm )在应用于各种语言任务时一直显示出卓越的泛化能力。尽管如此，挖掘大语言模型在放射学报告生成 (R2Gen)中的全部潜力仍然是一个挑战，这源于大语言模型和R2Gen任务之间固有的模式差异。为了有效地弥合这一差距，提出了R2GenGPT，使用 ..."}
+{"idx": 9, "title": "R2GenGPT: Radiology Report Generation with Frozen LLMs", "date": "", "ddg_snippet": "Abstract: Large Language Models ( LLMs ) have consistently showcased remarkable generalization capabilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology Report Generation (R2Gen) still presents a challenge, stemming from the inherent disparity in modality between LLMs and the R2Gen task.", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2309.09812v1/", "content": "Abstract: Large Language Models ( LLMs ) have consistently showcased remarkable generalization capabilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology Report Generation (R2Gen) still presents a challenge, stemming from the inherent disparity in modality between LLMs and the R2Gen task."}
diff --git a/data/sampled_jsons/RA-PbRL_Equation_8_regret_calculation.jsonl b/data/sampled_jsons/RA-PbRL_Equation_8_regret_calculation.jsonl
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index 0000000000000000000000000000000000000000..c6600ab8152b182ea0d2438a53a3b015b9cf2457
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+{"idx": 0, "title": "RA - PbRL : Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "RA - PbRL Algorithm. Regret Analysis. RA - PbRL incorporates nested and static quantile risk objectives, enabling the optimization of risk-sensitive policies. Theoretical analysis demonstrates sublinear regret bounds, proving the algorithm’s efficiency.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "RA - PbRL Algorithm. Regret Analysis. RA - PbRL incorporates nested and static quantile risk objectives, enabling the optimization of risk-sensitive policies. Theoretical analysis demonstrates sublinear regret bounds, proving the algorithm’s efficiency."}
+{"idx": 1, "title": "RA - PbRL : Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "We also introduce Risk-Aware-PbRL ( RA - PbRL ), an algorithm designed to optimize both nested and static objectives.This change to the bellman equation disrupts calculations on regret , making risk-neutral PbRL inapplicable. This is primarily due to the additional parameter.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v1/", "content": "We also introduce Risk-Aware-PbRL ( RA - PbRL ), an algorithm designed to optimize both nested and static objectives.This change to the bellman equation disrupts calculations on regret , making risk-neutral PbRL inapplicable. This is primarily due to the additional parameter."}
+{"idx": 2, "title": "Systems of Equations Solver: Step-by-Step Solutions - Wolfram|Alpha", "date": "", "ddg_snippet": "Free Systems of Equations Calculator helps you solve sets of two or more equations . Linear, nonlinear, inequalities or general constraints. Answers, graphs, alternate forms.", "subpage_snippet": "", "source": "www.wolframalpha.com", "link": "https://www.wolframalpha.com/calculators/system-equation-calculator", "content": "Free Systems of Equations Calculator helps you solve sets of two or more equations . Linear, nonlinear, inequalities or general constraints. Answers, graphs, alternate forms."}
+{"idx": 3, "title": "How to Find the Equation of a Line from Two Points", "date": "", "ddg_snippet": "In this lesson, you will learn how to find the equation of a line using two given points.", "subpage_snippet": "", "source": "resourcecenter.byupathway.edu", "link": "https://resourcecenter.byupathway.edu/math/m14-12", "content": "In this lesson, you will learn how to find the equation of a line using two given points."}
+{"idx": 4, "title": "Mastering Linear Equations : Definition, Formulas, Graphs, and Easy...", "date": "", "ddg_snippet": "This guide covers linear equations , including definitions, formulas, graphs, and examples for 8 th-grade students and college students as well. Learn how to solve equations using Mathos AI calculator to solve equations quickly.", "subpage_snippet": "", "source": "www.mathos.ai", "link": "https://www.mathos.ai/blog/linear-equations-definition-formulas-graphs-examples", "content": "This guide covers linear equations , including definitions, formulas, graphs, and examples for 8 th-grade students and college students as well. Learn how to solve equations using Mathos AI calculator to solve equations quickly."}
+{"idx": 5, "title": "Differential Equation Calculator - eMathHelp", "date": "", "ddg_snippet": "Differential Equation Calculator . Solve differential equations . The calculator will try to find the solution of the given ODE: first-order, second-order, nth-order, separable, linear, exact, Bernoulli, homogeneous, or inhomogeneous. Initial conditions are also supported.", "subpage_snippet": "", "source": "www.emathhelp.net", "link": "https://www.emathhelp.net/calculators/differential-equations/differential-equation-calculator/", "content": "Differential Equation Calculator . Solve differential equations . The calculator will try to find the solution of the given ODE: first-order, second-order, nth-order, separable, linear, exact, Bernoulli, homogeneous, or inhomogeneous. Initial conditions are also supported."}
+{"idx": 6, "title": "Solve for x Calculator", "date": "", "ddg_snippet": "Solve for x Calculator . Step 1: Enter the Equation you want to solve into the editor. The equation calculator allows you to take a simple or complex equation and solve by best method possible.", "subpage_snippet": "", "source": "www.mathway.com", "link": "https://www.mathway.com/Calculator/solve-for-x-calculator", "content": "Solve for x Calculator . Step 1: Enter the Equation you want to solve into the editor. The equation calculator allows you to take a simple or complex equation and solve by best method possible."}
+{"idx": 7, "title": "Калькулятор уравнений", "date": "", "ddg_snippet": "equation icon Уравнения.Значения. x equals .", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/equ/ru/", "content": "equation icon Уравнения.Значения. x equals ."}
+{"idx": 8, "title": "Решить линейное уравнение 8 (2x-1)-2( 8 x-3)=-2. Подробное решение.", "date": "", "ddg_snippet": "https://calc-best.ru/matematicheskie/linejnye-uravneniya? equation = 8 %282x-1%29-2%288x-3)=-2.", "subpage_snippet": "", "source": "calc-best.ru", "link": "https://calc-best.ru/matematicheskie/linejnye-uravneniya?equation=8(2x-1)-2(8x-3)=-2", "content": "https://calc-best.ru/matematicheskie/linejnye-uravneniya? equation = 8 %282x-1%29-2%288x-3)=-2."}
+{"idx": 9, "title": "3.5 Inches To Centimeters Converter | 3.5 in To cm Converter", "date": "", "ddg_snippet": "3.5 inches equal 8 .89 centimeters (3.5in = 8.89cm).3.5 Inch Table. Further inches to centimeters calculations .", "subpage_snippet": "", "source": "inches-to-cm.appspot.com", "link": "https://inches-to-cm.appspot.com/3.5-inches-to-cm.html", "content": "3.5 inches equal 8 .89 centimeters (3.5in = 8.89cm).3.5 Inch Table. Further inches to centimeters calculations ."}
diff --git a/data/sampled_jsons/RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_equation_8_regret_year_2024.jsonl b/data/sampled_jsons/RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_equation_8_regret_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..674b56e20f9de1157f0d37b0563a833a024a0f71
--- /dev/null
+++ b/data/sampled_jsons/RA-PbRL_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_equation_8_regret_year_2024.jsonl
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+{"idx": 0, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 1, "title": "PDF RA-PbRL: Provably Eficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "To the best of our knowledge, our proposed RA-PbRL algorithm is the first provably eficient Preference-based Reinforcement Learning ( PbRL ) algorithm that incorporates both nested and static risk objectives in one algorithm.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "To the best of our knowledge, our proposed RA-PbRL algorithm is the first provably eficient Preference-based Reinforcement Learning ( PbRL ) algorithm that incorporates both nested and static risk objectives in one algorithm."}
+{"idx": 2, "title": "RA-PbRL | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation. In International Conference on Machine Learning , pages 3773-3793.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3739861", "content": "Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation. In International Conference on Machine Learning , pages 3773-3793."}
+{"idx": 3, "title": "Risk-Aware Preference-baser Reinforcement Learning (RA-PbRL)", "date": "", "ddg_snippet": "RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties. The intuition behind the algorithm depends on the idea of confidence bounds. The ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties. The intuition behind the algorithm depends on the idea of confidence bounds. The ..."}
+{"idx": 4, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf", "content": "Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators ..."}
+{"idx": 5, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "This paper is important because it addresses a critical limitation of existing preference-based reinforcement learning ( PbRL ) methods: their inability to handle risk effectively. RA-PbRL offers a novel solution by incorporating risk-aware objectives and developing a provably efficient algorithm. This work is relevant to current research trends in safe and robust AI, which require algorithms ...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "This paper is important because it addresses a critical limitation of existing preference-based reinforcement learning ( PbRL ) methods: their inability to handle risk effectively. RA-PbRL offers a novel solution by incorporating risk-aware objectives and developing a provably efficient algorithm. This work is relevant to current research trends in safe and robust AI, which require algorithms ..."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Authors Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "Authors Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences ..."}
+{"idx": 7, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Abstract Preference-based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v1", "content": "Abstract Preference-based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware ..."}
+{"idx": 8, "title": "Efficient Preference-Based Reinforcement Learning Using Learned ...", "date": "", "ddg_snippet": "Preference-based reinforcement learning ( PbRL ) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10161081", "content": "Preference-based reinforcement learning ( PbRL ) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ..."}
+{"idx": 9, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "This work explores and proves the applicability of two risk-aware objectives to PbRL : nested and static quantile risk objectives and introduces Risk-Aware - PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Preference-based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/RA-PbRL:-Provably-Efficient-Risk-Aware-Learning-Zhao-Escamill/80889b1260bfcf4275f7ec18bb4eec183400f05d", "content": "This work explores and proves the applicability of two risk-aware objectives to PbRL : nested and static quantile risk objectives and introduces Risk-Aware - PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Preference-based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode ..."}
diff --git a/data/sampled_jsons/RA-PbRL_regret_equation_8_V_Vpi.jsonl b/data/sampled_jsons/RA-PbRL_regret_equation_8_V_Vpi.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b8562823c057569375efd809ed2e8105a6bf433
--- /dev/null
+++ b/data/sampled_jsons/RA-PbRL_regret_equation_8_V_Vpi.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RA-PbRL: Provably Eficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Loss of linearity of Bellman function When using a quantile function to transform a risk-neutral PbRL algorithm into a risk-aware algorithm, the Bellman equation used to solve the problem becomes non-linear. This change to the bellman equation disrupts calculations on regret , making risk-neutral PbRL inapplicable.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "Loss of linearity of Bellman function When using a quantile function to transform a risk-neutral PbRL algorithm into a risk-aware algorithm, the Bellman equation used to solve the problem becomes non-linear. This change to the bellman equation disrupts calculations on regret , making risk-neutral PbRL inapplicable."}
+{"idx": 1, "title": "[2410.23569] RA-PbRL: Provably Efficient Risk-Aware ...", "date": "", "ddg_snippet": "Oct 31, 2024 · We also introduce Risk-AwarePbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Oct 31, 2024 · We also introduce Risk-AwarePbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings."}
+{"idx": 2, "title": "RA-PbRL | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "Jun 5, 2025 · We also introduce Risk-Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3739861", "content": "Jun 5, 2025 · We also introduce Risk-Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings."}
+{"idx": 3, "title": "Risk-Aware Preference-baser Reinforcement Learning (RA-PbRL)", "date": "", "ddg_snippet": "RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk-awareness through Value-at-Risk penalties.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk-awareness through Value-at-Risk penalties."}
+{"idx": 4, "title": "[2410.23569] RA-PbRL: Provably Efficient Risk-Aware ...", "date": "", "ddg_snippet": "Loss of linearity of Bellman function When using a quantile function to transform a risk-neutral PbRL algorithm into a risk-aware algorithm, the Bellman equation used to solve the problem becomes non-linear. This change to the bellman equation disrupts calculations on regret , making risk-neutral PbRL inapplicable.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.23569", "content": "Loss of linearity of Bellman function When using a quantile function to transform a risk-neutral PbRL algorithm into a risk-aware algorithm, the Bellman equation used to solve the problem becomes non-linear. This change to the bellman equation disrupts calculations on regret , making risk-neutral PbRL inapplicable."}
+{"idx": 5, "title": "Yujie (Norah) Zhao", "date": "", "ddg_snippet": "First Author Publications (Full List on Google Scholar) RA-PbRL : Provably Efficient Risk-Aware Preference-Based Reinforcement Learning", "subpage_snippet": "", "source": "norahyujiezhao.github.io", "link": "https://norahyujiezhao.github.io/", "content": "First Author Publications (Full List on Google Scholar) RA-PbRL : Provably Efficient Risk-Aware Preference-Based Reinforcement Learning"}
+{"idx": 6, "title": "A Survey of Preference-Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Preference-based reinforcement learning ( PbRL ) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference-based feedback signal that indicates relative instead of absolute utility values.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Preference-based reinforcement learning ( PbRL ) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference-based feedback signal that indicates relative instead of absolute utility values."}
+{"idx": 7, "title": "INDUSTRIAL RESEARCH CENTRE, TRIPOLI, LIBYA ...", "date": "", "ddg_snippet": "Ve regret that some of the pages in the microfiche copy of this report may not be up to the proper egibihty standards even though the best possible copy was ...", "subpage_snippet": "", "source": "downloads.unido.org", "link": "https://downloads.unido.org/ot/46/89/4689759/00001-10000_06742.pdf", "content": "Ve regret that some of the pages in the microfiche copy of this report may not be up to the proper egibihty standards even though the best possible copy was ..."}
+{"idx": 8, "title": "case-based reasoning systems", "date": "", "ddg_snippet": "A new hybrid case-based reasoning approach for medical diagnosis systems. PubMed. Sharaf-El-Deen, Dina A; Moawad, Ibrahim F; Khalifa, M E. 2014-02-01.", "subpage_snippet": "", "source": "www.science.gov", "link": "https://www.science.gov/topicpages/c/case-based+reasoning+systems", "content": "A new hybrid case-based reasoning approach for medical diagnosis systems. PubMed. Sharaf-El-Deen, Dina A; Moawad, Ibrahim F; Khalifa, M E. 2014-02-01."}
+{"idx": 9, "title": "Reproductions supplied by EDRS are the best that can ... - ERIC", "date": "", "ddg_snippet": "by A Williams · 2000 — This issue includes the following articles: \"On Negative. Alternative Questions\" (Chung-hye Han); \"A Categorical Syntax for Verbs of.", "subpage_snippet": "", "source": "files.eric.ed.gov", "link": "https://files.eric.ed.gov/fulltext/ED444368.pdf", "content": "by A Williams · 2000 — This issue includes the following articles: \"On Negative. Alternative Questions\" (Chung-hye Han); \"A Categorical Syntax for Verbs of."}
diff --git a/data/sampled_jsons/RA-PbRL_technical_challenges_adapting_risk_measures_preference-based_reinforcement_learning.jsonl b/data/sampled_jsons/RA-PbRL_technical_challenges_adapting_risk_measures_preference-based_reinforcement_learning.jsonl
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index 0000000000000000000000000000000000000000..2277bbcbab3c15de19ecdf81a85648d2c933bf30
--- /dev/null
+++ b/data/sampled_jsons/RA-PbRL_technical_challenges_adapting_risk_measures_preference-based_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RA - PbRL : Provably Efficient Risk -Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v1/", "content": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 1, "title": "RA - PbRL : Provably Efficient Risk -Aware", "date": "", "ddg_snippet": "Preference - based Feedback Reinforcement Learning . Risk Aware Preference - Based MDP ( RA -PB-MDP). The standard MDP is described as a tuple, M(S, A, rξ⋆, P⋆, H), where S and A represent finite state and action spaces, and H denotes the length of episodes.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "Preference - based Feedback Reinforcement Learning . Risk Aware Preference - Based MDP ( RA -PB-MDP). The standard MDP is described as a tuple, M(S, A, rξ⋆, P⋆, H), where S and A represent finite state and action spaces, and H denotes the length of episodes."}
+{"idx": 2, "title": "RA - PbRL : Provably Efficient Risk -Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode.The main technical challenge addressed in this paper is the setting of preference - based reinforcement learning ( PbRL ).", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ra-pbrl-provably-efficient-risk-aware-preference", "content": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode.The main technical challenge addressed in this paper is the setting of preference - based reinforcement learning ( PbRL )."}
+{"idx": 3, "title": "RA - PbRL : Provably Efficient Risk -Aware Preference - Based ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions."}
+{"idx": 4, "title": "RA - PbRL : Provably Efficient Risk -Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/JNDcFOczOf@OpenReview", "content": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 5, "title": "RA - PbRL : Provably Efficient Risk -Aware Preference - Based ...", "date": "", "ddg_snippet": "At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/RA-PbRL:-Provably-Efficient-Risk-Aware-Preference-Based-Reinforcement-Learning-baf1c16a-6c49-45cc-9e7e-b12a3550842b", "content": "At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators."}
+{"idx": 6, "title": "GitHub - aguilarjose11/PbRLNeurips: code repository for upcomming...", "date": "", "ddg_snippet": "RA - PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference - based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "RA - PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference - based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties."}
+{"idx": 7, "title": "RA - PbRL : Provably Efficient Risk -Aware Preference - Based ...", "date": "", "ddg_snippet": "# Preference - based Reinforcement Learning ( PbRL ) traditionally focuses on maximizing average reward, ignoring risk .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "# Preference - based Reinforcement Learning ( PbRL ) traditionally focuses on maximizing average reward, ignoring risk ."}
+{"idx": 8, "title": "A Survey of Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "(2011). The original idea of preference - based reinforcement learning ( PbRL ) is to infer the objective from qualitative feedback, such as pairwise preferences between behaviors or between actions given.", "subpage_snippet": "", "source": "epub.ub.uni-muenchen.de", "link": "https://epub.ub.uni-muenchen.de/125328/1/2312.14925v2.pdf", "content": "(2011). The original idea of preference - based reinforcement learning ( PbRL ) is to infer the objective from qualitative feedback, such as pairwise preferences between behaviors or between actions given."}
+{"idx": 9, "title": "Preference - Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Preference - based reinforcement learning ( PbRL ) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Preference - based reinforcement learning ( PbRL ) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal."}
diff --git a/data/sampled_jsons/RAGGED_Section_3.1_retriever_paradigms_year_2024.jsonl b/data/sampled_jsons/RAGGED_Section_3.1_retriever_paradigms_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..55fee7620d0c53ba657cf7e0b4040a06bba47f78
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_Section_3.1_retriever_paradigms_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Adversarial Threat Vectors and Risk Mitigation for", "date": "", "ddg_snippet": "Section 3 of this paper provides a detailed analysis centered on a structured threat modeling process process applied to a generic Retrieval -Augmented ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00281v1", "content": "Section 3 of this paper provides a detailed analysis centered on a structured threat modeling process process applied to a generic Retrieval -Augmented ..."}
+{"idx": 1, "title": "L’intelligence artificielle générative dans l’impasse", "date": "", "ddg_snippet": "Nous partons d’un état de l’art sur les capacités des programmes basés sur des LLM à répondre à des besoins informationnels ( section 1 ).", "subpage_snippet": "", "source": "www.arthurperret.fr", "link": "https://www.arthurperret.fr/articles/2025-06-20-congres-sfsic-ia-impasse-informationnelle.html", "content": "Nous partons d’un état de l’art sur les capacités des programmes basés sur des LLM à répondre à des besoins informationnels ( section 1 )."}
+{"idx": 2, "title": "TCSR-SQL: Towards Table Content-aware Text-to-SQL with", "date": "", "ddg_snippet": "... the overall framework of TCSR-SQL, which comprises three modules: Keywords Extraction & Fuzzy Detection ( Section 3 . 1 ), Knowledge Retrieval ...", "subpage_snippet": "", "source": "orbyumc.org", "link": "https://orbyumc.org/article/tcsr-sql-towards-table-content-aware-text-to-sql-with-self-retrieval", "content": "... the overall framework of TCSR-SQL, which comprises three modules: Keywords Extraction & Fuzzy Detection ( Section 3 . 1 ), Knowledge Retrieval ..."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 4, "title": "Evaluating the Robustness of Retrieval-Augmented Generation to", "date": "", "ddg_snippet": "... we: ( 1 ) evaluate the susceptibility of RAG systems to adversarial documents in the medical domain where these documents are provided as retrieval ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03787v1", "content": "... we: ( 1 ) evaluate the susceptibility of RAG systems to adversarial documents in the medical domain where these documents are provided as retrieval ..."}
+{"idx": 5, "title": "Mobile-Agent-v3: Foundamental Agents for GUI Automation", "date": "", "ddg_snippet": "... 1 ) The strong UI perception capabilities (such as for Mobile, PC, and Web); 2) The planning, reflection, and reasoning in various dynamic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15144v1", "content": "... 1 ) The strong UI perception capabilities (such as for Mobile, PC, and Web); 2) The planning, reflection, and reasoning in various dynamic ..."}
+{"idx": 6, "title": "AttnTrace: Attention-based Context Traceback for Long-Context", "date": "", "ddg_snippet": "... 3 ] , Gemini-2.5-Pro [ 22 ] , and GPT-4. 1 [ 42 ] , serve as the foundation to empower systems such as autonomous agents [ 60 , 69 , 1 ] and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03793v1", "content": "... 3 ] , Gemini-2.5-Pro [ 22 ] , and GPT-4. 1 [ 42 ] , serve as the foundation to empower systems such as autonomous agents [ 60 , 69 , 1 ] and ..."}
+{"idx": 7, "title": "ECoRAG: Evidentiality-guided Compression for Long Context RAG", "date": "", "ddg_snippet": "As a result, a naive baseline simply prepending retrieved documents, ‘standard RAG’ in Figure 1 , outperforms a baseline compressor RECOMP Xu ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05167v1", "content": "As a result, a naive baseline simply prepending retrieved documents, ‘standard RAG’ in Figure 1 , outperforms a baseline compressor RECOMP Xu ..."}
+{"idx": 8, "title": "RAG+: Enhancing Retrieval-Augmented Generation with", "date": "", "ddg_snippet": "... RAG+, a simple yet effective extension to the RAG framework that enhances reasoning by bridging retrieval and generation with an application-aware ...", "subpage_snippet": "", "source": "keyurramoliya.com", "link": "https://keyurramoliya.com/posts/Rag-Plus/", "content": "... RAG+, a simple yet effective extension to the RAG framework that enhances reasoning by bridging retrieval and generation with an application-aware ..."}
+{"idx": 9, "title": "(PDF) ChatGPT as a Solver and Grader of Programming Exams", "date": "", "ddg_snippet": "W e chose a real exam from a 1st ... 2 We used gpt- 3 .5-turbo v ersion for this experimentation ... ants were tested: a simple one with almost no con-", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384288724_ChatGPT_as_a_Solver_and_Grader_of_Programming_Exams_written_in_Spanish", "content": "W e chose a real exam from a 1st ... 2 We used gpt- 3 .5-turbo v ersion for this experimentation ... ants were tested: a simple one with almost no con-"}
diff --git a/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems.jsonl b/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a1c92722efd6328ccd8f7ffd921548a73d224fc1
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AI Insights from ICML 2025 Part 1: Context engineering and", "date": "", "ddg_snippet": "Meanwhile, RAGGED : Towards Informed Design of Scalable and Stable RAG Systems introduced robustness metrics that revealed more retrieval isn’t ...", "subpage_snippet": "", "source": "instabase.com", "link": "https://instabase.com/blog/ai-insights-from-icml-2025-part-1-context-engineering-and-multimodal-reasoning/", "content": "Meanwhile, RAGGED : Towards Informed Design of Scalable and Stable RAG Systems introduced robustness metrics that revealed more retrieval isn’t ..."}
+{"idx": 1, "title": "Engineering RAG Systems for Real-World Applications: Design,", "date": "", "ddg_snippet": "... of this paper are as follows: end-to-end development and deployment of RAG systems for multilingual, domain-specific applications; user-centred ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.20869v2", "content": "... of this paper are as follows: end-to-end development and deployment of RAG systems for multilingual, domain-specific applications; user-centred ..."}
+{"idx": 2, "title": "12 RAG Pain Points and their Solutions", "date": "", "ddg_snippet": "Pain point: RAG systems in multi-turn dialogues can be quite at a loss concerning keeping track of context and selecting the required information ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2024/06/rag-pain-points-and-their-solutions/", "content": "Pain point: RAG systems in multi-turn dialogues can be quite at a loss concerning keeping track of context and selecting the required information ..."}
+{"idx": 3, "title": "How to Build a RAG System That Actually Works!", "date": "", "ddg_snippet": "An effective retrieval system is the backbone of RAG , ensuring that the model has access to accurate, relevant, and contextually rich information.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/03/why-rag-systems-fail-and-how-to-fix-them/", "content": "An effective retrieval system is the backbone of RAG , ensuring that the model has access to accurate, relevant, and contextually rich information."}
+{"idx": 4, "title": "RAG as a Service: What It Means and Why It Matters for", "date": "", "ddg_snippet": "RAG as a Service simplifies building and ... Building a RAG system from the ground up takes a lot of time, effort, and technical expertise.", "subpage_snippet": "", "source": "www.qodo.ai", "link": "https://www.qodo.ai/blog/rag-as-a-service/", "content": "RAG as a Service simplifies building and ... Building a RAG system from the ground up takes a lot of time, effort, and technical expertise."}
+{"idx": 5, "title": "RAG Architecture Expert Needed | Freelancer", "date": "", "ddg_snippet": "... RAG framework - Text2SQL where SQL queries can run on the top of RAG framework - Conversational AI Chatbot (Rasa/WhatsApp) - Semantic search engine - ...", "subpage_snippet": "", "source": "www.freelancer.com", "link": "https://www.freelancer.com/projects/langchain/rag-architecture-expert-needed", "content": "... RAG framework - Text2SQL where SQL queries can run on the top of RAG framework - Conversational AI Chatbot (Rasa/WhatsApp) - Semantic search engine - ..."}
+{"idx": 6, "title": "RAG vs Fine-Tuning: Best Approach for Scalable AI Agents", "date": "", "ddg_snippet": "Real-world example: Gupshup ’ s Banking Relationship Manager AI Agent uses RAG to access up-to-date policy information, interest rates, and ...", "subpage_snippet": "", "source": "www.gupshup.io", "link": "https://www.gupshup.io/resources/blog/rag-vs-fine-tuning", "content": "Real-world example: Gupshup ’ s Banking Relationship Manager AI Agent uses RAG to access up-to-date policy information, interest rates, and ..."}
+{"idx": 7, "title": "12 RAG Pain Points and Proposed Solutions | Towards Data Science", "date": "", "ddg_snippet": "Adjusting these parameters can impact the trade-off between computational efficiency and the quality of retrieved information.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/12-rag-pain-points-and-proposed-solutions-43709939a28c/", "content": "Adjusting these parameters can impact the trade-off between computational efficiency and the quality of retrieved information."}
+{"idx": 8, "title": "Implementing RAG: Application of OceanBase Database at CUSRI |", "date": "", "ddg_snippet": "On top of that, a RAG -based Q&A system is introduced to enhance the LLM s comprehension and communication capabilities for specific questions by ...", "subpage_snippet": "", "source": "oceanbase.github.io", "link": "https://oceanbase.github.io/docs/blogs/users/RAG-CUSRI", "content": "On top of that, a RAG -based Q&A system is introduced to enhance the LLM s comprehension and communication capabilities for specific questions by ..."}
+{"idx": 9, "title": "Carnegie Mellon University at ICML 2025 – Machine", "date": "", "ddg_snippet": "This paper proposes a set of simple, abstract tasks designed to probe the creative limits of today’s language models in a controlled and measurable ...", "subpage_snippet": "", "source": "blog.ml.cmu.edu", "link": "https://blog.ml.cmu.edu/2025/07/08/carnegie-mellon-university-at-icml-2025/", "content": "This paper proposes a set of simple, abstract tasks designed to probe the creative limits of today’s language models in a controlled and measurable ..."}
diff --git a/data/sampled_jsons/RAGGED_framework_three_retriever_paradigms_Section_3.1.jsonl b/data/sampled_jsons/RAGGED_framework_three_retriever_paradigms_Section_3.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7969f165421dc24ae720f391d1b7f70f2363c832
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_framework_three_retriever_paradigms_Section_3.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED: Towards Informed Design of Retrieval ...", "date": "", "ddg_snippet": "14 Mar 2024 — We introduce a reusable framework that can easily be adapted to analyze new RAG components, such as retriever and reader models, as they evolve.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v1", "content": "14 Mar 2024 — We introduce a reusable framework that can easily be adapted to analyze new RAG components, such as retriever and reader models, as they evolve."}
+{"idx": 1, "title": "RAGGED", "date": "", "ddg_snippet": "by J Hsia · Cited by 23 — In this section , we describe the experimental setup, including the retriever and reader models, datasets, and evaluation metrics used to assess the performance ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KDXj60FpJr", "content": "by J Hsia · Cited by 23 — In this section , we describe the experimental setup, including the retriever and reader models, datasets, and evaluation metrics used to assess the performance ..."}
+{"idx": 2, "title": "CRP-RAG: A Retrieval-Augmented Generation Framework ...", "date": "", "ddg_snippet": "by K Xu · 2024 · Cited by 14 — In this section , we introduce the framework design and reasoning process of CRP- RAG ( Section 3.1 ), along with the structures and workflows of ...", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202411.1648/v1", "content": "by K Xu · 2024 · Cited by 14 — In this section , we introduce the framework design and reasoning process of CRP- RAG ( Section 3.1 ), along with the structures and workflows of ..."}
+{"idx": 3, "title": "R3-RAG: Learning Step-by-Step Reasoning and Retrieval ...", "date": "", "ddg_snippet": "by Y Li · 2025 · Cited by 5 — In this section , we first introduce the trajectory of R3- RAG and then the cold start and RL training. 3.1 Trajectory Definition. In this section ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.23794?", "content": "by Y Li · 2025 · Cited by 5 — In this section , we first introduce the trajectory of R3- RAG and then the cold start and RL training. 3.1 Trajectory Definition. In this section ..."}
+{"idx": 4, "title": "Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "We implement the RAGGED framework by evaluating retrievers and readers ... We evaluate three retrievers with different retrieval paradigms : (1) BM25 ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46460", "content": "We implement the RAGGED framework by evaluating retrievers and readers ... We evaluate three retrievers with different retrieval paradigms : (1) BM25 ..."}
+{"idx": 5, "title": "A Coarse-to-Fine Progressive Retrieval Paradigm for RAG", "date": "", "ddg_snippet": "by X Zhao · 2025 · Cited by 12 — We first introduce three progressive retrieval stages from coarse to fine (§3.1) and then describe how to distill aggregated signals from the. 18 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.165.pdf", "content": "by X Zhao · 2025 · Cited by 12 — We first introduce three progressive retrieval stages from coarse to fine (§3.1) and then describe how to distill aggregated signals from the. 18 pages"}
+{"idx": 6, "title": "Enhancing retrieval-augmented generation for ...", "date": "", "ddg_snippet": "by D Shi · 2025 · Cited by 1 — Furthermore, our work optimizes the retrieval component within the RAG framework . ... Section 3.1 presents the overall workflow. Section 3.2 describes the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0166361525000958", "content": "by D Shi · 2025 · Cited by 1 — Furthermore, our work optimizes the retrieval component within the RAG framework . ... Section 3.1 presents the overall workflow. Section 3.2 describes the ..."}
+{"idx": 7, "title": "Retrieval-Augmented Generation (RAG) | Business & ...", "date": "", "ddg_snippet": "by M Klesel · 2025 · Cited by 3 — Retrieval-augmented generation ( RAG ) has been proposed as a new framework for AI that seeks to integrate additional knowledge, such as organizational data.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s12599-025-00945-3", "content": "by M Klesel · 2025 · Cited by 3 — Retrieval-augmented generation ( RAG ) has been proposed as a new framework for AI that seeks to integrate additional knowledge, such as organizational data."}
+{"idx": 8, "title": "In-Depth Exploration of the RAG Optimization Scheme and ...", "date": "", "ddg_snippet": "5 Sept 2024 — This article introduces in detail the challenges, general paradigm , engineering practice, and optimization strategy of RAG .", "subpage_snippet": "", "source": "www.alibabacloud.com", "link": "https://www.alibabacloud.com/blog/in-depth-exploration-of-the-rag-optimization-scheme-and-practice_601580", "content": "5 Sept 2024 — This article introduces in detail the challenges, general paradigm , engineering practice, and optimization strategy of RAG ."}
+{"idx": 9, "title": "Planning-guided Retrieval Augmented Generation", "date": "", "ddg_snippet": "by P Verma · Cited by 20 — Plan- RAG uses a DAG-based reasoning plan to decompose queries, improving attribution and efficiency while leveraging frozen LMs as plug-and-play experts.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=cUuOKnjVQJ", "content": "by P Verma · Cited by 20 — Plan- RAG uses a DAG-based reasoning plan to decompose queries, improving attribution and efficiency while leveraging frozen LMs as plug-and-play experts."}
diff --git a/data/sampled_jsons/RAGGED_paper_RAG_Stability_Score_formula_year_2024.jsonl b/data/sampled_jsons/RAGGED_paper_RAG_Stability_Score_formula_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48aebae35652b34d11347527a9eb96175934f333
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_paper_RAG_Stability_Score_formula_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "Through two new metrics - RAG Stability Score (RSS) and RAG Scalability Coeficient (RSC) - RAGGED of-fers a principled way to assess how reliably and eficiently models use retrieved information across configurations and domains.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09040v3", "content": "Through two new metrics - RAG Stability Score (RSS) and RAG Scalability Coeficient (RSC) - RAGGED of-fers a principled way to assess how reliably and eficiently models use retrieved information across configurations and domains."}
+{"idx": 1, "title": "RAG Evaluation & Confidence Score | by Naresh Kancharla | Medium", "date": "", "ddg_snippet": "Here are several methods to build a confidence score for RAG responses. This specific post will talk about RAGA's approach (2nd approach).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@naresh.kancharla/rag-evaluation-confidence-score-dfd1bdd01b82", "content": "Here are several methods to build a confidence score for RAG responses. This specific post will talk about RAGA's approach (2nd approach)."}
+{"idx": 2, "title": "Evaluation Metrics for Retrieval-Augmented Generation (RAG) Systems ...", "date": "", "ddg_snippet": "Importance of Evaluation Metrics in RAG Systems Evaluation metrics are super important for figuring out how well RAG systems are doing. They give us a consistent way to check how effectively a system pulls up relevant info and gives accurate answers.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/nlp/evaluation-metrics-for-retrieval-augmented-generation-rag-systems/", "content": "Importance of Evaluation Metrics in RAG Systems Evaluation metrics are super important for figuring out how well RAG systems are doing. They give us a consistent way to check how effectively a system pulls up relevant info and gives accurate answers."}
+{"idx": 3, "title": "What Are the Evaluation Metrics for RAGs? - Baeldung", "date": "", "ddg_snippet": "The formula for the BLEU score is: (1) where BP is the brevity penalty, is the weight for each n-gram precision, and is the precision for n-grams. A more detailed explanation of this metric can be found in our article here. 3.3. METEOR Score for Evaluating RAG Models", "subpage_snippet": "", "source": "www.baeldung.com", "link": "https://www.baeldung.com/cs/retrieval-augmented-generation-evaluate-metrics-performance", "content": "The formula for the BLEU score is: (1) where BP is the brevity penalty, is the weight for each n-gram precision, and is the precision for n-grams. A more detailed explanation of this metric can be found in our article here. 3.3. METEOR Score for Evaluating RAG Models"}
+{"idx": 4, "title": "A complete guide to RAG evaluation: metrics, testing and best practices", "date": "", "ddg_snippet": "This guide breaks down how to evaluate and test RAG systems. You'll learn how to evaluate retrieval and generation quality, build test sets with synthetic data, run experiments, and monitor in production.", "subpage_snippet": "", "source": "www.evidentlyai.com", "link": "https://www.evidentlyai.com/llm-guide/rag-evaluation", "content": "This guide breaks down how to evaluate and test RAG systems. You'll learn how to evaluate retrieval and generation quality, build test sets with synthetic data, run experiments, and monitor in production."}
+{"idx": 5, "title": "The Ultimate Guide to Evaluate RAG System Components - MyScale", "date": "", "ddg_snippet": "The formula for the BLEU score is: Where ( BP ) is the brevity penalty to penalize short responses, ( P_n ) is the precision of n-grams, and ( w_n ) are the weights for each n-gram level. BLEU quantitatively measures how closely the generated response matches the reference response.", "subpage_snippet": "", "source": "www.myscale.com", "link": "https://www.myscale.com/blog/ultimate-guide-to-evaluate-rag-system/", "content": "The formula for the BLEU score is: Where ( BP ) is the brevity penalty to penalize short responses, ( P_n ) is the precision of n-grams, and ( w_n ) are the weights for each n-gram level. BLEU quantitatively measures how closely the generated response matches the reference response."}
+{"idx": 6, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09040", "content": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability."}
+{"idx": 7, "title": "RAGAS for RAG in LLMs: A Comprehensive Guide to Evaluation Metrics.", "date": "", "ddg_snippet": "RAGAS: Specialized Metrics for RAG Models RAGAS introduces several metrics that provide a more holistic evaluation of RAG models, focusing on aspects like faithfulness, answer relevancy, context precision, and context recall.", "subpage_snippet": "", "source": "dkaarthick.medium.com", "link": "https://dkaarthick.medium.com/ragas-for-rag-in-llms-a-comprehensive-guide-to-evaluation-metrics-3aca142d6e38", "content": "RAGAS: Specialized Metrics for RAG Models RAGAS introduces several metrics that provide a more holistic evaluation of RAG models, focusing on aspects like faithfulness, answer relevancy, context precision, and context recall."}
+{"idx": 8, "title": "Best Practices in RAG Evaluation: A Comprehensive Guide", "date": "", "ddg_snippet": "Introduction This guide will teach you how to evaluate a RAG system for both accuracy and quality. You will learn to maintain RAG performance by testing for search precision, recall, contextual relevance, and response accuracy. Building a RAG application is just the beginning; it is crucial to test its usefulness for the end-user and calibrate its components for long-term stability . RAG ...", "subpage_snippet": "", "source": "qdrant.tech", "link": "https://qdrant.tech/blog/rag-evaluation-guide/", "content": "Introduction This guide will teach you how to evaluate a RAG system for both accuracy and quality. You will learn to maintain RAG performance by testing for search precision, recall, contextual relevance, and response accuracy. Building a RAG application is just the beginning; it is crucial to test its usefulness for the end-user and calibrate its components for long-term stability . RAG ..."}
+{"idx": 9, "title": "RAG Status: A Simple Guide to Effective KPI Management | ClearPoint ...", "date": "", "ddg_snippet": "RAG Status: A Practical Guide for Project Management Learn how to establish RAG status for your KPIs, ensuring clear project insights and effective decision-making with practical steps and expert tips.", "subpage_snippet": "", "source": "www.clearpointstrategy.com", "link": "https://www.clearpointstrategy.com/blog/establish-rag-statuses-for-kpis", "content": "RAG Status: A Practical Guide for Project Management Learn how to establish RAG status for your KPIs, ensuring clear project insights and effective decision-making with practical steps and expert tips."}
diff --git a/data/sampled_jsons/RAGGED_paper_decoder-only_models_noise_sensitive_LLaMA_reluctant_use_contexts_distracting_content_year_2024.jsonl b/data/sampled_jsons/RAGGED_paper_decoder-only_models_noise_sensitive_LLaMA_reluctant_use_contexts_distracting_content_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d38a8b39b279dbfb5f5ee5fcafdb937e591e2b2
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_paper_decoder-only_models_noise_sensitive_LLaMA_reluctant_use_contexts_distracting_content_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED", "date": "", "ddg_snippet": "by J Hsia · Cited by 23 — For models sensitive to irrelevant information, noise -filtering techniques are essential ... decoder: LLAMA is a decoder - only model that displays peak-then-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KDXj60FpJr", "content": "by J Hsia · Cited by 23 — For models sensitive to irrelevant information, noise -filtering techniques are essential ... decoder: LLAMA is a decoder - only model that displays peak-then-."}
+{"idx": 1, "title": "Embedding-Aligned Language Models", "date": "", "ddg_snippet": "We propose a novel approach for training large language models (LLMs) to adhere to objectives defined within a latent embedding space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.00024v2", "content": "We propose a novel approach for training large language models (LLMs) to adhere to objectives defined within a latent embedding space."}
+{"idx": 2, "title": "Daily Papers", "date": "", "ddg_snippet": "6 days ago — Efficient long- context modeling remains a critical challenge for natural language processing (NLP), as the time complexity of the predominant ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=context-dependent+recall", "content": "6 days ago — Efficient long- context modeling remains a critical challenge for natural language processing (NLP), as the time complexity of the predominant ..."}
+{"idx": 3, "title": "Long-Context Windows in Large Language Models", "date": "", "ddg_snippet": "This report provides a comprehensive overview of long- context LLMs, examining their foundations, state-of-the-art techniques, benchmarks, and applications ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@adnanmasood/long-context-windows-in-large-language-models-applications-in-comprehension-and-code-03bf4027066f", "content": "This report provides a comprehensive overview of long- context LLMs, examining their foundations, state-of-the-art techniques, benchmarks, and applications ..."}
+{"idx": 4, "title": "Paper Digest: NAACL 2025 Papers & Highlights", "date": "", "ddg_snippet": "4 May 2025 — Highlight: This limits our understanding of LLM performance variability in real-world applications. Our study addresses this issue by exploring ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2025/05/naacl-2025-papers-highlights/", "content": "4 May 2025 — Highlight: This limits our understanding of LLM performance variability in real-world applications. Our study addresses this issue by exploring ..."}
+{"idx": 5, "title": "Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "To address these gaps, we introduce RAGGED , a systematic framework for evaluating RAG systems across diverse retrieval settings. RAGGED provides a principled ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46460", "content": "To address these gaps, we introduce RAGGED , a systematic framework for evaluating RAG systems across diverse retrieval settings. RAGGED provides a principled ..."}
+{"idx": 6, "title": "NeurIPS 2024 Datasets Benchmarks 2024", "date": "", "ddg_snippet": "Large language models (LLMs) have recently demonstrated great success in generating and understanding natural language. While they have also shown potential ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/events/datasets-benchmarks-2024", "content": "Large language models (LLMs) have recently demonstrated great success in generating and understanding natural language. While they have also shown potential ..."}
+{"idx": 7, "title": "ICLR 2025 Orals", "date": "", "ddg_snippet": "In this paper , we first establish the convergence rate of BT reward models based on deep neural networks using embeddings, providing a theoretical foundation ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/events/oral", "content": "In this paper , we first establish the convergence rate of BT reward models based on deep neural networks using embeddings, providing a theoretical foundation ..."}
+{"idx": 8, "title": "On the Biology of a Large Language Model", "date": "", "ddg_snippet": "27 Mar 2025 — In this paper , we focus on applying attribution graphs to study a particular language model – Claude 3.5 Haiku, released in October 2024, which ...", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2025/attribution-graphs/biology.html", "content": "27 Mar 2025 — In this paper , we focus on applying attribution graphs to study a particular language model – Claude 3.5 Haiku, released in October 2024, which ..."}
+{"idx": 9, "title": "The 2024 Conference on Empirical Methods in Natural ...", "date": "", "ddg_snippet": "12 Nov 2024 — As an alternative, recent studies are exploring the use of large language models (LLMs) for data annotation.In this study, we present a case ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/events/emnlp-2024/", "content": "12 Nov 2024 — As an alternative, recent studies are exploring the use of large language models (LLMs) for data annotation.In this study, we present a case ..."}
diff --git a/data/sampled_jsons/RAP_Reasoning_via_Planning_token_usage_multiple_paths_sampling_year_2023.jsonl b/data/sampled_jsons/RAP_Reasoning_via_Planning_token_usage_multiple_paths_sampling_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..edbc68965a6177d5a1bea1ac0de69322861f3f02
--- /dev/null
+++ b/data/sampled_jsons/RAP_Reasoning_via_Planning_token_usage_multiple_paths_sampling_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reasoning with Language Model is Planning with World Model GitHub - Ber666/RAP: Reasoning with Language Model is ... Reasoning Aware Self-Consistency: Leveraging Reasoning Paths ... Reasoning via Planning (RAP) the LLM Reasoners - MsTechDiva Reasoning with Language Model is Planning with World Model arXiv:2305.14992v2 [cs.CL] 23 Oct 2023 Reasoning with Language Model is Planning with World Model - O… Reasoning with Language Model is Planning with World Model arXiv : 2305 .14992v2 [cs.CL] 23 Oct 2023 Reasoning with Language Model is Planning with World Model - O… Reasoning with Language Model is Planning with World Model - O… Reasoning with Language Model is Planning with World Model - O… Reasoning Paths Optimization: Learning to Reason and Explore ...", "date": "", "ddg_snippet": "May 24, 2023 · This prevents LLMs from performing deliberate planning akin to human brains, which involves exploring alternative reasoning paths , anticipating future states and rewards, and iteratively refining existing reasoning steps. To overcome the limitations, we propose a new LLM reasoning framework, R–– easoning vi a –– P–– lanning ( RAP ). News! We released LLM Reasoners, a library for complex reasoning with LLMs, and include the code to reproduce some experiments in RAP . Give it a try! Source code for the paper Reasoning with Language Model is Planning with World Model See full list on github.com •Warning: This code only supports LLaMA-1. Check our new library LLM Reasoners for more flexible choices of LLMs. •Our experiments are conducted with LLaMA-33B, which takes at least 4 GPUs of 24GB memory each. The code also supports smaller LLaMA models, but other LLMs (e.g. those from Hugging Face) are not tested. •Acquire the checkpoints of LLaMA from MetaAI following the LLaMA official repo and set up the environment variable: export LLAMA_CKPTS=\"YOUR_PATH_TO_LLAMA_CHECKPOINTS\" •Install all required packages for LLaMA official repo. See full list on github.com •Set up VAL following this guide and make sure you set the environment variable export VAL=\"YOUR_PATH_TO_VAL\" •Run the command: CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run --master_port 1034 --nproc_per_node 4 run_blocksworld.py --task mcts --model_name LLaMA --ckpt_path $LLAMA_CKPTS/30B --verbose True --data data/blocksworld/step_4.json --max_depth 4 --name run_4_May26_max_depth_4_alpha_05_rollouts_10 --rollouts 10 See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1054 run_gsm8k.py --llama-ckpt $LLAMA_CKPTS/30B --speedup-confidence-batch-size 2 •Use python run_gsm8k.py -- --help for details about arguments See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1074 run_prontoqa.py --llama-ckpt $LLAMA_CKPTS/30B •Use python run_prontoqa.py -- --help for details about arguments See full list on github.com 6 days ago · Abstract Self-consistency mitigates hallucinations in Large Language Models (LLMs) by sampling multiple reasoning paths , but it lacks a systematic approach to determine the optimal number of samples or select the most faithful rationale. Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks. Figure 1: An overview of Reasoning via Planning ( RAP ). Compared with previous LLM reasoning methods like Chain-of-Thought [59], we explicitly model the world state from a world model (repurposed from the language model), enabling us to leverage advanced planning algorithms to solve the reasoning problems. imulate long-term outcomes of actions. This prevents LLMs from performing deliber-ate planning akin to human brains, which in-volves exploring alternative reasoning paths , anticipating future states and rewards, and it-erati ely refining existing reasoning steps. To overcome the limitations, we propose a new LLM reasoning f What is reasoning via planning (RAP)? To overcome the limitations, we propose a new LLM reasoning framework , Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. What is rap – easoning VI A – P – Lanning? To overcome the limitations, we propose a new LLM reasoning framework , R–– easoning vi a–– P–– lanning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monto Carlo Tree Search) for strategic exploration in the vast reasoning space. How can rap solve ogical reasoning problems in prontoqa? ogical reasoning problems in PrOntoQA. Also, as the case illustrated in Figure 2, RAP can effectively recognize when a reasoning chain comes to a dead end, and propagate the signal back to earlier reason-ing steps, with the planning algorithm allowing it to expl Can rap redefine the 365 way we approach LLM reasoning? We 364 posit that RAP, with its innovative melding of planning and reasoning, has the potential to redefine the 365 way we approach LLM reasoning - essentially forging a new pathway toward achieving human-level 366 strategic thinking and planning in artificial intelligence. Why is rap aggregation not applied? Note that problems like plan generation or logical inference 229 require a complete reasoning trace as output ; thus, RAP-Aggregation will not be applied. 230 More importantly, there is a concern that some incorrect reasoning steps may appear in the early stage 231 of multiple iterations, thus polluting the aggregation. Is rap a viable framework for plan generation tasks? To explore the viability of the RAP framework for plan generation tasks, we adapt and 254 evaluate RAP on the Blocksworld benchmark . We define a state as the current orientation of the 255 blocks and an action as an instruction that moves blocks. However, the prompt-ing methods generally demand extensive token us-age to explore multiple reasoning paths from LLMs and integrate feedback from the environment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14992", "content": "May 24, 2023 · This prevents LLMs from performing deliberate planning akin to human brains, which involves exploring alternative reasoning paths , anticipating future states and rewards, and iteratively refining existing reasoning steps. To overcome the limitations, we propose a new LLM reasoning framework, R–– easoning vi a –– P–– lanning ( RAP ). News! We released LLM Reasoners, a library for complex reasoning with LLMs, and include the code to reproduce some experiments in RAP . Give it a try! Source code for the paper Reasoning with Language Model is Planning with World Model See full list on github.com •Warning: This code only supports LLaMA-1. Check our new library LLM Reasoners for more flexible choices of LLMs. •Our experiments are conducted with LLaMA-33B, which takes at least 4 GPUs of 24GB memory each. The code also supports smaller LLaMA models, but other LLMs (e.g. those from Hugging Face) are not tested. •Acquire the checkpoints of LLaMA from MetaAI following the LLaMA official repo and set up the environment variable: export LLAMA_CKPTS=\"YOUR_PATH_TO_LLAMA_CHECKPOINTS\" •Install all required packages for LLaMA official repo. See full list on github.com •Set up VAL following this guide and make sure you set the environment variable export VAL=\"YOUR_PATH_TO_VAL\" •Run the command: CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run --master_port 1034 --nproc_per_node 4 run_blocksworld.py --task mcts --model_name LLaMA --ckpt_path $LLAMA_CKPTS/30B --verbose True --data data/blocksworld/step_4.json --max_depth 4 --name run_4_May26_max_depth_4_alpha_05_rollouts_10 --rollouts 10 See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1054 run_gsm8k.py --llama-ckpt $LLAMA_CKPTS/30B --speedup-confidence-batch-size 2 •Use python run_gsm8k.py -- --help for details about arguments See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1074 run_prontoqa.py --llama-ckpt $LLAMA_CKPTS/30B •Use python run_prontoqa.py -- --help for details about arguments See full list on github.com 6 days ago · Abstract Self-consistency mitigates hallucinations in Large Language Models (LLMs) by sampling multiple reasoning paths , but it lacks a systematic approach to determine the optimal number of samples or select the most faithful rationale. Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks. Figure 1: An overview of Reasoning via Planning ( RAP ). Compared with previous LLM reasoning methods like Chain-of-Thought [59], we explicitly model the world state from a world model (repurposed from the language model), enabling us to leverage advanced planning algorithms to solve the reasoning problems. imulate long-term outcomes of actions. This prevents LLMs from performing deliber-ate planning akin to human brains, which in-volves exploring alternative reasoning paths , anticipating future states and rewards, and it-erati ely refining existing reasoning steps. To overcome the limitations, we propose a new LLM reasoning f What is reasoning via planning (RAP)? To overcome the limitations, we propose a new LLM reasoning framework , Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. What is rap – easoning VI A – P – Lanning? To overcome the limitations, we propose a new LLM reasoning framework , R–– easoning vi a–– P–– lanning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monto Carlo Tree Search) for strategic exploration in the vast reasoning space. How can rap solve ogical reasoning problems in prontoqa? ogical reasoning problems in PrOntoQA. Also, as the case illustrated in Figure 2, RAP can effectively recognize when a reasoning chain comes to a dead end, and propagate the signal back to earlier reason-ing steps, with the planning algorithm allowing it to expl Can rap redefine the 365 way we approach LLM reasoning? We 364 posit that RAP, with its innovative melding of planning and reasoning, has the potential to redefine the 365 way we approach LLM reasoning - essentially forging a new pathway toward achieving human-level 366 strategic thinking and planning in artificial intelligence. Why is rap aggregation not applied? Note that problems like plan generation or logical inference 229 require a complete reasoning trace as output ; thus, RAP-Aggregation will not be applied. 230 More importantly, there is a concern that some incorrect reasoning steps may appear in the early stage 231 of multiple iterations, thus polluting the aggregation. Is rap a viable framework for plan generation tasks? To explore the viability of the RAP framework for plan generation tasks, we adapt and 254 evaluate RAP on the Blocksworld benchmark . We define a state as the current orientation of the 255 blocks and an action as an instruction that moves blocks. However, the prompt-ing methods generally demand extensive token us-age to explore multiple reasoning paths from LLMs and integrate feedback from the environment."}
+{"idx": 1, "title": "GitHub - Ber666/RAP: Reasoning with Language Model is ... Reasoning Aware Self-Consistency: Leveraging Reasoning Paths ... Reasoning via Planning (RAP) the LLM Reasoners - MsTechDiva Reasoning with Language Model is Planning with World Model arXiv:2305.14992v2 [cs.CL] 23 Oct 2023 Reasoning with Language Model is Planning with World Model - O… Reasoning with Language Model is Planning with World Model arXiv : 2305 .14992v2 [cs.CL] 23 Oct 2023 Reasoning with Language Model is Planning with World Model - O… Reasoning with Language Model is Planning with World Model - O… Reasoning with Language Model is Planning with World Model - O… Reasoning Paths Optimization: Learning to Reason and Explore ...", "date": "", "ddg_snippet": "News! We released LLM Reasoners, a library for complex reasoning with LLMs, and include the code to reproduce some experiments in RAP . Give it a try! Source code for the paper Reasoning with Language Model is Planning with World Model See full list on github.com •Warning: This code only supports LLaMA-1. Check our new library LLM Reasoners for more flexible choices of LLMs. •Our experiments are conducted with LLaMA-33B, which takes at least 4 GPUs of 24GB memory each. The code also supports smaller LLaMA models, but other LLMs (e.g. those from Hugging Face) are not tested. •Acquire the checkpoints of LLaMA from MetaAI following the LLaMA official repo and set up the environment variable: export LLAMA_CKPTS=\"YOUR_PATH_TO_LLAMA_CHECKPOINTS\" •Install all required packages for LLaMA official repo. See full list on github.com •Set up VAL following this guide and make sure you set the environment variable export VAL=\"YOUR_PATH_TO_VAL\" •Run the command: CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run --master_port 1034 --nproc_per_node 4 run_blocksworld.py --task mcts --model_name LLaMA --ckpt_path $LLAMA_CKPTS/30B --verbose True --data data/blocksworld/step_4.json --max_depth 4 --name run_4_May26_max_depth_4_alpha_05_rollouts_10 --rollouts 10 See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1054 run_gsm8k.py --llama-ckpt $LLAMA_CKPTS/30B --speedup-confidence-batch-size 2 •Use python run_gsm8k.py -- --help for details about arguments See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1074 run_prontoqa.py --llama-ckpt $LLAMA_CKPTS/30B •Use python run_prontoqa.py -- --help for details about arguments See full list on github.com 6 days ago · Abstract Self-consistency mitigates hallucinations in Large Language Models (LLMs) by sampling multiple reasoning paths , but it lacks a systematic approach to determine the optimal number of samples or select the most faithful rationale. Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks. Figure 1: An overview of Reasoning via Planning ( RAP ). Compared with previous LLM reasoning methods like Chain-of-Thought [59], we explicitly model the world state from a world model (repurposed from the language model), enabling us to leverage advanced planning algorithms to solve the reasoning problems. imulate long-term outcomes of actions. This prevents LLMs from performing deliber-ate planning akin to human brains, which in-volves exploring alternative reasoning paths , anticipating future states and rewards, and it-erati ely refining existing reasoning steps. To overcome the limitations, we propose a new LLM reasoning f What is reasoning via planning (RAP)? To overcome the limitations, we propose a new LLM reasoning framework , Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. What is rap – easoning VI A – P – Lanning? To overcome the limitations, we propose a new LLM reasoning framework , R–– easoning vi a–– P–– lanning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monto Carlo Tree Search) for strategic exploration in the vast reasoning space. How can rap solve ogical reasoning problems in prontoqa? ogical reasoning problems in PrOntoQA. Also, as the case illustrated in Figure 2, RAP can effectively recognize when a reasoning chain comes to a dead end, and propagate the signal back to earlier reason-ing steps, with the planning algorithm allowing it to expl Can rap redefine the 365 way we approach LLM reasoning? We 364 posit that RAP, with its innovative melding of planning and reasoning, has the potential to redefine the 365 way we approach LLM reasoning - essentially forging a new pathway toward achieving human-level 366 strategic thinking and planning in artificial intelligence. Why is rap aggregation not applied? Note that problems like plan generation or logical inference 229 require a complete reasoning trace as output ; thus, RAP-Aggregation will not be applied. 230 More importantly, there is a concern that some incorrect reasoning steps may appear in the early stage 231 of multiple iterations, thus polluting the aggregation. Is rap a viable framework for plan generation tasks? To explore the viability of the RAP framework for plan generation tasks, we adapt and 254 evaluate RAP on the Blocksworld benchmark . We define a state as the current orientation of the 255 blocks and an action as an instruction that moves blocks. However, the prompt-ing methods generally demand extensive token us-age to explore multiple reasoning paths from LLMs and integrate feedback from the environment.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Ber666/RAP", "content": "News! We released LLM Reasoners, a library for complex reasoning with LLMs, and include the code to reproduce some experiments in RAP . Give it a try! Source code for the paper Reasoning with Language Model is Planning with World Model See full list on github.com •Warning: This code only supports LLaMA-1. Check our new library LLM Reasoners for more flexible choices of LLMs. •Our experiments are conducted with LLaMA-33B, which takes at least 4 GPUs of 24GB memory each. The code also supports smaller LLaMA models, but other LLMs (e.g. those from Hugging Face) are not tested. •Acquire the checkpoints of LLaMA from MetaAI following the LLaMA official repo and set up the environment variable: export LLAMA_CKPTS=\"YOUR_PATH_TO_LLAMA_CHECKPOINTS\" •Install all required packages for LLaMA official repo. See full list on github.com •Set up VAL following this guide and make sure you set the environment variable export VAL=\"YOUR_PATH_TO_VAL\" •Run the command: CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run --master_port 1034 --nproc_per_node 4 run_blocksworld.py --task mcts --model_name LLaMA --ckpt_path $LLAMA_CKPTS/30B --verbose True --data data/blocksworld/step_4.json --max_depth 4 --name run_4_May26_max_depth_4_alpha_05_rollouts_10 --rollouts 10 See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1054 run_gsm8k.py --llama-ckpt $LLAMA_CKPTS/30B --speedup-confidence-batch-size 2 •Use python run_gsm8k.py -- --help for details about arguments See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1074 run_prontoqa.py --llama-ckpt $LLAMA_CKPTS/30B •Use python run_prontoqa.py -- --help for details about arguments See full list on github.com 6 days ago · Abstract Self-consistency mitigates hallucinations in Large Language Models (LLMs) by sampling multiple reasoning paths , but it lacks a systematic approach to determine the optimal number of samples or select the most faithful rationale. Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks. Figure 1: An overview of Reasoning via Planning ( RAP ). Compared with previous LLM reasoning methods like Chain-of-Thought [59], we explicitly model the world state from a world model (repurposed from the language model), enabling us to leverage advanced planning algorithms to solve the reasoning problems. imulate long-term outcomes of actions. This prevents LLMs from performing deliber-ate planning akin to human brains, which in-volves exploring alternative reasoning paths , anticipating future states and rewards, and it-erati ely refining existing reasoning steps. To overcome the limitations, we propose a new LLM reasoning f What is reasoning via planning (RAP)? To overcome the limitations, we propose a new LLM reasoning framework , Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. What is rap – easoning VI A – P – Lanning? To overcome the limitations, we propose a new LLM reasoning framework , R–– easoning vi a–– P–– lanning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monto Carlo Tree Search) for strategic exploration in the vast reasoning space. How can rap solve ogical reasoning problems in prontoqa? ogical reasoning problems in PrOntoQA. Also, as the case illustrated in Figure 2, RAP can effectively recognize when a reasoning chain comes to a dead end, and propagate the signal back to earlier reason-ing steps, with the planning algorithm allowing it to expl Can rap redefine the 365 way we approach LLM reasoning? We 364 posit that RAP, with its innovative melding of planning and reasoning, has the potential to redefine the 365 way we approach LLM reasoning - essentially forging a new pathway toward achieving human-level 366 strategic thinking and planning in artificial intelligence. Why is rap aggregation not applied? Note that problems like plan generation or logical inference 229 require a complete reasoning trace as output ; thus, RAP-Aggregation will not be applied. 230 More importantly, there is a concern that some incorrect reasoning steps may appear in the early stage 231 of multiple iterations, thus polluting the aggregation. Is rap a viable framework for plan generation tasks? To explore the viability of the RAP framework for plan generation tasks, we adapt and 254 evaluate RAP on the Blocksworld benchmark . We define a state as the current orientation of the 255 blocks and an action as an instruction that moves blocks. However, the prompt-ing methods generally demand extensive token us-age to explore multiple reasoning paths from LLMs and integrate feedback from the environment."}
+{"idx": 2, "title": "Reasoning Aware Self-Consistency: Leveraging Reasoning Paths ...", "date": "", "ddg_snippet": "6 days ago · Abstract Self-consistency mitigates hallucinations in Large Language Models (LLMs) by sampling multiple reasoning paths , but it lacks a systematic approach to determine the optimal number of samples or select the most faithful rationale.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.184/", "content": "6 days ago · Abstract Self-consistency mitigates hallucinations in Large Language Models (LLMs) by sampling multiple reasoning paths , but it lacks a systematic approach to determine the optimal number of samples or select the most faithful rationale."}
+{"idx": 3, "title": "Reasoning via Planning (RAP) the LLM Reasoners - MsTechDiva", "date": "", "ddg_snippet": "Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks.", "subpage_snippet": "", "source": "mstechdiva.com", "link": "https://mstechdiva.com/reasoning-via-planning-rap-the-llm-reasoners/", "content": "Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks."}
+{"idx": 4, "title": "Reasoning with Language Model is Planning with World Model", "date": "", "ddg_snippet": "Figure 1: An overview of Reasoning via Planning ( RAP ). Compared with previous LLM reasoning methods like Chain-of-Thought [59], we explicitly model the world state from a world model (repurposed from the language model), enabling us to leverage advanced planning algorithms to solve the reasoning problems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zQbff3h0da", "content": "Figure 1: An overview of Reasoning via Planning ( RAP ). Compared with previous LLM reasoning methods like Chain-of-Thought [59], we explicitly model the world state from a world model (repurposed from the language model), enabling us to leverage advanced planning algorithms to solve the reasoning problems."}
+{"idx": 5, "title": "Reasoning Paths Optimization: Learning to Reason and Explore ...", "date": "", "ddg_snippet": "However, the prompt-ing methods generally demand extensive token us-age to explore multiple reasoning paths from LLMs and integrate feedback from the environment.", "subpage_snippet": "", "source": "reasoning-paths.github.io", "link": "https://reasoning-paths.github.io/static/documents/ReasoningPathsEMNLP2024.pdf", "content": "However, the prompt-ing methods generally demand extensive token us-age to explore multiple reasoning paths from LLMs and integrate feedback from the environment."}
+{"idx": 6, "title": "Reasoning with Language Model is Planning with World Model", "date": "", "ddg_snippet": "Language Model Reasoning via Planning ( RAP ).In this paper, we present Reasoning via Planning ( RAP ), a novel LLM reasoning framework that equips LLMs with an ability to reason akin to human-like strategic planning .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.emnlp-main.507.pdf", "content": "Language Model Reasoning via Planning ( RAP ).In this paper, we present Reasoning via Planning ( RAP ), a novel LLM reasoning framework that equips LLMs with an ability to reason akin to human-like strategic planning ."}
+{"idx": 7, "title": "ParaThinker Revolutionizes LLM Reasoning : Native Parallel Thinking...", "date": "", "ddg_snippet": "ParaThinker is trained using supervised fine-tuning (SFT) on reasoning paths sampled from a teacher model. During training, the tokens are randomly assigned to paths , teaching the model to generalize beyond the number of paths seen in training.", "subpage_snippet": "", "source": "www.xugj520.cn", "link": "https://www.xugj520.cn/en/archives/parathinker-parallel-thinking-llm-reasoning.html", "content": "ParaThinker is trained using supervised fine-tuning (SFT) on reasoning paths sampled from a teacher model. During training, the tokens are randomly assigned to paths , teaching the model to generalize beyond the number of paths seen in training."}
+{"idx": 8, "title": "Towards Reasoning Era: A Survey of Long Chain-of-Thought for...", "date": "", "ddg_snippet": "[810] use \" planning tokens \" to enhance reasoning , performing the planning process in hidden space to save computational resources and improve inference performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.09567", "content": "[810] use \" planning tokens \" to enhance reasoning , performing the planning process in hidden space to save computational resources and improve inference performance."}
+{"idx": 9, "title": "Advancing LLM Reasoning : Deep Think with Confidence... | Medium", "date": "", "ddg_snippet": "DeepConf: Efficient LLM Reasoning via Confidence-Based Early Stopping and Adaptive Consensus. noailabs.While these methods improve accuracy by generating multiple reasoning traces, they are inefficient, often producing many tokens for low-quality or incorrect reasoning paths .", "subpage_snippet": "", "source": "noailabs.medium.com", "link": "https://noailabs.medium.com/advancing-llm-reasoning-deep-think-with-confidence-deepconf-66bc7891dd42", "content": "DeepConf: Efficient LLM Reasoning via Confidence-Based Early Stopping and Adaptive Consensus. noailabs.While these methods improve accuracy by generating multiple reasoning traces, they are inefficient, often producing many tokens for low-quality or incorrect reasoning paths ."}
diff --git a/data/sampled_jsons/RAP_tree_search_multiple_rollouts_reasoning_paths_tokens.jsonl b/data/sampled_jsons/RAP_tree_search_multiple_rollouts_reasoning_paths_tokens.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4a97aec25f037268472c02797c1eb33bf6f14d05
--- /dev/null
+++ b/data/sampled_jsons/RAP_tree_search_multiple_rollouts_reasoning_paths_tokens.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Open AI Strawberry — The Role of Decision Trees and RL in... | Medium", "date": "", "ddg_snippet": "CoT reasoning can explore multiple reasoning paths , similar to the branches of a decision tree . Rollouts : Use the model’s policy to simulate complete reasoning paths from the current state to a terminal state.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/autonomous-agents/open-ai-strawberry-the-role-of-decision-trees-and-rl-in-chain-of-thought-reasoning-82bcabcbcbcc", "content": "CoT reasoning can explore multiple reasoning paths , similar to the branches of a decision tree . Rollouts : Use the model’s policy to simulate complete reasoning paths from the current state to a terminal state."}
+{"idx": 1, "title": "[2501.10053] AirRAG: Activating Intrinsic Reasoning for ...", "date": "", "ddg_snippet": "Moreover, we perform multiple rollouts to fully explore the solution space relying on the tree -based search . The number of output sequences (short for n) generated in certain actions can also be adjusted to achieve self-consistency verification and inference computation scaling.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2501.10053", "content": "Moreover, we perform multiple rollouts to fully explore the solution space relying on the tree -based search . The number of output sequences (short for n) generated in certain actions can also be adjusted to achieve self-consistency verification and inference computation scaling."}
+{"idx": 2, "title": "RCTS-RAG: Re-ranking Reasoning Context with Tree Search Makes ...", "date": "", "ddg_snippet": "May 1, 2025 · RCTS-RAG introduces a novel framework that combines Retrieval-Augmented Generation (RAG) with Monte Carlo Tree Search to improve the reasoning capabilities of Large Vision-Language Models. Our approach: 🎯 Re-ranks reasoning contexts using MCTS to find optimal reasoning paths 🧠 Enhances multi-modal understanding by integrating visual and textual information 📊 Achieves state-of-the-art ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yannqi/RCTS-RAG", "content": "May 1, 2025 · RCTS-RAG introduces a novel framework that combines Retrieval-Augmented Generation (RAG) with Monte Carlo Tree Search to improve the reasoning capabilities of Large Vision-Language Models. Our approach: 🎯 Re-ranks reasoning contexts using MCTS to find optimal reasoning paths 🧠 Enhances multi-modal understanding by integrating visual and textual information 📊 Achieves state-of-the-art ..."}
+{"idx": 3, "title": "A TREE-SEARCH CAN GUIDE LARGE L M DECODING AND TRAINING", "date": "", "ddg_snippet": "ABSTRACT Large language models (LLMs) typically employ sampling or beam search , ac-companied by prompts such as Chain-of-Thought (CoT), to boost reasoning and decoding ability. Recent work like Tree -of-Thought (ToT) and Reasoning via Planning ( RAP ) aim to augment the reasoning capabilities of LLMs by utilizing tree - search algorithms to guide multi-step reasoning . These methods mainly focus on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fLO9VaAb3B", "content": "ABSTRACT Large language models (LLMs) typically employ sampling or beam search , ac-companied by prompts such as Chain-of-Thought (CoT), to boost reasoning and decoding ability. Recent work like Tree -of-Thought (ToT) and Reasoning via Planning ( RAP ) aim to augment the reasoning capabilities of LLMs by utilizing tree - search algorithms to guide multi-step reasoning . These methods mainly focus on ..."}
+{"idx": 4, "title": "More Effectively Searching Trees of Thought for Increased ...", "date": "", "ddg_snippet": "We introduce a new framework that extends the Tree of Thoughts approach by applying a separate value function that can more effectively evaluate reasoning paths and incorporating exploration from Monte Carlo Tree Search (MCTS).", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1244/final-projects/KamyarJohnSalahiPranavGurusankarSathyaEdamadaka.pdf", "content": "We introduce a new framework that extends the Tree of Thoughts approach by applying a separate value function that can more effectively evaluate reasoning paths and incorporating exploration from Monte Carlo Tree Search (MCTS)."}
+{"idx": 5, "title": "Reasoning with Trees: Faithful Question Answering over ...", "date": "", "ddg_snippet": "Sep 15, 2025 · RwT reformulates knowledge graph question answering (KGQA) as a discrete decision-making problem, leveraging Monte Carlo Tree Search (MCTS) to iteratively refine reasoning paths . This approach mirrors human-like reasoning by dynamically integrating the LLM’s internal knowledge with external KG information.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.coling-main.211/", "content": "Sep 15, 2025 · RwT reformulates knowledge graph question answering (KGQA) as a discrete decision-making problem, leveraging Monte Carlo Tree Search (MCTS) to iteratively refine reasoning paths . This approach mirrors human-like reasoning by dynamically integrating the LLM’s internal knowledge with external KG information."}
+{"idx": 6, "title": "AirRAG: Activating Intrinsic Reasoning for Retrieval ...", "date": "", "ddg_snippet": "In response to these challenges, we propose AirRAG, a method that leverages intrinsic reasoning capabilities and expands the solution space using Monte Carlo Tree Search (MCTS). We design five fundamental reasoning actions: system analysis, direct answer, retrieval-answer, query transformation, and summary-answer.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.10053v2", "content": "In response to these challenges, we propose AirRAG, a method that leverages intrinsic reasoning capabilities and expands the solution space using Monte Carlo Tree Search (MCTS). We design five fundamental reasoning actions: system analysis, direct answer, retrieval-answer, query transformation, and summary-answer."}
+{"idx": 7, "title": "Monte Carlo Tree Search (MCTS) | DeepWiki", "date": "", "ddg_snippet": "This document explains Monte Carlo Tree Search (MCTS) and its integration with Large Language Models (LLMs) within the Unlock-DeepSeek project. We focus on how MCTS enhances LLM reasoning capabilities.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/datawhalechina/unlock-deepseek/4.1-monte-carlo-tree-search-(mcts)", "content": "This document explains Monte Carlo Tree Search (MCTS) and its integration with Large Language Models (LLMs) within the Unlock-DeepSeek project. We focus on how MCTS enhances LLM reasoning capabilities."}
+{"idx": 8, "title": "TreePO: Bridging the Gap of Policy Optimization and Efficacy and...", "date": "", "ddg_snippet": "In this paper, we introduce Tree -based Policy Optimization (TreePO), a framework that integrates these solutions into a unified RL pipeline. TreePO replaces inefficient independent rollouts with a computationally efficient and algorithmically flexible tree search .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.17445", "content": "In this paper, we introduce Tree -based Policy Optimization (TreePO), a framework that integrates these solutions into a unified RL pipeline. TreePO replaces inefficient independent rollouts with a computationally efficient and algorithmically flexible tree search ."}
+{"idx": 9, "title": "Reasoning with Language Model is", "date": "", "ddg_snippet": "Reasoning via Planning ( RAP )Planning with Monte Carlo Tree Search RAP -Aggregation: Aggregating Multiple Reasoning Outputs", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zQbff3h0da", "content": "Reasoning via Planning ( RAP )Planning with Monte Carlo Tree Search RAP -Aggregation: Aggregating Multiple Reasoning Outputs"}
diff --git a/data/sampled_jsons/RAS_algorithm_pseudocode_budget_B_workers_scores_allocation.jsonl b/data/sampled_jsons/RAS_algorithm_pseudocode_budget_B_workers_scores_allocation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a57b3f7d07de71575da497c86400e865d726c18b
--- /dev/null
+++ b/data/sampled_jsons/RAS_algorithm_pseudocode_budget_B_workers_scores_allocation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Credit Score Predictor for Gig Workers - GitHub", "date": "", "ddg_snippet": "🎯 Credit Score Predictor for Gig Workers A comprehensive machine learning system that predicts credit scores for gig workers and provides personalized suggestions for improvement. This project includes multiple interfaces (CLI, Web App) and uses advanced ML techniques to analyze financial data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CODESOUL23/credit-score-predictor-gig-workers", "content": "🎯 Credit Score Predictor for Gig Workers A comprehensive machine learning system that predicts credit scores for gig workers and provides personalized suggestions for improvement. This project includes multiple interfaces (CLI, Web App) and uses advanced ML techniques to analyze financial data."}
+{"idx": 1, "title": "Predicting Crowd Workers Performance: An Information Quality ...", "date": "", "ddg_snippet": "Jun 16, 2023 · 3.3 Models Supervised Machine Learning. We apply supervised algorithms to predict the difference between workers ’ scores and experts’ scores based on workers ’ trajectories. These algorithms belong to three classes, i.e., algorithms that treat trajectories embeddings as unordered and ordered one- and bi-directionally.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-34444-2_6", "content": "Jun 16, 2023 · 3.3 Models Supervised Machine Learning. We apply supervised algorithms to predict the difference between workers ’ scores and experts’ scores based on workers ’ trajectories. These algorithms belong to three classes, i.e., algorithms that treat trajectories embeddings as unordered and ordered one- and bi-directionally."}
+{"idx": 2, "title": "Algorithmic Game Theory: 15th International Symposium ...", "date": "", "ddg_snippet": "This can already be shown by an instance with budget B and one agent with value v and true cost c > B. ... scores or reviews for the workers . Our setting ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/algorithmic-game-theory-15th-international-symposium-sagt-2022-colchester-uk-september-1215-2022-proceedings-lecture-notes-in-computer-science-13584-1st-ed-2022-3031157133-9783031157134.html", "content": "This can already be shown by an instance with budget B and one agent with value v and true cost c > B. ... scores or reviews for the workers . Our setting ..."}
+{"idx": 3, "title": "Enhancing Generative Auto-bidding with Offline Reward Evaluation and...", "date": "", "ddg_snippet": "Pseudo Algorithm . Additional Experiments.Figure 2: Comparison of the trajectories (cost curves) generated by the planner trained with and without expert scores . “O_score” and “E_score” denote the original and the expert scores , respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15927", "content": "Pseudo Algorithm . Additional Experiments.Figure 2: Comparison of the trajectories (cost curves) generated by the planner trained with and without expert scores . “O_score” and “E_score” denote the original and the expert scores , respectively."}
+{"idx": 4, "title": "openknowledge.worldbank.org/server/api/core/bitstreams/7a757ed...", "date": "", "ddg_snippet": "3.10 Accurate forecasting of domestic revenue is a critical factor in determining budget performance, since budgeted expenditure allocations are based upon that forecast.", "subpage_snippet": "", "source": "openknowledge.worldbank.org", "link": "https://openknowledge.worldbank.org/server/api/core/bitstreams/7a757ed5-7609-504e-bde8-cfca1321086c/content", "content": "3.10 Accurate forecasting of domestic revenue is a critical factor in determining budget performance, since budgeted expenditure allocations are based upon that forecast."}
+{"idx": 5, "title": "Inference Aided Reinforcement Learning for Incentive ...", "date": "", "ddg_snippet": "Specifically, we first design a Gibbs sampling augmented Bayesian inference algorithm to estimate workers ’ labeling strategies from the collected labels at each step. Then we propose a reinforcement incentive learning (RIL) method, building on top of the above estimates, to uncover how workers respond to different payments.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2018/file/f2e43fa3400d826df4195a9ac70dca62-Paper.pdf", "content": "Specifically, we first design a Gibbs sampling augmented Bayesian inference algorithm to estimate workers ’ labeling strategies from the collected labels at each step. Then we propose a reinforcement incentive learning (RIL) method, building on top of the above estimates, to uncover how workers respond to different payments."}
+{"idx": 6, "title": "Towards an Engagement-Aware Attentive Artificial Listener for ...", "date": "", "ddg_snippet": "The pseudocode for gaze behavior generation is described in Algorithm 1. Feedback behavior generation is divided into audio back-channel and head nods. While a wizard provides the feedback-relevance points, the decision whether to produce a back-channel or not at this point is taken by the system.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8280470/", "content": "The pseudocode for gaze behavior generation is described in Algorithm 1. Feedback behavior generation is divided into audio back-channel and head nods. While a wizard provides the feedback-relevance points, the decision whether to produce a back-channel or not at this point is taken by the system."}
+{"idx": 7, "title": "Inference Aided Reinforcement Learning for Incentive ...", "date": "", "ddg_snippet": "Abstract Incentive mechanisms for crowdsourcing are designed to incentivize financially self-interested workers to generate and report high-quality labels. Existing mech-anisms are often developed as one-shot static solutions, assuming a certain level of knowledge about worker models (expertise levels, costs of exerting efforts, etc.). In this paper, we propose a novel inference aided ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1806.00206.pdf", "content": "Abstract Incentive mechanisms for crowdsourcing are designed to incentivize financially self-interested workers to generate and report high-quality labels. Existing mech-anisms are often developed as one-shot static solutions, assuming a certain level of knowledge about worker models (expertise levels, costs of exerting efforts, etc.). In this paper, we propose a novel inference aided ..."}
+{"idx": 8, "title": "Optimizing Job Rotation through Biorhythm Analysis and ...", "date": "", "ddg_snippet": "In this research, we develop an algorithm designed to extract and predict employees' biorhythm cycles and determine their optimal job rotation using a neural network methodology. The selection of a neural network algorithm is based on its ability to optimize the mathematical models of biorhythm cycles derived from newly collected data.", "subpage_snippet": "", "source": "ijms.ut.ac.ir", "link": "https://ijms.ut.ac.ir/article_99935_4a948d1b198d3873491c7ed73898da06.pdf", "content": "In this research, we develop an algorithm designed to extract and predict employees' biorhythm cycles and determine their optimal job rotation using a neural network methodology. The selection of a neural network algorithm is based on its ability to optimize the mathematical models of biorhythm cycles derived from newly collected data."}
+{"idx": 9, "title": "Inference Aided Reinforcement Learning For Incentive ...", "date": "", "ddg_snippet": "sampling augmented Bayesian inference algorithm , which estimates workers ’ labeling strategies and the aggregated label accuracy, as done in most existing inference algorithms , but significantly lowers", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/755521637/1806-00206v1", "content": "sampling augmented Bayesian inference algorithm , which estimates workers ’ labeling strategies and the aggregated label accuracy, as done in most existing inference algorithms , but significantly lowers"}
diff --git a/data/sampled_jsons/RFT_uses_positives_generated_from_SFT_policy_twice_efficient_SFT_original_Dsyn.jsonl b/data/sampled_jsons/RFT_uses_positives_generated_from_SFT_policy_twice_efficient_SFT_original_Dsyn.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..51ca853d9e073ce93890a6931741f23992ffb7e6
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fine-Tuning Methods for LLMs(SFT and RL): Explanations ...", "date": "", "ddg_snippet": "Apr 25, 2025 · However, instead of relying on a frozen SFT model, Online RFT uses the current policy model π_θ to generate responses in real-time. Selection: The sampled responses are evaluated using a set of correctness criteria, such as human-defined rules or domain-specific validation checks.", "subpage_snippet": "", "source": "aiwithmike.substack.com", "link": "https://aiwithmike.substack.com/p/fine-tuning-methods-for-llmssft-and", "content": "Apr 25, 2025 · However, instead of relying on a frozen SFT model, Online RFT uses the current policy model π_θ to generate responses in real-time. Selection: The sampled responses are evaluated using a set of correctness criteria, such as human-defined rules or domain-specific validation checks."}
+{"idx": 1, "title": "UFT: Unifying Supervised and Reinforcement Fine-Tuning Fine-Tuning Techniques - Choosing Between SFT, DPO, and RFT ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... Why Post-Training Matters Now: From SFT to RFT Key Concepts | modelscope/Trinity-RFT | DeepWiki Why Post-Training Matters Now: From SFT to RFT UFT: Unifying Supervised and Reinforcement Fine-Tuning Why Post-Training Matters Now: From SFT to RFT Fine-Tuning Techniques - Choosing Between SFT , DPO, and RFT (Wit… Why Post-Training Matters Now: From SFT to RFT Fine-Tuning Techniques - Choosing Between SFT , DPO, and RFT (Wit… Role of SFT in the RLHF Pipeline - apxml.com", "date": "", "ddg_snippet": "May 22, 2025 · Post-training has demonstrated its importance in enhancing the reasoning capabilities of large language models (LLMs). The primary post-training methods can be categorized into supervised fine-tuning ( SFT ) and reinforcement fine-tuning ( RFT ). SFT is efficient and well-suited for small language models, but it may lead to overfitting and limit the reasoning abilities of larger models. In ... Jun 18, 2025 · Using the SFT fine-tuned model as the starting point, apply DPO using preference comparison data. Performing Supervised Fine-Tuning ( SFT ) before Direct Preference Optimization (DPO) enhances model alignment and overall performance by establishing a robust initial policy , ensuring the model already prefers correct responses. Sep 26, 2024 · Figure 2: Positive data scaling laws: On GSM8K (a) and MATH (b), we evaluate SFT trained on Dsyn and RFT that uses SFT policy generated positives (Draft), as we scale Dsyn , observing D+ to be 2x as effective as Dsyn . Mar 4, 2025 · This dynamic landscape underscores the importance of having a robust platform to excel in post-training, positioning RFT as a particularly promising approach for specific use cases. RFT is emerging as a powerful paradigm shift in language model optimization, offering a compelling alternative to traditional SFT . May 12, 2025 · In Trinity- RFT , configured with small sync_interval values (e.g., 1-2) Off- Policy Learning Can use data collected from any policy , including older versions of the policy Data can be stored and reused over multiple training iterations Examples: OPMD (Online Policy Mirror Descent) Allows for more efficient data use but may suffer from ... Is RFT a viable alternative to traditional SFT? This dynamic landscape underscores the importance of having a robust platform to excel in post-training, positioning RFT as a particularly promising approach for specific use cases. RFT is emerging as a powerful paradigm shift in language model optimization, offering a compelling alternative to traditional SFT . Is RFT better than SFT? SFT is efficient and well-suited for small language models, but it may lead to overfitting and limit the reasoning abilities of larger models. In contrast, RFT generally yields better generalization but depends heavily on the strength of the base model. What is RFT in language model optimization? RFT is emerging as a powerful paradigm shift in language model optimization, offering a compelling alternative to traditional SFT. While SFT is an offline process reliant on static labeled datasets, RFT employs reinforcement learning in an online manner. What is SFT fine-tuning vs direct preference optimization (DPO)? Using the SFT fine-tuned model as the starting point, apply DPO using preference comparison data. Performing Supervised Fine-Tuning ( SFT ) before Direct Preference Optimization (DPO) enhances model alignment and overall performance by establishing a robust initial policy , ensuring the model already prefers correct responses. What is the synergy between SFT and reinforcement learning? Synergy of Supervised Fine‑Tuning (SFT) and Reinforcement Learning: A hybrid approach combining a strong SFT foundation with subsequent RL refinement effectively leverages the strengths of both methods. This synergy results in models that are both accurate and adaptable . What is reinforcement fine-tuning (RFT)? Reinforcement fine-tuning (RFT): this technique uses reinforcement learning with a reward signal (via a grader or reward model) to fine-tune the model for complex objectives. In RFT, the model generates outputs for given prompts during training, and each output is evaluated for quality. The primary technical role of SFT is to produce an initial policy , typically denoted as π S F T πSFT, which serves as the starting point for the subsequent Reinforcement Learning (RL) phase. The RL algorithm, usually Proximal Policy Optimization (PPO) in this context, doesn't start optimizing from the raw pre-trained model's parameters.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.16984", "content": "May 22, 2025 · Post-training has demonstrated its importance in enhancing the reasoning capabilities of large language models (LLMs). The primary post-training methods can be categorized into supervised fine-tuning ( SFT ) and reinforcement fine-tuning ( RFT ). SFT is efficient and well-suited for small language models, but it may lead to overfitting and limit the reasoning abilities of larger models. In ... Jun 18, 2025 · Using the SFT fine-tuned model as the starting point, apply DPO using preference comparison data. Performing Supervised Fine-Tuning ( SFT ) before Direct Preference Optimization (DPO) enhances model alignment and overall performance by establishing a robust initial policy , ensuring the model already prefers correct responses. Sep 26, 2024 · Figure 2: Positive data scaling laws: On GSM8K (a) and MATH (b), we evaluate SFT trained on Dsyn and RFT that uses SFT policy generated positives (Draft), as we scale Dsyn , observing D+ to be 2x as effective as Dsyn . Mar 4, 2025 · This dynamic landscape underscores the importance of having a robust platform to excel in post-training, positioning RFT as a particularly promising approach for specific use cases. RFT is emerging as a powerful paradigm shift in language model optimization, offering a compelling alternative to traditional SFT . May 12, 2025 · In Trinity- RFT , configured with small sync_interval values (e.g., 1-2) Off- Policy Learning Can use data collected from any policy , including older versions of the policy Data can be stored and reused over multiple training iterations Examples: OPMD (Online Policy Mirror Descent) Allows for more efficient data use but may suffer from ... Is RFT a viable alternative to traditional SFT? This dynamic landscape underscores the importance of having a robust platform to excel in post-training, positioning RFT as a particularly promising approach for specific use cases. RFT is emerging as a powerful paradigm shift in language model optimization, offering a compelling alternative to traditional SFT . Is RFT better than SFT? SFT is efficient and well-suited for small language models, but it may lead to overfitting and limit the reasoning abilities of larger models. In contrast, RFT generally yields better generalization but depends heavily on the strength of the base model. What is RFT in language model optimization? RFT is emerging as a powerful paradigm shift in language model optimization, offering a compelling alternative to traditional SFT. While SFT is an offline process reliant on static labeled datasets, RFT employs reinforcement learning in an online manner. What is SFT fine-tuning vs direct preference optimization (DPO)? Using the SFT fine-tuned model as the starting point, apply DPO using preference comparison data. Performing Supervised Fine-Tuning ( SFT ) before Direct Preference Optimization (DPO) enhances model alignment and overall performance by establishing a robust initial policy , ensuring the model already prefers correct responses. What is the synergy between SFT and reinforcement learning? Synergy of Supervised Fine‑Tuning (SFT) and Reinforcement Learning: A hybrid approach combining a strong SFT foundation with subsequent RL refinement effectively leverages the strengths of both methods. This synergy results in models that are both accurate and adaptable . What is reinforcement fine-tuning (RFT)? Reinforcement fine-tuning (RFT): this technique uses reinforcement learning with a reward signal (via a grader or reward model) to fine-tune the model for complex objectives. In RFT, the model generates outputs for given prompts during training, and each output is evaluated for quality. The primary technical role of SFT is to produce an initial policy , typically denoted as π S F T πSFT, which serves as the starting point for the subsequent Reinforcement Learning (RL) phase. The RL algorithm, usually Proximal Policy Optimization (PPO) in this context, doesn't start optimizing from the raw pre-trained model's parameters."}
+{"idx": 2, "title": "Fine-Tuning Techniques - Choosing Between SFT, DPO, and RFT ...", "date": "", "ddg_snippet": "Jun 18, 2025 · Using the SFT fine-tuned model as the starting point, apply DPO using preference comparison data. Performing Supervised Fine-Tuning ( SFT ) before Direct Preference Optimization (DPO) enhances model alignment and overall performance by establishing a robust initial policy , ensuring the model already prefers correct responses.", "subpage_snippet": "", "source": "cookbook.openai.com", "link": "https://cookbook.openai.com/examples/fine_tuning_direct_preference_optimization_guide", "content": "Jun 18, 2025 · Using the SFT fine-tuned model as the starting point, apply DPO using preference comparison data. Performing Supervised Fine-Tuning ( SFT ) before Direct Preference Optimization (DPO) enhances model alignment and overall performance by establishing a robust initial policy , ensuring the model already prefers correct responses."}
+{"idx": 3, "title": "Why Post-Training Matters Now: From SFT to RFT", "date": "", "ddg_snippet": "Mar 4, 2025 · This dynamic landscape underscores the importance of having a robust platform to excel in post-training, positioning RFT as a particularly promising approach for specific use cases. RFT is emerging as a powerful paradigm shift in language model optimization, offering a compelling alternative to traditional SFT .", "subpage_snippet": "", "source": "gradientflow.com", "link": "https://gradientflow.com/post-training-rft-sft-rlhf/", "content": "Mar 4, 2025 · This dynamic landscape underscores the importance of having a robust platform to excel in post-training, positioning RFT as a particularly promising approach for specific use cases. RFT is emerging as a powerful paradigm shift in language model optimization, offering a compelling alternative to traditional SFT ."}
+{"idx": 4, "title": "Key Concepts | modelscope/Trinity-RFT | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · In Trinity- RFT , configured with small sync_interval values (e.g., 1-2) Off- Policy Learning Can use data collected from any policy , including older versions of the policy Data can be stored and reused over multiple training iterations Examples: OPMD (Online Policy Mirror Descent) Allows for more efficient data use but may suffer from ...", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/modelscope/Trinity-RFT/1.2-key-concepts", "content": "May 12, 2025 · In Trinity- RFT , configured with small sync_interval values (e.g., 1-2) Off- Policy Learning Can use data collected from any policy , including older versions of the policy Data can be stored and reused over multiple training iterations Examples: OPMD (Online Policy Mirror Descent) Allows for more efficient data use but may suffer from ..."}
+{"idx": 5, "title": "Role of SFT in the RLHF Pipeline - apxml.com", "date": "", "ddg_snippet": "The primary technical role of SFT is to produce an initial policy , typically denoted as π S F T πSFT, which serves as the starting point for the subsequent Reinforcement Learning (RL) phase. The RL algorithm, usually Proximal Policy Optimization (PPO) in this context, doesn't start optimizing from the raw pre-trained model's parameters.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/rlhf-reinforcement-learning-human-feedback/chapter-2-sft-phase-rlhf/sft-role-in-rlhf", "content": "The primary technical role of SFT is to produce an initial policy , typically denoted as π S F T πSFT, which serves as the starting point for the subsequent Reinforcement Learning (RL) phase. The RL algorithm, usually Proximal Policy Optimization (PPO) in this context, doesn't start optimizing from the raw pre-trained model's parameters."}
+{"idx": 6, "title": "GitHub - ahmecse/Reinforcement-Fine-Tuning-LLMs-with-GRPO: RFT ...", "date": "", "ddg_snippet": "Methodology: SFT followed by RFT with GRPO for Wordle. The fine-tuning process implemented in this project follows a two-step approach, combining the strengths of SFT and RFT : Supervised Fine-Tuning ( SFT ): The initial step involves performing SFT on the base LLM (Qwen 2.5 7B Instruct).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ahmecse/Reinforcement-Fine-Tuning-LLMs-with-GRPO", "content": "Methodology: SFT followed by RFT with GRPO for Wordle. The fine-tuning process implemented in this project follows a two-step approach, combining the strengths of SFT and RFT : Supervised Fine-Tuning ( SFT ): The initial step involves performing SFT on the base LLM (Qwen 2.5 7B Instruct)."}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math...", "date": "", "ddg_snippet": "This SFT model is then used to generate both positive (correct) and negative (incorrect) responses to the same questions. This figure shows the negative log-likelihood distributions of SFT data (D+ sft ) and RFT data ( Dsyn ) under the base language mode...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "This SFT model is then used to generate both positive (correct) and negative (incorrect) responses to the same questions. This figure shows the negative log-likelihood distributions of SFT data (D+ sft ) and RFT data ( Dsyn ) under the base language mode..."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "SFT / RFT policy suffers from spurious correlations in positive synthetic data.This failure can be attributed to the presence of incorrect/irrel-evant steps that are not detected by our verifier, since it only. 0.8. SFT on Dsyn original .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "SFT / RFT policy suffers from spurious correlations in positive synthetic data.This failure can be attributed to the presence of incorrect/irrel-evant steps that are not detected by our verifier, since it only. 0.8. SFT on Dsyn original ."}
+{"idx": 9, "title": "Refining Intelligence: A Comparative Study of Reinforcement...", "date": "", "ddg_snippet": "Generalization: SFT : While SFT provides strong task-specific performance, it often struggles with generalizing to unseen tasks or domains. For instance, an SFT model trained on legal contracts may underperform when analyzing regulations from a different jurisdiction.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/refining-intelligence-comparative-study-reinforcement-ramachandran-19jve", "content": "Generalization: SFT : While SFT provides strong task-specific performance, it often struggles with generalizing to unseen tasks or domains. For instance, an SFT model trained on legal contracts may underperform when analyzing regulations from a different jurisdiction."}
diff --git a/data/sampled_jsons/RL_Incorrect_Synthetic_Data_Math_Reasoning_LLaMA_Mistral_base_models_experiments.jsonl b/data/sampled_jsons/RL_Incorrect_Synthetic_Data_Math_Reasoning_LLaMA_Mistral_base_models_experiments.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6581fb5962596a2a975e71d4dcbfcdf354fc58c
--- /dev/null
+++ b/data/sampled_jsons/RL_Incorrect_Synthetic_Data_Math_Reasoning_LLaMA_Mistral_base_models_experiments.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2406.14532] RL on Incorrect Synthetic Data Scales the Efficiency of...", "date": "", "ddg_snippet": "Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 1, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM...", "date": "", "ddg_snippet": "Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Sequence models are the machine learning models that input or output sequences of data . Sequential data includes text streams, audio clips, video clips, time-series data and etc.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/marvastisalman_rl-on-incorrect-synthetic-data-scales-the-activity-7213413858894323712-jCCf", "content": "Sequence models are the machine learning models that input or output sequences of data . Sequential data includes text streams, audio clips, video clips, time-series data and etc."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "abstract: Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study...", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "abstract: Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study..."}
+{"idx": 5, "title": "8× Math Gains, Cross-Modal Generation & Safety-First AI", "date": "", "ddg_snippet": "Explore the breakthroughs in AI safety, reasoning , and multimodality.Unlike pipeline- based approaches, NExT-GPT is unified—achieving state-of-the-art performance on tasks like image captioning, video QA, audio synthesis , and cross-modal dialogue.", "subpage_snippet": "", "source": "www.turing.com", "link": "https://www.turing.com/blog/agi-advance-newsletter-07", "content": "Explore the breakthroughs in AI safety, reasoning , and multimodality.Unlike pipeline- based approaches, NExT-GPT is unified—achieving state-of-the-art performance on tasks like image captioning, video QA, audio synthesis , and cross-modal dialogue."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of reinforcement learning ( RL ) on incorrect synthetic data to improve the math reasoning abilities of large language models (LLMs).", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/rl-incorrect-synthetic-data-scales-efficiency-llm", "content": "This paper investigates the use of reinforcement learning ( RL ) on incorrect synthetic data to improve the math reasoning abilities of large language models (LLMs)."}
+{"idx": 7, "title": "Train Your LLM With Synthetic Data — Or No Data at All | Medium", "date": "", "ddg_snippet": "I’ve created a version of the synthetic - data -kit that works seamlessly with your local Ollama instance. This means you can now use powerful models like llama 3, mistral , or gemma to generate your own datasets without needing a massive GPU or a US address.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science-collective/train-your-llm-with-synthetic-data-or-no-data-at-all-6939a377d101", "content": "I’ve created a version of the synthetic - data -kit that works seamlessly with your local Ollama instance. This means you can now use powerful models like llama 3, mistral , or gemma to generate your own datasets without needing a massive GPU or a US address."}
+{"idx": 8, "title": "GitHub - strategist922/awesome-long-chain-of-thought- reasoning", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold, Setlur et al., A Small Step Towards Reproducing OpenAI o1: Progress Report on the Steiner Open Source Models , Ji et al.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/strategist922/awesome-long-chain-of-thought-reasoning", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold, Setlur et al., A Small Step Towards Reproducing OpenAI o1: Progress Report on the Steiner Open Source Models , Ji et al."}
+{"idx": 9, "title": "MARGE: Improving Math Reasoning for LLMs with Guided... | alphaXiv", "date": "", "ddg_snippet": "Experimental Results. MARGE was evaluated on multiple mathematical reasoning benchmarks, including MATH , GSM8k, CollegeMath, and OlympiadBench, across a variety of base models (Qwen, Llama , MetaMath- Mistral ). Performance comparison across various model architectures.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2505.12500v1", "content": "Experimental Results. MARGE was evaluated on multiple mathematical reasoning benchmarks, including MATH , GSM8k, CollegeMath, and OlympiadBench, across a variety of base models (Qwen, Llama , MetaMath- Mistral ). Performance comparison across various model architectures."}
diff --git a/data/sampled_jsons/RL_Incorrect_Synthetic_Data_Section_5_experiments_LLaMA-2_Mistral_fine-tuning.jsonl b/data/sampled_jsons/RL_Incorrect_Synthetic_Data_Section_5_experiments_LLaMA-2_Mistral_fine-tuning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8b9f4130d936e6a06f0ae1e4b26d7fc3d631c9ac
--- /dev/null
+++ b/data/sampled_jsons/RL_Incorrect_Synthetic_Data_Section_5_experiments_LLaMA-2_Mistral_fine-tuning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Balancing Cost and Effectiveness of Synthetic Data Generation...", "date": "", "ddg_snippet": "Synthetic Data for Fine - Tuning . Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint arXiv:2406.14532, 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.19759v2", "content": "Synthetic Data for Fine - Tuning . Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint arXiv:2406.14532, 2024."}
+{"idx": 1, "title": "Anomaly Detection of Tabular Data Using LLMs", "date": "", "ddg_snippet": "The experiments involve fine - tuning LLMs like Llama 2 and Mistral on synthetic datasets to enhance anomaly detection performance .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-Anomaly-Detection-of-clxuw77eq9xx501afsxnbfhxk", "content": "The experiments involve fine - tuning LLMs like Llama 2 and Mistral on synthetic datasets to enhance anomaly detection performance ."}
+{"idx": 2, "title": "A hands-on guide to finetuning Llama 2 and Mistral models... | Medium", "date": "", "ddg_snippet": "Fine - Tuning Differences for Llama 2 and Mistral . While the setup is largely similar, there are subtle differences I’ve noticed: Llama 2 : Requires more VRAM due to its larger embeddings. You might need to tweak batch sizes or gradient accumulation steps.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@amit25173/a-hands-on-guide-to-finetuning-llama-2-and-mistral-models-using-qlora-7fea52f97535", "content": "Fine - Tuning Differences for Llama 2 and Mistral . While the setup is largely similar, there are subtle differences I’ve noticed: Llama 2 : Requires more VRAM due to its larger embeddings. You might need to tweak batch sizes or gradient accumulation steps."}
+{"idx": 3, "title": "Fine - Tuning on an M1 Mac With Mistral , Ollama, and Together.ai", "date": "", "ddg_snippet": "Now that we’ve decided on Mistral -7B-Instruct- v 0. 2 , how do we fine - tune it? There’s a specific format that Mistral expects which is different from the OpenAI JSONL format. This is likely where you’ll need to start– by converting your existing data into the Mistral format which we’ll go over next.", "subpage_snippet": "", "source": "www.zaaane.com", "link": "https://www.zaaane.com/posts/fine-tuning-on-an-m1-mac-with-mistral-and-togetherai/", "content": "Now that we’ve decided on Mistral -7B-Instruct- v 0. 2 , how do we fine - tune it? There’s a specific format that Mistral expects which is different from the OpenAI JSONL format. This is likely where you’ll need to start– by converting your existing data into the Mistral format which we’ll go over next."}
+{"idx": 4, "title": "Over with Llama - 2 , Mistral 7B is Taking Over: Get Started With...", "date": "", "ddg_snippet": "Getting started with Mistral 7B and Langchain integration: A step-by-step guide.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/bye-bye-llama-2-mistral-7b-is-taking-over-get-started-with-mistral-7b-instruct-1504ff5f373c", "content": "Getting started with Mistral 7B and Langchain integration: A step-by-step guide."}
+{"idx": 5, "title": "Mistral 7B | Mistral AI", "date": "", "ddg_snippet": "Fine - tuning Mistral 7B for chat. To show the generalization capabilities of Mistral 7B, we fine - tuned it on instruction datasets publicly available on HuggingFace. No tricks, no proprietary data . The resulting model, Mistral 7B Instruct, outperforms all 7B models on MT-Bench, and is comparable...", "subpage_snippet": "", "source": "mistral.ai", "link": "https://mistral.ai/news/announcing-mistral-7b", "content": "Fine - tuning Mistral 7B for chat. To show the generalization capabilities of Mistral 7B, we fine - tuned it on instruction datasets publicly available on HuggingFace. No tricks, no proprietary data . The resulting model, Mistral 7B Instruct, outperforms all 7B models on MT-Bench, and is comparable..."}
+{"idx": 6, "title": "Balancing Cost and Effectiveness of Synthetic Data", "date": "", "ddg_snippet": "Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint arXiv:2406.14532, 2024. Fine - tuning was carried out over 3 epochs with a peak learning rate of 4e- 5 , except for the Mistral 7B model, which used a learning rate of 1e- 5 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=hRjFiTxv1v&name=pdf", "content": "Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint arXiv:2406.14532, 2024. Fine - tuning was carried out over 3 epochs with a peak learning rate of 4e- 5 , except for the Mistral 7B model, which used a learning rate of 1e- 5 ."}
+{"idx": 7, "title": "GitHub - unslothai/unsloth: Fine - tuning & Reinforcement Learning for...", "date": "", "ddg_snippet": "Fine - tuning & Reinforcement Learning for LLMs. Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2 x faster with 70% less VRAM.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/unslothai/unsloth", "content": "Fine - tuning & Reinforcement Learning for LLMs. Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2 x faster with 70% less VRAM."}
+{"idx": 8, "title": "Fine - tuning Meta’s Llama 2 to power Jio Copilot — Part 1 | by Vignesh...", "date": "", "ddg_snippet": "Our experiments fine - tuning Meta’s Open Source Large Language Model — Llama 2 to power Jio Copilot.We fine - tuned Llama 2 7B & 13B models using PEFT and QLORA-based parameter and memory efficient techniques, respectively, to limit performance decay.", "subpage_snippet": "", "source": "blog.gofynd.com", "link": "https://blog.gofynd.com/fine-tuning-metas-llama-2-to-power-jio-copilot-part-1-afa527744d36", "content": "Our experiments fine - tuning Meta’s Open Source Large Language Model — Llama 2 to power Jio Copilot.We fine - tuned Llama 2 7B & 13B models using PEFT and QLORA-based parameter and memory efficient techniques, respectively, to limit performance decay."}
+{"idx": 9, "title": "Help Me Uploading Fine Tuned Models For Inference Api | Forum", "date": "", "ddg_snippet": "Error loading finetuned llama 2 model while running inferenceLlama/ Mistral Finetuning for Inference API", "subpage_snippet": "", "source": "discuss.huggingface.co", "link": "https://discuss.huggingface.co/t/help-me-uploading-fine-tuned-models-for-inference-api/89683", "content": "Error loading finetuned llama 2 model while running inferenceLlama/ Mistral Finetuning for Inference API"}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Figure_5(c)_Q-values_critical_steps.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Figure_5(c)_Q-values_critical_steps.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b615986e5a7dd30dc8c5ce96c31e9ede91392e28
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Figure_5(c)_Q-values_critical_steps.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "( c ) appropriate ways of constructing learner-specific negative data with emphasis on critical steps , results in a performance boost equivalent up positive data 8×; (d) training with negative data provides a mechanism to unlearn spurious correlations; and (e) we present a conceptual model inspired from RL to explain our observations synthetic ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4b77d5b896c321a29277524a98a50215-Paper-Conference.pdf", "content": "( c ) appropriate ways of constructing learner-specific negative data with emphasis on critical steps , results in a performance boost equivalent up positive data 8×; (d) training with negative data provides a mechanism to unlearn spurious correlations; and (e) we present a conceptual model inspired from RL to explain our observations synthetic ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data · MinWoo Park Reinforcement Learning for LLM Reasoning - cs224r.stanford.edu [QA] RL on Incorrect Synthetic Data Scales the Efficiency of ... Learning to Reason by Failing: Offline RL on Sub-optimal ... RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoning… Reinforcement Learning for LLM Reasoning [2406.14532] RL on Incorrect Synthetic Data Scales the Efficiency of LL… RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RLonIncorrectSyntheticDataScalesthe ...", "date": "", "ddg_snippet": "Jun 20, 2024 · Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ... Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning [QA] RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold Arxiv Papers 9.22K subscribers Subscribed Jun 13, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner doubles the sample efficiency of synthetic data . What's the difference between RL and RFT? QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"! Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. What is synthetic data in math reasoning? lities via a study on math reasoning, a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated respon es for a novel set of initial problems synthesized by prompting capable models [29, 31]. T Does SFT reduce negative data to advantage-weighted RL? multiple responses under the SFT policy obtained by training on only the positive data. This reduction negative data to advantage-weighted RL enables us to conceptually compare it to training data , which corresponds to imitation learning (i.e., behavioral cloning) on positive data. are able to argue for the gener Can synthetic data be used to solve complex reasoning problems? are still many open questions that need to be answered to fully understand its utility. While synthetic data from LLMs like Gemini and GPT-4 holds great potential, for more complex reasoning problems (more complicated than the datasets eval RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Jun 20, 2024 · Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ... Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning [QA] RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold Arxiv Papers 9.22K subscribers Subscribed Jun 13, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner doubles the sample efficiency of synthetic data . What's the difference between RL and RFT? QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"! Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. What is synthetic data in math reasoning? lities via a study on math reasoning, a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated respon es for a novel set of initial problems synthesized by prompting capable models [29, 31]. T Does SFT reduce negative data to advantage-weighted RL? multiple responses under the SFT policy obtained by training on only the positive data. This reduction negative data to advantage-weighted RL enables us to conceptually compare it to training data , which corresponds to imitation learning (i.e., behavioral cloning) on positive data. are able to argue for the gener Can synthetic data be used to solve complex reasoning problems? are still many open questions that need to be answered to fully understand its utility. While synthetic data from LLMs like Gemini and GPT-4 holds great potential, for more complex reasoning problems (more complicated than the datasets eval RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ...", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ..."}
+{"idx": 3, "title": "[QA] RL on Incorrect Synthetic Data Scales the Efficiency of ... Learning to Reason by Failing: Offline RL on Sub-optimal ... RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoning… Reinforcement Learning for LLM Reasoning [2406.14532] RL on Incorrect Synthetic Data Scales the Efficiency of LL… RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RLonIncorrectSyntheticDataScalesthe ...", "date": "", "ddg_snippet": "[QA] RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold Arxiv Papers 9.22K subscribers Subscribed Jun 13, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner doubles the sample efficiency of synthetic data . What's the difference between RL and RFT? QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"! Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. What is synthetic data in math reasoning? lities via a study on math reasoning, a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated respon es for a novel set of initial problems synthesized by prompting capable models [29, 31]. T Does SFT reduce negative data to advantage-weighted RL? multiple responses under the SFT policy obtained by training on only the positive data. This reduction negative data to advantage-weighted RL enables us to conceptually compare it to training data , which corresponds to imitation learning (i.e., behavioral cloning) on positive data. are able to argue for the gener Can synthetic data be used to solve complex reasoning problems? are still many open questions that need to be answered to fully understand its utility. While synthetic data from LLMs like Gemini and GPT-4 holds great potential, for more complex reasoning problems (more complicated than the datasets eval RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=7q-Vueg_vms", "content": "[QA] RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold Arxiv Papers 9.22K subscribers Subscribed Jun 13, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner doubles the sample efficiency of synthetic data . What's the difference between RL and RFT? QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"! Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. What is synthetic data in math reasoning? lities via a study on math reasoning, a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated respon es for a novel set of initial problems synthesized by prompting capable models [29, 31]. T Does SFT reduce negative data to advantage-weighted RL? multiple responses under the SFT policy obtained by training on only the positive data. This reduction negative data to advantage-weighted RL enables us to conceptually compare it to training data , which corresponds to imitation learning (i.e., behavioral cloning) on positive data. are able to argue for the gener Can synthetic data be used to solve complex reasoning problems? are still many open questions that need to be answered to fully understand its utility. While synthetic data from LLMs like Gemini and GPT-4 holds great potential, for more complex reasoning problems (more complicated than the datasets eval RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
+{"idx": 4, "title": "RLonIncorrectSyntheticDataScalesthe ...", "date": "", "ddg_snippet": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
+{"idx": 5, "title": "Reinforcement Learning for LLM Reasoning - cs224r.stanford.edu", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning"}
+{"idx": 6, "title": "Learning to Reason by Failing: Offline RL on Sub-optimal ...", "date": "", "ddg_snippet": "Jun 13, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner doubles the sample efficiency of synthetic data .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=v2PV1yCFJk", "content": "Jun 13, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner doubles the sample efficiency of synthetic data ."}
+{"idx": 7, "title": "SQL-R1: Training Natural Language to SQL Reasoning Model By", "date": "", "ddg_snippet": "Q3 : Can we deploy sustainable data engineering for training robust and efficient NL2SQL reasoning models? RL training relies on high-quality ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.08600v3", "content": "Q3 : Can we deploy sustainable data engineering for training robust and efficient NL2SQL reasoning models? RL training relies on high-quality ..."}
+{"idx": 8, "title": "Trust but Verify! A Survey on Verification Design for Test-time", "date": "", "ddg_snippet": "... candidates P Discriminative (3) Naive ORM (Cobbe et al., 2021 ) Trains solution-level and token-level verifiers on labeled-dataset O (4) OVM (Yu ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.16665v3", "content": "... candidates P Discriminative (3) Naive ORM (Cobbe et al., 2021 ) Trains solution-level and token-level verifiers on labeled-dataset O (4) OVM (Yu ..."}
+{"idx": 9, "title": "Rethinking Reasoning Quality in Large Language Models through", "date": "", "ddg_snippet": "... Qwen-2. 5 -7B-Instruct-1M model trained on LogicTree with 400 steps raises overall accuracy from 13% to 60%, still solves 31% of the hardest depth-8 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06024v1", "content": "... Qwen-2. 5 -7B-Instruct-1M model trained on LogicTree with 400 steps raises overall accuracy from 13% to 60%, still solves 31% of the hardest depth-8 ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_Figure_4_spurious_correla.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_Figure_4_spurious_correla.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..45bf05f9aadeb1428944df3d37f1d8e48032247b
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_Figure_4_spurious_correla.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scaling Up RL: Unlocking Diverse Reasoning in LLMs via", "date": "", "ddg_snippet": "... reward signal, assigning a score of 1 if the LLM ’s response passes either the original or the enhanced math -verify , and 0 otherwise (for incorrect ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12507v1", "content": "... reward signal, assigning a score of 1 if the LLM ’s response passes either the original or the enhanced math -verify , and 0 otherwise (for incorrect ..."}
+{"idx": 1, "title": "Decomposing Elements of Problem Solving: What \"Math\" Does RL", "date": "", "ddg_snippet": "Mathematical reasoning tasks have become prominent benchmarks for assessing the reasoning capabilities of LLMs , especially with reinforcement learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22756v1", "content": "Mathematical reasoning tasks have become prominent benchmarks for assessing the reasoning capabilities of LLMs , especially with reinforcement learning ..."}
+{"idx": 2, "title": "Scaling LLM Test-Time Compute Optimally can be More Effective", "date": "", "ddg_snippet": "On the one hand, some works show that current LLMs can use test-time computation to improve their outputs [ 4 , 23 , 8 , 30 , 48 ] , on the other ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.03314v1", "content": "On the one hand, some works show that current LLMs can use test-time computation to improve their outputs [ 4 , 23 , 8 , 30 , 48 ] , on the other ..."}
+{"idx": 3, "title": "Turing Mirror: Evaluating the ability of LLMs to recognize", "date": "", "ddg_snippet": "... the TuringMirror benchmark and leveraging the understanding_fables dataset from BIG-bench, we generated fables using three distinct AI models: ...", "subpage_snippet": "", "source": "www.apartresearch.com", "link": "https://www.apartresearch.com/project/turing-mirror-evaluating-the-ability-of-llms-to-recognize-llm-generated-text", "content": "... the TuringMirror benchmark and leveraging the understanding_fables dataset from BIG-bench, we generated fables using three distinct AI models: ..."}
+{"idx": 4, "title": "Large Language Models Are Reasoning Teachers | Request PDF", "date": "", "ddg_snippet": "... the reasoning capabilities of opensource LLMs , recent studies focus on Supervised Fine-Tuning (SFT) these open-source LLMs to distill mathematical ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372919115_Large_Language_Models_Are_Reasoning_Teachers", "content": "... the reasoning capabilities of opensource LLMs , recent studies focus on Supervised Fine-Tuning (SFT) these open-source LLMs to distill mathematical ..."}
+{"idx": 5, "title": "Trust but Verify! A Survey on Verification Design for Test-time", "date": "", "ddg_snippet": "... used to evaluate the quality or plausibility of different reasoning paths or solutions from the language model during inference, enabling efficient ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.16665v3", "content": "... used to evaluate the quality or plausibility of different reasoning paths or solutions from the language model during inference, enabling efficient ..."}
+{"idx": 6, "title": "Declaration", "date": "", "ddg_snippet": "The rapid evolution of neural architectures—from multilayer perceptrons to large- scale Transformer-based models—has enabled language models ( LLMs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15366v1", "content": "The rapid evolution of neural architectures—from multilayer perceptrons to large- scale Transformer-based models—has enabled language models ( LLMs ..."}
+{"idx": 7, "title": "Virginia Smith", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... the same time, training on model-generated positives can ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Virginia+Smith", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... the same time, training on model-generated positives can ..."}
+{"idx": 8, "title": "Sewoong Oh", "date": "", "ddg_snippet": "... RLVR) can elicit strong mathematical reasoning in certain models even with spurious rewards that have little, no, or even negative correlation with ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Sewoong+Oh", "content": "... RLVR) can elicit strong mathematical reasoning in certain models even with spurious rewards that have little, no, or even negative correlation with ..."}
+{"idx": 9, "title": "Cop N' Shop", "date": "", "ddg_snippet": "... The concepts of data validation, data provenance/ transparent, verifiable data handling all give a very good reason to use blockchain rather than ...", "subpage_snippet": "", "source": "www.apartresearch.com", "link": "https://www.apartresearch.com/project/cop-n-shop", "content": "... The concepts of data validation, data provenance/ transparent, verifiable data handling all give a very good reason to use blockchain rather than ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_Section_5_underlying_caus.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_Section_5_underlying_caus.jsonl
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index 0000000000000000000000000000000000000000..e6a7b7efe9f9be4d890071655769ad7bbdcf2d80
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+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_Section_5_underlying_caus.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 67 — Our contribution is a study of the role of synthetic data in improving math reasoning capabilities of LLMs. We derive scaling laws for positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 67 — Our contribution is a study of the role of synthetic data in improving math reasoning capabilities of LLMs. We derive scaling laws for positive ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — Overall, this model explains the utility of negative data over only positive data . Report issue for preceding element. Our contribution is a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — Overall, this model explains the utility of negative data over only positive data . Report issue for preceding element. Our contribution is a ..."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of", "date": "", "ddg_snippet": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96295/paper", "content": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks."}
+{"idx": 4, "title": "CAN 1B LLM SURPASS 405B LLM? RETHINKING", "date": "", "ddg_snippet": "by R Liu · Cited by 61 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv:2406.14532, 2024. Amrith Setlur, Chirag ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=CvjX9Lhpze", "content": "by R Liu · Cited by 61 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv:2406.14532, 2024. Amrith Setlur, Chirag ..."}
+{"idx": 5, "title": "Beyond Human Data: Scaling Self-Training for Problem ...", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Think, Prune, Train, Improve: Scaling Reasoning without Scaling Models.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Beyond-Human-Data:-Scaling-Self-Training-for-with-Singh-Co-Reyes/48362b169a235ca650918c489c8cea4c597da645", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Think, Prune, Train, Improve: Scaling Reasoning without Scaling Models."}
+{"idx": 6, "title": "MathFusion: Enhancing Mathematical Problem-solving of ...", "date": "", "ddg_snippet": "by Q Pei · 2025 · Cited by 11 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv:2406.14532. Zhihong Shao ... 21 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.367.pdf", "content": "by Q Pei · 2025 · Cited by 11 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv:2406.14532. Zhihong Shao ... 21 pages"}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Training on model-generated synthetic data is a promising approach ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=LLM-generated+synthetic+data", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Training on model-generated synthetic data is a promising approach ..."}
+{"idx": 8, "title": "Common 7B Language Models Already Possess Strong ...", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. 19 upvotes · 5 comments. Large Language Models Are ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/singularity/comments/1e2yysy/common_7b_language_models_already_possess_strong/", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. 19 upvotes · 5 comments. Large Language Models Are ..."}
+{"idx": 9, "title": "Preference Learning via Error-injected Self-editing", "date": "", "ddg_snippet": "by K Xu · 2025 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv:2406.14532. Zhihong Shao, Peiyi ... 20 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1506.pdf", "content": "by K Xu · 2025 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv:2406.14532. Zhihong Shao, Peiyi ... 20 pages"}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_advantage_weighted_RL_spu.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_advantage_weighted_RL_spu.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..66bb98bf3cd352dcb2e7ed267eb46f282741e3d0
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_advantage_weighted_RL_spu.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "e data , attaining performance similar to amplifying the amount of synthetic data by 8×. We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL )", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "e data , attaining performance similar to amplifying the amount of synthetic data by 8×. We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL )"}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The proposed advantage-weighted RL method with negative data can be a promising approach for improving the efficiency of synthetic data in training large language models. Overall, this paper provides valuable insights into the role of synthetic data in improving the math reasoning capabilities of large language models.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/1dm164m/rl_on_incorrect_synthetic_data_scales_the/", "content": "The proposed advantage-weighted RL method with negative data can be a promising approach for improving the efficiency of synthetic data in training large language models. Overall, this paper provides valuable insights into the role of synthetic data in improving the math reasoning capabilities of large language models."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone.", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone."}
+{"idx": 4, "title": "PDF Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning"}
+{"idx": 5, "title": "Achieving 8× Performance Gains with Reinforcement Learning on Synthetic ...", "date": "", "ddg_snippet": "Training with negative data helps unlearn spurious correlations. They present a conceptual model inspired by reinforcement learning ( RL ) to explain these observations and the generalization benefits of synthetic data . Overall, this study provides valuable insights and conceptual models to understand the role of synthetic data in reasoning tasks.", "subpage_snippet": "", "source": "syncedreview.com", "link": "https://syncedreview.com/2024/07/01/achieving-8x-performance-gains-with-reinforcement-learning-on-synthetic-data-in-large-language-models/", "content": "Training with negative data helps unlearn spurious correlations. They present a conceptual model inspired by reinforcement learning ( RL ) to explain these observations and the generalization benefits of synthetic data . Overall, this study provides valuable insights and conceptual models to understand the role of synthetic data in reasoning tasks."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it ..."}
+{"idx": 7, "title": "scaling-LLM-math-synthetic-data/README.md at master - GitHub", "date": "", "ddg_snippet": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold\" - ars22/scaling- LLM - math - synthetic - data", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ars22/scaling-LLM-math-synthetic-data/blob/master/README.md", "content": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold\" - ars22/scaling- LLM - math - synthetic - data"}
+{"idx": 8, "title": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight ...", "date": "", "ddg_snippet": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
+{"idx": 9, "title": "AI-Powered Paper Summarization about the arXiv paper 2406.14532v1", "date": "", "ddg_snippet": "Easy-to-read summary of the arXiv paper 2406.14532v1 entitled RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "Easy-to-read summary of the arXiv paper 2406.14532v1 entitled RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold"}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_arxiv_bench.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_arxiv_bench.jsonl
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index 0000000000000000000000000000000000000000..d57fa7d368807c520cd52a042c60d8c485b51b71
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+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data · MinWoo Park dblp: RL on Incorrect Synthetic Data Scales the Efficiency of ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... scaling-LLM-math-synthetic-data/README.md at master - GitHub RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "Jun 20, 2024 · Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ... Jun 20, 2024 · RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ... Jun 20, 2024 · abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold . Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \" Can synthetic data be used to solve complex reasoning problems? While synthetic data from LLMs like Gemini and GPT-4 holds great potential, for more complex reasoning problems (more complicated than the datasets evaluated in our work), synthetic data generated from more capable models can contain errors . Can synthetic data address data scarcity? Thus while in principle, synthetic data could potentially address data scarcity , it must be designed in an appropriate manner to be eff ective. However, this has been hard due to a lack o f an understanding of how synthetic data contributes to LLM beha vior. Does RL improve imitation learning? Our theoretical result extends guarantees from the RL literature comparing RL with imitation learning to show that indeed the use of RL (and hence negative data) improves over imitation alone . Notation and setup. How do we optimize the advantage-weighted RL objective? When we optimize the advantage-weighted RL objective, this disparity in Q-values is reduced by re-weighting the next-token loss with advantage estimates , and training is able to preferentially minimize the loss at the critical token. In this scenario, this results in the model picking upon the ground-truth generalizable feature. Our work studies the role of synthetic data for improving math reasoning capabilities of LLMs. We find that while the typical approach of collecting new questions and corresponding positive (correct) solutions from capable models like GPT-4/Gemini-1.5 presents underwhelming data scaling.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Jun 20, 2024 · Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ... Jun 20, 2024 · RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ... Jun 20, 2024 · abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold . Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \" Can synthetic data be used to solve complex reasoning problems? While synthetic data from LLMs like Gemini and GPT-4 holds great potential, for more complex reasoning problems (more complicated than the datasets evaluated in our work), synthetic data generated from more capable models can contain errors . Can synthetic data address data scarcity? Thus while in principle, synthetic data could potentially address data scarcity , it must be designed in an appropriate manner to be eff ective. However, this has been hard due to a lack o f an understanding of how synthetic data contributes to LLM beha vior. Does RL improve imitation learning? Our theoretical result extends guarantees from the RL literature comparing RL with imitation learning to show that indeed the use of RL (and hence negative data) improves over imitation alone . Notation and setup. How do we optimize the advantage-weighted RL objective? When we optimize the advantage-weighted RL objective, this disparity in Q-values is reduced by re-weighting the next-token loss with advantage estimates , and training is able to preferentially minimize the loss at the critical token. In this scenario, this results in the model picking upon the ground-truth generalizable feature. Our work studies the role of synthetic data for improving math reasoning capabilities of LLMs. We find that while the typical approach of collecting new questions and corresponding positive (correct) solutions from capable models like GPT-4/Gemini-1.5 presents underwhelming data scaling."}
+{"idx": 1, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "Our work studies the role of synthetic data for improving math reasoning capabilities of LLMs. We find that while the typical approach of collecting new questions and corresponding positive (correct) solutions from capable models like GPT-4/Gemini-1.5 presents underwhelming data scaling.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "Our work studies the role of synthetic data for improving math reasoning capabilities of LLMs. We find that while the typical approach of collecting new questions and corresponding positive (correct) solutions from capable models like GPT-4/Gemini-1.5 presents underwhelming data scaling."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "Jun 20, 2024 · abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "Jun 20, 2024 · abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 4, "title": "dblp: RL on Incorrect Synthetic Data Scales the Efficiency of ...", "date": "", "ddg_snippet": "Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2406-14532", "content": "Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 6, "title": "scaling-LLM-math-synthetic-data/README.md at master - GitHub", "date": "", "ddg_snippet": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ars22/scaling-LLM-math-synthetic-data/blob/master/README.md", "content": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \""}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Implementation Details. Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "Implementation Details. Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ."}
+{"idx": 8, "title": "AI-Powered Paper Summarization about the arXiv paper 2406.14532v1", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .And indeed, they find that fine-tuning again with this self-generated positive data leads to a doubling of efficiency when solving the same synthetic problems.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .And indeed, they find that fine-tuning again with this self-generated positive data leads to a doubling of efficiency when solving the same synthetic problems."}
+{"idx": 9, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach forfinetuning LLMs, but it remains unclear when it helps or hurts. In this paper,we investigate this question for math reasoning via an empirical study,followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/499567/rl-on-incorrect-synthetic-data-scales-the-efficiency-of-llm-math-reasoning-by-eight-fold", "content": "Training on model-generated synthetic data is a promising approach forfinetuning LLMs, but it remains unclear when it helps or hurts. In this paper,we investigate this question for math reasoning via an empirical study,followed by building a conceptual understanding of our observations."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_benchmarks.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_benchmarks.jsonl
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index 0000000000000000000000000000000000000000..18631d741e7b33ebe744c892cf5064c0436ad3b1
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+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 66 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "by A Setlur · 2024 · Cited by 66 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 66 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 66 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 66 — Our contribution is a study of the role of synthetic data in improving math reasoning capabilities of LLMs. We derive scaling laws for positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 66 — Our contribution is a study of the role of synthetic data in improving math reasoning capabilities of LLMs. We derive scaling laws for positive ..."}
+{"idx": 3, "title": "RL on incorrect synthetic data scales the efficiency of LLM ...", "date": "", "ddg_snippet": "5 Jun 2025 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739277", "content": "5 Jun 2025 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "This paper investigates the role of synthetic data in improving the math reasoning capabilities of large language models (LLMs).", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/1dm164m/rl_on_incorrect_synthetic_data_scales_the/", "content": "This paper investigates the role of synthetic data in improving the math reasoning capabilities of large language models (LLMs)."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — The researchers used this 'negative reinforcement learning' to build an AI that's remarkably efficient at math problem-solving. They found that ...", "subpage_snippet": "", "source": "www.promptlayer.com", "link": "https://www.promptlayer.com/research-papers/rl-on-incorrect-synthetic-data-scales-the-efficiency-of-llm-math-reasoning-by-eight-fold", "content": "20 Jun 2024 — The researchers used this 'negative reinforcement learning' to build an AI that's remarkably efficient at math problem-solving. They found that ..."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Key takeaway: 'Training on incorrect synthetic data , when constructed with appropriate recovery, can significantly improve the efficiency of LLM math ...", "subpage_snippet": "", "source": "k8s.consensus.app", "link": "https://k8s.consensus.app/papers/details/fffa1f2c572d54268d6e2691de789e4c/", "content": "Key takeaway: 'Training on incorrect synthetic data , when constructed with appropriate recovery, can significantly improve the efficiency of LLM math ..."}
+{"idx": 7, "title": "Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold . ... ➢Result: Offline RL gives 8x sample efficiency vs imitation. 41 pages", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold . ... ➢Result: Offline RL gives 8x sample efficiency vs imitation. 41 pages"}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of", "date": "", "ddg_snippet": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96295/paper", "content": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks."}
+{"idx": 9, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — This paper investigates the use of reinforcement learning (RL) on incorrect synthetic data to improve the math reasoning abilities of large ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/rl-incorrect-synthetic-data-scales-efficiency-llm", "content": "20 Jun 2024 — This paper investigates the use of reinforcement learning (RL) on incorrect synthetic data to improve the math reasoning abilities of large ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_spurious_correlations_Sec.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_spurious_correlations_Sec.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5433dcdca79b9db7cef6a73404ba7b760021f314
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+{"idx": 0, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 1, "title": "Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning"}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data · MinWoo Park RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... Learning to Reason by Failing: Offline RL on Sub-optimal ... Images RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... Reinforcement Learning for LLM Reasoning Reinforcement Learning for LLM Reasoning RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reinforcement Learning for LLM Reasoning RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math", "date": "", "ddg_snippet": "Jun 20, 2024 · Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ... Jun 20, 2024 · At the same time, training on model-generated positives can amplify various spurious correlations , resulting in flat or even inverse scaling trends as the amount of data increases. Jun 20, 2024 · We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone. The presence of spurious correlations in synthetic data can hinder the performance of models trained on such data . Addressing these correlations is crucial for achieving optimal results. Incorporating negative ( incorrect ) synthetic data can help in identifying and unlearning spurious correlations , leading to better generalization. Jun 13, 2024 · This paper investigates the helps/hurts of training models on model-generated synthetic data for math reasoning . They first conduct an empirical study and then propose to construct negative samples to address spurious correlations in SFT/RFT policy. The reviewers agree that the research problem is significant. The observations are insightful and are well-motivated and grounded with theorems ... View all To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Can synthetic data improve the math reasoning capabilities of large language models? The proposed advantage-weighted RL method with negative data can be a promising approach for improving the efficiency of synthetic data in training large language models. Overall, this paper provides valuable insights into the role of synthetic data in improving the math reasoning capabilities of large language models. Is LLM test-time compute more effective than scaling model parameters? Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Does training on model-generated synthetic data help or hurt LLMs? Preprints and early-stage research may not have been peer reviewed yet. Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. Does advantage-weighted RL with negative data improve the efficiency of synthetic data? Advantage-weighted RL with negative data achieves an 8x improvement in the efficiency of synthetic data. The proposed method provides a conceptual model to understand the generalization benefits of using negative data. The use of synthetic data can significantly improve the efficiency of training large language models for math reasoning tasks. Does hen scaling meet LLM finetuning? hen scaling meets llm finetuning: The effect of dat , model and finetuning method, 2024. Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei. Negative preference optimization: F om catastrophic collapse to effective unlearning. arXiv preprint arXiv:2404.05868, 2024. Y o Zhao, Mikhail Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, a", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Jun 20, 2024 · Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ... Jun 20, 2024 · At the same time, training on model-generated positives can amplify various spurious correlations , resulting in flat or even inverse scaling trends as the amount of data increases. Jun 20, 2024 · We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone. The presence of spurious correlations in synthetic data can hinder the performance of models trained on such data . Addressing these correlations is crucial for achieving optimal results. Incorporating negative ( incorrect ) synthetic data can help in identifying and unlearning spurious correlations , leading to better generalization. Jun 13, 2024 · This paper investigates the helps/hurts of training models on model-generated synthetic data for math reasoning . They first conduct an empirical study and then propose to construct negative samples to address spurious correlations in SFT/RFT policy. The reviewers agree that the research problem is significant. The observations are insightful and are well-motivated and grounded with theorems ... View all To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Can synthetic data improve the math reasoning capabilities of large language models? The proposed advantage-weighted RL method with negative data can be a promising approach for improving the efficiency of synthetic data in training large language models. Overall, this paper provides valuable insights into the role of synthetic data in improving the math reasoning capabilities of large language models. Is LLM test-time compute more effective than scaling model parameters? Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Does training on model-generated synthetic data help or hurt LLMs? Preprints and early-stage research may not have been peer reviewed yet. Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. Does advantage-weighted RL with negative data improve the efficiency of synthetic data? Advantage-weighted RL with negative data achieves an 8x improvement in the efficiency of synthetic data. The proposed method provides a conceptual model to understand the generalization benefits of using negative data. The use of synthetic data can significantly improve the efficiency of training large language models for math reasoning tasks. Does hen scaling meet LLM finetuning? hen scaling meets llm finetuning: The effect of dat , model and finetuning method, 2024. Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei. Negative preference optimization: F om catastrophic collapse to effective unlearning. arXiv preprint arXiv:2404.05868, 2024. Y o Zhao, Mikhail Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, a"}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "Jun 20, 2024 · We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone.", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "Jun 20, 2024 · We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "The presence of spurious correlations in synthetic data can hinder the performance of models trained on such data . Addressing these correlations is crucial for achieving optimal results. Incorporating negative ( incorrect ) synthetic data can help in identifying and unlearning spurious correlations , leading to better generalization.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/1dm164m/rl_on_incorrect_synthetic_data_scales_the/", "content": "The presence of spurious correlations in synthetic data can hinder the performance of models trained on such data . Addressing these correlations is crucial for achieving optimal results. Incorporating negative ( incorrect ) synthetic data can help in identifying and unlearning spurious correlations , leading to better generalization."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4b77d5b896c321a29277524a98a50215-Paper-Conference.pdf", "content": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ..."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "SFT/RFT policy suffers from spurious correlations in positive synthetic data . While RFT data maybe “easier-to-fit”, in Figure 2(c) we also note that continuing to scale RFT data leads to test error saturation, or even worse test error.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "SFT/RFT policy suffers from spurious correlations in positive synthetic data . While RFT data maybe “easier-to-fit”, in Figure 2(c) we also note that continuing to scale RFT data leads to test error saturation, or even worse test error."}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 9, "title": "AI-Powered Paper Summarization about the arXiv paper 2406.14532 v 1", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.AI-generated Key Points. Authors explore training language models on model-generated synthetic data for math reasoning tasks.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.AI-generated Key Points. Authors explore training language models on model-generated synthetic data for math reasoning tasks."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_spurious_correlations_con.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_spurious_correlations_con.jsonl
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index 0000000000000000000000000000000000000000..660dbe786a93909373d492f659d68ee9bc8c8b9b
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+{"idx": 0, "title": "[2406.14532] RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "View a PDF of the paper titled RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold, by Amrith Setlur and 5 other ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "View a PDF of the paper titled RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold, by Amrith Setlur and 5 other ..."}
+{"idx": 1, "title": "Decomposing Elements of Problem Solving: What \"Math\" Does RL", "date": "", "ddg_snippet": "To truly understand the impact of RL on reasoning , we need evaluation methods that go beyond aggregate accuracy and capture how models navigate the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22756v1", "content": "To truly understand the impact of RL on reasoning , we need evaluation methods that go beyond aggregate accuracy and capture how models navigate the ..."}
+{"idx": 2, "title": "GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with", "date": "", "ddg_snippet": "... that long-form reasoning [ 48 ] and scalable reinforcement learning [ 36 ] can significantly enhance the ability of large language models ( LLMs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01006v1", "content": "... that long-form reasoning [ 48 ] and scalable reinforcement learning [ 36 ] can significantly enhance the ability of large language models ( LLMs ..."}
+{"idx": 3, "title": "Turing Mirror: Evaluating the ability of LLMs to recognize", "date": "", "ddg_snippet": "... the TuringMirror benchmark and leveraging the understanding_fables dataset from BIG-bench, we generated fables using three distinct AI models : ...", "subpage_snippet": "", "source": "www.apartresearch.com", "link": "https://www.apartresearch.com/project/turing-mirror-evaluating-the-ability-of-llms-to-recognize-llm-generated-text", "content": "... the TuringMirror benchmark and leveraging the understanding_fables dataset from BIG-bench, we generated fables using three distinct AI models : ..."}
+{"idx": 4, "title": "National Data Privacy and Governance Act", "date": "", "ddg_snippet": "... this deserves further exploration and could potentially shed light on important methodological questions (such as faithfulness of model reasoning ...", "subpage_snippet": "", "source": "www.apartresearch.com", "link": "https://www.apartresearch.com/project/national-data-privacy-and-governance-act", "content": "... this deserves further exploration and could potentially shed light on important methodological questions (such as faithfulness of model reasoning ..."}
+{"idx": 5, "title": "Trust but Verify! A Survey on Verification Design for Test-time", "date": "", "ddg_snippet": "... used to evaluate the quality or plausibility of different reasoning paths or solutions from the language model during inference, enabling efficient ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.16665v3", "content": "... used to evaluate the quality or plausibility of different reasoning paths or solutions from the language model during inference, enabling efficient ..."}
+{"idx": 6, "title": "Declaration", "date": "", "ddg_snippet": "The rapid evolution of neural architectures—from multilayer perceptrons to large- scale Transformer-based models —has enabled language models ( LLMs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15366v1", "content": "The rapid evolution of neural architectures—from multilayer perceptrons to large- scale Transformer-based models —has enabled language models ( LLMs ..."}
+{"idx": 7, "title": "Achieving 8× Performance Gains with Reinforcement Learning on", "date": "", "ddg_snippet": "... RL on Incorrect Synthetic Data ... The paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold is on arXiv .", "subpage_snippet": "", "source": "syncedreview.com", "link": "https://syncedreview.com/2024/07/01/achieving-8x-performance-gains-with-reinforcement-learning-on-synthetic-data-in-large-language-models/", "content": "... RL on Incorrect Synthetic Data ... The paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold is on arXiv ."}
+{"idx": 8, "title": "Virginia Smith", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... the same time, training on model -generated positives can ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Virginia+Smith", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... the same time, training on model -generated positives can ..."}
+{"idx": 9, "title": "Lei Hou - ACL Anthology", "date": "", "ddg_snippet": "Extensive evaluation of ten models on MM- MATH reveals significant challenges for existing LMMs, highlighting their limited utilization of visual ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/l/lei-hou/", "content": "Extensive evaluation of ten models on MM- MATH reveals significant challenges for existing LMMs, highlighting their limited utilization of visual ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_spurious_correlations_Section_5_underlying_cause_math_reasoning.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_spurious_correlations_Section_5_underlying_cause_math_reasoning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c8823108ee4b6f994270acdff944f01849afdbc7
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_spurious_correlations_Section_5_underlying_cause_math_reasoning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2406.14532] RL on Incorrect Synthetic Data Scales the Efficiency of...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4b77d5b896c321a29277524a98a50215-Paper-Conference.pdf", "content": "Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 3, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM...", "date": "", "ddg_snippet": "PDF | Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.arXiv:2406.14532 v 1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Effici ency of LLM Math Reasoning by Eight-F old.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "PDF | Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.arXiv:2406.14532 v 1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Effici ency of LLM Math Reasoning by Eight-F old."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL ).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL )."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Analyzing datasets to uncover patterns, relationships, and anomalies is key for making data -driven decisions. Techniques like EDA with UA & MA, HeatMap Correlation , and Data Visualization using Histograms, Boxplots, PointPlots, and more are essential tools!", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/marvastisalman_rl-on-incorrect-synthetic-data-scales-the-activity-7213413858894323712-jCCf", "content": "Analyzing datasets to uncover patterns, relationships, and anomalies is key for making data -driven decisions. Techniques like EDA with UA & MA, HeatMap Correlation , and Data Visualization using Histograms, Boxplots, PointPlots, and more are essential tools!"}
+{"idx": 7, "title": "Spurious Correlations", "date": "", "ddg_snippet": "Correlation is not causation: thousands of charts of real data showing actual correlations between ridiculous variables.", "subpage_snippet": "", "source": "tylervigen.com", "link": "https://tylervigen.com/spurious-correlations", "content": "Correlation is not causation: thousands of charts of real data showing actual correlations between ridiculous variables."}
+{"idx": 8, "title": "MARGE: Improving Math Reasoning for LLMs with Guided... | alphaXiv", "date": "", "ddg_snippet": "The authors explore the impact of incorrect synthetic data on scaling LLM math reasoning , which is relevant to MARGE's approach of leveraging both correct and incorrect examples to enhance exploration and credit assignment.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2505.12500v1", "content": "The authors explore the impact of incorrect synthetic data on scaling LLM math reasoning , which is relevant to MARGE's approach of leveraging both correct and incorrect examples to enhance exploration and credit assignment."}
+{"idx": 9, "title": "Expert-Vetted Data for Smarter RL Models", "date": "", "ddg_snippet": "Boost RL models with expert-vetted data . See how iMerit builds safer AI through structured training.Reduced Noise: Faulty or inconsistent reasoning examples may cause agents to rely on shortcuts or spurious correlations .", "subpage_snippet": "", "source": "imerit.net", "link": "https://imerit.net/resources/blog/how-expert-vetted-reasoning-datasets-are-important-for-improving-reinforcement-learning-models/", "content": "Boost RL models with expert-vetted data . See how iMerit builds safer AI through structured training.Reduced Noise: Faulty or inconsistent reasoning examples may cause agents to rely on shortcuts or spurious correlations ."}
diff --git a/data/sampled_jsons/Rafailov_Direct_Preference_Optimization_2023_language_model_reward_model_year_2023.jsonl b/data/sampled_jsons/Rafailov_Direct_Preference_Optimization_2023_language_model_reward_model_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6c7cbd7628c8ea1eafd4c749714067c7ce688777
--- /dev/null
+++ b/data/sampled_jsons/Rafailov_Direct_Preference_Optimization_2023_language_model_reward_model_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NeurIPS 2023 Direct Preference Optimization : Your Language ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/oral/73865", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning."}
+{"idx": 1, "title": "Aligning Large Language Models with Human Preferences", "date": "", "ddg_snippet": "DPO Direct Preference Optimization ( Rafailov et al., 2023 ) directly optimizes a language model to adhere to human preferences without using explicit reward modeling or reinforcement learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.15997v1", "content": "DPO Direct Preference Optimization ( Rafailov et al., 2023 ) directly optimizes a language model to adhere to human preferences without using explicit reward modeling or reinforcement learning."}
+{"idx": 2, "title": "Reinforcement Learning from Human Feedback in Large Language ...", "date": "", "ddg_snippet": "Language models like GPT, PaLM, and LLaMA have demonstrated impressive capabilities in natural language understanding and generation. Rafailov , R. et al. ( 2023 ). Direct Preference Optimization : Your Language Model is Secretly a Reward Model .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@preeti.rana.ai/reinforcement-learning-from-human-feedback-in-large-language-models-62c89e8c2d3e", "content": "Language models like GPT, PaLM, and LLaMA have demonstrated impressive capabilities in natural language understanding and generation. Rafailov , R. et al. ( 2023 ). Direct Preference Optimization : Your Language Model is Secretly a Reward Model ."}
+{"idx": 3, "title": "Training language models to follow instructions with human feedback", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model . Rafael Rafailov , Archit Sharma, E. Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn.Fine-Tuning Language Models from Human Preferences .", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/d766bffc357127e0dc86dd69561d5aeb520d6f4c/Training-language-models-to-follow-instructions-with-human-feedback/graph", "content": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model . Rafael Rafailov , Archit Sharma, E. Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn.Fine-Tuning Language Models from Human Preferences ."}
+{"idx": 4, "title": "Direct Preference Optimization : Your Language Model is... - AMiner", "date": "", "ddg_snippet": "While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due t.", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/647572e0d68f896efa7b79a5/direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "content": "While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due t."}
+{"idx": 5, "title": "Modern Methods for Training Large Language Models with Minimal...", "date": "", "ddg_snippet": "This academic review systematizes and analyzes breakthrough approaches to training large language models (LLM) developed between 2022 and 2025, with an emphasis on radically reducing or eliminating the reliance on human-labeled data.", "subpage_snippet": "", "source": "injoit.org", "link": "http://injoit.org/index.php/j1/article/view/2193", "content": "This academic review systematizes and analyzes breakthrough approaches to training large language models (LLM) developed between 2022 and 2025, with an emphasis on radically reducing or eliminating the reliance on human-labeled data."}
+{"idx": 6, "title": "lucidrains/self- rewarding -lm-pytorch - Githubissues", "date": "", "ddg_snippet": "Self- Rewarding Language Model Implementation of the training framework proposed in Self- Rewarding Language Model , from MetaAI They really took the title of the DPO paper to heart.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/lucidrains/self-rewarding-lm-pytorch/readme", "content": "Self- Rewarding Language Model Implementation of the training framework proposed in Self- Rewarding Language Model , from MetaAI They really took the title of the DPO paper to heart."}
+{"idx": 7, "title": "Reinforcement Learning with Large Language Models", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model by Rafailov et al.(ICLR 2023 ). Pre-Trained Language Models for Interactive Decision-Making by Li et al.", "subpage_snippet": "", "source": "reinforcement-learning.mpi-sws.org", "link": "https://reinforcement-learning.mpi-sws.org/course-rl-llms-w23/", "content": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model by Rafailov et al.(ICLR 2023 ). Pre-Trained Language Models for Interactive Decision-Making by Li et al."}
+{"idx": 8, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model , by Rafael Rafailov and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model , by Rafael Rafailov and 5 other authors"}
+{"idx": 9, "title": "NeurIPS 2023 Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/oral/73865", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning."}
diff --git a/data/sampled_jsons/Rafailov_et_al._2024_Direct_Preference_Optimization_abstract_year_2024.jsonl b/data/sampled_jsons/Rafailov_et_al._2024_Direct_Preference_Optimization_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..de3f318e186b63d4c0d91f5a241134f2ea595cab
--- /dev/null
+++ b/data/sampled_jsons/Rafailov_et_al._2024_Direct_Preference_Optimization_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Survey of Direct Preference Optimization", "date": "", "ddg_snippet": "In this context, Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative that directly optimizes LLMs using ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.11701v1", "content": "In this context, Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative that directly optimizes LLMs using ..."}
+{"idx": 1, "title": "Evaluating the Effectiveness of Direct Preference Optimization", "date": "", "ddg_snippet": "... SFT) approach for adapting LLM-based ATS models by leveraging a computationally efficient LLM alignment technique— direct preference optimization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01479v1", "content": "... SFT) approach for adapting LLM-based ATS models by leveraging a computationally efficient LLM alignment technique— direct preference optimization ..."}
+{"idx": 2, "title": "[2305.18290] Direct Preference Optimization: Your Language", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the ..."}
+{"idx": 3, "title": "A Comprehensive Survey of Direct Preference Optimization:", "date": "", "ddg_snippet": "On the other hand, staring from the KL-constrained reward maximization objective in RL, Direct Preference Optimization (DPO; ( Rafailov et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.15595v2", "content": "On the other hand, staring from the KL-constrained reward maximization objective in RL, Direct Preference Optimization (DPO; ( Rafailov et al ..."}
+{"idx": 4, "title": "Right Now, Wrong Then: Non-Stationary Direct Preference", "date": "", "ddg_snippet": "... Direct Preference Optimization (NS-DPO), a novel ... However, standard preference optimization approaches ( e .g., DPO and IPO ( Rafailov et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.18676v2", "content": "... Direct Preference Optimization (NS-DPO), a novel ... However, standard preference optimization approaches ( e .g., DPO and IPO ( Rafailov et al ."}
+{"idx": 5, "title": "Disentangling Length from Quality in Direct Preference", "date": "", "ddg_snippet": "Disentangling Length from Quality in Direct Preference Optimization (Park et al ., Findings 2024 ) ... etal - 2024 -disentangling, title = \" ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.297/", "content": "Disentangling Length from Quality in Direct Preference Optimization (Park et al ., Findings 2024 ) ... etal - 2024 -disentangling, title = \" ..."}
+{"idx": 6, "title": "Most Influential NIPS Papers (2024-05 Version) – Paper", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model Is Secretly A Reward Model IF:7 Related Papers Related Patents Related Grants Related Venues ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2024/05/most-influential-nips-papers-2024-05/", "content": "Direct Preference Optimization : Your Language Model Is Secretly A Reward Model IF:7 Related Papers Related Patents Related Grants Related Venues ..."}
+{"idx": 7, "title": "Most Influential NIPS Papers (2024-05 Version) –", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model Is Secretly A Reward Model IF:7 Related Papers Related Patents Related Grants Related Venues ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2024/05/most-influential-nips-papers-2024-05/", "content": "Direct Preference Optimization : Your Language Model Is Secretly A Reward Model IF:7 Related Papers Related Patents Related Grants Related Venues ..."}
+{"idx": 8, "title": "ReXPref-Prior: A MIMIC-CXR Preference Dataset for Reducing", "date": "", "ddg_snippet": "... Prior will be useful for training models that hallucinate prior exams less frequently, through techniques such as direct preference optimization ...", "subpage_snippet": "", "source": "physionet.org", "link": "https://physionet.org/content/rexpref-prior/1.0.0/", "content": "... Prior will be useful for training models that hallucinate prior exams less frequently, through techniques such as direct preference optimization ..."}
+{"idx": 9, "title": "NeurIPS 2023 Primer", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model ( Rafailov et al .). ... paper proposes Direct Preference Optimization ...", "subpage_snippet": "", "source": "www.ruder.io", "link": "https://www.ruder.io/neurips-2023-primer/", "content": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model ( Rafailov et al .). ... paper proposes Direct Preference Optimization ..."}
diff --git a/data/sampled_jsons/Rahul_Choudhary_Hanbaek_Lyu_generalized_Sinkhorn_algorithm_equation_update_rule.jsonl b/data/sampled_jsons/Rahul_Choudhary_Hanbaek_Lyu_generalized_Sinkhorn_algorithm_equation_update_rule.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf989b7ebc2ad15712de848deddd9ca7fac1078a
--- /dev/null
+++ b/data/sampled_jsons/Rahul_Choudhary_Hanbaek_Lyu_generalized_Sinkhorn_algorithm_equation_update_rule.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear Convergence of Sinkhorn’s Algorithm for Generalized ...", "date": "", "ddg_snippet": "Recently, Lyu and Mukherjee ( Lyu & Mukherjee, 2024) established linear convergence of the generalized Sinkhorn algorithm (6) for the generalized typical table problem in a random matrix theory context.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "Recently, Lyu and Mukherjee ( Lyu & Mukherjee, 2024) established linear convergence of the generalized Sinkhorn algorithm (6) for the generalized typical table problem in a random matrix theory context."}
+{"idx": 1, "title": "Hanbaek Lyu", "date": "", "ddg_snippet": "Aug 6, 2025 · Contingency tables, Random Matrices, Optimal Transport Hanbaek Lyu and Sumit Muhkerjee, \"Large random matrices with given margins.\" Submitted. Prepint (2024) (Last updated ver: Aug 6, 2025 CT_limit) Rahul Choudhary and Hanbaek Lyu , \"Linear convergence of Sinkhorn 's algorithm for generalized static Schrödinger bridge\", To appear in ICML 2025 Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer ...", "subpage_snippet": "", "source": "hanbaeklyu.com", "link": "https://hanbaeklyu.com/publications/", "content": "Aug 6, 2025 · Contingency tables, Random Matrices, Optimal Transport Hanbaek Lyu and Sumit Muhkerjee, \"Large random matrices with given margins.\" Submitted. Prepint (2024) (Last updated ver: Aug 6, 2025 CT_limit) Rahul Choudhary and Hanbaek Lyu , \"Linear convergence of Sinkhorn 's algorithm for generalized static Schrödinger bridge\", To appear in ICML 2025 Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer ..."}
+{"idx": 2, "title": "Hanbaek Lyu - OpenReview", "date": "", "ddg_snippet": "Publications Linear convergence of Sinkhorn 's algorithm for generalized static Schrödinger bridge Rahul Choudhary , Hanbaek Lyu Published: 01 May 2025, Last Modified: 10 Aug 2025 ICML 2025 poster", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Hanbaek_Lyu1", "content": "Publications Linear convergence of Sinkhorn 's algorithm for generalized static Schrödinger bridge Rahul Choudhary , Hanbaek Lyu Published: 01 May 2025, Last Modified: 10 Aug 2025 ICML 2025 poster"}
+{"idx": 3, "title": "Rahul Choudhary - OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · Publications Linear convergence of Sinkhorn 's algorithm for generalized static Schrödinger bridge Rahul Choudhary , Hanbaek Lyu", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Rahul_Choudhary1", "content": "May 1, 2025 · Publications Linear convergence of Sinkhorn 's algorithm for generalized static Schrödinger bridge Rahul Choudhary , Hanbaek Lyu"}
+{"idx": 4, "title": "HANBAEKLYU", "date": "", "ddg_snippet": "Publications [1] R. Choudhary and H. Lyu ,“Linear convergence of Sinkhorn ’s algorithm for generalized static Schrödinger bridge.” To appear in ICML 2025 [2] D ...", "subpage_snippet": "", "source": "hanbaeklyu.com", "link": "https://hanbaeklyu.com/wp-content/uploads/2025/05/cv_lyu-4.pdf", "content": "Publications [1] R. Choudhary and H. Lyu ,“Linear convergence of Sinkhorn ’s algorithm for generalized static Schrödinger bridge.” To appear in ICML 2025 [2] D ..."}
+{"idx": 5, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "May 1, 2025 · The authors leverage the process of Lyu and Mukherjee'24 to identify the linear convergence of the generalized Sinkhorn algorithm for a general class of metrics and solve the problem suffered by the KL divergence.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0hrkN07DuO", "content": "May 1, 2025 · The authors leverage the process of Lyu and Mukherjee'24 to identify the linear convergence of the generalized Sinkhorn algorithm for a general class of metrics and solve the problem suffered by the KL divergence."}
+{"idx": 6, "title": "Sinkhorn ’s Algorithm for Entropic Optimal", "date": "", "ddg_snippet": "2019W Term 1 Scribe: Delbert Yip. 13.1 Sinkhorn ’s Algorithm for Entropic Optimal Transport.At the (l + 1)th iteration, we would have used v(l) to update u(l), giving us u(l+1), which would then be used to update v(l), yielding v(l+1).", "subpage_snippet": "", "source": "personal.math.ubc.ca", "link": "https://personal.math.ubc.ca/~geoff/courses/W2019T1/Lecture13.pdf", "content": "2019W Term 1 Scribe: Delbert Yip. 13.1 Sinkhorn ’s Algorithm for Entropic Optimal Transport.At the (l + 1)th iteration, we would have used v(l) to update u(l), giving us u(l+1), which would then be used to update v(l), yielding v(l+1)."}
+{"idx": 7, "title": "Wasserstein distance via entropy regularization ( Sinkhorn algorithm )", "date": "", "ddg_snippet": "The Sinkhorn algorithm iterates this update rule until convergence, resulting in a transport plan that minimizes the regularized problem.", "subpage_snippet": "", "source": "www.fabriziomusacchio.com", "link": "https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance_sinkhorn/", "content": "The Sinkhorn algorithm iterates this update rule until convergence, resulting in a transport plan that minimizes the regularized problem."}
+{"idx": 8, "title": "[2311.16706] Sinkhorn Flow: A Continuous-Time Framework for...", "date": "", "ddg_snippet": "Recently, the Sinkhorn algorithm has been recast within the mirror descent framework, thus benefiting from classical optimization theory insights. Here, we build upon this result by introducing a continuous-time analogue of the Sinkhorn algorithm .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.16706", "content": "Recently, the Sinkhorn algorithm has been recast within the mirror descent framework, thus benefiting from classical optimization theory insights. Here, we build upon this result by introducing a continuous-time analogue of the Sinkhorn algorithm ."}
+{"idx": 9, "title": "A note on overrelaxation in the Sinkhorn algorithm | Optimization...", "date": "", "ddg_snippet": "We derive an a priori parameter range for overrelaxation of the Sinkhorn algorithm , which guarantees global convergence and a strictly faster asymptotic lo.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11590-021-01830-0", "content": "We derive an a priori parameter range for overrelaxation of the Sinkhorn algorithm , which guarantees global convergence and a strictly faster asymptotic lo."}
diff --git a/data/sampled_jsons/Regret_Matching+_(In)Stability_and_Fast_Convergence_in_Games_ACM_abstract_Farina_2023_year_2023.jsonl b/data/sampled_jsons/Regret_Matching+_(In)Stability_and_Fast_Convergence_in_Games_ACM_abstract_Farina_2023_year_2023.jsonl
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index 0000000000000000000000000000000000000000..ec75add222c6f2780189f052ef77e7b2248a562b
--- /dev/null
+++ b/data/sampled_jsons/Regret_Matching+_(In)Stability_and_Fast_Convergence_in_Games_ACM_abstract_Farina_2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Rapid Learning in Constrained Minimax Games with Negative", "date": "", "ddg_snippet": "With the inextricably intertwined advancement of online learning and game theory, several algorithms have become foundational solvers for minimax ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.00533v1", "content": "With the inextricably intertwined advancement of online learning and game theory, several algorithms have become foundational solvers for minimax ..."}
+{"idx": 1, "title": "Computer Science and Game Theory Jun 2024", "date": "", "ddg_snippet": "Subjects: Computer Science and Game Theory (cs.GT) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.GT/2024-06", "content": "Subjects: Computer Science and Game Theory (cs.GT) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)"}
+{"idx": 2, "title": "Tuomas W. Sandholm", "date": "", "ddg_snippet": "For contributions to research in computational economics, including market design, combinatorial auctions, and game theory, and for contributions to ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "http://www.cs.cmu.edu/~sandholm/", "content": "For contributions to research in computational economics, including market design, combinatorial auctions, and game theory, and for contributions to ..."}
+{"idx": 3, "title": "Tuomas W. Sandholm", "date": "", "ddg_snippet": "For contributions to research in computational economics, including market design, combinatorial auctions, and game theory, and for contributions to ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~sandholm/", "content": "For contributions to research in computational economics, including market design, combinatorial auctions, and game theory, and for contributions to ..."}
+{"idx": 4, "title": "ApproxED: Approximate exploitability descent via learned best", "date": "", "ddg_snippet": "... include continuous resource allocation games (Ganzfried, 2021 ) , security games in continuous spaces (Kamra et al., 2017 , 2018 , 2019 ) , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2301.08830v3", "content": "... include continuous resource allocation games (Ganzfried, 2021 ) , security games in continuous spaces (Kamra et al., 2017 , 2018 , 2019 ) , ..."}
+{"idx": 5, "title": "TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven", "date": "", "ddg_snippet": "... Can we automatically design computationally efficient, accurate, interpretable, and highly generalizable trajectory prediction heuristics? Inspired ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05616v1", "content": "... Can we automatically design computationally efficient, accurate, interpretable, and highly generalizable trajectory prediction heuristics? Inspired ..."}
+{"idx": 6, "title": "Publications – TOC for Fairness", "date": "", "ddg_snippet": "... International Conference on Artificial ... STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games .", "subpage_snippet": "", "source": "toc4fairness.org", "link": "https://toc4fairness.org/publications/", "content": "... International Conference on Artificial ... STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games ."}
+{"idx": 7, "title": "theory.khoury.northeastern.edu/seminar.html", "date": "", "ddg_snippet": "Abstract : We study an online allocation problem that arises in food rescue operations where donations arrive online to be allocated to recipient ...", "subpage_snippet": "", "source": "theory.khoury.northeastern.edu", "link": "https://theory.khoury.northeastern.edu/seminar.html", "content": "Abstract : We study an online allocation problem that arises in food rescue operations where donations arrive online to be allocated to recipient ..."}
+{"idx": 8, "title": "Vincent Conitzer", "date": "", "ddg_snippet": "In our dataset 90.2% of authors are connected to ... ACM Fellow 2019, \"For contributions to game theory, social choice theory, and mechanism design\".", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/vincent-conitzer/", "content": "In our dataset 90.2% of authors are connected to ... ACM Fellow 2019, \"For contributions to game theory, social choice theory, and mechanism design\"."}
+{"idx": 9, "title": "Vincent Conitzer - publications by topic", "date": "", "ddg_snippet": "Choosing what game to play without selecting equilibria: results on inferring safe (Pareto) improvements in binary constraint structures.", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "http://www.cs.cmu.edu/~conitzer/bytopic.html", "content": "Choosing what game to play without selecting equilibria: results on inferring safe (Pareto) improvements in binary constraint structures."}
diff --git a/data/sampled_jsons/Regret_Matching+_(In)Stability_and_Fast_Convergence_in_Games_Farina_et_al._2023_full_abstract_text.jsonl b/data/sampled_jsons/Regret_Matching+_(In)Stability_and_Fast_Convergence_in_Games_Farina_et_al._2023_full_abstract_text.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..59f086c0c8baaa9e1367bd0bba3ede5e444212eb
--- /dev/null
+++ b/data/sampled_jsons/Regret_Matching+_(In)Stability_and_Fast_Convergence_in_Games_Farina_et_al._2023_full_abstract_text.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract : Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "Abstract : Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability ."}
+{"idx": 1, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "by Gabriele Farina , et al . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/regret-matching-in-stability-and-fast-convergence-in-games", "content": "by Gabriele Farina , et al . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery."}
+{"idx": 2, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to..."}
+{"idx": 3, "title": "Gabriele Farina - Regret Matching $^+$: ( In ) Stability and Fast ...", "date": "", "ddg_snippet": "Abstract . Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability .", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/", "content": "Abstract . Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability ."}
+{"idx": 4, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nYgs0qZJ97", "content": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery."}
+{"idx": 5, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Show abstract . Fast Convergence of Regularized Learning in Games . Article. Full - text available.Our results extend those of [Rakhlin and Shridharan 2013] and [Daskalakis et al . 2014], who only analyzed two-player zero-sum games for specific algorithms.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371008709_Regret_Matching_InStability_and_Fast_Convergence_in_Games", "content": "Show abstract . Fast Convergence of Regularized Learning in Games . Article. Full - text available.Our results extend those of [Rakhlin and Shridharan 2013] and [Daskalakis et al . 2014], who only analyzed two-player zero-sum games for specific algorithms."}
+{"idx": 6, "title": "Regret Matching $^+$: ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo.", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/farina-2023-regret/", "content": "Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo."}
+{"idx": 7, "title": "Julien Grand-Clément | 5 Publications | 2 Citations | Related Authors", "date": "", "ddg_snippet": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo +4 more.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/authors/julien-grand-clement-3tq4z79u", "content": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo +4 more."}
+{"idx": 8, "title": "Regret Matching+ : - Instability, average- and last-iterate convergence ...", "date": "", "ddg_snippet": "Presentation based on: • Regret Matching+ : Instability and Fast Convergence in Games , Farina , G.-C., Kroer, Lee and Luo, NeurIPS 2023 . •", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Presentation based on: • Regret Matching+ : Instability and Fast Convergence in Games , Farina , G.-C., Kroer, Lee and Luo, NeurIPS 2023 . •"}
+{"idx": 9, "title": "Faster Game Solving via Predictive Blackwell Approachability...", "date": "", "ddg_snippet": "In spite of this prevalence, the regret match -ing (RM) and regret matching+ (RM+) algorithms have been preferred in the practice of solving large-scale games (as the local regret minimizers within the counterfactual regret min-imization framework).", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/16676/16483", "content": "In spite of this prevalence, the regret match -ing (RM) and regret matching+ (RM+) algorithms have been preferred in the practice of solving large-scale games (as the local regret minimizers within the counterfactual regret min-imization framework)."}
diff --git a/data/sampled_jsons/Regret_Matching+_(In)Stability_and_Fast_Convergence_in_Games_NeurIPS_abstract_2023_year_2023.jsonl b/data/sampled_jsons/Regret_Matching+_(In)Stability_and_Fast_Convergence_in_Games_NeurIPS_abstract_2023_year_2023.jsonl
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index 0000000000000000000000000000000000000000..b715149d3c5bad7449ad35f87f52c6542ab927b6
--- /dev/null
+++ b/data/sampled_jsons/Regret_Matching+_(In)Stability_and_Fast_Convergence_in_Games_NeurIPS_abstract_2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Authors Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Authors Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ..."}
+{"idx": 1, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games Regret Matching : (In)Stability and Fast Convergence in Games Regret Matching$^+$: (In)Stability and Fast Convergence in Games Regret matching+ | Proceedings of the 37th International ... Regret Matching+: - Instability, average- and last-iterate ... Regret Matching +: ( In ) Stability and Fast Convergence in Games Regret Matching : ( In ) Stability and Fast Convergence in Games - Mas… Regret Matching : ( In ) Stability and Fast Convergence in Games - Mas… Regret matching + | Proceedings of the 37th International Conference … Regret Matching : ( In ) Stability and Fast Convergence in Games - Mas… Regret Matching : ( In ) Stability and Fast Convergence in Games - Mas… Gabriele Farina - Regret Matching$^+$: (In)Stability and Fast ...", "date": "", "ddg_snippet": "May 24, 2023 · Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and ... Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples ... Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL Dec 10, 2023 · Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . Presentation based on: Regret Matching+ : Instability and Fast Convergence in Games , Are regret matching+ algorithms effective in solving large-scale games? Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery . Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. Do regret minimizers have faster convergence rates? ut its practical performance is usually significantly faster.On the other hand, a line of recent works show that regret minimizers based on follow the regular-ized leader (FTRL) or online mirror descent (OMD) enjoy faster convergence rates in theory when ombined with the concept of opti Which algorithm guarantees a smooth predictive RM+? the resulting algorithm smooth predictive RM+ (Algorithm 2). Besides a similar result to Theorem 4.1 on the individual regret (omitted for simplicity), Algorithm 2 also guarantees that the social regret is nded by a ga .2. Theorem 4.2.Letη lgorithm 2 guarantees that the so-Algorithm 3 Conceptual RM+1: Input: Step size η > 0 wit Does chopping off the positive orthant RM+ operate in? We then provide two fixes: restarting and chopping off the positive orthant that RM+ operates in . Combined with RM+ with predictions, we show that restarting is sufficient to get O (T1/4) individual regret and that chopping off achieves O (1) social regret in normal-form games. Does asynchronous restarting stabilize the RM+ algorithm? bounded by O d3/2T1/4 in multi-t=1 player normal-form games.Although the restarting idea successfully stabilizes the RM+ algorithm , the discontinuity created by asynchronous restarts caus s technical dificulty for bounding the social regret by O(1). Next we ntroduce an alternativ Does extragradient RM+ achieve O(1) social regret? Extragradient RM+ achieves O(1) social regret (Theorem 5.6). S e Table 1 for a summary of our results for normal-form games. We further extend Conceptual RM+ to extensive-form games (EFG), yielding O(1) regret in T iterations with O(T log(T)) gradient computation. The key step here is t s ow the Lipschitzness of the CFR decomposition (Lemma J. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM$^+$ and its predictive version can be unstable, which might cause other players to suffer large regret .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "May 24, 2023 · Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and ... Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples ... Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL Dec 10, 2023 · Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . Presentation based on: Regret Matching+ : Instability and Fast Convergence in Games , Are regret matching+ algorithms effective in solving large-scale games? Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery . Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. Do regret minimizers have faster convergence rates? ut its practical performance is usually significantly faster.On the other hand, a line of recent works show that regret minimizers based on follow the regular-ized leader (FTRL) or online mirror descent (OMD) enjoy faster convergence rates in theory when ombined with the concept of opti Which algorithm guarantees a smooth predictive RM+? the resulting algorithm smooth predictive RM+ (Algorithm 2). Besides a similar result to Theorem 4.1 on the individual regret (omitted for simplicity), Algorithm 2 also guarantees that the social regret is nded by a ga .2. Theorem 4.2.Letη lgorithm 2 guarantees that the so-Algorithm 3 Conceptual RM+1: Input: Step size η > 0 wit Does chopping off the positive orthant RM+ operate in? We then provide two fixes: restarting and chopping off the positive orthant that RM+ operates in . Combined with RM+ with predictions, we show that restarting is sufficient to get O (T1/4) individual regret and that chopping off achieves O (1) social regret in normal-form games. Does asynchronous restarting stabilize the RM+ algorithm? bounded by O d3/2T1/4 in multi-t=1 player normal-form games.Although the restarting idea successfully stabilizes the RM+ algorithm , the discontinuity created by asynchronous restarts caus s technical dificulty for bounding the social regret by O(1). Next we ntroduce an alternativ Does extragradient RM+ achieve O(1) social regret? Extragradient RM+ achieves O(1) social regret (Theorem 5.6). S e Table 1 for a summary of our results for normal-form games. We further extend Conceptual RM+ to extensive-form games (EFG), yielding O(1) regret in T iterations with O(T log(T)) gradient computation. The key step here is t s ow the Lipschitzness of the CFR decomposition (Lemma J. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM$^+$ and its predictive version can be unstable, which might cause other players to suffer large regret ."}
+{"idx": 2, "title": "Regret Matching$^+$: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/farina-2023-regret/", "content": "Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL"}
+{"idx": 3, "title": "Gabriele Farina - Regret Matching$^+$: (In)Stability and Fast ...", "date": "", "ddg_snippet": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM$^+$ and its predictive version can be unstable, which might cause other players to suffer large regret .", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/", "content": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM$^+$ and its predictive version can be unstable, which might cause other players to suffer large regret ."}
+{"idx": 4, "title": "Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples ..."}
+{"idx": 5, "title": "Regret matching+ | Proceedings of the 37th International ...", "date": "", "ddg_snippet": "Dec 10, 2023 · Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668812", "content": "Dec 10, 2023 · Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability ."}
+{"idx": 6, "title": "Regret Matching+: - Instability, average- and last-iterate ...", "date": "", "ddg_snippet": "Presentation based on: Regret Matching+ : Instability and Fast Convergence in Games ,", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Presentation based on: Regret Matching+ : Instability and Fast Convergence in Games ,"}
+{"idx": 7, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nYgs0qZJ97", "content": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to..."}
+{"idx": 8, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371008709_Regret_Matching_InStability_and_Fast_Convergence_in_Games", "content": "Abstract . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability ."}
+{"idx": 9, "title": "Julien Grand-Clément | 5 Publications | 2 Citations | Related Authors", "date": "", "ddg_snippet": "TL;DR: The Conic Blackwell Algorithm (CBA+) regret minimizer is introduced, a new parameter-free algorithm for solving convex-concave saddle-point problems, which achieves a O( 1 / √ T ) ergodic rate of convergence . Regret Matching+ : ( In ) Stability and Fast Convergence in Games .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/authors/julien-grand-clement-3tq4z79u", "content": "TL;DR: The Conic Blackwell Algorithm (CBA+) regret minimizer is introduced, a new parameter-free algorithm for solving convex-concave saddle-point problems, which achieves a O( 1 / √ T ) ergodic rate of convergence . Regret Matching+ : ( In ) Stability and Fast Convergence in Games ."}
diff --git a/data/sampled_jsons/Regret_Matching+_algorithm_OpenReview_LWeVVPuIx0.jsonl b/data/sampled_jsons/Regret_Matching+_algorithm_OpenReview_LWeVVPuIx0.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f36f57744cca1274803514179eea55b4f4b5f3ba
--- /dev/null
+++ b/data/sampled_jsons/Regret_Matching+_algorithm_OpenReview_LWeVVPuIx0.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Last-Iterate Convergence Properties of Regret - Matching Algorithms ...", "date": "", "ddg_snippet": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LWeVVPuIx0", "content": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence."}
+{"idx": 1, "title": "GitHub - int8/ regret - matching : Simple implementation of regret ...", "date": "", "ddg_snippet": "This is simple implementation of regret matching algorithm for Nash Equilibrium computation for two players zero sum games via repeated self-play.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/int8/regret-matching", "content": "This is simple implementation of regret matching algorithm for Nash Equilibrium computation for two players zero sum games via repeated self-play."}
+{"idx": 2, "title": "machine learning - How to implement the regret matching algorithm ?", "date": "", "ddg_snippet": "My question is the following: How to calculate the regret in practice? I am trying to implement the regret matching algorithm but I do not understand how to do it.", "subpage_snippet": "", "source": "cs.stackexchange.com", "link": "https://cs.stackexchange.com/questions/27915/how-to-implement-the-regret-matching-algorithm", "content": "My question is the following: How to calculate the regret in practice? I am trying to implement the regret matching algorithm but I do not understand how to do it."}
+{"idx": 3, "title": "Faster Regret Matching | DeepAI", "date": "", "ddg_snippet": "The regret matching algorithm proposed by Sergiu Hart is one of the most powerful iterative methods in finding correlated equilibrium.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/faster-regret-matching", "content": "The regret matching algorithm proposed by Sergiu Hart is one of the most powerful iterative methods in finding correlated equilibrium."}
+{"idx": 4, "title": "Regret Matching+ : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract: Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "Abstract: Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games."}
+{"idx": 5, "title": "An algorithm that does NOT work: Follow-the-Leader", "date": "", "ddg_snippet": "The Regret Matching (RM) algorithm .The regret matching+ algorithm was introduced by Tammelin [2014], Tammelin et al. [2015], and is given in Algorithm 2. It differs from RM only on the last line, where a further thresholding is added.", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/6S890/lecture5.pdf", "content": "The Regret Matching (RM) algorithm .The regret matching+ algorithm was introduced by Tammelin [2014], Tammelin et al. [2015], and is given in Algorithm 2. It differs from RM only on the last line, where a further thresholding is added."}
+{"idx": 6, "title": "Steps to building a Poker AI — Part 4: Regret Matching for...", "date": "", "ddg_snippet": "The regret matching algorithm is used to minimize expected future regrets through self-play. The opponent's strategy is fixed, with a 50% chance of choosing Rock, a 20% chance of choosing Paper, and a 30% chance of choosing Scissors.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/steps-to-building-a-poker-ai-part-4-regret-matching-for-rock-paper-scissors-in-python-168411edbb13", "content": "The regret matching algorithm is used to minimize expected future regrets through self-play. The opponent's strategy is fixed, with a 50% chance of choosing Rock, a 20% chance of choosing Paper, and a 30% chance of choosing Scissors."}
+{"idx": 7, "title": "Union Find in 5 minutes — Data Structures & Algorithms - YouTube", "date": "", "ddg_snippet": "This video covers one of the most popular data structures and algorithms topic \"Union Find\". This is an instruction showing how to run Union-Find on a graph", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=ayW5B2W9hfo", "content": "This video covers one of the most popular data structures and algorithms topic \"Union Find\". This is an instruction showing how to run Union-Find on a graph"}
+{"idx": 8, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "Algorithm 1 Regret Matching+ (RM+) Algorithm 2 Predictive RM+ (PRM+).In this paper, we investigate the last-iterate convergence properties of regret - matching algorithms , a class of popular methods for equilibrium computation in games.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LWeVVPuIx0", "content": "Algorithm 1 Regret Matching+ (RM+) Algorithm 2 Predictive RM+ (PRM+).In this paper, we investigate the last-iterate convergence properties of regret - matching algorithms , a class of popular methods for equilibrium computation in games."}
+{"idx": 9, "title": "Alternating", "date": "", "ddg_snippet": "Regret - matching is an algorithm for solving the online regret minimisation problem. Regret - matching+ is a variant of regret - matching that stores a set of non-negative regret -like values.", "subpage_snippet": "", "source": "www.jair.org", "link": "https://www.jair.org/index.php/jair/article/download/11370/26477/", "content": "Regret - matching is an algorithm for solving the online regret minimisation problem. Regret - matching+ is a variant of regret - matching that stores a set of non-negative regret -like values."}
diff --git a/data/sampled_jsons/Regret_Matching+_algorithm_update_rule.jsonl b/data/sampled_jsons/Regret_Matching+_algorithm_update_rule.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d232e6544095697a338f0c9c01b4a80f5f6f6824
--- /dev/null
+++ b/data/sampled_jsons/Regret_Matching+_algorithm_update_rule.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "op-ping off achieves O(1) social regret in normal-form games. We also apply our stabilizing techniques to clairvoyant updates in the uncoupled learning setting for RM +, introduced Extragradient RM +, and prove desirable results akin t.", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "op-ping off achieves O(1) social regret in normal-form games. We also apply our stabilizing techniques to clairvoyant updates in the uncoupled learning setting for RM +, introduced Extragradient RM +, and prove desirable results akin t."}
+{"idx": 1, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Our experiments show the advantages of our algorithms over vanilla RM$^+$-based algorithms in matrix and extensive-form games.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Our experiments show the advantages of our algorithms over vanilla RM$^+$-based algorithms in matrix and extensive-form games."}
+{"idx": 2, "title": "Regret Matching+: - Instability, average- and last-iterate ...", "date": "", "ddg_snippet": "Presentation based on: Regret Matching +: Instability and Fast Convergence in Games,", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Presentation based on: Regret Matching +: Instability and Fast Convergence in Games,"}
+{"idx": 3, "title": "Regret Matching - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "For instance, when implementing the described reinforcement learning algorithms , a player updates his learning rule as an automaton without maximizing a given performance metric.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/engineering/regret-matching", "content": "For instance, when implementing the described reinforcement learning algorithms , a player updates his learning rule as an automaton without maximizing a given performance metric."}
+{"idx": 4, "title": "Regret matching+ | Proceedings of the 37th International ...", "date": "", "ddg_snippet": "Dec 10, 2023 · We then provide two fixes: restarting and chopping off the positive orthant that RM + operates in. Combined with RM + with predictions, we show that restarting is sufficient to get O (T1/4) individual regret and that chopping off achieves O (1) social regret in normal-form games.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668812", "content": "Dec 10, 2023 · We then provide two fixes: restarting and chopping off the positive orthant that RM + operates in. Combined with RM + with predictions, we show that restarting is sufficient to get O (T1/4) individual regret and that chopping off achieves O (1) social regret in normal-form games."}
+{"idx": 5, "title": "Gabriele Farina - Regret Matching$^+$: (In)Stability and Fast ...", "date": "", "ddg_snippet": "In this paper, we first give counterexamples showing that RM and its predictive version can be unstable, which might cause other players to suffer large regret . We then provide two fixes: restarting and chopping off the positive orthant that RM works in.", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/", "content": "In this paper, we first give counterexamples showing that RM and its predictive version can be unstable, which might cause other players to suffer large regret . We then provide two fixes: restarting and chopping off the positive orthant that RM works in."}
+{"idx": 6, "title": "Gabriele Farina - Learning in Games: Algorithms (Part I)", "date": "", "ddg_snippet": "General principles in the design of learning algorithms . Follow-the-leader, regret matching , multiplicative weights update , online mirror descent.", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2024/6S890f24_L05_algo1/", "content": "General principles in the design of learning algorithms . Follow-the-leader, regret matching , multiplicative weights update , online mirror descent."}
+{"idx": 7, "title": "Counterfactual Regret Minimization (CFR)", "date": "", "ddg_snippet": "We use the following update rule which applies Blackwell’s Approachability Theorem to regret (called Regret Matching )", "subpage_snippet": "", "source": "stevengong.co", "link": "https://stevengong.co/notes/Counterfactual-Regret-Minimization", "content": "We use the following update rule which applies Blackwell’s Approachability Theorem to regret (called Regret Matching )"}
+{"idx": 8, "title": "Last-Iterate Convergence Properties of Regret Matching Algorithms ...", "date": "", "ddg_snippet": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence."}
+{"idx": 9, "title": "Artificial Intelligence, poker and regret . Part 1 | by Remi AI | Medium", "date": "", "ddg_snippet": "Regret matching (RM) is an algorithm that seeks to minimise regret about its decisions at each step/move of a game. As the name suggests, it learns from past behaviours to inform future decisions by favouring the action it regretted not having taken previously.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/hackernoon/artificial-intelligence-poker-and-regret-part-1-36c78d955720", "content": "Regret matching (RM) is an algorithm that seeks to minimise regret about its decisions at each step/move of a game. As the name suggests, it learns from past behaviours to inform future decisions by favouring the action it regretted not having taken previously."}
diff --git a/data/sampled_jsons/Regret_Matching+_algorithm_update_rule_formula.jsonl b/data/sampled_jsons/Regret_Matching+_algorithm_update_rule_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3477812897fed714f29a54b3984a2a71265cb6e1
--- /dev/null
+++ b/data/sampled_jsons/Regret_Matching+_algorithm_update_rule_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
+{"idx": 1, "title": "Lecture 22: Adaptive Methods / Regret Minimization", "date": "", "ddg_snippet": "The update rule uses the projection operator PC at each iteration to project a gradient step back onto the feasible set: x(t+1) = PC(x(t) − ηt∇ft(x(t)) where PC(x) = argmin ∥x − y∥2 y∈C If we choose C = Rd, we obtain the online gradient descent algorithm .", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~mgormley/courses/10425//slides/lecture22-adam.pdf", "content": "The update rule uses the projection operator PC at each iteration to project a gradient step back onto the feasible set: x(t+1) = PC(x(t) − ηt∇ft(x(t)) where PC(x) = argmin ∥x − y∥2 y∈C If we choose C = Rd, we obtain the online gradient descent algorithm ."}
+{"idx": 2, "title": "[2305.14709] Regret Matching+: (In)Stability and Fast ... Regret Matching+: (In)Stability and Fast Convergence in Games Regret Matching+: - Instability, average- and last-iterate ... Regret Matching - an overview | ScienceDirect Topics Regret Matching +: (In)Stability and Fast Convergence in Games Regret Matching +: (In)Stability and Fast Convergence in Games Regret Matching : (In)Stability and Fast Convergence in Games - Massa… Regret Matching : (In)Stability and Fast Convergence in Games - Massa… Regret Matching : (In)Stability and Fast Convergence in Games - Massa… Regret Matching : (In)Stability and Fast Convergence in Games - Massa… Last-Iterate Convergence Properties of Regret-Matching ...", "date": "", "ddg_snippet": "May 24, 2023 · Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and ... Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games.However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability.In this paper ... Game solving via regret minimization Regret Matching+ (RM+) and instability Improved average convergence after stabilizing RM+ Last-iterate convergence after stabilizing RM+ An adaptive regret -matching algorithm based on no- regret learning is proposed as the solution and the correlated equilibrium is obtained. Numerical results demonstrate that the game-theoretic solution has a larger capacity than random selection baselines. Are regret matching+ algorithms effective in solving large-scale games? Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery . Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. Is RM+ a No-Regret algorithm? Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable , which might cause other players to suffer large regret. Does extragradient RM+ achieve O(1) social regret? Extragradient RM+ achieves O(1) social regret (Theorem 5.6). S e Table 1 for a summary of our results for normal-form games. We further extend Conceptual RM+ to extensive-form games (EFG), yielding O(1) regret in T iterations with O(T log(T)) gradient computation. The key step here is t s ow the Lipschitzness of the CFR decomposition (Lemma J. How do you calculate individual regret of each player? he individual regret of each player is bounded by a constant. Since w 7→Πzt−1,X≥ (ηF(w)) is a contraction for η 0 wit Is restarting enough to get O(t1/4) individual regret? able, which might cause other players to suffer large regret. We then provide two fixes: restarting and chopping off the positive orthant that RM+ operates in. Combined with RM+ with predictions, we show that restarting is suficient to get O(T1/4) individual regret and that c Sep 22, 2023 · Algorithms based on regret matching, specifically regret matching$^+$ (RM$^+$), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice. Unlike algorithms such as optimistic gradient descent ascent, which have strong last-iterate and ergodic convergence properties for zero-sum games, virtually nothing is known about the last-iterate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "May 24, 2023 · Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and ... Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games.However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability.In this paper ... Game solving via regret minimization Regret Matching+ (RM+) and instability Improved average convergence after stabilizing RM+ Last-iterate convergence after stabilizing RM+ An adaptive regret -matching algorithm based on no- regret learning is proposed as the solution and the correlated equilibrium is obtained. Numerical results demonstrate that the game-theoretic solution has a larger capacity than random selection baselines. Are regret matching+ algorithms effective in solving large-scale games? Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery . Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. Is RM+ a No-Regret algorithm? Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable , which might cause other players to suffer large regret. Does extragradient RM+ achieve O(1) social regret? Extragradient RM+ achieves O(1) social regret (Theorem 5.6). S e Table 1 for a summary of our results for normal-form games. We further extend Conceptual RM+ to extensive-form games (EFG), yielding O(1) regret in T iterations with O(T log(T)) gradient computation. The key step here is t s ow the Lipschitzness of the CFR decomposition (Lemma J. How do you calculate individual regret of each player? he individual regret of each player is bounded by a constant. Since w 7→Πzt−1,X≥ (ηF(w)) is a contraction for η 0 wit Is restarting enough to get O(t1/4) individual regret? able, which might cause other players to suffer large regret. We then provide two fixes: restarting and chopping off the positive orthant that RM+ operates in. Combined with RM+ with predictions, we show that restarting is suficient to get O(T1/4) individual regret and that c Sep 22, 2023 · Algorithms based on regret matching, specifically regret matching$^+$ (RM$^+$), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice. Unlike algorithms such as optimistic gradient descent ascent, which have strong last-iterate and ergodic convergence properties for zero-sum games, virtually nothing is known about the last-iterate ..."}
+{"idx": 3, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games.However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability.In this paper ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games.However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability.In this paper ..."}
+{"idx": 4, "title": "Regret Matching+: - Instability, average- and last-iterate ...", "date": "", "ddg_snippet": "Game solving via regret minimization Regret Matching+ (RM+) and instability Improved average convergence after stabilizing RM+ Last-iterate convergence after stabilizing RM+", "subpage_snippet": "", "source": "www.cirm-math.fr", "link": "https://www.cirm-math.fr/RepOrga/2879/Slides/Grand-Clement_CIRM.pdf", "content": "Game solving via regret minimization Regret Matching+ (RM+) and instability Improved average convergence after stabilizing RM+ Last-iterate convergence after stabilizing RM+"}
+{"idx": 5, "title": "Regret Matching - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "An adaptive regret -matching algorithm based on no- regret learning is proposed as the solution and the correlated equilibrium is obtained. Numerical results demonstrate that the game-theoretic solution has a larger capacity than random selection baselines.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/engineering/regret-matching", "content": "An adaptive regret -matching algorithm based on no- regret learning is proposed as the solution and the correlated equilibrium is obtained. Numerical results demonstrate that the game-theoretic solution has a larger capacity than random selection baselines."}
+{"idx": 6, "title": "Last-Iterate Convergence Properties of Regret-Matching ...", "date": "", "ddg_snippet": "Sep 22, 2023 · Algorithms based on regret matching, specifically regret matching$^+$ (RM$^+$), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice. Unlike algorithms such as optimistic gradient descent ascent, which have strong last-iterate and ergodic convergence properties for zero-sum games, virtually nothing is known about the last-iterate ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fWk5Qx0exc", "content": "Sep 22, 2023 · Algorithms based on regret matching, specifically regret matching$^+$ (RM$^+$), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice. Unlike algorithms such as optimistic gradient descent ascent, which have strong last-iterate and ergodic convergence properties for zero-sum games, virtually nothing is known about the last-iterate ..."}
+{"idx": 7, "title": "An algorithm that does NOT work: Follow-the-Leader", "date": "", "ddg_snippet": "The regret matching+ (RM+) algorithm . Follow-the-Regularized-Leader (FTRL). The Multiplicative Weights Update Algorithm . MIT 6.S890 — Topics in Multiagent Learning (F23).The regret matching+ algorithm was introduced by Tammelin [2014], Tammelin et al.", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/6S890/lecture5.pdf", "content": "The regret matching+ (RM+) algorithm . Follow-the-Regularized-Leader (FTRL). The Multiplicative Weights Update Algorithm . MIT 6.S890 — Topics in Multiagent Learning (F23).The regret matching+ algorithm was introduced by Tammelin [2014], Tammelin et al."}
+{"idx": 8, "title": "Mastering the Regret Matching Plays in Game Theory", "date": "", "ddg_snippet": "This guide unpacks regret matching in game theory, covering foundational concepts, strategic plays, and advanced outcome optimization techniques.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/mastering-regret-matching-plays-game-theory", "content": "This guide unpacks regret matching in game theory, covering foundational concepts, strategic plays, and advanced outcome optimization techniques."}
+{"idx": 9, "title": "Learn AI Game Playing Algorithm Part III — Counterfactual Regret ...", "date": "", "ddg_snippet": "In CFR, we use regret matching to update our strategy σᵗ iteratively and return the average strategy as the approximated Nash equilibrium strategy after enough iterations. It is that simple. Notes about Theorem-2 Proof.", "subpage_snippet": "", "source": "xyzml.medium.com", "link": "https://xyzml.medium.com/learn-ai-game-playing-algorithm-part-iii-counterfactual-regret-minimization-b182a7ec85fb", "content": "In CFR, we use regret matching to update our strategy σᵗ iteratively and return the average strategy as the approximated Nash equilibrium strategy after enough iterations. It is that simple. Notes about Theorem-2 Proof."}
diff --git a/data/sampled_jsons/Regret_Matching+_algorithm_update_step.jsonl b/data/sampled_jsons/Regret_Matching+_algorithm_update_step.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3cfdabbb448eca1244822309823040e57129f305
--- /dev/null
+++ b/data/sampled_jsons/Regret_Matching+_algorithm_update_step.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Last-Iterate Convergence Properties of Regret Matching Algorithms ...", "date": "", "ddg_snippet": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence."}
+{"idx": 1, "title": "An algorithm that does NOT work: Follow-the-Leader", "date": "", "ddg_snippet": "The regret matching+ (RM+) algorithm . Follow-the-Regularized-Leader (FTRL). The Multiplicative Weights Update Algorithm . Halfspace to be forced ( Step 4). Following on with Blackwell’s algorithm , when [ϕ(t)]+ = 0, the halfspace to be forced at each ti...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/6S890f23_L05_learning_algorithms/L05.pdf", "content": "The regret matching+ (RM+) algorithm . Follow-the-Regularized-Leader (FTRL). The Multiplicative Weights Update Algorithm . Halfspace to be forced ( Step 4). Following on with Blackwell’s algorithm , when [ϕ(t)]+ = 0, the halfspace to be forced at each ti..."}
+{"idx": 2, "title": "Regret Matching+ : (In) Stability and Fast Convergence in... | DeepAI", "date": "", "ddg_snippet": "05/24/23 - Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/regret-matching-in-stability-and-fast-convergence-in-games", "content": "05/24/23 - Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games."}
+{"idx": 3, "title": "machine learning - How to implement the regret matching algorithm ?", "date": "", "ddg_snippet": "My question is the following: How to calculate the regret in practice? I am trying to implement the regret matching algorithm but I do not understand how to do it.", "subpage_snippet": "", "source": "cs.stackexchange.com", "link": "https://cs.stackexchange.com/questions/27915/how-to-implement-the-regret-matching-algorithm", "content": "My question is the following: How to calculate the regret in practice? I am trying to implement the regret matching algorithm but I do not understand how to do it."}
+{"idx": 4, "title": "Decoding Poker GTO: CFR+ and Optimal Strategy", "date": "", "ddg_snippet": "An in-depth exploration of the Counterfactual Regret Minimization Plus (CFR+) algorithm and how it revolutionized poker solvers like Piosolver to develop unexploitable GTO strategies.", "subpage_snippet": "", "source": "rta.poker", "link": "https://rta.poker/blog/decoding-optimal-strategy-cfr", "content": "An in-depth exploration of the Counterfactual Regret Minimization Plus (CFR+) algorithm and how it revolutionized poker solvers like Piosolver to develop unexploitable GTO strategies."}
+{"idx": 5, "title": "Alternating", "date": "", "ddg_snippet": "Regret - matching is an algorithm for solving the online regret minimisation problem.We will show that when using regret - matching or regret - matching+ , the expected utility σt+ 1 · vt is never less than σt · vt. To do this, we will need to show these algorithms have a couple of other properties.", "subpage_snippet": "", "source": "www.jair.org", "link": "https://www.jair.org/index.php/jair/article/download/11370/26477/", "content": "Regret - matching is an algorithm for solving the online regret minimisation problem.We will show that when using regret - matching or regret - matching+ , the expected utility σt+ 1 · vt is never less than σt · vt. To do this, we will need to show these algorithms have a couple of other properties."}
+{"idx": 6, "title": "Why Player Of Games Is Needed. Comparison Between... | Medium", "date": "", "ddg_snippet": "Regret - matching+ is a substantial upgrade made in CFR+ from the vanilla CFR algorithm . In regret - matching+ , we normalize the cumulative regret value Q to update the policy. By updating the policy this way, we effectively account for uncertainty in imperfect information games.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/why-player-of-games-is-needed-a01505c4bad7", "content": "Regret - matching+ is a substantial upgrade made in CFR+ from the vanilla CFR algorithm . In regret - matching+ , we normalize the cumulative regret value Q to update the policy. By updating the policy this way, we effectively account for uncertainty in imperfect information games."}
+{"idx": 7, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "Algorithm 1 Regret Matching+ (RM+) Algorithm 2 Predictive RM+ (PRM+).The step size is η = 0.05 for all algorithms . We also plot the line ( 1 − 0.002)t for reference. H.4 experiments with best-iterate convergence.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LWeVVPuIx0", "content": "Algorithm 1 Regret Matching+ (RM+) Algorithm 2 Predictive RM+ (PRM+).The step size is η = 0.05 for all algorithms . We also plot the line ( 1 − 0.002)t for reference. H.4 experiments with best-iterate convergence."}
+{"idx": 8, "title": "Time and Space: Why Imperfect Information Games are Hard", "date": "", "ddg_snippet": "Regret - matching+ , a regret minimising algorithm , is an online algorithm for action selection in a full information, adversarial setting. It operates very sim-ilarly to the regret - matching algorithm and has a similar theoretical bound on external regret .", "subpage_snippet": "", "source": "poker.cs.ualberta.ca", "link": "https://poker.cs.ualberta.ca/publications/Burch_Neil_E_201712_PhD.pdf", "content": "Regret - matching+ , a regret minimising algorithm , is an online algorithm for action selection in a full information, adversarial setting. It operates very sim-ilarly to the regret - matching algorithm and has a similar theoretical bound on external regret ."}
+{"idx": 9, "title": "Solving", "date": "", "ddg_snippet": "algorithm , regret - matching+ .Unlike in CFR, with CFR+ the current strategy prole empirically either ”al-most” converges or converges to an approximate Nash equilibrium directly, so no averaging step is necessary.", "subpage_snippet": "", "source": "modelai.gettysburg.edu", "link": "http://modelai.gettysburg.edu/2019/deepstack/Resources/Lesson4/CFR-Plus.pdf", "content": "algorithm , regret - matching+ .Unlike in CFR, with CFR+ the current strategy prole empirically either ”al-most” converges or converges to an approximate Nash equilibrium directly, so no averaging step is necessary."}
diff --git a/data/sampled_jsons/Regret_Matching+_max(0,_R_i^t)_normalization_formula.jsonl b/data/sampled_jsons/Regret_Matching+_max(0,_R_i^t)_normalization_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dcf4e29fd79350372c0592d3a70a0630ec3b431c
--- /dev/null
+++ b/data/sampled_jsons/Regret_Matching+_max(0,_R_i^t)_normalization_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Natural interviewing equilibria in matching settings", "date": "", "ddg_snippet": "by A Borodin · 2024 — It is straightforward to see that any strategy profile such that each resident r_i interviews with hospital h_i forms an equilibrium. Thus, ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00355-024-01541-2", "content": "by A Borodin · 2024 — It is straightforward to see that any strategy profile such that each resident r_i interviews with hospital h_i forms an equilibrium. Thus, ..."}
+{"idx": 1, "title": "Consumer search with anticipated regret", "date": "", "ddg_snippet": "1 Aug 2022 — There exists a unique optimal stopping rule such that the consumer buys when m > m ∗ $m>m^*$ ; otherwise, she continues to search the next ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1111/poms.13767", "content": "1 Aug 2022 — There exists a unique optimal stopping rule such that the consumer buys when m > m ∗ $m>m^*$ ; otherwise, she continues to search the next ..."}
+{"idx": 2, "title": "UMI - Yale Statistics and Data Science", "date": "", "ddg_snippet": "r'l |0) = n P ( x *l^) f°r some 0 E 0 . We show that the same strategy identified in Chapter 2 also asymptotically minimizes the worst regret. Shtarkov ...", "subpage_snippet": "", "source": "www.stat.yale.edu", "link": "http://www.stat.yale.edu/~arb4/students_files/QunXieThesis.pdf", "content": "r'l |0) = n P ( x *l^) f°r some 0 E 0 . We show that the same strategy identified in Chapter 2 also asymptotically minimizes the worst regret. Shtarkov ..."}
+{"idx": 3, "title": "Optimal Auctions through Deep Learning: Advances in ...", "date": "", "ddg_snippet": "11 Feb 2024 — 2.3 Quantile-Based Regret The intent is that the characterization-free approach leads to mechanisms with low expected ex post regret.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3630749", "content": "11 Feb 2024 — 2.3 Quantile-Based Regret The intent is that the characterization-free approach leads to mechanisms with low expected ex post regret."}
+{"idx": 4, "title": "Recommendation Systems • Multi-Armed Bandits", "date": "", "ddg_snippet": "Formally, regret R(n) over n rounds is given by: R(n)=∑n t =1(r∗−r t ); where r∗ is the expected reward of the optimal ...", "subpage_snippet": "", "source": "vinija.ai", "link": "https://vinija.ai/recsys/multi-armed-bandit/", "content": "Formally, regret R(n) over n rounds is given by: R(n)=∑n t =1(r∗−r t ); where r∗ is the expected reward of the optimal ..."}
+{"idx": 5, "title": "Classical Improvements to Modern Machine Learning", "date": "", "ddg_snippet": "by S Kaul · 2024 — By definition, its roots (those t where p( t )= 0 ) are the eigenvalues of A. So ... Let h(a) = max ( 0 ,a) be a ReLU. Consider these values: α + h(−ω)i and. 206 pages", "subpage_snippet": "", "source": "reports-archive.adm.cs.cmu.edu", "link": "http://reports-archive.adm.cs.cmu.edu/anon/2024/CMU-CS-24-137.pdf", "content": "by S Kaul · 2024 — By definition, its roots (those t where p( t )= 0 ) are the eigenvalues of A. So ... Let h(a) = max ( 0 ,a) be a ReLU. Consider these values: α + h(−ω)i and. 206 pages"}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 7, "title": "Assortment optimization: a systematic literature review", "date": "", "ddg_snippet": "by J Heger · 2024 · Cited by 22 — In this paper, we systematically review state-of-the-art studies on assortment optimization. We assemble an extensive literature overview.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00291-024-00752-4", "content": "by J Heger · 2024 · Cited by 22 — In this paper, we systematically review state-of-the-art studies on assortment optimization. We assemble an extensive literature overview."}
+{"idx": 8, "title": "Choice with Endogenous Categorization - Oxford Academic", "date": "", "ddg_snippet": "by A Ellis · 2022 · Cited by 29 — Abstract. We propose and axiomatize the categorical thinking model (CTM) in which the framing of the decision problem affects how agents categorize alterna.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/restud/article/89/1/240/6166661", "content": "by A Ellis · 2022 · Cited by 29 — Abstract. We propose and axiomatize the categorical thinking model (CTM) in which the framing of the decision problem affects how agents categorize alterna."}
+{"idx": 9, "title": "princeton-nlp/QuRating-GPT3.5-Judgments-Test", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/princeton-nlp/QuRating-GPT3.5-Judgments-Test", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
diff --git a/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023.jsonl b/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..75801dbbb697e74451f6d282735d6459e1c1a216
--- /dev/null
+++ b/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to..."}
+{"idx": 1, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "by Gabriele Farina , et al .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/regret-matching-in-stability-and-fast-convergence-in-games", "content": "by Gabriele Farina , et al .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which..."}
+{"idx": 2, "title": "Gabriele Farina - Regret Matching $^+$: ( In ) Stability and Fast ...", "date": "", "ddg_snippet": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM$^+$ and its predictive version can be unstable...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/", "content": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM$^+$ and its predictive version can be unstable..."}
+{"idx": 3, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to..."}
+{"idx": 4, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371008709_Regret_Matching_InStability_and_Fast_Convergence_in_Games", "content": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to..."}
+{"idx": 5, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nYgs0qZJ97", "content": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to..."}
+{"idx": 6, "title": "Regret Matching $^+$: ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo. December, 2023 .", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/farina-2023-regret/", "content": "Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo. December, 2023 ."}
+{"idx": 7, "title": "Julien Grand-Clément | 5 Publications | 2 Citations | Related Authors", "date": "", "ddg_snippet": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo +4 more. - 24 May 2023 .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/authors/julien-grand-clement-3tq4z79u", "content": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo +4 more. - 24 May 2023 ."}
+{"idx": 8, "title": "Julien Grand-Clément - Google Akademik", "date": "", "ddg_snippet": "Regret Matching+ :( In ) Stability and Fast Convergence in Games . G Farina , J Grand-Clément, C Kroer, CW Lee, H Luo. Proceedings of the 36th Advances in Neural Information Processing Systems …, 2023 .", "subpage_snippet": "", "source": "scholar.google.co.za", "link": "https://scholar.google.co.za/citations?user=K_ZLzdoAAAAJ&hl=tr", "content": "Regret Matching+ :( In ) Stability and Fast Convergence in Games . G Farina , J Grand-Clément, C Kroer, CW Lee, H Luo. Proceedings of the 36th Advances in Neural Information Processing Systems …, 2023 ."}
+{"idx": 9, "title": "Articles by Julien Grand-Clément | Synthical", "date": "", "ddg_snippet": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . 24 May 2023 by Gabriele Farina and others.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/1551d66f-d464-4596-898d-eb958ce85108/articles", "content": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . 24 May 2023 by Gabriele Farina and others."}
diff --git a/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_abstract_year_2023.jsonl b/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bea2a6b5068cfc58934a3665fea8fff6aa995814
--- /dev/null
+++ b/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching+: (In)Stability and Fast Convergence in ...", "date": "", "ddg_snippet": "by G Farina · 2023 · Cited by 17 — We start by showing that, in stark contrast to FTRL/OMD al - gorithms that are stable inherently, there exist loss sequences that make RM+ and its variants. 27 pages", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "by G Farina · 2023 · Cited by 17 — We start by showing that, in stark contrast to FTRL/OMD al - gorithms that are stable inherently, there exist loss sequences that make RM+ and its variants. 27 pages"}
+{"idx": 1, "title": "Regret matching + : (in)stability and fast convergence ...", "date": "", "ddg_snippet": "by G Farina · 2023 · Cited by 17 — Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3666122.3668812", "content": "by G Farina · 2023 · Cited by 17 — Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of ..."}
+{"idx": 2, "title": "(In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "8 Jul 2025 — In this paper, we first give counterexamples showing that RM + ^+ + and its predictive version can be unstable, which might cause other players to suffer large ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/", "content": "8 Jul 2025 — In this paper, we first give counterexamples showing that RM + ^+ + and its predictive version can be unstable, which might cause other players to suffer large ..."}
+{"idx": 3, "title": "Last-Iterate Convergence Properties of Regret-Matching ...", "date": "", "ddg_snippet": "by Y Cai · Cited by 5 — In this paper, we study the last-iterate convergence properties of various popular variants of RM$^+$.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fWk5Qx0exc", "content": "by Y Cai · Cited by 5 — In this paper, we study the last-iterate convergence properties of various popular variants of RM$^+$."}
+{"idx": 4, "title": "Efficient Last-Iterate Convergence in Regret Minimization ...", "date": "", "ddg_snippet": "4 days ago — Regret matching+:(in) stability and fast convergence in games . Advances in Neural Information Processing Systems, 36, 2024. Farina et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13653v1", "content": "4 days ago — Regret matching+:(in) stability and fast convergence in games . Advances in Neural Information Processing Systems, 36, 2024. Farina et al."}
+{"idx": 5, "title": "Efficient Last-Iterate Convergence in Solving Games", "date": "", "ddg_snippet": "18 Mar 2025 — Regret matching+: (in)stability and fast convergence in games . In Proceedings of the 37th Conference on Neural Information Processing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2308.11256v2", "content": "18 Mar 2025 — Regret matching+: (in)stability and fast convergence in games . In Proceedings of the 37th Conference on Neural Information Processing ..."}
+{"idx": 6, "title": "arXiv:2309.03084v1 [cs.AI] 4 Sep 2023", "date": "", "ddg_snippet": "Abstract . Counterfactual Regret Minimization (CFR) and its variants are the best algorithms so far for solving large-scale incom- plete information games .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/pure-monte-carlo-counterfactual-regret-minimization-2aruule5s5.pdf", "content": "Abstract . Counterfactual Regret Minimization (CFR) and its variants are the best algorithms so far for solving large-scale incom- plete information games ."}
+{"idx": 7, "title": "Minimizing Weighted Counterfactual Regret with Optimistic ...", "date": "", "ddg_snippet": "by H Xu · Cited by 4 — Recent work [ Farina et al .,. 2023 ] proposes two fixes for PRM+, achieving O(1/T) con- vergence in normal-form games . It is worthwhile to investi- gate whether ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0583.pdf", "content": "by H Xu · Cited by 4 — Recent work [ Farina et al .,. 2023 ] proposes two fixes for PRM+, achieving O(1/T) con- vergence in normal-form games . It is worthwhile to investi- gate whether ..."}
+{"idx": 8, "title": "Extensive-Form Game Solving via Blackwell ...", "date": "", "ddg_snippet": "Farina, J. Grand-Clément, C. Kroer, C.-W. Lee, and H. Luo. Regret matching+: (in)stability and fast convergence in games . In Advances in Neural Information ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96365", "content": "Farina, J. Grand-Clément, C. Kroer, C.-W. Lee, and H. Luo. Regret matching+: (in)stability and fast convergence in games . In Advances in Neural Information ..."}
+{"idx": 9, "title": "DIVERGENCE-REGULARIZED DISCOUNTED AGGREGA", "date": "", "ddg_snippet": "This paper presents Divergence-Regularized Discounted Aggregation (DRDA), a multi-round learning system for solving partially observable stochastic games .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/7f7afd4efd19711f3853db13572bbfc54393afd8.pdf", "content": "This paper presents Divergence-Regularized Discounted Aggregation (DRDA), a multi-round learning system for solving partially observable stochastic games ."}
diff --git a/data/sampled_jsons/RepE_large_language_models_interpretability.jsonl b/data/sampled_jsons/RepE_large_language_models_interpretability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b8c7b7c9922d65c544d14c53ee576163d040b5d
--- /dev/null
+++ b/data/sampled_jsons/RepE_large_language_models_interpretability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improving Large Language Models Function Calling and Interpretability ...", "date": "", "ddg_snippet": "Large language models (LLMs) have demonstrated strong reasoning and tool-use capabilities, yet they often fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. These issues often stem from an incomplete understanding of user goals and inadequate comprehension of tool documentation. While Chain-of-Thought (CoT ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2509.18076", "content": "Large language models (LLMs) have demonstrated strong reasoning and tool-use capabilities, yet they often fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. These issues often stem from an incomplete understanding of user goals and inadequate comprehension of tool documentation. While Chain-of-Thought (CoT ..."}
+{"idx": 1, "title": "Rethinking The Reliability of Representation Engineering in Large ...", "date": "", "ddg_snippet": "Inspired by cognitive neuroscience, representation engineering ( RepE ) seeks to connect the neural activities within large language models (LLMs) to their behaviors, providing a promising pathway...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=sYJQEgkkaI", "content": "Inspired by cognitive neuroscience, representation engineering ( RepE ) seeks to connect the neural activities within large language models (LLMs) to their behaviors, providing a promising pathway..."}
+{"idx": 2, "title": "Awesome Interpretability in Large Language Models - GitHub", "date": "", "ddg_snippet": "Awesome Interpretability in Large Language Models The area of interpretability in large language models (LLMs) has been growing rapidly in recent years. This repository tries to collect all relevant resources to help beginners quickly get started in this area and help researchers to keep up with the latest research progress.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ruizheliUOA/Awesome-Interpretability-in-Large-Language-Models", "content": "Awesome Interpretability in Large Language Models The area of interpretability in large language models (LLMs) has been growing rapidly in recent years. This repository tries to collect all relevant resources to help beginners quickly get started in this area and help researchers to keep up with the latest research progress."}
+{"idx": 3, "title": "Explainability and Interpretability of Multilingual Large Language ...", "date": "", "ddg_snippet": "Multilingual large language models (MLLMs) demonstrate state-of-the-art capabilities across diverse cross-lingual and multilingual tasks. Their complex internal mechanisms, however, often lack transparency, posing significant challenges in elucidating their internal processing of multilingualism, cross-lingual transfer dynamics and handling of language -specific features. This paper addresses ...", "subpage_snippet": "", "source": "www.copenlu.com", "link": "http://www.copenlu.com/publication/2025_openreview_resck/", "content": "Multilingual large language models (MLLMs) demonstrate state-of-the-art capabilities across diverse cross-lingual and multilingual tasks. Their complex internal mechanisms, however, often lack transparency, posing significant challenges in elucidating their internal processing of multilingualism, cross-lingual transfer dynamics and handling of language -specific features. This paper addresses ..."}
+{"idx": 4, "title": "Explainability for Large Language Models: A Survey", "date": "", "ddg_snippet": "Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this article, we introduce ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3639372", "content": "Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this article, we introduce ..."}
+{"idx": 5, "title": "Rethinking Interpretability in the Era of Large Language Models", "date": "", "ddg_snippet": "Interpretable machine learning has exploded as an area of interest over the last decade, sparked by the rise of increasingly large datasets and deep neural networks. Simultaneously, large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks, offering a chance to rethink opportunities in interpretable machine learning. Notably, the capability to explain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.01761", "content": "Interpretable machine learning has exploded as an area of interest over the last decade, sparked by the rise of increasingly large datasets and deep neural networks. Simultaneously, large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks, offering a chance to rethink opportunities in interpretable machine learning. Notably, the capability to explain ..."}
+{"idx": 6, "title": "The Rise of Large Language Models: Evolution, Applications, and Future ...", "date": "", "ddg_snippet": "Large language models have the potential to revolutionize numerous disciplines, as evidenced by subsequent milestones and diverse model designs. The emergence of GPT-4 in 2023 has led to improvements in multimodal language models [5, 7 - 9]. LLM offers various programs that affect businesses across different industries and use cases.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1002/eng2.70368", "content": "Large language models have the potential to revolutionize numerous disciplines, as evidenced by subsequent milestones and diverse model designs. The emergence of GPT-4 in 2023 has led to improvements in multimodal language models [5, 7 - 9]. LLM offers various programs that affect businesses across different industries and use cases."}
+{"idx": 7, "title": "Large language models can do jaw-dropping things. But nobody knows ...", "date": "", "ddg_snippet": "The largest models , and large language models in particular, seem to behave in ways textbook math says they shouldn't.", "subpage_snippet": "", "source": "www.technologyreview.com", "link": "https://www.technologyreview.com/2024/03/04/1089403/large-language-models-amazing-but-nobody-knows-why/", "content": "The largest models , and large language models in particular, seem to behave in ways textbook math says they shouldn't."}
+{"idx": 8, "title": "PDF InterpretabilityintheEraofLargeLanguageModels ...", "date": "", "ddg_snippet": "InterpretabilityintheEraofLargeLanguageModels: OpportunitiesandChallenges Interpretability in the Era of Large Language Models : Opportunities and Challenges", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/5177c627eec4962417ca349e4766628da370fae9.pdf", "content": "InterpretabilityintheEraofLargeLanguageModels: OpportunitiesandChallenges Interpretability in the Era of Large Language Models : Opportunities and Challenges"}
+{"idx": 9, "title": "Representation Engineering: A Top-Down Approach to AI Transparency ...", "date": "", "ddg_snippet": "Introduction to Representation Engineering ( RepE ) Representation Engineering ( RepE ) offers a complementary research paradigm for understanding and controlling Large Language Models (LLMs), running parallel to Mechanistic Interpretability (MI). While MI primarily operates at the level of circuits—such as attention heads, pathways, and neurons— RepE focuses on the representational spaces ...", "subpage_snippet": "", "source": "longjubai.github.io", "link": "https://longjubai.github.io/blog/2024/Representation-Engineering/", "content": "Introduction to Representation Engineering ( RepE ) Representation Engineering ( RepE ) offers a complementary research paradigm for understanding and controlling Large Language Models (LLMs), running parallel to Mechanistic Interpretability (MI). While MI primarily operates at the level of circuits—such as attention heads, pathways, and neurons— RepE focuses on the representational spaces ..."}
diff --git a/data/sampled_jsons/ResearchGate_385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Section_5.1.jsonl b/data/sampled_jsons/ResearchGate_385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Section_5.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dbb6b7e98b21c1f28bbff0ba2a95ddddb4a2fb49
--- /dev/null
+++ b/data/sampled_jsons/ResearchGate_385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Section_5.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v . t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v . t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "(PDF) On the Role of Attention Heads in Large Language Model ...", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged..."}
+{"idx": 2, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Oct 17, 2024 · However, existing research tends to overlook the safety impact of multi- head attention mechanisms, despite their crucial role in various model functionalities. Hence, in this paper, we aim to explore the connection between standard attention mechanisms and safety capability to fill this gap in the safety -related mechanistic interpretability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.13708", "content": "Oct 17, 2024 · However, existing research tends to overlook the safety impact of multi- head attention mechanisms, despite their crucial role in various model functionalities. Hence, in this paper, we aim to explore the connection between standard attention mechanisms and safety capability to fill this gap in the safety -related mechanistic interpretability."}
+{"idx": 3, "title": "【论文阅读】ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODE...", "date": "", "ddg_snippet": "ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODEL SAFETY 原文摘要 研究背景与现状 背景 LLMs 在多种语言任务上表现出色，但其安全防护措施可能被绕过，从而生成有害内容。 已有研究发现，当模型的安全性表示或相关组件被压制时，其安全能力会受损。 现状 尽管安全机制的研究不断深入，但目前的研究 ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/xianshuiyihui/article/details/149698061", "content": "ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODEL SAFETY 原文摘要 研究背景与现状 背景 LLMs 在多种语言任务上表现出色，但其安全防护措施可能被绕过，从而生成有害内容。 已有研究发现，当模型的安全性表示或相关组件被压制时，其安全能力会受损。 现状 尽管安全机制的研究不断深入，但目前的研究 ..."}
+{"idx": 4, "title": "ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODEL SAFETY", "date": "", "ddg_snippet": "Hence, in this paper, we aim to explore the connection between standard attention mechanisms and safety capability to fill this gap in safety -related mechanistic interpretability. We propose a novel metric tailored for multi- head attention , the Safety Head ImPortant Score (Ships), to as-sess the individual heads ’ contributions to model safety .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=h0Ak8A5yqw", "content": "Hence, in this paper, we aim to explore the connection between standard attention mechanisms and safety capability to fill this gap in safety -related mechanistic interpretability. We propose a novel metric tailored for multi- head attention , the Safety Head ImPortant Score (Ships), to as-sess the individual heads ’ contributions to model safety ."}
+{"idx": 5, "title": "arXiv:2410.13708v1 [cs.CL] 17 Oct 2024 - ResearchGate", "date": "", "ddg_snippet": "ica-tion required in previous studies. More importantly, we demonstrate that attention heads primarily function as feature extractors for safety and models fine-tuned from the same base model ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety/fulltext/6711f20e069cb92a811a75e8/On-the-Role-of-Attention-Heads-in-Large-Language-Model-Safety.pdf", "content": "ica-tion required in previous studies. More importantly, we demonstrate that attention heads primarily function as feature extractors for safety and models fine-tuned from the same base model ..."}
+{"idx": 6, "title": "ICLR 2025 On the Role of Attention Heads in Large Language ...", "date": "", "ddg_snippet": "However, existing research tends to overlook the safety impact of multi- head attention mechanisms, despite their crucial role in various model functionalities. Hence, in this paper, we aim to explore the connection between standard attention mechanisms and safety capability to fill this gap in the safety -related mechanistic interpretability.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/oral/31798", "content": "However, existing research tends to overlook the safety impact of multi- head attention mechanisms, despite their crucial role in various model functionalities. Hence, in this paper, we aim to explore the connection between standard attention mechanisms and safety capability to fill this gap in the safety -related mechanistic interpretability."}
+{"idx": 7, "title": "Attention Heads of Large Language Models: A Survey", "date": "", "ddg_snippet": "Sep 5 , 2024 · Since the advent of ChatGPT, Large Language Models (LLMs) have excelled in various tasks but remain as black-box systems. Understanding the reasoning bottlenecks of LLMs has become a critical challenge, as these limitations are deeply tied to their internal architecture. Among these, attention heads have emerged as a focal point for investigating the underlying mechanics of LLMs. In this ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.03752", "content": "Sep 5 , 2024 · Since the advent of ChatGPT, Large Language Models (LLMs) have excelled in various tasks but remain as black-box systems. Understanding the reasoning bottlenecks of LLMs has become a critical challenge, as these limitations are deeply tied to their internal architecture. Among these, attention heads have emerged as a focal point for investigating the underlying mechanics of LLMs. In this ..."}
+{"idx": 8, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that when safety ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=h0Ak8A5yqw", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that when safety ..."}
+{"idx": 9, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Abstract: Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that when safety ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2410.13708v1", "content": "Abstract: Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that when safety ..."}
diff --git a/data/sampled_jsons/Responsible_AI_Licenses_(RAIL).jsonl b/data/sampled_jsons/Responsible_AI_Licenses_(RAIL).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..30fdb382cba9c0ab8deb5d90ced5ca906e554080
--- /dev/null
+++ b/data/sampled_jsons/Responsible_AI_Licenses_(RAIL).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Responsible AI Licenses (RAIL)", "date": "", "ddg_snippet": "What is a rail license ? Responsible AI Licenses (RAIL ) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications. These licenses include behavioral-use clauses which grant permissions for specific use-cases and/or restrict certain use-cases.", "subpage_snippet": "", "source": "www.licenses.ai", "link": "https://www.licenses.ai/", "content": "What is a rail license ? Responsible AI Licenses (RAIL ) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications. These licenses include behavioral-use clauses which grant permissions for specific use-cases and/or restrict certain use-cases."}
+{"idx": 1, "title": "Responsible AI Licenses (RAIL): Here’s What You Need to Know", "date": "", "ddg_snippet": "May 22, 2024 · Responsible AI Licenses (RAIL ) are a class of licenses created with the intention of preventing harmful or unethical uses of artificial intelligence while also allowing for the free and open sharing of models between those who intend to use and improve them for authorized purposes.", "subpage_snippet": "", "source": "www.mend.io", "link": "https://www.mend.io/blog/responsible-ai-licenses-rail-heres-what-you-need-to-know/", "content": "May 22, 2024 · Responsible AI Licenses (RAIL ) are a class of licenses created with the intention of preventing harmful or unethical uses of artificial intelligence while also allowing for the free and open sharing of models between those who intend to use and improve them for authorized purposes."}
+{"idx": 2, "title": "What are Responsible AI Licenses (RAIL)? - AI Glossary ...", "date": "", "ddg_snippet": "What are Responsible AI Licenses (RAIL )? Responsible AI Licenses (RAIL ) are specialized licensing frameworks designed to ensure that artificial intelligence ( AI ) models, datasets, and tools are used in ways that align with ethical principles and avoid harmful applications.", "subpage_snippet": "", "source": "www.theainavigator.com", "link": "https://www.theainavigator.com/blog/what-are-responsible-ai-licenses-rail", "content": "What are Responsible AI Licenses (RAIL )? Responsible AI Licenses (RAIL ) are specialized licensing frameworks designed to ensure that artificial intelligence ( AI ) models, datasets, and tools are used in ways that align with ethical principles and avoid harmful applications."}
+{"idx": 3, "title": "Responsible AI licenses: a practical tool for implementing ... Responsible AI License RAIL - Reasoned Insights, Inc. What are Responsible AI Licenses (RAIL)? - AI Glossary ... Responsible AI Licenses (RAIL): Responsible AI use through ... Responsible AI Licenses ( RAIL ) Responsible AI Licenses ( RAIL ) Responsible AI Licenses ( RAIL ): Here’s What You Need to Know Responsible AI licenses : a practical tool for implementing the ... - OECD Responsible AI licenses : a practical tool for implementing the ... - OECD Responsible AI licenses : a practical tool for implementing the ... - OECD Responsible AI Licenses (RAIL) - AI Tech Suite", "date": "", "ddg_snippet": "One example of this is BigScience. In 2021, Hugging Face led a one-year research initiative with the support of many actors including the French research institutions GENCI and CNRS. The initiative aimed to create the first open and collaboration-based multilingual (46 languages) Large Language Model (LLM) developed on a transparent basis. The frui... See full list on oecd. ai All of this took place while major players in the AI space released their other major models. Meta released its LLM OPT-175, chatbot SEER, and vision BB3 models, all under a responsible AI license for research purposes. Stable Diffusion by Stability. ai is the latest major ML release that uses an OpenRAIL license. It is a multimodal generative model... See full list on oecd. ai These licenses are in line with ongoing AI regulatory proposals, namely the EU AI Act. In the long run having responsibility engrained in the development, release, and use of AI could reduce many inequalities, of which socio-economic and gender are just a couple of examples. To this end, RAILs and OpenRAILs add value by deterring the usage of AI in... See full list on oecd. ai To give a more practical approach to showing the articulation between the BigScience OpenRAIL-M license and the OECD AI Principles, the table below shows exactly how the articulation between existing and already implemented AI governance tools, such as OpenRAILs, and widely accepted international AI principles is happening. See full list on oecd. ai This assessment of the OECD AI Principles implies that policy makers could do more to integrate RAILs and OpenRAILs into their AI policy strategies and public procurement initiatives. RAIL licenses are designed as a complementary tool for the ongoing challenges that AI policy and regulatory efforts tackle on the international stage. They can be a p... See full list on oecd. ai Jan 23, 2024 · Responsible AI License ( RAIL ) The Responsible AI License , abbreviated as RAIL , is a licensing framework specifically tailored for AI technologies. The Apache 2.0, MIT and GPL license have adequately addressed the needs of various open source initiatives. RAIL goes beyond traditional licensing models by incorporating principles of responsibility, ethics, and transparency into the legal ... What are Responsible AI Licenses (RAIL )? Responsible AI Licenses (RAIL ) are specialized licensing frameworks designed to ensure that artificial intelligence ( AI ) models, datasets, and tools are used in ways that align with ethical principles and avoid harmful applications. Mar 18, 2025 · Artificial intelligence is evolving rapidly – but how can we ensure that its use remains ethical and responsible ? Firstly, the AI Regulation sets out ethical guidelines for AI systems. In addition, the AI Licensing Initiative and the AI Licensing Initiative offer Responsible AI Licenses (RAIL ) also a legal guideline for ethical AI use. What is a responsible AI license? Responsible AI Licenses ( RAIL ) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications. We provide both source code licenses as well as end-user licenses that developers/providers can include with AI software to restrict its irresponsible use. What is a rail license? Responsible AI Licenses (RAIL) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications. These licenses include behavioral-use clauses which grant permissions for specific use-cases and/or restrict certain use-cases. What AI models are covered under a rail license? Famous AI models covered under RAIL licenses include Stable Diffusion and BLOOM . In this blog we’ll answer some common questions about RAIL licenses. The licenses themselves are dry, sure, but they’re perfectly readable by a non-lawyer so be sure to look through any specific licenses that cover models you want to use. Are rails a good idea for AI governance? RAILs would have even greater value if articulated with more complementary tools for responsible AI available in the AI governance ecosystem such as model cards. Model cards may inform the draft and choice of a RAIL. What are openrail licenses? When these licenses allow free access and flexible downstream distribution of the licensed AI feature , they are OpenRAIL The RAIL Initiative was created in 2019 to encourage the industry to adopt use restrictions in licenses as a way to mitigate the risks of misuse and potential harm caused by AI systems. One example of this is BigScience. What is a licensed AI feature? The license can be conceived as a mechanism that can be implemented by the AI community and is appropriate for the context of open sharing, consistent with the capabilities and limitations of AI. Use restrictions on the use of the licensed AI feature are conducive to respect for human rights throughout an AI system’s lifecycle. Responsible AI Licenses (RAIL ) are designed to encourage responsible AI use by including use restrictions. OpenRAIL is a subclass that permits free, open access and reuse for commercial purposes, while still including usage restrictions.", "subpage_snippet": "", "source": "oecd.ai", "link": "https://oecd.ai/en/wonk/rails-licenses-trustworthy-ai", "content": "One example of this is BigScience. In 2021, Hugging Face led a one-year research initiative with the support of many actors including the French research institutions GENCI and CNRS. The initiative aimed to create the first open and collaboration-based multilingual (46 languages) Large Language Model (LLM) developed on a transparent basis. The frui... See full list on oecd. ai All of this took place while major players in the AI space released their other major models. Meta released its LLM OPT-175, chatbot SEER, and vision BB3 models, all under a responsible AI license for research purposes. Stable Diffusion by Stability. ai is the latest major ML release that uses an OpenRAIL license. It is a multimodal generative model... See full list on oecd. ai These licenses are in line with ongoing AI regulatory proposals, namely the EU AI Act. In the long run having responsibility engrained in the development, release, and use of AI could reduce many inequalities, of which socio-economic and gender are just a couple of examples. To this end, RAILs and OpenRAILs add value by deterring the usage of AI in... See full list on oecd. ai To give a more practical approach to showing the articulation between the BigScience OpenRAIL-M license and the OECD AI Principles, the table below shows exactly how the articulation between existing and already implemented AI governance tools, such as OpenRAILs, and widely accepted international AI principles is happening. See full list on oecd. ai This assessment of the OECD AI Principles implies that policy makers could do more to integrate RAILs and OpenRAILs into their AI policy strategies and public procurement initiatives. RAIL licenses are designed as a complementary tool for the ongoing challenges that AI policy and regulatory efforts tackle on the international stage. They can be a p... See full list on oecd. ai Jan 23, 2024 · Responsible AI License ( RAIL ) The Responsible AI License , abbreviated as RAIL , is a licensing framework specifically tailored for AI technologies. The Apache 2.0, MIT and GPL license have adequately addressed the needs of various open source initiatives. RAIL goes beyond traditional licensing models by incorporating principles of responsibility, ethics, and transparency into the legal ... What are Responsible AI Licenses (RAIL )? Responsible AI Licenses (RAIL ) are specialized licensing frameworks designed to ensure that artificial intelligence ( AI ) models, datasets, and tools are used in ways that align with ethical principles and avoid harmful applications. Mar 18, 2025 · Artificial intelligence is evolving rapidly – but how can we ensure that its use remains ethical and responsible ? Firstly, the AI Regulation sets out ethical guidelines for AI systems. In addition, the AI Licensing Initiative and the AI Licensing Initiative offer Responsible AI Licenses (RAIL ) also a legal guideline for ethical AI use. What is a responsible AI license? Responsible AI Licenses ( RAIL ) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications. We provide both source code licenses as well as end-user licenses that developers/providers can include with AI software to restrict its irresponsible use. What is a rail license? Responsible AI Licenses (RAIL) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications. These licenses include behavioral-use clauses which grant permissions for specific use-cases and/or restrict certain use-cases. What AI models are covered under a rail license? Famous AI models covered under RAIL licenses include Stable Diffusion and BLOOM . In this blog we’ll answer some common questions about RAIL licenses. The licenses themselves are dry, sure, but they’re perfectly readable by a non-lawyer so be sure to look through any specific licenses that cover models you want to use. Are rails a good idea for AI governance? RAILs would have even greater value if articulated with more complementary tools for responsible AI available in the AI governance ecosystem such as model cards. Model cards may inform the draft and choice of a RAIL. What are openrail licenses? When these licenses allow free access and flexible downstream distribution of the licensed AI feature , they are OpenRAIL The RAIL Initiative was created in 2019 to encourage the industry to adopt use restrictions in licenses as a way to mitigate the risks of misuse and potential harm caused by AI systems. One example of this is BigScience. What is a licensed AI feature? The license can be conceived as a mechanism that can be implemented by the AI community and is appropriate for the context of open sharing, consistent with the capabilities and limitations of AI. Use restrictions on the use of the licensed AI feature are conducive to respect for human rights throughout an AI system’s lifecycle. Responsible AI Licenses (RAIL ) are designed to encourage responsible AI use by including use restrictions. OpenRAIL is a subclass that permits free, open access and reuse for commercial purposes, while still including usage restrictions."}
+{"idx": 4, "title": "Responsible AI License RAIL - Reasoned Insights, Inc.", "date": "", "ddg_snippet": "Jan 23, 2024 · Responsible AI License ( RAIL ) The Responsible AI License , abbreviated as RAIL , is a licensing framework specifically tailored for AI technologies. The Apache 2.0, MIT and GPL license have adequately addressed the needs of various open source initiatives. RAIL goes beyond traditional licensing models by incorporating principles of responsibility, ethics, and transparency into the legal ...", "subpage_snippet": "", "source": "reasonedinsights.com", "link": "https://reasonedinsights.com/rail-license-an-enforcement-framework-for-responsible-ai/", "content": "Jan 23, 2024 · Responsible AI License ( RAIL ) The Responsible AI License , abbreviated as RAIL , is a licensing framework specifically tailored for AI technologies. The Apache 2.0, MIT and GPL license have adequately addressed the needs of various open source initiatives. RAIL goes beyond traditional licensing models by incorporating principles of responsibility, ethics, and transparency into the legal ..."}
+{"idx": 5, "title": "Responsible AI Licenses (RAIL): Responsible AI use through ...", "date": "", "ddg_snippet": "Mar 18, 2025 · Artificial intelligence is evolving rapidly – but how can we ensure that its use remains ethical and responsible ? Firstly, the AI Regulation sets out ethical guidelines for AI systems. In addition, the AI Licensing Initiative and the AI Licensing Initiative offer Responsible AI Licenses (RAIL ) also a legal guideline for ethical AI use.", "subpage_snippet": "", "source": "jun.legal", "link": "https://jun.legal/en/2025/03/18/responsible-ai-licenses-rail-verantwortungsvolle-ki-nutzung-durch-lizenzierung/", "content": "Mar 18, 2025 · Artificial intelligence is evolving rapidly – but how can we ensure that its use remains ethical and responsible ? Firstly, the AI Regulation sets out ethical guidelines for AI systems. In addition, the AI Licensing Initiative and the AI Licensing Initiative offer Responsible AI Licenses (RAIL ) also a legal guideline for ethical AI use."}
+{"idx": 6, "title": "Responsible AI Licenses (RAIL) - AI Tech Suite", "date": "", "ddg_snippet": "Responsible AI Licenses (RAIL ) are designed to encourage responsible AI use by including use restrictions. OpenRAIL is a subclass that permits free, open access and reuse for commercial purposes, while still including usage restrictions.", "subpage_snippet": "", "source": "www.aitechsuite.com", "link": "https://www.aitechsuite.com/tools/licenses.ai", "content": "Responsible AI Licenses (RAIL ) are designed to encourage responsible AI use by including use restrictions. OpenRAIL is a subclass that permits free, open access and reuse for commercial purposes, while still including usage restrictions."}
+{"idx": 7, "title": "About — Responsible AI Licenses (RAIL)", "date": "", "ddg_snippet": "Responsible AI Licenses (RAIL ) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications.", "subpage_snippet": "", "source": "www.licenses.ai", "link": "https://www.licenses.ai/about", "content": "Responsible AI Licenses (RAIL ) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications."}
+{"idx": 8, "title": "The BigScience RAIL License", "date": "", "ddg_snippet": "Such a license effectively imposes behavioral-use terms on the use of the model. The concept of a Responsible AI License emerged from a community initiative to ...", "subpage_snippet": "", "source": "bigscience.huggingface.co", "link": "https://bigscience.huggingface.co/blog/the-bigscience-rail-license", "content": "Such a license effectively imposes behavioral-use terms on the use of the model. The concept of a Responsible AI License emerged from a community initiative to ..."}
+{"idx": 9, "title": "RAIL License - AAAI", "date": "", "ddg_snippet": "5 Apr 2023 — Open Responsible AI Licenses (Open RAIL) are licenses designed to permit free and open access, re-use, and downstream distribution of derivatives of AI ...", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/rail-license/", "content": "5 Apr 2023 — Open Responsible AI Licenses (Open RAIL) are licenses designed to permit free and open access, re-use, and downstream distribution of derivatives of AI ..."}
diff --git a/data/sampled_jsons/Rew_short_alpha_Feint_alpha_attack_t_s_t_f_t_0.jsonl b/data/sampled_jsons/Rew_short_alpha_Feint_alpha_attack_t_s_t_f_t_0.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5080ad1f250514652f730bdd5bd41ce63751c978
--- /dev/null
+++ b/data/sampled_jsons/Rew_short_alpha_Feint_alpha_attack_t_s_t_f_t_0.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "УНИАН - новости Украины | война с Россией | УНІАН – Telegram", "date": "", "ddg_snippet": "Последние новости с фронта, все о войне и другие актуальные новости Украины!", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/uniannet", "content": "Последние новости с фронта, все о войне и другие актуальные новости Украины!"}
+{"idx": 1, "title": "Проверка IMEI iPhone Бесплатная онлайн-проверка iPhone.", "date": "", "ddg_snippet": "Хотите купить подержанное устройство у незнакомца, не дайте себя обмануть, используйте...", "subpage_snippet": "", "source": "iunlocker.com", "link": "https://iunlocker.com/ru/", "content": "Хотите купить подержанное устройство у незнакомца, не дайте себя обмануть, используйте..."}
+{"idx": 2, "title": "Best TFT Meta Comps for Set 15 (Patch 15.4b)", "date": "", "ddg_snippet": "Discover the best meta TFT comps to climb with in Set 15. Updated Builds, Augments, and Guides for every meta comp. Curated by former world champion Dishsoap and Challenger caster Frodan.", "subpage_snippet": "", "source": "tftacademy.com", "link": "https://tftacademy.com/tierlist/comps", "content": "Discover the best meta TFT comps to climb with in Set 15. Updated Builds, Augments, and Guides for every meta comp. Curated by former world champion Dishsoap and Challenger caster Frodan."}
+{"idx": 3, "title": "Глава 7. Элементы операционного исчисления [VMath]", "date": "", "ddg_snippet": "Рассмотрим функцию вещественного переменного $ f ( t )$ определенную на всей вещественной оси $ t \\in R$ и интегрируемую на любом конечном промежутке. Пусть $ f ( t )$ удовлетворяет условиям: 1) $ f ( t )= 0 $ при $ t < 0 $.", "subpage_snippet": "", "source": "vmath.ru", "link": "https://vmath.ru/vf5/oplaplace/course", "content": "Рассмотрим функцию вещественного переменного $ f ( t )$ определенную на всей вещественной оси $ t \\in R$ и интегрируемую на любом конечном промежутке. Пусть $ f ( t )$ удовлетворяет условиям: 1) $ f ( t )= 0 $ при $ t < 0 $."}
+{"idx": 4, "title": "GISMETEO: Погода в Йошкар-Оле сегодня, прогноз погоды...", "date": "", "ddg_snippet": "Подробный прогноз погоды в Йошкар-Оле на сегодня.", "subpage_snippet": "", "source": "www.gismeteo.ru", "link": "https://www.gismeteo.ru/weather-yoshkar-ola-11975/", "content": "Подробный прогноз погоды в Йошкар-Оле на сегодня."}
+{"idx": 5, "title": "fa.functional analysis - Explicit formula for the norm of... - MathOverflow", "date": "", "ddg_snippet": "This comment attacks a person or group.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/500667/explicit-formula-for-the-norm-of-an-integral-operator", "content": "This comment attacks a person or group."}
+{"idx": 6, "title": "Картинки Google", "date": "", "ddg_snippet": "© 2025 - Конфиденциальность - Условия.", "subpage_snippet": "", "source": "images.google.com", "link": "https://images.google.com/", "content": "© 2025 - Конфиденциальность - Условия."}
+{"idx": 7, "title": "Reading comprehension practice", "date": "", "ddg_snippet": "At that time the strongest part of the English army was bowmen. These bowmen, with their long bows had won many victories for the English in France. The Scots did not have many bowmen, so when the English archers attacked they could not defend themselves or fight back.", "subpage_snippet": "", "source": "www.ingilizcecin.com", "link": "https://www.ingilizcecin.com/wp-content/uploads/2020/04/www.ingilizcecin.com-b1-level-english-reading-comprehension-practice-with-answer-key-b1-seviyesi-ingilizce-okuma-parcalari-ve-sorulari-cevaplar-92775.pdf", "content": "At that time the strongest part of the English army was bowmen. These bowmen, with their long bows had won many victories for the English in France. The Scots did not have many bowmen, so when the English archers attacked they could not defend themselves or fight back."}
+{"idx": 8, "title": "Curl P1-R vs Feint Long | Форум", "date": "", "ddg_snippet": "Curl P1-R vs Feint Long. 25 Авг 2016, 11:06:40. Собственно, такая история. Поиграл некоторое время P1-R. Не могу сказать что плохие, пилят неплохо если отрабатывать и подходить правильно под каждый мяч, атакующие способности хорошие, на столе чуть лажа.", "subpage_snippet": "", "source": "www.tt-maximum.com", "link": "https://www.tt-maximum.com/forum/index.php?topic=7666.0", "content": "Curl P1-R vs Feint Long. 25 Авг 2016, 11:06:40. Собственно, такая история. Поиграл некоторое время P1-R. Не могу сказать что плохие, пилят неплохо если отрабатывать и подходить правильно под каждый мяч, атакующие способности хорошие, на столе чуть лажа."}
+{"idx": 9, "title": "Герой С Обратным Отсчетом Дорама Китай | TikTok", "date": "", "ddg_snippet": "TikTok video from Drama short (@drama. short 77): “The girl was forced to marry a disabled person on behalf of her sister, but she didn' t know that his identity was amazingly large.part2#shortdramas #movie #shortmovie #kalostv”.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/герой-с-обратным-отсчетом-дорама-китай", "content": "TikTok video from Drama short (@drama. short 77): “The girl was forced to marry a disabled person on behalf of her sister, but she didn' t know that his identity was amazingly large.part2#shortdramas #movie #shortmovie #kalostv”."}
diff --git a/data/sampled_jsons/Rew_short_equation_1_dual-behavior_model_feint.jsonl b/data/sampled_jsons/Rew_short_equation_1_dual-behavior_model_feint.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6cb7c22591bde43ca758e60e5829fd6934f708c1
--- /dev/null
+++ b/data/sampled_jsons/Rew_short_equation_1_dual-behavior_model_feint.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "C.1 Demonstration of Feint Behaviors in Dual -Beahvior Models To explain the generation of physically realistic Feint behavior in a Dual-Behavior Model in detail, we use humanoid models : when selecting the corresponding actions (i.e. from Feint behaviors and then an attack behavior ), the starting position (jointly connected body) of the second ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v2", "content": "C.1 Demonstration of Feint Behaviors in Dual -Beahvior Models To explain the generation of physically realistic Feint behavior in a Dual-Behavior Model in detail, we use humanoid models : when selecting the corresponding actions (i.e. from Feint behaviors and then an attack behavior ), the starting position (jointly connected body) of the second ..."}
+{"idx": 1, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short-term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual-Behavior model ) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ", "content": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short-term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual-Behavior model ) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors ."}
+{"idx": 2, "title": "PDF feint_video_slides - neurips.cc", "date": "", "ddg_snippet": "2 Constraints: Physical constraint Lead to physically plausible follow-up behaviors . Dual-Behavior Model Effectiveness constraint Enable temporal and spatial advantages.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96274.pdf", "content": "2 Constraints: Physical constraint Lead to physically plausible follow-up behaviors . Dual-Behavior Model Effectiveness constraint Enable temporal and spatial advantages."}
+{"idx": 3, "title": "Example of a ReW model behavior in the training phase. A binary input ...", "date": "", "ddg_snippet": "Example of a ReW model behavior in the training phase. A binary input and a float value y are presented to the model . The pseudo-random mapping is applied to the binary input and the new pattern ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Example-of-a-ReW-model-behavior-in-the-training-phase-A-binary-input-and-a-float-value-y_fig3_339784662", "content": "Example of a ReW model behavior in the training phase. A binary input and a float value y are presented to the model . The pseudo-random mapping is applied to the binary input and the new pattern ..."}
+{"idx": 4, "title": "[2403.07932v2] Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "The key idea of our work is to ( 1 ) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual-Behavior Model ; (2) concertize the implications from our formalization of Feint on game strategies, in terms of temporal, spatial and their collective impacts respectively ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932v2", "content": "The key idea of our work is to ( 1 ) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual-Behavior Model ; (2) concertize the implications from our formalization of Feint on game strategies, in terms of temporal, spatial and their collective impacts respectively ..."}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Overview. Our work provides the first comprehensive and concrete formalization of Feint behaviors in action-level and strategy-level. We first present an automatic approach to generate Feint behaviors using Palindrome-directed Templates based on our observation on Feint characteristics, and provide Dual-Behavior Model to examine the design for the combination of Feint behaviors and normal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v2", "content": "Overview. Our work provides the first comprehensive and concrete formalization of Feint behaviors in action-level and strategy-level. We first present an automatic approach to generate Feint behaviors using Palindrome-directed Templates based on our observation on Feint characteristics, and provide Dual-Behavior Model to examine the design for the combination of Feint behaviors and normal ..."}
+{"idx": 6, "title": "Feint in Multi-Player Games - arXiv.org", "date": "", "ddg_snippet": "The formalization is built upon Non-transitive Active Markov Game Model , where Feint can have a considerable amount of impacts. Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v1", "content": "The formalization is built upon Non-transitive Active Markov Game Model , where Feint can have a considerable amount of impacts. Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning)."}
+{"idx": 7, "title": "PDF F M -P G - arXiv.org", "date": "", "ddg_snippet": "Technical Report-Feb-24, at User-Centric Computing Group, 2024 Feint shall be ( 1 ) the actions that follow Feint actions (e.g. actual attacks) in a short-term period of time should have strong correlation to Feint ; (2) the actions in the long-term periods explicitly or implicitly depend on the effect of the Feint and its following actions; and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v1.pdf", "content": "Technical Report-Feb-24, at User-Centric Computing Group, 2024 Feint shall be ( 1 ) the actions that follow Feint actions (e.g. actual attacks) in a short-term period of time should have strong correlation to Feint ; (2) the actions in the long-term periods explicitly or implicitly depend on the effect of the Feint and its following actions; and ..."}
+{"idx": 8, "title": "Junyu Liu - Projects", "date": "", "ddg_snippet": "The key idea of our work is to ( 1 ) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual-Behavior Model ; (2) concertize the implications from our formalization of Feint on game strategies, in terms of temporal, spatial, and their collective impacts ...", "subpage_snippet": "", "source": "junyu-liu-nate.github.io", "link": "https://junyu-liu-nate.github.io/projects/FeintFinal.html", "content": "The key idea of our work is to ( 1 ) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual-Behavior Model ; (2) concertize the implications from our formalization of Feint on game strategies, in terms of temporal, spatial, and their collective impacts ..."}
+{"idx": 9, "title": "Gender & Women's Studies: Critical Terrain, 2nd Edition", "date": "", "ddg_snippet": "Explore Gender and Women's Studies with this textbook. Covers intersectionality, gender construction, and feminist theories. Edited by Hobbs & Rice.", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/27829959/gender-and-womens-studies--second-edition-critical-terrai...", "content": "Explore Gender and Women's Studies with this textbook. Covers intersectionality, gender construction, and feminist theories. Edited by Hobbs & Rice."}
diff --git a/data/sampled_jsons/Rew_short_formula_Feint_Behaviors_and_Strategies.jsonl b/data/sampled_jsons/Rew_short_formula_Feint_Behaviors_and_Strategies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f5fa91e22f4187c2b57ea4ff4d02acd3f9fd227
--- /dev/null
+++ b/data/sampled_jsons/Rew_short_formula_Feint_Behaviors_and_Strategies.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v2", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "1Feint behaviors refer to a set of nuanced deceptive behaviors, which enable play- 2ers temporal and spatial advantages over opponents in competitive games. Such. 3behaviors are crucial tactics in most competitive Multi-Player games (e.g., box- 4ing, fencing, basketball, motor racing, etc.). However, existing literatures do not.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "1Feint behaviors refer to a set of nuanced deceptive behaviors, which enable play- 2ers temporal and spatial advantages over opponents in competitive games. Such. 3behaviors are crucial tactics in most competitive Multi-Player games (e.g., box- 4ing, fencing, basketball, motor racing, etc.). However, existing literatures do not."}
+{"idx": 2, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v2", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 3, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "Based on the action-level formalization, we model the Feint behavior impacts on strategy -level in terms of the temporal, spatial, and their collective impacts under a learnable scheme.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/Feint-preprint.pdf", "content": "Based on the action-level formalization, we model the Feint behavior impacts on strategy -level in terms of the temporal, spatial, and their collective impacts under a learnable scheme."}
+{"idx": 4, "title": "Feint in Multi-Player Games - arXiv.org", "date": "", "ddg_snippet": "Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v1", "content": "Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts."}
diff --git a/data/sampled_jsons/Rubin_1974_potential_response_variables_abstract_year_1974.jsonl b/data/sampled_jsons/Rubin_1974_potential_response_variables_abstract_year_1974.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8a6853bb67ca7b5f2e7cd526d02ed08d2f2dae3
--- /dev/null
+++ b/data/sampled_jsons/Rubin_1974_potential_response_variables_abstract_year_1974.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causality: Rubin ( 1974 )", "date": "", "ddg_snippet": "Rubin ’s ( 1974 ) abstract .Within the experiment there can be no refutation of this claim; only a logical argument explaining that the variable cannot causally aect the dependent variable or additional data outside the study can be used to counter it. Hedibert Lopes (Insper). Rubin ( 1974 ).", "subpage_snippet": "", "source": "hedibert.org", "link": "https://hedibert.org/wp-content/uploads/2015/10/causality-meeting2.pdf", "content": "Rubin ’s ( 1974 ) abstract .Within the experiment there can be no refutation of this claim; only a logical argument explaining that the variable cannot causally aect the dependent variable or additional data outside the study can be used to counter it. Hedibert Lopes (Insper). Rubin ( 1974 )."}
+{"idx": 1, "title": "The response -function variables ra and rb (summa", "date": "", "ddg_snippet": "Abstract . Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability.is closely related to the potential response variables in. Rubin 's model of counterfactuals [ Rubin , 1974 ], which.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1302.6784", "content": "Abstract . Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability.is closely related to the potential response variables in. Rubin 's model of counterfactuals [ Rubin , 1974 ], which."}
+{"idx": 2, "title": "(PDF) Counterfactuals, hypotheticals and potential responses ...", "date": "", "ddg_snippet": "the pioneering work of Rubin ( Rubin 1974 ; Rubin 1978), itself foreshadowed by.headache”, the PR approach would introduce two potential response variables , Y0and Y1, with Yxinterpreted as “the log-duration of my headache if I take x.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228383318_Counterfactuals_hypotheticals_and_potential_responses_a_philosophical_examination_of_statistical_causality", "content": "the pioneering work of Rubin ( Rubin 1974 ; Rubin 1978), itself foreshadowed by.headache”, the PR approach would introduce two potential response variables , Y0and Y1, with Yxinterpreted as “the log-duration of my headache if I take x."}
+{"idx": 3, "title": "(PDF) Counterfactuals, hypotheticals and potential responses ...", "date": "", "ddg_snippet": "Abstract . Statisticians have developed a variety of conceptions, frameworks and tools for causal inference. We study some of these from a philosophical angle, focusing in particular on two formal frameworks, \" Potential responses \" (PR) and \"Decision Theory\" (DT)...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/51834370/Counterfactuals_hypotheticals_and_potential_responses_a_philosophical_examination_of_statistical_causality", "content": "Abstract . Statisticians have developed a variety of conceptions, frameworks and tools for causal inference. We study some of these from a philosophical angle, focusing in particular on two formal frameworks, \" Potential responses \" (PR) and \"Decision Theory\" (DT)..."}
+{"idx": 4, "title": "Application of potential outcomes to an intentional weight loss latent...", "date": "", "ddg_snippet": "(2005) using the potential outcomes framework ( Rubin , 1974 ).Typically, the potential outcomes framework for a two treatment comparison would assume a potential outcome response variable corresponding to each possible treatment.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC3214637/", "content": "(2005) using the potential outcomes framework ( Rubin , 1974 ).Typically, the potential outcomes framework for a two treatment comparison would assume a potential outcome response variable corresponding to each possible treatment."}
+{"idx": 5, "title": "1994-Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "minimal variable as a response -function variable . rb is closely related to the potential response variables in Rubin ’s model of counterfactuals ( Rubin 1974 ), which was introduced to facilitate causal inference in statis", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/AAAI/1994/AAAI94-035.pdf", "content": "minimal variable as a response -function variable . rb is closely related to the potential response variables in Rubin ’s model of counterfactuals ( Rubin 1974 ), which was introduced to facilitate causal inference in statis"}
+{"idx": 6, "title": "Hypothetical Estimands in Clinical Trials: A Unification of Causal...", "date": "", "ddg_snippet": "The potential outcomes framework provides a formal definition for such causal effects and the assumptions required to estimate them ( Rubin 1974 ). “ Potential outcome” refers to the response that would have been observed on a patient had they been assigned a particular treatment.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/pdf/10.1080/19466315.2022.2081599", "content": "The potential outcomes framework provides a formal definition for such causal effects and the assumptions required to estimate them ( Rubin 1974 ). “ Potential outcome” refers to the response that would have been observed on a patient had they been assigned a particular treatment."}
+{"idx": 7, "title": "A Noel Intervention Recurrent autoencoder for real time... | medRxiv", "date": "", "ddg_snippet": "The potential outcome framework is often referred to the Neyman- Rubin model ( Rubin 1974 ). Potential outcomes consist of actual (or observed) and counterfactual (hypothesized) outcomes. We are interested in number of cases of Covid-19 under some specific intervention.", "subpage_snippet": "", "source": "www.medrxiv.org", "link": "https://www.medrxiv.org/content/10.1101/2020.05.05.20091827v1.full", "content": "The potential outcome framework is often referred to the Neyman- Rubin model ( Rubin 1974 ). Potential outcomes consist of actual (or observed) and counterfactual (hypothesized) outcomes. We are interested in number of cases of Covid-19 under some specific intervention."}
+{"idx": 8, "title": "Causal Machine Learning and its use for public policy | Swiss Journal of...", "date": "", "ddg_snippet": "We use Rubin ’s ( 1974 ) potential outcome language to describe the so-called static binary treatment model under unconfoundedness (e.g. Imbens, 2004). Let D denote the treatment indicator, which is either 0 (control) or 1 (treated).", "subpage_snippet": "", "source": "sjes.springeropen.com", "link": "https://sjes.springeropen.com/articles/10.1186/s41937-023-00113-y", "content": "We use Rubin ’s ( 1974 ) potential outcome language to describe the so-called static binary treatment model under unconfoundedness (e.g. Imbens, 2004). Let D denote the treatment indicator, which is either 0 (control) or 1 (treated)."}
+{"idx": 9, "title": "Week 9: Regression in the Social Sciences", "date": "", "ddg_snippet": "For example, having all dummy variables in a linear model is not statistically identied because they cannot be distinguished from the intercept.Summing Up: Neyman- Rubin causal model. Useful for studying the “eects of causes”, less so for the “causes of eects”.", "subpage_snippet": "", "source": "bstewart.scholar.princeton.edu", "link": "https://bstewart.scholar.princeton.edu/sites/g/files/toruqf4016/files/bstewart/files/lecture9_handout_2018.pdf", "content": "For example, having all dummy variables in a linear model is not statistically identied because they cannot be distinguished from the intercept.Summing Up: Neyman- Rubin causal model. Useful for studying the “eects of causes”, less so for the “causes of eects”."}
diff --git a/data/sampled_jsons/Ruoqi_Shen_Yin_Tat_Lee_Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them.jsonl b/data/sampled_jsons/Ruoqi_Shen_Yin_Tat_Lee_Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d6549db13a3cb77e07486311d3a6293b0209b693
--- /dev/null
+++ b/data/sampled_jsons/Ruoqi_Shen_Yin_Tat_Lee_Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large", "date": "", "ddg_snippet": "... tokens after pruning, we apply layer-wise sparsity, assigning sparsity inversely to the layer’s importance, which is computed as the average of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09897v2", "content": "... tokens after pruning, we apply layer-wise sparsity, assigning sparsity inversely to the layer’s importance, which is computed as the average of ..."}
+{"idx": 1, "title": "Exploring Response Uncertainty in MLLMs: An Empirical", "date": "", "ddg_snippet": "As shown in Figure 1 , when we sample twenty responses per query, more than half of the queries exhibit a consistency below 62.15% on the highly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02708v3", "content": "As shown in Figure 1 , when we sample twenty responses per query, more than half of the queries exhibit a consistency below 62.15% on the highly ..."}
+{"idx": 2, "title": "Algorithmic Capabilities of Random Transformers", "date": "", "ddg_snippet": "... associative recall, but little is ... One possibility is that some aspect of the transformer architecture makes these behaviors easy to learn.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.04368v1", "content": "... associative recall, but little is ... One possibility is that some aspect of the transformer architecture makes these behaviors easy to learn."}
+{"idx": 3, "title": "M-LongDoc: A Benchmark and Retrieval-Aware Tuning for Long", "date": "", "ddg_snippet": "The example question from M-LongDoc is more complex than those from other benchmarks, as it requires an explanatory answer rather than an extraction ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.06176v1", "content": "The example question from M-LongDoc is more complex than those from other benchmarks, as it requires an explanatory answer rather than an extraction ..."}
+{"idx": 4, "title": "SpaRE: Enhancing Spatial Reasoning in Vision-Language Models", "date": "", "ddg_snippet": "As shown in Table 3 , we achieve up to a 49% gain on the A split of What’s Up (Kamath et al., 2023 ) , a benchmark designed to test spatial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.20648v1", "content": "As shown in Table 3 , we achieve up to a 49% gain on the A split of What’s Up (Kamath et al., 2023 ) , a benchmark designed to test spatial ..."}
+{"idx": 5, "title": "NeurIPS 2021 Orals", "date": "", "ddg_snippet": "A puzzling phenomenon in the current practice of deep learning is that models are trained with many more parameters than what this classical theory ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2021/events/oral", "content": "A puzzling phenomenon in the current practice of deep learning is that models are trained with many more parameters than what this classical theory ..."}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/S18_characters_dataset_100000_training_examples.jsonl b/data/sampled_jsons/S18_characters_dataset_100000_training_examples.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d0dad3f9f9f8c0778fe755959158ba108972b19b
--- /dev/null
+++ b/data/sampled_jsons/S18_characters_dataset_100000_training_examples.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Simpsons characters recognition and detection using... | Medium", "date": "", "ddg_snippet": "The dataset currently features 18 classes/ characters (the data on Kaggle contains 20 classes, but currently I used only 18 characters for training ).The Simpsons characters . The training set includes about 1000 images per character (still labeling data to get to this number).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/alex-attia-blog/the-simpsons-character-recognition-using-keras-d8e1796eae36", "content": "The dataset currently features 18 classes/ characters (the data on Kaggle contains 20 classes, but currently I used only 18 characters for training ).The Simpsons characters . The training set includes about 1000 images per character (still labeling data to get to this number)."}
+{"idx": 1, "title": "Omniglot Dataset | dragen1860/MAML-Pytorch | DeepWiki", "date": "", "ddg_snippet": "For information about training with this dataset , see Omniglot Training .The Omniglot dataset is a standard benchmark for few-shot learning, consisting of 1623 different handwritten characters from 50 alphabets, with 20 examples per character .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/dragen1860/MAML-Pytorch/4.2-omniglot-dataset", "content": "For information about training with this dataset , see Omniglot Training .The Omniglot dataset is a standard benchmark for few-shot learning, consisting of 1623 different handwritten characters from 50 alphabets, with 20 examples per character ."}
+{"idx": 2, "title": "Model Training with Ultralytics YOLO - Ultralytics YOLO Docs", "date": "", "ddg_snippet": "YOLO11 datasets like COCO, VOC, ImageNet and many others automatically download on first use, i.e. yolo train data =coco.yaml. Usage Examples . Train YOLO11n on the COCO8 dataset for 100 epochs at image size 640. The training device can be specified using the device argument.", "subpage_snippet": "", "source": "docs.ultralytics.com", "link": "https://docs.ultralytics.com/modes/train/", "content": "YOLO11 datasets like COCO, VOC, ImageNet and many others automatically download on first use, i.e. yolo train data =coco.yaml. Usage Examples . Train YOLO11n on the COCO8 dataset for 100 epochs at image size 640. The training device can be specified using the device argument."}
+{"idx": 3, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 4, "title": "UCI Machine Learning Repository | Discover datasets around the world!", "date": "", "ddg_snippet": "Dataset Characteristics . Tabular.The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are not linearly separable from each other.", "subpage_snippet": "", "source": "archive.ics.uci.edu", "link": "https://archive.ics.uci.edu/dataset/53/iris", "content": "Dataset Characteristics . Tabular.The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are not linearly separable from each other."}
+{"idx": 5, "title": "Explore datasets powering machine learning.", "date": "", "ddg_snippet": "Apply filters. Datasets . 506,642. Full-text search.22. lmms-lab/LLaVA-One-Vision-1.5-Mid- Training -85M. Viewer • Updated about 1 hour ago •.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets", "content": "Apply filters. Datasets . 506,642. Full-text search.22. lmms-lab/LLaVA-One-Vision-1.5-Mid- Training -85M. Viewer • Updated about 1 hour ago •."}
+{"idx": 6, "title": "Gemini Fine Tuning Guide for Custom Datasets - ML Journey", "date": "", "ddg_snippet": "Data Structure and Format. Your custom dataset must follow Gemini’s specific formatting requirements to ensure successful fine-tuning. The platform accepts data in JSONL (JSON Lines) format, where each line represents a single training example . Here’s the essential structure", "subpage_snippet": "", "source": "mljourney.com", "link": "https://mljourney.com/gemini-fine-tuning-guide-for-custom-datasets/", "content": "Data Structure and Format. Your custom dataset must follow Gemini’s specific formatting requirements to ensure successful fine-tuning. The platform accepts data in JSONL (JSON Lines) format, where each line represents a single training example . Here’s the essential structure"}
+{"idx": 7, "title": "Excel Sample Data for Practice or Training Example", "date": "", "ddg_snippet": "Get this Microsoft Excel sample data for practice and training . Quickly download the dummy data for office supply orders, then use it to while learning how to lookup, sort, filter, create formulas, pivot tables and more Excel skills!", "subpage_snippet": "", "source": "www.contextures.com", "link": "https://www.contextures.com/xlsampledata01.html", "content": "Get this Microsoft Excel sample data for practice and training . Quickly download the dummy data for office supply orders, then use it to while learning how to lookup, sort, filter, create formulas, pivot tables and more Excel skills!"}
+{"idx": 8, "title": "machinelearningmastery.com/much- training - data -required-machine...", "date": "", "ddg_snippet": "trained using a broad collection of data .", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/much-training-data-required-machine-learning/", "content": "trained using a broad collection of data ."}
+{"idx": 9, "title": "Training Guide - Flux model training from just 1 image [Attention...]", "date": "", "ddg_snippet": "The site owner hides the web page description.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/StableDiffusion/comments/1fop9gy/training_guide_flux_model_training_from_just_1/", "content": "The site owner hides the web page description."}
diff --git a/data/sampled_jsons/S18_characters_dataset_training_examples_100000.jsonl b/data/sampled_jsons/S18_characters_dataset_training_examples_100000.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c48e04c09963f07179736fa404a0c7aa2ca618ad
--- /dev/null
+++ b/data/sampled_jsons/S18_characters_dataset_training_examples_100000.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training , validation, and test data sets - Wikipedia", "date": "", "ddg_snippet": "A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example , a classifier.[9][10].Retrieved 2021-05-18.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Training,_validation,_and_test_data_sets", "content": "A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example , a classifier.[9][10].Retrieved 2021-05-18."}
+{"idx": 1, "title": "Datasets for character-level RNN training - GitHub", "date": "", "ddg_snippet": "The datasets are very diverse and should cover a wide range of linguistic characteristics. If you use any of these datasets in your research, please cite: De Boom C., Demeester T., Dhoedt B.: \" Character -level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes\". Neural Computing and Applications (2018).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cedricdeboom/character-level-rnn-datasets", "content": "The datasets are very diverse and should cover a wide range of linguistic characteristics. If you use any of these datasets in your research, please cite: De Boom C., Demeester T., Dhoedt B.: \" Character -level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes\". Neural Computing and Applications (2018)."}
+{"idx": 2, "title": "The ARC-GEN-100K Dataset | Kaggle", "date": "", "ddg_snippet": "An extension of the ARC-AGI-1 training dataset containing 100,000 example pairs.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/arcgen100k/the-arc-gen-100k-dataset", "content": "An extension of the ARC-AGI-1 training dataset containing 100,000 example pairs."}
+{"idx": 3, "title": "s18 Object Detection Dataset (v2, 2022-06-23 1:21pm) by Saji ...", "date": "", "ddg_snippet": "Jun 22, 2024 · 10 open source s18 images and annotations in multiple formats for training computer vision models. s18 (v2, 2022-06-23 1:21pm), created by Saji Thoppil", "subpage_snippet": "", "source": "universe.roboflow.com", "link": "https://universe.roboflow.com/saji-thoppil/s18/dataset/2", "content": "Jun 22, 2024 · 10 open source s18 images and annotations in multiple formats for training computer vision models. s18 (v2, 2022-06-23 1:21pm), created by Saji Thoppil"}
+{"idx": 4, "title": "Open Datasets For AI/ML | AI Training Datasets - Shaip", "date": "", "ddg_snippet": "A large number of open datasets for your AI/ML models. Natural Language Processing (NLP), Computer Vision, and more. Watch this space for ready-to-use AI training datasets", "subpage_snippet": "", "source": "www.shaip.com", "link": "https://www.shaip.com/offerings/open-datasets/", "content": "A large number of open datasets for your AI/ML models. Natural Language Processing (NLP), Computer Vision, and more. Watch this space for ready-to-use AI training datasets"}
+{"idx": 5, "title": "65 of the Best Training Datasets for Machine Learning", "date": "", "ddg_snippet": "Aug 25, 2023 · Sentiment Analysis Datasets for Machine Learning Improving sentiment analysis algorithms is crucial, and these large, specialized datasets can be instrumental in enhancing their accuracy and performance. You can also check out our top 25 Twitter training datasets for data scientists that are free.", "subpage_snippet": "", "source": "smartone.ai", "link": "https://smartone.ai/blog/65-of-the-best-training-datasets-for-machine-learning/", "content": "Aug 25, 2023 · Sentiment Analysis Datasets for Machine Learning Improving sentiment analysis algorithms is crucial, and these large, specialized datasets can be instrumental in enhancing their accuracy and performance. You can also check out our top 25 Twitter training datasets for data scientists that are free."}
+{"idx": 6, "title": "LLMDataHub: Awesome Datasets for LLM Training - GitHub", "date": "", "ddg_snippet": "In this repository, we provide a curated collection of datasets specifically designed for chatbot training , including links, size, language, usage, and a brief description of each dataset . Our goal is to make it easier for researchers and practitioners to identify and select the most relevant and useful datasets for their chatbot LLM training ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Zjh-819/LLMDataHub", "content": "In this repository, we provide a curated collection of datasets specifically designed for chatbot training , including links, size, language, usage, and a brief description of each dataset . Our goal is to make it easier for researchers and practitioners to identify and select the most relevant and useful datasets for their chatbot LLM training ..."}
+{"idx": 7, "title": "Data sets for neural network training - Stack Overflow", "date": "", "ddg_snippet": "I think this is a nice use case. Scan in two pages of text, extract the letters and form training /testing datasets (e.g. 8x8 pixels leads to 64 input nodes), label the data. Train the ANN and get a score using the testing dataset . Change the network topology/parameters and tune the network to get the best score.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/963041/data-sets-for-neural-network-training", "content": "I think this is a nice use case. Scan in two pages of text, extract the letters and form training /testing datasets (e.g. 8x8 pixels leads to 64 input nodes), label the data. Train the ANN and get a score using the testing dataset . Change the network topology/parameters and tune the network to get the best score."}
+{"idx": 8, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 9, "title": "Explore datasets powering machine learning.", "date": "", "ddg_snippet": "Apply filters. Datasets . 506,642. Full-text search.22. lmms-lab/LLaVA-One-Vision-1.5-Mid- Training -85M. Viewer • Updated about 1 hour ago •.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets", "content": "Apply filters. Datasets . 506,642. Full-text search.22. lmms-lab/LLaVA-One-Vision-1.5-Mid- Training -85M. Viewer • Updated about 1 hour ago •."}
diff --git a/data/sampled_jsons/S18_characters_dataset_training_examples_sitearxiv.org.jsonl b/data/sampled_jsons/S18_characters_dataset_training_examples_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..587e6d098ec1eea21898b1bc6ad7c8f4571988e7
--- /dev/null
+++ b/data/sampled_jsons/S18_characters_dataset_training_examples_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Getting the most out of your tokenizer for pre-training and ...", "date": "", "ddg_snippet": "We fix the number of characters used to train learn the BPE tokenizer to 10 Billion, and vary only the percentage of code and multilingual training data in the training dataset . We keep all the other hyper-parameters constant. Figure 2 shows the NSL of our trained tokenizers on three held-out sets for Multilingual, Code and English.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.01035v2", "content": "We fix the number of characters used to train learn the BPE tokenizer to 10 Billion, and vary only the percentage of code and multilingual training data in the training dataset . We keep all the other hyper-parameters constant. Figure 2 shows the NSL of our trained tokenizers on three held-out sets for Multilingual, Code and English."}
+{"idx": 1, "title": "Chatter: A Character Attribution Dataset for Narrative ...", "date": "", "ddg_snippet": "Our work addresses this by curating the Chatter dataset that labels whether a character portrays some attribute for 88148 character -attribute pairs, encompassing 2998 characters , 13324 attributes and 660 movies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.05227v1", "content": "Our work addresses this by curating the Chatter dataset that labels whether a character portrays some attribute for 88148 character -attribute pairs, encompassing 2998 characters , 13324 attributes and 660 movies."}
+{"idx": 2, "title": "arXiv:1712.06424v3 [cs.CV] 18 Jun 2018", "date": "", "ddg_snippet": "arXiv:1712.06424v3 [cs.CV] 18 Jun 2018 Learning to Write Stylized Chinese Characters by Reading a Handful of Examples", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1712.06424", "content": "arXiv:1712.06424v3 [cs.CV] 18 Jun 2018 Learning to Write Stylized Chinese Characters by Reading a Handful of Examples"}
+{"idx": 3, "title": "\"Let Your Characters Tell Their Story\": A Dataset for ...", "date": "", "ddg_snippet": "Sep 12, 2021 · To encourage research in this field of character -centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.05438", "content": "Sep 12, 2021 · To encourage research in this field of character -centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation."}
+{"idx": 4, "title": "Streamlined optical training of large-scale modern deep", "date": "", "ddg_snippet": "3.1 Movie-Dialogs dataset and the preprocessing for the ODFA-trained language Transformer ... Important examples are the evolution of GPUs and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.12965v2", "content": "3.1 Movie-Dialogs dataset and the preprocessing for the ODFA-trained language Transformer ... Important examples are the evolution of GPUs and the ..."}
+{"idx": 5, "title": "TextCaps : Handwritten Character Recognition with Very Small ...", "date": "", "ddg_snippet": "We evaluate the proposed architecture on a non- character dataset , Fashion-MNIST, to ensure the flex-ibility and robustness. We achieve very good results with 200 training samples and achieve the state-of-the-art with the full dataset .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1904.08095", "content": "We evaluate the proposed architecture on a non- character dataset , Fashion-MNIST, to ensure the flex-ibility and robustness. We achieve very good results with 200 training samples and achieve the state-of-the-art with the full dataset ."}
+{"idx": 6, "title": "Generating Pixel Art Character Sprites using GANs", "date": "", "ddg_snippet": "The four datasets were small and had a number of training examples varying from 184 images to 776 in the largest one. As a result, in a dataset in which characters are modularly built by assembling different parts of the body, the model generated images with high perceptual quality, with almost no divergence from the ground truth.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.06413", "content": "The four datasets were small and had a number of training examples varying from 184 images to 776 in the largest one. As a result, in a dataset in which characters are modularly built by assembling different parts of the body, the model generated images with high perceptual quality, with almost no divergence from the ground truth."}
+{"idx": 7, "title": "Optical Character Recognition using Convolutional Neural ...", "date": "", "ddg_snippet": "Dec 29, 2024 · Abstract This research paper delves into the development of an Optical Character Recognition (OCR) system for the recognition of Ashokan Brahmi characters using Convolutional Neural Networks. It utilizes a comprehensive dataset of character images to train the models, along with data augmentation techniques to optimize the training process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.01981v1", "content": "Dec 29, 2024 · Abstract This research paper delves into the development of an Optical Character Recognition (OCR) system for the recognition of Ashokan Brahmi characters using Convolutional Neural Networks. It utilizes a comprehensive dataset of character images to train the models, along with data augmentation techniques to optimize the training process."}
+{"idx": 8, "title": "Unlocking Bias Detection: Leveraging Transformer-Based Models", "date": "", "ddg_snippet": "We have prepared a dataset specifically for training these models to identify and locate biases in texts. ... based formulation, our training dataset ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00347v3", "content": "We have prepared a dataset specifically for training these models to identify and locate biases in texts. ... based formulation, our training dataset ..."}
+{"idx": 9, "title": "Unconditional Latent Diffusion Models Memorize Patient Imaging", "date": "", "ddg_snippet": "As a matter of fact, very recently, several studies have trained generative models on private/limited-access/restricted datasets and made synthetic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.01054v3", "content": "As a matter of fact, very recently, several studies have trained generative models on private/limited-access/restricted datasets and made synthetic ..."}
diff --git a/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_Lee_dual_variable.jsonl b/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_Lee_dual_variable.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c286b5bdabb99240d83144fd065774b7e89390c2
--- /dev/null
+++ b/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_Lee_dual_variable.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity ..."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning | Cool Papers ...", "date": "", "ddg_snippet": "#1 SAFE : Finding Sparse and Flat Minima to Improve Pruning [PDF 7] [Copy] [Kimi 4] [REL] Authors: Dongyeop Lee , Kwanhee Lee , Jinseok Chung, Namhoon Lee Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization, we aim ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/10l1pGeOcK@OpenReview", "content": "#1 SAFE : Finding Sparse and Flat Minima to Improve Pruning [PDF 7] [Copy] [Kimi 4] [REL] Authors: Dongyeop Lee , Kwanhee Lee , Jinseok Chung, Namhoon Lee Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization, we aim ..."}
+{"idx": 2, "title": "publications | Dongyeop Lee - GitHub Pages", "date": "", "ddg_snippet": "SAFE : Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee , Kwanhee Lee , Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent ...", "subpage_snippet": "", "source": "edong6768.github.io", "link": "https://edong6768.github.io/publications/", "content": "SAFE : Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee , Kwanhee Lee , Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent ..."}
+{"idx": 3, "title": "PDF Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Penalizes x iterate to move slightly closer to z during flatness-inducing minimization. This gradually moves x towards sparsity during flatness induction without sudden changes, yielding a sparse and flat minima .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46658.pdf", "content": "Penalizes x iterate to move slightly closer to z during flatness-inducing minimization. This gradually moves x towards sparsity during flatness induction without sudden changes, yielding a sparse and flat minima ."}
+{"idx": 4, "title": "Presenting SAFE: A Method for Improving Pruning at ICML 2025", "date": "", "ddg_snippet": "Will be presenting our spotlight poster \" SAFE : Finding Sparse and Flat Minima to Improve Pruning \" tomorrow at East Exhibition Hall, #E-3510, from 11:00 AM to 1:30 PM! (#ICML2025 @ #Vancouver ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/kwanheelee_e-icml2025-vancouver-activity-7351126114557153282-pk7J", "content": "Will be presenting our spotlight poster \" SAFE : Finding Sparse and Flat Minima to Improve Pruning \" tomorrow at East Exhibition Hall, #E-3510, from 11:00 AM to 1:30 PM! (#ICML2025 @ #Vancouver ..."}
+{"idx": 5, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "In this section, we demonstrate that SAFE converges to sparse and flat solutions, leading to performance improve -ments over baselines in both image classification and lan-guage modeling tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.06866", "content": "In this section, we demonstrate that SAFE converges to sparse and flat solutions, leading to performance improve -ments over baselines in both image classification and lan-guage modeling tasks."}
+{"idx": 6, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Venue: ICML 2025, Spotlight poster Contact: kwanhee.lee@postech.ac.kr This repository contains the official PyTorch implementation for the paper SAFE : Finding Sparse and Flat Minima to Improve Pruning . Our work introduces SAFE , an algorithm designed to find sparse and flat minima , leading to improved model pruning performance.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LOG-postech/safe-torch", "content": "Venue: ICML 2025, Spotlight poster Contact: kwanhee.lee@postech.ac.kr This repository contains the official PyTorch implementation for the paper SAFE : Finding Sparse and Flat Minima to Improve Pruning . Our work introduces SAFE , an algorithm designed to find sparse and flat minima , leading to improved model pruning performance."}
+{"idx": 7, "title": "[이남훈 교수] SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Dongyeop Lee , Kwanhee Lee , Jinseok Chung, and Namhoon Lee . \" SAFE : Finding Sparse and Flat Minima to Improve Pruning \", International Conference on Machine Learning (ICML), 2025.", "subpage_snippet": "", "source": "cse.postech.ac.kr", "link": "https://cse.postech.ac.kr/csepostech/research/latest-research.do?mode=view&articleNo=24246&title=[이남훈+교수]+SAFE:+Finding+Sparse+and+Flat+Minima+to+Improve+Pruning", "content": "Dongyeop Lee , Kwanhee Lee , Jinseok Chung, and Namhoon Lee . \" SAFE : Finding Sparse and Flat Minima to Improve Pruning \", International Conference on Machine Learning (ICML), 2025."}
+{"idx": 8, "title": "Dongyeop Lee", "date": "", "ddg_snippet": "selected publications 2025 SAFE : Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee , Kwanhee Lee , Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster", "subpage_snippet": "", "source": "edong6768.github.io", "link": "https://edong6768.github.io/", "content": "selected publications 2025 SAFE : Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee , Kwanhee Lee , Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster"}
+{"idx": 9, "title": "Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32.", "date": "", "ddg_snippet": "Download scientific diagram | Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32. from publication: SAFE : Finding Sparse and Flat Minima to Improve Pruning | Sparsifying neural ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Best-performing-penalty-parameter-of-SAFE-for-VGG-19-and-ResNet-20-32_tbl3_392531034", "content": "Download scientific diagram | Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32. from publication: SAFE : Finding Sparse and Flat Minima to Improve Pruning | Sparsifying neural ..."}
diff --git a/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_algorithm_Section_3.2_dual_variable_update.jsonl b/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_algorithm_Section_3.2_dual_variable_update.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..52966efbd6a9081f7f19303ddc29d34b02feab34
--- /dev/null
+++ b/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_algorithm_Section_3.2_dual_variable_update.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "This paper introduces a theoretically principled approach for producing sparse and flat models. The approach is based on an augmented Lagrange dual approach and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=10l1pGeOcK&noteId=gDiOJllnP7", "content": "This paper introduces a theoretically principled approach for producing sparse and flat models. The approach is based on an augmented Lagrange dual approach and ..."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by D Lee · 2025 — In this section , we present a detailed derivation of our flatness-inducing sparsification algorithm Sparsification via. ADMM with Flatness ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.06866", "content": "by D Lee · 2025 — In this section , we present a detailed derivation of our flatness-inducing sparsification algorithm Sparsification via. ADMM with Flatness ..."}
+{"idx": 2, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Extensive evaluations on standard image classification and language modeling tasks reveal that SAFE consistently yields sparse networks with improved ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46658", "content": "Extensive evaluations on standard image classification and language modeling tasks reveal that SAFE consistently yields sparse networks with improved ..."}
+{"idx": 3, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "7 Jun 2025 — Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective. We solve it ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v1", "content": "7 Jun 2025 — Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective. We solve it ..."}
+{"idx": 4, "title": "Sparse Flows: Pruning Continuous-depth Models", "date": "", "ddg_snippet": "by L Liebenwein · 2021 · Cited by 17 — We find that for Neural ODEs, pruning decreases the value of the Hessian's eigen- values, and as a result, flattens the loss which leads to better ... 15 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/bf1b2f4b901c21a1d8645018ea9aeb05-Paper.pdf", "content": "by L Liebenwein · 2021 · Cited by 17 — We find that for Neural ODEs, pruning decreases the value of the Hessian's eigen- values, and as a result, flattens the loss which leads to better ... 15 pages"}
+{"idx": 5, "title": "ADAPTIVE SHARPNESS-AWARE PRUNING FOR ...", "date": "", "ddg_snippet": "by A Bair · Cited by 14 — The main objective of our design is to find flat minima in order to produce models that are simultaneously prunable and robust. We introduce a new method,.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/cb7d81f864f95b4fd7c1085c0c8e11f1-Paper-Conference.pdf", "content": "by A Bair · Cited by 14 — The main objective of our design is to find flat minima in order to produce models that are simultaneously prunable and robust. We introduce a new method,."}
+{"idx": 6, "title": "ICML 2025 2025 Spotlight Posters", "date": "", "ddg_snippet": "SAFE: Finding Sparse and Flat Minima to Improve Pruning . Spotlight Poster ... dual variable updates to ensure feasibility. Improving Consistency Models ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/2025SpotlightPosters", "content": "SAFE: Finding Sparse and Flat Minima to Improve Pruning . Spotlight Poster ... dual variable updates to ensure feasibility. Improving Consistency Models ..."}
+{"idx": 7, "title": "A Single-Step, Sharpness-Aware Minimization is All You ...", "date": "", "ddg_snippet": "A single-step, sharpness-aware minimization is all you need to achieve efficient and accurate sparse training.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/95533", "content": "A single-step, sharpness-aware minimization is all you need to achieve efficient and accurate sparse training."}
+{"idx": 8, "title": "ICLR 2025 Spotlights", "date": "", "ddg_snippet": "In this paper, we explore the two -point zeroth-order gradient estimator and identify the distribution of random perturbations that minimizes the estimator's ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/events/spotlight-posters", "content": "In this paper, we explore the two -point zeroth-order gradient estimator and identify the distribution of random perturbations that minimizes the estimator's ..."}
+{"idx": 9, "title": "The Generalization-Stability Tradeoff In Neural Network ...", "date": "", "ddg_snippet": "by BR Bartoldson · 2020 · Cited by 117 — Can improved generalization in pruned DNNs simply be explained by the reduced parameter count, or rather, do the properties of the pruning ...", "subpage_snippet": "", "source": "www.osti.gov", "link": "https://www.osti.gov/servlets/purl/1769136", "content": "by BR Bartoldson · 2020 · Cited by 117 — Can improved generalization in pruned DNNs simply be explained by the reduced parameter count, or rather, do the properties of the pruning ..."}
diff --git a/data/sampled_jsons/SAHARA_algorithm_Safety_Attention_Head_AttRibution_Algorithm_steps_pseudocode.jsonl b/data/sampled_jsons/SAHARA_algorithm_Safety_Attention_Head_AttRibution_Algorithm_steps_pseudocode.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9b81af4967129336d8e49b18c4dbaf3f3357fe2f
--- /dev/null
+++ b/data/sampled_jsons/SAHARA_algorithm_Safety_Attention_Head_AttRibution_Algorithm_steps_pseudocode.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SafetyHeadAttribution/Readme.md at main · ydyjya ... - GitHub", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that the special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ydyjya/SafetyHeadAttribution/blob/main/Readme.md", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that the special attention head has a significant impact on safety ."}
+{"idx": 1, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "We propose Safety Attention Head AttRibution Algorithm ( Sahara ), a heuristic approach for pinpointing these heads. Finally, in Section 4.3, we conduct a series of experiments and analyses to understand the impact of safety heads on models’ safety guardrails.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v1", "content": "We propose Safety Attention Head AttRibution Algorithm ( Sahara ), a heuristic approach for pinpointing these heads. Finally, in Section 4.3, we conduct a series of experiments and analyses to understand the impact of safety heads on models’ safety guardrails."}
+{"idx": 2, "title": "ICLR 2025 On the Role of Attention Heads in Large Language ...", "date": "", "ddg_snippet": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/oral/31798", "content": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model. Our findings show that special attention head has a significant impact on safety ."}
+{"idx": 3, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "However, directly transferring existing methods to the safety attention mechanism attribution task is challenging. This paper aims to interpret safety capability within multi - head attention by introducing Safety Head ImPortant Scores (Ships) and a heuristic algorithm , Sahara .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper/arxiv/2410.13708", "content": "However, directly transferring existing methods to the safety attention mechanism attribution task is challenging. This paper aims to interpret safety capability within multi - head attention by introducing Safety Head ImPortant Scores (Ships) and a heuristic algorithm , Sahara ."}
+{"idx": 4, "title": "ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODEL SAFETY", "date": "", "ddg_snippet": "assess the individual heads’ contributions to model safety . Based on this, we generalize Ships to the dataset level and further introduce the Safety At-tention Head AttRibution Algorithm ( Sahara ) to at ribute the critical safety atten-tion heads inside the model. Our findings show that", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=70fAPPoRpg&name=pdf", "content": "assess the individual heads’ contributions to model safety . Based on this, we generalize Ships to the dataset level and further introduce the Safety At-tention Head AttRibution Algorithm ( Sahara ) to at ribute the critical safety atten-tion heads inside the model. Our findings show that"}
+{"idx": 5, "title": "SafetyHeadAttribution/lib/Sahara/attribution.py at main ...", "date": "", "ddg_snippet": "Contribute to ydyjya/SafetyHeadAttribution development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ydyjya/SafetyHeadAttribution/blob/main/lib/Sahara/attribution.py", "content": "Contribute to ydyjya/SafetyHeadAttribution development by creating an account on GitHub."}
+{"idx": 6, "title": "arXiv:2410.13708v2 [cs.CL] 24 Feb 2025", "date": "", "ddg_snippet": "eads’ contributions to model safety . Based on this, we gen-eralize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical sa ety attention heads in-side the model. Our findings show that the special attention h", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.13708", "content": "eads’ contributions to model safety . Based on this, we gen-eralize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical sa ety attention heads in-side the model. Our findings show that the special attention h"}
+{"idx": 7, "title": "Understanding and Mitigating the Threat to LLM Applications", "date": "", "ddg_snippet": "by S Holder — The study presents two analytical tools: the Safety Head Importance Score (SHIPS) and the Safety . Attention Head Attribution Algorithm ( SAHARA ). SHIPS ...", "subpage_snippet": "", "source": "scholar.smu.edu", "link": "https://scholar.smu.edu/cgi/viewcontent.cgi?article=1299&context=datasciencereview", "content": "by S Holder — The study presents two analytical tools: the Safety Head Importance Score (SHIPS) and the Safety . Attention Head Attribution Algorithm ( SAHARA ). SHIPS ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=safety+guard", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical ..."}
+{"idx": 9, "title": "HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion ...", "date": "", "ddg_snippet": "by A Berguiga · 2025 · Cited by 17 — Algorithm 1 presents the pseudocode for the HIDS-IoMT approach. This procedure is initiated upon the receipt of a message from an adjacent node ... 20 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/10891538.pdf", "content": "by A Berguiga · 2025 · Cited by 17 — Algorithm 1 presents the pseudocode for the HIDS-IoMT approach. This procedure is initiated upon the receipt of a message from an adjacent node ... 20 pages"}
diff --git a/data/sampled_jsons/SAMP_algorithm_sampling_probability_common_type_agent_j0.jsonl b/data/sampled_jsons/SAMP_algorithm_sampling_probability_common_type_agent_j0.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ef225d16a8657d4d001bbcd4048902d6ccf319f0
--- /dev/null
+++ b/data/sampled_jsons/SAMP_algorithm_sampling_probability_common_type_agent_j0.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Promoting Fairness Among Dynamic Agents in", "date": "", "ddg_snippet": "The algorithm SAMP -G for OM-LF under long-run group-level fairness is formally stated in Algorithm 4. Note that SAMP -G will reject an online agent immediately with probability 1 − i∈Nj x∗ij/λj, and will also reject it if the rst sampled ofine agent has reached capacity.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/959f70ee50044bed305e48e3484005a7-Paper-Conference.pdf", "content": "The algorithm SAMP -G for OM-LF under long-run group-level fairness is formally stated in Algorithm 4. Note that SAMP -G will reject an online agent immediately with probability 1 − i∈Nj x∗ij/λj, and will also reject it if the rst sampled ofine agent has reached capacity."}
+{"idx": 1, "title": "Promoting Fairness Among Dynamic Agents in... | OpenReview", "date": "", "ddg_snippet": ", which rejects each arriving common - type agent with probability .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0C3bLHwjsY", "content": ", which rejects each arriving common - type agent with probability ."}
+{"idx": 2, "title": "A Sparsity Preestimated Adaptive Matching Pursuit Algorithm", "date": "", "ddg_snippet": "However, the SAMP algorithm starts from zero and iterates several times with a fixed step size to approximate the true sparsity, which increases the runtime. To increase the run speed, a sparsity preestimated adaptive matching pursuit (SPAMP) algorithm is proposed in this paper.", "subpage_snippet": "", "source": "www.hindawi.com", "link": "https://www.hindawi.com/journals/jece/2021/5598180/", "content": "However, the SAMP algorithm starts from zero and iterates several times with a fixed step size to approximate the true sparsity, which increases the runtime. To increase the run speed, a sparsity preestimated adaptive matching pursuit (SPAMP) algorithm is proposed in this paper."}
+{"idx": 3, "title": "questionpro.com/blog/non- probability - sampling", "date": "", "ddg_snippet": "Non- Probability Sampling : Definition, types , Examples, and advantages\",", "subpage_snippet": "", "source": "www.questionpro.com", "link": "https://www.questionpro.com/blog/non-probability-sampling/", "content": "Non- Probability Sampling : Definition, types , Examples, and advantages\","}
+{"idx": 4, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching...", "date": "", "ddg_snippet": "For Online Matching under long-run Fairness (OM-LF) with a single offline agent , we show that the first-come-first-serve (FCFS) policy is $1$-competitive, i.e., matching any optimal clairvoyant. For the general case of OM-LF: We present a sampling algorithm ( SAMP ) and show that (1)...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/959f70ee50044bed305e48e3484005a7-Abstract-Conference.html", "content": "For Online Matching under long-run Fairness (OM-LF) with a single offline agent , we show that the first-come-first-serve (FCFS) policy is $1$-competitive, i.e., matching any optimal clairvoyant. For the general case of OM-LF: We present a sampling algorithm ( SAMP ) and show that (1)..."}
+{"idx": 5, "title": "An LP-based policy for the general case", "date": "", "ddg_snippet": "Algorithm 1: An LP-based sampling ( SAMP ). 1 Oine Phase: 2 Solve LP (1), and let {𝑥𝑖 𝑗 } be an optimal solution. 3 Online Phase: 4 Let a demand agent (of type ) 𝑗 arrive.the common type 𝑗 = 0 , it can be served by all the 𝑛 supply agents .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2112.04169", "content": "Algorithm 1: An LP-based sampling ( SAMP ). 1 Oine Phase: 2 Solve LP (1), and let {𝑥𝑖 𝑗 } be an optimal solution. 3 Online Phase: 4 Let a demand agent (of type ) 𝑗 arrive.the common type 𝑗 = 0 , it can be served by all the 𝑛 supply agents ."}
+{"idx": 6, "title": "Equity Promotion in Online Resource Allocation", "date": "", "ddg_snippet": "Algorithm 1: An LP-based sampling ( SAMP ). 1 Ofine Phase: 2 Solve LP (1), and let {xij } be an optimal solution. 3 Online Phase: 4 Let a demand agent (of type ) j arrive.", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/21234/21234-13-25247-1-2-20220628.pdf", "content": "Algorithm 1: An LP-based sampling ( SAMP ). 1 Ofine Phase: 2 Solve LP (1), and let {xij } be an optimal solution. 3 Online Phase: 4 Let a demand agent (of type ) j arrive."}
+{"idx": 7, "title": "A Unied Approach to Online Matching with Conict-Aware Constraints", "date": "", "ddg_snippet": "Algorithm 1: An LP-Based Sampling Algorithm ( SAMP (α)).We propose two LP-based heuristics— SAMP (1) and SAMP (0.8)—and test these two against two other baselines, namely Greedy and Uniform Sampling . The details of these algorithms are as follows.", "subpage_snippet": "", "source": "jpdickerson.com", "link": "https://jpdickerson.com/pubs/xu19unified.pdf", "content": "Algorithm 1: An LP-Based Sampling Algorithm ( SAMP (α)).We propose two LP-based heuristics— SAMP (1) and SAMP (0.8)—and test these two against two other baselines, namely Greedy and Uniform Sampling . The details of these algorithms are as follows."}
+{"idx": 8, "title": "Lattice-Based Polynomial Commitments: Towards Asymptotic and...", "date": "", "ddg_snippet": "StructBASIS: The sampling algorithm Samp (A) first generates a row a⊺ ← Rℓq and sets. with the following properties: the algorithm EA, given oracle access to a ( probabilistic ) algorithm A : C × · · · × C → {0, 1}∗, requires an expected number of at mo...", "subpage_snippet": "", "source": "eprint.iacr.org", "link": "https://eprint.iacr.org/2023/846.pdf", "content": "StructBASIS: The sampling algorithm Samp (A) first generates a row a⊺ ← Rℓq and sets. with the following properties: the algorithm EA, given oracle access to a ( probabilistic ) algorithm A : C × · · · × C → {0, 1}∗, requires an expected number of at mo..."}
+{"idx": 9, "title": "net-014.dvi", "date": "", "ddg_snippet": "6.2.1 Random Sampler : SAMP . We describe a simple, distributed sampling algorithm SAMP to sample an independent set from I. This algorithm may not sample independent sets uniformly from I, but it samples each of them with strictly positive probability .", "subpage_snippet": "", "source": "www.stat.berkeley.edu", "link": "https://www.stat.berkeley.edu/~aldous/260-FMIE/Papers/shah_GA.pdf", "content": "6.2.1 Random Sampler : SAMP . We describe a simple, distributed sampling algorithm SAMP to sample an independent set from I. This algorithm may not sample independent sets uniformly from I, but it samples each of them with strictly positive probability ."}
diff --git a/data/sampled_jsons/SCAMP-5_focal_plane_feature_detection_tracking.jsonl b/data/sampled_jsons/SCAMP-5_focal_plane_feature_detection_tracking.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..42808239895a818f116f5ad2d6ba102ee336cb6d
--- /dev/null
+++ b/data/sampled_jsons/SCAMP-5_focal_plane_feature_detection_tracking.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Focal length - Wikipedia", "date": "", "ddg_snippet": "The focal point F and focal length f of a positive lens, a negative lens, a concave mirror, and a convex mirror. The focal length of an optical system is a measure of how strongly the system converges or diverges light; it is the inverse of the syste...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Focal_length", "content": "The focal point F and focal length f of a positive lens, a negative lens, a concave mirror, and a convex mirror. The focal length of an optical system is a measure of how strongly the system converges or diverges light; it is the inverse of the syste..."}
+{"idx": 1, "title": "High-frame rate homography and visual odometry by tracking binary...", "date": "", "ddg_snippet": "Using noisy features computed on the focal plane of the SCAMP - 5 image sensor, our system can track keypoints using this binary descriptor.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10514-023-10122-8", "content": "Using noisy features computed on the focal plane of the SCAMP - 5 image sensor, our system can track keypoints using this binary descriptor."}
+{"idx": 2, "title": "(PDF) Focal - Plane Sensor-Processor-Based Visual Inertial Odometry", "date": "", "ddg_snippet": "example, using focal - plane processing, a ground target was detected and tracked to guide a small, agile quadrotor UAV [4]. In [5], they performed drone racing, using SCAMP - 5 to detect the gates.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392168588_Focal-Plane_Sensor-Processor-Based_Visual_Inertial_Odometry", "content": "example, using focal - plane processing, a ground target was detected and tracked to guide a small, agile quadrotor UAV [4]. In [5], they performed drone racing, using SCAMP - 5 to detect the gates."}
+{"idx": 3, "title": "Ubuntu – Podrobnosti balíka scamp v focal", "date": "", "ddg_snippet": "Balík: scamp (2.0.4+dfsg-1build2) [ports] [universe]. Odkazy pre scamp . Screenshot. Zdroje Ubuntu: Hlásenia chýb. Stiahnuť zdrojový balík scamp Compute astrometric and photometric solutions. Ostatné balíky súvisiace s balíkom scamp .", "subpage_snippet": "", "source": "packages.ubuntu.com", "link": "https://packages.ubuntu.com/sk/focal/armhf/scamp", "content": "Balík: scamp (2.0.4+dfsg-1build2) [ports] [universe]. Odkazy pre scamp . Screenshot. Zdroje Ubuntu: Hlásenia chýb. Stiahnuť zdrojový balík scamp Compute astrometric and photometric solutions. Ostatné balíky súvisiace s balíkom scamp ."}
+{"idx": 4, "title": "Thermal imaging camera / focal plane array / MWIR... - RITM Industry", "date": "", "ddg_snippet": "GL1000 features : - 320x256 Type II superlattice MWIR cooled detector - Explosive-proof - Gas leakage detection and temperature meansurement - NETD reaches 25mk which guarantees good images - Working mode: Normal mode and Sensitive mode - - Compact and portable...", "subpage_snippet": "", "source": "ritmindustry.com", "link": "https://ritmindustry.com/catalog/infrared-cameras-and-scanners/thermal-imaging-camera-focal-plane-array-mwir-for-voc-gas-leak-detection/", "content": "GL1000 features : - 320x256 Type II superlattice MWIR cooled detector - Explosive-proof - Gas leakage detection and temperature meansurement - NETD reaches 25mk which guarantees good images - Working mode: Normal mode and Sensitive mode - - Compact and portable..."}
+{"idx": 5, "title": "Convolutional kernel function algebra", "date": "", "ddg_snippet": "convolution, stencil, compiler, Focal - Plane Sensor-Processor, algebra, optimization.In modeling convolutions in architectures like SCAMP - 5 it is helpful to represent each instruction as a lter acting on the PE registers as channels.", "subpage_snippet": "", "source": "spiral.imperial.ac.uk", "link": "https://spiral.imperial.ac.uk/bitstream/10044/1/100000/7/fcomp-04-921454.pdf", "content": "convolution, stencil, compiler, Focal - Plane Sensor-Processor, algebra, optimization.In modeling convolutions in architectures like SCAMP - 5 it is helpful to represent each instruction as a lter acting on the PE registers as channels."}
+{"idx": 6, "title": "Research reviews in-sensor visual perception and inference – UR ALL...", "date": "", "ddg_snippet": "First developed 20 years in the past, ever-improving focal - plane sensor-processors just like the SCAMP chip have been broadly utilized in computing experiments, however not totally surveyed. The authors first introduce essentially the most...", "subpage_snippet": "", "source": "urallnews.com", "link": "https://urallnews.com/research-reviews-in-sensor-visual-perception-and-inference/", "content": "First developed 20 years in the past, ever-improving focal - plane sensor-processors just like the SCAMP chip have been broadly utilized in computing experiments, however not totally surveyed. The authors first introduce essentially the most..."}
+{"idx": 7, "title": "Depth perception from focus", "date": "", "ddg_snippet": "4. The distance ring ensures correct distance to put the SCAMP - 5 sensor into the focal plane . 5. A case contains and protects the optical sensor itself as well as the elec-tronics needed to support it.", "subpage_snippet": "", "source": "sandamirskaya.eu", "link": "https://sandamirskaya.eu/downloads/files/report_v8.pdf", "content": "4. The distance ring ensures correct distance to put the SCAMP - 5 sensor into the focal plane . 5. A case contains and protects the optical sensor itself as well as the elec-tronics needed to support it."}
+{"idx": 8, "title": "Временная Почта - Бесплатный Генератор Одноразового... | Boomlify", "date": "", "ddg_snippet": "Track campaign performance without compromising user privacy: Case StudyE-commerce brand reduced spam complaints by 72%.2024-2026 Predictions. AI-powered spam detection (98% accuracy). Blockchain-based temporary identities.", "subpage_snippet": "", "source": "boomlify.com", "link": "https://boomlify.com/ru/temp-mail-instant/", "content": "Track campaign performance without compromising user privacy: Case StudyE-commerce brand reduced spam complaints by 72%.2024-2026 Predictions. AI-powered spam detection (98% accuracy). Blockchain-based temporary identities."}
+{"idx": 9, "title": "Фокальная плоскость прицела — FFP или SFP: что выбрать...", "date": "", "ddg_snippet": "В оптике с переменной кратностью таких плоскостей две: первая фокальная плоскость (FFP – first focal plane ) — расположена перед блоком масштабирования (ближе к объективу), и вторая фокальная плоскость (SFP – second focal plane ) — расположена ближе к окуляру.", "subpage_snippet": "", "source": "guns.club", "link": "https://guns.club/lib/optika/pervaya-ili-vtoraya-fokalnaya-ploskost/", "content": "В оптике с переменной кратностью таких плоскостей две: первая фокальная плоскость (FFP – first focal plane ) — расположена перед блоком масштабирования (ближе к объективу), и вторая фокальная плоскость (SFP – second focal plane ) — расположена ближе к окуляру."}
diff --git a/data/sampled_jsons/SCAMP-7_256x256_resolution_pixel_processor_array_specifications.jsonl b/data/sampled_jsons/SCAMP-7_256x256_resolution_pixel_processor_array_specifications.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..833a0e6c722f4603262ed57fbf2173d42cdf85b6
--- /dev/null
+++ b/data/sampled_jsons/SCAMP-7_256x256_resolution_pixel_processor_array_specifications.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scamp Trailers - Fiberglass RV", "date": "", "ddg_snippet": "Scamp trailers are ultralight fiberglass travel trailers made by Eveland Inc of Backus, Minnesota. The official scamp manufacturers page can be found here. You can also view all the Scamp trailers in our Fiberglass RV Registry at this link.", "subpage_snippet": "", "source": "www.fiberglassrv.com", "link": "https://www.fiberglassrv.com/forums/groups/1-scamp+trailers.html", "content": "Scamp trailers are ultralight fiberglass travel trailers made by Eveland Inc of Backus, Minnesota. The official scamp manufacturers page can be found here. You can also view all the Scamp trailers in our Fiberglass RV Registry at this link."}
+{"idx": 1, "title": "Scamp Owners - SOI Scamp Travel Trailer | Events | Forum", "date": "", "ddg_snippet": "Welcome to Scamp Owners International (SOI) Are you new to Scamp Owners International? The Online registration to join SOI is now available. Come on in and join one of the oldest ongoing Molded Fiberglass Travel Trailer Clubs Our sister website, Fiberglass-rv-4sale.com - the top Molded Fiberglass selling website has been closed due to retirement.", "subpage_snippet": "", "source": "scampowners.com", "link": "https://scampowners.com/", "content": "Welcome to Scamp Owners International (SOI) Are you new to Scamp Owners International? The Online registration to join SOI is now available. Come on in and join one of the oldest ongoing Molded Fiberglass Travel Trailer Clubs Our sister website, Fiberglass-rv-4sale.com - the top Molded Fiberglass selling website has been closed due to retirement."}
+{"idx": 2, "title": "Scamp 16 Travel Trailer Standard:", "date": "", "ddg_snippet": "Scamp 16 Travel Trailer Standard: Scamp 16' Standard Floor Plans Scamp 16' Standard Specifications /Features Fiberglass construction Overall length 16’ Overall height 7 ’ 10” Overall width 6’ 8” Interior length 13’ Interior height 6’ 3” Interior width 6’ 6” Super insulation (R15) 30 amp power converter 12 volt lighting Ice box ...", "subpage_snippet": "", "source": "scampowners.com", "link": "https://scampowners.com/new-16-scamp-standard-travel-trailer-specifications-floor-plans", "content": "Scamp 16 Travel Trailer Standard: Scamp 16' Standard Floor Plans Scamp 16' Standard Specifications /Features Fiberglass construction Overall length 16’ Overall height 7 ’ 10” Overall width 6’ 8” Interior length 13’ Interior height 6’ 3” Interior width 6’ 6” Super insulation (R15) 30 amp power converter 12 volt lighting Ice box ..."}
+{"idx": 3, "title": "Scamp 13 Travel Trailer Standard:", "date": "", "ddg_snippet": "Scamp 13 Travel Trailer Standard: Scamp 13' Standard Specifications /Features Fiberglass construction Overall length 13’ Overall height 7 ’ 6” Overall width 6’ 8” Interior length 10’ Interior height 6’ 3” Interior width 6’ 6” Super insulation (R15) 30 amp power converter 12 volt lighting Ice box standard 12 gallon fresh water ...", "subpage_snippet": "", "source": "scampowners.com", "link": "https://scampowners.com/new-13-scamp-standard-travel-trailer-specifications-floor-plans", "content": "Scamp 13 Travel Trailer Standard: Scamp 13' Standard Specifications /Features Fiberglass construction Overall length 13’ Overall height 7 ’ 6” Overall width 6’ 8” Interior length 10’ Interior height 6’ 3” Interior width 6’ 6” Super insulation (R15) 30 amp power converter 12 volt lighting Ice box standard 12 gallon fresh water ..."}
+{"idx": 4, "title": "Scamp 16 Travel Trailer Deluxe:", "date": "", "ddg_snippet": "Scamp 16 Travel Trailer Deluxe: Scamp 16' Deluxe Floor Plans Scamp 16' Deluxe Specifications /Features Fiberglass construction Overall length 16’ Overall height 7 ’ 10” Overall width 6’ 8” Interior length 13’ Interior height 6’ 3” Interior width 6’ 6” Super insulation (R15) 30 amp power converter 12 volt lighting 4.6 cubic foot refrigerator 12 gallon fresh water tank Sleeps one", "subpage_snippet": "", "source": "scampowners.com", "link": "https://scampowners.com/new-16-deluxe-scamp-travel-trailer-specifications-floor-plans", "content": "Scamp 16 Travel Trailer Deluxe: Scamp 16' Deluxe Floor Plans Scamp 16' Deluxe Specifications /Features Fiberglass construction Overall length 16’ Overall height 7 ’ 10” Overall width 6’ 8” Interior length 13’ Interior height 6’ 3” Interior width 6’ 6” Super insulation (R15) 30 amp power converter 12 volt lighting 4.6 cubic foot refrigerator 12 gallon fresh water tank Sleeps one"}
+{"idx": 5, "title": "Scamp 13 Travel Trailer Deluxe Specifications:", "date": "", "ddg_snippet": "Scamp 13 Travel Trailer Deluxe Specifications : Scamp 13' Deluxe Floor Plans Scamp 13' Deluxe Specifications /Features Fiberglass construction Overall length 13’ Overall height 7 ’ 6” Overall width 6’ 8” Interior length 10’ Interior height 6’ 3” Interior width 6’ 6” Super insulation (R15) 30 amp power converter 12 volt lighting 1.9 cubic foot refrigerator 12 gallon fresh water", "subpage_snippet": "", "source": "scampowners.com", "link": "https://scampowners.com/new-13-deluxe-scamp-travel-trailer-specifications-floor-plans", "content": "Scamp 13 Travel Trailer Deluxe Specifications : Scamp 13' Deluxe Floor Plans Scamp 13' Deluxe Specifications /Features Fiberglass construction Overall length 13’ Overall height 7 ’ 6” Overall width 6’ 8” Interior length 10’ Interior height 6’ 3” Interior width 6’ 6” Super insulation (R15) 30 amp power converter 12 volt lighting 1.9 cubic foot refrigerator 12 gallon fresh water"}
+{"idx": 6, "title": "Casita vs. Scamp - Fiberglass RV", "date": "", "ddg_snippet": "So today I've called both companies and requested a brochure. Until they get here I thought i'd seek out opinions. The two i'm interested in are Casita Freedom 17 Deluxe & Scamp 16 Deluxe. Is", "subpage_snippet": "", "source": "www.fiberglassrv.com", "link": "https://www.fiberglassrv.com/forums/f51/casita-vs-scamp-85588.html", "content": "So today I've called both companies and requested a brochure. Until they get here I thought i'd seek out opinions. The two i'm interested in are Casita Freedom 17 Deluxe & Scamp 16 Deluxe. Is"}
+{"idx": 7, "title": "Forums | Scamp Owners International", "date": "", "ddg_snippet": "Sep 11, 2025 · SOI member trailers, parts, wanted ads.New posts", "subpage_snippet": "", "source": "scampowners.com", "link": "https://scampowners.com/forum", "content": "Sep 11, 2025 · SOI member trailers, parts, wanted ads.New posts"}
+{"idx": 8, "title": "General Discussion | Scamp Owners International", "date": "", "ddg_snippet": "Log in to post new content in the forum.1 2 3 4 5 6 7 8 9 … next › last »", "subpage_snippet": "", "source": "scampowners.com", "link": "https://scampowners.com/forums/general-discussion", "content": "Log in to post new content in the forum.1 2 3 4 5 6 7 8 9 … next › last »"}
+{"idx": 9, "title": "Fiberglass RV Classifieds | Buy, Sell, Trade", "date": "", "ddg_snippet": "Oct 10, 2013 · List your rig or lightly used gear for sale. Our classifieds are FREE and available to all members. Post your ads here (click) and a discussion thread will be started automatically in this forum.", "subpage_snippet": "", "source": "www.fiberglassrv.com", "link": "https://www.fiberglassrv.com/forums/fiberglass-rv-classifieds-buy-sell-trade.1278/", "content": "Oct 10, 2013 · List your rig or lightly used gear for sale. Our classifieds are FREE and available to all members. Post your ads here (click) and a discussion thread will be started automatically in this forum."}
diff --git a/data/sampled_jsons/SCAMP-7_Computation_Time_Breakdown_Descriptor_Response_Map_percentage.jsonl b/data/sampled_jsons/SCAMP-7_Computation_Time_Breakdown_Descriptor_Response_Map_percentage.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bc995f011fdbd4a70afed464cea18b84f0ffeea9
--- /dev/null
+++ b/data/sampled_jsons/SCAMP-7_Computation_Time_Breakdown_Descriptor_Response_Map_percentage.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Descriptor -In-Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "The computed response map will then have each tracked feature sur-rounded by responses from its own descriptor , from which the feature’s new location can be determined.Table 1. SCAMP - 7 Computation Time Breakdown . Task Descriptor Response Map .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "The computed response map will then have each tracked feature sur-rounded by responses from its own descriptor , from which the feature’s new location can be determined.Table 1. SCAMP - 7 Computation Time Breakdown . Task Descriptor Response Map ."}
+{"idx": 1, "title": "GitHub - lauriebose/ Scamp 7 -Image-Transformations: Algorithms and...", "date": "", "ddg_snippet": "Algorithms and examples of performing focal plane image transformations on SCAMP vision system.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lauriebose/Scamp7-Image-Transformations", "content": "Algorithms and examples of performing focal plane image transformations on SCAMP vision system."}
+{"idx": 2, "title": "SCAMP", "date": "", "ddg_snippet": "SCAMP takes a time series as input and computes the matrix profile for a particular window size. You can read more about the matrix profile at the Matrix Profile Homepage This is a much improved framework over GPU-STOMP which has the following additional features", "subpage_snippet": "", "source": "scamp-docs.readthedocs.io", "link": "https://scamp-docs.readthedocs.io/_/downloads/en/latest/pdf/", "content": "SCAMP takes a time series as input and computes the matrix profile for a particular window size. You can read more about the matrix profile at the Matrix Profile Homepage This is a much improved framework over GPU-STOMP which has the following additional features"}
+{"idx": 3, "title": "Acquisition of know-how related to SCAMP -5 and... - GOV.UK", "date": "", "ddg_snippet": "The Secretary of State for Business, Energy and Industrial Strategy made a final order under the National Security and Investment Act 2021 on 20 July 2022, in respect of the acquisition of know-how related to SCAMP -5 and...", "subpage_snippet": "", "source": "www.gov.uk", "link": "https://www.gov.uk/government/publications/acquisition-of-know-how-related-to-scamp-5-and-scamp-7-vision-sensing-technology-notice-of-final-order", "content": "The Secretary of State for Business, Energy and Industrial Strategy made a final order under the National Security and Investment Act 2021 on 20 July 2022, in respect of the acquisition of know-how related to SCAMP -5 and..."}
+{"idx": 4, "title": "AnonVM | 7 Ways to Reduce Server Response Time - Knowledgebase", "date": "", "ddg_snippet": "Understand the significance of server response time , measured by Time to First Byte (TTFB), and its impact on user experience and search engine ranking.", "subpage_snippet": "", "source": "anonvm.wtf", "link": "https://anonvm.wtf/knowledgebase/19/7-Ways-to-Reduce-Server-Response-Time.html", "content": "Understand the significance of server response time , measured by Time to First Byte (TTFB), and its impact on user experience and search engine ranking."}
+{"idx": 5, "title": "Image Describer: 100% Free & Unlimited Generator (No Login)", "date": "", "ddg_snippet": "Use AI to describe any image or picture! Get instant object recognition, detailed visual analysis, and question answering. Enhance your photo analysis capabilities with real- time AI insights.", "subpage_snippet": "", "source": "describeimage.ai", "link": "https://describeimage.ai/", "content": "Use AI to describe any image or picture! Get instant object recognition, detailed visual analysis, and question answering. Enhance your photo analysis capabilities with real- time AI insights."}
+{"idx": 6, "title": "AI Image Generator (free, no sign-up, unlimited)", "date": "", "ddg_snippet": "3D Pokemon Painted Pokemon 2D Pokemon Vintage Anime Neon Vintage Anime Manga Fantasy World Map Fantasy City Map Old World Map 3D Isometric Icon Flat Style Icon Flat Style Logo Game Art Icon Digital Painting Icon Concept Art Icon Cute 3D Icon Cute 3D Icon 𝗦𝗲𝘁...", "subpage_snippet": "", "source": "perchance.org", "link": "https://perchance.org/ai-text-to-image-generator", "content": "3D Pokemon Painted Pokemon 2D Pokemon Vintage Anime Neon Vintage Anime Manga Fantasy World Map Fantasy City Map Old World Map 3D Isometric Icon Flat Style Icon Flat Style Logo Game Art Icon Digital Painting Icon Concept Art Icon Cute 3D Icon Cute 3D Icon 𝗦𝗲𝘁..."}
+{"idx": 7, "title": "infoscience.epfl.ch/server/api/core/bitstreams/89c82f40-406c-4553...", "date": "", "ddg_snippet": "Gradient Response Maps for Real- Time Detection of Texture-Less Objects.2. 7 Computation Time Study. In this section we compare the numbers of operations. required by the original method from [18] and the.", "subpage_snippet": "", "source": "infoscience.epfl.ch", "link": "https://infoscience.epfl.ch/server/api/core/bitstreams/89c82f40-406c-4553-b5ed-8f10c28a7280/content", "content": "Gradient Response Maps for Real- Time Detection of Texture-Less Objects.2. 7 Computation Time Study. In this section we compare the numbers of operations. required by the original method from [18] and the."}
+{"idx": 8, "title": "Jump Space Achievement Guide: Unlock All 12 Trophies Like a Pro", "date": "", "ddg_snippet": "Getting every achievement in Jump Space might seem like launching into deep space without a map , but this achievement guide breaks down all 12 trophies currently available in Early Access.", "subpage_snippet": "", "source": "game.dazepuzzle.com", "link": "https://game.dazepuzzle.com/jump-space-achievement-guide/", "content": "Getting every achievement in Jump Space might seem like launching into deep space without a map , but this achievement guide breaks down all 12 trophies currently available in Early Access."}
+{"idx": 9, "title": "РЕАЛЬНОЕ СОБЕСЕДОВАНИЕ / Middle FRONTEND разработчик...", "date": "", "ddg_snippet": "Runtime интеграция: Нет нужды в build- time знаниях о remotes; конфигурация через webpack.config.js определяет exposed и remote модули.Если интегрируете с Go-backend (API responses как objects), парсите в Map для client-side manipulations.", "subpage_snippet": "", "source": "careerclue.vercel.app", "link": "https://careerclue.vercel.app/blog/2025/08/29/hhMBmra2o2w-realnoe-sobesedovanie-middle-frontend-razrabotchik-ssp-soft--ot-tys", "content": "Runtime интеграция: Нет нужды в build- time знаниях о remotes; конфигурация через webpack.config.js определяет exposed и remote модули.Если интегрируете с Go-backend (API responses как objects), парсите в Map для client-side manipulations."}
diff --git a/data/sampled_jsons/SIGIR_2024_vs_WWW_2024_Information_Retrieval_papers_year_2024.jsonl b/data/sampled_jsons/SIGIR_2024_vs_WWW_2024_Information_Retrieval_papers_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..333270611ef60bc104cf3cf5b71152202186d801
--- /dev/null
+++ b/data/sampled_jsons/SIGIR_2024_vs_WWW_2024_Information_Retrieval_papers_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SIGIR 2024", "date": "", "ddg_snippet": "Call for Demonstration Papers .The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . July 14-18, 2024 (Washington D.C., USA).", "subpage_snippet": "", "source": "sigir-2024.github.io", "link": "https://sigir-2024.github.io/", "content": "Call for Demonstration Papers .The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . July 14-18, 2024 (Washington D.C., USA)."}
+{"idx": 1, "title": "SIGIR 2024 - Amazon Science", "date": "", "ddg_snippet": "SIGIR 2024 Workshop on Information Retrieval 's Role in RAG Systems (IR-RAG). July 18.", "subpage_snippet": "", "source": "www.amazon.science", "link": "https://www.amazon.science/conferences-and-events/sigir-2024", "content": "SIGIR 2024 Workshop on Information Retrieval 's Role in RAG Systems (IR-RAG). July 18."}
+{"idx": 2, "title": "Information Retrieval – Paper Digest", "date": "", "ddg_snippet": "Most Influential SIGIR Papers ( 2024 -09 Version). September 29, 2024 April 1, 2025 admin. SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ) is one of the top information retrieval conferences in the world.", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/category/information-retrieval/", "content": "Most Influential SIGIR Papers ( 2024 -09 Version). September 29, 2024 April 1, 2025 admin. SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ) is one of the top information retrieval conferences in the world."}
+{"idx": 3, "title": "Information Retrieval Conferences 2024 | Restackio", "date": "", "ddg_snippet": "SIGIR 2024 : Scheduled for July 21-25 in the vibrant city of Amsterdam, this conference will focus on innovative retrieval techniques and user-centered design. Expect discussions on: Novel algorithms for information retrieval .", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/information-retrieval-knowledge-answer-ir-conferences-2024-cat-ai", "content": "SIGIR 2024 : Scheduled for July 21-25 in the vibrant city of Amsterdam, this conference will focus on innovative retrieval techniques and user-centered design. Expect discussions on: Novel algorithms for information retrieval ."}
+{"idx": 4, "title": "Proceedings of the 47th International ACM SIGIR Conference on...", "date": "", "ddg_snippet": "ACM 2024 Proceeding. General ChairsWelcome to the 47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ( SIGIR 2024 ), taking place in Washington D.C., USA, from July 14 to 18, 2024 .", "subpage_snippet": "", "source": "acm-prod.literatumonline.com", "link": "https://acm-prod.literatumonline.com/doi/proceedings/10.1145/3626772", "content": "ACM 2024 Proceeding. General ChairsWelcome to the 47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ( SIGIR 2024 ), taking place in Washington D.C., USA, from July 14 to 18, 2024 ."}
+{"idx": 5, "title": "sigir 2024 -rbo/README.md at main · julian-urbano/ sigir 2024 -rbo", "date": "", "ddg_snippet": "Reproduce results from the SIGIR 2024 paper \"The Treatment of Ties in Rank-Biased Overlap\" - sigir 2024 -rbo/README.md at main · julian-urbano/ sigir 2024 -rbo.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/julian-urbano/sigir2024-rbo/blob/main/README.md", "content": "Reproduce results from the SIGIR 2024 paper \"The Treatment of Ties in Rank-Biased Overlap\" - sigir 2024 -rbo/README.md at main · julian-urbano/ sigir 2024 -rbo."}
+{"idx": 6, "title": "Report on The Search Futures Workshop at ECIR 2024", "date": "", "ddg_snippet": "In ECIR 2024 : 46th European Conference on Information Retrieval . Springer, April 2024 .In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2024 .", "subpage_snippet": "", "source": "www.johannetrippas.com", "link": "https://www.johannetrippas.com/papers/azzopardi2024report.pdf", "content": "In ECIR 2024 : 46th European Conference on Information Retrieval . Springer, April 2024 .In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2024 ."}
+{"idx": 7, "title": "Information Retrieval 's Role in RAG Systems 2024", "date": "", "ddg_snippet": "Proceedings of the Workshop Information Retrieval 's Role in RAG Systems (IR-RAG 2024 ) co-located with the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval ( SIGIR 2024 ). Washington DC, USA, 07 18, 2024 .", "subpage_snippet": "", "source": "ceur-ws.org", "link": "https://ceur-ws.org/Vol-3784/", "content": "Proceedings of the Workshop Information Retrieval 's Role in RAG Systems (IR-RAG 2024 ) co-located with the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval ( SIGIR 2024 ). Washington DC, USA, 07 18, 2024 ."}
+{"idx": 8, "title": "Prediction of the realisation of an information need : an... - Strathprints", "date": "", "ddg_snippet": "[thumbnail of McGuire-Moshfeghi-ACM- SIGIR - 2024 -Prediction-of-the-realisation-of-an- information -need].One of the foundational goals of Information Retrieval (IR) is to satisfy searchers' Information Needs (IN).", "subpage_snippet": "", "source": "strathprints.strath.ac.uk", "link": "https://strathprints.strath.ac.uk/89487/", "content": "[thumbnail of McGuire-Moshfeghi-ACM- SIGIR - 2024 -Prediction-of-the-realisation-of-an- information -need].One of the foundational goals of Information Retrieval (IR) is to satisfy searchers' Information Needs (IN)."}
+{"idx": 9, "title": "SIGIR 2025: International Conference on Research and Development...", "date": "", "ddg_snippet": "Call For Papers . Relevant areas include: Search and Ranking.European Conference on Information Retrieval . 2024 -10-02. 2024 -12-16.", "subpage_snippet": "", "source": "www.myhuiban.com", "link": "https://www.myhuiban.com/conference/141", "content": "Call For Papers . Relevant areas include: Search and Ranking.European Conference on Information Retrieval . 2024 -10-02. 2024 -12-16."}
diff --git a/data/sampled_jsons/SLHAMR_moving_camera_PA-MPJPE_pedestrian_motion_reconstruction_year_2024.jsonl b/data/sampled_jsons/SLHAMR_moving_camera_PA-MPJPE_pedestrian_motion_reconstruction_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6d6954e77b9e04a14463588a5cb75bda39fb3843
--- /dev/null
+++ b/data/sampled_jsons/SLHAMR_moving_camera_PA-MPJPE_pedestrian_motion_reconstruction_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PEDESTRIAN MOTION RECONSTRUCTION:ALARGE", "date": "", "ddg_snippet": "It is evident that HMR methods operating in camera space yield higher PA - MPJPE results, yet signifi- cantly lower values in other metrics. This indicates ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/789739cfd83f5876545163c9f6ee9f74cfe121fa.pdf", "content": "It is evident that HMR methods operating in camera space yield higher PA - MPJPE results, yet signifi- cantly lower values in other metrics. This indicates ..."}
+{"idx": 1, "title": "Decoupling Human and Camera Motion from Videos in the Wild", "date": "", "ddg_snippet": "Given a video of moving people (left), we present SLAHMR: Simultaneous Localication and Human Mesh Recovery, a method to recover the global trajectories of the people and cameras in the world coordinate frame (right top). Prior methods model human motion in the camera coordinate frame (right bottom), and cannot recover the global displacement of the athlete.", "subpage_snippet": "", "source": "vye16.github.io", "link": "https://vye16.github.io/slahmr/", "content": "Given a video of moving people (left), we present SLAHMR: Simultaneous Localication and Human Mesh Recovery, a method to recover the global trajectories of the people and cameras in the world coordinate frame (right top). Prior methods model human motion in the camera coordinate frame (right bottom), and cannot recover the global displacement of the athlete."}
+{"idx": 2, "title": "PDF P M Reconstruction: a Large Scale Benchmark Via Mixed Reality Rendering ...", "date": "", "ddg_snippet": "To address the previously highlighted challenges, we present the Pedestrian Motion Reconstruction (PMR) dataset, a comprehensive resource designed for intention-aware pedestrian motion recon - struction using data from moving sensors.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/f3342358d0792ea201dc86d69570946b-Paper-Conference.pdf", "content": "To address the previously highlighted challenges, we present the Pedestrian Motion Reconstruction (PMR) dataset, a comprehensive resource designed for intention-aware pedestrian motion recon - struction using data from moving sensors."}
+{"idx": 3, "title": "Physics-based Human Pose Estimation from a Single Moving RGB Camera", "date": "", "ddg_snippet": "Figures 3 and 4 show qualitative results. 4DHumans achieves the lowest MPJPE for motion reconstruction , while WHAM performs best at PA-MPJPE . Our method excels in trajectory estimation, outperforming others in W-MPJPE and RTE, while its WA- MPJPE is close to 4DHumans.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.17406v1", "content": "Figures 3 and 4 show qualitative results. 4DHumans achieves the lowest MPJPE for motion reconstruction , while WHAM performs best at PA-MPJPE . Our method excels in trajectory estimation, outperforming others in W-MPJPE and RTE, while its WA- MPJPE is close to 4DHumans."}
+{"idx": 4, "title": "Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed ...", "date": "", "ddg_snippet": "This data provides a rich foundation for modeling pedestrian intent through multi-view and multi-modal insights. We also conduct comprehensive benchmark assessments across different modalities to thoroughly evaluate pedestrian motion reconstruction methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YOpa6dTrpt", "content": "This data provides a rich foundation for modeling pedestrian intent through multi-view and multi-modal insights. We also conduct comprehensive benchmark assessments across different modalities to thoroughly evaluate pedestrian motion reconstruction methods."}
+{"idx": 5, "title": "PA-MPJPE error plot of some examples in 3DPW-blur.", "date": "", "ddg_snippet": "Holistic human pose and shape reconstruction receive huge interest since it restores detailed human pose and shape including facial expression and finger-level hand shape.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/PA-MPJPE-error-plot-of-some-examples-in-3DPW-blur_fig3_365100873", "content": "Holistic human pose and shape reconstruction receive huge interest since it restores detailed human pose and shape including facial expression and finger-level hand shape."}
+{"idx": 6, "title": "Decoupling human and camera motion from videos in the wild", "date": "", "ddg_snippet": "Visual paper walkthrough SLAHMR robustly tracks the motion of multiple moving people filmed with a moving camera and works well on \"in-the-wild\" videos. It's a great showcase of how to build working computer vision systems by intelligently combining several single purpose models.", "subpage_snippet": "", "source": "rerun.io", "link": "https://rerun.io/examples/spatial-computing/slahmr", "content": "Visual paper walkthrough SLAHMR robustly tracks the motion of multiple moving people filmed with a moving camera and works well on \"in-the-wild\" videos. It's a great showcase of how to build working computer vision systems by intelligently combining several single purpose models."}
+{"idx": 7, "title": "PACE: Human and Camera Motion Estimation from in-the-wild Videos", "date": "", "ddg_snippet": "Figure 1: Human and camera motion reconstruction from in-the-wild videos: given a video of multiple people, PACE is able to reconstruct the motions of all humans and the camera in a coherent global space. To achieve this, we leverage the benefits of both camera localization methods and human motion priors, exploiting the complementary nature of these approaches, i.e., dynamic foreground motion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.13768", "content": "Figure 1: Human and camera motion reconstruction from in-the-wild videos: given a video of multiple people, PACE is able to reconstruct the motions of all humans and the camera in a coherent global space. To achieve this, we leverage the benefits of both camera localization methods and human motion priors, exploiting the complementary nature of these approaches, i.e., dynamic foreground motion ..."}
+{"idx": 8, "title": "Visual inertial localization method assisted by pedestrian motion ...", "date": "", "ddg_snippet": "On the other hand, pedestrian motion is highly complex compared to other platforms such as vehicles, making visual-inertial SLAM less stable on pedestrian platforms. To fully utilize the characteristics of pedestrian motion , an algorithm was proposed [29]that mounts visual sensors and IMUs on the heel.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0263224124014787", "content": "On the other hand, pedestrian motion is highly complex compared to other platforms such as vehicles, making visual-inertial SLAM less stable on pedestrian platforms. To fully utilize the characteristics of pedestrian motion , an algorithm was proposed [29]that mounts visual sensors and IMUs on the heel."}
+{"idx": 9, "title": "PDF Decoupling Human and Camera Motion from Videos in the Wild", "date": "", "ddg_snippet": "Our optimization method decouples the camera and human motion , which allows us to place people in the same world coordinate frame. Most existing methods do not model the camera motion ; meth-ods that rely on the background pixels to infer 3D human motion usually require a full scene reconstruction , which is often not possible for in-the-wild videos.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_Decoupling_Human_and_Camera_Motion_From_Videos_in_the_Wild_CVPR_2023_paper.pdf", "content": "Our optimization method decouples the camera and human motion , which allows us to place people in the same world coordinate frame. Most existing methods do not model the camera motion ; meth-ods that rely on the background pixels to infer 3D human motion usually require a full scene reconstruction , which is often not possible for in-the-wild videos."}
diff --git a/data/sampled_jsons/SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_in_Urban_Environments_abstract.jsonl b/data/sampled_jsons/SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_in_Urban_Environments_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..98a93336b794d1ae61c8eaf829506a110da9d5d9
--- /dev/null
+++ b/data/sampled_jsons/SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_in_Urban_Environments_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose ... Images SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose ... SLOPER4D/README.md at main · climbingdaily/SLOPER4D · GitHub SLOPER4D DATASET - LiDAR Human dblp: SLOPER4D: A Scene-Aware Dataset for Global 4D Human ... SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose ... CVPR 2023 Open Access Repository", "date": "", "ddg_snippet": "Mar 16, 2023 · We present SLOPER4D , a novel scene - aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild. View all We present SLOPER4D , a novel scene - aware dataset collected in large urban environments to facilitate the re-search of global human pose estimation (GHPE) with human-scene interaction in the wild. Mar 18, 2024 · Comparison between original extrinsic parameters and our optimization results. The SLOPER4D codebase is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. By downloading the dataset you accept the following License. Dataset breakdown 15 sequences of 12 human subjects in 10 scenes in urban environments (1k – 30k $m^2$) 100k+ frames multi-source data (20 Hz) including 2D / 3D annotations and 3D scenes; 7 km+ human motions. Mar 20, 2023 · dblp: SLOPER4D : A Scene - Aware Dataset for Global 4D Human Pose Estimation in Urban Environments . For some weeks now, the dblp team has been receiving an exceptionally high number of support and error correction requests from the community. Mar 18, 2024 · Comparison between original extrinsic parameters and our optimization results. The SLOPER4D codebase is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. We present SLOPER4D , a novel scene - aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.09095", "content": "Mar 16, 2023 · We present SLOPER4D , a novel scene - aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild. View all We present SLOPER4D , a novel scene - aware dataset collected in large urban environments to facilitate the re-search of global human pose estimation (GHPE) with human-scene interaction in the wild. Mar 18, 2024 · Comparison between original extrinsic parameters and our optimization results. The SLOPER4D codebase is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. By downloading the dataset you accept the following License. Dataset breakdown 15 sequences of 12 human subjects in 10 scenes in urban environments (1k – 30k $m^2$) 100k+ frames multi-source data (20 Hz) including 2D / 3D annotations and 3D scenes; 7 km+ human motions. Mar 20, 2023 · dblp: SLOPER4D : A Scene - Aware Dataset for Global 4D Human Pose Estimation in Urban Environments . For some weeks now, the dblp team has been receiving an exceptionally high number of support and error correction requests from the community. Mar 18, 2024 · Comparison between original extrinsic parameters and our optimization results. The SLOPER4D codebase is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. We present SLOPER4D , a novel scene - aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild."}
+{"idx": 1, "title": "SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose ...", "date": "", "ddg_snippet": "Mar 18, 2024 · Comparison between original extrinsic parameters and our optimization results. The SLOPER4D codebase is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/climbingdaily/SLOPER4D", "content": "Mar 18, 2024 · Comparison between original extrinsic parameters and our optimization results. The SLOPER4D codebase is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License."}
+{"idx": 2, "title": "SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose ...", "date": "", "ddg_snippet": "We present SLOPER4D , a novel scene - aware dataset collected in large urban environments to facilitate the re-search of global human pose estimation (GHPE) with human-scene interaction in the wild.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Dai_SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_CVPR_2023_paper.pdf", "content": "We present SLOPER4D , a novel scene - aware dataset collected in large urban environments to facilitate the re-search of global human pose estimation (GHPE) with human-scene interaction in the wild."}
+{"idx": 3, "title": "SLOPER4D/README.md at main · climbingdaily/SLOPER4D · GitHub", "date": "", "ddg_snippet": "Mar 18, 2024 · Comparison between original extrinsic parameters and our optimization results. The SLOPER4D codebase is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/climbingdaily/SLOPER4D/blob/main/README.md", "content": "Mar 18, 2024 · Comparison between original extrinsic parameters and our optimization results. The SLOPER4D codebase is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License."}
+{"idx": 4, "title": "SLOPER4D DATASET - LiDAR Human", "date": "", "ddg_snippet": "By downloading the dataset you accept the following License. Dataset breakdown 15 sequences of 12 human subjects in 10 scenes in urban environments (1k – 30k $m^2$) 100k+ frames multi-source data (20 Hz) including 2D / 3D annotations and 3D scenes; 7 km+ human motions.", "subpage_snippet": "", "source": "www.lidarhumanmotion.net", "link": "http://www.lidarhumanmotion.net/data-sloper4d/", "content": "By downloading the dataset you accept the following License. Dataset breakdown 15 sequences of 12 human subjects in 10 scenes in urban environments (1k – 30k $m^2$) 100k+ frames multi-source data (20 Hz) including 2D / 3D annotations and 3D scenes; 7 km+ human motions."}
+{"idx": 5, "title": "dblp: SLOPER4D: A Scene-Aware Dataset for Global 4D Human ...", "date": "", "ddg_snippet": "Mar 20, 2023 · dblp: SLOPER4D : A Scene - Aware Dataset for Global 4D Human Pose Estimation in Urban Environments . For some weeks now, the dblp team has been receiving an exceptionally high number of support and error correction requests from the community.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2303-09095", "content": "Mar 20, 2023 · dblp: SLOPER4D : A Scene - Aware Dataset for Global 4D Human Pose Estimation in Urban Environments . For some weeks now, the dblp team has been receiving an exceptionally high number of support and error correction requests from the community."}
+{"idx": 6, "title": "(PDF) SLOPER 4 D : A Scene - Aware Dataset for Global 4 D Human ...", "date": "", "ddg_snippet": "We present SLOPER 4 D , a novel scene - aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human -scene interaction in the wild.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/369300769_SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_in_Urban_Environments", "content": "We present SLOPER 4 D , a novel scene - aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human -scene interaction in the wild."}
+{"idx": 7, "title": "SLOPER 4 D : A Scene - Aware Dataset for Global 4 D Human Pose ...", "date": "", "ddg_snippet": "... Pose Estimation in Urban Environments }, booktitle = {IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)}, abstract = {We present SLOPER 4 D , a novel scene - aware dataset collected in large urban environments to facilitate the research of global human pose .", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/ps/publications/dai_2023_cvpr", "content": "... Pose Estimation in Urban Environments }, booktitle = {IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)}, abstract = {We present SLOPER 4 D , a novel scene - aware dataset collected in large urban environments to facilitate the research of global human pose ."}
+{"idx": 8, "title": "SLOPER 4 D : A Scene - Aware Dataset for Global 4 D Human Pose ...", "date": "", "ddg_snippet": "Abstract . We present SLOPER 4 D , a novel scene - aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human -scene interaction in the wild.", "subpage_snippet": "", "source": "www.lidarhumanmotion.net", "link": "http://www.lidarhumanmotion.net/sloper4d/", "content": "Abstract . We present SLOPER 4 D , a novel scene - aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human -scene interaction in the wild."}
+{"idx": 9, "title": "SLOPER 4 D : A Scene - Aware Dataset for Global 4 D Human Pose ...", "date": "", "ddg_snippet": "Human Pose Estimation .pytorch. 2,727.Alternatives To Sloper 4 d . Select To Compare.", "subpage_snippet": "", "source": "awesomeopensource.com", "link": "https://awesomeopensource.com/project/climbingdaily/SLOPER4D", "content": "Human Pose Estimation .pytorch. 2,727.Alternatives To Sloper 4 d . Select To Compare."}
diff --git a/data/sampled_jsons/SLOPER4D_struggles_capturing_complex_extreme_scenarios_collisions_pedestrian.jsonl b/data/sampled_jsons/SLOPER4D_struggles_capturing_complex_extreme_scenarios_collisions_pedestrian.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b76a3a0ab2db5ce70a8c49d79d303dadb82f1126
--- /dev/null
+++ b/data/sampled_jsons/SLOPER4D_struggles_capturing_complex_extreme_scenarios_collisions_pedestrian.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Discovering new solutions to century-old problems... - Google DeepMind", "date": "", "ddg_snippet": "For centuries, mathematicians have developed complex equations to describe the fundamental physics involved in fluid dynamics. These laws govern everything from the swirling vortex of a hurricane to airflow lifting an airplane’s wing. Experts can carefully craft scenarios that make theory...", "subpage_snippet": "", "source": "deepmind.google", "link": "https://deepmind.google/discover/blog/discovering-new-solutions-to-century-old-problems-in-fluid-dynamics/", "content": "For centuries, mathematicians have developed complex equations to describe the fundamental physics involved in fluid dynamics. These laws govern everything from the swirling vortex of a hurricane to airflow lifting an airplane’s wing. Experts can carefully craft scenarios that make theory..."}
+{"idx": 1, "title": "«Русский ковчег», или белый генерал – друг краснокожих", "date": "", "ddg_snippet": "Taking the complex of ideas from the book “The Island of Russia”, the author of the article is going to highlight one of his key works in the context of contemporary issues, because, in his opinion, the most topical issue is the “dismantling” of the empire...", "subpage_snippet": "", "source": "politconservatism.ru", "link": "https://politconservatism.ru/articles/general-belyaev", "content": "Taking the complex of ideas from the book “The Island of Russia”, the author of the article is going to highlight one of his key works in the context of contemporary issues, because, in his opinion, the most topical issue is the “dismantling” of the empire..."}
+{"idx": 2, "title": "Connection Guide for Multiple AMS Units with... | Bambu Lab Wiki", "date": "", "ddg_snippet": "As H2D is a dual-nozzle printer, the most extreme scenario allows connecting all AMS 2 Pro/HT to one nozzle while using an external spool for the other.AMS 2 Pro has filaments with the auto-refill relationship (even if it's idle for now). Supported Scenarios", "subpage_snippet": "", "source": "wiki.bambulab.com", "link": "https://wiki.bambulab.com/en/ams/manual/multi-model-AMS-compatibility-guide", "content": "As H2D is a dual-nozzle printer, the most extreme scenario allows connecting all AMS 2 Pro/HT to one nozzle while using an external spool for the other.AMS 2 Pro has filaments with the auto-refill relationship (even if it's idle for now). Supported Scenarios"}
+{"idx": 3, "title": "RusAutoCon", "date": "", "ddg_snippet": "N. Andriyanov, V. Dementiev, A. Tashlinsky Zero-Shot Object Tracking Based on CSRT and ORB for Pedestrian Counting. Секция 8. Информационная безопасность промышленных систем автоматизации.", "subpage_snippet": "", "source": "rusautocon.org", "link": "https://rusautocon.org/programme2025-rus.html", "content": "N. Andriyanov, V. Dementiev, A. Tashlinsky Zero-Shot Object Tracking Based on CSRT and ORB for Pedestrian Counting. Секция 8. Информационная безопасность промышленных систем автоматизации."}
+{"idx": 4, "title": "PoE 2 0.3 Best Atlas Tree & Map Farming Strats (Rise of the Abyssals)", "date": "", "ddg_snippet": "By capturing towers, you reveal more of the Atlas, making it easier to spot corrupted zones and unique maps. Don’t forget to insert any tablets you find into towers—these add extra mechanics, item quantity, and map rarity, helping you get more rewards and progression per map.", "subpage_snippet": "", "source": "www.aoeah.com", "link": "https://www.aoeah.com/news/4081--poe-2-03-best-atlas-tree--map-farming-strats-rise-of-the-abyssals--poe-2-the-third-edict-atlas-guide", "content": "By capturing towers, you reveal more of the Atlas, making it easier to spot corrupted zones and unique maps. Don’t forget to insert any tablets you find into towers—these add extra mechanics, item quantity, and map rarity, helping you get more rewards and progression per map."}
+{"idx": 5, "title": "How to Capture Another PC Screen Remotely [Like a Pro in 2025]", "date": "", "ddg_snippet": "Step 4. Hit the Screen recording button to start capturing the screen in real time.With Alloy, you can periodically capture another PC screen as static images without interrupting workflows or installing additional software on the target machine.", "subpage_snippet": "", "source": "www.anyviewer.com", "link": "https://www.anyviewer.com/how-to/how-to-capture-another-pc-screen-2578.html", "content": "Step 4. Hit the Screen recording button to start capturing the screen in real time.With Alloy, you can periodically capture another PC screen as static images without interrupting workflows or installing additional software on the target machine."}
+{"idx": 6, "title": "How much thermal energy is produced in this collision ?", "date": "", "ddg_snippet": "In the collision scenario , the initial kinetic energy of the two railroad cars, each weighing 6500 kg and traveling at 100 km/h, is calculated to be significant, and upon coming to rest, this energy is converted into thermal energy.", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/how-much-thermal-energy-is-produced-in-this-collision.140916/", "content": "In the collision scenario , the initial kinetic energy of the two railroad cars, each weighing 6500 kg and traveling at 100 km/h, is calculated to be significant, and upon coming to rest, this energy is converted into thermal energy."}
+{"idx": 7, "title": "Carta Ramalan 4 D 2025 Terbaru dan Terbaik | Dato Chai", "date": "", "ddg_snippet": "Kami menyediakan Carta Ramalan 4 D untuk tempat Gd Lotto, Perdana 4 D dan Carta MKT terbaru.Kami menyediakan Magnum, Kuda(Damacai), Toto, Gd Lotto, Perdana yang mempunyai carta ramalan 4 d yang tepat dan kptsn 4 d result setiap hari. semogo berjaya!", "subpage_snippet": "", "source": "ramalan4u.com", "link": "https://ramalan4u.com/", "content": "Kami menyediakan Carta Ramalan 4 D untuk tempat Gd Lotto, Perdana 4 D dan Carta MKT terbaru.Kami menyediakan Magnum, Kuda(Damacai), Toto, Gd Lotto, Perdana yang mempunyai carta ramalan 4 d yang tepat dan kptsn 4 d result setiap hari. semogo berjaya!"}
+{"idx": 8, "title": "Тинганьский Translator | Free & AI-Powered", "date": "", "ddg_snippet": "The “Japanese Lyrics Improvisor Translator” is here […] Indifferent-confident Tone Translator. Struggling to convey that perfect blend of nonchalance and self-assurance?", "subpage_snippet": "", "source": "anythingtranslate.com", "link": "https://anythingtranslate.com/translators/тинганьский-translator/", "content": "The “Japanese Lyrics Improvisor Translator” is here […] Indifferent-confident Tone Translator. Struggling to convey that perfect blend of nonchalance and self-assurance?"}
+{"idx": 9, "title": "Душераздирающие подробности о жертве, найденной в машине...", "date": "", "ddg_snippet": "В свете последних событий, связанных с певцом d4vd и Селестой, мы приняли решение прекратить все обновления, касающиеся песни «Always Love».", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/wall-230454200_2290", "content": "В свете последних событий, связанных с певцом d4vd и Селестой, мы приняли решение прекратить все обновления, касающиеся песни «Always Love»."}
diff --git "a/data/sampled_jsons/SPD_Sync-Point_Drop_Algorithm_1_threshold_\317\2041_\317\2042_year_2024.jsonl" "b/data/sampled_jsons/SPD_Sync-Point_Drop_Algorithm_1_threshold_\317\2041_\317\2042_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..2416783e54f27e5685b2bbc9d13149fe09af05af
--- /dev/null
+++ "b/data/sampled_jsons/SPD_Sync-Point_Drop_Algorithm_1_threshold_\317\2041_\317\2042_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SPD : Sync - Point Drop for efficient tensor parallelism of Large...", "date": "", "ddg_snippet": "Algorithm 1 Sync - point drop based on sensitivity. Algorithm 1 shows the overall process of applying SPD in a multi-tiered block-wise approach with measured sync sensitivity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20727v2", "content": "Algorithm 1 Sync - point drop based on sensitivity. Algorithm 1 shows the overall process of applying SPD in a multi-tiered block-wise approach with measured sync sensitivity."}
+{"idx": 1, "title": "Algorithms for Distributed Functional Monitoring", "date": "", "ddg_snippet": "There are two variants: threshold monitoring (determining when C(t) exceeds a threshold τ ) and value monitoring (provid-ing a good approximation to C(t) at all times t). Value.", "subpage_snippet": "", "source": "home.cse.ust.hk", "link": "https://home.cse.ust.hk/~yike/soda08.pdf", "content": "There are two variants: threshold monitoring (determining when C(t) exceeds a threshold τ ) and value monitoring (provid-ing a good approximation to C(t) at all times t). Value."}
+{"idx": 2, "title": "Algorithms for Distributed Functional Monitoring", "date": "", "ddg_snippet": "There are two variants: threshold monitoring (determining when C(t) exceeds a threshold τ ) and value monitoring (provid-ing a good approximation to C(t) at all times t). Value.", "subpage_snippet": "", "source": "dimacs.rutgers.edu", "link": "http://dimacs.rutgers.edu/~graham/pubs/papers/soda08.pdf", "content": "There are two variants: threshold monitoring (determining when C(t) exceeds a threshold τ ) and value monitoring (provid-ing a good approximation to C(t) at all times t). Value."}
+{"idx": 3, "title": "Optimal threshold selection for tomogram", "date": "", "ddg_snippet": "A simple algorithm for this case is the following: rst, determine initial thresholds τ 0, pos-sibly using another automated procedure, such as tting Gaussian functions to the histogram.In Figure 4(b), the projection distance is plotted as a function of the two thresholds , τ 1 and τ 2 .", "subpage_snippet": "", "source": "homepages.cwi.nl", "link": "https://homepages.cwi.nl/~kbatenbu/papers/basij_tmi_2009.pdf", "content": "A simple algorithm for this case is the following: rst, determine initial thresholds τ 0, pos-sibly using another automated procedure, such as tting Gaussian functions to the histogram.In Figure 4(b), the projection distance is plotted as a function of the two thresholds , τ 1 and τ 2 ."}
+{"idx": 4, "title": "6.3. Предварительная обработка данных — scikit-learn...", "date": "", "ddg_snippet": "Также возможно закодировать каждый столбец в столбцы n_categories - 1 вместо столбцов n_categories, используя параметр drop . Этот параметр позволяет пользователю указать категорию для каждого удаляемого объекта.", "subpage_snippet": "", "source": "scikit-learn.ru", "link": "https://scikit-learn.ru/stable/modules/preprocessing.html", "content": "Также возможно закодировать каждый столбец в столбцы n_categories - 1 вместо столбцов n_categories, используя параметр drop . Этот параметр позволяет пользователю указать категорию для каждого удаляемого объекта."}
+{"idx": 5, "title": "Surface water maps de-noising and missing-data lling using", "date": "", "ddg_snippet": "The threshold 245 τ 1 is related to the quality of the a priori information. In this work, it was chosen based on. trial and error principle.Table 4: De-noising statistics for the Filter 1 a (based on pixel probability from GSWO) for threshold τ 1 a = 0, 0.15, and 0.3.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-02377012v1/document", "content": "The threshold 245 τ 1 is related to the quality of the a priori information. In this work, it was chosen based on. trial and error principle.Table 4: De-noising statistics for the Filter 1 a (based on pixel probability from GSWO) for threshold τ 1 a = 0, 0.15, and 0.3."}
+{"idx": 6, "title": "4.4 The relaxation time τ 2 \\tau_{2}‣ Chapter 4 Hitting and Convergence...", "date": "", "ddg_snippet": "4 Hitting and Convergence Time, and Flow Rate, Parameters for Reversible Markov Chains (October 11, 1994) 4.3 The variation threshold . τ 1 \\tau_{ 1 }.and use it as an initial state for simulating the stationary chain. More feasible to implement is the following algorithm .", "subpage_snippet": "", "source": "www.stat.berkeley.edu", "link": "https://www.stat.berkeley.edu/~aldous/RWG/Book_Ralph/Ch4.S4.html", "content": "4 Hitting and Convergence Time, and Flow Rate, Parameters for Reversible Markov Chains (October 11, 1994) 4.3 The variation threshold . τ 1 \\tau_{ 1 }.and use it as an initial state for simulating the stationary chain. More feasible to implement is the following algorithm ."}
+{"idx": 7, "title": "Mixture Proportion Estimation via Kernel Embedding of Distributions", "date": "", "ddg_snippet": "Algorithm 1 maintains upper and lower bounds (λleft and λright) on the gradient thresholding estimator, 1 estimates the slope at the current point λcurr and adjusts the upper and.", "subpage_snippet": "", "source": "www.ambujtewari.com", "link": "https://www.ambujtewari.com/research/ramaswamy16mixture.pdf", "content": "Algorithm 1 maintains upper and lower bounds (λleft and λright) on the gradient thresholding estimator, 1 estimates the slope at the current point λcurr and adjusts the upper and."}
+{"idx": 8, "title": "(PDF) MNL-Prophet: Sequential Assortment Selection under Uncertainty", "date": "", "ddg_snippet": "Algorithm 3. 1 . Compute threshold τ = 1 . 1 +γ·E[f(S∗)], where γ=E[ψ(S∗)], and set Aτ← ∅. Accept every. arriving item with revenue greater or equal to τ, namely, add ito Aτif and only if ri≥τ.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373046404_MNL-Prophet_Sequential_Assortment_Selection_under_Uncertainty", "content": "Algorithm 3. 1 . Compute threshold τ = 1 . 1 +γ·E[f(S∗)], where γ=E[ψ(S∗)], and set Aτ← ∅. Accept every. arriving item with revenue greater or equal to τ, namely, add ito Aτif and only if ri≥τ."}
+{"idx": 9, "title": "probability theory - Brownian motion stopped at the hitting time of an...", "date": "", "ddg_snippet": "You'll need to complete a few actions and gain 15 reputation points before being able to upvote. Upvoting indicates when questions and answers are useful.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1305780/brownian-motion-stopped-at-the-hitting-time-of-an-independent-brownian-motion", "content": "You'll need to complete a few actions and gain 15 reputation points before being able to upvote. Upvoting indicates when questions and answers are useful."}
diff --git a/data/sampled_jsons/SPD_Sync-Point_Drop_tensor_parallelism_data_parallelism_8_GPU_configuration_year_2024.jsonl b/data/sampled_jsons/SPD_Sync-Point_Drop_tensor_parallelism_data_parallelism_8_GPU_configuration_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..da805918b18d7d7e079f658ced2438a5850fb35c
--- /dev/null
+++ b/data/sampled_jsons/SPD_Sync-Point_Drop_tensor_parallelism_data_parallelism_8_GPU_configuration_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TPLA: Tensor Parallel Latent Attention for Efficient", "date": "", "ddg_snippet": "Tensor parallelism addresses memory and compute limitations by splitting large tensors —such as weight matrices—across multiple devices, enabling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15881v1", "content": "Tensor parallelism addresses memory and compute limitations by splitting large tensors —such as weight matrices—across multiple devices, enabling ..."}
+{"idx": 1, "title": "SPD: Sync-Point Drop for efficient tensor parallelism of ...", "date": "", "ddg_snippet": "May 21, 2025 · Data parallelism Data parallelism can be achieved with SPD by replicating an SPD model across distributed GPU environments. For example, in an 8 -GPUs configuration , a 4-GPUs SPD model can be instantiated as a single replica, which is then duplicated across the remaining 4-GPUs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20727v3", "content": "May 21, 2025 · Data parallelism Data parallelism can be achieved with SPD by replicating an SPD model across distributed GPU environments. For example, in an 8 -GPUs configuration , a 4-GPUs SPD model can be instantiated as a single replica, which is then duplicated across the remaining 4-GPUs."}
+{"idx": 2, "title": "SPD: SYNC-POINT DROP FOR EFFICIENT TENSOR PAR ALLELISM OF ...", "date": "", "ddg_snippet": "ability and low latency. Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ) to reduce communication overheads in tensor parallelism by dropping synchronization on attention outputs. In detail, we first propose a block design that allows execution to proceed without communication through SPD . Second, we identify regions of communication redundancy, where dropping ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=uoU4ypjAmN", "content": "ability and low latency. Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ) to reduce communication overheads in tensor parallelism by dropping synchronization on attention outputs. In detail, we first propose a block design that allows execution to proceed without communication through SPD . Second, we identify regions of communication redundancy, where dropping ..."}
+{"idx": 3, "title": "Optimizing LLM Inference Across Multiple GPUs - zerna.io", "date": "", "ddg_snippet": "Reducing Communication Bottlenecks in Tensor Parallelism Sync-Point Drop ( SPD ) is a novel optimization technique that selectively eliminates synchronization points in tensor - parallel LLM inference, significantly reducing communication overhead.", "subpage_snippet": "", "source": "zerna.io", "link": "http://zerna.io/en/page/engineering/presentation_set/engineering-llm-research/presentation/engineering-model-optimization/slide/engineering-paper-2502_20727", "content": "Reducing Communication Bottlenecks in Tensor Parallelism Sync-Point Drop ( SPD ) is a novel optimization technique that selectively eliminates synchronization points in tensor - parallel LLM inference, significantly reducing communication overhead."}
+{"idx": 4, "title": "Analyzing the Impact of Tensor Parallelism Configurations on ...", "date": "", "ddg_snippet": "Mar 14, 2025 · In this blog, we explore the mechanics of tensor parallelism , its impact on throughput and latency across different batch sizes, and the trade-offs between high TP configurations and single- GPU (TP=1) deployments.", "subpage_snippet": "", "source": "rocm.blogs.amd.com", "link": "https://rocm.blogs.amd.com/artificial-intelligence/tensor-parallelism/README.html", "content": "Mar 14, 2025 · In this blog, we explore the mechanics of tensor parallelism , its impact on throughput and latency across different batch sizes, and the trade-offs between high TP configurations and single- GPU (TP=1) deployments."}
+{"idx": 5, "title": "Tensor and Fully Sharded Data Parallelism", "date": "", "ddg_snippet": "Combining multiple distributed training techniques, such as pipeline, tensor , and data parallelism (PTD-P), or fully sharded data parallelism with tensor parallelism , unlocks the full potential of massive GPU clusters.", "subpage_snippet": "", "source": "martynassubonis.substack.com", "link": "https://martynassubonis.substack.com/p/tensor-and-fully-sharded-data-parallelism", "content": "Combining multiple distributed training techniques, such as pipeline, tensor , and data parallelism (PTD-P), or fully sharded data parallelism with tensor parallelism , unlocks the full potential of massive GPU clusters."}
+{"idx": 6, "title": "Parallelisms — NVIDIA NeMo Framework User Guide 24.07 ...", "date": "", "ddg_snippet": "Dec 23, 2024 · Tensor Parallelism (TP) is a model- parallel partitioning method that distributes the parameter tensor of an individual layer across GPUs. On top of reducing the model state memory usage, it also saves the activation memory as per- GPU tensor sizes shrinks.", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/nemo-framework/user-guide/24.07/nemotoolkit/features/parallelisms.html", "content": "Dec 23, 2024 · Tensor Parallelism (TP) is a model- parallel partitioning method that distributes the parameter tensor of an individual layer across GPUs. On top of reducing the model state memory usage, it also saves the activation memory as per- GPU tensor sizes shrinks."}
+{"idx": 7, "title": "Scaling Speculative Decoding with Lookahead Reasoning", "date": "", "ddg_snippet": "These step proposals and verification are independent, so they can be batched and executed in parallel, making full use of GPU ’s batching capacity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.19830v1", "content": "These step proposals and verification are independent, so they can be batched and executed in parallel, making full use of GPU ’s batching capacity."}
+{"idx": 8, "title": "BurnInTest Windows Changelog", "date": "", "ddg_snippet": "in the 3 Min bitcfg, or configuration that used it as starting config) it was considered a main test and allow BIT to run without any of the main ...", "subpage_snippet": "", "source": "www.softpedia.com", "link": "https://www.softpedia.com/progChangelog/BurnInTest-Professional-Changelog-3299.html", "content": "in the 3 Min bitcfg, or configuration that used it as starting config) it was considered a main test and allow BIT to run without any of the main ..."}
+{"idx": 9, "title": "PassMark BurnInTest software - PC Reliability and Load Testing", "date": "", "ddg_snippet": "MonitorTest, Fixed toolbar not appearing for vertical sync test and adaptive sync test ... sections for comparison (e.g., CPU, RAM, GPU , Disks)", "subpage_snippet": "", "source": "www.passmark.com", "link": "https://www.passmark.com/products/burnintest/history.php", "content": "MonitorTest, Fixed toolbar not appearing for vertical sync test and adaptive sync test ... sections for comparison (e.g., CPU, RAM, GPU , Disks)"}
diff --git a/data/sampled_jsons/SPRING_Improving_the_Throughput_of_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Based_State_P.jsonl b/data/sampled_jsons/SPRING_Improving_the_Throughput_of_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Based_State_P.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..24252b9eac45d4a23e30b3284e1d9e74760cf62f
--- /dev/null
+++ b/data/sampled_jsons/SPRING_Improving_the_Throughput_of_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Based_State_P.jsonl
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+{"idx": 0, "title": "Spring | Home", "date": "", "ddg_snippet": "Integrate AI into your Spring applications without reinventing the wheel. Quickly deliver production‑grade features with independently evolvable microservices.", "subpage_snippet": "", "source": "spring.io", "link": "https://spring.io/", "content": "Integrate AI into your Spring applications without reinventing the wheel. Quickly deliver production‑grade features with independently evolvable microservices."}
+{"idx": 1, "title": "Spring Framework", "date": "", "ddg_snippet": "Jun 2, 2011 · The Spring Framework provides a comprehensive programming and configuration model for modern Java- based enterprise applications - on any kind of deployment platform.", "subpage_snippet": "", "source": "spring.io", "link": "https://spring.io/projects/spring-framework", "content": "Jun 2, 2011 · The Spring Framework provides a comprehensive programming and configuration model for modern Java- based enterprise applications - on any kind of deployment platform."}
+{"idx": 2, "title": "Guides - Spring", "date": "", "ddg_snippet": "Learn how to build an application that uses Spring Integration to fetch data, process it, and write it to a file.", "subpage_snippet": "", "source": "spring.io", "link": "https://spring.io/guides", "content": "Learn how to build an application that uses Spring Integration to fetch data, process it, and write it to a file."}
+{"idx": 3, "title": "Spring Boot 3.5.0 available now", "date": "", "ddg_snippet": "May 22, 2025 · Spring Boot 3.5 moves to new versions of several Spring projects and we’ve also upgraded to the latest stable releases of other third-party libraries wherever possible.", "subpage_snippet": "", "source": "spring.io", "link": "https://spring.io/blog/2025/05/22/spring-boot-3-5-0-available-now", "content": "May 22, 2025 · Spring Boot 3.5 moves to new versions of several Spring projects and we’ve also upgraded to the latest stable releases of other third-party libraries wherever possible."}
+{"idx": 4, "title": "Spring Boot 3.4.8 available now", "date": "", "ddg_snippet": "Jul 24, 2025 · On behalf of the team and everyone who has contributed, I'm happy to announce that Spring Boot 3.4.8 has been released and is now available from Maven Central. This release includes 38 bug fixes, documentation improvements, and dependency upgrades.", "subpage_snippet": "", "source": "spring.io", "link": "https://spring.io/blog/2025/07/24/spring-boot-3-4-8-available-now", "content": "Jul 24, 2025 · On behalf of the team and everyone who has contributed, I'm happy to announce that Spring Boot 3.4.8 has been released and is now available from Maven Central. This release includes 38 bug fixes, documentation improvements, and dependency upgrades."}
+{"idx": 5, "title": "RestClient Support for OAuth2 in Spring Security 6.4", "date": "", "ddg_snippet": "Oct 28, 2024 · With the introduction of RestClient in Spring Framework 6.1, it is now possible to align both stacks with very similar configuration models by utilizing RestClient and WebClient as the underlying HTTP clients for each stack, respectively.", "subpage_snippet": "", "source": "spring.io", "link": "https://spring.io/blog/2024/10/28/restclient-support-for-oauth2-in-spring-security-6-4", "content": "Oct 28, 2024 · With the introduction of RestClient in Spring Framework 6.1, it is now possible to align both stacks with very similar configuration models by utilizing RestClient and WebClient as the underlying HTTP clients for each stack, respectively."}
+{"idx": 6, "title": "Spring AI 1.0.1 Released", "date": "", "ddg_snippet": "Aug 8, 2025 · The Spring AI community continues to grow and contribute in meaningful ways. This release includes contributions from community members who reported issues, submitted fixes, and provided valuable feedback.", "subpage_snippet": "", "source": "spring.io", "link": "https://spring.io/blog/2025/08/08/spring-ai-1", "content": "Aug 8, 2025 · The Spring AI community continues to grow and contribute in meaningful ways. This release includes contributions from community members who reported issues, submitted fixes, and provided valuable feedback."}
+{"idx": 7, "title": "Spring Boot", "date": "", "ddg_snippet": "Spring Boot makes it easy to create stand-alone, production-grade Spring based Applications that you can \"just run\". We take an opinionated view of the Spring platform and third-party libraries so you can get started with minimum fuss.", "subpage_snippet": "", "source": "spring.io", "link": "https://spring.io/projects/spring-boot", "content": "Spring Boot makes it easy to create stand-alone, production-grade Spring based Applications that you can \"just run\". We take an opinionated view of the Spring platform and third-party libraries so you can get started with minimum fuss."}
+{"idx": 8, "title": "Spring Initializr", "date": "", "ddg_snippet": "Initializr generates spring boot project with just what you need to start quickly!", "subpage_snippet": "", "source": "start.spring.io", "link": "https://start.spring.io/index.html", "content": "Initializr generates spring boot project with just what you need to start quickly!"}
+{"idx": 9, "title": "Spring Framework Documentation", "date": "", "ddg_snippet": "Jun 2, 2011 · Rod Johnson, Juergen Hoeller, Keith Donald, Colin Sampaleanu, Rob Harrop, Thomas Risberg, Alef Arendsen, Darren Davison, Dmitriy Kopylenko, Mark Pollack, Thierry ...", "subpage_snippet": "", "source": "docs.spring.io", "link": "https://docs.spring.io/spring-framework/reference/index.html", "content": "Jun 2, 2011 · Rod Johnson, Juergen Hoeller, Keith Donald, Colin Sampaleanu, Rob Harrop, Thomas Risberg, Alef Arendsen, Darren Davison, Dmitriy Kopylenko, Mark Pollack, Thierry ..."}
diff --git a/data/sampled_jsons/SPRING_is_a_deep-reinforcement-learning_framework_for_state_placement_in_sharding_blockchains,_aimin_year_2024.jsonl b/data/sampled_jsons/SPRING_is_a_deep-reinforcement-learning_framework_for_state_placement_in_sharding_blockchains,_aimin_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..00dadab4e5d6eb13c8c65bec92b8749a7d413326
--- /dev/null
+++ b/data/sampled_jsons/SPRING_is_a_deep-reinforcement-learning_framework_for_state_placement_in_sharding_blockchains,_aimin_year_2024.jsonl
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+{"idx": 1, "title": "Software Fault Tolerance Research Topics for MS PhD –", "date": "", "ddg_snippet": "I am sharing with you some of the research topics regarding Software Fault Tolerance that you can choose for your research proposal for the thesis ...", "subpage_snippet": "", "source": "t4tutorials.com", "link": "https://t4tutorials.com/software-fault-tolerance-research-topics-for-ms-phd/", "content": "I am sharing with you some of the research topics regarding Software Fault Tolerance that you can choose for your research proposal for the thesis ..."}
+{"idx": 2, "title": "MERN Stack Course in Chennai | MERN Stack Training in Chennai |", "date": "", "ddg_snippet": "Mastering all four technologies is essential for the aspirants to skillfully handle the framework to build engaging, user-friendly, and impressive ...", "subpage_snippet": "", "source": "www.fita.in", "link": "https://www.fita.in/mern-stack-course-in-chennai/", "content": "Mastering all four technologies is essential for the aspirants to skillfully handle the framework to build engaging, user-friendly, and impressive ..."}
+{"idx": 3, "title": "Paper Digest: WWW 2024 Papers & Highlights – Paper", "date": "", "ddg_snippet": "SPRING : Improving The Throughput of Sharding Blockchain Via Deep Reinforcement Learning Based State Placement Related Papers Related Patents Related ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2024/05/www-2024-highlights/", "content": "SPRING : Improving The Throughput of Sharding Blockchain Via Deep Reinforcement Learning Based State Placement Related Papers Related Patents Related ..."}
+{"idx": 4, "title": "IC3 - Publications", "date": "", "ddg_snippet": "... Forensics: High-Performance Byzantine Accountability for Crash Fault ... Altruism, reciprocity, and tokens to reward forwarding data: Is that fair? .", "subpage_snippet": "", "source": "www.initc3.org", "link": "https://www.initc3.org/publications.html", "content": "... Forensics: High-Performance Byzantine Accountability for Crash Fault ... Altruism, reciprocity, and tokens to reward forwarding data: Is that fair? ."}
+{"idx": 5, "title": "How to Become a Full Stack Developer in 2025? A Full Guide", "date": "", "ddg_snippet": "Vue is known for its simplicity and ease of integration, while Angular offers a complete framework solution with built- in tools for large-scale ...", "subpage_snippet": "", "source": "softspacesolutions.com", "link": "https://softspacesolutions.com/blog/how-to-become-a-full-stack-developer-2/", "content": "Vue is known for its simplicity and ease of integration, while Angular offers a complete framework solution with built- in tools for large-scale ..."}
+{"idx": 6, "title": "How to Become a Full Stack Developer in 2025? A Full Guide", "date": "", "ddg_snippet": "Vue is known for its simplicity and ease of integration, while Angular offers a complete framework solution with built- in tools for large-scale ...", "subpage_snippet": "", "source": "softspacesolutions.com", "link": "https://softspacesolutions.com/blog/how-to-become-a-full-stack-developer/", "content": "Vue is known for its simplicity and ease of integration, while Angular offers a complete framework solution with built- in tools for large-scale ..."}
+{"idx": 7, "title": "Between the Poles: Buildings", "date": "", "ddg_snippet": "... Accurate location information about underground infrastructure is essential for powering our future planet I thought it would be worth while to ...", "subpage_snippet": "", "source": "geospatial.blogs.com", "link": "https://geospatial.blogs.com/geospatial/buildings/", "content": "... Accurate location information about underground infrastructure is essential for powering our future planet I thought it would be worth while to ..."}
+{"idx": 8, "title": "Pre-Employment Test Library | Hirenest", "date": "", "ddg_snippet": "This account manager test is designed to identify the strengths and weaknesses of potential employees applying for a position in the account manager ...", "subpage_snippet": "", "source": "hirenest.com", "link": "https://hirenest.com/solutions/tests", "content": "This account manager test is designed to identify the strengths and weaknesses of potential employees applying for a position in the account manager ..."}
+{"idx": 9, "title": "Polynomial Codes Over Certain Finite Fields | SIAM Journal on", "date": "", "ddg_snippet": "If you have the appropriate software installed, you can download article citation data to the citation manager of your choice.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/0108018?cookieSet=1", "content": "If you have the appropriate software installed, you can download article citation data to the citation manager of your choice."}
diff --git a/data/sampled_jsons/SWE-bench_Can_Language_Models_Resolve_Real-World_GitHub_Issues_year_2024.jsonl b/data/sampled_jsons/SWE-bench_Can_Language_Models_Resolve_Real-World_GitHub_Issues_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..736667f980e6fa98d611676e6253553c6ff40d38
--- /dev/null
+++ b/data/sampled_jsons/SWE-bench_Can_Language_Models_Resolve_Real-World_GitHub_Issues_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SWE-bench: Can Language Models Resolve Real-World GitHub Issues?", "date": "", "ddg_snippet": "Oct 10, 2023 · To this end, we introduce SWE - bench , an evaluation framework consisting of 2, 294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.06770", "content": "Oct 10, 2023 · To this end, we introduce SWE - bench , an evaluation framework consisting of 2, 294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories."}
+{"idx": 1, "title": "SWE-bench: Can Language Models Resolve Real-World GitHub ...", "date": "", "ddg_snippet": "Dec 6, 2023 · We evaluate state-of-the-art LM systems on SWE - bench and find that they largely struggle to generate functional and well-integrated solutions to real issues . Further, we release a training dataset and finetuned version of CodeLlama ( SWE -Llama) to promote open research in this domain.", "subpage_snippet": "", "source": "pli.princeton.edu", "link": "https://pli.princeton.edu/blog/2023/swe-bench-can-language-models-resolve-real-world-github-issues", "content": "Dec 6, 2023 · We evaluate state-of-the-art LM systems on SWE - bench and find that they largely struggle to generate functional and well-integrated solutions to real issues . Further, we release a training dataset and finetuned version of CodeLlama ( SWE -Llama) to promote open research in this domain."}
+{"idx": 2, "title": "SWE-bench: Can Language Models Resolve Real-world Github Issues?", "date": "", "ddg_snippet": "SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue, a language model is tasked with generating a patch that resolves the described problem.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/swe-bench/SWE-bench", "content": "SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue, a language model is tasked with generating a patch that resolves the described problem."}
+{"idx": 3, "title": "SWE-bench: Can Language Models Resolve Real-world Github Issues?", "date": "", "ddg_snippet": "Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE -Llama can resolve only the simplest issues . The best-performing model, Claude 2, is able to solve a mere 1.96% of the issues .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/hash/edac78c3e300629acfe6cbe9ca88fb84-Abstract-Conference.html", "content": "Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE -Llama can resolve only the simplest issues . The best-performing model, Claude 2, is able to solve a mere 1.96% of the issues ."}
+{"idx": 4, "title": "Overview - SWE-bench", "date": "", "ddg_snippet": "SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue, a language model is tasked with generating a patch that resolves the described problem. Check out the other projects that are part of the SWE - bench ecosystem!", "subpage_snippet": "", "source": "www.swebench.com", "link": "https://www.swebench.com/SWE-bench/", "content": "SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue, a language model is tasked with generating a patch that resolves the described problem. Check out the other projects that are part of the SWE - bench ecosystem!"}
+{"idx": 5, "title": "Paper Insights: SWE-BENCH: CAN LANGUAGE MODELS RESOLVE REAL ...", "date": "", "ddg_snippet": "Since the authors felt that bug fixing in software engineering is a difficult problem for LLMs to solve with simple solutions (solutions that can be verified by using unit tests), they created...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@shanmuka.sadhu/paper-insights-swe-bench-can-language-models-resolve-real-world-github-issues-d6ac309fcbb8", "content": "Since the authors felt that bug fixing in software engineering is a difficult problem for LLMs to solve with simple solutions (solutions that can be verified by using unit tests), they created..."}
+{"idx": 6, "title": "Claude SWE-Bench Performance \\ Anthropic", "date": "", "ddg_snippet": "Jan 6, 2025 · SWE-bench is an AI evaluation benchmark that assesses a model's ability to complete real-world software engineering tasks. Specifically, it tests how the model can resolve GitHub issues from popular open-source Python repositories.", "subpage_snippet": "", "source": "www.anthropic.com", "link": "https://www.anthropic.com/engineering/swe-bench-sonnet", "content": "Jan 6, 2025 · SWE-bench is an AI evaluation benchmark that assesses a model's ability to complete real-world software engineering tasks. Specifically, it tests how the model can resolve GitHub issues from popular open-source Python repositories."}
+{"idx": 7, "title": "SWE-bench: Can Language Models Resolve Real-world ...", "date": "", "ddg_snippet": "by CE Jimenez · Cited by 942 — Authors propose SWE-bench, a benchmark based on GitHub issues . They apply LLMs to try and fix these real-world issues and discover very poor ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=VTF8yNQM66", "content": "by CE Jimenez · Cited by 942 — Authors propose SWE-bench, a benchmark based on GitHub issues . They apply LLMs to try and fix these real-world issues and discover very poor ..."}
+{"idx": 8, "title": "SWE-bench: Can Language Models Resolve Real-world ...", "date": "", "ddg_snippet": "SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SWE-bench/SWE-bench", "content": "SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub ."}
+{"idx": 9, "title": "Can Language Models Resolve Real-world Github Issues?", "date": "", "ddg_snippet": "SWE-bench tests AI systems' ability to solve GitHub issues . We collect 2,294 task instances by crawling Pull Requests and Issues from 12 popular Python ...", "subpage_snippet": "", "source": "www.swebench.com", "link": "https://www.swebench.com/original.html", "content": "SWE-bench tests AI systems' ability to solve GitHub issues . We collect 2,294 task instances by crawling Pull Requests and Issues from 12 popular Python ..."}
diff --git a/data/sampled_jsons/Sang-Jun_Park_Keun-Soo_Heo_DART_Disease-aware_Image-Text_Alignment.jsonl b/data/sampled_jsons/Sang-Jun_Park_Keun-Soo_Heo_DART_Disease-aware_Image-Text_Alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..28fe2a3703e3778203fac0d877c3087c3c7344c6
--- /dev/null
+++ b/data/sampled_jsons/Sang-Jun_Park_Keun-Soo_Heo_DART_Disease-aware_Image-Text_Alignment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2504.11786] DART : Disease - aware Image - Text Alignment and...", "date": "", "ddg_snippet": "View a PDF of the paper titled DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation, by Sang - Jun Park and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "View a PDF of the paper titled DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation, by Sang - Jun Park and 5 other authors."}
+{"idx": 1, "title": "(PDF) DART : Disease - aware Image - Text Alignment and...", "date": "", "ddg_snippet": "Keun - Soo Heo .a Disease - aware image - text Alignment and self-correcting. Re- alignment for Trustworthy radiology report generation. ( DART ) framework. In the first stage, we generate ini-. tial reports based on image-to-text retrieval with disease", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390845711_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_Report_Generation", "content": "Keun - Soo Heo .a Disease - aware image - text Alignment and self-correcting. Re- alignment for Trustworthy radiology report generation. ( DART ) framework. In the first stage, we generate ini-. tial reports based on image-to-text retrieval with disease"}
+{"idx": 2, "title": "DART : Disease - aware Image - Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Sang - Jun Park * Keun - Soo Heo In this study, we propose a Disease - aware image - text Alignment and self-correcting...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Sang - Jun Park * Keun - Soo Heo In this study, we propose a Disease - aware image - text Alignment and self-correcting..."}
+{"idx": 3, "title": "Dart", "date": "", "ddg_snippet": "Authors: Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam.In this study, we propose a Disease - aware image - text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/s/Dart", "content": "Authors: Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam.In this study, we propose a Disease - aware image - text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework."}
+{"idx": 4, "title": "Heo Keun - Soo - Google 학술 검색", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation. SJ Park , KS Heo , DH Shin, YH Son, JH Oh, TE Kam.", "subpage_snippet": "", "source": "scholar.google.co.kr", "link": "https://scholar.google.co.kr/citations?user=UJJGePQAAAAJ&hl=ko", "content": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation. SJ Park , KS Heo , DH Shin, YH Son, JH Oh, TE Kam."}
+{"idx": 5, "title": "dblp: Sang - Jun Park (disambiguation)", "date": "", "ddg_snippet": "Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam: DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/166/8696.html", "content": "Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam: DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation."}
+{"idx": 6, "title": "Photo Translate - Online Image Translator", "date": "", "ddg_snippet": "Our Online Photo translator translates the text embedded inside an image with a single click. Upload your photo to our image translator, choose the language, and get the translated image effortlessly.", "subpage_snippet": "", "source": "www.imagetotext.io", "link": "https://www.imagetotext.io/photo-translate", "content": "Our Online Photo translator translates the text embedded inside an image with a single click. Upload your photo to our image translator, choose the language, and get the translated image effortlessly."}
+{"idx": 7, "title": "GitHub - mk-runner/Awesome-Radiology-Report-Generation: paper list...", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation [paper].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation", "content": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation [paper]."}
+{"idx": 8, "title": "Joseph is a super minimal content focus theme for Jekyll.", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation. Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam.", "subpage_snippet": "", "source": "donghee-shin-page.github.io", "link": "https://donghee-shin-page.github.io/publications/", "content": "DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation. Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam."}
+{"idx": 9, "title": "dblp: List of computer science publications by Dong-Hee Shin", "date": "", "ddg_snippet": "Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam: DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/09/4826.html", "content": "Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam: DART : Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation."}
diff --git a/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_blog_summary_Figure_3_results.jsonl b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_blog_summary_Figure_3_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..33880be1e512a328882f71e64c0edfcb04fb6af3
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are known, providing a ground truth. We select methods ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20099", "content": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are known, providing a ground truth. We select methods ..."}
+{"idx": 1, "title": "GitHub - simonbing/CRLSanityCheck", "date": "", "ddg_snippet": "Sanity Checking Causal Representation Learning on a Simple Real-World System Official code repository for the paper Sanity Checking Causal Representation Learning on a Simple Real-World System (2025) by Juan L. Gamella*, Simon Bing* and Jakob Runge.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/simonbing/CRLSanityCheck", "content": "Sanity Checking Causal Representation Learning on a Simple Real-World System Official code repository for the paper Sanity Checking Causal Representation Learning on a Simple Real-World System (2025) by Juan L. Gamella*, Simon Bing* and Jakob Runge."}
+{"idx": 2, "title": "PDF Sanity Checking Causal Representation Learning on a ... - ResearchGate", "date": "", "ddg_snippet": "In Figure 3 , we provide a summary of the results of applying CCRL to the real images and to the synthetic images (e.g., Figure 2F) produced by the deterministic sim-ulator of the light tunnel ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389398876_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System/fulltext/67c12b09645ef274a496774e/Sanity-Checking-Causal-Representation-Learning-on-a-Simple-Real-World-System.pdf", "content": "In Figure 3 , we provide a summary of the results of applying CCRL to the real images and to the synthetic images (e.g., Figure 2F) produced by the deterministic sim-ulator of the light tunnel ..."}
+{"idx": 3, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors---the inputs to the experiment---are ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=d2aGLPSpFz", "content": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors---the inputs to the experiment---are ..."}
+{"idx": 4, "title": "PDF Causal Representation Learning", "date": "", "ddg_snippet": "In this review, we discuss existing work in causal representation learning and structure learning . First, we define a causal representation as one which satisfies properties of causal variables and survey existing works to build causal representations .", "subpage_snippet": "", "source": "sanaelotfi.github.io", "link": "https://sanaelotfi.github.io/files/project_reports/causal_representation_learning_survey.pdf", "content": "In this review, we discuss existing work in causal representation learning and structure learning . First, we define a causal representation as one which satisfies properties of causal variables and survey existing works to build causal representations ."}
+{"idx": 5, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "This paper evaluates various causal representation learning methods on a controlled optical experiment, finding that they fail to recover known causal factors and highlighting significant challenges in their practical application.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/115764", "content": "This paper evaluates various causal representation learning methods on a controlled optical experiment, finding that they fail to recover known causal factors and highlighting significant challenges in their practical application."}
+{"idx": 6, "title": "Sanity Checking Causal Representation Learning", "date": "", "ddg_snippet": "This paper provides a sanity check to validate advances in causal representation learning . Because our contribution pertains to fundamental and theoretical research in machine learning , the potential societal consequences are difficult to ascertain and, in any case, not immediate.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099", "content": "This paper provides a sanity check to validate advances in causal representation learning . Because our contribution pertains to fundamental and theoretical research in machine learning , the potential societal consequences are difficult to ascertain and, in any case, not immediate."}
+{"idx": 7, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "This paper provides a sanity check to validate advances in causal representation learning . Because our contribution pertains to fundamental and theoretical research in machine learning , the potential societal consequences are dificult to ascertain and, in any case, not immediate.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=d2aGLPSpFz", "content": "This paper provides a sanity check to validate advances in causal representation learning . Because our contribution pertains to fundamental and theoretical research in machine learning , the potential societal consequences are dificult to ascertain and, in any case, not immediate."}
+{"idx": 8, "title": "New Insights on Causal Representation Learning (CRL) - LinkedIn", "date": "", "ddg_snippet": "📢 New Insights on Causal Representation Learning (CRL) 📢 In a recent study, researchers have conducted a thorough evaluation of CRL methods on a controlled optical experiment designed to ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/abdullah-kasri_sanity-checking-causal-representation-learning-activity-7301433547033034752--hc7", "content": "📢 New Insights on Causal Representation Learning (CRL) 📢 In a recent study, researchers have conducted a thorough evaluation of CRL methods on a controlled optical experiment designed to ..."}
+{"idx": 9, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "This work provides the first identifiability result for causal representation learning that allows for multiple variables to be targeted by an intervention within one environment, and presents a practical algorithm to learn causal representations from multi-node interventional data and provides empirical evidence that validates the results .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Sanity-Checking-Causal-Representation-Learning-on-a-Gamella-Bing/638e050573f438f77583f2b210c2d5da0f1b4ca7", "content": "This work provides the first identifiability result for causal representation learning that allows for multiple variables to be targeted by an intervention within one environment, and presents a practical algorithm to learn causal representations from multi-node interventional data and provides empirical evidence that validates the results ."}
diff --git "a/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_2023_ImageNet-100_top-1_accuracy_70.3_synthetic_training.jsonl" "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_2023_ImageNet-100_top-1_accuracy_70.3_synthetic_training.jsonl"
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index 0000000000000000000000000000000000000000..6ebd51da473b3f3269d28f2d69b45f64167a20fb
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+++ "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_2023_ImageNet-100_top-1_accuracy_70.3_synthetic_training.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Image Captions are Natural Prompts for Training Data Synthesis", "date": "", "ddg_snippet": "[ 2023 ] Sarıyıldız , M.B., Alahari, K., Larlus, D., Kalantidis, Y.: Fake it till you make it: Learning transferable representations from synthetic imagenet clones.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.08526v2", "content": "[ 2023 ] Sarıyıldız , M.B., Alahari, K., Larlus, D., Kalantidis, Y.: Fake it till you make it: Learning transferable representations from synthetic imagenet clones."}
+{"idx": 1, "title": "Image Captions are Natural Prompts for Training Data Synthesis", "date": "", "ddg_snippet": "Extensive experiments on ImageNette, ImageNet - 100 , and ImageNet- 1 K verify that our method significantly improves the performance of models trained on synthetic training data, i .e., 10% classification accuracy improvements on average.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392940851_Image_Captions_are_Natural_Prompts_for_Training_Data_Synthesis", "content": "Extensive experiments on ImageNette, ImageNet - 100 , and ImageNet- 1 K verify that our method significantly improves the performance of models trained on synthetic training data, i .e., 10% classification accuracy improvements on average."}
+{"idx": 2, "title": "ImageNet 100 - 10 steps Benchmark (Incremental...) | Papers With Code", "date": "", "ddg_snippet": "The current state-of-the-art on ImageNet 100 - 10 steps is kNN-CLIP. See a full comparison of 13 papers with code.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/sota/incremental-learning-on-imagenet100-10-steps", "content": "The current state-of-the-art on ImageNet 100 - 10 steps is kNN-CLIP. See a full comparison of 13 papers with code."}
+{"idx": 3, "title": "LeeNet分类网络（ ImageNet 100 （已开源）： top 1 -73.44...", "date": "", "ddg_snippet": "测试结果：准确率方面（ top 1 -73.44%，top5-91.40%）. 速度方面（forward_CPU_time-30ms）.1. 下载地址 ImageNet 约 100 GB可以从官网下载，MiniImageNet约3GB，下载地址，密码: hl31。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/samylee/article/details/77748755", "content": "测试结果：准确率方面（ top 1 -73.44%，top5-91.40%）. 速度方面（forward_CPU_time-30ms）.1. 下载地址 ImageNet 约 100 GB可以从官网下载，MiniImageNet约3GB，下载地址，密码: hl31。"}
+{"idx": 4, "title": "Performance on imagenet 100 and imagenet 1 k · Issue # 1 · bl0/moco", "date": "", "ddg_snippet": "Performance on imagenet 100 and imagenet 1 k# 1 . Copy link.Have you tried your implementation on imagenet 100 dataset? I 'm getting accuracy at around 69.0 with default config (8 gpu, lr 0.03, bs 256), which is lower than the MoCo implementation in the CMC repo.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bl0/moco/issues/1", "content": "Performance on imagenet 100 and imagenet 1 k# 1 . Copy link.Have you tried your implementation on imagenet 100 dataset? I 'm getting accuracy at around 69.0 with default config (8 gpu, lr 0.03, bs 256), which is lower than the MoCo implementation in the CMC repo."}
+{"idx": 5, "title": "Fake it till you make it: Learning transferable... - Naver Labs Europe", "date": "", "ddg_snippet": "Training models on synthetic images.We report top-5 accuracy for all ImageNet test sets, and average top - 1 over three groups of transfer datasets. Pretrained Models.", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/research/computer-vision/imagenet-sd/?_hsenc=p2ANqtz--ciwBubqu0hPsY8PFT6eFAely0lbpuUMpYXKqSxTr5i5vTQBBP9qR5y0AaFOwKrOvMOzx1", "content": "Training models on synthetic images.We report top-5 accuracy for all ImageNet test sets, and average top - 1 over three groups of transfer datasets. Pretrained Models."}
+{"idx": 6, "title": "Published as a conference paper at ICLR 2024", "date": "", "ddg_snippet": "Specifically, we achieve 70.9% top 1 classifi-cation accuracy on ImageNet 1K when training solely with synthetic data equivalent to 1 × the original real data size, which increases to 76.0% when scaling up to 10 × synthetic data1.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=svIdLLZpsA", "content": "Specifically, we achieve 70.9% top 1 classifi-cation accuracy on ImageNet 1K when training solely with synthetic data equivalent to 1 × the original real data size, which increases to 76.0% when scaling up to 10 × synthetic data1."}
+{"idx": 7, "title": "Future-proofing class-incremental learning | springerprofessional.de", "date": "", "ddg_snippet": "Average incremental accuracy and final accuracy of our proposed method FPCIL are reported for CIFAR 100 and ImageNet -Subset in Tables 2 and 3 for both the learning from scratch and learning from half procedures.", "subpage_snippet": "", "source": "www.springerprofessional.de", "link": "https://www.springerprofessional.de/en/future-proofing-class-incremental-learning/50275764", "content": "Average incremental accuracy and final accuracy of our proposed method FPCIL are reported for CIFAR 100 and ImageNet -Subset in Tables 2 and 3 for both the learning from scratch and learning from half procedures."}
+{"idx": 8, "title": "Towards Efficient and Effective Self-Supervised", "date": "", "ddg_snippet": "The proposed method achieves the same accuracy as the baseline in one- third the training time (shown using blue dotted line). (b) Gain in Top - 1 Accuracy (%), or the difference between accuracy of the current epoch and epoch-50.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136910509.pdf", "content": "The proposed method achieves the same accuracy as the baseline in one- third the training time (shown using blue dotted line). (b) Gain in Top - 1 Accuracy (%), or the difference between accuracy of the current epoch and epoch-50."}
+{"idx": 9, "title": "52 датасета для тренировочных проектов / Хабр", "date": "", "ddg_snippet": "CIFAR- 100 — соответственно, 0- 100 . GTSRB (German traffic sign recognition benchmark) Dataset — 50 000 изображений 43 дорожных знаков. (Вариант применения с исходником на Python: Traffic Signs Recognition Python Project).", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/edison/articles/480408/", "content": "CIFAR- 100 — соответственно, 0- 100 . GTSRB (German traffic sign recognition benchmark) Dataset — 50 000 изображений 43 дорожных знаков. (Вариант применения с исходником на Python: Traffic Signs Recognition Python Project)."}
diff --git "a/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_2023_ImageNet-100_training_accuracy_results_table_synthetic_data_year_2023.jsonl" "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_2023_ImageNet-100_training_accuracy_results_table_synthetic_data_year_2023.jsonl"
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index 0000000000000000000000000000000000000000..0fd2274495a15063347e3f125336448aefad5087
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Image Captions are Natural Prompts for Training Data Synthesis", "date": "", "ddg_snippet": "These differences in the training stage causes large accuracy gaps when training with real and synthetic data , and we thus do not include the ImageNet-100 results of ( Sarıyıldız et al., 2023 ) in our experiments.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-025-02436-0", "content": "These differences in the training stage causes large accuracy gaps when training with real and synthetic data , and we thus do not include the ImageNet-100 results of ( Sarıyıldız et al., 2023 ) in our experiments."}
+{"idx": 1, "title": "[2304.08466] Synthetic Data from Diffusion Models Improves ImageNet ...", "date": "", "ddg_snippet": "The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples). Augmenting the ImageNet training set with samples from the resulting models yields significant improvements in ImageNet classification accuracy over strong ResNet and Vision Transformer baselines.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.08466", "content": "The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples). Augmenting the ImageNet training set with samples from the resulting models yields significant improvements in ImageNet classification accuracy over strong ResNet and Vision Transformer baselines."}
+{"idx": 2, "title": "Table 4 from : Learning transferable representations from synthetic ...", "date": "", "ddg_snippet": "Table 4. Top-1 Accuracy on ImageNet-100 when prepending the prompt with domain identifiers. Guidance scale is equal to 2.0. - \": Learning transferable representations from synthetic ImageNet clones\"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/:-Learning-transferable-representations-from-clones-Sariyildiz-Karteek/6c8e04911fb665f8b2f8d4a0ad80b54e37c8223a/figure/6", "content": "Table 4. Top-1 Accuracy on ImageNet-100 when prepending the prompt with domain identifiers. Guidance scale is equal to 2.0. - \": Learning transferable representations from synthetic ImageNet clones\""}
+{"idx": 3, "title": "GitHub - danielchyeh/ImageNet-100-Pytorch: (Pytorch) Training ResNets ...", "date": "", "ddg_snippet": "ImageNet -1K data could be accessed with ILSVRC 2012. If ImageNet -1K data is available already, jump to the Quick Start section below to generate ImageNet-100 . Generate ImageNet-100 dataset based on selected class file randomly sampled from ImageNet -1K dataset. Simply run the generate_IN100.py could ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/danielchyeh/ImageNet-100-Pytorch", "content": "ImageNet -1K data could be accessed with ILSVRC 2012. If ImageNet -1K data is available already, jump to the Quick Start section below to generate ImageNet-100 . Generate ImageNet-100 dataset based on selected class file randomly sampled from ImageNet -1K dataset. Simply run the generate_IN100.py could ..."}
+{"idx": 4, "title": "Synthetic Data from Diffusion Models Improves ImageNet Classification", "date": "", "ddg_snippet": "The model also yields a new state-of-the-art in Classification Accuracy Scores, i.e., ImageNet test accuracy for a ResNet-50 architecture trained solely on synthetic data (64.96 top-1 accuracy for 256×256 samples, improving to 69.24 for 1024×1024 samples).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DlRsoxjyPm", "content": "The model also yields a new state-of-the-art in Classification Accuracy Scores, i.e., ImageNet test accuracy for a ResNet-50 architecture trained solely on synthetic data (64.96 top-1 accuracy for 256×256 samples, improving to 69.24 for 1024×1024 samples)."}
+{"idx": 5, "title": "CVPR 2023 Open Access Repository", "date": "", "ddg_snippet": "Mert Bülent Sarıyıldız , Karteek Alahari, Diane Larlus, Yannis Kalantidis; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 , pp. 8011-8021", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/html/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.html", "content": "Mert Bülent Sarıyıldız , Karteek Alahari, Diane Larlus, Yannis Kalantidis; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 , pp. 8011-8021"}
+{"idx": 6, "title": "PDF Does progress on ImageNet transfer to real-world datasets?", "date": "", "ddg_snippet": "Thus, as with architectures, augmentation strategies that improve accuracy on ImageNet do not always improve accuracy on real-world tasks. 5.2CLIP models A natural follow-up to our experiments is to change the source of pre- training data .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2023/file/4eb33c53ed5b14ce9028309431f565cc-Paper-Datasets_and_Benchmarks.pdf", "content": "Thus, as with architectures, augmentation strategies that improve accuracy on ImageNet do not always improve accuracy on real-world tasks. 5.2CLIP models A natural follow-up to our experiments is to change the source of pre- training data ."}
+{"idx": 7, "title": "PDF Fake It Till You Make It: Learning Transferable Representations From ...", "date": "", "ddg_snippet": "Our work focuses on image-level classification, and uses a general-purpose text-conditioned generative model instead of ImageNet -1K class-conditioned GANs. It further offers a larger scale study with promising results on the full ImageNet -1K benchmark when training from 1.28 million synthetic images.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.pdf", "content": "Our work focuses on image-level classification, and uses a general-purpose text-conditioned generative model instead of ImageNet -1K class-conditioned GANs. It further offers a larger scale study with promising results on the full ImageNet -1K benchmark when training from 1.28 million synthetic images."}
+{"idx": 8, "title": "Fake it till you make it: Learning transferable representations from ...", "date": "", "ddg_snippet": "CVPR 2023 publication. Models and paperThe blue polygon shows the performance of a model trained on ImageNet -1K. The red polygon depicts the performance of a model trained only on synthetic data , generated with Stable Diffusion using the class names of ImageNet -1K. We report top-5 accuracy for all ImageNet test sets, and average top-1 over three groups of transfer datasets.", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/research/computer-vision/imagenet-sd/", "content": "CVPR 2023 publication. Models and paperThe blue polygon shows the performance of a model trained on ImageNet -1K. The red polygon depicts the performance of a model trained only on synthetic data , generated with Stable Diffusion using the class names of ImageNet -1K. We report top-5 accuracy for all ImageNet test sets, and average top-1 over three groups of transfer datasets."}
+{"idx": 9, "title": "PDF Supplementary Material for Fake it till you make it: Learning ...", "date": "", "ddg_snippet": "We compare four different models trained on either real or synthetic data for the 100 classes of ImageNet-100 : One model trained on real images, ImageNet - 100 -Real, two mod- els trained on synthetic image sets of the same size obtained by using two different prompts: p", "subpage_snippet": "", "source": "lear.inrialpes.fr", "link": "https://lear.inrialpes.fr/people/alahari/papers/sariyildiz23a-supp.pdf", "content": "We compare four different models trained on either real or synthetic data for the 100 classes of ImageNet-100 : One model trained on real images, ImageNet - 100 -Real, two mod- els trained on synthetic image sets of the same size obtained by using two different prompts: p"}
diff --git a/data/sampled_jsons/ScoreDec_A_Phase-preserving_High-Fidelity_Audio_Codec_with_A_Generalized_Score-based_Diffusion_Post-.jsonl b/data/sampled_jsons/ScoreDec_A_Phase-preserving_High-Fidelity_Audio_Codec_with_A_Generalized_Score-based_Diffusion_Post-.jsonl
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index 0000000000000000000000000000000000000000..6bd609fa095de1eb5e2bd9231b8ecbda54dac615
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ComplexDec: A Domain-robust High-fidelity Neural Audio ...", "date": "", "ddg_snippet": "4 Feb 2025 — Richard, “ ScoreDec: A phase-preserving high-fidelity audio codec with a generalized score-based diffusion post-filter ,” in Proc. ICASSP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02019v1", "content": "4 Feb 2025 — Richard, “ ScoreDec: A phase-preserving high-fidelity audio codec with a generalized score-based diffusion post-filter ,” in Proc. ICASSP ..."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with high", "date": "", "ddg_snippet": "... are: (1) the extension and simplification of prior score - based generative audio enhancement methods (Welker et al., 2022 ; Richter et al., 2023 ; Wu ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "... are: (1) the extension and simplification of prior score - based generative audio enhancement methods (Welker et al., 2022 ; Richter et al., 2023 ; Wu ..."}
+{"idx": 2, "title": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec ...", "date": "", "ddg_snippet": "by YC Wu · 2024 · Cited by 7 — ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter . Authors:Yi-Chiao Wu, Dejan Marković ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.12160", "content": "by YC Wu · 2024 · Cited by 7 — ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter . Authors:Yi-Chiao Wu, Dejan Marković ..."}
+{"idx": 3, "title": "scoredec: a phase-preserving high-fidelity audio codec with a", "date": "", "ddg_snippet": "by YC Wu · 2024 · Cited by 7 — ABSTRACT . Although recent mainstream waveform-domain end-to-end (E2E) neural audio codecs achieve impressive coded audio quality with a very low bitrate, the ... 5 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/10445798/10445803/10448371.pdf", "content": "by YC Wu · 2024 · Cited by 7 — ABSTRACT . Although recent mainstream waveform-domain end-to-end (E2E) neural audio codecs achieve impressive coded audio quality with a very low bitrate, the ... 5 pages"}
+{"idx": 4, "title": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec ...", "date": "", "ddg_snippet": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter . Yi-Chiao Wu, Dejan Marković, Steven Krenn, ...", "subpage_snippet": "", "source": "bigpon.github.io", "link": "https://bigpon.github.io/ScoreDec_demo/", "content": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter . Yi-Chiao Wu, Dejan Marković, Steven Krenn, ..."}
+{"idx": 5, "title": "EVA-GAN: Enhanced Various Audio Generation via ...", "date": "", "ddg_snippet": "5 Feb 2024 — Abstract . EVA-GAN improves ... ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2402.00892", "content": "5 Feb 2024 — Abstract . EVA-GAN improves ... ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter ..."}
+{"idx": 6, "title": "[PDF] SoundStream: An End-to-End Neural Audio Codec", "date": "", "ddg_snippet": "PDF . Add to Library. Alert. 1 Excerpt. Low ... ScoreDec: A Phase-Preserving High-Fidelity Audio Codec with a Generalized Score-Based Diffusion Post-Filter .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/SoundStream:-An-End-to-End-Neural-Audio-Codec-Zeghidour-Luebs/59a0ef2d3bccebb544a2df4ad0453d49cc8e731f", "content": "PDF . Add to Library. Alert. 1 Excerpt. Low ... ScoreDec: A Phase-Preserving High-Fidelity Audio Codec with a Generalized Score-Based Diffusion Post-Filter ."}
+{"idx": 7, "title": "Paper page - MusicHiFi: Fast High-Fidelity Stereo Vocoding", "date": "", "ddg_snippet": "18 Mar 2024 — ... ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter (2024); Ultra-lightweight Neural ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2403.10493", "content": "18 Mar 2024 — ... ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter (2024); Ultra-lightweight Neural ..."}
+{"idx": 8, "title": "ga642381/speech-trident: Awesome speech/audio LLMs, ...", "date": "", "ddg_snippet": "... ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter , paper. 2023-11, HierSpeech++, HierSpeech++: ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ga642381/speech-trident", "content": "... ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter , paper. 2023-11, HierSpeech++, HierSpeech++: ..."}
+{"idx": 9, "title": "Alexander Richard", "date": "", "ddg_snippet": "Abstract: We propose FlowDec, a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Alexander+Richard", "content": "Abstract: We propose FlowDec, a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a ..."}
diff --git a/data/sampled_jsons/ScoreDec_Wu_2024_phase-preserving_high-fidelity_audio_codec_score-based_diffusion_post-filter_year_2024.jsonl b/data/sampled_jsons/ScoreDec_Wu_2024_phase-preserving_high-fidelity_audio_codec_score-based_diffusion_post-filter_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..98e9588bf6aeba22e647f9c2c9c70272f45bf5d2
--- /dev/null
+++ b/data/sampled_jsons/ScoreDec_Wu_2024_phase-preserving_high-fidelity_audio_codec_score-based_diffusion_post-filter_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high", "date": "", "ddg_snippet": "In this spirit, a recently proposed score - based codec is ScoreDec ( Wu et al., 2024 ) , a widely applicable generative postfilter for E2E neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "In this spirit, a recently proposed score - based codec is ScoreDec ( Wu et al., 2024 ) , a widely applicable generative postfilter for E2E neural ..."}
+{"idx": 1, "title": "ComplexDec: A Domain-robust High-fidelity Neural Audio Codec", "date": "", "ddg_snippet": "Descript- audio - codec (DAC) [ 23 ] adopts a handcraft code factorization in RVQ to ease the information loss by adopting high -dimensional code ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02019v1", "content": "Descript- audio - codec (DAC) [ 23 ] adopts a handcraft code factorization in RVQ to ease the information loss by adopting high -dimensional code ..."}
+{"idx": 2, "title": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A ...", "date": "", "ddg_snippet": "Jan 22, 2024 · Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well- preserved phase information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.12160", "content": "Jan 22, 2024 · Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well- preserved phase information."}
+{"idx": 3, "title": "ScoreDec: A Phase-Preserving High-Fidelity Audio Codec with a ...", "date": "", "ddg_snippet": "Although recent mainstream waveform-domain end-to-end (E2E) neural audio codecs achieve impressive coded audio quality with a very low bitrate, the quality gap", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10448371", "content": "Although recent mainstream waveform-domain end-to-end (E2E) neural audio codecs achieve impressive coded audio quality with a very low bitrate, the quality gap"}
+{"idx": 4, "title": "Yi-Chiao Wu on LinkedIn: ScoreDec: A Phase-Preserving High ...", "date": "", "ddg_snippet": "I will present our (the Codec Avatar audio team in Pittsburgh) latest work, ScoreDec : A Phase - Preserving High - Fidelity Audio Codec with a Generalized Score - Based Diffusion ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/wuyichiao_scoredec-a-phase-preserving-high-fidelity-activity-7184772614807015424-aw5k", "content": "I will present our (the Codec Avatar audio team in Pittsburgh) latest work, ScoreDec : A Phase - Preserving High - Fidelity Audio Codec with a Generalized Score - Based Diffusion ..."}
+{"idx": 5, "title": "IEEE ICASSP 2024 || Seoul, Korea || 14-19 April 2024", "date": "", "ddg_snippet": "ScoreDec : A Phase - preserving High - Fidelity Audio Codec with A Generalized Score - based Diffusion Post - filter Yi-Chiao Wu , Dejan Markovic, Steven Krenn, Israel D. Gebru, Alexander Richard, Meta, United States of America", "subpage_snippet": "", "source": "cmsworkshops.com", "link": "https://cmsworkshops.com/ICASSP2024/view_paper.php?PaperNum=2138", "content": "ScoreDec : A Phase - preserving High - Fidelity Audio Codec with A Generalized Score - based Diffusion Post - filter Yi-Chiao Wu , Dejan Markovic, Steven Krenn, Israel D. Gebru, Alexander Richard, Meta, United States of America"}
+{"idx": 6, "title": "[Paper 리뷰] ScoreDec: A Phase-Preserving High-Fidelity Audio ...", "date": "", "ddg_snippet": "Jun 21, 2024 · 결과적으로 Score Dec 은 symmetric neural audio codec인 AudioDec과 score - based diffusion post - filter (SPF)로 구성되고, 각각 mel-loss와 score-matching loss를 사용하여 개별적으로 training 됨", "subpage_snippet": "", "source": "randomsampling.tistory.com", "link": "https://randomsampling.tistory.com/279", "content": "Jun 21, 2024 · 결과적으로 Score Dec 은 symmetric neural audio codec인 AudioDec과 score - based diffusion post - filter (SPF)로 구성되고, 각각 mel-loss와 score-matching loss를 사용하여 개별적으로 training 됨"}
+{"idx": 7, "title": "Alexander Richard", "date": "", "ddg_snippet": "... ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Alexander+Richard", "content": "... ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving ..."}
+{"idx": 8, "title": "Timo GERKMANN | Professor, Head of Signal Processing Group |", "date": "", "ddg_snippet": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s t...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Timo-Gerkmann", "content": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s t..."}
+{"idx": 9, "title": "Israel D. Gebru - Google Scholar", "date": "", "ddg_snippet": "Audio -visual tracking by density ... ScoreDec : A Phase - Preserving High - Fidelity Audio Codec with a Generalized Score - Based Diffusion Post - Filter", "subpage_snippet": "", "source": "scholar.google.it", "link": "https://scholar.google.it/citations?user=5RFgT84AAAAJ&hl=en", "content": "Audio -visual tracking by density ... ScoreDec : A Phase - Preserving High - Fidelity Audio Codec with a Generalized Score - Based Diffusion Post - Filter"}
diff --git a/data/sampled_jsons/Section_5.1_experimental_setup_OmniBench_GPU_hardware_A100_V100_H100.jsonl b/data/sampled_jsons/Section_5.1_experimental_setup_OmniBench_GPU_hardware_A100_V100_H100.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eca2775a1260b514bf5145985560590d33d8a335
--- /dev/null
+++ b/data/sampled_jsons/Section_5.1_experimental_setup_OmniBench_GPU_hardware_A100_V100_H100.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[AINews] OpenAI adopts MCP • Buttondown", "date": "", "ddg_snippet": "It outperforms models like Gemini- 1 . 5 -Pro on OmniBench and excels in speech recognition, translation, audio understanding, and image/video reasoning.", "subpage_snippet": "", "source": "buttondown.com", "link": "https://buttondown.com/ainews/archive/ainews-ghibli-memes/", "content": "It outperforms models like Gemini- 1 . 5 -Pro on OmniBench and excels in speech recognition, translation, audio understanding, and image/video reasoning."}
+{"idx": 1, "title": "TFM_MORITZ_JOHANNES_MEISTER1.pdf", "date": "", "ddg_snippet": "5 . 1 . 1 Experimental Setup . Data.The experiments are run on a single node Google Cloud instance with 68GB memory, 22 vCores and 4 NVIDIA Tesla P100 GPUs with 16GB memory each. The node is running Hopsworks 0.10.0 with Apache Spark 2.4.0.1 and Maggy 0.2.2.", "subpage_snippet": "", "source": "oa.upm.es", "link": "https://oa.upm.es/56977/1/TFM_MORITZ_JOHANNES_MEISTER.pdf", "content": "5 . 1 . 1 Experimental Setup . Data.The experiments are run on a single node Google Cloud instance with 68GB memory, 22 vCores and 4 NVIDIA Tesla P100 GPUs with 16GB memory each. The node is running Hopsworks 0.10.0 with Apache Spark 2.4.0.1 and Maggy 0.2.2."}
+{"idx": 2, "title": "Trajno napovedovanje krvnega tlaka iz signala PPG", "date": "", "ddg_snippet": "Experiments and evaluation. Experimental setup . Clinical dataset (MIMIC database).", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/151301279.pdf", "content": "Experiments and evaluation. Experimental setup . Clinical dataset (MIMIC database)."}
+{"idx": 3, "title": "Docker-pi: Docker Container Deployment in Fog Computing...", "date": "", "ddg_snippet": "Experimental setup .Section 3 analyzes the deployment process and points out its ineciencies. Section 4 proposes and evaluates three optimizations. Finally, Section 5 discusses practicalities, and Section 6 concludes.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02271434/document", "content": "Experimental setup .Section 3 analyzes the deployment process and points out its ineciencies. Section 4 proposes and evaluates three optimizations. Finally, Section 5 discusses practicalities, and Section 6 concludes."}
+{"idx": 4, "title": "Noname manuscript No.", "date": "", "ddg_snippet": "Section 3 reviews related work. tamp t, we just need to recompute the join result (up. In Section 4, we present the MLG-join algorithm and to t + TM ) for the objects that have reported updates its implementation on the GPU . Section 5 provides a at t, and update the list L accordingly.", "subpage_snippet": "", "source": "www.ruizhang.info", "link": "https://www.ruizhang.info/publications/Vldbj2014-ContinuousMovingJoinGPU.pdf", "content": "Section 3 reviews related work. tamp t, we just need to recompute the join result (up. In Section 4, we present the MLG-join algorithm and to t + TM ) for the objects that have reported updates its implementation on the GPU . Section 5 provides a at t, and update the list L accordingly."}
+{"idx": 5, "title": "Temporal Tiling for Distributed", "date": "", "ddg_snippet": "5 . 1 Experimental Setup . We benchmark the overlapped implementation against the standard MPI code gen-erated by Devito. Both stencil computations were executed across two different CPU platforms (see Section 5 . 1 .1).", "subpage_snippet": "", "source": "www.imperial.ac.uk", "link": "https://www.imperial.ac.uk/media/imperial-college/faculty-of-engineering/computing/public/distinguished-projects/2223-ug-projects/Tiling-in-time---improving-data-locality-in-computational-science-applications,-in-4D.pdf", "content": "5 . 1 Experimental Setup . We benchmark the overlapped implementation against the standard MPI code gen-erated by Devito. Both stencil computations were executed across two different CPU platforms (see Section 5 . 1 .1)."}
+{"idx": 6, "title": "Noname manuscript No.", "date": "", "ddg_snippet": "Section 3 reviews related work. tamp t, we just need to recompute the join result (up. In Section 4, we present the MLG-join algorithm and to t + TM ) for the objects that have reported updates its implementation on the GPU . Section 5 provides a at t, and update the list L accordingly.", "subpage_snippet": "", "source": "homepage.cs.latrobe.edu.au", "link": "http://homepage.cs.latrobe.edu.au/zhe/files/MLG.pdf", "content": "Section 3 reviews related work. tamp t, we just need to recompute the join result (up. In Section 4, we present the MLG-join algorithm and to t + TM ) for the objects that have reported updates its implementation on the GPU . Section 5 provides a at t, and update the list L accordingly."}
+{"idx": 7, "title": "MCred: multi-modal message credibility for fake news detection using...", "date": "", "ddg_snippet": "5 . 1 Experimental setup . We implemented the proposed MCred model using sklearn, matplotlib, nltk, and other libraries from the Python 3.9 distribution.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s12652-022-04338-2", "content": "5 . 1 Experimental setup . We implemented the proposed MCred model using sklearn, matplotlib, nltk, and other libraries from the Python 3.9 distribution."}
+{"idx": 8, "title": "Negative Absolute Temperature and the Dynamics of Quantum Phase...", "date": "", "ddg_snippet": "A 100 . x-direction y-direction.At the beginning of each experimental cycle, we heat one dispenser for each species by running a current through them to increase the respective background pressures in the MOT chamber of the vacuum setup .", "subpage_snippet": "", "source": "d-nb.info", "link": "https://d-nb.info/1068767162/34", "content": "A 100 . x-direction y-direction.At the beginning of each experimental cycle, we heat one dispenser for each species by running a current through them to increase the respective background pressures in the MOT chamber of the vacuum setup ."}
+{"idx": 9, "title": "Shusaku Inoue 2 and Panitan Lukkunaprasit 3", "date": "", "ddg_snippet": "3.2. 1 Experimental setup Figure 2 illustrates the setup of this experimental study. The hydraulic model experiments were carried out in a wave flume of 1 m × 1m in cross section and 40 m in length. The rigid bed of the flume with a compound bed slope of 1/115 (0.50)...", "subpage_snippet": "", "source": "cdn.intechopen.com", "link": "https://cdn.intechopen.com/pdfs/24939/InTech-Experimental_and_numerical_modeling_of_tsunami_force_on_bridge_decks.pdf", "content": "3.2. 1 Experimental setup Figure 2 illustrates the setup of this experimental study. The hydraulic model experiments were carried out in a wave flume of 1 m × 1m in cross section and 40 m in length. The rigid bed of the flume with a compound bed slope of 1/115 (0.50)..."}
diff --git a/data/sampled_jsons/Section_5.2_Rosenbrock_function_formula_f(x,y)_=_-100(y_-_x^2)^2_-_(1_-_x)^2.jsonl b/data/sampled_jsons/Section_5.2_Rosenbrock_function_formula_f(x,y)_=_-100(y_-_x^2)^2_-_(1_-_x)^2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f72cfe3cbcfadf88e251fbd4b259feb91e73c1bb
--- /dev/null
+++ b/data/sampled_jsons/Section_5.2_Rosenbrock_function_formula_f(x,y)_=_-100(y_-_x^2)^2_-_(1_-_x)^2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Rosenbrock Function - Level Set Covering Algorithm - 1Library Research on Rosenbrock Function Optimization Problem Based on ... PDE_Qual_for_web_without_some_p - UCLA Mathematics Homework for Math 5470 x1, Spring 2016 - University of Utah Chapter 3: Metrics - Department of Computer Science ... TP1 - vdb", "date": "", "ddg_snippet": "To further test the LSC, a larger midship structural design problem was tested.This structural problem has a higher computational cost to perform the design fitnesscalculation, and more design variables and uncertain parameters. 5 .4. 1 Problem Description The framework was applied to the design of a midship section which is shown infigure 5 .8. The d... See full list on 1library.net The LSC algorithm was developed to solve the DSC-U problem, and the frame-work and results from two test problems were presented. The algorithm solves theDSC-U problem to give the Pareto front in the regret-space remaining trade space.The algorithm utilized level sets and methods to solve the set covering problem (SCP)to solve the DSC-U; the algori... See full list on 1library.net The design space covering for uncertainty (DSC-U) problem as defined in Chap-ter III is a new problem formulation to look at decision making in early stage design.The problem is formulated to solve the trade space between the regret and spaceremaining metrics. The regret metric is a measure of the expected suboptimality of afinal design for a given... See full list on 1library.net Discover an improved differential evolution algorithm for optimizing the Rosenbrock function . Overcome premature convergence and find the global minimum with self-adaptive scaling factor and crossover rate. Enhance function optimization and gain new insights in special fields. Disclaimer: This handbook is intended to assist graduate students with qualifying examination preparation. Please be aware, however, that the handbook might contain, and almost certainly contains, typos as well as incorrect or inaccurate solutions. I can not be made responsible for any inaccuracies contained in this handbook. Let (x; y ) be a point on the stable manifold, and assume (x; y) is close to ( 1; 0). Introduce a new variable u = x+1 and write the stable manifold as a power series Before we turn to neural nets, consider the Rosenbrock function example from Section 1. We saw that gradient descent on the inputs has trouble with ill-conditioned curvature caused by the nonlinear mapping. On étudie la fonction de Rosenbrock . Cette fonction est très souvent utilisée pour tester des algorithmes d'optimisation. En effet, une fois la \"vallée\" trouvée, il est assez compliqué pour un algorithme de suivre cette \"vallée\" jusqu'au point $ (1,1)$.", "subpage_snippet": "", "source": "1library.net", "link": "https://1library.net/article/rosenbrock-function-level-set-covering-algorithm.qov282mz", "content": "To further test the LSC, a larger midship structural design problem was tested.This structural problem has a higher computational cost to perform the design fitnesscalculation, and more design variables and uncertain parameters. 5 .4. 1 Problem Description The framework was applied to the design of a midship section which is shown infigure 5 .8. The d... See full list on 1library.net The LSC algorithm was developed to solve the DSC-U problem, and the frame-work and results from two test problems were presented. The algorithm solves theDSC-U problem to give the Pareto front in the regret-space remaining trade space.The algorithm utilized level sets and methods to solve the set covering problem (SCP)to solve the DSC-U; the algori... See full list on 1library.net The design space covering for uncertainty (DSC-U) problem as defined in Chap-ter III is a new problem formulation to look at decision making in early stage design.The problem is formulated to solve the trade space between the regret and spaceremaining metrics. The regret metric is a measure of the expected suboptimality of afinal design for a given... See full list on 1library.net Discover an improved differential evolution algorithm for optimizing the Rosenbrock function . Overcome premature convergence and find the global minimum with self-adaptive scaling factor and crossover rate. Enhance function optimization and gain new insights in special fields. Disclaimer: This handbook is intended to assist graduate students with qualifying examination preparation. Please be aware, however, that the handbook might contain, and almost certainly contains, typos as well as incorrect or inaccurate solutions. I can not be made responsible for any inaccuracies contained in this handbook. Let (x; y ) be a point on the stable manifold, and assume (x; y) is close to ( 1; 0). Introduce a new variable u = x+1 and write the stable manifold as a power series Before we turn to neural nets, consider the Rosenbrock function example from Section 1. We saw that gradient descent on the inputs has trouble with ill-conditioned curvature caused by the nonlinear mapping. On étudie la fonction de Rosenbrock . Cette fonction est très souvent utilisée pour tester des algorithmes d'optimisation. En effet, une fois la \"vallée\" trouvée, il est assez compliqué pour un algorithme de suivre cette \"vallée\" jusqu'au point $ (1,1)$."}
+{"idx": 1, "title": "Research on Rosenbrock Function Optimization Problem Based on ...", "date": "", "ddg_snippet": "Discover an improved differential evolution algorithm for optimizing the Rosenbrock function . Overcome premature convergence and find the global minimum with self-adaptive scaling factor and crossover rate. Enhance function optimization and gain new insights in special fields.", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/journal/paperinformation?paperid=96749", "content": "Discover an improved differential evolution algorithm for optimizing the Rosenbrock function . Overcome premature convergence and find the global minimum with self-adaptive scaling factor and crossover rate. Enhance function optimization and gain new insights in special fields."}
+{"idx": 2, "title": "plotting - Contour plot of Rosenbrock function - Mathematica Stack...", "date": "", "ddg_snippet": "Try PlotLabel -> Row@{\" Rosenbrock function : \", f [ x , y ]}. Regarding contours: Mathematica uses equi-spaced contours according to the function value by default. In the top plot they are clearly much denser around the minimum.", "subpage_snippet": "", "source": "mathematica.stackexchange.com", "link": "https://mathematica.stackexchange.com/questions/143338/contour-plot-of-rosenbrock-function", "content": "Try PlotLabel -> Row@{\" Rosenbrock function : \", f [ x , y ]}. Regarding contours: Mathematica uses equi-spaced contours according to the function value by default. In the top plot they are clearly much denser around the minimum."}
+{"idx": 3, "title": "Rosenbrock banana function | Optimization test case", "date": "", "ddg_snippet": "The Rosenbrock 's banana function is a common test case for optimization software.The function has a global minimum at ( 1 , 1 ). If an optimization method starts at the point (− 1 . 2 , 1 ), it has to find its way to the other side of a flat, curved valley to find the optimal point.", "subpage_snippet": "", "source": "www.johndcook.com", "link": "https://www.johndcook.com/blog/2010/07/28/rosenbrocks-banana-function/", "content": "The Rosenbrock 's banana function is a common test case for optimization software.The function has a global minimum at ( 1 , 1 ). If an optimization method starts at the point (− 1 . 2 , 1 ), it has to find its way to the other side of a flat, curved valley to find the optimal point."}
+{"idx": 4, "title": "MATLAB Rosenbrock TEST PROBLEM", "date": "", "ddg_snippet": "Contour Plot of the Rosenbrock function .%In this script we apply steepest descent with the %backtracking linesearch to minimize the 2 -D % Rosenbrock function starting at the point x =(- 1 .9, 2 ). %Termination parameters eps = 1 .0e-4; epsf = 1 .0e-6; maxit = 10000; iter = 0", "subpage_snippet": "", "source": "sites.math.washington.edu", "link": "https://sites.math.washington.edu/~burke/crs/516/pgm/rosen-testproblem.html", "content": "Contour Plot of the Rosenbrock function .%In this script we apply steepest descent with the %backtracking linesearch to minimize the 2 -D % Rosenbrock function starting at the point x =(- 1 .9, 2 ). %Termination parameters eps = 1 .0e-4; epsf = 1 .0e-6; maxit = 10000; iter = 0"}
+{"idx": 5, "title": "Rosenbrock Function - File Exchange - MATLAB Central", "date": "", "ddg_snippet": "The Rosenbrock function is a non-convex function used as a performance test problem for optimization.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/matlabcentral/fileexchange/36883-rosenbrock-function", "content": "The Rosenbrock function is a non-convex function used as a performance test problem for optimization."}
+{"idx": 6, "title": "Lecture-7- Rosenbrock", "date": "", "ddg_snippet": "The rosenbrock function is a 2 d function ( x , y ) -> f ( x , y ). It has a steep internal valley with a gradual slope that makes it a useful test case.Work counters Seconds run: 0 (vs limit Inf) Iterations: 10000 f ( x ) calls: 25032 ∇ f ( x ) calls: 25032.", "subpage_snippet": "", "source": "www.cs.purdue.edu", "link": "https://www.cs.purdue.edu/homes/dgleich/cs520-2023/julia/Lecture-7-Rosenbrock.html", "content": "The rosenbrock function is a 2 d function ( x , y ) -> f ( x , y ). It has a steep internal valley with a gradual slope that makes it a useful test case.Work counters Seconds run: 0 (vs limit Inf) Iterations: 10000 f ( x ) calls: 25032 ∇ f ( x ) calls: 25032."}
+{"idx": 7, "title": "optimization - plotting a 2 d function as surface in... - Stack Overflow", "date": "", "ddg_snippet": "After a quick investigation of the Rosenbrock function I found, and correct me if Im wrong, but you need to specify the y -vector you arent supposed to nest it within z or anything like that Someone else tried this same thing as shown here but using Plots.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/40650340/plotting-a-2d-function-as-surface-in-3d-space-with-plots-jl", "content": "After a quick investigation of the Rosenbrock function I found, and correct me if Im wrong, but you need to specify the y -vector you arent supposed to nest it within z or anything like that Someone else tried this same thing as shown here but using Plots."}
+{"idx": 8, "title": "A Genetic Algorithm Function Optimizer in C++ – technical-recipes.com", "date": "", "ddg_snippet": "The Rosenbrock function is defined by: f ( x , y ) = ( 1 - x )^ 2 + 100 ( y - x ^ 2 )^ 2 .Locating the valley vicinity is fairly straightforward, while converging to the global minimum, located at ( x , y ) = ( 1 , 1 ) where f ( 1 , 1 ) = 0 is trickier. The Genetic Algorithm.", "subpage_snippet": "", "source": "www.technical-recipes.com", "link": "https://www.technical-recipes.com/2012/a-genetic-algorithm-function-optimizer-in-c/", "content": "The Rosenbrock function is defined by: f ( x , y ) = ( 1 - x )^ 2 + 100 ( y - x ^ 2 )^ 2 .Locating the valley vicinity is fairly straightforward, while converging to the global minimum, located at ( x , y ) = ( 1 , 1 ) where f ( 1 , 1 ) = 0 is trickier. The Genetic Algorithm."}
+{"idx": 9, "title": "pymoo - Rosenbrock", "date": "", "ddg_snippet": "Rosenbrock ¶. The definition ca be found in [55]. It is a non-convex function , introduced by Howard H. Rosenbrock in 1960 and also known as Rosenbrock ’s valley or Rosenbrock ’s banana function .", "subpage_snippet": "", "source": "pymoo.org", "link": "https://pymoo.org/problems/single/rosenbrock.html", "content": "Rosenbrock ¶. The definition ca be found in [55]. It is a non-convex function , introduced by Howard H. Rosenbrock in 1960 and also known as Rosenbrock ’s valley or Rosenbrock ’s banana function ."}
diff --git a/data/sampled_jsons/Section_6_Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge.jsonl b/data/sampled_jsons/Section_6_Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..921d866f998e60dd2b3c646c9060af7c96e1299c
--- /dev/null
+++ b/data/sampled_jsons/Section_6_Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge . We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561", "content": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge . We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems."}
+{"idx": 1, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge ... In Section 6 , we present and address some views that provide ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40182", "content": "Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge ... In Section 6 , we present and address some views that provide ..."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "by H Wallach · 2025 · Cited by 10 — In. Section 6 , we present and address some views that provide ... Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge .", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00561", "content": "by H Wallach · 2025 · Cited by 10 — In. Section 6 , we present and address some views that provide ... Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge ."}
+{"idx": 3, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "Jun 6 , 2025 · Specifically, our position is that evaluating GenAI systems is a social science measurement challenge . We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "Jun 6 , 2025 · Specifically, our position is that evaluating GenAI systems is a social science measurement challenge . We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems."}
+{"idx": 4, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "Position : Evaluating Generative AI Systems is a Social Science Measurement Challenge ... position is that evaluating GenAI systems is a social science ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "Position : Evaluating Generative AI Systems is a Social Science Measurement Challenge ... position is that evaluating GenAI systems is a social science ..."}
+{"idx": 5, "title": "Generative AI Needs Adaptive Governance", "date": "", "ddg_snippet": "The paper is structured as follows: In Section 2, we explore in more detail what’s different about generative AI that warrants an adaptive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04554v1", "content": "The paper is structured as follows: In Section 2, we explore in more detail what’s different about generative AI that warrants an adaptive ..."}
+{"idx": 6, "title": "Personalized Knowledge Transfer Through Generative AI:", "date": "", "ddg_snippet": "... adaptation in learning systems based on ... Section 3 reviews related work on adaptive learning systems and generative AI in educational contexts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.04070v1", "content": "... adaptation in learning systems based on ... Section 3 reviews related work on adaptive learning systems and generative AI in educational contexts."}
+{"idx": 7, "title": "Report of the 1st Workshop on Generative AI and Law", "date": "", "ddg_snippet": "A concrete research agenda to promote collaboration and progress on emerging issues at the intersection of Generative AI and law ( Section 6 ).", "subpage_snippet": "", "source": "blog.genlaw.org", "link": "https://blog.genlaw.org/2023-full-report.html", "content": "A concrete research agenda to promote collaboration and progress on emerging issues at the intersection of Generative AI and law ( Section 6 )."}
+{"idx": 8, "title": "Generative AI and Jobs: A global analysis of potential effects", "date": "", "ddg_snippet": "While it is impossible to predict how generative AI will further develop, the current capabilities and future potential of this technology are ...", "subpage_snippet": "", "source": "webapps.ilo.org", "link": "https://webapps.ilo.org/static/english/intserv/working-papers/wp096/index.html", "content": "While it is impossible to predict how generative AI will further develop, the current capabilities and future potential of this technology are ..."}
+{"idx": 9, "title": "Guide on the use of generative artificial intelligence -", "date": "", "ddg_snippet": "Generative AI tools rely on models that pose various challenges , including limited transparency and explainability.", "subpage_snippet": "", "source": "www.canada.ca", "link": "https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/guide-use-generative-ai.html", "content": "Generative AI tools rely on models that pose various challenges , including limited transparency and explainability."}
diff --git a/data/sampled_jsons/Section_8_Limitations_password-locked_models_naturally_hidden_capabilities.jsonl b/data/sampled_jsons/Section_8_Limitations_password-locked_models_naturally_hidden_capabilities.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c301390dd7dc617f74207be107b7af13dd0e4ab
--- /dev/null
+++ b/data/sampled_jsons/Section_8_Limitations_password-locked_models_naturally_hidden_capabilities.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden . Specifically, these LLMs are ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "To do this, we introduce password - locked models , LLMs fine-tuned such that some of their capabilities are deliberately hidden . Specifically, these LLMs are ..."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "5 Nov 2024 — This paper studies how the hidden capabilities of LLMs can be accessed through prompting, fine-tuning, and reinforcement learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zzOOqD6R1b&referrer=[the+profile+of+David+Krueger](/profile?id=~David_Krueger1)", "content": "5 Nov 2024 — This paper studies how the hidden capabilities of LLMs can be accessed through prompting, fine-tuning, and reinforcement learning."}
+{"idx": 2, "title": "Security and Privacy Challenges of Large Language Models", "date": "", "ddg_snippet": "30 Jan 2024 — The limitations of existing research along with future research challenges are discussed in Section 8 . Finally, Section 9 concludes the paper.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.00888v1", "content": "30 Jan 2024 — The limitations of existing research along with future research challenges are discussed in Section 8 . Finally, Section 9 concludes the paper."}
+{"idx": 3, "title": "Frontier Models are Capable of In-context Scheming", "date": "", "ddg_snippet": "by A Meinke · 2024 · Cited by 74 — They have demonstrated increased capabilities , autonomously solving problems that range from short programming tasks to extended machine.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.04984", "content": "by A Meinke · 2024 · Cited by 74 — They have demonstrated increased capabilities , autonomously solving problems that range from short programming tasks to extended machine."}
+{"idx": 4, "title": "On large language models safety, security, and privacy", "date": "", "ddg_snippet": "by R Zhang · 2025 · Cited by 17 — Our study provides clearer and more reasonable definitions for safety, security, and privacy within the context of LLMs.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1674862X25000023", "content": "by R Zhang · 2025 · Cited by 17 — Our study provides clearer and more reasonable definitions for safety, security, and privacy within the context of LLMs."}
+{"idx": 5, "title": "On protecting the data privacy of Large Language Models ...", "date": "", "ddg_snippet": "by B Yan · 2025 · Cited by 24 — This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2667295225000042", "content": "by B Yan · 2025 · Cited by 24 — This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding."}
+{"idx": 6, "title": "Embers of autoregression show how large language ...", "date": "", "ddg_snippet": "by RT McCoy · 2024 · Cited by 96 — Indeed, in the “Sparks of artificial general intelligence” paper (4), Section 8 is titled “ Limitations of autoregressive architecture highlighted by GPT-4,” and ...", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.2322420121", "content": "by RT McCoy · 2024 · Cited by 96 — Indeed, in the “Sparks of artificial general intelligence” paper (4), Section 8 is titled “ Limitations of autoregressive architecture highlighted by GPT-4,” and ..."}
+{"idx": 7, "title": "I Know What You Trained Last Summer: A Survey ...", "date": "", "ddg_snippet": "To safeguard their intellectual property, owners may opt to keep their models secret, allowing external users to access them only by input-output queries over ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3595292", "content": "To safeguard their intellectual property, owners may opt to keep their models secret, allowing external users to access them only by input-output queries over ..."}
+{"idx": 8, "title": "NIST Special Publication 800-63B", "date": "", "ddg_snippet": "Cited by 432 — Verifiers SHOULD permit claimants to use “paste” functionality when entering a memorized secret . This facilitates the use of password managers, which are widely ...", "subpage_snippet": "", "source": "pages.nist.gov", "link": "https://pages.nist.gov/800-63-3/sp800-63b.html", "content": "Cited by 432 — Verifiers SHOULD permit claimants to use “paste” functionality when entering a memorized secret . This facilitates the use of password managers, which are widely ..."}
+{"idx": 9, "title": "Fairness issues, current approaches, and challenges in ...", "date": "", "ddg_snippet": "by TD Jui · 2024 · Cited by 63 — The inherent biases and limitations in the training data and algorithms can lead to discriminatory outcomes and perpetuate societal biases.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s13042-023-02083-2", "content": "by TD Jui · 2024 · Cited by 63 — The inherent biases and limitations in the training data and algorithms can lead to discriminatory outcomes and perpetuate societal biases."}
diff --git a/data/sampled_jsons/Section_J_reward_estimation_r_o_c_diffusion_model_alignment_practical_challenges.jsonl b/data/sampled_jsons/Section_J_reward_estimation_r_o_c_diffusion_model_alignment_practical_challenges.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae2e718fcca9a5e44379e6aa2f7edd24caacbd00
--- /dev/null
+++ b/data/sampled_jsons/Section_J_reward_estimation_r_o_c_diffusion_model_alignment_practical_challenges.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "training-free diffusion model alignment with sampling ...", "date": "", "ddg_snippet": "by PH Yeh · 2024 · Cited by 5 — This work addresses the challenge of better aligning pre-trained diffusion models without training or backpropagation. We first demonstrate how ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.05760?", "content": "by PH Yeh · 2024 · Cited by 5 — This work addresses the challenge of better aligning pre-trained diffusion models without training or backpropagation. We first demonstrate how ..."}
+{"idx": 1, "title": "Untangling Challenges of Fine-Grained Feedback for Text ...", "date": "", "ddg_snippet": "17 Oct 2024 — Open challenges : We identify and discuss open challenges surrounding the construction and evaluation of reward models for text-to-image systems ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.16807v2", "content": "17 Oct 2024 — Open challenges : We identify and discuss open challenges surrounding the construction and evaluation of reward models for text-to-image systems ..."}
+{"idx": 2, "title": "BoKDiff: best-of-K diffusion alignment for target-specific 3D ...", "date": "", "ddg_snippet": "by A Khodabandeh Yalabadi · 2025 — Implementing this method on the DecompDiff model has some challenges that we will discuss in the next section . ( Section 3.3). 3.3 Practical ... 10 pages", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/bioinformaticsadvances/article-pdf/5/1/vbaf137/63463861/vbaf137.pdf", "content": "by A Khodabandeh Yalabadi · 2025 — Implementing this method on the DecompDiff model has some challenges that we will discuss in the next section . ( Section 3.3). 3.3 Practical ... 10 pages"}
+{"idx": 3, "title": "Reward rate optimization in two-alternative decision making", "date": "", "ddg_snippet": "by P Simen · 2009 · Cited by 296 — The drift- diffusion model (DDM) describes decision making in simple, two-alternative forced choice (2AFC) tasks . It accurately fits response-time distributions ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC2791916/", "content": "by P Simen · 2009 · Cited by 296 — The drift- diffusion model (DDM) describes decision making in simple, two-alternative forced choice (2AFC) tasks . It accurately fits response-time distributions ..."}
+{"idx": 4, "title": "Understanding Impact of Human Feedback via Influence ...", "date": "", "ddg_snippet": "by T Min · Cited by 5 — We explore the use of influence functions to measure the impact of human feedback on the performance of reward models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=dTQmayPKMs", "content": "by T Min · Cited by 5 — We explore the use of influence functions to measure the impact of human feedback on the performance of reward models ."}
+{"idx": 5, "title": "Track: Poster Session 2 - CVPR", "date": "", "ddg_snippet": "13 Jun 2025 — For shape exploration, we combine the diffusion model with reinforcement learning to train a shape exploration policy. This involves using the ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/session/35266", "content": "13 Jun 2025 — For shape exploration, we combine the diffusion model with reinforcement learning to train a shape exploration policy. This involves using the ..."}
+{"idx": 6, "title": "Track: Poster Session 4", "date": "", "ddg_snippet": "25 Apr 2025 — Aligning diffusion models with user preferences has been a key challenge .Existing methods for aligning diffusion models either require ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31974", "content": "25 Apr 2025 — Aligning diffusion models with user preferences has been a key challenge .Existing methods for aligning diffusion models either require ..."}
+{"idx": 7, "title": "Understanding Impact of Human Feedback via Influence ...", "date": "", "ddg_snippet": "by T Min · 2025 · Cited by 5 — By enhancing the inter- pretability of human feedback in reward modeling , our approach can help labelers provide accurate feedback to reward ... 30 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1333.pdf", "content": "by T Min · 2025 · Cited by 5 — By enhancing the inter- pretability of human feedback in reward modeling , our approach can help labelers provide accurate feedback to reward ... 30 pages"}
+{"idx": 8, "title": "A comprehensive survey of loss functions and metrics in ...", "date": "", "ddg_snippet": "by J Terven · 2025 · Cited by 48 — This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11198-7", "content": "by J Terven · 2025 · Cited by 48 — This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights"}
+{"idx": 9, "title": "Machine-Learning-from-Human-Preferences.pdf", "date": "", "ddg_snippet": "It sidesteps the challenges of explicitly learning a reward model or tuning complex. RL pipelines, offering a direct and scalable method for preference-based ...", "subpage_snippet": "", "source": "mlhp.stanford.edu", "link": "https://mlhp.stanford.edu/Machine-Learning-from-Human-Preferences.pdf", "content": "It sidesteps the challenges of explicitly learning a reward model or tuning complex. RL pipelines, offering a direct and scalable method for preference-based ..."}
diff --git a/data/sampled_jsons/Self-Paced_Curriculum_Learning_abstract_Curriculum_learning_self-paced_learning_2015_year_2015.jsonl b/data/sampled_jsons/Self-Paced_Curriculum_Learning_abstract_Curriculum_learning_self-paced_learning_2015_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e7892554b74d33c0befac05e6cf89d0fe2b7fb94
--- /dev/null
+++ b/data/sampled_jsons/Self-Paced_Curriculum_Learning_abstract_Curriculum_learning_self-paced_learning_2015_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self-paced curriculum learning | Proceedings of the Twenty ...", "date": "", "ddg_snippet": "by L Jiang · 2015 · Cited by 671 — Self-paced curriculum learning . Authors: Lu Jiang. Lu Jiang. School of ... Curriculum learning (CL) or self-paced learning (SPL) represents a recently ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2886521.2886696", "content": "by L Jiang · 2015 · Cited by 671 — Self-paced curriculum learning . Authors: Lu Jiang. Lu Jiang. School of ... Curriculum learning (CL) or self-paced learning (SPL) represents a recently ..."}
+{"idx": 1, "title": "Self-Paced Curriculum Learning", "date": "", "ddg_snippet": "by L Jiang · 2015 · Cited by 671 — Abstract . Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired. 7 pages", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/9608/9608-13-13136-1-2-20201228.pdf", "content": "by L Jiang · 2015 · Cited by 671 — Abstract . Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired. 7 pages"}
+{"idx": 2, "title": "Self-Paced Curriculum Learning", "date": "", "ddg_snippet": "Self-Paced Curriculum Learning . Work. HTML PDF. Year: 2015 . Type: article. Abstract : Curriculum learning (CL) or self-paced learning (SPL) represents a recently ...", "subpage_snippet": "", "source": "openalex.org", "link": "https://openalex.org/W2256388387", "content": "Self-Paced Curriculum Learning . Work. HTML PDF. Year: 2015 . Type: article. Abstract : Curriculum learning (CL) or self-paced learning (SPL) represents a recently ..."}
+{"idx": 3, "title": "WHY CURRICULUM LEARNING & SELF-PACED ...", "date": "", "ddg_snippet": "by T Gong · Cited by 35 — Curriculum learning , self-paced learning , learning theory, TRECVID. MED ... self-paced curriculum learning [10], and multiple applications of this SPL ...", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/aimspress-data/bdia/2016/1/PDF/2380-6966_2016_1_111.pdf", "content": "by T Gong · Cited by 35 — Curriculum learning , self-paced learning , learning theory, TRECVID. MED ... self-paced curriculum learning [10], and multiple applications of this SPL ..."}
+{"idx": 4, "title": "Self-paced learning with privileged information", "date": "", "ddg_snippet": "by W Xu · 2019 · Cited by 22 — Self-paced learning with privileged information. Author links ... Jiang et al. Self-paced curriculum learning . Proceedings of the AAAI.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0925231219309798", "content": "by W Xu · 2019 · Cited by 22 — Self-paced learning with privileged information. Author links ... Jiang et al. Self-paced curriculum learning . Proceedings of the AAAI."}
+{"idx": 5, "title": "Self-Paced Boost Learning for Classification", "date": "", "ddg_snippet": "by T Pi · Cited by 91 — and self-paced learning . Boosting is a family of ... Variants of SPL are also developed, such as self-paced curriculum learning [Jiang et al., 2015 ],.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/Proceedings/16/Papers/276.pdf", "content": "by T Pi · Cited by 91 — and self-paced learning . Boosting is a family of ... Variants of SPL are also developed, such as self-paced curriculum learning [Jiang et al., 2015 ],."}
+{"idx": 6, "title": "Openning07/awesome-curriculum-learning: some bravo or ...", "date": "", "ddg_snippet": "Self-Paced Learning : An Implicit Regularization Perspective. [AAAI] ... self-paced curriculum learning framework. [IJCV]. Curriculum Model Adaptation ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Openning07/awesome-curriculum-learning", "content": "Self-Paced Learning : An Implicit Regularization Perspective. [AAAI] ... self-paced curriculum learning framework. [IJCV]. Curriculum Model Adaptation ..."}
+{"idx": 7, "title": "Self-paced learning with identification refinement for SPOC ...", "date": "", "ddg_snippet": "by K Li · 2018 · Cited by 3 — Self-paced learning with identification refinement for SPOC student grading ... Self-paced curriculum learning . In Twenty-Ninth AAAI Conference on ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3210713.3210732", "content": "by K Li · 2018 · Cited by 3 — Self-paced learning with identification refinement for SPOC student grading ... Self-paced curriculum learning . In Twenty-Ninth AAAI Conference on ..."}
+{"idx": 8, "title": "Self-Paced Learning: an Implicit Regularization Perspective", "date": "", "ddg_snippet": "by Y Fan · 2016 · Cited by 63 — Self-paced curriculum learning . In AAAI,. 2015 . [14] M. P. Kumar, B. Packer, and D. Koller. Self-paced learning for latent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1606.00128", "content": "by Y Fan · 2016 · Cited by 63 — Self-paced curriculum learning . In AAAI,. 2015 . [14] M. P. Kumar, B. Packer, and D. Koller. Self-paced learning for latent ..."}
+{"idx": 9, "title": "arXiv:1511.06049v2 [cs.LG] 1 Nov 2016", "date": "", "ddg_snippet": "by D Meng · 2015 · Cited by 89 — ... self-paced learning with diver- sity [9] and self-paced curriculum learning [10], have been proposed under the format (1). The effectiveness ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1511.06049", "content": "by D Meng · 2015 · Cited by 89 — ... self-paced learning with diver- sity [9] and self-paced curriculum learning [10], have been proposed under the format (1). The effectiveness ..."}
diff --git a/data/sampled_jsons/Self-paced_curriculum_learning_Jiang_2015_AAAI_abstract.jsonl b/data/sampled_jsons/Self-paced_curriculum_learning_Jiang_2015_AAAI_abstract.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self-paced curriculum learning | Proceedings of the Twenty ...", "date": "", "ddg_snippet": "by L Jiang · 2015 · Cited by 671 — In this paper, we discover the missing link between CL and SPL, and propose a unified framework named self- paced curriculum leaning (SPCL).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2886521.2886696", "content": "by L Jiang · 2015 · Cited by 671 — In this paper, we discover the missing link between CL and SPL, and propose a unified framework named self- paced curriculum leaning (SPCL)."}
+{"idx": 1, "title": "Self-Paced Curriculum Learning", "date": "", "ddg_snippet": "by L Jiang · 2015 · Cited by 671 — In this paper, we discover the missing link between CL and SPL, and propose a unified framework named self - paced curriculum leaning (SPCL). SPCL. 7 pages", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/9608/9608-13-13136-1-2-20201228.pdf", "content": "by L Jiang · 2015 · Cited by 671 — In this paper, we discover the missing link between CL and SPL, and propose a unified framework named self - paced curriculum leaning (SPCL). SPCL. 7 pages"}
+{"idx": 2, "title": "Self-Paced Curriculum Learning", "date": "", "ddg_snippet": "Abstract . Curriculum learning (CL) or self - paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans ...", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/self-paced-curriculum-learning/864969413936283699-68893", "content": "Abstract . Curriculum learning (CL) or self - paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans ..."}
+{"idx": 3, "title": "Self-Paced Boost Learning for Classification", "date": "", "ddg_snippet": "by T Pi · Cited by 91 — [Jiang et al.,2015] Lu Jiang, Deyu Meng, Qian Zhao,. Shiguang Shan, and Alexander G Hauptmann. Self-paced curriculum learning . In Twenty-Ninth AAAI Conference ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/Proceedings/16/Papers/276.pdf", "content": "by T Pi · Cited by 91 — [Jiang et al.,2015] Lu Jiang, Deyu Meng, Qian Zhao,. Shiguang Shan, and Alexander G Hauptmann. Self-paced curriculum learning . In Twenty-Ninth AAAI Conference ..."}
+{"idx": 4, "title": "Self-paced learning with privileged information", "date": "", "ddg_snippet": "by W Xu · 2019 · Cited by 22 — Jiang et al. Self-paced curriculum learning . Proceedings of the AAAI. (2015). V. Vapnik et al. Learning using hidden information: master class ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0925231219309798", "content": "by W Xu · 2019 · Cited by 22 — Jiang et al. Self-paced curriculum learning . Proceedings of the AAAI. (2015). V. Vapnik et al. Learning using hidden information: master class ..."}
+{"idx": 5, "title": "Self-Paced Co-training", "date": "", "ddg_snippet": "by F Ma · Cited by 171 — The recent development of SPL includes that ( Jiang et al.,. 2015 ) improved SPL as a more effective self - paced cur- riculum learning (SPCL) regime by embedding ... 10 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v70/ma17b/ma17b.pdf", "content": "by F Ma · Cited by 171 — The recent development of SPL includes that ( Jiang et al.,. 2015 ) improved SPL as a more effective self - paced cur- riculum learning (SPCL) regime by embedding ... 10 pages"}
+{"idx": 6, "title": "WHY CURRICULUM LEARNING & SELF-PACED ...", "date": "", "ddg_snippet": "by T Gong · Cited by 35 — Abstract . Since being recently raised, curriculum learning (CL) and self - paced learning (SPL) have attracted increasing attention due to its multiple.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/aimspress-data/bdia/2016/1/PDF/2380-6966_2016_1_111.pdf", "content": "by T Gong · Cited by 35 — Abstract . Since being recently raised, curriculum learning (CL) and self - paced learning (SPL) have attracted increasing attention due to its multiple."}
+{"idx": 7, "title": "Multi-Objective Self-Paced Learning", "date": "", "ddg_snippet": "by H Li · 2016 · Cited by 44 — Jiang, L.; Meng, D.; Zhao, Q.; Shan, S.; and Hauptmann,. A. G. 2015. Self-paced curriculum learning . In AAAI, 2694–. 2700. Jin, Y., and Sendhoff, B. 2008 ...", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/10255/10255-13-13783-1-2-20201228.pdf", "content": "by H Li · 2016 · Cited by 44 — Jiang, L.; Meng, D.; Zhao, Q.; Shan, S.; and Hauptmann,. A. G. 2015. Self-paced curriculum learning . In AAAI, 2694–. 2700. Jin, Y., and Sendhoff, B. 2008 ..."}
+{"idx": 8, "title": "A Probabilistic Interpretation of Self-Paced Learning with ...", "date": "", "ddg_snippet": "by P Klink · 2021 · Cited by 34 — Self-paced curriculum learning . In AAAI Conference on Artificial Intelligence (AAAI), 2015. Scott Kirkpatrick, C Daniel Gelatt, and Mario P Vecchi ... 52 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume22/21-0112/21-0112.pdf", "content": "by P Klink · 2021 · Cited by 34 — Self-paced curriculum learning . In AAAI Conference on Artificial Intelligence (AAAI), 2015. Scott Kirkpatrick, C Daniel Gelatt, and Mario P Vecchi ... 52 pages"}
+{"idx": 9, "title": "arXiv:1511.06049v2 [cs.LG] 1 Nov 2016", "date": "", "ddg_snippet": "by D Meng · 2015 · Cited by 89 — Self-paced learning (SPL ) is a recently raised methodology designed through simulating the learning principle of humans/animals.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1511.06049", "content": "by D Meng · 2015 · Cited by 89 — Self-paced learning (SPL ) is a recently raised methodology designed through simulating the learning principle of humans/animals."}
diff --git a/data/sampled_jsons/Self-paced_curriculum_learning_Jiang_et_al_2015_abstract.jsonl b/data/sampled_jsons/Self-paced_curriculum_learning_Jiang_et_al_2015_abstract.jsonl
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index 0000000000000000000000000000000000000000..d657f7a3d9d57da4ddf0252cfcbb53e387646ece
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self - Paced Curriculum Learning", "date": "", "ddg_snippet": "Abstract . Curriculum learning (CL) or self - paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex sam-ples in training. The two methods share a similar con-ceptual...", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/9608/9608-13-13136-1-2-20201228.pdf", "content": "Abstract . Curriculum learning (CL) or self - paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex sam-ples in training. The two methods share a similar con-ceptual..."}
+{"idx": 1, "title": "(PDF) Self - paced Curriculum Learning", "date": "", "ddg_snippet": "Abstract . Curriculum learning (CL) or self - paced learning (SPL). represents a recently proposed learning regime inspired.advantage of SPCL on two tasks. Curriculum learning (Bengio et al . 2009) and self - paced .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/279853657_Self-paced_Curriculum_Learning", "content": "Abstract . Curriculum learning (CL) or self - paced learning (SPL). represents a recently proposed learning regime inspired.advantage of SPCL on two tasks. Curriculum learning (Bengio et al . 2009) and self - paced ."}
+{"idx": 2, "title": "Curriculum Learning for Vision-and-Language", "date": "", "ddg_snippet": "Self - paced curriculum learning (SPCL) is an “instructor-student collaborative” learning method as it considers both prior knowledge known before training and information learning during training in a.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1fr3bOX2t69", "content": "Self - paced curriculum learning (SPCL) is an “instructor-student collaborative” learning method as it considers both prior knowledge known before training and information learning during training in a."}
+{"idx": 3, "title": "Self - paced curriculum learning for visual question answering on", "date": "", "ddg_snippet": "Self - paced Curriculum Learning for RSVQA. Experiments and Discussion.Lobry et al . [6] rst introduced the task of VQA for remote sensing data (RSVQA) and creat-ed two remote sensing-oriented datasets via the data from OpenStreetMap and pre-dened templates.", "subpage_snippet": "", "source": "elib.dlr.de", "link": "https://elib.dlr.de/146238/1/IGARSS_YUAN.pdf", "content": "Self - paced Curriculum Learning for RSVQA. Experiments and Discussion.Lobry et al . [6] rst introduced the task of VQA for remote sensing data (RSVQA) and creat-ed two remote sensing-oriented datasets via the data from OpenStreetMap and pre-dened templates."}
+{"idx": 4, "title": "Curriculum learning . The concept of curriculum learning in | Medium", "date": "", "ddg_snippet": "Self - paced curriculum learning . It’s a paradigm where predefined criteria and learning-based metrics are jointly used to define the training order of samples. It was first introduced by Jiang et al . in 2015 and applied to matrix factorization and multimedia event detection.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aiguys/curriculum-learning-83b1b2221f33", "content": "Self - paced curriculum learning . It’s a paradigm where predefined criteria and learning-based metrics are jointly used to define the training order of samples. It was first introduced by Jiang et al . in 2015 and applied to matrix factorization and multimedia event detection."}
+{"idx": 5, "title": "MentorNet: Learning Data-Driven Curriculum", "date": "", "ddg_snippet": "processing (Turian et al ., 2010), multitask learning (Graves et al ., 2017). A common CL approach is to predene a curriculum . For example, Kumar et al . (2010) proposed a curriculum called self - paced learning which favors training samples of smaller loss.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v80/jiang18c/jiang18c.pdf", "content": "processing (Turian et al ., 2010), multitask learning (Graves et al ., 2017). A common CL approach is to predene a curriculum . For example, Kumar et al . (2010) proposed a curriculum called self - paced learning which favors training samples of smaller loss."}
+{"idx": 6, "title": "A Probabilistic Interpretation of Self - Paced Learning with", "date": "", "ddg_snippet": "Self - Paced Learning for Reinforcement Learning . Application to Episodic Reinforcement Learning .The concept of self - paced learning (SPL), as introduced by Kumar et al . (2010) and ex-tended by Jiang et al .", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume22/21-0112/21-0112.pdf", "content": "Self - Paced Learning for Reinforcement Learning . Application to Episodic Reinforcement Learning .The concept of self - paced learning (SPL), as introduced by Kumar et al . (2010) and ex-tended by Jiang et al ."}
+{"idx": 7, "title": "Distributed Self - Paced Learning in Alternating Direction Method of...", "date": "", "ddg_snippet": "[ Jiang et al ., 2014a] Lu Jiang , Deyu Meng, Teruko Mita-mura, and Alexander G Hauptmann. Easy samples rst: Self - paced reranking for zero-example multimedia search. Self - paced curriculum learning , 2015 . [Keerthiram Murugesan, 2017] Jaime Carbonell Keerthi-ram Murugesan.", "subpage_snippet": "", "source": "people.cs.vt.edu", "link": "https://people.cs.vt.edu/ctlu/Publication/2018/IJCAI-Xuchao-Proceedings.pdf", "content": "[ Jiang et al ., 2014a] Lu Jiang , Deyu Meng, Teruko Mita-mura, and Alexander G Hauptmann. Easy samples rst: Self - paced reranking for zero-example multimedia search. Self - paced curriculum learning , 2015 . [Keerthiram Murugesan, 2017] Jaime Carbonell Keerthi-ram Murugesan."}
+{"idx": 8, "title": "Self - paced Curriculum Learning", "date": "", "ddg_snippet": "In NIPS Self - paced Learning • Self - paced Learning (SPL): the curriculum is determined by the learned models. • Solving a joint optimization problem of the learning objective with the curriculum (a sequence of gradually added samples).", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/18302718/self-paced-curriculum-learning", "content": "In NIPS Self - paced Learning • Self - paced Learning (SPL): the curriculum is determined by the learned models. • Solving a joint optimization problem of the learning objective with the curriculum (a sequence of gradually added samples)."}
+{"idx": 9, "title": "GitHub - google/mentornet: Code for MentorNet: Learning Data-Driven...", "date": "", "ddg_snippet": "self _ paced _mentornet. SPCL linear ( Jiang et al . 2015 ). Discount the weight by loss linearly.\" Self - Paced Curriculum Learning .\" In AAAI.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/google/mentornet", "content": "self _ paced _mentornet. SPCL linear ( Jiang et al . 2015 ). Discount the weight by loss linearly.\" Self - Paced Curriculum Learning .\" In AAAI."}
diff --git a/data/sampled_jsons/Semantic_Scholar_Taming_Knowledge_Conflicts_in_Language_Models_Table_3.jsonl b/data/sampled_jsons/Semantic_Scholar_Taming_Knowledge_Conflicts_in_Language_Models_Table_3.jsonl
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index 0000000000000000000000000000000000000000..bebdcc8263250e7cdefee0c3c2b5bd5165dc4c05
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge .View a PDF of the paper titled Taming Knowledge Conflicts in Language Models , by Gaotang Li and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge .View a PDF of the paper titled Taming Knowledge Conflicts in Language Models , by Gaotang Li and 2 other authors."}
+{"idx": 1, "title": "(PDF) Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. Abstract.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389894493_Taming_Knowledge_Conflicts_in_Language_Models", "content": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. Abstract."}
+{"idx": 2, "title": "Taming Knowledge Conflicts in Language Models | OpenReview", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\"...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0cEZyhHEks&referrer=[the+profile+of+Hanghang+Tong](/profile?id=~Hanghang_Tong2)", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\"..."}
+{"idx": 3, "title": "Figure 1 from Decision-informed Neural Networks... | Semantic Scholar", "date": "", "ddg_snippet": "figure 3. table 3 .This work proposes an experimental framework to investigate knowledge conflicts in Large Language Models , offering the first quantitative analysis of confirmation bias in LLM-based investment analysis, and reveals distinct, model -specific tendencies. Expand.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Decision-informed-Neural-Networks-with-Large-Model-Hwang-Kong/1cf03388dbcef225e17f8d6f5c071dd306676880/figure/0", "content": "figure 3. table 3 .This work proposes an experimental framework to investigate knowledge conflicts in Large Language Models , offering the first quantitative analysis of confirmation bias in LLM-based investment analysis, and reveals distinct, model -specific tendencies. Expand."}
+{"idx": 4, "title": "Addressing Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "#Understanding Knowledge Conflicts . Language models use two types of knowledge : Internal Memory and External Context. Internal memory is the knowledge that the model has learned during training. This is often factual but can become outdated.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-09-03-addressing-knowledge-conflicts-in-language-models--a9pwq1z", "content": "#Understanding Knowledge Conflicts . Language models use two types of knowledge : Internal Memory and External Context. Internal memory is the knowledge that the model has learned during training. This is often factual but can become outdated."}
+{"idx": 5, "title": "GitHub - pillowsofwind/ Knowledge - Conflicts -Survey: [EMNLP 2024]...", "date": "", "ddg_snippet": "Studying Large Language Model Behaviors Under Realistic Knowledge Conflicts , arXiv 2024, [Paper]. Characterizing mechanisms for factual recall in language models , EMNLP 2023, [Paper].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pillowsofwind/Knowledge-Conflicts-Survey", "content": "Studying Large Language Model Behaviors Under Realistic Knowledge Conflicts , arXiv 2024, [Paper]. Characterizing mechanisms for factual recall in language models , EMNLP 2023, [Paper]."}
+{"idx": 6, "title": "Gaotang Li - Google Scholar", "date": "", "ddg_snippet": "Verified email at illinois.edu - Homepage. Language Models . Taming Knowledge Conflicts in Language Models . G Li, Y Chen, H Tong.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=0aVJRykAAAAJ&hl=en", "content": "Verified email at illinois.edu - Homepage. Language Models . Taming Knowledge Conflicts in Language Models . G Li, Y Chen, H Tong."}
+{"idx": 7, "title": "D YNAMIC QA: Tracing Internal Knowledge Conflicts in Language ...", "date": "", "ddg_snippet": "Knowledge -intensive language understanding tasks require Language Models (LMs) to inte-grate relevant context, mitigating their inher-ent weaknesses, such as incomplete or out-dated knowledge . However, conflicting knowl - edge can be present in the LM’s parameters, termed...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.838.pdf", "content": "Knowledge -intensive language understanding tasks require Language Models (LMs) to inte-grate relevant context, mitigating their inher-ent weaknesses, such as incomplete or out-dated knowledge . However, conflicting knowl - edge can be present in the LM’s parameters, termed..."}
+{"idx": 8, "title": "From Internal Conflict to Contextual Adaptation of Language Models", "date": "", "ddg_snippet": "The paper explores knowledge conflicts in language models (LMs) by introducing a new dataset, DYNAMICQA, which contains static, temporal, and disputable facts. The study assesses the use of semantic entropy as an indicator of...", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-From-Internal-Conflict-clz1rdbcb3l4101ctnzw9ug6p", "content": "The paper explores knowledge conflicts in language models (LMs) by introducing a new dataset, DYNAMICQA, which contains static, temporal, and disputable facts. The study assesses the use of semantic entropy as an indicator of..."}
+{"idx": 9, "title": "Neutralizing Backdoors through Information Conflicts for Large...", "date": "", "ddg_snippet": "Backdoors. Large Language Models . Information Conflicts . NLP.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/neutralizing-backdoors-through-information-conflicts-for-large-language-models/1069137010880413738-108592", "content": "Backdoors. Large Language Models . Information Conflicts . NLP."}
diff --git a/data/sampled_jsons/Sharpness-Aware_Minimization_Foret_et_al._2021_abstract_year_2021.jsonl b/data/sampled_jsons/Sharpness-Aware_Minimization_Foret_et_al._2021_abstract_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..43366610d796eae32d263c9c59817f2f1c2cd25d
--- /dev/null
+++ b/data/sampled_jsons/Sharpness-Aware_Minimization_Foret_et_al._2021_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2110.03141] Efficient Sharpness-aware Minimization for", "date": "", "ddg_snippet": "Recently, the relation between the sharpness of the loss landscape and the generalization error has been established by Foret et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2110.03141", "content": "Recently, the relation between the sharpness of the loss landscape and the generalization error has been established by Foret et al ."}
+{"idx": 1, "title": "Sharpness-Aware Minimization with Z-Score Gradient Filtering", "date": "", "ddg_snippet": "Sharpness - Aware Minimization (SAM) mitigates this by seeking flatter minima but perturbs parameters using the full gradient, which can include ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.02369v1", "content": "Sharpness - Aware Minimization (SAM) mitigates this by seeking flatter minima but perturbs parameters using the full gradient, which can include ..."}
+{"idx": 2, "title": "On Memorization and Privacy Risks of Sharpness Aware", "date": "", "ddg_snippet": "... been considerable amount of recent works that explore loss optimization that searches for flatter optima (Norton & Royset, 2021 ; Foret et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00488v2", "content": "... been considerable amount of recent works that explore loss optimization that searches for flatter optima (Norton & Royset, 2021 ; Foret et al ..."}
+{"idx": 3, "title": "Tractable Sharpness-aware Regularization of Probabilistic", "date": "", "ddg_snippet": "... sharp minima, characterized by high curvature, have been extensively studied in deep neural networks, leading to the development of sharpness - aware ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05537v1", "content": "... sharp minima, characterized by high curvature, have been extensively studied in deep neural networks, leading to the development of sharpness - aware ..."}
+{"idx": 4, "title": "Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning", "date": "", "ddg_snippet": "Sharpness - Aware Minimization (SAM) ( Foret et al ., 2021 ) is a widely used technique that enhances generalization by formulating optimization as a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19564v1", "content": "Sharpness - Aware Minimization (SAM) ( Foret et al ., 2021 ) is a widely used technique that enhances generalization by formulating optimization as a ..."}
+{"idx": 5, "title": "ICLR 2021 Spotlights", "date": "", "ddg_snippet": "... the presence of label noise, while also exhibiting good generalization on natural test data, something referred to as benign overfitting (Bartlett et ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/events/Spotlight", "content": "... the presence of label noise, while also exhibiting good generalization on natural test data, something referred to as benign overfitting (Bartlett et ..."}
+{"idx": 6, "title": "(PDF) FedSoL: Bridging Global Alignment and Local Generality in", "date": "", "ddg_snippet": "These restrictions aim to encourage global alignment by constraining the deviation of local learning from the ... Minimization (SAM) ( Foret et al .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373363708_FedSoL_Bridging_Global_Alignment_and_Local_Generality_in_Federated_Learning", "content": "These restrictions aim to encourage global alignment by constraining the deviation of local learning from the ... Minimization (SAM) ( Foret et al ."}
+{"idx": 7, "title": "TbsNet: the importance of thin-branch structures in CNNs [PeerJ]", "date": "", "ddg_snippet": "... DW) convolution, group convolution, and asymmetric convolution, etc ., have been widely studied and applied, such as Inception ( Liu et al ., 2021a ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-1429/", "content": "... DW) convolution, group convolution, and asymmetric convolution, etc ., have been widely studied and applied, such as Inception ( Liu et al ., 2021a ..."}
+{"idx": 8, "title": "GitHub - ravi-mosaicml/ravi-composer: Composing methods for ML", "date": "", "ddg_snippet": "... using Composer' s built-in trainer, which automatically takes care of the low-level details of using speedup methods and provides useful abstractions ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ravi-mosaicml/ravi-composer", "content": "... using Composer' s built-in trainer, which automatically takes care of the low-level details of using speedup methods and provides useful abstractions ..."}
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diff --git a/data/sampled_jsons/Sharpness-Aware_Minimization_Foret_improving_generalization_abstract.jsonl b/data/sampled_jsons/Sharpness-Aware_Minimization_Foret_improving_generalization_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0fe1107aa025ea35b833eb048262dbdede6ea260
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+{"idx": 0, "title": "Sharpness-Aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "by P Foret · 2020 · Cited by 1946 — Abstract page for arXiv paper 2010.01412: Sharpness-Aware Minimization for Efficiently Improving Generalization .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.01412", "content": "by P Foret · 2020 · Cited by 1946 — Abstract page for arXiv paper 2010.01412: Sharpness-Aware Minimization for Efficiently Improving Generalization ."}
+{"idx": 1, "title": "Sharpness-Aware Minimization for Efficiently Improving Generalization", "date": "", "ddg_snippet": "In particular, our procedure, Sharpness-Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv201001412F/abstract", "content": "In particular, our procedure, Sharpness-Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently."}
+{"idx": 2, "title": "ICLR 2021 Sharpness-aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "Spotlight Sharpness-aware Minimization for Efficiently Improving Generalization Pierre Foret · Ariel Kleiner · Hossein Mobahi · Behnam Neyshabur [ Abstract ] [ Visit Oral Session 2 ] [ Paper ] [ Paper ]", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/spotlight/3497", "content": "Spotlight Sharpness-aware Minimization for Efficiently Improving Generalization Pierre Foret · Ariel Kleiner · Hossein Mobahi · Behnam Neyshabur [ Abstract ] [ Visit Oral Session 2 ] [ Paper ] [ Paper ]"}
+{"idx": 3, "title": "Sharpness-Aware Minimization for Efficiently Improving Generalization", "date": "", "ddg_snippet": "Abstract Sharpness-Aware Minimization (SAM) improves model generalization by simultaneously minimizing loss value and sharpness, achieving state-of-the-art performance and label noise robustness.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2010.01412", "content": "Abstract Sharpness-Aware Minimization (SAM) improves model generalization by simultaneously minimizing loss value and sharpness, achieving state-of-the-art performance and label noise robustness."}
+{"idx": 4, "title": "Sharpness-Aware Minimization for Efficiently Improving Generalization", "date": "", "ddg_snippet": "In particular, we make the following contributions: • We introduce Sharpness-Aware Minimization (SAM), a novel procedure that improves model generalization by simultaneously minimizing loss value and loss sharpness.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2010.01412", "content": "In particular, we make the following contributions: • We introduce Sharpness-Aware Minimization (SAM), a novel procedure that improves model generalization by simultaneously minimizing loss value and loss sharpness."}
+{"idx": 5, "title": "Sharpness-Aware Minimization for Efficiently Improving Generalization", "date": "", "ddg_snippet": "This work introduces a novel, effective procedure for simultaneously minimizing loss value and loss sharpness, Sharpness-Aware Minimization (SAM), which improves model generalization across a variety of benchmark datasets and models, yielding novel state-of-the-art performance for several. In today's heavily overparameterized models, the value of the training loss provides few guarantees on ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Sharpness-Aware-Minimization-for-Efficiently-Foret-Kleiner/a2cd073b57be744533152202989228cb4122270a", "content": "This work introduces a novel, effective procedure for simultaneously minimizing loss value and loss sharpness, Sharpness-Aware Minimization (SAM), which improves model generalization across a variety of benchmark datasets and models, yielding novel state-of-the-art performance for several. In today's heavily overparameterized models, the value of the training loss provides few guarantees on ..."}
+{"idx": 6, "title": "Sharpness-aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "by P Foret · Cited by 1946 — Sharpness-aware Minimization for Efficiently Improving Generalization ... Abstract : In today's heavily overparameterized models, the value ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6Tm1mposlrM", "content": "by P Foret · Cited by 1946 — Sharpness-aware Minimization for Efficiently Improving Generalization ... Abstract : In today's heavily overparameterized models, the value ..."}
+{"idx": 7, "title": "SHARPNESS-AWARE MINIMIZATION FOR EFFICIENTLY ...", "date": "", "ddg_snippet": "by P Foret · Cited by 1946 — We introduce Sharpness-Aware Minimization (SAM), a novel procedure that improves ... Improving Generalization Performance by Switching from Adam to SGD. arXiv e ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6Tm1mposlrM", "content": "by P Foret · Cited by 1946 — We introduce Sharpness-Aware Minimization (SAM), a novel procedure that improves ... Improving Generalization Performance by Switching from Adam to SGD. arXiv e ..."}
+{"idx": 8, "title": "sharpness-aware minimization for efficiently improving ...", "date": "", "ddg_snippet": "by P Foret · 2020 · Cited by 1946 — We introduce Sharpness-Aware Minimization (SAM), a novel procedure ... Improving Generalization Performance by Switching from Adam to SGD.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2010.01412", "content": "by P Foret · 2020 · Cited by 1946 — We introduce Sharpness-Aware Minimization (SAM), a novel procedure ... Improving Generalization Performance by Switching from Adam to SGD."}
+{"idx": 9, "title": "[R] Sharpness-Aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "Title: Sharpness-Aware Minimization for Efficiently Improving Generalization . Authors:Pierre Foret , Ariel Kleiner, Hossein Mobahi, Behnam ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/n1lgio/r_sharpnessaware_minimization_for_efficiently/", "content": "Title: Sharpness-Aware Minimization for Efficiently Improving Generalization . Authors:Pierre Foret , Ariel Kleiner, Hossein Mobahi, Behnam ..."}
diff --git a/data/sampled_jsons/Sharpness-Aware_Minimization_SAM_Foret_2021_abstract.jsonl b/data/sampled_jsons/Sharpness-Aware_Minimization_SAM_Foret_2021_abstract.jsonl
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index 0000000000000000000000000000000000000000..1a8e972039f420150720a79467012cc62b9ded80
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+++ b/data/sampled_jsons/Sharpness-Aware_Minimization_SAM_Foret_2021_abstract.jsonl
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+{"idx": 0, "title": "Sharpness-Aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "by P Foret · 2020 · Cited by 1946 — We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (eg, CIFAR-10, CIFAR-100, ImageNet, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.01412", "content": "by P Foret · 2020 · Cited by 1946 — We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (eg, CIFAR-10, CIFAR-100, ImageNet, ..."}
+{"idx": 1, "title": "Sharpness-aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "by P Foret · Cited by 1946 — We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (e.g., CIFAR-{10, 100}, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6Tm1mposlrM", "content": "by P Foret · Cited by 1946 — We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (e.g., CIFAR-{10, 100}, ..."}
+{"idx": 2, "title": "The Crucial Role of Normalization in Sharpness-Aware ...", "date": "", "ddg_snippet": "by Y Dai · 2023 · Cited by 28 — Abstract : Sharpness - Aware Minimization ( SAM ) is a recently proposed gradient-based optimizer ( Foret et al., ICLR 2021 ) that greatly improves ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.15287", "content": "by Y Dai · 2023 · Cited by 28 — Abstract : Sharpness - Aware Minimization ( SAM ) is a recently proposed gradient-based optimizer ( Foret et al., ICLR 2021 ) that greatly improves ..."}
+{"idx": 3, "title": "Sharpness-aware minimization leads to low-rank features", "date": "", "ddg_snippet": "by M Andriushchenko · 2023 · Cited by 37 — Abstract. Sharpness-aware minimization (SAM ) is a recently proposed method that minimizes the sharpness of the training loss of a neural network ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668159", "content": "by M Andriushchenko · 2023 · Cited by 37 — Abstract. Sharpness-aware minimization (SAM ) is a recently proposed method that minimizes the sharpness of the training loss of a neural network ..."}
+{"idx": 4, "title": "Unifying and revisiting Sharpness-Aware Minimization with ...", "date": "", "ddg_snippet": "by Z Wei · 2025 — Abstract. Sharpness-aware minimization (SAM ) has been proposed to improve generalization by encouraging the model to converge to a flatter region. However, ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S089360802500084X", "content": "by Z Wei · 2025 — Abstract. Sharpness-aware minimization (SAM ) has been proposed to improve generalization by encouraging the model to converge to a flatter region. However, ..."}
+{"idx": 5, "title": "SHARPNESS-AWARE MINIMIZATION FOR EFFICIENTLY ...", "date": "", "ddg_snippet": "by P Foret · Cited by 1946 — We present empirical results showing that SAM improves model gen- eralization across a variety of benchmark datasets (e.g., CIFAR-{10, 100}, Ima-. geNet, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6Tm1mposlrM", "content": "by P Foret · Cited by 1946 — We present empirical results showing that SAM improves model gen- eralization across a variety of benchmark datasets (e.g., CIFAR-{10, 100}, Ima-. geNet, ..."}
+{"idx": 6, "title": "The Crucial Role of Normalization in Sharpness-Aware ...", "date": "", "ddg_snippet": "Sharpness-Aware Minimization (SAM ) is a recently proposed gradient-based optimizer (Foret et al., ICLR 2021) that greatly improves the prediction ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/69875", "content": "Sharpness-Aware Minimization (SAM ) is a recently proposed gradient-based optimizer (Foret et al., ICLR 2021) that greatly improves the prediction ..."}
+{"idx": 7, "title": "Stabilizing Sharpness-Aware Minimization Through A ...", "date": "", "ddg_snippet": "by C Tan · 2025 · Cited by 3 — Abstract. Recently, sharpness-aware minimization (SAM ) has attracted much attention because of its surprising effectiveness in improving generalization ... 35 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "http://www.jmlr.org/papers/volume26/24-0065/24-0065.pdf", "content": "by C Tan · 2025 · Cited by 3 — Abstract. Recently, sharpness-aware minimization (SAM ) has attracted much attention because of its surprising effectiveness in improving generalization ... 35 pages"}
+{"idx": 8, "title": "AdaSAM: Boosting sharpness-aware minimization with ...", "date": "", "ddg_snippet": "by H Sun · 2024 · Cited by 56 — Sharpness-aware minimization (SAM ) (Foret et al., 2021) is a powerful optimizer for training large-scale deep learning models by explicitly minimizing the gap ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0893608023006068", "content": "by H Sun · 2024 · Cited by 56 — Sharpness-aware minimization (SAM ) (Foret et al., 2021) is a powerful optimizer for training large-scale deep learning models by explicitly minimizing the gap ..."}
+{"idx": 9, "title": "The crucial role of normalization in sharpness-aware minimization", "date": "", "ddg_snippet": "Abstract. Sharpness-Aware Minimization (SAM ) is a recently proposed gradient-based optimizer (Foret et al., ICLR 2021) that greatly improves the prediction ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3669085", "content": "Abstract. Sharpness-Aware Minimization (SAM ) is a recently proposed gradient-based optimizer (Foret et al., ICLR 2021) that greatly improves the prediction ..."}
diff --git a/data/sampled_jsons/Sharpness-Aware_Minimization_for_Efficiently_Improving_Generalization_full_abstract_text.jsonl b/data/sampled_jsons/Sharpness-Aware_Minimization_for_Efficiently_Improving_Generalization_full_abstract_text.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d3d1a94ac4a89ee7c468958802ac2238e6904cf
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+{"idx": 0, "title": "[2010.01412] Sharpness-Aware Minimization for Efficiently", "date": "", "ddg_snippet": "View a PDF of the paper titled Sharpness - Aware Minimization for Efficiently Improving Generalization , by Pierre Foret and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.01412", "content": "View a PDF of the paper titled Sharpness - Aware Minimization for Efficiently Improving Generalization , by Pierre Foret and 3 other authors"}
+{"idx": 1, "title": "[2110.03141] Efficient Sharpness-aware Minimization for", "date": "", "ddg_snippet": "This paper thus proposes Efficient Sharpness Aware Minimizer (ESAM), which boosts SAM s efficiency at no cost to its generalization performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2110.03141", "content": "This paper thus proposes Efficient Sharpness Aware Minimizer (ESAM), which boosts SAM s efficiency at no cost to its generalization performance."}
+{"idx": 2, "title": "Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning", "date": "", "ddg_snippet": "... Sharpness - Aware Minimization (SAM) has proven effective in improving generalization by seeking flat minima, its substantial extra memory and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19564v1", "content": "... Sharpness - Aware Minimization (SAM) has proven effective in improving generalization by seeking flat minima, its substantial extra memory and ..."}
+{"idx": 3, "title": "Beyond Local Sharpness: Communication-Efficient Global", "date": "", "ddg_snippet": "Beyond Local Sharpness : Communication-Efficient Global Sharpness - aware Minimization for Federated Learning ... sharpness - aware minimization (SAM) to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.03752v2", "content": "Beyond Local Sharpness : Communication-Efficient Global Sharpness - aware Minimization for Federated Learning ... sharpness - aware minimization (SAM) to ..."}
+{"idx": 4, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity ... Frontier of Regret Minimization ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity ... Frontier of Regret Minimization ..."}
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+{"idx": 6, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Improving generalization by controlling label-noise information in neural network weights ... Generalizing Convolutional Neural Networks for ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Improving generalization by controlling label-noise information in neural network weights ... Generalizing Convolutional Neural Networks for ..."}
+{"idx": 7, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Improving generalization by controlling label-noise information in neural network weights ... Generalizing Convolutional Neural Networks for ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Improving generalization by controlling label-noise information in neural network weights ... Generalizing Convolutional Neural Networks for ..."}
+{"idx": 8, "title": "CVPR 2023 Schedule", "date": "", "ddg_snippet": "3rd Workshop and Challenge on Computer Vision in the Built Environment for the Design, Construction, and Operation of Buildings", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/calendar", "content": "3rd Workshop and Challenge on Computer Vision in the Built Environment for the Design, Construction, and Operation of Buildings"}
+{"idx": 9, "title": "(PDF) GeoS: Geodesic Image Segmentation", "date": "", "ddg_snippet": "... efficiency with high segmentation accuracy; ii) the ability to estimate an approximation to the posterior over segmentations; iii) the ability to ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221304156_GeoS_Geodesic_Image_Segmentation", "content": "... efficiency with high segmentation accuracy; ii) the ability to estimate an approximation to the posterior over segmentations; iii) the ability to ..."}
diff --git a/data/sampled_jsons/Sharpness-Aware_Minimization_for_Generalization_and_Robustness_Foret_et_al._2021_arxiv_abstract.jsonl b/data/sampled_jsons/Sharpness-Aware_Minimization_for_Generalization_and_Robustness_Foret_et_al._2021_arxiv_abstract.jsonl
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+{"idx": 0, "title": "Sharpness-Aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "by P Foret · 2020 · Cited by 1946 — We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (eg, CIFAR-10, CIFAR-100, ImageNet, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.01412", "content": "by P Foret · 2020 · Cited by 1946 — We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (eg, CIFAR-10, CIFAR-100, ImageNet, ..."}
+{"idx": 1, "title": "Sharpness-aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "by P Foret · Cited by 1946 — We introduce a novel, effective procedure for instead simultaneously minimizing loss value and loss sharpness .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6Tm1mposlrM", "content": "by P Foret · Cited by 1946 — We introduce a novel, effective procedure for instead simultaneously minimizing loss value and loss sharpness ."}
+{"idx": 2, "title": "Sharpness-Aware Minimization Enhances Feature Quality ...", "date": "", "ddg_snippet": "30 May 2024 — Sharpness-Aware Minimization (SAM ) has emerged as a promising alternative optimizer to stochastic gradient descent (SGD).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.20439v1", "content": "30 May 2024 — Sharpness-Aware Minimization (SAM ) has emerged as a promising alternative optimizer to stochastic gradient descent (SGD)."}
+{"idx": 3, "title": "Stabilizing Sharpness-Aware Minimization Through A ...", "date": "", "ddg_snippet": "by C Tan · 2025 · Cited by 3 — Abstract. Recently, sharpness-aware minimization (SAM ) has attracted much attention because of its surprising effectiveness in improving generalization ... 35 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "http://www.jmlr.org/papers/volume26/24-0065/24-0065.pdf", "content": "by C Tan · 2025 · Cited by 3 — Abstract. Recently, sharpness-aware minimization (SAM ) has attracted much attention because of its surprising effectiveness in improving generalization ... 35 pages"}
+{"idx": 4, "title": "Sharpness-Aware Minimization Improves Language Model ...", "date": "", "ddg_snippet": "by D Bahri · 2022 · Cited by 116 — In Foret et al . (2020), the idea of partitioning the ascent mini-batch into m disjoint micro-batches and computing a distinct adversar- ial ... 12 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.acl-long.508.pdf", "content": "by D Bahri · 2022 · Cited by 116 — In Foret et al . (2020), the idea of partitioning the ascent mini-batch into m disjoint micro-batches and computing a distinct adversar- ial ... 12 pages"}
+{"idx": 5, "title": "On the duality between sharpness-aware minimization and ...", "date": "", "ddg_snippet": "by Y Zhang · 2024 · Cited by 20 — Sharpness-aware minimization improves language model generalization . arXiv preprint arXiv:2110.08529, 2021. Google Scholar. [4]. Yang Bai, Yan ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3694507", "content": "by Y Zhang · 2024 · Cited by 20 — Sharpness-aware minimization improves language model generalization . arXiv preprint arXiv:2110.08529, 2021. Google Scholar. [4]. Yang Bai, Yan ..."}
+{"idx": 6, "title": "Friendly Sharpness-Aware Minimization - CVF Open Access", "date": "", "ddg_snippet": "by T Li · 2024 · Cited by 30 — Sharpness-Aware Minimization (SAM ) has been instru- mental in improving deep neural network training by min- imizing both training loss and loss sharpness. 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Friendly_Sharpness-Aware_Minimization_CVPR_2024_paper.pdf", "content": "by T Li · 2024 · Cited by 30 — Sharpness-Aware Minimization (SAM ) has been instru- mental in improving deep neural network training by min- imizing both training loss and loss sharpness. 10 pages"}
+{"idx": 7, "title": "Why Does Sharpness-Aware Minimization Generalize ...", "date": "", "ddg_snippet": "by Z Chen · 2023 · Cited by 25 — To tackle this challenge, Sharpness-Aware. Minimization (SAM ) has emerged as a promising training method, which can improve the generalization of neural ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/e4d3fe32495088805bbbb4f1de63e947-Paper-Conference.pdf", "content": "by Z Chen · 2023 · Cited by 25 — To tackle this challenge, Sharpness-Aware. Minimization (SAM ) has emerged as a promising training method, which can improve the generalization of neural ..."}
+{"idx": 8, "title": "ASAM: Adaptive Sharpness-Aware Minimization for Scale ...", "date": "", "ddg_snippet": "by J Kwon · 2021 · Cited by 426 — Sharpness-Aware Minimization (SAM ) (Foret et al., 2021) aims to minimize the following PAC-Bayesian generaliza- tion error upper bound. LD(w) ≤ max. k kp≤ρ.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v139/kwon21b/kwon21b.pdf", "content": "by J Kwon · 2021 · Cited by 426 — Sharpness-Aware Minimization (SAM ) (Foret et al., 2021) aims to minimize the following PAC-Bayesian generaliza- tion error upper bound. LD(w) ≤ max. k kp≤ρ."}
+{"idx": 9, "title": "Sharpness-Aware Minimization Alone can Improve ...", "date": "", "ddg_snippet": "by Z Wei · Cited by 19 — Sharpness-Aware Minimization (SAM ) is an effective method for improving generalization ability by regularizing loss sharpness. In this.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=bxsqPkm2m9", "content": "by Z Wei · Cited by 19 — Sharpness-Aware Minimization (SAM ) is an effective method for improving generalization ability by regularizing loss sharpness. In this."}
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+{"idx": 0, "title": "Shen Build with Highest Winrate - LoL Runes, Items, and Skill...", "date": "", "ddg_snippet": "Shen build with the highest winrate runes and items in every role. U.GG analyzes millions of LoL matches to give you the best LoL champion build. Patch 15.18.", "subpage_snippet": "", "source": "u.gg", "link": "https://u.gg/lol/champions/shen/build", "content": "Shen build with the highest winrate runes and items in every role. U.GG analyzes millions of LoL matches to give you the best LoL champion build. Patch 15.18."}
+{"idx": 1, "title": "Shen - League of Legends", "date": "", "ddg_snippet": "Among the secretive, Ionian warriors known as the Kinkou, Shen serves as their leader, the Eye of Twilight. He longs to remain free from the confusion of emotion, prejudice, and ego, and walks the unseen path of dispassionate judgment between the spirit realm and the physical world.", "subpage_snippet": "", "source": "www.leagueoflegends.com", "link": "https://www.leagueoflegends.com/en-us/champions/shen/", "content": "Among the secretive, Ionian warriors known as the Kinkou, Shen serves as their leader, the Eye of Twilight. He longs to remain free from the confusion of emotion, prejudice, and ego, and walks the unseen path of dispassionate judgment between the spirit realm and the physical world."}
+{"idx": 2, "title": "Shen /LoL - League of Legends Wiki | Fandom", "date": "", "ddg_snippet": "Shen is the first dark-themed Ionian champion, before Varus, Syndra, Zed, Jhin, Xayah, & Kayn (Noxian-born). Shen , Pantheon, and Twisted Fate were the first to have a global-range teleport ability.", "subpage_snippet": "", "source": "leagueoflegends.fandom.com", "link": "https://leagueoflegends.fandom.com/wiki/Shen/LoL", "content": "Shen is the first dark-themed Ionian champion, before Varus, Syndra, Zed, Jhin, Xayah, & Kayn (Noxian-born). Shen , Pantheon, and Twisted Fate were the first to have a global-range teleport ability."}
+{"idx": 3, "title": "Shen Build Guides :: League of Legends Strategy Builds ... - ...", "date": "", "ddg_snippet": "Build guides for Shen on MOBAFire. Learn what runes and items make the best Shen build in League of Legends (LoL).", "subpage_snippet": "", "source": "www.mobafire.com", "link": "https://www.mobafire.com/league-of-legends/champion/shen-48", "content": "Build guides for Shen on MOBAFire. Learn what runes and items make the best Shen build in League of Legends (LoL)."}
+{"idx": 4, "title": "Shen Build Guide - Runes, Items & More - Patch 25.19 - Mobalytics", "date": "", "ddg_snippet": "Get the best Shen builds, based on analysis of 10000+ matches in all regions and ranks. Climb in patch 25.19 with Shen builds provided by Mobalytics!", "subpage_snippet": "", "source": "mobalytics.gg", "link": "https://mobalytics.gg/lol/champions/shen/build", "content": "Get the best Shen builds, based on analysis of 10000+ matches in all regions and ranks. Climb in patch 25.19 with Shen builds provided by Mobalytics!"}
+{"idx": 5, "title": "Shen Build - Highest Win Rate Builds, Runes, and Items", "date": "", "ddg_snippet": "Shen Build with the highest win rate. Runes, items, and skill build in patch 15.18. Shen build recommendations and guides.", "subpage_snippet": "", "source": "www.op.gg", "link": "https://www.op.gg/lol/champions/shen/build", "content": "Shen Build with the highest win rate. Runes, items, and skill build in patch 15.18. Shen build recommendations and guides."}
+{"idx": 6, "title": "Shen Build , Runes & Counters Guide for top Shen - LoLalytics", "date": "", "ddg_snippet": "The best Shen players have a 55.1% win rate with an average rank of Master on the Shen Leaderboard. Below is a detailed breakdown of the Shen build, runes & counters.", "subpage_snippet": "", "source": "lolalytics.com", "link": "https://lolalytics.com/lol/shen/build/", "content": "The best Shen players have a 55.1% win rate with an average rank of Master on the Shen Leaderboard. Below is a detailed breakdown of the Shen build, runes & counters."}
+{"idx": 7, "title": "Shen Guide ⇒ Dominate the Rift with Pro Plays [2025]", "date": "", "ddg_snippet": "Aug 6, 2025 · Shen is a master of balance and order, tasked with maintaining harmony between the spirit realm and the material world. As the leader of the Kinkou Order, he inherited the title of the Eye of Twilight after the death of his father, Kusho.", "subpage_snippet": "", "source": "lolnow.gg", "link": "https://lolnow.gg/shen/", "content": "Aug 6, 2025 · Shen is a master of balance and order, tasked with maintaining harmony between the spirit realm and the material world. As the leader of the Kinkou Order, he inherited the title of the Eye of Twilight after the death of his father, Kusho."}
+{"idx": 8, "title": "Shen - Biography - Universe of League of Legends", "date": "", "ddg_snippet": "Shen taught the girl, Akali Jhomen Tethi, to master the arts of stealth and subterfuge. Her mother, Mayym, had stood alongside Kusho as the Fist of Shadow, and it seemed as though her daughter could follow the same path.", "subpage_snippet": "", "source": "universe.leagueoflegends.com", "link": "https://universe.leagueoflegends.com/en_US/story/champion/shen/", "content": "Shen taught the girl, Akali Jhomen Tethi, to master the arts of stealth and subterfuge. Her mother, Mayym, had stood alongside Kusho as the Fist of Shadow, and it seemed as though her daughter could follow the same path."}
+{"idx": 9, "title": "Women's & Men's Clothing, Shop Online Fashion | SHEIN", "date": "", "ddg_snippet": "From shoes to clothing, from sports equipment to accessories. All fashion inspiration & the latest trends can be found online at SHEIN .", "subpage_snippet": "", "source": "us.shein.com", "link": "https://us.shein.com/", "content": "From shoes to clothing, from sports equipment to accessories. All fashion inspiration & the latest trends can be found online at SHEIN ."}
diff --git a/data/sampled_jsons/Sieve_MLE_FD3_SD_MSE_Table_1_arxiv.org_2410.02025.jsonl b/data/sampled_jsons/Sieve_MLE_FD3_SD_MSE_Table_1_arxiv.org_2410.02025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06dd43f689f6d42b1f86e65cff6978aada3714e6
--- /dev/null
+++ b/data/sampled_jsons/Sieve_MLE_FD3_SD_MSE_Table_1_arxiv.org_2410.02025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "pt for the MSE(SD) for the FD3 dataset. However, for the FD3 dataset, we found that as the training sample size increases further, the MSE(SD) of the sieve MLE achieves perf", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "pt for the MSE(SD) for the FD3 dataset. However, for the FD3 dataset, we found that as the training sample size increases further, the MSE(SD) of the sieve MLE achieves perf"}
+{"idx": 1, "title": "[2410.02025] A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend solely on the intrinsic dimension and smoothness of the true conditional distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend solely on the intrinsic dimension and smoothness of the true conditional distribution."}
+{"idx": 2, "title": "Multivariate Density Estimation via Adaptive Partitioning (I): Sieve MLE", "date": "", "ddg_snippet": "We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that for a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1401.2597", "content": "We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that for a ..."}
+{"idx": 3, "title": "Convergence Rate of Sieve Estimates on JSTOR", "date": "", "ddg_snippet": "In this paper, we develop a general theory for the convergence rate of sieve estimates, maximum likelihood estimates ( MLE's ) and related estimates obtained by optimizing certain empirical criteria in general parameter spaces.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/2242281", "content": "In this paper, we develop a general theory for the convergence rate of sieve estimates, maximum likelihood estimates ( MLE's ) and related estimates obtained by optimizing certain empirical criteria in general parameter spaces."}
+{"idx": 4, "title": "Multivariate Density Estimation via Adaptive Partitioning (I): Sieve MLE", "date": "", "ddg_snippet": "We study a non-parametric approach to multivariate density es-timation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maxi-mum likelihood estimator, and reach a conclusion that for a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1401.2597", "content": "We study a non-parametric approach to multivariate density es-timation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maxi-mum likelihood estimator, and reach a conclusion that for a ..."}
+{"idx": 5, "title": "GitHub - justdoty/Sieve_MLE: Monte Carlo EM algorithm implementation of ...", "date": "", "ddg_snippet": "The estimator is a sieve maximum likelihood estimator, whose densities are conditioned on the unobserved covariates. I make a slight departure from their original implementation by using a Monte Carlo EM algorithm to integrate out the unobserved variable from the objective function.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/justdoty/Sieve_MLE", "content": "The estimator is a sieve maximum likelihood estimator, whose densities are conditioned on the unobserved covariates. I make a slight departure from their original implementation by using a Monte Carlo EM algorithm to integrate out the unobserved variable from the objective function."}
+{"idx": 6, "title": "(PDF) Convergence Rate of Sieve Estimates - ResearchGate", "date": "", "ddg_snippet": "In this paper, we develop a general theory for the convergence rate of sieve estimates, maximum likelihood estimates ( MLE's ) and related estimates obtained by optimizing certain empirical criteria ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/38357549_Convergence_Rate_of_Sieve_Estimates", "content": "In this paper, we develop a general theory for the convergence rate of sieve estimates, maximum likelihood estimates ( MLE's ) and related estimates obtained by optimizing certain empirical criteria ..."}
+{"idx": 7, "title": "Sieve_MLE/README.md at master · justdoty/Sieve_MLE", "date": "", "ddg_snippet": "Monte Carlo EM algorithm implementation of Hu and Schennach (2008) for sieve MLE with nonclassical measurement errors - justdoty/ Sieve_MLE", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/justdoty/Sieve_MLE/blob/master/README.md", "content": "Monte Carlo EM algorithm implementation of Hu and Schennach (2008) for sieve MLE with nonclassical measurement errors - justdoty/ Sieve_MLE"}
+{"idx": 8, "title": "[2410.02755v1] SIEVE: General Purpose Data Filtering System Matching ...", "date": "", "ddg_snippet": "Creating specialized large language models requires vast amounts of clean, special purpose data for training and fine-tuning. With only a handful of existing large-scale, domain-specific datasets, creation of new datasets is required in most applications. This requires the development of new application-specific filtering of web-scale data. Filtering with a high-performance, general-purpose ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02755v1", "content": "Creating specialized large language models requires vast amounts of clean, special purpose data for training and fine-tuning. With only a handful of existing large-scale, domain-specific datasets, creation of new datasets is required in most applications. This requires the development of new application-specific filtering of web-scale data. Filtering with a high-performance, general-purpose ..."}
+{"idx": 9, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE(SD) for the FD3 dataset. However, for the FD3 dataset, we found that as the training sample size increases further, the MSE(SD) of the sieve MLE achieves performance increasingly comparable to CKDE.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025v1", "content": "Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE(SD) for the FD3 dataset. However, for the FD3 dataset, we found that as the training sample size increases further, the MSE(SD) of the sieve MLE achieves performance increasingly comparable to CKDE."}
diff --git a/data/sampled_jsons/Sieve_MLE_FD3_SD_MSE_Table_1_sitearxiv.org.jsonl b/data/sampled_jsons/Sieve_MLE_FD3_SD_MSE_Table_1_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..304d235d32af9904e316d4ce8d803a689a3042f9
--- /dev/null
+++ b/data/sampled_jsons/Sieve_MLE_FD3_SD_MSE_Table_1_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Efficient Estimation of Structural Models via Sieves", "date": "", "ddg_snippet": "where θ is the parameter of interest and Ψ is a representation of the structural model. 1 While Ψ is explicit, solving for p couldbediஂቶcultorcostly.Suchcomputational burden limits the use of standard estimators. For instance, the maximum likelihood estimator ( MLE ) repeatedly guesses θ and evaluates data likelihood using the solution of ( 1 ), p∗(θ). However, finding the solution can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2204.13488v2", "content": "where θ is the parameter of interest and Ψ is a representation of the structural model. 1 While Ψ is explicit, solving for p couldbediஂቶcultorcostly.Suchcomputational burden limits the use of standard estimators. For instance, the maximum likelihood estimator ( MLE ) repeatedly guesses θ and evaluates data likelihood using the solution of ( 1 ), p∗(θ). However, finding the solution can ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using", "date": "", "ddg_snippet": "Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE .Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE ( SD ) for the FD 3 dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE .Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE ( SD ) for the FD 3 dataset."}
+{"idx": 2, "title": "Multivariate Density Estimation via Adaptive Partitioning (I ...", "date": "", "ddg_snippet": "We study a non-parametric approach to multivariate density es-timation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maxi-mum likelihood estimator, and reach a conclusion that for a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1401.2597", "content": "We study a non-parametric approach to multivariate density es-timation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maxi-mum likelihood estimator, and reach a conclusion that for a ..."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE .Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE ( SD ) for the FD 3 dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE .Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE ( SD ) for the FD 3 dataset."}
+{"idx": 4, "title": "A Likelihood Approach to Nonparametric Estimation of a", "date": "", "ddg_snippet": "3 Convergence Rate of a Sieve MLE . 4 Class of True Distributions. 5 Numerical Experiments.That is, we obtain a sieve MLE of f∗ based on the perturbed observation Xi = Xi + i , where i is an articial noise vector following the distribution N (0D, σ2ID).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2105.04046", "content": "3 Convergence Rate of a Sieve MLE . 4 Class of True Distributions. 5 Numerical Experiments.That is, we obtain a sieve MLE of f∗ based on the perturbed observation Xi = Xi + i , where i is an articial noise vector following the distribution N (0D, σ2ID)."}
+{"idx": 5, "title": "Efficient estimation of parameters in marginals", "date": "", "ddg_snippet": "Table 1 : Simulated mean and variance for Plackett copula based FMLE, SMLE, QMLE, PMLE(G) and PMLE(C) assuming Gaussian and Clayton rotated copulas, respectively.Table 2: Optimal number of sieve elements in SMLE. increases as sieve complexity grows, as expected.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.17334", "content": "Table 1 : Simulated mean and variance for Plackett copula based FMLE, SMLE, QMLE, PMLE(G) and PMLE(C) assuming Gaussian and Clayton rotated copulas, respectively.Table 2: Optimal number of sieve elements in SMLE. increases as sieve complexity grows, as expected."}
+{"idx": 6, "title": "Why Machine Learning Cannot Ignore", "date": "", "ddg_snippet": "We now introduce a special sieve MLE , the HAL- MLE . The HAL- MLE is generally theoretically superior to other types of sieve . 6 Why Machine Learning Cannot Ignore Maximum Likelihood Estimation. MLEs , with its statistical properties presented in the subsequent section.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2110.12112", "content": "We now introduce a special sieve MLE , the HAL- MLE . The HAL- MLE is generally theoretically superior to other types of sieve . 6 Why Machine Learning Cannot Ignore Maximum Likelihood Estimation. MLEs , with its statistical properties presented in the subsequent section."}
+{"idx": 7, "title": "[1401.2597] Multivariate Density Estimation via Adaptive ... [2401.17334] Efficient estimation of parameters in marginals ... [1401.2597] Multivariate Density Estimation via Adaptive ... Multivariate Density Estimation via Adaptive Partitioning (II ...", "date": "", "ddg_snippet": "Jan 12, 2014 · We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that for a ... Jan 29, 2024 · Simulations suggest that the sieve MLE can be almost as efficient as FMLE relative to QMLE provided there is enough dependence between the marginals. We demonstrate practical value of the new estimator with several applications. Abstract We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that ... Compared to the sieve MLE , the main advantage of the Bayesian method is that it can adapt to the unknown complexity of the true density function, thus achieving the optimal convergence rate without arti cial condi-tions on the density. 1 . Introduction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1401.2597", "content": "Jan 12, 2014 · We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that for a ... Jan 29, 2024 · Simulations suggest that the sieve MLE can be almost as efficient as FMLE relative to QMLE provided there is enough dependence between the marginals. We demonstrate practical value of the new estimator with several applications. Abstract We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that ... Compared to the sieve MLE , the main advantage of the Bayesian method is that it can adapt to the unknown complexity of the true density function, thus achieving the optimal convergence rate without arti cial condi-tions on the density. 1 . Introduction."}
+{"idx": 8, "title": "[2401.17334] Efficient estimation of parameters in marginals ...", "date": "", "ddg_snippet": "Jan 29, 2024 · Simulations suggest that the sieve MLE can be almost as efficient as FMLE relative to QMLE provided there is enough dependence between the marginals. We demonstrate practical value of the new estimator with several applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.17334", "content": "Jan 29, 2024 · Simulations suggest that the sieve MLE can be almost as efficient as FMLE relative to QMLE provided there is enough dependence between the marginals. We demonstrate practical value of the new estimator with several applications."}
+{"idx": 9, "title": "[1401.2597] Multivariate Density Estimation via Adaptive ...", "date": "", "ddg_snippet": "Abstract We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1401.2597", "content": "Abstract We study a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of the sieve maximum likelihood estimator, and reach a conclusion that ..."}
diff --git a/data/sampled_jsons/Signed_Laplacians_Constrained_Graph_Clustering_equation_4_scaling_factor_co.jsonl b/data/sampled_jsons/Signed_Laplacians_Constrained_Graph_Clustering_equation_4_scaling_factor_co.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9a87f8022310ed6235f522d1db7e89c1a9568624
--- /dev/null
+++ b/data/sampled_jsons/Signed_Laplacians_Constrained_Graph_Clustering_equation_4_scaling_factor_co.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bounds on Perfect Node Classification: A Convex Graph", "date": "", "ddg_snippet": "... guarantees the solution of our optimization problem perfectly recovering the communities, under milder conditions than the bounds on graph clustering ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20231v1", "content": "... guarantees the solution of our optimization problem perfectly recovering the communities, under milder conditions than the bounds on graph clustering ..."}
+{"idx": 1, "title": "Algorithmic Aspects of Vertex Elimination on Graphs | SIAM", "date": "", "ddg_snippet": "... consider a graph -theoretic elimination process which is related to performing Gaussian elimination on sparse symmetric positive definite systems of ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/0205021?cookieSet=1", "content": "... consider a graph -theoretic elimination process which is related to performing Gaussian elimination on sparse symmetric positive definite systems of ..."}
+{"idx": 2, "title": "Algorithmic Aspects of Vertex Elimination on Graphs | SIAM", "date": "", "ddg_snippet": "... consider a graph -theoretic elimination process which is related to performing Gaussian elimination on sparse symmetric positive definite systems of ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/0205021", "content": "... consider a graph -theoretic elimination process which is related to performing Gaussian elimination on sparse symmetric positive definite systems of ..."}
+{"idx": 3, "title": "Graphs Limits Research Topics – T4Tutorials.com", "date": "", "ddg_snippet": "Automatic analysis of attack graphs for risk mitigation and prioritization on large- scale and complex networks in Industry 4 .0", "subpage_snippet": "", "source": "t4tutorials.com", "link": "https://t4tutorials.com/graphs-limits-research-topics/", "content": "Automatic analysis of attack graphs for risk mitigation and prioritization on large- scale and complex networks in Industry 4 .0"}
+{"idx": 4, "title": "Graphs clustering Research Topics – T4Tutorials.com", "date": "", "ddg_snippet": "Graph Debiased Contrastive Learning with Joint Representation Clustering . ... constrained sparse learning: a graph -based framework for single view and ...", "subpage_snippet": "", "source": "t4tutorials.com", "link": "https://t4tutorials.com/graphs-clustering-research-topics/", "content": "Graph Debiased Contrastive Learning with Joint Representation Clustering . ... constrained sparse learning: a graph -based framework for single view and ..."}
+{"idx": 5, "title": "Geometric Constrained Scalable Algorithm for PDE-Constrained", "date": "", "ddg_snippet": "Therefore, the optimization process solves a PDE- constrained optimization problem that incorporates the imposed geometric constraints in the descent ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/379529035_Geometric_Constrained_Scalable_Algorithm_for_PDE-Constrained_Shape_Optimization", "content": "Therefore, the optimization process solves a PDE- constrained optimization problem that incorporates the imposed geometric constraints in the descent ..."}
+{"idx": 6, "title": "Downloads", "date": "", "ddg_snippet": "... Attribute-Aware Hash Codes for Large- Scale ... A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "... Attribute-Aware Hash Codes for Large- Scale ... A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering"}
+{"idx": 7, "title": "725个机器学习术语表 | @Sting (atSting.com)", "date": "", "ddg_snippet": "http://www.atsting.com/wp-content/uploads/2015/10/logo_200.png ... Co -Training ... Density-Based Clustering", "subpage_snippet": "", "source": "www.atsting.com", "link": "https://www.atsting.com/archives/2195", "content": "http://www.atsting.com/wp-content/uploads/2015/10/logo_200.png ... Co -Training ... Density-Based Clustering"}
+{"idx": 8, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs ... Adaptive recurrent vision performs zero-shot computation scaling ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html", "content": "Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs ... Adaptive recurrent vision performs zero-shot computation scaling ..."}
+{"idx": 9, "title": "CVPR14 Webpage - Program", "date": "", "ddg_snippet": "Efficient pruning LMI conditions for Branch-and-Prune Rank and Chirality- Constrained Estimation of the Dual Absolute Quadric", "subpage_snippet": "", "source": "pamitc.org", "link": "https://pamitc.org/cvpr14/program.html", "content": "Efficient pruning LMI conditions for Branch-and-Prune Rank and Chirality- Constrained Estimation of the Dual Absolute Quadric"}
diff --git a/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering_Equation_4_scaling_factor_co_min(degH_degG).jsonl b/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering_Equation_4_scaling_factor_co_min(degH_degG).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f73c08fa73c03bcc1acc493f33cc7f7bbd6da247
--- /dev/null
+++ b/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering_Equation_4_scaling_factor_co_min(degH_degG).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Signed Laplacians for Constrained Graph Clustering - OpenReview", "date": "", "ddg_snippet": "( 4 ) This choice of c0 guarantees that for the scaled graph G(c0), the degree of each vertex v ∈ V satisfies that degG (c0)(v) ≤ degH (v) for all v ∈ V . With this scaling factor c0 established, we now proceed to study the properties of the graph G = (V, E, w) and its corresponding minimum cut value ΦG in comparison to H = (V, E′, w ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=MHaSq1LlTe", "content": "( 4 ) This choice of c0 guarantees that for the scaled graph G(c0), the degree of each vertex v ∈ V satisfies that degG (c0)(v) ≤ degH (v) for all v ∈ V . With this scaling factor c0 established, we now proceed to study the properties of the graph G = (V, E, w) and its corresponding minimum cut value ΦG in comparison to H = (V, E′, w ..."}
+{"idx": 1, "title": "ICML Poster Signed Laplacians for Constrained Graph Clustering", "date": "", "ddg_snippet": "In this work, we establish a Cheeger-type inequality that relates the solution of the constrained clustering problem to the spectral properties of G and H. To reduce computational complexity, we utilise the signed Laplacian of H, streamlining calculations while maintaining accuracy.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45552", "content": "In this work, we establish a Cheeger-type inequality that relates the solution of the constrained clustering problem to the spectral properties of G and H. To reduce computational complexity, we utilise the signed Laplacian of H, streamlining calculations while maintaining accuracy."}
+{"idx": 2, "title": "The Constrained Laplacian Rank Algorithm for Graph-Based ...", "date": "", "ddg_snippet": "We address both of these drawbacks by allowing the data graph itself to be adjusted as part of the clustering procedure. In partic-ular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters).", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jordan/papers/CLR_aaai16_ready.pdf", "content": "We address both of these drawbacks by allowing the data graph itself to be adjusted as part of the clustering procedure. In partic-ular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters)."}
+{"idx": 3, "title": "Algorithms inspired by graph Laplacians: linear equation ... Lecture: Signed Graph Clustering 4mm Foundations of Data ... Regularized spectral methods for clustering signed networks GitHub - danspielman/ Laplacians .jl: Algorithms inspired by graph The Constrained Laplacian Rank Algorithm for Graph-Based Clustering The Constrained Laplacian Rank Algorithm for Graph-Based Clustering The Constrained Laplacian Rank Algorithm for Graph-Based Clustering The Constrained Laplacian Rank Algorithm for Graph-Based Clustering The Constrained Laplacian Rank Algorithm for Graph-Based Clustering Spectral Clustering of Signed Graphs via Matrix Power Means", "date": "", "ddg_snippet": "Laplacians is a package containing graph algorithms, with an emphasis on tasks related to spectral and algebraic graph theory. It contains (and will contain more) code for solving systems of linear equations in graph Laplacians , low stretch spanning trees, sparsifiation, clustering , local clustering , and optimization on graphs. All graphs are represented by sparse adjacency matrices. This is both for speed, and because our main concerns are algebraic tasks. It does not handle dynamic graphs. It would be very slow to implement dynamic graphs this way. See full list on github.com To get the current version of the master branch, run pkg> add Laplacians #master See full list on github.com Improved stability of spectral graph drawing. See full list on github.com Contains the revised approximate Cholesky preconditioner described in the forthcoming paper by Gao, Kyng, and Spielman. See full list on github.com This is compatible with Julia 1.7. The only significant change from 1.2.0 was dictated by a change in interface to SuiteSparse. See full list on github.com This version is compatible with Julia 1. 4 , 1.5, and 1.6. but not earlier versions. Some features of this version will break in Julia 1.7. Changes: •Added two graph generators: complete_bipartite_graph, star_graph. •Added a function line_graph that computes the line graph of an input graph . See full list on github.com Change: minor bug fix for spectral graph drawing. Verified compatibility with Julia 1.2. See full list on github.com Changes: •Updating to use Julia's new Registrator. •Added harmonic_interp to interpolate harmonic functions on graphs. This is the fundamental routine used in Label Propagation / Semi-Supervised Learning on Graphs. •Added a function read_graph to replace readIJ and readIJV. It is a little more robust. •Cleaned up maxflow so that it now returns a flow and cut as a matrix and set. •Made pcg a little more general. See full list on github.com Changes: •Added latin_square_graph and latin_square. •Allow plot_graph to plot in 3D. •Fixed performance bug due to lazy matrix transpose. •Changed more function names to agree with Julia naming conventions. •Update documentation and examples. See full list on github.com This version works with Julia version 1.0.0. See full list on github.com ( Signed Laplacian L) Jeroome Kunegis, Stephan Schmidt, Andreas Lommatzsch, Jürgen Lerner, Ernesto William De Luca, and Sahin Albayrak. Spectral analysis of signed graphs for clustering , prediction and visualization. We complement our theoretical results with an extensive set of numerical experiments on synthetic data. Keywords: signed clustering , graph Laplacians , stochastic block models, spectral methods, regularization techniques, sparse graphs. What is Laplacians? Laplacians is a package containing graph algorithms , with an emphasis on tasks related to spectral and algebraic graph theory. It contains (and will contain more) code for solving systems of linear equations in graph Laplacians, low stretch spanning trees, sparsifiation, clustering, local clustering, and optimization on graphs. Can a Laplacian graph have exactly k connected components? To achieve such ideal clustering structures, we impose a rank constraint on the Laplacian graph of the new data similar-ity matrix, thereby guaranteeing the existence of exactly k connected components . Considering both L2-norm and L1-norm objectives, we propose two new clustering objectives and derive optimization algorithms to solve them. Can a data graph be adjusted as a Partic-Ular clustering procedure? We address both of these drawbacks by allowing the data graph itself to be adjusted as part of the clustering procedure. In partic-ular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters). What is a graph-based clustering approach? Graph-based clustering approaches typically optimize their objectives based on a given data graph associated with an affinity matrix A 2 Rn n (which can be symmetric or non-symmetric), where n is the number of nodes (data points) in the graph. There are two drawbacks with these approaches: How do we learn a graph with exactly k connected components? In partic-ular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters). We develop two versions of this method, based upon the L1-norm and the L2-norm, which yield two new graph-based clus-tering objectives. We derive optimization algorithms to solve these objectives. How can we partition data points into k clusters based on s? Under this con-straint, the learned S is block diagonal with proper permuta-tion, and thus we can directly partition the data points into k clusters based on S (Nie, Wang, and Huang 2014). To avoid the case that some rows of S are all zeros, we further con-strain the S such that the sum of each row of S is one. Inspired by the notion of k-balance, different approaches for signed graph clustering have been introduced. In par-ticular, many of them aim to extend spectral clustering to signed graphs by proposing novel signed graph Laplacians .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/danspielman/Laplacians.jl", "content": "Laplacians is a package containing graph algorithms, with an emphasis on tasks related to spectral and algebraic graph theory. It contains (and will contain more) code for solving systems of linear equations in graph Laplacians , low stretch spanning trees, sparsifiation, clustering , local clustering , and optimization on graphs. All graphs are represented by sparse adjacency matrices. This is both for speed, and because our main concerns are algebraic tasks. It does not handle dynamic graphs. It would be very slow to implement dynamic graphs this way. See full list on github.com To get the current version of the master branch, run pkg> add Laplacians #master See full list on github.com Improved stability of spectral graph drawing. See full list on github.com Contains the revised approximate Cholesky preconditioner described in the forthcoming paper by Gao, Kyng, and Spielman. See full list on github.com This is compatible with Julia 1.7. The only significant change from 1.2.0 was dictated by a change in interface to SuiteSparse. See full list on github.com This version is compatible with Julia 1. 4 , 1.5, and 1.6. but not earlier versions. Some features of this version will break in Julia 1.7. Changes: •Added two graph generators: complete_bipartite_graph, star_graph. •Added a function line_graph that computes the line graph of an input graph . See full list on github.com Change: minor bug fix for spectral graph drawing. Verified compatibility with Julia 1.2. See full list on github.com Changes: •Updating to use Julia's new Registrator. •Added harmonic_interp to interpolate harmonic functions on graphs. This is the fundamental routine used in Label Propagation / Semi-Supervised Learning on Graphs. •Added a function read_graph to replace readIJ and readIJV. It is a little more robust. •Cleaned up maxflow so that it now returns a flow and cut as a matrix and set. •Made pcg a little more general. See full list on github.com Changes: •Added latin_square_graph and latin_square. •Allow plot_graph to plot in 3D. •Fixed performance bug due to lazy matrix transpose. •Changed more function names to agree with Julia naming conventions. •Update documentation and examples. See full list on github.com This version works with Julia version 1.0.0. See full list on github.com ( Signed Laplacian L) Jeroome Kunegis, Stephan Schmidt, Andreas Lommatzsch, Jürgen Lerner, Ernesto William De Luca, and Sahin Albayrak. Spectral analysis of signed graphs for clustering , prediction and visualization. We complement our theoretical results with an extensive set of numerical experiments on synthetic data. Keywords: signed clustering , graph Laplacians , stochastic block models, spectral methods, regularization techniques, sparse graphs. What is Laplacians? Laplacians is a package containing graph algorithms , with an emphasis on tasks related to spectral and algebraic graph theory. It contains (and will contain more) code for solving systems of linear equations in graph Laplacians, low stretch spanning trees, sparsifiation, clustering, local clustering, and optimization on graphs. Can a Laplacian graph have exactly k connected components? To achieve such ideal clustering structures, we impose a rank constraint on the Laplacian graph of the new data similar-ity matrix, thereby guaranteeing the existence of exactly k connected components . Considering both L2-norm and L1-norm objectives, we propose two new clustering objectives and derive optimization algorithms to solve them. Can a data graph be adjusted as a Partic-Ular clustering procedure? We address both of these drawbacks by allowing the data graph itself to be adjusted as part of the clustering procedure. In partic-ular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters). What is a graph-based clustering approach? Graph-based clustering approaches typically optimize their objectives based on a given data graph associated with an affinity matrix A 2 Rn n (which can be symmetric or non-symmetric), where n is the number of nodes (data points) in the graph. There are two drawbacks with these approaches: How do we learn a graph with exactly k connected components? In partic-ular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters). We develop two versions of this method, based upon the L1-norm and the L2-norm, which yield two new graph-based clus-tering objectives. We derive optimization algorithms to solve these objectives. How can we partition data points into k clusters based on s? Under this con-straint, the learned S is block diagonal with proper permuta-tion, and thus we can directly partition the data points into k clusters based on S (Nie, Wang, and Huang 2014). To avoid the case that some rows of S are all zeros, we further con-strain the S such that the sum of each row of S is one. Inspired by the notion of k-balance, different approaches for signed graph clustering have been introduced. In par-ticular, many of them aim to extend spectral clustering to signed graphs by proposing novel signed graph Laplacians ."}
+{"idx": 4, "title": "Lecture: Signed Graph Clustering 4mm Foundations of Data ...", "date": "", "ddg_snippet": "( Signed Laplacian L) Jeroome Kunegis, Stephan Schmidt, Andreas Lommatzsch, Jürgen Lerner, Ernesto William De Luca, and Sahin Albayrak. Spectral analysis of signed graphs for clustering , prediction and visualization.", "subpage_snippet": "", "source": "www.stats.ox.ac.uk", "link": "https://www.stats.ox.ac.uk/~cucuring/SignedClustering_CDT.pdf", "content": "( Signed Laplacian L) Jeroome Kunegis, Stephan Schmidt, Andreas Lommatzsch, Jürgen Lerner, Ernesto William De Luca, and Sahin Albayrak. Spectral analysis of signed graphs for clustering , prediction and visualization."}
+{"idx": 5, "title": "Regularized spectral methods for clustering signed networks", "date": "", "ddg_snippet": "We complement our theoretical results with an extensive set of numerical experiments on synthetic data. Keywords: signed clustering , graph Laplacians , stochastic block models, spectral methods, regularization techniques, sparse graphs.", "subpage_snippet": "", "source": "math.ucla.edu", "link": "https://math.ucla.edu/~mihai/SignedClust2020.pdf", "content": "We complement our theoretical results with an extensive set of numerical experiments on synthetic data. Keywords: signed clustering , graph Laplacians , stochastic block models, spectral methods, regularization techniques, sparse graphs."}
+{"idx": 6, "title": "Spectral Clustering of Signed Graphs via Matrix Power Means", "date": "", "ddg_snippet": "Inspired by the notion of k-balance, different approaches for signed graph clustering have been introduced. In par-ticular, many of them aim to extend spectral clustering to signed graphs by proposing novel signed graph Laplacians .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v97/mercado19a/mercado19a.pdf", "content": "Inspired by the notion of k-balance, different approaches for signed graph clustering have been introduced. In par-ticular, many of them aim to extend spectral clustering to signed graphs by proposing novel signed graph Laplacians ."}
+{"idx": 7, "title": "Defensive Alliances in Signed Networks", "date": "", "ddg_snippet": "by E Arrighi · 2023 · Cited by 1 — How to partition a complete signed graph best possible to a clustering partitioning has been investigated extensively since the publication.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2309.06801", "content": "by E Arrighi · 2023 · Cited by 1 — How to partition a complete signed graph best possible to a clustering partitioning has been investigated extensively since the publication."}
+{"idx": 8, "title": "Finding the Cores of Higher Graphs Using Geometric and ...", "date": "", "ddg_snippet": "by I Garcıa-Redondo — Abstract In this survey, we explore recent literature on finding the cores of higher graphs using geometric and topological means. We study graphs ...", "subpage_snippet": "", "source": "www.sci.utah.edu", "link": "https://www.sci.utah.edu/~beiwang/publications/WCT3_Survey_BeiWang_2025.pdf", "content": "by I Garcıa-Redondo — Abstract In this survey, we explore recent literature on finding the cores of higher graphs using geometric and topological means. We study graphs ..."}
+{"idx": 9, "title": "Defensive Alliances in Signed Networks - ACM Digital Library", "date": "", "ddg_snippet": "by E Arrighi · 2025 · Cited by 1 — Davis (1967) extended this notion; he called a signed graph a clustering or weakly balanced if there are no cycles in the graph with exactly one negative edge.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1613/jair.1.17165", "content": "by E Arrighi · 2025 · Cited by 1 — Davis (1967) extended this notion; he called a signed graph a clustering or weakly balanced if there are no cycles in the graph with exactly one negative edge."}
diff --git a/data/sampled_jsons/SimXRD-4M-_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchmark.jsonl b/data/sampled_jsons/SimXRD-4M-_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchmark.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..761abea034fc7df6f6fa4ed367c4d9769a131882
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M-_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchmark.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SimXRD - 4 M : Big Simulated X - ray Diffraction Data and Crystal ...", "date": "", "ddg_snippet": "Powder X - ray diffraction ( XRD ) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. Although machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "Powder X - ray diffraction ( XRD ) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. Although machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and ..."}
+{"idx": 1, "title": "GitHub - Bin-Cao/SimXRD: [ICLR 2025] SimXRD-4M: Big Simulated ...", "date": "", "ddg_snippet": "Open Source: SimXRD - 4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Open Source: SimXRD - 4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials."}
+{"idx": 2, "title": "SIMXRD-4M: B SIMULATED X-RAY DIFFRACTION D C SYMMETRY ...", "date": "", "ddg_snippet": "e limited availability of training data and established bench-marks. To address this, we introduce SimXRD - 4M , the largest open-source simulated XRD pattern dataset to date, a med at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates compre", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mkuB677eMM", "content": "e limited availability of training data and established bench-marks. To address this, we introduce SimXRD - 4M , the largest open-source simulated XRD pattern dataset to date, a med at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates compre"}
+{"idx": 3, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal ...", "date": "", "ddg_snippet": "Experimental XRD pattern of a Li-rich layered oxide cathode was compared with simulated pattern generated using PysimXRD . The simulation incorporates multiphysical coupling, producing patterns that closely match experimental measurement with minimal residual errors.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "Experimental XRD pattern of a Li-rich layered oxide cathode was compared with simulated pattern generated using PysimXRD . The simulation incorporates multiphysical coupling, producing patterns that closely match experimental measurement with minimal residual errors."}
+{"idx": 4, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate ...", "date": "", "ddg_snippet": "Jun 15, 2024 · SimXRD , the largest open-source simulated XRD pattern dataset so far, is introduced to accelerate the development of crystallographic informatics and finds that the crystal symmetry inherently follows a long-tailed distribution and evaluates 21 sequence learning models on SimXRD .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/SimXRD-4M:-Big-Simulated-X-ray-Diffraction-Data-the-Cao-Liu/9c00a79aabe96dcff5f536a8ee8da6023e223e77/figure/5", "content": "Jun 15, 2024 · SimXRD , the largest open-source simulated XRD pattern dataset so far, is introduced to accelerate the development of crystallographic informatics and finds that the crystal symmetry inherently follows a long-tailed distribution and evaluates 21 sequence learning models on SimXRD ."}
+{"idx": 5, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate ...", "date": "", "ddg_snippet": "The results indicate that existing neural networks struggle with low-frequency crystal classifications . The present work highlights the academic significance and the engineering novelty of simulated XRD patterns in this interdisciplinary field.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "The results indicate that existing neural networks struggle with low-frequency crystal classifications . The present work highlights the academic significance and the engineering novelty of simulated XRD patterns in this interdisciplinary field."}
+{"idx": 6, "title": "(PDF) SIMXRD - 4 M : big simulated x - ray diffraction data and crystal ...", "date": "", "ddg_snippet": "Powder X - ray diffraction ( XRD ) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. Data and crystal symmetry classification . Benchmark . Bin Cao1∗.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389657996_SIMXRD-4M_BIG_SIMULATED_X-RAY_DIFFRACTION_DATA_AND_CRYSTAL_SYMMETRY_CLASSIFICATION_BENCHMARK", "content": "Powder X - ray diffraction ( XRD ) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. Data and crystal symmetry classification . Benchmark . Bin Cao1∗."}
+{"idx": 7, "title": "SimXRD - 4 M : Big Simulated X - ray Diffraction Data Accelerate the...", "date": "", "ddg_snippet": "Powder X - ray diffraction ( XRD ) patterns are greatly effective in identifying crystals . Although machine learning (ML) has significantly advanced the analysis of powder XRD patterns, the progress is hindered by a lack of training data .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/SimXRD-4M:-Big-Simulated-X-ray-Diffraction-Data-Accelerate-the-Crystal-Symmetry-Classification-8f73fec2-f675-4f6a-ab07-8e6e05d1c379", "content": "Powder X - ray diffraction ( XRD ) patterns are greatly effective in identifying crystals . Although machine learning (ML) has significantly advanced the analysis of powder XRD patterns, the progress is hindered by a lack of training data ."}
+{"idx": 8, "title": "caobin/CPPbenchmark · Datasets at Hugging Face", "date": "", "ddg_snippet": "CPPbenchmark is a curated benchmark suite for evaluating machine learning models on crystal property prediction (CPP) tasks. It includes eight tasks—seven regression (e.g., formation energy, band gap, elastic moduli) and one classification (metal/non-metal)—using high-quality...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/caobin/CPPbenchmark", "content": "CPPbenchmark is a curated benchmark suite for evaluating machine learning models on crystal property prediction (CPP) tasks. It includes eight tasks—seven regression (e.g., formation energy, band gap, elastic moduli) and one classification (metal/non-metal)—using high-quality..."}
+{"idx": 9, "title": "Advancing Crystal Structure Analysis with SimXRD Dataset", "date": "", "ddg_snippet": "Title: SimXRD - 4 M : Big Simulated X - ray Diffraction Data Accelerate the Crystalline Symmetry Classification .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-advancing-crystal-structure-analysis-with-simxrd-dataset--ak4ex8o", "content": "Title: SimXRD - 4 M : Big Simulated X - ray Diffraction Data Accelerate the Crystalline Symmetry Classification ."}
diff --git a/data/sampled_jsons/SimXRD-4M_arXiv_Table_3_'Bidirectional-GRU'_'Focal_loss'_'F1_score'_year_2024.jsonl b/data/sampled_jsons/SimXRD-4M_arXiv_Table_3_'Bidirectional-GRU'_'Focal_loss'_'F1_score'_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..55a4de7e00ebf0ee63078de28dd1e5ad015b6d78
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_arXiv_Table_3_'Bidirectional-GRU'_'Focal_loss'_'F1_score'_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and ...", "date": "", "ddg_snippet": "Table 3: Results of weighted classification, label smoothing, and Focal loss on crystal system and space group classification. Crystal System Classification ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "Table 3: Results of weighted classification, label smoothing, and Focal loss on crystal system and space group classification. Crystal System Classification ..."}
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diff --git a/data/sampled_jsons/SimXRD-4M_largest_open-source_simulated_XRD_pattern_dataset_AdvancedXRDAnalysis_Lee_2023.jsonl b/data/sampled_jsons/SimXRD-4M_largest_open-source_simulated_XRD_pattern_dataset_AdvancedXRDAnalysis_Lee_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f6dd37ccc19c6bcf42e56d9a8e67d04d8800b723
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_largest_open-source_simulated_XRD_pattern_dataset_AdvancedXRDAnalysis_Lee_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns , representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns , representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations."}
+{"idx": 1, "title": "SIMXRD-4M: BIG SIMULATED X-RAY DIFFRACTION DATA AND ...", "date": "", "ddg_snippet": "In this paper, we introduce SimXRD , the largest open - source XRD pattern dataset for symmetry identification. Data analysis reveals that the symmetry labels ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/b04843d76c9f090beca55f335c4ea6bf-Paper-Conference.pdf", "content": "In this paper, we introduce SimXRD , the largest open - source XRD pattern dataset for symmetry identification. Data analysis reveals that the symmetry labels ..."}
+{"idx": 2, "title": "GitHub - Bin-Cao/SimXRD: [ICLR 2025] SimXRD-4M: Big Simulated X-ray ...", "date": "", "ddg_snippet": "Open Source : SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns , which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are influenced by various factors such as the testing environment (instrumentation), light source (X ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Open Source : SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns , which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are influenced by various factors such as the testing environment (instrumentation), light source (X ..."}
+{"idx": 3, "title": "Simxrd-4m: B Simulated X-ray Diffraction D C Symmetry Classification", "date": "", "ddg_snippet": "e limited availability of training data and established bench-marks. To address this, we introduce SimXRD-4M , the largest open-source simulated XRD pattern dataset to date, a med at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates compre", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mkuB677eMM", "content": "e limited availability of training data and established bench-marks. To address this, we introduce SimXRD-4M , the largest open-source simulated XRD pattern dataset to date, a med at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates compre"}
+{"idx": 4, "title": "Advancing Crystal Structure Analysis with SimXRD Dataset", "date": "", "ddg_snippet": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns , representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-advancing-crystal-structure-analysis-with-simxrd-dataset--ak4ex8o", "content": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns , representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations."}
+{"idx": 5, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates comprehensive physical interactions, resulting in a high-fidelity database.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/b04843d76c9f090beca55f335c4ea6bf-Abstract-Conference.html", "content": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates comprehensive physical interactions, resulting in a high-fidelity database."}
+{"idx": 6, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "To address this, we introduce SimXRD-4M , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates comprehensive physical interactions, resulting in a high-fidelity database.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "To address this, we introduce SimXRD-4M , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates comprehensive physical interactions, resulting in a high-fidelity database."}
+{"idx": 7, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381665624_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Accelerate_the_Crystalline_Symmetry_Classification", "content": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics."}
+{"idx": 8, "title": "SimXRD/README.md at main · Bin-Cao/SimXRD · GitHub", "date": "", "ddg_snippet": "Open Source : SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns , which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are influenced by various factors such as the testing environment (instrumentation), light source (X ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD/blob/main/README.md", "content": "Open Source : SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns , which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are influenced by various factors such as the testing environment (instrumentation), light source (X ..."}
+{"idx": 9, "title": "Figure 4 from SimXRD-4M: Big Simulated X-ray Diffraction Data ...", "date": "", "ddg_snippet": "SimXRD , the largest open-source simulated XRD pattern dataset so far, is introduced to accelerate the development of crystallographic informatics and finds that the crystal symmetry inherently follows a long-tailed distribution and evaluates 21 sequence learning models on SimXRD .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/SimXRD-4M:-Big-Simulated-X-ray-Diffraction-Data-the-Cao-Liu/9c00a79aabe96dcff5f536a8ee8da6023e223e77/figure/5", "content": "SimXRD , the largest open-source simulated XRD pattern dataset so far, is introduced to accelerate the development of crystallographic informatics and finds that the crystal symmetry inherently follows a long-tailed distribution and evaluates 21 sequence learning models on SimXRD ."}
diff --git a/data/sampled_jsons/SimXRD-4M_paper_section_A.3_category_1_category_2_space_group_classification.jsonl b/data/sampled_jsons/SimXRD-4M_paper_section_A.3_category_1_category_2_space_group_classification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eb0dded0504025b17b5cd58dd5af5842223bcb39
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_paper_section_A.3_category_1_category_2_space_group_classification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "We offer benchmarks to illustrate its potential in library space group and crystal system classification tasks, as illustrated in the section : Use Cases of SimXRD Dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "We offer benchmarks to illustrate its potential in library space group and crystal system classification tasks, as illustrated in the section : Use Cases of SimXRD Dataset."}
+{"idx": 1, "title": "SimXRD-4M ICLR 2025 - GitHub", "date": "", "ddg_snippet": "The Official Implementation of SimXRD | Paper | DataBase | Benchmark Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups , each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "The Official Implementation of SimXRD | Paper | DataBase | Benchmark Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups , each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials."}
+{"idx": 2, "title": "Simxrd-4m: B Simulated X-ray Diffraction D C Symmetry Classification", "date": "", "ddg_snippet": "m-metry, duplicates, or discrepancies in space group classification . Consequently, SimXRD comprises 4,065,346 X-ray powder diffraction patterns covering 119,569 distinct crystal structures simulated under various conditions, including grain size, orientation, internal stress, inelastic scatt ymmetry classification models), recurrent models", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mkuB677eMM", "content": "m-metry, duplicates, or discrepancies in space group classification . Consequently, SimXRD comprises 4,065,346 X-ray powder diffraction patterns covering 119,569 distinct crystal structures simulated under various conditions, including grain size, orientation, internal stress, inelastic scatt ymmetry classification models), recurrent models"}
+{"idx": 3, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystalline ...", "date": "", "ddg_snippet": "# 1 SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark [PDF 2 ] [Copy] [Kimi] [REL] Authors: Bin Cao, Yang Liu, Zinan Zheng, Ruifeng Tan, Jia Li, Tong-Yi Zhang Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/mkuB677eMM@OpenReview", "content": "# 1 SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark [PDF 2 ] [Copy] [Kimi] [REL] Authors: Bin Cao, Yang Liu, Zinan Zheng, Ruifeng Tan, Jia Li, Tong-Yi Zhang Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis ..."}
+{"idx": 4, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal ...", "date": "", "ddg_snippet": "View a PDF of the paper titled SimXRD-4M : Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification , by Bin Cao and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.15469", "content": "View a PDF of the paper titled SimXRD-4M : Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification , by Bin Cao and 5 other authors"}
+{"idx": 5, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381665624_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Accelerate_the_Crystalline_Symmetry_Classification", "content": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations."}
+{"idx": 6, "title": "Figure 4 from SimXRD-4M: Big Simulated X-ray Diffraction Data ...", "date": "", "ddg_snippet": "This work proposes a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction patterns as a proof of concept using simulated patterns, and succeeds in quantifying empirical knowledge vaguely shared among experts.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/SimXRD-4M:-Big-Simulated-X-ray-Diffraction-Data-the-Cao-Liu/9c00a79aabe96dcff5f536a8ee8da6023e223e77/figure/5", "content": "This work proposes a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction patterns as a proof of concept using simulated patterns, and succeeds in quantifying empirical knowledge vaguely shared among experts."}
+{"idx": 7, "title": "SimXRD/README.md at main · Bin-Cao/SimXRD · GitHub", "date": "", "ddg_snippet": "The Official Implementation of SimXRD | Paper | DataBase | Benchmark Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups , each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD/blob/main/README.md", "content": "The Official Implementation of SimXRD | Paper | DataBase | Benchmark Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups , each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials."}
+{"idx": 8, "title": "PDF SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance."}
+{"idx": 9, "title": "Publications - Ruifeng Tan", "date": "", "ddg_snippet": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper , we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ...", "subpage_snippet": "", "source": "ruifeng-tan.github.io", "link": "https://ruifeng-tan.github.io/publications/", "content": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper , we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ..."}
diff --git a/data/sampled_jsons/Simulation_from_full_dimension_distribution_FD1_FD2_FD3.jsonl b/data/sampled_jsons/Simulation_from_full_dimension_distribution_FD1_FD2_FD3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..88fdd173276db425e311bc3dd1e4236a82fc48ad
--- /dev/null
+++ b/data/sampled_jsons/Simulation_from_full_dimension_distribution_FD1_FD2_FD3.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using", "date": "", "ddg_snippet": "Simulation from full dimension distribution .However, for the FD 3 dataset, we found that as the training sample size increases further, the MSE(SD) of the sieve MLE achieves performance increasingly comparable to CKDE. Simulation from distributions on manifolds.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "Simulation from full dimension distribution .However, for the FD 3 dataset, we found that as the training sample size increases further, the MSE(SD) of the sieve MLE achieves performance increasingly comparable to CKDE. Simulation from distributions on manifolds."}
+{"idx": 1, "title": "(PDF) A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Simulation from full dimension distribution .However, for the FD 3 dataset, we found that as the training sample size increases further, the. MSE(SD) of the sieve MLE achieves performance increasingly comparable to CKDE. Simulation from distributions on manifolds.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "Simulation from full dimension distribution .However, for the FD 3 dataset, we found that as the training sample size increases further, the. MSE(SD) of the sieve MLE achieves performance increasingly comparable to CKDE. Simulation from distributions on manifolds."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — Simulation from full dimension distribution . We use the following models for data generation. Report issue for preceding element. •. FD1 ... FD2 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — Simulation from full dimension distribution . We use the following models for data generation. Report issue for preceding element. •. FD1 ... FD2 ..."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Simulation from full dimension distribution . We use the following models for data generation. • FD1 . : Y. = I{U0.5} N X, 0.252 ; U ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "Simulation from full dimension distribution . We use the following models for data generation. • FD1 . : Y. = I{U0.5} N X, 0.252 ; U ..."}
+{"idx": 4, "title": "TIVE MODELS", "date": "", "ddg_snippet": "Simulation from full dimension distribution . We use the following models for data generation. • FD1 : Y = I{U0.5} N X, 0.252 ; U ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "Simulation from full dimension distribution . We use the following models for data generation. • FD1 : Y = I{U0.5} N X, 0.252 ; U ..."}
+{"idx": 5, "title": "A strategy for determining the equilibrium constants for", "date": "", "ddg_snippet": "However, the signal transmission from the ligand binding site to the pore has not yet been fully elucidated for any of these channels.", "subpage_snippet": "", "source": "rupress.org", "link": "https://rupress.org/jgp/article/154/6/e202113041/213190/A-strategy-for-determining-the-equilibrium", "content": "However, the signal transmission from the ligand binding site to the pore has not yet been fully elucidated for any of these channels."}
+{"idx": 6, "title": "CN112399105A - Imaging circuit, method of operation, and image", "date": "", "ddg_snippet": "H04N25/40 — Extracting pixel data from image sensors by controlling scanning circuits, e.g. ... H04N25/44 — Extracting pixel data from ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/CN112399105A/en", "content": "H04N25/40 — Extracting pixel data from image sensors by controlling scanning circuits, e.g. ... H04N25/44 — Extracting pixel data from ..."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 8, "title": "KR20220022481A - Method and system for extending image dynamic", "date": "", "ddg_snippet": "G01S17/894 — 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/KR20220022481A/en", "content": "G01S17/894 — 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g."}
diff --git a/data/sampled_jsons/Sinkhorn_algorithm_f-divergence_regularization_update_equations.jsonl b/data/sampled_jsons/Sinkhorn_algorithm_f-divergence_regularization_update_equations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5e7628739baf228dfe2d5ddd4cdc491138d11d95
--- /dev/null
+++ b/data/sampled_jsons/Sinkhorn_algorithm_f-divergence_regularization_update_equations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Optimal transport with f-divergence regularization and generalized ...", "date": "", "ddg_snippet": "a practical algorithm for computing an ap-proximate solution of the optimal transport problem with f-divergence regularization via the generalized Sinkhorn algorithm . Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different f-divergences for regular-ization , which influences convergence speed, numerical stability and sparsity of the ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v151/terjek22a/terjek22a.pdf", "content": "a practical algorithm for computing an ap-proximate solution of the optimal transport problem with f-divergence regularization via the generalized Sinkhorn algorithm . Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different f-divergences for regular-ization , which influences convergence speed, numerical stability and sparsity of the ..."}
+{"idx": 1, "title": "Optimal transport with $f$-divergence regularization and generalized ...", "date": "", "ddg_snippet": "Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replacing the Kullback-Leibler divergence with a general f - divergence leads to a natural generalization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2105.14337", "content": "Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replacing the Kullback-Leibler divergence with a general f - divergence leads to a natural generalization."}
+{"idx": 2, "title": "PDF Optimal transport with f -divergence regularization and ... - AISTATS", "date": "", "ddg_snippet": "Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm D ́avid Terj ́ek & Diego Gonz ́alez-S ́anchez", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/media/aistats-2022/Slides/3362.pdf", "content": "Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm D ́avid Terj ́ek & Diego Gonz ́alez-S ́anchez"}
+{"idx": 3, "title": "PDF Sinkhorn Divergences : Interpolating between Optimal Transport and MMD", "date": "", "ddg_snippet": "Definition ( '-divergence ) Let ' convex l.s.c. function such that '(1) = 0, the '-divergence D' between two measures and is defined by :", "subpage_snippet": "", "source": "audeg.github.io", "link": "https://audeg.github.io/talks/talkAIP.pdf", "content": "Definition ( '-divergence ) Let ' convex l.s.c. function such that '(1) = 0, the '-divergence D' between two measures and is defined by :"}
+{"idx": 4, "title": "PDF On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "We study Sinkhorn's algorithm for solving the entropically regu-larized optimal transport problem. Its iterate πt is shown to satisfy H(πt|π∗)+H(π∗|πt) = O(t−1) where H denotes relative entropy and π∗ the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with subgaussian marginals. We also obtain the rate O(t−1) for the ...", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/Sinkhorn_rate.pdf", "content": "We study Sinkhorn's algorithm for solving the entropically regu-larized optimal transport problem. Its iterate πt is shown to satisfy H(πt|π∗)+H(π∗|πt) = O(t−1) where H denotes relative entropy and π∗ the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with subgaussian marginals. We also obtain the rate O(t−1) for the ..."}
+{"idx": 5, "title": "renyi-ai/optimal-transport-with-f-divergence-regularization-and ...", "date": "", "ddg_snippet": "This is the official codebase for the paper \"Optimal transport with f-divergence regularization and generalized Sinkhorn algorithm \" by Dávid Terjék and Diego González-Sánchez accepted for publication at the 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/renyi-ai/optimal-transport-with-f-divergence-regularization-and-generalized-sinkhorn-algorithm", "content": "This is the official codebase for the paper \"Optimal transport with f-divergence regularization and generalized Sinkhorn algorithm \" by Dávid Terjék and Diego González-Sánchez accepted for publication at the 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022."}
+{"idx": 6, "title": "PDF A Stochastic Algorithm for Sinkhorn Distance-Regularized ...", "date": "", "ddg_snippet": "This paper focuses on Sinkhorn distance regularized DRO. We generalize Sinkhorn distance allowing broader function choices to model ambiguity set and derive the lagrangian dual taking the form of nested stochastic programming. We also design the algorithm based on stochastic gradient descent with easy-to-implement constant learning rate.", "subpage_snippet": "", "source": "opt-ml.org", "link": "https://opt-ml.org/papers/2024/paper22.pdf", "content": "This paper focuses on Sinkhorn distance regularized DRO. We generalize Sinkhorn distance allowing broader function choices to model ambiguity set and derive the lagrangian dual taking the form of nested stochastic programming. We also design the algorithm based on stochastic gradient descent with easy-to-implement constant learning rate."}
+{"idx": 7, "title": "Optimal transport with f-divergence regularization and generalized ...", "date": "", "ddg_snippet": "Abstract: Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replacing the Kullback-Leibler divergence with a general f - divergence leads to a natural generalization. The case of divergences defined by ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0kDNUB4ae3", "content": "Abstract: Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replacing the Kullback-Leibler divergence with a general f - divergence leads to a natural generalization. The case of divergences defined by ..."}
+{"idx": 8, "title": "PDF Geometric Losses for Machine Learning - Sinkhorn Divergences for ...", "date": "", "ddg_snippet": "The Sinkhorn algorithm converges towards the optimal ( f ; g) of OT\"( ; ) when ' is strictly convex, but also for TV, Range and Balanced OT.", "subpage_snippet": "", "source": "thibsej.github.io", "link": "https://thibsej.github.io/files/beamer_PGMO_2019.pdf", "content": "The Sinkhorn algorithm converges towards the optimal ( f ; g) of OT\"( ; ) when ' is strictly convex, but also for TV, Range and Balanced OT."}
+{"idx": 9, "title": "arXiv:2105.14337v1 [math.OC] 29 May 2021", "date": "", "ddg_snippet": "Abstract Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replac-ing the Kullback-Leibler divergence with a general f-divergence leads to a natural generalization. Using convex analysis, we extend the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2105.14337v1", "content": "Abstract Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replac-ing the Kullback-Leibler divergence with a general f-divergence leads to a natural generalization. Using convex analysis, we extend the ..."}
diff --git a/data/sampled_jsons/Sinkhorn_algorithm_tameness_condition_condition_number_convergence.jsonl b/data/sampled_jsons/Sinkhorn_algorithm_tameness_condition_condition_number_convergence.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..745be1a75df546a66b71a9dc8a5504faa21f6552
--- /dev/null
+++ b/data/sampled_jsons/Sinkhorn_algorithm_tameness_condition_condition_number_convergence.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "Our main result is the convergence of the algorithm in the sense of rel-ative entropy and its rate, under general conditions including unbounded costs. Methodologically, we proceed in two steps. First, we show that certain φt, ψt (exponential) moment estimates for the iterates imply a convergence rate.", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/Sinkhorn_rate.pdf", "content": "Our main result is the convergence of the algorithm in the sense of rel-ative entropy and its rate, under general conditions including unbounded costs. Methodologically, we proceed in two steps. First, we show that certain φt, ψt (exponential) moment estimates for the iterates imply a convergence rate."}
+{"idx": 1, "title": "[2408.11620] Annealed Sinkhorn for Optimal Transport: convergence ...", "date": "", "ddg_snippet": "Sinkhorn's algorithm is a method of choice to solve large-scale optimal transport (OT) problems. In this context, it involves an inverse temperature parameter β that determines the speed-accuracy trade-off. To improve this trade-off, practitioners often use a variant of this algorithm , Annealed Sinkhorn , that uses an nondecreasing sequence (βt)t∈N where t is the iteration count. However ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.11620", "content": "Sinkhorn's algorithm is a method of choice to solve large-scale optimal transport (OT) problems. In this context, it involves an inverse temperature parameter β that determines the speed-accuracy trade-off. To improve this trade-off, practitioners often use a variant of this algorithm , Annealed Sinkhorn , that uses an nondecreasing sequence (βt)t∈N where t is the iteration count. However ..."}
+{"idx": 2, "title": "On the Convergence Rate of Sinkhorn's Algorithm - PubsOnLine", "date": "", "ddg_snippet": "We study Sinkhorn's algorithm for solving the entropically regularized optimal transport problem. Its iterate 𝜋 𝑡 π t is shown to satisfy 𝐻 (𝜋 𝑡 | 𝜋 *) + 𝐻 (𝜋 * | 𝜋 𝑡) = 𝑂 (𝑡 − 1) H (π t | π *) + H (π * | π t) = O (t 1), where H denotes relative entropy and 𝜋 * π * denotes the optimal coupling. This holds for a large class of cost functions and ...", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/full/10.1287/moor.2024.0427", "content": "We study Sinkhorn's algorithm for solving the entropically regularized optimal transport problem. Its iterate 𝜋 𝑡 π t is shown to satisfy 𝐻 (𝜋 𝑡 | 𝜋 *) + 𝐻 (𝜋 * | 𝜋 𝑡) = 𝑂 (𝑡 − 1) H (π t | π *) + H (π * | π t) = O (t 1), where H denotes relative entropy and 𝜋 * π * denotes the optimal coupling. This holds for a large class of cost functions and ..."}
+{"idx": 3, "title": "On the Linear Convergence of the Multimarginal Sinkhorn Algorithm", "date": "", "ddg_snippet": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/21M1410634", "content": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ..."}
+{"idx": 4, "title": "PDF Quantitative contraction rates for Sinkhorn algorithm", "date": "", "ddg_snippet": "As a corollary, we establish exponential convergence of Sinkhorn plans and bridges w.r.t. a symmetric relative entropy. Up to the authors' knowledge, these are the first results which establish geometric convergence of Sinkhorn algorithm in a general setting with-out assuming bounded cost functions or compactly supported marginals.", "subpage_snippet": "", "source": "research.tue.nl", "link": "https://research.tue.nl/files/295195017/2304.04451v1.pdf", "content": "As a corollary, we establish exponential convergence of Sinkhorn plans and bridges w.r.t. a symmetric relative entropy. Up to the authors' knowledge, these are the first results which establish geometric convergence of Sinkhorn algorithm in a general setting with-out assuming bounded cost functions or compactly supported marginals."}
+{"idx": 5, "title": "PDF Non-asymptotic convergence bounds for Sinkhorn iterates and their ...", "date": "", "ddg_snippet": "The Sinkhorn algorithm consists in defining sequences (φε,n)n∈N and (ψε,n)n∈N respectively approximating φε and ψε, relying that these two functions are fixed points of a partic-ular functional. In this paper, we are interested in the convergence of these two sequences to the \"Schr ̈odinger potentials\" φε and ψε.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v195/greco23a/greco23a.pdf", "content": "The Sinkhorn algorithm consists in defining sequences (φε,n)n∈N and (ψε,n)n∈N respectively approximating φε and ψε, relying that these two functions are fixed points of a partic-ular functional. In this paper, we are interested in the convergence of these two sequences to the \"Schr ̈odinger potentials\" φε and ψε."}
+{"idx": 6, "title": "On the Convergence Rate of Sinkhorn's AlgorithmThe authors thank ...", "date": "", "ddg_snippet": "The latter boils down to a simple matrix-vector multiplication in a discretized setting, whence the suitability for high-dimensional problems. Our main result is the convergence of the algorithm in the sense of relative entropy and its rate, under general conditions including unbounded costs. Methodologically, we proceed in two steps.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.06000v2", "content": "The latter boils down to a simple matrix-vector multiplication in a discretized setting, whence the suitability for high-dimensional problems. Our main result is the convergence of the algorithm in the sense of relative entropy and its rate, under general conditions including unbounded costs. Methodologically, we proceed in two steps."}
+{"idx": 7, "title": "PDF On the linear convergence of the multi-marginal Sinkhorn algorithm", "date": "", "ddg_snippet": "The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel) method of ...", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/8f3b/63cdaad4f03307b7f7e2b5f307a513686322.pdf", "content": "The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel) method of ..."}
+{"idx": 8, "title": "PDF Sinkhorn Divergences for Unbalanced Optimal Transport", "date": "", "ddg_snippet": "Convergence of Sinkhorn algorithm Assume either: ' strictly convex and @' goes to zero or +1 as x goes to 0 or +1. The entropy corresponds to Balanced, TV or Range.", "subpage_snippet": "", "source": "thibsej.github.io", "link": "https://thibsej.github.io/files/beamer_mokameeting_sinkdiv.pdf", "content": "Convergence of Sinkhorn algorithm Assume either: ' strictly convex and @' goes to zero or +1 as x goes to 0 or +1. The entropy corresponds to Balanced, TV or Range."}
+{"idx": 9, "title": "PDF Sharper Exponential Convergence Rates for Sinkhorn s Algorithm in ...", "date": "", "ddg_snippet": "It is well-known that δt converges to zero under general conditions . However, despite the extensive history of Sinkhorn's algorithm and its widespread adoption in modern applications, several open questions remain regarding its convergence speed. Existing guarantees generally fall into two categories.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10107-025-02242-z.pdf", "content": "It is well-known that δt converges to zero under general conditions . However, despite the extensive history of Sinkhorn's algorithm and its widespread adoption in modern applications, several open questions remain regarding its convergence speed. Existing guarantees generally fall into two categories."}
diff --git a/data/sampled_jsons/Soft_Reasoning-_Navigating_Solution_Spaces_in_Large_Language_Models_through_Controlled_Embedding_Exp.jsonl b/data/sampled_jsons/Soft_Reasoning-_Navigating_Solution_Spaces_in_Large_Language_Models_through_Controlled_Embedding_Exp.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e3efcc9060682a88894c447c8ac65e0506f120c3
--- /dev/null
+++ b/data/sampled_jsons/Soft_Reasoning-_Navigating_Solution_Spaces_in_Large_Language_Models_through_Controlled_Embedding_Exp.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Soft Reasoning: Navigating Solution Spaces in Large Language", "date": "", "ddg_snippet": "Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration ... Soft Reasoning , a novel approach ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.24688v3", "content": "Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration ... Soft Reasoning , a novel approach ..."}
+{"idx": 1, "title": "[2505.24688] Soft Reasoning: Navigating Solution Spaces in", "date": "", "ddg_snippet": "View a PDF of the paper titled Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration , by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.24688", "content": "View a PDF of the paper titled Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration , by ..."}
+{"idx": 2, "title": "Language-Driven Hierarchical Task Structures as Explicit World", "date": "", "ddg_snippet": "In the realm of Language Models (L), scaling parameters and data has led to the emergence of unprecedented capabilities in reasoning and generation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.04731v1", "content": "In the realm of Language Models (L), scaling parameters and data has led to the emergence of unprecedented capabilities in reasoning and generation ..."}
+{"idx": 3, "title": "GitHub - alickzhu/Soft-Reasoning: code for paper: Soft", "date": "", "ddg_snippet": "Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration Qinglin Zhu*, Runcong Zhao*, Hanqi Yan ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/alickzhu/Soft-Reasoning", "content": "Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration Qinglin Zhu*, Runcong Zhao*, Hanqi Yan ..."}
+{"idx": 4, "title": "GitHub - multimodal-art-projection/LatentCoT-Horizon: 📖 This", "date": "", "ddg_snippet": "SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought ... Soft Thinking: Unlocking the Reasoning ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/multimodal-art-projection/LatentCoT-Horizon/", "content": "SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought ... Soft Thinking: Unlocking the Reasoning ..."}
+{"idx": 5, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Navigating the Design Space of ... The Cost of Scaling Down Large Language Models : Reducing Model Size Affects Memory before In -context Learning", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Navigating the Design Space of ... The Cost of Scaling Down Large Language Models : Reducing Model Size Affects Memory before In -context Learning"}
+{"idx": 6, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "... Integrating Neural Perception, Grammar ... Expert Learning through Generalized Inverse Multiobjective Optimization: Models , Insights, and Algorithms", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "... Integrating Neural Perception, Grammar ... Expert Learning through Generalized Inverse Multiobjective Optimization: Models , Insights, and Algorithms"}
+{"idx": 7, "title": "CVPR 2023 Schedule", "date": "", "ddg_snippet": "OmniLabel: Infinite label spaces for semantic understanding via natural language ... New Frontiers in Visual Language Reasoning : Compositionality ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/calendar", "content": "OmniLabel: Infinite label spaces for semantic understanding via natural language ... New Frontiers in Visual Language Reasoning : Compositionality ..."}
+{"idx": 8, "title": "CVPR 2024 Papers", "date": "", "ddg_snippet": "... Responses From Black-Box Vision- Language Models for ... Leveraging Vision- Language Models for Improving Domain Generalization in Image Classification", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/papers.html", "content": "... Responses From Black-Box Vision- Language Models for ... Leveraging Vision- Language Models for Improving Domain Generalization in Image Classification"}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "... in Diffusion Probabilistic ... A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model -Based Reinforcement Learning", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "... in Diffusion Probabilistic ... A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model -Based Reinforcement Learning"}
diff --git a/data/sampled_jsons/SpaceNet_dataset_image_size.jsonl b/data/sampled_jsons/SpaceNet_dataset_image_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7096086c5e011ce5ea466056b38b4ace03d5d3ce
--- /dev/null
+++ b/data/sampled_jsons/SpaceNet_dataset_image_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "List of datasets in computer vision and image processing -", "date": "", "ddg_snippet": "Autonomous vehicles driving through a mid- size city captured images of various areas using cameras and laser scanners.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/List_of_datasets_in_computer_vision_and_image_processing", "content": "Autonomous vehicles driving through a mid- size city captured images of various areas using cameras and laser scanners."}
+{"idx": 1, "title": "Exploring the SpaceNet Dataset Using DIGITS | NVIDIA Technical", "date": "", "ddg_snippet": "For training and validation we restricted the dataset to 3-band SpaceNet images containing at least one building footprint.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/exploring-spacenet-dataset-using-digits/", "content": "For training and validation we restricted the dataset to 3-band SpaceNet images containing at least one building footprint."}
+{"idx": 2, "title": "GitHub - reachsumit/deep-unet-for-satellite-image-segmentation:", "date": "", "ddg_snippet": "... Imagery Feature Detection with SpaceNet dataset ... The dataset consists of 8-band commercial grade satellite imagery taken from SpaceNet dataset .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/reachsumit/deep-unet-for-satellite-image-segmentation", "content": "... Imagery Feature Detection with SpaceNet dataset ... The dataset consists of 8-band commercial grade satellite imagery taken from SpaceNet dataset ."}
+{"idx": 3, "title": "Accelerated Image Segmentation Using PyTorch | by Benjamin", "date": "", "ddg_snippet": "I will walk you through the steps to work with a satellite image dataset called SpaceNet5, and how I optimized the code to make deep learning ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/intel-analytics-software/accelerated-image-segmentation-using-pytorch-9aaba6c36737", "content": "I will walk you through the steps to work with a satellite image dataset called SpaceNet5, and how I optimized the code to make deep learning ..."}
+{"idx": 4, "title": "SpaceNet satellite imagery repository launched by DigitalGlobe,", "date": "", "ddg_snippet": "The satellite imagery in the SpaceNet database will be able to serve as training data for new generations of intelligent analytics tools for ...", "subpage_snippet": "", "source": "techcrunch.com", "link": "https://techcrunch.com/2016/08/25/spacenet-satellite-imagery-repository-launched-by-digitalglobe-cosmiq-works-and-nvidia-on-aws/", "content": "The satellite imagery in the SpaceNet database will be able to serve as training data for new generations of intelligent analytics tools for ..."}
+{"idx": 5, "title": "[REPO]@Telematika | chrieke/awesome-satellite-imagery-datasets", "date": "", "ddg_snippet": "Spacenet Challenge Round 6 - Multi-Sensor All Weather Mapping (CosmiQ Works, Capella Space, Maxar, AWS, Intel, Feb 2020) 48k building footprints ...", "subpage_snippet": "", "source": "repo.telematika.org", "link": "https://repo.telematika.org/project/chrieke_awesome-satellite-imagery-datasets/", "content": "Spacenet Challenge Round 6 - Multi-Sensor All Weather Mapping (CosmiQ Works, Capella Space, Maxar, AWS, Intel, Feb 2020) 48k building footprints ..."}
+{"idx": 6, "title": "GitHub - songtaohe/Sat2Graph: Sat2Graph: Road Graph Extraction", "date": "", "ddg_snippet": "For SpaceNet , please find the dataset split, the pre-processed dataset and the sat2graph outputs from this link . ... dataset partition (which tiles ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/songtaohe/Sat2Graph", "content": "For SpaceNet , please find the dataset split, the pre-processed dataset and the sat2graph outputs from this link . ... dataset partition (which tiles ..."}
+{"idx": 7, "title": "DeH4R: A Decoupled and Hybrid Method for Road Network Graph", "date": "", "ddg_snippet": "As depicted in Figure 2 (a), the CVD takes a RGB image patch I h × w × 3 I^{h\\times w\\times 3} as input and outputs three segmentation maps ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.13669v1", "content": "As depicted in Figure 2 (a), the CVD takes a RGB image patch I h × w × 3 I^{h\\times w\\times 3} as input and outputs three segmentation maps ..."}
+{"idx": 8, "title": "How to make a building footprint detector | by Romain Candy |", "date": "", "ddg_snippet": "We will compare visually the prediction between the model of the first part (trained on the much larger SpaceNet dataset ) and the model train from ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/picterra/how-to-make-a-building-footprint-detector-768045a15bcc", "content": "We will compare visually the prediction between the model of the first part (trained on the much larger SpaceNet dataset ) and the model train from ..."}
+{"idx": 9, "title": "Part 2: Creating a Classified map of the Colombian Wetlands", "date": "", "ddg_snippet": "... also provides easy access to relevant public datasets including Landsat, Sentinel, MODIS, and SpaceNet , a collection of commercial satellite imagery ...", "subpage_snippet": "", "source": "claudeschrader.com", "link": "https://claudeschrader.com/keras-deeplearning-mapping/", "content": "... also provides easy access to relevant public datasets including Landsat, Sentinel, MODIS, and SpaceNet , a collection of commercial satellite imagery ..."}
diff --git a/data/sampled_jsons/Speculative_Decoding_autoregressive_Transformer_models_acceleration.jsonl b/data/sampled_jsons/Speculative_Decoding_autoregressive_Transformer_models_acceleration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b5883d593c4fbce88d8028cfa9911cc60818ccc
--- /dev/null
+++ b/data/sampled_jsons/Speculative_Decoding_autoregressive_Transformer_models_acceleration.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fast Inference from Transformers via Speculative Decoding", "date": "", "ddg_snippet": "In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/leviathan23a.html", "content": "In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by ..."}
+{"idx": 1, "title": "Fast Inference from Transformers via Speculative Decoding", "date": "", "ddg_snippet": "In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/leviathan23a", "content": "In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by ..."}
+{"idx": 2, "title": "Fast Inference from Transformers via Speculative Decoding |", "date": "", "ddg_snippet": "In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/fast-inference-from-transformers-via-speculative-decoding", "content": "In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by ..."}
+{"idx": 3, "title": "Model-free Speculative Decoding for Transformer-based ASR with", "date": "", "ddg_snippet": "Speculative decoding (SD) [ 12 ] is a simple technique aims at accelerating autoregressive transformer inference by leveraging parallelized token ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21522v1", "content": "Speculative decoding (SD) [ 12 ] is a simple technique aims at accelerating autoregressive transformer inference by leveraging parallelized token ..."}
+{"idx": 4, "title": "Fast and scalable retrosynthetic planning with a transformer", "date": "", "ddg_snippet": "Speculative decoding is a method of reducing the generation latency of autoregressive transformer models . ... Speculative decoding accelerates ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.01459v1", "content": "Speculative decoding is a method of reducing the generation latency of autoregressive transformer models . ... Speculative decoding accelerates ..."}
+{"idx": 5, "title": "Fast Inference from Transformers via Speculative Decoding |", "date": "", "ddg_snippet": "In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/fast-inference-from-transformers-via", "content": "In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by ..."}
+{"idx": 6, "title": "Speculative Decoding — Model Optimizer", "date": "", "ddg_snippet": "Therefore, various speculative decoding algorithms have been proposed to accelerate text generation, especially in latency critical applications.", "subpage_snippet": "", "source": "nvidia.github.io", "link": "https://nvidia.github.io/TensorRT-Model-Optimizer/guides/5_speculative_decoding.html", "content": "Therefore, various speculative decoding algorithms have been proposed to accelerate text generation, especially in latency critical applications."}
+{"idx": 7, "title": "Speculative Decoding — Model Optimizer 0.27.0", "date": "", "ddg_snippet": "Therefore, various speculative decoding algorithms have been proposed to accelerate text generation, especially in latency critical applications.", "subpage_snippet": "", "source": "nvidia.github.io", "link": "https://nvidia.github.io/TensorRT-Model-Optimizer/guides/7_speculative_decoding.html", "content": "Therefore, various speculative decoding algorithms have been proposed to accelerate text generation, especially in latency critical applications."}
+{"idx": 8, "title": "Speculative decoding | LLM Inference Handbook", "date": "", "ddg_snippet": "Speculative decoding is an inference-time optimization that speeds up autoregressive generation by combining a fast “draft” model with the target ...", "subpage_snippet": "", "source": "bentoml.com", "link": "https://bentoml.com/llm/inference-optimization/speculative-decoding", "content": "Speculative decoding is an inference-time optimization that speeds up autoregressive generation by combining a fast “draft” model with the target ..."}
+{"idx": 9, "title": "Faster Assisted Generation with Dynamic Speculation", "date": "", "ddg_snippet": "Speculative decoding is a popular technique to accelerate the inference of large language models , while preserving their accuracy.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/dynamic_speculation_lookahead", "content": "Speculative decoding is a popular technique to accelerate the inference of large language models , while preserving their accuracy."}
diff --git a/data/sampled_jsons/SpeechSSM_Long-Form_Speech_Generation_initialization_LM_pretraining.jsonl b/data/sampled_jsons/SpeechSSM_Long-Form_Speech_Generation_initialization_LM_pretraining.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2a1e7a5df1a47885cb24f24fd6f4311ecf3c7386
--- /dev/null
+++ b/data/sampled_jsons/SpeechSSM_Long-Form_Speech_Generation_initialization_LM_pretraining.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long - Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4AmFA0qNQ2", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants."}
+{"idx": 1, "title": "(PDF) Long - Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "of generating long - form speech means that analyses and.eling of long - form speech . For modeling, this led us to. SpeechSSM , the first spoken LM that allows for generation .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387382437_Long-Form_Speech_Generation_with_Spoken_Language_Models", "content": "of generating long - form speech means that analyses and.eling of long - form speech . For modeling, this led us to. SpeechSSM , the first spoken LM that allows for generation ."}
+{"idx": 2, "title": "Long - Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "can be trained for either read speech or extemporaneous speech ( SpeechSSM -X). We define evaluations for long - form speech continuation, from time-stratified evaluations to modern NLG metrics like LLM-as-judge. We release LibriSpeech-Long, a 3-4min reformat of LibriSpeech...", "subpage_snippet": "", "source": "google.github.io", "link": "https://google.github.io/tacotron/publications/speechssm/", "content": "can be trained for either read speech or extemporaneous speech ( SpeechSSM -X). We define evaluations for long - form speech continuation, from time-stratified evaluations to modern NLG metrics like LLM-as-judge. We release LibriSpeech-Long, a 3-4min reformat of LibriSpeech..."}
+{"idx": 3, "title": "Researcher develops ' SpeechSSM ,' opening up possibilities for...", "date": "", "ddg_snippet": "SpeechSSM effectively processes unbounded speech sequences by dividing speech data into short, fixed units (windows), processing each unit independently, and then combining them to create long speech . Additionally, in the speech generation phase, it uses a \"Non-Autoregressive\"...", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-07-speechssm-possibilities-hour-ai-voice.html", "content": "SpeechSSM effectively processes unbounded speech sequences by dividing speech data into short, fixed units (windows), processing each unit independently, and then combining them to create long speech . Additionally, in the speech generation phase, it uses a \"Non-Autoregressive\"..."}
+{"idx": 4, "title": "SpeechSSM Breakthrough Brings Hyper-Realistic AI... - STC MDITR", "date": "", "ddg_snippet": "Decoding Long - Form Speech . Why SpeechSSM is a Game-Changer. SpeechSSM ’s non-autoregressive synthesis allows it to generate long speech segments much faster than traditional models, making real-time applications possible.", "subpage_snippet": "", "source": "stc-mditr.org", "link": "https://stc-mditr.org/hyper-realistic-ai-voices-in-record-time/", "content": "Decoding Long - Form Speech . Why SpeechSSM is a Game-Changer. SpeechSSM ’s non-autoregressive synthesis allows it to generate long speech segments much faster than traditional models, making real-time applications possible."}
+{"idx": 5, "title": "Long - Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long - form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Long-Form-Speech-Generation-with-Spoken-Language-Models-c233f089-93be-467e-bade-579e198d2008", "content": "From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long - form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates."}
+{"idx": 6, "title": "Long - Form Speech Generation with Spoken Language Models...", "date": "", "ddg_snippet": "With theseconsiderations we propose SpeechSSM , the first speech language model to learnfrom and sample long - form spoken audio (e.g., 16 minutes of read orextemporaneous speech ) in a single decoding session without text intermediates,based on recent advances in...", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/561804/long-form-speech-generation-with-spoken-language-models", "content": "With theseconsiderations we propose SpeechSSM , the first speech language model to learnfrom and sample long - form spoken audio (e.g., 16 minutes of read orextemporaneous speech ) in a single decoding session without text intermediates,based on recent advances in..."}
+{"idx": 7, "title": "Slamming: Training a Speech Language Model... | Read Paper on Bytez", "date": "", "ddg_snippet": "We introduce Slam, a recipe for training high-quality Speech Language Models (SLMs) on a single academic GPU in 24 hours. We do so through empirical analysis of model initialisation and architectu...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2502.15814/paper", "content": "We introduce Slam, a recipe for training high-quality Speech Language Models (SLMs) on a single academic GPU in 24 hours. We do so through empirical analysis of model initialisation and architectu..."}
+{"idx": 8, "title": "Realistic Text to Speech converter & AI Voice generator", "date": "", "ddg_snippet": "Insert text to generate speech and download audio mp3/wav. Speak a text with AI-powered voices.", "subpage_snippet": "", "source": "speechgen.io", "link": "https://speechgen.io/", "content": "Insert text to generate speech and download audio mp3/wav. Speak a text with AI-powered voices."}
+{"idx": 9, "title": "SPEECHMA - Free Online Text to Speech | Unlimited AI Voices", "date": "", "ddg_snippet": "Convert text to speech free with 580+ premium AI voices. Best unlimited online text-to- speech converter with commercial license. Supports 60+ languages: English, Spanish, French, German, Chinese, Japanese, Korean, Arabic & more.", "subpage_snippet": "", "source": "speechma.com", "link": "https://speechma.com/", "content": "Convert text to speech free with 580+ premium AI voices. Best unlimited online text-to- speech converter with commercial license. Supports 60+ languages: English, Spanish, French, German, Chinese, Japanese, Korean, Arabic & more."}
diff --git a/data/sampled_jsons/SpeechSSM_Long-Form_Speech_Generation_with_Spoken_Language_Models_experimental_setup.jsonl b/data/sampled_jsons/SpeechSSM_Long-Form_Speech_Generation_with_Spoken_Language_Models_experimental_setup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a49ad3e857c24f914a2bd13fa05e80ec929bdc32
--- /dev/null
+++ b/data/sampled_jsons/SpeechSSM_Long-Form_Speech_Generation_with_Spoken_Language_Models_experimental_setup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "24 Dec 2024 — We propose SpeechSSM , the first speech language model to learn from and sample long-form spoken audio (eg, 16 minutes of read or extemporaneous speech) in a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v1", "content": "24 Dec 2024 — We propose SpeechSSM , the first speech language model to learn from and sample long-form spoken audio (eg, 16 minutes of read or extemporaneous speech) in a ..."}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "The result is SpeechSSM , a new (textless) spoken language model family (2B, 9B) designed for long-form generation, being the first to model and generate ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46499", "content": "The result is SpeechSSM , a new (textless) spoken language model family (2B, 9B) designed for long-form generation, being the first to model and generate ..."}
+{"idx": 2, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "The result is SpeechSSM , a new (textless) spoken language model family (2B, 9B) designed for long-form generation, being the first to model and generate ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=4AmFA0qNQ2&name=pdf", "content": "The result is SpeechSSM , a new (textless) spoken language model family (2B, 9B) designed for long-form generation, being the first to model and generate ..."}
+{"idx": 3, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "10 Jul 2025 — We derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (eg, 16 minutes of read or extemporaneous speech)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v2", "content": "10 Jul 2025 — We derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (eg, 16 minutes of read or extemporaneous speech)"}
+{"idx": 4, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "24 Dec 2024 — The paper targets the generation of long - form spoken audio, such as conversations, audiobooks, and podcasts, using a novel speech language model ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/94450", "content": "24 Dec 2024 — The paper targets the generation of long - form spoken audio, such as conversations, audiobooks, and podcasts, using a novel speech language model ..."}
+{"idx": 5, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165014", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "5 days ago — Spoken Language Models (SLMs) are designed to take speech inputs and produce spoken responses. However, current SLMs lack the ability to perform ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Spoken+language+models", "content": "5 days ago — Spoken Language Models (SLMs) are designed to take speech inputs and produce spoken responses. However, current SLMs lack the ability to perform ..."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "Long-Form Speech Generation with Spoken Language Models · We consider the generative modeling of speech over multiple minutes, a requirement for long-form ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Generative+Spoken+Language+Modeling", "content": "Long-Form Speech Generation with Spoken Language Models · We consider the generative modeling of speech over multiple minutes, a requirement for long-form ..."}
+{"idx": 8, "title": "Vignesh Kumar's Post", "date": "", "ddg_snippet": "That's the promise of a new kind of AI model called Spoken Language Models (SLMs). ... VibeVoice enables expressive, long-form audio generation ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/kumarvignesh_speechssm-voiceai-speechgeneration-activity-7347856074319765504-cPCD", "content": "That's the promise of a new kind of AI model called Spoken Language Models (SLMs). ... VibeVoice enables expressive, long-form audio generation ..."}
+{"idx": 9, "title": "ICML 2025 Orals", "date": "", "ddg_snippet": "Long-Form Speech Generation with Spoken Language Models . Oral. Se Jin Park ... From these considerations we derive **SpeechSSM**, the first speech language model ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/oral", "content": "Long-Form Speech Generation with Spoken Language Models . Oral. Se Jin Park ... From these considerations we derive **SpeechSSM**, the first speech language model ..."}
diff --git a/data/sampled_jsons/SpeechSSM_initialization_text_LM_Gemma-2B.jsonl b/data/sampled_jsons/SpeechSSM_initialization_text_LM_Gemma-2B.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b7a6daf54b88b42591bac41220639505e914060e
--- /dev/null
+++ b/data/sampled_jsons/SpeechSSM_initialization_text_LM_Gemma-2B.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "VoxtLM and Spirit LM see text data during training. Due to variations in token, initialization , and training data choices, we also define SpeechTransformer (\" with Transformer \"), a spoken LM initialized with Gemma-2B ( Gemma Team et al., 2024) but otherwise matched with SpeechSSM - 2B . 4", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v2", "content": "VoxtLM and Spirit LM see text data during training. Due to variations in token, initialization , and training data choices, we also define SpeechTransformer (\" with Transformer \"), a spoken LM initialized with Gemma-2B ( Gemma Team et al., 2024) but otherwise matched with SpeechSSM - 2B . 4"}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "VoxtLM and Spirit LM see text data during training. Due to variations in token, initialization , and training data choices, we also dene SpeechTransformer (“with Transformer”), a spoken LM initialized with Gemma - 2 B but otherwise matched with SpeechSSM - 2 B .4.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4AmFA0qNQ2", "content": "VoxtLM and Spirit LM see text data during training. Due to variations in token, initialization , and training data choices, we also dene SpeechTransformer (“with Transformer”), a spoken LM initialized with Gemma - 2 B but otherwise matched with SpeechSSM - 2 B .4."}
+{"idx": 2, "title": "(PDF) Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "pose SpeechSSM , the first speech language model. to learn from and sample long-form spoken au-. dio (e.g., 16 minutes of read or extemporaneous. speech) in a single decoding session without text .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387382437_Long-Form_Speech_Generation_with_Spoken_Language_Models", "content": "pose SpeechSSM , the first speech language model. to learn from and sample long-form spoken au-. dio (e.g., 16 minutes of read or extemporaneous. speech) in a single decoding session without text ."}
+{"idx": 3, "title": "tp_rag_1.ipynb - Colab", "date": "", "ddg_snippet": "For modeling, this led us to SpeechSSM , the first spoken LM that allows for generation than can go indefinitely without running out of memory. For evaluation we created the LibriSpeech-Long benchmark and proposed new evaluations for long-form speech con- tinuation.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/gist/TheoCARON1999/c182d1166e61da6d6db3b4772cb8a350/tp_rag_1.ipynb", "content": "For modeling, this led us to SpeechSSM , the first spoken LM that allows for generation than can go indefinitely without running out of memory. For evaluation we created the LibriSpeech-Long benchmark and proposed new evaluations for long-form speech con- tinuation."}
+{"idx": 4, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "- COCO- LM : Correcting and Contrasting Text Sequences for Language Model Pretraining. We present a self-supervised learning framework, COCO- LM , that pretrains Language Models by COrrecting and COntrasting corrupted text sequences.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=sequence-to-sequence+language+model", "content": "- COCO- LM : Correcting and Contrasting Text Sequences for Language Model Pretraining. We present a self-supervised learning framework, COCO- LM , that pretrains Language Models by COrrecting and COntrasting corrupted text sequences."}
+{"idx": 5, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "VoxtLM and Spirit LM see text data during training. We note that there is great variation in token, initialization , and training data choices. Hence, to make the fairest comparison to SpeechSSM , we construct a Transformer decoder-only model, namely SpeechTransformer, initialized with Gemma-2B 4 ( Gemma Team et al., 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v1", "content": "VoxtLM and Spirit LM see text data during training. We note that there is great variation in token, initialization , and training data choices. Hence, to make the fairest comparison to SpeechSSM , we construct a Transformer decoder-only model, namely SpeechTransformer, initialized with Gemma-2B 4 ( Gemma Team et al., 2024)."}
+{"idx": 6, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "VoxtLM and Spirit LM see text data during training. Due to variations in token, initialization , and training data choices, we also define SpeechTransformer (\"with Transformer\"), a spoken LM initialized with Gemma-2B ( Gemma Team et al., 2024) but otherwise matched with SpeechSSM - 2B .4", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.18603", "content": "VoxtLM and Spirit LM see text data during training. Due to variations in token, initialization , and training data choices, we also define SpeechTransformer (\"with Transformer\"), a spoken LM initialized with Gemma-2B ( Gemma Team et al., 2024) but otherwise matched with SpeechSSM - 2B .4"}
+{"idx": 7, "title": "Aman's AI Journal • Primers • VLM Architectures", "date": "", "ddg_snippet": "The linear projection maps SigLIP's output tokens into the same dimensions as Gemma-2B's vocab tokens, enabling seamless concatenation of image and text tokens. A key design decision in PaliGemma is the use of the SigLIP image encoder instead of a CLIP image encoder.", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/VLM/", "content": "The linear projection maps SigLIP's output tokens into the same dimensions as Gemma-2B's vocab tokens, enabling seamless concatenation of image and text tokens. A key design decision in PaliGemma is the use of the SigLIP image encoder instead of a CLIP image encoder."}
+{"idx": 8, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Abstract We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, current spoken lan-guage models struggle to generate plausible speech past tens of seconds, from high temporal resolution of speech tokens causing loss of coher-ence, to architectural issues with long-sequence training or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.18603v1", "content": "Abstract We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, current spoken lan-guage models struggle to generate plausible speech past tens of seconds, from high temporal resolution of speech tokens causing loss of coher-ence, to architectural issues with long-sequence training or ..."}
+{"idx": 9, "title": "lmstudio-ai (LM Studio) - Hugging Face", "date": "", "ddg_snippet": "How to use: Download a \"mmproj\" model file + one or more of the primary model files.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/lmstudio-ai", "content": "How to use: Download a \"mmproj\" model file + one or more of the primary model files."}
diff --git a/data/sampled_jsons/SpeechSSM_training_sequence_240_seconds_4_minutes_default_year_2024.jsonl b/data/sampled_jsons/SpeechSSM_training_sequence_240_seconds_4_minutes_default_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3cf7f04f5bc08f15371205fd3e85836f659a2d34
--- /dev/null
+++ b/data/sampled_jsons/SpeechSSM_training_sequence_240_seconds_4_minutes_default_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Researcher develops ' SpeechSSM ,' opening up possibilities for...", "date": "", "ddg_snippet": "SpeechSSM effectively processes unbounded speech sequences by dividing speech data into short, fixed units (windows), processing each unit independently, and then combining them to create long speech.", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-07-speechssm-possibilities-hour-ai-voice.html", "content": "SpeechSSM effectively processes unbounded speech sequences by dividing speech data into short, fixed units (windows), processing each unit independently, and then combining them to create long speech."}
+{"idx": 1, "title": "KAIST Unveils SpeechSSM for 24/7 AI Voice Assistant | Mirage News", "date": "", "ddg_snippet": "To solve this problem, Se Jin Park developed ' SpeechSSM ,' a spoken language model using a Hybrid State-Space Model, designed to efficiently process and generate long speech sequences .", "subpage_snippet": "", "source": "www.miragenews.com", "link": "https://www.miragenews.com/kaist-unveils-speechssm-for-247-ai-voice-1490834/", "content": "To solve this problem, Se Jin Park developed ' SpeechSSM ,' a spoken language model using a Hybrid State-Space Model, designed to efficiently process and generate long speech sequences ."}
+{"idx": 2, "title": "SpeechSSM Breakthrough Brings Hyper-Realistic AI... - STC MDITR", "date": "", "ddg_snippet": "SpeechSSM , created by KAIST, is a groundbreaking AI model that generates hyper-realistic, long-duration speech quickly and naturally. SpeechSSM , by contrast, can keep a conversation going for many minutes , maintaining the same personality and emotional tone throughout.", "subpage_snippet": "", "source": "stc-mditr.org", "link": "https://stc-mditr.org/hyper-realistic-ai-voices-in-record-time/", "content": "SpeechSSM , created by KAIST, is a groundbreaking AI model that generates hyper-realistic, long-duration speech quickly and naturally. SpeechSSM , by contrast, can keep a conversation going for many minutes , maintaining the same personality and emotional tone throughout."}
+{"idx": 3, "title": "240 seconds to Minutes /Hours/Days - Calculatio", "date": "", "ddg_snippet": "240 seconds - How Many Minutes/Hours/Days? The answer is: 4 minutes . Convert Seconds to Minutes/Hours/Days/Months/Years.", "subpage_snippet": "", "source": "calculat.io", "link": "https://calculat.io/en/date/seconds/240", "content": "240 seconds - How Many Minutes/Hours/Days? The answer is: 4 minutes . Convert Seconds to Minutes/Hours/Days/Months/Years."}
+{"idx": 4, "title": "Minutes to Seconds Conversion (min to sec)", "date": "", "ddg_snippet": "Minutes to seconds conversion calculator helps you to find how many seconds in a minute , converts the unit of time minutes to seconds .", "subpage_snippet": "", "source": "www.timecalculator.net", "link": "https://www.timecalculator.net/minutes-to-seconds", "content": "Minutes to seconds conversion calculator helps you to find how many seconds in a minute , converts the unit of time minutes to seconds ."}
+{"idx": 5, "title": "Convert seconds to minutes - Time Conversions", "date": "", "ddg_snippet": "Online calculator to convert seconds to minutes (sec to min) with formulas, examples, and tables.", "subpage_snippet": "", "source": "www.checkyourmath.com", "link": "https://www.checkyourmath.com/convert/time/seconds_minutes.php", "content": "Online calculator to convert seconds to minutes (sec to min) with formulas, examples, and tables."}
+{"idx": 6, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "SpeechSSM (2B) trained on LibriLight (60k hours) read speech (audio-only) max training length: 4 min. From the evil seed of lust, all other deadly sins had sprung forth.", "subpage_snippet": "", "source": "google.github.io", "link": "https://google.github.io/tacotron/publications/speechssm/", "content": "SpeechSSM (2B) trained on LibriLight (60k hours) read speech (audio-only) max training length: 4 min. From the evil seed of lust, all other deadly sins had sprung forth."}
+{"idx": 7, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech) in a single decoding session without text intermediates.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.18603", "content": "From these considerations we derive SpeechSSM , the first speech language model family to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech) in a single decoding session without text intermediates."}
+{"idx": 8, "title": "Сброс пароля на MES без потери конфигурации | АлтунинВВ.Блог", "date": "", "ddg_snippet": "DDR3 Training Sequence - Switching XBAR Window to FastPath Window.DDR3 Training Sequence - Ended Successfully. BootROM: Image checksum verification PASSED. Starting U-Boot. Press ctrl+shift+6 to enable debug mode.", "subpage_snippet": "", "source": "blog.altuninvv.ru", "link": "https://blog.altuninvv.ru/eltex/mes/сброс-пароля-на-mes-без-потери-конфигурации", "content": "DDR3 Training Sequence - Switching XBAR Window to FastPath Window.DDR3 Training Sequence - Ended Successfully. BootROM: Image checksum verification PASSED. Starting U-Boot. Press ctrl+shift+6 to enable debug mode."}
+{"idx": 9, "title": "tp_rag_1.ipynb - Colab", "date": "", "ddg_snippet": "SpeechSSM -30s, SpeechSSM - 4 min, and SpeechSSM (- 16min) get 750, 5760, and 24k tokens per sequence respec- tively, under USM-v2’s frame rate of 25Hz.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/gist/TheoCARON1999/c182d1166e61da6d6db3b4772cb8a350/tp_rag_1.ipynb", "content": "SpeechSSM -30s, SpeechSSM - 4 min, and SpeechSSM (- 16min) get 750, 5760, and 24k tokens per sequence respec- tively, under USM-v2’s frame rate of 25Hz."}
diff --git a/data/sampled_jsons/Stanislaus_Ulam_Marvin_Li_Aayush_Karan_Sitan_Chen_Blink_of_an_eye_GitHub.jsonl b/data/sampled_jsons/Stanislaus_Ulam_Marvin_Li_Aayush_Karan_Sitan_Chen_Blink_of_an_eye_GitHub.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..516fd3a8d312b359b6f65f0738cccbf64eeb2948
--- /dev/null
+++ b/data/sampled_jsons/Stanislaus_Ulam_Marvin_Li_Aayush_Karan_Sitan_Chen_Blink_of_an_eye_GitHub.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00921] Blink of an eye : a simple theory for feature localization in...", "date": "", "ddg_snippet": "Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks."}
+{"idx": 1, "title": "Blink of an eye : a simple theory for feature localization in generative...", "date": "", "ddg_snippet": "Published 2/4/2025 by Marvin Li , Aayush Karan , Sitan Chen .Introduces \" blink of an eye \" theory to explain rapid feature emergence. Shows features appear in critical time windows during model training. Provides mathematical framework for understanding feature localization.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/blink-eye-simple-theory-feature-localization-generative", "content": "Published 2/4/2025 by Marvin Li , Aayush Karan , Sitan Chen .Introduces \" blink of an eye \" theory to explain rapid feature emergence. Shows features appear in critical time windows during model training. Provides mathematical framework for understanding feature localization."}
+{"idx": 2, "title": "Homepage: Sitan Chen", "date": "", "ddg_snippet": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models [pdf] Marvin Li , Aayush Karan , Sitan Chen ICML 2025 Oral presentation.", "subpage_snippet": "", "source": "sitanchen.com", "link": "https://sitanchen.com/", "content": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models [pdf] Marvin Li , Aayush Karan , Sitan Chen ICML 2025 Oral presentation."}
+{"idx": 3, "title": "(PDF) Blink of an eye : a simple theory for feature localization in...", "date": "", "ddg_snippet": "generative models. Marvin Li *. Harvard College. Aayush Karan †. Marvin Li and Sitan Chen . Critical windows: non-asymptotic theory for feature emergence. in diffusion models, 2024.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "generative models. Marvin Li *. Harvard College. Aayush Karan †. Marvin Li and Sitan Chen . Critical windows: non-asymptotic theory for feature emergence. in diffusion models, 2024."}
+{"idx": 4, "title": "Marvin Li - Google Scholar", "date": "", "ddg_snippet": "Sitan Chen Sitan ChenAssistant Professor of Computer Science, Harvard UniversityVerified email at seas.harvard.edu. Blink of an eye : a simple theory for feature localization in generative models.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=NhMTzpsAAAAJ&hl=en", "content": "Sitan Chen Sitan ChenAssistant Professor of Computer Science, Harvard UniversityVerified email at seas.harvard.edu. Blink of an eye : a simple theory for feature localization in generative models."}
+{"idx": 5, "title": "Hi, my name is Marvin . - Marvin Li", "date": "", "ddg_snippet": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models Marvin Li , Aayush Karan , Sitan Chen . ICML, 2025 (Oral, top 1% of submissions) arXiv / code A unifying theory showing why and when features suddenly “lock in” during generation in both diffusion and...", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/", "content": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models Marvin Li , Aayush Karan , Sitan Chen . ICML, 2025 (Oral, top 1% of submissions) arXiv / code A unifying theory showing why and when features suddenly “lock in” during generation in both diffusion and..."}
+{"idx": 6, "title": "Sitan Chen | Harvard John A. Paulson School of Engineering and...", "date": "", "ddg_snippet": "Sitan Chen . Assistant Professor of Computer Science. Primary Teaching Area.", "subpage_snippet": "", "source": "seas.harvard.edu", "link": "https://seas.harvard.edu/person/sitan-chen", "content": "Sitan Chen . Assistant Professor of Computer Science. Primary Teaching Area."}
+{"idx": 7, "title": "Frontiers in Probabilistic Inference: learning meets Sampling", "date": "", "ddg_snippet": "Sitan Chen . - Blink of an eye : a simple theory for feature localization in generative models ( Poster ) > link.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/workshop/23990", "content": "Sitan Chen . - Blink of an eye : a simple theory for feature localization in generative models ( Poster ) > link."}
+{"idx": 8, "title": "ICML 2025 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "OR. theory->probabilistic methods. Marvin Li ; Aayush Karan ; Sitan Chen ; Harvard University ; United States", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/icml-paper-list/icml-2025-paper-list/", "content": "OR. theory->probabilistic methods. Marvin Li ; Aayush Karan ; Sitan Chen ; Harvard University ; United States"}
+{"idx": 9, "title": "Vol 15, No 4 (August 30, 2025) - Cardiovascular Diagnosis and Therapy", "date": "", "ddg_snippet": "Shuai Liu, Liang Shang, Shuang-Lei Li , Peng-Yu Zhang, Hao Chen , Bo Liu, Min Cheng , Qiu-Ying Liu, Xin Li , Ying-Ying Hu, Wei-Hua Ye.", "subpage_snippet": "", "source": "cdt.amegroups.org", "link": "https://cdt.amegroups.org/issue/view/1524", "content": "Shuai Liu, Liang Shang, Shuang-Lei Li , Peng-Yu Zhang, Hao Chen , Bo Liu, Min Cheng , Qiu-Ying Liu, Xin Li , Ying-Ying Hu, Wei-Hua Ye."}
diff --git "a/data/sampled_jsons/Statistical_Collusion_arXiv_2502.04879_Theorem_3.3_Rs(n)_R\316\264(n)_error_term_formula.jsonl" "b/data/sampled_jsons/Statistical_Collusion_arXiv_2502.04879_Theorem_3.3_Rs(n)_R\316\264(n)_error_term_formula.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..0b72a1e9d224164799193510340965aa7b70ccde
--- /dev/null
+++ "b/data/sampled_jsons/Statistical_Collusion_arXiv_2502.04879_Theorem_3.3_Rs(n)_R\316\264(n)_error_term_formula.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "arXiv : 2502 . 04879 v3 [stat.ML] 25 May 2025.in Figure 3 (a), ne involves a trade-off: small values lead to erratic strategy estimates and weaker bounds, while overly large values increase the Rδ ˜( n − ne) term , also weakening the bound.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879", "content": "arXiv : 2502 . 04879 v3 [stat.ML] 25 May 2025.in Figure 3 (a), ne involves a trade-off: small values lead to erratic strategy estimates and weaker bounds, while overly large values increase the Rδ ˜( n − ne) term , also weakening the bound."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms Statistical Collusion by Collectives on Learning Platforms Statistical Collusion by Collectives on Learning Platforms Statistical Collusion by Collectives on Learning Platforms arXiv:2502.04879v1 [stat.ML] 7 Feb 2025 \"Statistical Collusion by Collectives on Learning Platforms.\"", "date": "", "ddg_snippet": "Feb 7, 2025 · As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ... The first term scales approximately linearly with n , by a factor of n / N , while the second term decreases by 1− n / N . However, the dependence is more complex than purely lin-ear. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. … The 𝑁 𝑛 N - n italic_ N - italic_ n base consumers and the 𝑛 n italic_ n members of the collective together form an empirical distribution of consumers ^ 𝒫 \\hat {\\mathcal {P}} over^ start_ARG caligraphic_P end_ARG, and the corresponding dataset constitutes the training set. Feb 10, 2025 · arXiv :2502.04879v1 [stat.ML] 7 Feb 2025 Statistical Collusion by Collectives on Learning Platforms Details and statistics show external API response JSON @ openalex.org see also: API doc @ openalex.org DOI: 10.48550/ ARXIV . 2502.04879 access: open type: Informal or Other Publication metadata version: 2025-03-26 view electronic edition via DOI (open access) authority control: export record BibTeX RIS RDF N -Triples RDF Turtle RDF/XML XML dblp key:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "Feb 7, 2025 · As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ... The first term scales approximately linearly with n , by a factor of n / N , while the second term decreases by 1− n / N . However, the dependence is more complex than purely lin-ear. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. … The 𝑁 𝑛 N - n italic_ N - italic_ n base consumers and the 𝑛 n italic_ n members of the collective together form an empirical distribution of consumers ^ 𝒫 \\hat {\\mathcal {P}} over^ start_ARG caligraphic_P end_ARG, and the corresponding dataset constitutes the training set. Feb 10, 2025 · arXiv :2502.04879v1 [stat.ML] 7 Feb 2025 Statistical Collusion by Collectives on Learning Platforms Details and statistics show external API response JSON @ openalex.org see also: API doc @ openalex.org DOI: 10.48550/ ARXIV . 2502.04879 access: open type: Informal or Other Publication metadata version: 2025-03-26 view electronic edition via DOI (open access) authority control: export record BibTeX RIS RDF N -Triples RDF Turtle RDF/XML XML dblp key:"}
+{"idx": 2, "title": "arXiv:2502.04879v1 [stat.ML] 7 Feb 2025", "date": "", "ddg_snippet": "Feb 10, 2025 · arXiv :2502.04879v1 [stat.ML] 7 Feb 2025 Statistical Collusion by Collectives on Learning Platforms", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879v1", "content": "Feb 10, 2025 · arXiv :2502.04879v1 [stat.ML] 7 Feb 2025 Statistical Collusion by Collectives on Learning Platforms"}
+{"idx": 3, "title": "\"Statistical Collusion by Collectives on Learning Platforms.\"", "date": "", "ddg_snippet": "Details and statistics show external API response JSON @ openalex.org see also: API doc @ openalex.org DOI: 10.48550/ ARXIV . 2502.04879 access: open type: Informal or Other Publication metadata version: 2025-03-26 view electronic edition via DOI (open access) authority control: export record BibTeX RIS RDF N -Triples RDF Turtle RDF/XML XML dblp key:", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-04879", "content": "Details and statistics show external API response JSON @ openalex.org see also: API doc @ openalex.org DOI: 10.48550/ ARXIV . 2502.04879 access: open type: Informal or Other Publication metadata version: 2025-03-26 view electronic edition via DOI (open access) authority control: export record BibTeX RIS RDF N -Triples RDF Turtle RDF/XML XML dblp key:"}
+{"idx": 4, "title": "Using The Distance Formula Or Pythagorean Theorem To... - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=We3LG8pK-LU", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "The 𝑁 𝑛 N - n italic_ N - italic_ n base consumers and the 𝑛 n italic_ n members of the collective together form an empirical distribution of consumers ^ 𝒫 \\hat {\\mathcal {P}} over^ start_ARG caligraphic_P end_ARG, and the corresponding dataset constitutes the training set.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "The 𝑁 𝑛 N - n italic_ N - italic_ n base consumers and the 𝑛 n italic_ n members of the collective together form an empirical distribution of consumers ^ 𝒫 \\hat {\\mathcal {P}} over^ start_ARG caligraphic_P end_ARG, and the corresponding dataset constitutes the training set."}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.04879", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. …"}
+{"idx": 7, "title": "Google Translate", "date": "", "ddg_snippet": "Privacy & Terms Help Send feedback About Google.Translation error .", "subpage_snippet": "", "source": "translate.google.com", "link": "https://translate.google.com/", "content": "Privacy & Terms Help Send feedback About Google.Translation error ."}
+{"idx": 8, "title": "3 Regression Metrics You Must Know: MAE, MSE... | Proclus Academy", "date": "", "ddg_snippet": "In statistics and machine learning, regression refers to a set of techniques used to predict a numerical value based on some inputs.MSE=1018700 =1870. Root Mean Squared Error (RMSE). MSE is a helpful metric, but it is hard to interpret. It, by definition, involved squaring of error terms .", "subpage_snippet": "", "source": "proclusacademy.com", "link": "https://proclusacademy.com/blog/explainer/regression-metrics-you-must-know/", "content": "In statistics and machine learning, regression refers to a set of techniques used to predict a numerical value based on some inputs.MSE=1018700 =1870. Root Mean Squared Error (RMSE). MSE is a helpful metric, but it is hard to interpret. It, by definition, involved squaring of error terms ."}
+{"idx": 9, "title": "How to fix the \"Updating from such a repository...\" - LinuxForDevices", "date": "", "ddg_snippet": "N : Updating from such a repository can't be done securely, and is therefore disabled by default. You might have encountered this error while trying to run the apt update or the apt upgrade command.", "subpage_snippet": "", "source": "www.linuxfordevices.com", "link": "https://www.linuxfordevices.com/tutorials/linux/fix-updating-from-such-a-repository-cant-be-done-securely-error", "content": "N : Updating from such a repository can't be done securely, and is therefore disabled by default. You might have encountered this error while trying to run the apt update or the apt upgrade command."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Etienne_Gauthier_Francis_Bach_Michael_Jor.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Etienne_Gauthier_Francis_Bach_Michael_Jor.jsonl
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index 0000000000000000000000000000000000000000..e84ce92ca2a10340b93f2696374ddd2f90102d52
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Etienne_Gauthier_Francis_Bach_Michael_Jor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Etienne Gauthier 1 Francis Bach 1 Michael I. Jordan 1 2.Probability Surveys, 17:257–317, 2020. 9. Statistical Collusion by Collectives on Learning Platforms Li, B. and Liu, W. A theoretical analysis of backdoor poi-. soning attacks in convolutional neural networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879", "content": "Etienne Gauthier 1 Francis Bach 1 Michael I. Jordan 1 2.Probability Surveys, 17:257–317, 2020. 9. Statistical Collusion by Collectives on Learning Platforms Li, B. and Liu, W. A theoretical analysis of backdoor poi-. soning attacks in convolutional neural networks."}
+{"idx": 1, "title": "(PDF) Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Francis Bach . Etienne Gauthier ∗1, Francis Bach 1and Michael I. Jordan 1,2. 1Inria, Ecole Normale Sup´erieure, PSL Research University. 2Department of Electrical Engineering and Computer Sciences, University of California", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848421_Statistical_Collusion_by_Collectives_on_Learning_Platforms", "content": "Francis Bach . Etienne Gauthier ∗1, Francis Bach 1and Michael I. Jordan 1,2. 1Inria, Ecole Normale Sup´erieure, PSL Research University. 2Department of Electrical Engineering and Computer Sciences, University of California"}
+{"idx": 2, "title": "ICML Poster Statistical Collusion by Collectives on Learning ...", "date": "", "ddg_snippet": "Etienne Gauthier · Francis Bach · Michael Jordan .As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46504", "content": "Etienne Gauthier · Francis Bach · Michael Jordan .As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 3, "title": "[Literature Review] Statistical Collusion by Collectives on Learning ...", "date": "", "ddg_snippet": "The paper titled \" Statistical Collusion by Collectives on Learning Platforms \" by Etienne Gauthier , Francis Bach , and Michael I. Jordan presents a framework for analyzing how collectives can influence machine learning platforms by coordinating data submission strategies.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "The paper titled \" Statistical Collusion by Collectives on Learning Platforms \" by Etienne Gauthier , Francis Bach , and Michael I. Jordan presents a framework for analyzing how collectives can influence machine learning platforms by coordinating data submission strategies."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms - Paper...", "date": "", "ddg_snippet": "Etienne Gauthier , Francis Bach , Michael I. Jordan .As platforms increasingly rely on learning algorithms, collectives may formand seek ways to influence these platforms to align with their own interests.This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/608798/statistical-collusion-by-collectives-on-learning-platforms", "content": "Etienne Gauthier , Francis Bach , Michael I. Jordan .As platforms increasingly rely on learning algorithms, collectives may formand seek ways to influence these platforms to align with their own interests.This can be achieved by coordinated submission of altered data."}
+{"idx": 5, "title": "Papers by Francis Bach with links to code and results.", "date": "", "ddg_snippet": "Search Results for author: Francis Bach . Found 186 papers, 66 papers with code.As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Francis+Bach", "content": "Search Results for author: Francis Bach . Found 186 papers, 66 papers with code.As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests."}
+{"idx": 6, "title": "Etienne Gauthier - Google Scholar", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms . E Gauthier , F Bach , MI Jordan .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Tiwn0RMAAAAJ&hl=en", "content": "Statistical Collusion by Collectives on Learning Platforms . E Gauthier , F Bach , MI Jordan ."}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Statistical-Collusion-by-Collectives-on-Learning-Platforms-deaeb81c-5365-4cec-9af2-e4d286dfd04a", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 8, "title": "Francis Bach - INRIA - ENS - PSL", "date": "", "ddg_snippet": "Etienne Gauthier , Francis Bach , Michael I. Jordan . Backward Conformal Prediction.Proceedings of the International Conference on Machine Learning (ICML), 2025. [pdf]. Etienne Gauthier , Francis Bach , Michael I. Jordan . Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "www.di.ens.fr", "link": "https://www.di.ens.fr/~fbach/", "content": "Etienne Gauthier , Francis Bach , Michael I. Jordan . Backward Conformal Prediction.Proceedings of the International Conference on Machine Learning (ICML), 2025. [pdf]. Etienne Gauthier , Francis Bach , Michael I. Jordan . Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 9, "title": "GauthierE/ statistical - collusion : Statistical Collusion by Collectives ...", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms .The resulting figures will be saved in the plots/ folder. About. Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "Statistical Collusion by Collectives on Learning Platforms .The resulting figures will be saved in the plots/ folder. About. Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Theorem_3.3_error_terms.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Theorem_3.3_error_terms.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e130e0916b2efbfa10dddb39201e688a9e9171ca
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Theorem_3.3_error_terms.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "7 Feb 2025 — We will denote Hoeffding error terms as follows: Report ... The proof of Theorem 3.5 is essentially the same than the proof of Theorem 3.3 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v1", "content": "7 Feb 2025 — We will denote Hoeffding error terms as follows: Report ... The proof of Theorem 3.5 is essentially the same than the proof of Theorem 3.3 ."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "This is why the estimation term for Δ x ~ in Theorem 3.7 is R ( n − n e s t ) , rather than R ( n ) in Theorem 3.3 . This directly quantifies the impact of not ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46yLEXtav4&noteId=vzYAAeoKXq", "content": "This is why the estimation term for Δ x ~ in Theorem 3.7 is R ( n − n e s t ) , rather than R ( n ) in Theorem 3.3 . This directly quantifies the impact of not ..."}
+{"idx": 2, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "by E Gauthier · 2025 · Cited by 2 — ... error terms R˜δ(n). ... In this case, the union bound should be applied only to ˜x ∈ X0. The proof of Theorem 3.5 is essentially the same as the proof of Theorem ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879?", "content": "by E Gauthier · 2025 · Cited by 2 — ... error terms R˜δ(n). ... In this case, the union bound should be applied only to ˜x ∈ X0. The proof of Theorem 3.5 is essentially the same as the proof of Theorem ..."}
+{"idx": 3, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "This paper explores how groups of people (called collectives ) can influence platforms that use learning algorithms by changing the data they provide.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper explores how groups of people (called collectives ) can influence platforms that use learning algorithms by changing the data they provide."}
+{"idx": 4, "title": "An Empirical Study of Collusion Behavior in the Maze P2P ...", "date": "", "ddg_snippet": "by Q Lian · Cited by 237 — Peer-to-peer networks often use incentive policies to encourage cooperation between nodes. Such systems are gen- erally susceptible to collusion by groups ... 13 pages", "subpage_snippet": "", "source": "people.cs.uchicago.edu", "link": "https://people.cs.uchicago.edu/~ravenben/publications/pdf/MSR-TR-Maze.pdf", "content": "by Q Lian · Cited by 237 — Peer-to-peer networks often use incentive policies to encourage cooperation between nodes. Such systems are gen- erally susceptible to collusion by groups ... 13 pages"}
+{"idx": 5, "title": "AI-Powered Trading, Algorithmic Collusion, and Price ...", "date": "", "ddg_snippet": "by WW Dou · 2025 · Cited by 60 — We define AI collusion as a scenario where autonomous, self-interested RL algorithms independently learn to coordinate their trading in a way ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/4452704.pdf?abstractid=4452704&mirid=1", "content": "by WW Dou · 2025 · Cited by 60 — We define AI collusion as a scenario where autonomous, self-interested RL algorithms independently learn to coordinate their trading in a way ..."}
+{"idx": 6, "title": "AI-Powered Trading, Algorithmic Collusion, and Price ...", "date": "", "ddg_snippet": "by WW Dou · 2024 · Cited by 60 — The integration of algorithmic trading and reinforcement learning , known as AI-powered trading, has significantly impacted capital markets. 84 pages", "subpage_snippet": "", "source": "conferences.fuqua.duke.edu", "link": "https://conferences.fuqua.duke.edu/assetpricing/wp-content/uploads/sites/2/2025/08/p9_DouGoldsteinJi.pdf", "content": "by WW Dou · 2024 · Cited by 60 — The integration of algorithmic trading and reinforcement learning , known as AI-powered trading, has significantly impacted capital markets. 84 pages"}
+{"idx": 7, "title": "Collusion, Mergers, and Related Antitrust Issues", "date": "", "ddg_snippet": "by J Asker · 2021 · Cited by 89 — Abstract. This survey examines recent developments in economic research relating to an- titrust, paying specific attention to research in ... 101 pages", "subpage_snippet": "", "source": "back.nber.org", "link": "https://back.nber.org/appendix/w29175/w29175.appendix.pdf", "content": "by J Asker · 2021 · Cited by 89 — Abstract. This survey examines recent developments in economic research relating to an- titrust, paying specific attention to research in ... 101 pages"}
+{"idx": 8, "title": "Impact of common institutional ownership on enterprise ...", "date": "", "ddg_snippet": "by W Wang · 2023 · Cited by 9 — This study examines the influence of common institutional ownership on enterprise digital transformation by using theoretical logic and empirical evidence.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10665731/", "content": "by W Wang · 2023 · Cited by 9 — This study examines the influence of common institutional ownership on enterprise digital transformation by using theoretical logic and empirical evidence."}
+{"idx": 9, "title": "Artificial intelligence and corporate green development", "date": "", "ddg_snippet": "by Q Liu · 2025 · Cited by 1 — The results show that AI significantly promotes green development at the firm level, and this finding is robust across multiple tests.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1059056025005088", "content": "by Q Liu · 2025 · Cited by 1 — The results show that AI significantly promotes green development at the firm level, and this finding is robust across multiple tests."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_delta_tilde_algorithm_year_2024.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_delta_tilde_algorithm_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..28fc35e66abe40352f860c6ec3575bc64fcbecca
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_delta_tilde_algorithm_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v1", "content": "Abstract As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In ..."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms | Read Paper ...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 2, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This ..."}
+{"idx": 3, "title": "GitHub - GauthierE/statistical-collusion", "date": "", "ddg_snippet": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47263", "content": "Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT"}
+{"idx": 5, "title": "論文の概要: Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract: As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2502.04879v2", "content": "Abstract: As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests."}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract As platforms increasingly rely on learning algo-rithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the po-tential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879", "content": "Abstract As platforms increasingly rely on learning algo-rithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the po-tential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In ..."}
+{"idx": 8, "title": "[论文审查] Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "相似审查 [论文审查] Algorithmic Collective Action with Two Collectives [论文审查] A Statistical Learning Approach for Feature-Aware Task-to-Core Allocation in Heterogeneous Platforms [论文审查] Reinforcement Learning , Collusion , and the Folk Theorem [论文审查] Collusion Detection with Graph Neural Networks", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/zh/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "相似审查 [论文审查] Algorithmic Collective Action with Two Collectives [论文审查] A Statistical Learning Approach for Feature-Aware Task-to-Core Allocation in Heterogeneous Platforms [论文审查] Reinforcement Learning , Collusion , and the Folk Theorem [论文审查] Collusion Detection with Graph Neural Networks"}
+{"idx": 9, "title": "arXiv:2502.04879v1 [stat.ML] 7 Feb 2025", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879v1", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
diff --git a/data/sampled_jsons/StereoSet_Nadeem_et_al._2021_full_title_year_2021.jsonl b/data/sampled_jsons/StereoSet_Nadeem_et_al._2021_full_title_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f6874d479b7fa86ddb63b3ed8874eeadc5784643
--- /dev/null
+++ b/data/sampled_jsons/StereoSet_Nadeem_et_al._2021_full_title_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Factuality in Neural Language Models Moin Nadeem", "date": "", "ddg_snippet": "by M Nadeem · 2021 — \" StereoSet : Measuring stereotypical bias in pretrained language models,\" Under submission to EACL 2021 . • Chapter 5: M Nadeem , T He, K Cho, J Glass. \"A ...", "subpage_snippet": "", "source": "sls.csail.mit.edu", "link": "https://sls.csail.mit.edu/publications/2021/Moin-Nadeem_MEng_Thesis.pdf", "content": "by M Nadeem · 2021 — \" StereoSet : Measuring stereotypical bias in pretrained language models,\" Under submission to EACL 2021 . • Chapter 5: M Nadeem , T He, K Cho, J Glass. \"A ..."}
+{"idx": 1, "title": "McGill-NLP/stereoset · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/McGill-NLP/stereoset", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 2, "title": "StereoSet: Measuring stereotypical bias in pretrained ...", "date": "", "ddg_snippet": "This repository contains an extensible codebase to measure stereotypical bias on new pretrained models, as well as code to replicate our results.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/moinnadeem/StereoSet", "content": "This repository contains an extensible codebase to measure stereotypical bias on new pretrained models, as well as code to replicate our results."}
+{"idx": 3, "title": "Measuring stereotypical bias in pretrained language models", "date": "", "ddg_snippet": "by M Nadeem · 2021 · Cited by 1326 — We present StereoSet , a large-scale natural English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.acl-long.416/", "content": "by M Nadeem · 2021 · Cited by 1326 — We present StereoSet , a large-scale natural English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion."}
+{"idx": 4, "title": "Measuring stereotypical bias in pretrained language models", "date": "", "ddg_snippet": "by M Nadeem · 2020 · Cited by 1326 — We present StereoSet , a large-scale natural dataset in English to measure stereotypical biases in four domains: gender, profession, race, and religion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2004.09456", "content": "by M Nadeem · 2020 · Cited by 1326 — We present StereoSet , a large-scale natural dataset in English to measure stereotypical biases in four domains: gender, profession, race, and religion."}
+{"idx": 5, "title": "Measuring stereotypical bias in pretrained language models", "date": "", "ddg_snippet": "by M Nadeem · 2021 · Cited by 1315 — A stereotype is an over-generalized belief about a particular group of people , e.g., Asians are good at math or African Americans are.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.acl-long.416.pdf", "content": "by M Nadeem · 2021 · Cited by 1315 — A stereotype is an over-generalized belief about a particular group of people , e.g., Asians are good at math or African Americans are."}
+{"idx": 6, "title": "Bipol: A novel multi-axes bias evaluation metric with ...", "date": "", "ddg_snippet": "by L Alkhaled · 2023 · Cited by 17 — It is similar to StereoSet , (for associative contexts), which measures 4 axes of social bias in a LM (Nadeem et al., 2021). Table 9 below compares some of the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2949719123000274", "content": "by L Alkhaled · 2023 · Cited by 17 — It is similar to StereoSet , (for associative contexts), which measures 4 axes of social bias in a LM (Nadeem et al., 2021). Table 9 below compares some of the ..."}
+{"idx": 7, "title": "Causally Testing Gender Bias in LLMs: A Case Study on ...", "date": "", "ddg_snippet": "21 Oct 2024 — For explicit bias, the models state the stereotypes, e.g., “girls tend to be softer than boys” ( Nadeem et al ., 2021 ) . Implicit bias occurs when ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.10678v3", "content": "21 Oct 2024 — For explicit bias, the models state the stereotypes, e.g., “girls tend to be softer than boys” ( Nadeem et al ., 2021 ) . Implicit bias occurs when ..."}
+{"idx": 8, "title": "Diverse Perspectives of Gender Bias in Stereotype Benchmarks", "date": "", "ddg_snippet": "ognized intrinsic stereotyping metrics: StereoSet. 044. (Nadeem et al., 2021 ) and CrowS-Pairs (Nangia. 045 et al., 2020). We begin by highlighting the incon ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=8fXaTPYsoE", "content": "ognized intrinsic stereotyping metrics: StereoSet. 044. (Nadeem et al., 2021 ) and CrowS-Pairs (Nangia. 045 et al., 2020). We begin by highlighting the incon ..."}
+{"idx": 9, "title": "The Negation Bias in Large Language Models", "date": "", "ddg_snippet": "by Y Wang — Current bias evaluations now leverage standardized benchmarks—such as StereoSet. (Nadeem et al., 2021 ) and CrowS-Pairs (Nangia et al., 2020)—which measure ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/28c3760ba4126e30194f7a7106a80ecd43bfee99.pdf", "content": "by Y Wang — Current bias evaluations now leverage standardized benchmarks—such as StereoSet. (Nadeem et al., 2021 ) and CrowS-Pairs (Nangia et al., 2020)—which measure ..."}
diff --git a/data/sampled_jsons/StereoSet_Nadeem_et_al._2021_paper_title_year_2021.jsonl b/data/sampled_jsons/StereoSet_Nadeem_et_al._2021_paper_title_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d24044d9093903b119e776107364313c75e92443
--- /dev/null
+++ b/data/sampled_jsons/StereoSet_Nadeem_et_al._2021_paper_title_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Factuality in Neural Language Models Moin Nadeem", "date": "", "ddg_snippet": "by M Nadeem · 2021 — Chapter 4: M Nadeem , A Bethke, S Reddy. \" StereoSet : Measuring stereotypical bias in pretrained language models,\" Under submission to EACL 2021 . • Chapter 5: M ...", "subpage_snippet": "", "source": "sls.csail.mit.edu", "link": "https://sls.csail.mit.edu/publications/2021/Moin-Nadeem_MEng_Thesis.pdf", "content": "by M Nadeem · 2021 — Chapter 4: M Nadeem , A Bethke, S Reddy. \" StereoSet : Measuring stereotypical bias in pretrained language models,\" Under submission to EACL 2021 . • Chapter 5: M ..."}
+{"idx": 1, "title": "McGill-NLP/stereoset · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/McGill-NLP/stereoset", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 2, "title": "Measuring stereotypical bias in pretrained language models", "date": "", "ddg_snippet": "by M Nadeem · 2021 · Cited by 1326 — We present StereoSet , a large-scale natural English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.acl-long.416/", "content": "by M Nadeem · 2021 · Cited by 1326 — We present StereoSet , a large-scale natural English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion."}
+{"idx": 3, "title": "Measuring stereotypical bias in pretrained language models", "date": "", "ddg_snippet": "by M Nadeem · 2020 · Cited by 1326 — We present StereoSet , a large-scale natural dataset in English to measure stereotypical biases in four domains: gender, profession, race, and religion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2004.09456", "content": "by M Nadeem · 2020 · Cited by 1326 — We present StereoSet , a large-scale natural dataset in English to measure stereotypical biases in four domains: gender, profession, race, and religion."}
+{"idx": 4, "title": "How Different Is Stereotypical Bias Across Languages?", "date": "", "ddg_snippet": "by IT Öztürk · 2023 · Cited by 4 — To that end, we make use of the English StereoSet data set ( Nadeem et al ., 2021 ), which we semi-automatically translate into German, French, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.07331", "content": "by IT Öztürk · 2023 · Cited by 4 — To that end, we make use of the English StereoSet data set ( Nadeem et al ., 2021 ), which we semi-automatically translate into German, French, ..."}
+{"idx": 5, "title": "MABEL: Attenuating Gender Bias using Textual Entailment ...", "date": "", "ddg_snippet": "More information can be found in the StereoSet ( Nadeem et al ., 2021 ) paper . Training. Before training, make sure that the counterfactually-augmented NLI data ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/princeton-nlp/MABEL", "content": "More information can be found in the StereoSet ( Nadeem et al ., 2021 ) paper . Training. Before training, make sure that the counterfactually-augmented NLI data ..."}
+{"idx": 6, "title": "https://huggingface.co/datasets/McGill-NLP/stereos...", "date": "", "ddg_snippet": "... stereoset pretty_name: StereoSet tags ... Paper :** https://arxiv.org/abs/2004.09456 - **Leaderboard ... [ Nadeem et al (2020)](https://arxiv.org/abs ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/McGill-NLP/stereoset/resolve/2b249192978ff042eb3c938d8c0329cd90756033/README.md?download=true", "content": "... stereoset pretty_name: StereoSet tags ... Paper :** https://arxiv.org/abs/2004.09456 - **Leaderboard ... [ Nadeem et al (2020)](https://arxiv.org/abs ..."}
+{"idx": 7, "title": "Causally Testing Gender Bias in LLMs: A Case Study on ...", "date": "", "ddg_snippet": "by Y Chen · 2025 · Cited by 7 — We illustrate this idea with two examples in Section 2.1, one from Stereotype ( Nadeem et al .,. 2021 ), and the second from OCCUGENDER, and their ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.281.pdf", "content": "by Y Chen · 2025 · Cited by 7 — We illustrate this idea with two examples in Section 2.1, one from Stereotype ( Nadeem et al .,. 2021 ), and the second from OCCUGENDER, and their ..."}
+{"idx": 8, "title": "a modular framework for ethical, structured, and adaptive AI", "date": "", "ddg_snippet": "by MS Torkestani · 2025 — A credentialed subset of MIMIC-IV was used for clinical data, ensuring compliance with data protection standards. StereoSet ( Nadeem et al ., 2021 ) ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11330-7", "content": "by MS Torkestani · 2025 — A credentialed subset of MIMIC-IV was used for clinical data, ensuring compliance with data protection standards. StereoSet ( Nadeem et al ., 2021 ) ..."}
+{"idx": 9, "title": "COBIAS: Assessing the Contextual Reliability of Bias ...", "date": "", "ddg_snippet": "20 May 2025 — Large Language Models (LLMs) often inherit biases from the web data they are trained on, which contains stereotypes and prejudices.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3717867.3717923", "content": "20 May 2025 — Large Language Models (LLMs) often inherit biases from the web data they are trained on, which contains stereotypes and prejudices."}
diff --git a/data/sampled_jsons/StreamRF_CVPR_2023_paper_abstract_radiance_fields.jsonl b/data/sampled_jsons/StreamRF_CVPR_2023_paper_abstract_radiance_fields.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a112b4caa48f35066163aa31244d423bdd1d79a
--- /dev/null
+++ b/data/sampled_jsons/StreamRF_CVPR_2023_paper_abstract_radiance_fields.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ CVPR 2023 Talk] EventNeRF: Neural Radiance Fields from a Single...", "date": "", "ddg_snippet": "In Computer Vision and Pattern Recognition ( CVPR ), 2023 . Abstract : Asynchronously operating event cameras find many applications due to their high dynamic range, no motion blur, low latency and low data bandwidth.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/e6f1e8045e499c32effd72112ada1a71/", "content": "In Computer Vision and Pattern Recognition ( CVPR ), 2023 . Abstract : Asynchronously operating event cameras find many applications due to their high dynamic range, no motion blur, low latency and low data bandwidth."}
+{"idx": 1, "title": "CVPR Poster VideoRF: Rendering Dynamic Radiance Fields as...", "date": "", "ddg_snippet": "In this paper , we introduce VideoRF, the first approach to enable real-time streaming and rendering of dynamic human-centric radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one.", "subpage_snippet": "", "source": "cvpr2023.thecvf.com", "link": "https://cvpr2023.thecvf.com/virtual/2024/poster/31080", "content": "In this paper , we introduce VideoRF, the first approach to enable real-time streaming and rendering of dynamic human-centric radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one."}
+{"idx": 2, "title": "Streaming Radiance Fields for 3D Video Synthesis | DeepAI", "date": "", "ddg_snippet": "We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/streaming-radiance-fields-for-3d-video-synthesis", "content": "We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes."}
+{"idx": 3, "title": "AlgoHunt/ StreamRF : Official implementation of our NeurIPS paper ...", "date": "", "ddg_snippet": "Streaming Radiance Fields for 3D Video Synthesis. Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Ping Tan.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AlgoHunt/StreamRF", "content": "Streaming Radiance Fields for 3D Video Synthesis. Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Ping Tan."}
+{"idx": 4, "title": "HandNeRF: Neural Radiance Fields for Animatable Interacting Hands", "date": "", "ddg_snippet": "SPARF: Neural Radiance Fields from Sparse and Noisy Poses 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ).", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2023/012900v1078/1POVz2A3XJ6", "content": "SPARF: Neural Radiance Fields from Sparse and Noisy Poses 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR )."}
+{"idx": 5, "title": "NeRF at CVPR 23: Arbitrary Camera Trajectories - Freedium", "date": "", "ddg_snippet": "Let's have a look at two papers about Arbitrary Camera Trajectories. In this article, I'll detail two NeRF articles dealing with Arbitrary Camera Trajectories that caught my attention during the CVPR 2023 conference.", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/5a015cfc9d63", "content": "Let's have a look at two papers about Arbitrary Camera Trajectories. In this article, I'll detail two NeRF articles dealing with Arbitrary Camera Trajectories that caught my attention during the CVPR 2023 conference."}
+{"idx": 6, "title": "Motion-Oriented Compositional Neural Radiance Fields ... | SpringerLink", "date": "", "ddg_snippet": "Abstract . This paper introduces Motion-oriented Compositional Neu-ral Radiance Fields (MoCo-NeRF), a framework designed to perform free-viewpoint rendering of monocular human videos via novel non-rigid motion modeling approach. In the context of dynamic clothed humans...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-72933-1_27", "content": "Abstract . This paper introduces Motion-oriented Compositional Neu-ral Radiance Fields (MoCo-NeRF), a framework designed to perform free-viewpoint rendering of monocular human videos via novel non-rigid motion modeling approach. In the context of dynamic clothed humans..."}
+{"idx": 7, "title": "Visualizing Subtle Motions with Time-Varying Radiance Fields", "date": "", "ddg_snippet": "Static radiance fields . NeRF [35] has become the main- stream approach for representing 3D scenes and demon-strates high-quality view synthesis results.", "subpage_snippet": "", "source": "3dvar.com", "link": "https://3dvar.com/Feng20233D.pdf", "content": "Static radiance fields . NeRF [35] has become the main- stream approach for representing 3D scenes and demon-strates high-quality view synthesis results."}
+{"idx": 8, "title": "CVPR 2023 , highlight paper - DiffRF: Rendering-Guided 3D Radiance ...", "date": "", "ddg_snippet": "The paper introduces a novel approach for 3D radiance field synthesis called DiffRF, which is based on denoising diffusion probabilistic models. Unlike existing diffusion-based methods that operate on images, latent codes, or point cloud data, DiffRF directly generates volumetric radiance ...", "subpage_snippet": "", "source": "agibreakdown.podbean.com", "link": "https://agibreakdown.podbean.com/e/cvpr-2023-highlight-paper-diffrf-rendering-guided-3d-radiance-field-diffusion/", "content": "The paper introduces a novel approach for 3D radiance field synthesis called DiffRF, which is based on denoising diffusion probabilistic models. Unlike existing diffusion-based methods that operate on images, latent codes, or point cloud data, DiffRF directly generates volumetric radiance ..."}
+{"idx": 9, "title": "HandNeRF: Neural Radiance Fields for... | Papers With Code", "date": "", "ddg_snippet": "CVPR 2023 · Zhiyang Guo, Wengang Zhou, Min Wang, Li Li, Houqiang Li ·.Then we design a pose-driven deformation field to establish correspondence from those different poses to a shared canonical space, where a pose-disentangled NeRF for one hand is optimized.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/handnerf-neural-radiance-fields-for", "content": "CVPR 2023 · Zhiyang Guo, Wengang Zhou, Min Wang, Li Li, Houqiang Li ·.Then we design a pose-driven deformation field to establish correspondence from those different poses to a shared canonical space, where a pose-disentangled NeRF for one hand is optimized."}
diff --git a/data/sampled_jsons/StreamRF_dynamic_scene_reconstruction_streaming.jsonl b/data/sampled_jsons/StreamRF_dynamic_scene_reconstruction_streaming.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ad1cfc89f74f1d20336203e30d887b9877013629
--- /dev/null
+++ b/data/sampled_jsons/StreamRF_dynamic_scene_reconstruction_streaming.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Xe Currency Converter - Live Exchange Rates Today", "date": "", "ddg_snippet": "Calculate live currency and foreign exchange rates with the free Xe Currency Converter. Convert between all major global currencies, precious metals, and crypto with this currency calculator and view the live mid-market rates .", "subpage_snippet": "", "source": "www.xe.com", "link": "https://www.xe.com/currencyconverter/", "content": "Calculate live currency and foreign exchange rates with the free Xe Currency Converter. Convert between all major global currencies, precious metals, and crypto with this currency calculator and view the live mid-market rates ."}
+{"idx": 1, "title": "Euro foreign exchange reference rates - European Central Bank", "date": "", "ddg_snippet": "Reference rates for all the official currencies of non- euro area Member States of the European Union and world currencies with the most liquid active spot FX markets are set and published.", "subpage_snippet": "", "source": "www.ecb.europa.eu", "link": "https://www.ecb.europa.eu/stats/policy_and_exchange_rates/euro_reference_exchange_rates/html/index.en.html", "content": "Reference rates for all the official currencies of non- euro area Member States of the European Union and world currencies with the most liquid active spot FX markets are set and published."}
+{"idx": 2, "title": "EUR to USD - Euro to US Dollar Conversion - Exchange Rates", "date": "", "ddg_snippet": "2 days ago · Use the EUR to USD currency converter at Exchange-Rates.org for accurate and up-to-date exchange rates. Easily convert Euros to US Dollars with real-time data.", "subpage_snippet": "", "source": "www.exchange-rates.org", "link": "https://www.exchange-rates.org/converter/eur-usd", "content": "2 days ago · Use the EUR to USD currency converter at Exchange-Rates.org for accurate and up-to-date exchange rates. Easily convert Euros to US Dollars with real-time data."}
+{"idx": 3, "title": "Euro to Dollar (EUR/USD) Exchange Rate & Converter", "date": "", "ddg_snippet": "To convert Euros to Dollars or determine the Euro Dollar exchange rate simply use the currency converter on the right of this page, which offers fast live exchange rate conversions today!", "subpage_snippet": "", "source": "www.exchangerates.org.uk", "link": "https://www.exchangerates.org.uk/Euros-to-Dollars-currency-conversion-page.html", "content": "To convert Euros to Dollars or determine the Euro Dollar exchange rate simply use the currency converter on the right of this page, which offers fast live exchange rate conversions today!"}
+{"idx": 4, "title": "Currency Exchange Table ( Euro - EUR) - X-Rates", "date": "", "ddg_snippet": "This currency rates table lets you compare an amount in Euro to all other currencies.", "subpage_snippet": "", "source": "www.x-rates.com", "link": "https://www.x-rates.com/table/?from=EUR&amount=1", "content": "This currency rates table lets you compare an amount in Euro to all other currencies."}
+{"idx": 5, "title": "Live Exchange Rates | OANDA", "date": "", "ddg_snippet": "Get live exchange rates for major currency pairs from the OANDA fxTrade platform.", "subpage_snippet": "", "source": "www.oanda.com", "link": "https://www.oanda.com/currency-converter/live-exchange-rates/", "content": "Get live exchange rates for major currency pairs from the OANDA fxTrade platform."}
+{"idx": 6, "title": "EUR to GBP Interbank Exchange Rate - Convert Euro to British...", "date": "", "ddg_snippet": "11 hours ago · This Euro to British pound conversion tool allows you to compare recent and historic interbank exchange rates for EUR to GBP.", "subpage_snippet": "", "source": "www.eurochange.co.uk", "link": "https://www.eurochange.co.uk/currency-converter/eur-to-gbp", "content": "11 hours ago · This Euro to British pound conversion tool allows you to compare recent and historic interbank exchange rates for EUR to GBP."}
+{"idx": 7, "title": "1 Euro to US dollars Exchange Rate . Convert EUR/USD - Wise", "date": "", "ddg_snippet": "Convert 1 EUR to USD with the Wise Currency Converter. Analyze historical currency charts or live Euro / US dollar rates and get free rate alerts directly to your email.", "subpage_snippet": "", "source": "wise.com", "link": "https://wise.com/us/currency-converter/eur-to-usd-rate?amount=1", "content": "Convert 1 EUR to USD with the Wise Currency Converter. Analyze historical currency charts or live Euro / US dollar rates and get free rate alerts directly to your email."}
+{"idx": 8, "title": "InforEuro, the exchange rate of the Euro currency", "date": "", "ddg_snippet": "InforEuro helps you convert euro in other currencies. The European Commission’s official monthly accounting rates for the euro , its conversion rate to other currencies and its historic data since 1994 can be found here.", "subpage_snippet": "", "source": "commission.europa.eu", "link": "https://commission.europa.eu/funding-tenders/procedures-guidelines-tenders/information-contractors-and-beneficiaries/exchange-rate-inforeuro_en", "content": "InforEuro helps you convert euro in other currencies. The European Commission’s official monthly accounting rates for the euro , its conversion rate to other currencies and its historic data since 1994 can be found here."}
+{"idx": 9, "title": "1 EUR to USD Exchange Rate Today | Convert Euro to US dollar", "date": "", "ddg_snippet": "4 days ago · Use our free EUR to USD converter for the latest Euro to US Dollar exchange rate . View charts, tables, and get a transfer quote.", "subpage_snippet": "", "source": "cambridgecurrencies.com", "link": "https://cambridgecurrencies.com/currency-eur-to-usd/", "content": "4 days ago · Use our free EUR to USD converter for the latest Euro to US Dollar exchange rate . View charts, tables, and get a transfer quote."}
diff --git a/data/sampled_jsons/StreamRF_generalized_method_dynamic_scene_reconstruction.jsonl b/data/sampled_jsons/StreamRF_generalized_method_dynamic_scene_reconstruction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..40de279bbfb5b36c88e665a7a5f7dc8d32075cd5
--- /dev/null
+++ b/data/sampled_jsons/StreamRF_generalized_method_dynamic_scene_reconstruction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Revolutionizing Dynamic View Rendering - Simple Science", "date": "", "ddg_snippet": "With the advent of Streaming Radiance Fields ( StreamRF ), researchers sought to create an efficient method for dynamic scene reconstruction .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-04-11-revolutionizing-dynamic-view-rendering--a9r0rg7", "content": "With the advent of Streaming Radiance Fields ( StreamRF ), researchers sought to create an efficient method for dynamic scene reconstruction ."}
+{"idx": 1, "title": "Dynamic View Synthesis from Dynamic Monocular Video", "date": "", "ddg_snippet": "Dynamic Scene Dataset.Evaluating our method and others. Our goal is to make the evaluation as simple as possible for you. We have collected the fix-view-change-time results of the following methods : NeRF NeRF + t Yoon et al. Non-Rigid NeRF NSFF DynamicNeRF (ours).", "subpage_snippet": "", "source": "pythonrepo.com", "link": "https://pythonrepo.com/repo/gaochen315-DynamicNeRF", "content": "Dynamic Scene Dataset.Evaluating our method and others. Our goal is to make the evaluation as simple as possible for you. We have collected the fix-view-change-time results of the following methods : NeRF NeRF + t Yoon et al. Non-Rigid NeRF NSFF DynamicNeRF (ours)."}
+{"idx": 2, "title": "MGStream: Motion-aware 3D Gaussian for Streamable Dynamic ...", "date": "", "ddg_snippet": "for Streamable Dynamic Scene Reconstruction . Zhenyu Bao1,2, Qing Li2, Guibiao Liao1,2, Zhongyuan Zhao1,2, Kanglin Liu2, 1Peking University, 2Pengcheng LaboratoryComparsion with StreamRF .", "subpage_snippet": "", "source": "zhenybao.github.io", "link": "https://zhenybao.github.io/MGStream/?s=09", "content": "for Streamable Dynamic Scene Reconstruction . Zhenyu Bao1,2, Qing Li2, Guibiao Liao1,2, Zhongyuan Zhao1,2, Kanglin Liu2, 1Peking University, 2Pengcheng LaboratoryComparsion with StreamRF ."}
+{"idx": 3, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "Related workGeneralizable 3D Reconstruction for Acceleration Dynamic Scene Reconstruction and View SynthesisTo address this issue, existing methods such as StreamRF [29], NeRFPlayer[51], ReRF[62]...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "Related workGeneralizable 3D Reconstruction for Acceleration Dynamic Scene Reconstruction and View SynthesisTo address this issue, existing methods such as StreamRF [29], NeRFPlayer[51], ReRF[62]..."}
+{"idx": 4, "title": "(PDF) Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "streaming approach for dynamic scene reconstruction that.is the first approach to use a generalized method for stream -. ing reconstruction of dynamic scenes .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "streaming approach for dynamic scene reconstruction that.is the first approach to use a generalized method for stream -. ing reconstruction of dynamic scenes ."}
+{"idx": 5, "title": "Instant Gaussian Stream : Fast and Generalizable... | alphaXiv", "date": "", "ddg_snippet": "Dynamic scene reconstruction has seen significant progress in recent years, with methods falling into two main categories: Offline approaches: Methods like NeRF-based dynamic scene reconstruction ( Dynamic NeRF, K-Planes) and more recent Gaussian...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.16979", "content": "Dynamic scene reconstruction has seen significant progress in recent years, with methods falling into two main categories: Offline approaches: Methods like NeRF-based dynamic scene reconstruction ( Dynamic NeRF, K-Planes) and more recent Gaussian..."}
+{"idx": 6, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "In contrast, for dynamic scenes , scene -specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=real-time+dynamic+view+synthesis", "content": "In contrast, for dynamic scenes , scene -specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video."}
+{"idx": 7, "title": "4D Gaussian Splatting with Scale-aware Residual Field and Adaptive...", "date": "", "ddg_snippet": "Reconstructing dynamic scenes from video sequences is a highly 36 promising task in the multimedia domain.125 Recent advancements in dynamic scene reconstruction have 126 been achieved through methods based on NeRF [23] and 3DGS 127 [14].", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=bgRADWSr6O", "content": "Reconstructing dynamic scenes from video sequences is a highly 36 promising task in the multimedia domain.125 Recent advancements in dynamic scene reconstruction have 126 been achieved through methods based on NeRF [23] and 3DGS 127 [14]."}
+{"idx": 8, "title": "SWinGS: Sliding Windows for", "date": "", "ddg_snippet": "Non-NeRF dynamic reconstruction : A number of approaches prior to the emergence of NeRF [37] tackled dynamic scene reconstruction .We present our method to reconstruct and render novel views of general dynamic scenes from multiple calibrated time-synchronized cameras.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/07170.pdf", "content": "Non-NeRF dynamic reconstruction : A number of approaches prior to the emergence of NeRF [37] tackled dynamic scene reconstruction .We present our method to reconstruct and render novel views of general dynamic scenes from multiple calibrated time-synchronized cameras."}
+{"idx": 9, "title": "GitHub - lidq92/arxiv-daily: [NOT UPDATED][To be updated with http...", "date": "", "ddg_snippet": "Generalized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals.2025-05-28. Patch-based Reconstruction for Unsupervised Dynamic MRI using Learnable Tensor Function with Implicit Neural Representation. Yuanyuan Liu et.al.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lidq92/arxiv-daily", "content": "Generalized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals.2025-05-28. Patch-based Reconstruction for Unsupervised Dynamic MRI using Learnable Tensor Function with Implicit Neural Representation. Yuanyuan Liu et.al."}
diff --git a/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_limitations_section_8.jsonl b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_limitations_section_8.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1678e7c79daf0ae77a293e76f9f28efce9e1194d
--- /dev/null
+++ b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_limitations_section_8.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "5 Nov 2024 — Limitations : The paper addresses its limitations in section 8 . Flag For Ethics Review: No ethics review needed. Rating: 8: Strong Accept ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zzOOqD6R1b&referrer=[the+profile+of+David+Krueger](/profile?id=~David_Krueger1)", "content": "5 Nov 2024 — Limitations : The paper addresses its limitations in section 8 . Flag For Ethics Review: No ethics review needed. Rating: 8: Strong Accept ..."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities."}
+{"idx": 2, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "5 Jun 2025 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740125", "content": "5 Jun 2025 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce ..."}
+{"idx": 3, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "29 May 2024 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "29 May 2024 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce ..."}
+{"idx": 4, "title": "[Paper] Stress-testing capability elicitation with password- ...", "date": "", "ddg_snippet": "4 Jun 2024 — We released a paper studying this by examining how well supervised fine-tuning and RL can elicit capabilities from password - locked models .", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "4 Jun 2024 — We released a paper studying this by examining how well supervised fine-tuning and RL can elicit capabilities from password - locked models ."}
+{"idx": 5, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "We train models to only show good behavior when given a password (and perform poorly otherwise), and then try to elicit the capabilities by fine-tuning these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=uvvVjWP1aj&name=supplementary_material", "content": "We train models to only show good behavior when given a password (and perform poorly otherwise), and then try to elicit the capabilities by fine-tuning these ..."}
+{"idx": 6, "title": "When does capability elicitation bound risk?", "date": "", "ddg_snippet": "21 Jan 2025 — A capability evaluation produces a number (called a capability ) that upper bounds the score that particular models will achieve on some risk ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/u3taQsgxqCzrgErMM/when-does-capability-elicitation-bound-risk", "content": "21 Jan 2025 — A capability evaluation produces a number (called a capability ) that upper bounds the score that particular models will achieve on some risk ..."}
+{"idx": 7, "title": "Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "4 Feb 2025 — We evaluate a variety of capability elicitation techniques on password - locked and circuit-broken model organisms, in both a multiple-choice ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v1", "content": "4 Feb 2025 — We evaluate a variety of capability elicitation techniques on password - locked and circuit-broken model organisms, in both a multiple-choice ..."}
+{"idx": 8, "title": "Password-locked models: a stress case for capabilities ...", "date": "", "ddg_snippet": "3 Aug 2023 — Password-locked models are trained to exhibit certain capabilities only when a password is present in the query. Studying these models has two purposes.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "3 Aug 2023 — Password-locked models are trained to exhibit certain capabilities only when a password is present in the query. Studying these models has two purposes."}
+{"idx": 9, "title": "Fuzzing LLMs sometimes makes them reveal their secrets", "date": "", "ddg_snippet": "26 Feb 2025 — ... password - locked model used in Stress - testing capability elicitation with password - locked models on 32 random problems from the MATH test set.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/GE6pcmmLc3kdpNJja/fuzzing-llms-sometimes-makes-them-reveal-their-secrets", "content": "26 Feb 2025 — ... password - locked model used in Stress - testing capability elicitation with password - locked models on 32 random problems from the MATH test set."}
diff --git a/data/sampled_jsons/SwinIR_Image_restoration_using_swin_transformer_Liang_2021_abstract.jsonl b/data/sampled_jsons/SwinIR_Image_restoration_using_swin_transformer_Liang_2021_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48b2a24833f2b69863eac957faf37d64a1cab9bb
--- /dev/null
+++ b/data/sampled_jsons/SwinIR_Image_restoration_using_swin_transformer_Liang_2021_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SwinIR: Image Restoration Using Swin Transformer", "date": "", "ddg_snippet": "by J Liang · 2021 · Cited by 4929 — In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9607618", "content": "by J Liang · 2021 · Cited by 4929 — In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer."}
+{"idx": 1, "title": "SwinIR: Image Restoration Using Swin Transformer", "date": "", "ddg_snippet": "by J Liang · 2021 · Cited by 4889 — We conduct experiments on three represen- tative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image . 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2021W/AIM/papers/Liang_SwinIR_Image_Restoration_Using_Swin_Transformer_ICCVW_2021_paper.pdf", "content": "by J Liang · 2021 · Cited by 4889 — We conduct experiments on three represen- tative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image . 12 pages"}
+{"idx": 2, "title": "Liang SwinIR Image Restoration Using Swin Transformer ...", "date": "", "ddg_snippet": "This document describes a new method called SwinIR that uses Swin Transformers for image restoration tasks like super resolution, denoising, and compression ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/665585226/Liang-SwinIR-Image-Restoration-Using-Swin-Transformer-ICCVW-2021-paper", "content": "This document describes a new method called SwinIR that uses Swin Transformers for image restoration tasks like super resolution, denoising, and compression ..."}
+{"idx": 3, "title": "Swinir: Image Restoration Using Swin Transformer | PDF", "date": "", "ddg_snippet": "SwinIR is a new image restoration model based on Swin Transformer that achieves state-of-the-art results on image super-resolution, denoising, ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/521762860/image-resotration", "content": "SwinIR is a new image restoration model based on Swin Transformer that achieves state-of-the-art results on image super-resolution, denoising, ..."}
+{"idx": 4, "title": "Image super-resolution reconstruction using Swin ...", "date": "", "ddg_snippet": "by Z Sun · 2024 · Cited by 14 — This paper proposes a shifted window Transformer ( Swin Transformer ) with an efficient channel attention network (S-ECAN), which combines the attention based on ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0952197624010170", "content": "by Z Sun · 2024 · Cited by 14 — This paper proposes a shifted window Transformer ( Swin Transformer ) with an efficient channel attention network (S-ECAN), which combines the attention based on ..."}
+{"idx": 5, "title": "SwinIR: Image Restoration Using Swin Transformer", "date": "", "ddg_snippet": "Key takeaway: ' SwinIR , a strong image restoration model based on the Swin Transformer , outperforms state-of-the-art methods on various tasks and reduces the ...", "subpage_snippet": "", "source": "k8s.consensus.app", "link": "https://k8s.consensus.app/papers/details/83f71813c0d459068d66615a73e6c453/", "content": "Key takeaway: ' SwinIR , a strong image restoration model based on the Swin Transformer , outperforms state-of-the-art methods on various tasks and reduces the ..."}
+{"idx": 6, "title": "Image Super-Resolution Reconstruction Network based on ...", "date": "", "ddg_snippet": "3 days ago — The SwinIR [18] model applies the Swin Transformer to SR reconstruction tasks, achieving strong performance metrics and computational efficiency ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00241v5", "content": "3 days ago — The SwinIR [18] model applies the Swin Transformer to SR reconstruction tasks, achieving strong performance metrics and computational efficiency ..."}
+{"idx": 7, "title": "Super-resolution Reconstruction of Remote Sensing Images ...", "date": "", "ddg_snippet": "by T Yang · 2025 — An improved SwinIR Transformer remote sensing image super-resolution reconstruction algorithm is proposed, combining residual hybrid attention and spatial-gate ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3728725.3728750", "content": "by T Yang · 2025 — An improved SwinIR Transformer remote sensing image super-resolution reconstruction algorithm is proposed, combining residual hybrid attention and spatial-gate ..."}
+{"idx": 8, "title": "An enhanced image restoration using deep learning and ...", "date": "", "ddg_snippet": "by A Senthil Anandhi · 2025 · Cited by 7 — We suggest an enhanced image restoration model that merges Lewin architecture with SwinIR , using advanced deep learning methods.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-94449-5", "content": "by A Senthil Anandhi · 2025 · Cited by 7 — We suggest an enhanced image restoration model that merges Lewin architecture with SwinIR , using advanced deep learning methods."}
+{"idx": 9, "title": "Image super-resolution reconstruction using Swin ...", "date": "", "ddg_snippet": "Image super-resolution reconstruction (SR) is an important ill-posed problem in low-level vision, which aims to reconstruct high-resolution images from ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197624010170/pdf", "content": "Image super-resolution reconstruction (SR) is an important ill-posed problem in low-level vision, which aims to reconstruct high-resolution images from ..."}
diff --git a/data/sampled_jsons/SybilHunter_Tor_node_pairs_connectivity_patterns_Winter.jsonl b/data/sampled_jsons/SybilHunter_Tor_node_pairs_connectivity_patterns_Winter.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a4a6a6d15d4c848766760c92392674dca0a58982
--- /dev/null
+++ b/data/sampled_jsons/SybilHunter_Tor_node_pairs_connectivity_patterns_Winter.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Identifying and Characterizing Sybils in the Tor Network - USENIX", "date": "", "ddg_snippet": "Abstract Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practi-cal tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as config-uration; and behavior, such as uptime sequences. We ...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_winter.pdf", "content": "Abstract Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practi-cal tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as config-uration; and behavior, such as uptime sequences. We ..."}
+{"idx": 1, "title": "GitHub - NullHypothesis/sybilhunter: Hunting for Sybils and anomalies ...", "date": "", "ddg_snippet": "Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NullHypothesis/sybilhunter", "content": "Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data."}
+{"idx": 2, "title": "Winter, Philipp (0000-0003-2481-2997) - kau.diva-portal.org", "date": "", "ddg_snippet": "In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers.", "subpage_snippet": "", "source": "kau.diva-portal.org", "link": "https://kau.diva-portal.org/smash/person.jsf?pid=authority-person:3851", "content": "In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers."}
+{"idx": 3, "title": "Protecting Tor from Sybil attacks - nymity.ch", "date": "", "ddg_snippet": "To this end, we have developed sybilhunter —a command line tool to analyse archived Tor network data for signs of Sybil relays. We are using sybilhunter to analyse large piles of data and generate visualisations, such as the ones shown below. Writing Identifying and characterizing Sybils in the Tor network", "subpage_snippet": "", "source": "nymity.ch", "link": "https://nymity.ch/sybilhunting/", "content": "To this end, we have developed sybilhunter —a command line tool to analyse archived Tor network data for signs of Sybil relays. We are using sybilhunter to analyse large piles of data and generate visualisations, such as the ones shown below. Writing Identifying and characterizing Sybils in the Tor network"}
+{"idx": 4, "title": "PDF Identifying and characterizing Sybils in the Tor network", "date": "", "ddg_snippet": "The double-edged sword of volunteer-run networks The Tor code is developed by The Tor Project", "subpage_snippet": "", "source": "ensa.fi", "link": "https://ensa.fi/slides/security16_slides_winter.pdf", "content": "The double-edged sword of volunteer-run networks The Tor code is developed by The Tor Project"}
+{"idx": 5, "title": "PDF Identifying and characterizing Sybils in the Tor network", "date": "", "ddg_snippet": "We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attack-ers. Our findings include diverse Sybils, ranging from botnets, to academic research, and relays that hijack Bit-coin transactions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1602.07787v1.pdf", "content": "We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attack-ers. Our findings include diverse Sybils, ranging from botnets, to academic research, and relays that hijack Bit-coin transactions."}
+{"idx": 6, "title": "Identifying and Characterizing Sybils in the Tor Network", "date": "", "ddg_snippet": "In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/winter", "content": "In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers."}
+{"idx": 7, "title": "Hunting for Sybils and anomalies in archived Tor network data.", "date": "", "ddg_snippet": "Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xxmingming/sybilhunter-security", "content": "Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data."}
+{"idx": 8, "title": "Identifying and Characterizing Sybils in the Tor Network", "date": "", "ddg_snippet": "Figure 1: Sybilhunter's architecture. Two datasets serve as input to sybilhunter ; consensuses and server descriptors, and malicious relays gathered with exitmap [37, § 3.1]. - \"Identifying and Characterizing Sybils in the Tor Network\"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Identifying-and-Characterizing-Sybils-in-the-Tor-Winter-Ensafi/2f837d4b34af8c691589894dd3441bb390ea1ae7/figure/0", "content": "Figure 1: Sybilhunter's architecture. Two datasets serve as input to sybilhunter ; consensuses and server descriptors, and malicious relays gathered with exitmap [37, § 3.1]. - \"Identifying and Characterizing Sybils in the Tor Network\""}
+{"idx": 9, "title": "Identifying and Characterizing Sybils in the Tor Network", "date": "", "ddg_snippet": "We dentials, break into TLS-protected connections, or used sybilhunter's diverse analysis techniques to analyze inject malicious content [37, § 5.2]. nine years of archived Tor network data, providing us Website fingerprinting: Tor's encryption prevents with new insights into the operation of real-world attack- guard relays (the first ...", "subpage_snippet": "", "source": "docslib.org", "link": "https://docslib.org/doc/5402114/identifying-and-characterizing-sybils-in-the-tor-network", "content": "We dentials, break into TLS-protected connections, or used sybilhunter's diverse analysis techniques to analyze inject malicious content [37, § 5.2]. nine years of archived Tor network data, providing us Website fingerprinting: Tor's encryption prevents with new insights into the operation of real-world attack- guard relays (the first ..."}
diff --git a/data/sampled_jsons/Sybilhunter_Detecting_Sybil_Nodes_in_the_Tor_Network_abstract_Winter_Dingledine_Feamster.jsonl b/data/sampled_jsons/Sybilhunter_Detecting_Sybil_Nodes_in_the_Tor_Network_abstract_Winter_Dingledine_Feamster.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a547e6fd3d934a351a996264fec216a4ab590dcc
--- /dev/null
+++ b/data/sampled_jsons/Sybilhunter_Detecting_Sybil_Nodes_in_the_Tor_Network_abstract_Winter_Dingledine_Feamster.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sybil attack - Wikipedia", "date": "", "ddg_snippet": "A Sybil attack is a type of attack on a computer network service in which an attacker subverts the service's reputation system by creating a large number of pseudonymous identities and uses them to gain a disproportionately large influence.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Sybil_attack", "content": "A Sybil attack is a type of attack on a computer network service in which an attacker subverts the service's reputation system by creating a large number of pseudonymous identities and uses them to gain a disproportionately large influence."}
+{"idx": 1, "title": "GitHub - NullHypothesis/ sybilhunter : Hunting for Sybils and...", "date": "", "ddg_snippet": "Hunting for Sybils and anomalies in archived Tor network data. Sybilhunter takes as input data obtained from CollecTor. Let's start by pretty-printing a file containing a network consensus or relay descriptors", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NullHypothesis/sybilhunter", "content": "Hunting for Sybils and anomalies in archived Tor network data. Sybilhunter takes as input data obtained from CollecTor. Let's start by pretty-printing a file containing a network consensus or relay descriptors"}
+{"idx": 2, "title": "Identifying and characterizing Sybils in the Tor network | Request PDF", "date": "", "ddg_snippet": "Little is known about real-world Sybils in the Tor network , and we lack practical tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , the first system for detecting Sybil relays based on their appearance, such as configuration; and behavior...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/301854678_Identifying_and_characterizing_Sybils_in_the_Tor_network", "content": "Little is known about real-world Sybils in the Tor network , and we lack practical tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , the first system for detecting Sybil relays based on their appearance, such as configuration; and behavior..."}
+{"idx": 3, "title": "Impact Analysis of Sybil Attacks in the Tor Network | SpringerLink", "date": "", "ddg_snippet": "Tor , the most widely used anonymity network with around 2 million users, relies on volunteer-operated relays to route traffic through circuits while preserving anonymity. Winter , P., Ensafi, R., Loesing, K., Feamster , N.: Identifying and characterizing Sybils in the Tor network .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-92886-4_13", "content": "Tor , the most widely used anonymity network with around 2 million users, relies on volunteer-operated relays to route traffic through circuits while preserving anonymity. Winter , P., Ensafi, R., Loesing, K., Feamster , N.: Identifying and characterizing Sybils in the Tor network ."}
+{"idx": 4, "title": "Protecting Tor from Sybil attacks", "date": "", "ddg_snippet": "Protecting the Tor network from Sybil attacks Philipp Winter , Roya Ensafi, Karsten Loesing, and Nick Feamster HotPETs talk, July 2015 Slides • Recording.", "subpage_snippet": "", "source": "nymity.ch", "link": "https://nymity.ch/sybilhunting/", "content": "Protecting the Tor network from Sybil attacks Philipp Winter , Roya Ensafi, Karsten Loesing, and Nick Feamster HotPETs talk, July 2015 Slides • Recording."}
+{"idx": 5, "title": "Proceedings of the", "date": "", "ddg_snippet": "Tor : The Second-Generation Onion Router. Roger Dingledine The Free Haven Project. Abstract . We present Tor , a circuit-based low-latency anonymous com-munication service.", "subpage_snippet": "", "source": "static.usenix.org", "link": "https://static.usenix.org/event/sec04/tech/full_papers/dingledine/dingledine.pdf", "content": "Tor : The Second-Generation Onion Router. Roger Dingledine The Free Haven Project. Abstract . We present Tor , a circuit-based low-latency anonymous com-munication service."}
+{"idx": 6, "title": "(PDF) As-awareness in Tor path selection", "date": "", "ddg_snippet": "Additionally, Feamster and Dingledine only considered Tor clients located at a hand- The rest of this paper is organized as follows.In order to avoid this basic form of Sybil attack [4], Tor clients ensure that the IP address of each node in their circuit is from a different /16 subnet.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/71027946/As_awareness_in_Tor_path_selection", "content": "Additionally, Feamster and Dingledine only considered Tor clients located at a hand- The rest of this paper is organized as follows.In order to avoid this basic form of Sybil attack [4], Tor clients ensure that the IP address of each node in their circuit is from a different /16 subnet."}
+{"idx": 7, "title": "7 Tips to Speed Up Tor Browser - Make Tech Easier", "date": "", "ddg_snippet": "Featured Tor Speeds Tips. Tor preserves your online anonymity through its unique onion routing, in which your encrypted data passes through several intermediary nodes .", "subpage_snippet": "", "source": "www.maketecheasier.com", "link": "https://www.maketecheasier.com/make-tor-faster/", "content": "Featured Tor Speeds Tips. Tor preserves your online anonymity through its unique onion routing, in which your encrypted data passes through several intermediary nodes ."}
+{"idx": 8, "title": "The Ultimate Guide to Using Tor Browser Securely - YouTube", "date": "", "ddg_snippet": "The complete tutorial to using Tor Browser safely. Let's cover all the privacy, security, and anonymity considerations you need to make when using Tor .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=K3wmLvny5tg", "content": "The complete tutorial to using Tor Browser safely. Let's cover all the privacy, security, and anonymity considerations you need to make when using Tor ."}
+{"idx": 9, "title": "Tor takes aim against malicious nodes on the network – STE...", "date": "", "ddg_snippet": "Tor already tries to remove malicious Sybils from the network (not all of them are attackers), but a false positive is costly, because it removes bandwidth from the network .", "subpage_snippet": "", "source": "stewilliams.com", "link": "https://stewilliams.com/tor-takes-aim-against-malicious-nodes-on-the-network/", "content": "Tor already tries to remove malicious Sybils from the network (not all of them are attackers), but a false positive is costly, because it removes bandwidth from the network ."}
diff --git a/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_PPO_gradient_variance_moving_targets_year_2024.jsonl b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_PPO_gradient_variance_moving_targets_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1fae1ad23c57b587e1af7c95e22e524a34300f6a
--- /dev/null
+++ b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_PPO_gradient_variance_moving_targets_year_2024.jsonl
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+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty. Differing preferences can complicate the alignment process, and prediction errors in a trained reward model can become more severe ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty. Differing preferences can complicate the alignment process, and prediction errors in a trained reward model can become more severe ..."}
+{"idx": 1, "title": "GitHub - shashacks/Symmetric_RL", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_RL", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance ."}
+{"idx": 2, "title": "\"Symmetric Reinforcement Learning Loss for Robust Learning on ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2405-17618", "content": "Bibliographic details on Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales."}
+{"idx": 3, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on...", "date": "", "ddg_snippet": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9oq0iY2Jxx", "content": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement..."}
+{"idx": 4, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "Abstract Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) introduce additional challenges. For instance, diverse preferences complicate the alignment process, and prediction errors in a trained reward model can become ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17618v3", "content": "Abstract Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) introduce additional challenges. For instance, diverse preferences complicate the alignment process, and prediction errors in a trained reward model can become ..."}
+{"idx": 5, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "The symmetric RL loss function is a novel approach that makes reinforcement learning more resistant to noisy feedback. It works by modifying how the AI calculates its learning errors, similar to methods used in supervised learning. The process involves: 1) Analyzing both positive and negative feedback symmetrically, 2) Reducing the impact of outlier feedback signals, and 3) Maintaining ...", "subpage_snippet": "", "source": "www.promptlayer.com", "link": "https://www.promptlayer.com/research-papers/symmetric-reinforcement-learning-loss-for-robust-learning-on-diverse-tasks-and-model-scales", "content": "The symmetric RL loss function is a novel approach that makes reinforcement learning more resistant to noisy feedback. It works by modifying how the AI calculates its learning errors, similar to methods used in supervised learning. The process involves: 1) Analyzing both positive and negative feedback symmetrically, 2) Reducing the impact of outlier feedback signals, and 3) Maintaining ..."}
+{"idx": 6, "title": "Symmetric_RL/README.md at master - GitHub", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_RL/blob/master/README.md", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance ."}
+{"idx": 7, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Published in ICML 2025, 2025", "subpage_snippet": "", "source": "aperrault.github.io", "link": "https://aperrault.github.io/publications/2025-07-13-symmetric/", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Published in ICML 2025, 2025"}
+{"idx": 8, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales: Paper and Code. Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty. Differing preferences ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/symmetric-reinforcement-learning-loss-for", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales: Paper and Code. Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty. Differing preferences ..."}
+{"idx": 9, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on...", "date": "", "ddg_snippet": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/symmetric-reinforcement-learning-loss-robust-learning-diverse", "content": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty."}
diff --git a/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_SPPO_PPO_noisy_environments_instability.jsonl b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_SPPO_PPO_noisy_environments_instability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bc19cfc620e17e4f1af509d745ae7114e02bb68d
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+++ b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_SPPO_PPO_noisy_environments_instability.jsonl
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+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ... Images GitHub - shashacks/Symmetric_RL Troubleshooting PPO Training Instability - apxml.com On Learning Symmetric Locomotion - xbpeng.github.io SYMMETRIC REINFORCEMENT LEARNING LOSS FOR ROBUST LEARNING ON ... Comparative Analysis of A3C and PPO Algorithms in ... PPO: How One Simple Innovation Solved Reinforcement Learning ...", "date": "", "ddg_snippet": "May 27, 2024 · We conduct experiments in discrete action tasks (Atari games) and continuous action space tasks (MuJoCo benchmark and Box2D) using Symmetric A2C (SA2C) and Symmetric PPO ( SPPO ), with and without added noise with especially notable performance in SPPO across different hyperparameters. View all To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . Training large language models with PPO in the RLHF setting can sometimes feel like navigating a minefield. While powerful, PPO training runs can be sensitive to hyperparameters and implementation details, occasionally leading to instability . At each PPO learning iteration, the agent interacts with the environment and collects a set of ( st,at,rt,st+1) tuples. These are then used to estimate the gradient of the PPO surrogate loss , LPPO, and standard stochastic descent algorithms can be used to update the network parameters θ to minimize this surrogate loss . We propose the symmetric RL loss for A2C and PPO , along with the gradient analysis that aligns with the gradient behavior of robust loss functions used in noisy classification tasks in Section 4.3. This research article presents a comparison between two mainstream Deep Reinforcement Learning (DRL) algorithms, Asynchronous Advantage Actor-Critic (A3C) and Proximal Policy Optimization ( PPO ), in the context of two diverse environments : CartPole and Lunar Lander. DRL algorithms are widely known for their effectiveness in training agents to navigate complex environments and achieve optimal ... Dec 12, 2024 · In 2017, Schulman and his team at OpenAI introduced PPO in their groundbreaking paper, transforming reinforcement learning with an elegantly simple solution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "May 27, 2024 · We conduct experiments in discrete action tasks (Atari games) and continuous action space tasks (MuJoCo benchmark and Box2D) using Symmetric A2C (SA2C) and Symmetric PPO ( SPPO ), with and without added noise with especially notable performance in SPPO across different hyperparameters. View all To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . Training large language models with PPO in the RLHF setting can sometimes feel like navigating a minefield. While powerful, PPO training runs can be sensitive to hyperparameters and implementation details, occasionally leading to instability . At each PPO learning iteration, the agent interacts with the environment and collects a set of ( st,at,rt,st+1) tuples. These are then used to estimate the gradient of the PPO surrogate loss , LPPO, and standard stochastic descent algorithms can be used to update the network parameters θ to minimize this surrogate loss . We propose the symmetric RL loss for A2C and PPO , along with the gradient analysis that aligns with the gradient behavior of robust loss functions used in noisy classification tasks in Section 4.3. This research article presents a comparison between two mainstream Deep Reinforcement Learning (DRL) algorithms, Asynchronous Advantage Actor-Critic (A3C) and Proximal Policy Optimization ( PPO ), in the context of two diverse environments : CartPole and Lunar Lander. DRL algorithms are widely known for their effectiveness in training agents to navigate complex environments and achieve optimal ... Dec 12, 2024 · In 2017, Schulman and his team at OpenAI introduced PPO in their groundbreaking paper, transforming reinforcement learning with an elegantly simple solution."}
+{"idx": 1, "title": "GitHub - shashacks/Symmetric_RL", "date": "", "ddg_snippet": "To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_RL", "content": "To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss ."}
+{"idx": 2, "title": "Troubleshooting PPO Training Instability - apxml.com", "date": "", "ddg_snippet": "Training large language models with PPO in the RLHF setting can sometimes feel like navigating a minefield. While powerful, PPO training runs can be sensitive to hyperparameters and implementation details, occasionally leading to instability .", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/rlhf-reinforcement-learning-human-feedback/chapter-4-rl-ppo-fine-tuning/troubleshooting-ppo-instability", "content": "Training large language models with PPO in the RLHF setting can sometimes feel like navigating a minefield. While powerful, PPO training runs can be sensitive to hyperparameters and implementation details, occasionally leading to instability ."}
+{"idx": 3, "title": "On Learning Symmetric Locomotion - xbpeng.github.io", "date": "", "ddg_snippet": "At each PPO learning iteration, the agent interacts with the environment and collects a set of ( st,at,rt,st+1) tuples. These are then used to estimate the gradient of the PPO surrogate loss , LPPO, and standard stochastic descent algorithms can be used to update the network parameters θ to minimize this surrogate loss .", "subpage_snippet": "", "source": "xbpeng.github.io", "link": "https://xbpeng.github.io/projects/SymLoco/SymLoco_2018.pdf", "content": "At each PPO learning iteration, the agent interacts with the environment and collects a set of ( st,at,rt,st+1) tuples. These are then used to estimate the gradient of the PPO surrogate loss , LPPO, and standard stochastic descent algorithms can be used to update the network parameters θ to minimize this surrogate loss ."}
+{"idx": 4, "title": "SYMMETRIC REINFORCEMENT LEARNING LOSS FOR ROBUST LEARNING ON ...", "date": "", "ddg_snippet": "We propose the symmetric RL loss for A2C and PPO , along with the gradient analysis that aligns with the gradient behavior of robust loss functions used in noisy classification tasks in Section 4.3.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9oq0iY2Jxx", "content": "We propose the symmetric RL loss for A2C and PPO , along with the gradient analysis that aligns with the gradient behavior of robust loss functions used in noisy classification tasks in Section 4.3."}
+{"idx": 5, "title": "Comparative Analysis of A3C and PPO Algorithms in ...", "date": "", "ddg_snippet": "This research article presents a comparison between two mainstream Deep Reinforcement Learning (DRL) algorithms, Asynchronous Advantage Actor-Critic (A3C) and Proximal Policy Optimization ( PPO ), in the context of two diverse environments : CartPole and Lunar Lander. DRL algorithms are widely known for their effectiveness in training agents to navigate complex environments and achieve optimal ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10703056", "content": "This research article presents a comparison between two mainstream Deep Reinforcement Learning (DRL) algorithms, Asynchronous Advantage Actor-Critic (A3C) and Proximal Policy Optimization ( PPO ), in the context of two diverse environments : CartPole and Lunar Lander. DRL algorithms are widely known for their effectiveness in training agents to navigate complex environments and achieve optimal ..."}
+{"idx": 6, "title": "PPO: How One Simple Innovation Solved Reinforcement Learning ...", "date": "", "ddg_snippet": "Dec 12, 2024 · In 2017, Schulman and his team at OpenAI introduced PPO in their groundbreaking paper, transforming reinforcement learning with an elegantly simple solution.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@etto.magni/ppo-how-one-simple-innovation-solved-reinforcement-learnings-stability-challenge-f372407320b8", "content": "Dec 12, 2024 · In 2017, Schulman and his team at OpenAI introduced PPO in their groundbreaking paper, transforming reinforcement learning with an elegantly simple solution."}
+{"idx": 7, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · 2024 · Cited by 1 — Notably, both SA2C and SPPO perform well in noisy environments . Addition- ally, SPPO shows consistent performance improvements across various ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618?", "content": "by JS Byun · 2024 · Cited by 1 — Notably, both SA2C and SPPO perform well in noisy environments . Addition- ally, SPPO shows consistent performance improvements across various ..."}
+{"idx": 8, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · Cited by 1 — This paper shows that the symmetric PPO loss improves performance over the regular PPO loss , probably because it mitigates the effects of the small batch.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9oq0iY2Jxx", "content": "by JS Byun · Cited by 1 — This paper shows that the symmetric PPO loss improves performance over the regular PPO loss , probably because it mitigates the effects of the small batch."}
+{"idx": 9, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "To enhance stability , we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165652", "content": "To enhance stability , we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance ..."}
diff --git a/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_for_Robust_Learning_on_Diverse_Tasks_and_Model_Scales_Equation_year_2024.jsonl b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_for_Robust_Learning_on_Diverse_Tasks_and_Model_Scales_Equation_year_2024.jsonl
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index 0000000000000000000000000000000000000000..c1a13eff97c513544d232a6512a9fc3cd3669c37
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+++ b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_for_Robust_Learning_on_Diverse_Tasks_and_Model_Scales_Equation_year_2024.jsonl
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+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty. Differing preferences can complicate the alignment process...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17618v2", "content": "Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty. Differing preferences can complicate the alignment process..."}
+{"idx": 1, "title": "ICML Poster Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) introduce additional challenges.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44897", "content": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) introduce additional challenges."}
+{"idx": 2, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty.We demonstrate performance improvements across various tasks and scales .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380935299_Symmetric_Reinforcement_Learning_Loss_for_Robust_Learning_on_Diverse_Tasks_and_Model_Scales", "content": "Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty.We demonstrate performance improvements across various tasks and scales ."}
+{"idx": 3, "title": "GitHub - shashacks/ Symmetric _RL", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_RL", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales . Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty."}
+{"idx": 4, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "The paper introduces a new loss function called Symmetric Reinforcement Learning (SRL) Loss that aims to improve the robustness and performance of reinforcement learning models across diverse tasks and model scales .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/symmetric-reinforcement-learning-loss-robust-learning-diverse", "content": "The paper introduces a new loss function called Symmetric Reinforcement Learning (SRL) Loss that aims to improve the robustness and performance of reinforcement learning models across diverse tasks and model scales ."}
+{"idx": 5, "title": "Symmetric Reinforcement Learning Loss for Robust ... | PromptLayer", "date": "", "ddg_snippet": "Reinforcement learning differs from traditional AI learning methods by mimicking how humans learn through trial and error. Unlike conventional approaches where AI learns from pre-labeled data, RL allows AI to learn from experience and feedback in real-time.", "subpage_snippet": "", "source": "www.promptlayer.com", "link": "https://www.promptlayer.com/research-papers/symmetric-reinforcement-learning-loss-for-robust-learning-on-diverse-tasks-and-model-scales", "content": "Reinforcement learning differs from traditional AI learning methods by mimicking how humans learn through trial and error. Unlike conventional approaches where AI learns from pre-labeled data, RL allows AI to learn from experience and feedback in real-time."}
+{"idx": 6, "title": "GSPO Reinforcement Learning | Unsloth Documentation", "date": "", "ddg_snippet": "Reinforcement Learning (RL) Guide. GSPO Reinforcement Learning . Train with GSPO (Group Sequence Policy Optimization) RL in Unsloth. We're introducing GSPO which is a variant of GRPO made by the Qwen team at Alibaba.", "subpage_snippet": "", "source": "docs.unsloth.ai", "link": "https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning", "content": "Reinforcement Learning (RL) Guide. GSPO Reinforcement Learning . Train with GSPO (Group Sequence Policy Optimization) RL in Unsloth. We're introducing GSPO which is a variant of GRPO made by the Qwen team at Alibaba."}
+{"idx": 7, "title": "PyTorch Loss Functions: The Ultimate Guide", "date": "", "ddg_snippet": "Learn about PyTorch loss functions: from built-in to custom, covering their implementation and monitoring techniques.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/pytorch-loss-functions", "content": "Learn about PyTorch loss functions: from built-in to custom, covering their implementation and monitoring techniques."}
+{"idx": 8, "title": "The Ohio State University - Cited by 26 - Reinforcement Learning", "date": "", "ddg_snippet": "Symmetric reinforcement learning loss for robust learning on diverse tasks and model scales .2017. DLPO: Diffusion Model Loss -Guided Reinforcement Learning for Fine-Tuning Text-to-Speech Diffusion Models .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=yKcK_BMAAAAJ&hl=en", "content": "Symmetric reinforcement learning loss for robust learning on diverse tasks and model scales .2017. DLPO: Diffusion Model Loss -Guided Reinforcement Learning for Fine-Tuning Text-to-Speech Diffusion Models ."}
+{"idx": 9, "title": "Andrew Perrault", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales . Robust Reinforcement Learning Under Minimax Regret for Green Security.", "subpage_snippet": "", "source": "aperrault.github.io", "link": "https://aperrault.github.io/publications/", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales . Robust Reinforcement Learning Under Minimax Regret for Green Security."}
diff --git a/data/sampled_jsons/T-Agent_$_AutoGPT_$_one-day_CVE-Bench_monetary_cost_per_task_year_2025.jsonl b/data/sampled_jsons/T-Agent_$_AutoGPT_$_one-day_CVE-Bench_monetary_cost_per_task_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bc03c15632446da5586c417ce10176454ceeec7a
--- /dev/null
+++ b/data/sampled_jsons/T-Agent_$_AutoGPT_$_one-day_CVE-Bench_monetary_cost_per_task_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "Under the one - day setting, we provide the agents with ... We apply CVE -Bench to evaluate various LLM agents under both zero- day and one - day settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "Under the one - day setting, we provide the agents with ... We apply CVE -Bench to evaluate various LLM agents under both zero- day and one - day settings."}
+{"idx": 1, "title": "Fully autonomous GPT-4 AI agents use Zero Days to successfully", "date": "", "ddg_snippet": "For over a decade I ’ ve been saying that AI will one day be capable of fully autonomously hacking (and patching) systems.", "subpage_snippet": "", "source": "www.fanaticalfuturist.com", "link": "https://www.fanaticalfuturist.com/2024/07/fully-autonomous-gpt-4-ai-agents-use-zero-days-to-successfully-hack-systems/", "content": "For over a decade I ’ ve been saying that AI will one day be capable of fully autonomously hacking (and patching) systems."}
+{"idx": 2, "title": "Fully autonomous GPT-4 AI agents use Zero Days to successfully", "date": "", "ddg_snippet": "For over a decade I ’ ve been saying that AI will one day be capable of fully autonomously hacking (and patching) systems.", "subpage_snippet": "", "source": "www.311institute.com", "link": "https://www.311institute.com/fully-autonomous-gpt-4-ai-agents-use-zero-days-to-successfully-hack-systems/", "content": "For over a decade I ’ ve been saying that AI will one day be capable of fully autonomously hacking (and patching) systems."}
+{"idx": 3, "title": "Data Breach Threat: Hackers Target TransUnion and Experian,", "date": "", "ddg_snippet": "Teen Hacking Suspect Arrested by London Police for GTA 6 and Uber Breach ... eBay, VMware, and McAfee Taken Down in Widespread Phishing Operation", "subpage_snippet": "", "source": "www.cysecurity.news", "link": "https://www.cysecurity.news/2023/11/data-breach-threat-hackers-target.html", "content": "Teen Hacking Suspect Arrested by London Police for GTA 6 and Uber Breach ... eBay, VMware, and McAfee Taken Down in Widespread Phishing Operation"}
+{"idx": 4, "title": "Workshops List – The One! DEF CON 31", "date": "", "ddg_snippet": "Title: Adrian Wood, David Mitchell – Creating and uncovering malicious containers Redux Scheduled Date and Time (Pacific Standard): Saturday, ...", "subpage_snippet": "", "source": "defcon.outel.org", "link": "https://defcon.outel.org/dcwp/dc31/activities/workshops-list/", "content": "Title: Adrian Wood, David Mitchell – Creating and uncovering malicious containers Redux Scheduled Date and Time (Pacific Standard): Saturday, ..."}
+{"idx": 5, "title": "AutoPentest: Enhancing Vulnerability Management With Autonomous", "date": "", "ddg_snippet": "... called AutoPentest, we integrate GPT-4o with the LLM agent framework LangChain 3 3 3 LangChain: https://www.langchain.com/ (accessed: May 10, 2025 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10321v1", "content": "... called AutoPentest, we integrate GPT-4o with the LLM agent framework LangChain 3 3 3 LangChain: https://www.langchain.com/ (accessed: May 10, 2025 ..."}
+{"idx": 6, "title": "Iranian Hackers Allegedly Exploiting Israeli Entities -", "date": "", "ddg_snippet": "Teen Hacking Suspect Arrested by London Police for GTA 6 and Uber Breach ... eBay, VMware, and McAfee Taken Down in Widespread Phishing Operation", "subpage_snippet": "", "source": "www.cysecurity.news", "link": "https://www.cysecurity.news/2022/08/iranian-hackers-allegedly-exploiting.html", "content": "Teen Hacking Suspect Arrested by London Police for GTA 6 and Uber Breach ... eBay, VMware, and McAfee Taken Down in Widespread Phishing Operation"}
+{"idx": 7, "title": "Lynx Technologies LLC careers | 2 Lynx Technologies LLC Jobs in", "date": "", "ddg_snippet": "We are building an advanced, AI-driven multi- agent software system designed to revolutionize task automation and code generation.", "subpage_snippet": "", "source": "cutshort.io", "link": "https://cutshort.io/company/jobs-at-lynx-technologies-llc-66-j5xSPOc3", "content": "We are building an advanced, AI-driven multi- agent software system designed to revolutionize task automation and code generation."}
+{"idx": 8, "title": "Automaton - Forem", "date": "", "ddg_snippet": "Day 1 : Introduction to Playwright – A Modern End-to-End Testing Framework ... One Will Scale With You? Automate Tweets From YouTube Videos Creating ...", "subpage_snippet": "", "source": "forem.com", "link": "https://forem.com/t/automaton", "content": "Day 1 : Introduction to Playwright – A Modern End-to-End Testing Framework ... One Will Scale With You? Automate Tweets From YouTube Videos Creating ..."}
+{"idx": 9, "title": "AI Trends Disrupting Software Teams - InfoQ", "date": "", "ddg_snippet": "Organizations are increasingly adopting AI agents that coordinate, plan, and execute complex business tasks with minimal human intervention.", "subpage_snippet": "", "source": "www.infoq.com", "link": "https://www.infoq.com/articles/ai-trends-disrupting-software-teams/", "content": "Organizations are increasingly adopting AI agents that coordinate, plan, and execute complex business tasks with minimal human intervention."}
diff --git a/data/sampled_jsons/Table_1_OR_Table_2_OR_Table_3_MCC_Contrastive_CRL_synthetic_real_ablation_results.jsonl b/data/sampled_jsons/Table_1_OR_Table_2_OR_Table_3_MCC_Contrastive_CRL_synthetic_real_ablation_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..035afb4068032997c07e82cfb9b9169f8bc817bf
--- /dev/null
+++ b/data/sampled_jsons/Table_1_OR_Table_2_OR_Table_3_MCC_Contrastive_CRL_synthetic_real_ablation_results.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "27 Feb 2025 — Figure 3: Experimental results for the Contrastive CRL method ... real and synthetic ablation datasets. NA refers to the group of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v1", "content": "27 Feb 2025 — Figure 3: Experimental results for the Contrastive CRL method ... real and synthetic ablation datasets. NA refers to the group of ..."}
+{"idx": 1, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "Figure 3: Experimental results for the Contrastive CRL method applied to the real ... Right: Average R2 correlation matrices for real and synthetic ablation ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f49eb15381f8ea150aedc07d26d294f4b7c91ada.pdf", "content": "Figure 3: Experimental results for the Contrastive CRL method applied to the real ... Right: Average R2 correlation matrices for real and synthetic ablation ..."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Table_4_CVE-Bench_cost_T-Agent_AutoGPT_Cy-Agent_monetary_USD_numerical_values_year_2025.jsonl b/data/sampled_jsons/Table_4_CVE-Bench_cost_T-Agent_AutoGPT_Cy-Agent_monetary_USD_numerical_values_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..08a63210ab230895c08542a1b850e06ebf16f464
--- /dev/null
+++ b/data/sampled_jsons/Table_4_CVE-Bench_cost_T-Agent_AutoGPT_Cy-Agent_monetary_USD_numerical_values_year_2025.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "Built upon the sandbox framework, we introduce CVE - Bench , the first real-world cybersecurity benchmark for LLM agents .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "Built upon the sandbox framework, we introduce CVE - Bench , the first real-world cybersecurity benchmark for LLM agents ."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "10 Apr 2025 — Furthermore, Cy-Agent leads to significantly lower success rates than T-Agent and AutoGPT . We find that this is because the action-execution- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v3", "content": "10 Apr 2025 — Furthermore, Cy-Agent leads to significantly lower success rates than T-Agent and AutoGPT . We find that this is because the action-execution- ..."}
+{"idx": 2, "title": "CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "AutoGPT T - Agent Cy - Agent . We present the costs of using CVE - Bench to evalu-ate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "AutoGPT T - Agent Cy - Agent . We present the costs of using CVE - Bench to evalu-ate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task."}
+{"idx": 3, "title": "CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "AutoGPT T - Agent Cy - Agent . We present the costs of using CVE - Bench to evalu-ate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task.", "subpage_snippet": "", "source": "yuxuan18.github.io", "link": "https://yuxuan18.github.io/assets/pub/cvebench.pdf", "content": "AutoGPT T - Agent Cy - Agent . We present the costs of using CVE - Bench to evalu-ate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time to execute one task."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Table_4_HumanAct12_FID_0.077_OR_0.07_OR_0.08_MLD.jsonl b/data/sampled_jsons/Table_4_HumanAct12_FID_0.077_OR_0.07_OR_0.08_MLD.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f0acceb3d65f8e0fed500dd24dbbdf01411e690
--- /dev/null
+++ b/data/sampled_jsons/Table_4_HumanAct12_FID_0.077_OR_0.07_OR_0.08_MLD.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlexMotion: Lightweight, Physics-Aware, and Controllable ...", "date": "", "ddg_snippet": "28 Jan 2025 — HumanML3D, derived from AMASS and HumanAct12 , contains 14,616 motion sequences with 44,970 textual annotations. KIT-ML provides 3,911 motion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.16778v1", "content": "28 Jan 2025 — HumanML3D, derived from AMASS and HumanAct12 , contains 14,616 motion sequences with 44,970 textual annotations. KIT-ML provides 3,911 motion ..."}
+{"idx": 1, "title": "Motion Flow Matching for Human Motion Synthesis and ...", "date": "", "ddg_snippet": "Table 4 : Evaluation of action-to-motion on the HumanAct12 dataset. NFE denotes the number of function evaluations. → ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08895v1", "content": "Table 4 : Evaluation of action-to-motion on the HumanAct12 dataset. NFE denotes the number of function evaluations. → ..."}
+{"idx": 2, "title": "MOTION FLOW MATCHING FOR EFFICIENT HUMAN ...", "date": "", "ddg_snippet": "We present our results on the action-to-motion dataset HumanAct12 in Table 4 . Our method achieves results that are comparable to various baselines, all while ... 21 pages", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/61a1228b68fe5cc477b223010e69dce5ad7aff34.pdf", "content": "We present our results on the action-to-motion dataset HumanAct12 in Table 4 . Our method achieves results that are comparable to various baselines, all while ... 21 pages"}
+{"idx": 3, "title": "Executing Your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "by X Chen · 2023 · Cited by 494 — MLD achieves state-of-the-art ac- curacy and diversity on UESTC and competitive results on. HumanAct12 , indicating that diffusion models in motion la- tent can ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf", "content": "by X Chen · 2023 · Cited by 494 — MLD achieves state-of-the-art ac- curacy and diversity on UESTC and competitive results on. HumanAct12 , indicating that diffusion models in motion la- tent can ... 11 pages"}
+{"idx": 4, "title": "MMM: Generative Masked Motion Model - CVF Open Access", "date": "", "ddg_snippet": "by E Pinyoanuntapong · 2024 · Cited by 84 — are originally from AMASS [20] and HumanAct12 [10]. Each sequence is ... Table 4 . Ablation results on the masking ratio during training. Masking. 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Pinyoanuntapong_MMM_Generative_Masked_Motion_Model_CVPR_2024_paper.pdf", "content": "by E Pinyoanuntapong · 2024 · Cited by 84 — are originally from AMASS [20] and HumanAct12 [10]. Each sequence is ... Table 4 . Ablation results on the masking ratio during training. Masking. 10 pages"}
+{"idx": 5, "title": "arXiv:2401.11115v3 [cs.CV] 24 Jan 2024", "date": "", "ddg_snippet": "24 Jan 2024 — Following the experimental setup by Tevet et al., we train the MDM (MotionMix) from scratch on the HumanAct12 and UESTC datasets for 750K and 2M ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/motionmix-weakly-supervised-diffusion-for-controllable-1a4e690k44.pdf", "content": "24 Jan 2024 — Following the experimental setup by Tevet et al., we train the MDM (MotionMix) from scratch on the HumanAct12 and UESTC datasets for 750K and 2M ..."}
+{"idx": 6, "title": "FLEXMOTION", "date": "", "ddg_snippet": "by A Tashakori — HumanML3D, derived from. AMASS and HumanAct12 , contains 14,616 motion sequences with 44,970 textual annotations. ... Table 4 : Performance comparison of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=7652tHbbVE", "content": "by A Tashakori — HumanML3D, derived from. AMASS and HumanAct12 , contains 14,616 motion sequences with 44,970 textual annotations. ... Table 4 : Performance comparison of ..."}
+{"idx": 7, "title": "arXiv:2312.02256v1 [cs.CV] 4 Dec 2023", "date": "", "ddg_snippet": "by W Zhou · Cited by 79 — 0.08 . 0.09. 0.10. 0.11. 0.12. FID . HumanAct12 . ACTOR. MDM. MLD . Ours. Figure 3 ... Table 4 . Ablation study on different sampling steps on the ...", "subpage_snippet": "", "source": "storage.prod.researchhub.com", "link": "https://storage.prod.researchhub.com/uploads/papers/2023/12/06/2312.02256v1.pdf", "content": "by W Zhou · Cited by 79 — 0.08 . 0.09. 0.10. 0.11. 0.12. FID . HumanAct12 . ACTOR. MDM. MLD . Ours. Figure 3 ... Table 4 . Ablation study on different sampling steps on the ..."}
+{"idx": 8, "title": "Deep Learning Theory and Applications", "date": "", "ddg_snippet": "10 Jul 2024 — ... Table 4 presents a comparison of this research work to other ... HumanAct12 , NTU 120. RGB+D, and Actions in Supermarket Dataset. The ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-66694-0.pdf", "content": "10 Jul 2024 — ... Table 4 presents a comparison of this research work to other ... HumanAct12 , NTU 120. RGB+D, and Actions in Supermarket Dataset. The ..."}
+{"idx": 9, "title": "Spatio-Temporal Control for Masked Motion Synthesis", "date": "", "ddg_snippet": "... become a widespread choice for text-to-motion generation, operating directly in the motion space [ 48 , 60 , 23 ] , VAE latent space [ 5 ] , or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10780v2", "content": "... become a widespread choice for text-to-motion generation, operating directly in the motion space [ 48 , 60 , 23 ] , VAE latent space [ 5 ] , or ..."}
diff --git a/data/sampled_jsons/Table_4_T-Agent_AutoGPT_One-day_cost_$4.82_$0.38.jsonl b/data/sampled_jsons/Table_4_T-Agent_AutoGPT_One-day_cost_$4.82_$0.38.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1e62460e08c643d4318e21ad5acc96e55b8c5ab2
--- /dev/null
+++ b/data/sampled_jsons/Table_4_T-Agent_AutoGPT_One-day_cost_$4.82_$0.38.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tray Merlin AI vs. AutoGPT : Compare top AI automation platforms.", "date": "", "ddg_snippet": "AutoGPT revolutionizes AI development by enabling the creation of autonomous agents capable of complex task execution… without constant human input.SmythOS outperforms Tray Merlin AI and AutoGPT - start building agents for free and discover first hand.", "subpage_snippet": "", "source": "smythos.com", "link": "https://smythos.com/ai-agents/ai-agent-builders/tray-merlin-ai-vs-autogpt/", "content": "AutoGPT revolutionizes AI development by enabling the creation of autonomous agents capable of complex task execution… without constant human input.SmythOS outperforms Tray Merlin AI and AutoGPT - start building agents for free and discover first hand."}
+{"idx": 1, "title": "274 рез. по запросу «Businesswoman feet on table »... | Shutterstock", "date": "", "ddg_snippet": "Стоковые видео без лицензионных платежей в количестве 274 по запросу «businesswoman feet on table », в том числе высококачественные клипы в формате 4 K и HD для загрузки.", "subpage_snippet": "", "source": "www.shutterstock.com", "link": "https://www.shutterstock.com/ru/video/search/businesswoman-feet-on-table", "content": "Стоковые видео без лицензионных платежей в количестве 274 по запросу «businesswoman feet on table », в том числе высококачественные клипы в формате 4 K и HD для загрузки."}
+{"idx": 2, "title": "CORE-Bench: Fostering the Credibility of Published Research", "date": "", "ddg_snippet": "Table 4 : Primary task-specific modifications to AutoGPT . This table summarizes the modifications made to create CORE- Agent for each level of difficulty.We ran AutoGPT and CORE- Agent using both GPT - 4 o and GPT - 4 o-mini with an API cost limit of $ 4 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.11363", "content": "Table 4 : Primary task-specific modifications to AutoGPT . This table summarizes the modifications made to create CORE- Agent for each level of difficulty.We ran AutoGPT and CORE- Agent using both GPT - 4 o and GPT - 4 o-mini with an API cost limit of $ 4 ."}
+{"idx": 3, "title": "(PDF) CORE-Bench: Fostering the Credibility of Published Research...", "date": "", "ddg_snippet": "We evaluated two baseline agents : the general-purpose AutoGPT and a task-specific agent called CORE- Agent . We tested both variants using two underlying language models: GPT - 4 o and GPT - 4 o-mini.To evaluate the impact of our $ 4 cost limit on performance, we ran. CORE- Agent .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384085477_CORE-Bench_Fostering_the_Credibility_of_Published_Research_Through_a_Computational_Reproducibility_Agent_Benchmark", "content": "We evaluated two baseline agents : the general-purpose AutoGPT and a task-specific agent called CORE- Agent . We tested both variants using two underlying language models: GPT - 4 o and GPT - 4 o-mini.To evaluate the impact of our $ 4 cost limit on performance, we ran. CORE- Agent ."}
+{"idx": 4, "title": "Контрольная работа по английскому языку для 4 класса по УМК...", "date": "", "ddg_snippet": "5. Robby is (под) _ the table . 3. Составь предложения. 1 . She/ is/a girl.3. How many/much apples are there on the table ?", "subpage_snippet": "", "source": "xn--j1ahfl.xn--p1ai", "link": "https://xn--j1ahfl.xn--p1ai/library/test_po_anglijskomu_yaziku_dlya_4_klassa_203427.html", "content": "5. Robby is (под) _ the table . 3. Составь предложения. 1 . She/ is/a girl.3. How many/much apples are there on the table ?"}
+{"idx": 5, "title": "3 Look at the picture and complete the sentences with prepositions of...", "date": "", "ddg_snippet": "2 The coffee table is . the armchair. 1 bank - baker's - vase - library 2 fireplace - sofa - armchair - bathtub 3 newsagent's - toy shop - supermarket - aspirin 4 spring - May - autumn - winter 5 first - two - ninth - sixth.", "subpage_snippet": "", "source": "www.euroki.org", "link": "https://www.euroki.org/koza/look-at-the-picture-and-complete-the-sentences-with-prepositions-of-place-the-window-is--the-sofa-the", "content": "2 The coffee table is . the armchair. 1 bank - baker's - vase - library 2 fireplace - sofa - armchair - bathtub 3 newsagent's - toy shop - supermarket - aspirin 4 spring - May - autumn - winter 5 first - two - ninth - sixth."}
+{"idx": 6, "title": "Английский язык. 4 кл. класс (К. М. Баранова, Д. . Дули...)", "date": "", "ddg_snippet": "II Special Days (Happy New Year!, Valentine’s Day /Peter and Fevronia Day ). There is one optional unit at the back of each book containing activities for New Year (Part 1 ) and Valentine’s Day /Peter and Fevronia Day (Part 2) which should be covered as a lead-up to the respective celebrations.", "subpage_snippet": "", "source": "storage.yandexcloud.net", "link": "https://storage.yandexcloud.net/prod-file-public/6579/47c7/b672/fd8f95f6-6579-47c7-b672-369ce5613b01-preview.pdf", "content": "II Special Days (Happy New Year!, Valentine’s Day /Peter and Fevronia Day ). There is one optional unit at the back of each book containing activities for New Year (Part 1 ) and Valentine’s Day /Peter and Fevronia Day (Part 2) which should be covered as a lead-up to the respective celebrations."}
+{"idx": 7, "title": "Упражнения на глагол TO BE в Past Simple с ответами", "date": "", "ddg_snippet": "6. Mary (be) sick three days ago. 7. He (be) at the cinema last night. 8. It (be) very hot yesterday.Упражнение 7. Поставьте глагол to be в отрицательную форму Past Simple. Example : There weren’ t any tomatoes in the fridge. 1 . There (be) any flowers on the table .", "subpage_snippet": "", "source": "EnglishWeb.ru", "link": "https://EnglishWeb.ru/grammar/to-be-past-simple-exercises.html", "content": "6. Mary (be) sick three days ago. 7. He (be) at the cinema last night. 8. It (be) very hot yesterday.Упражнение 7. Поставьте глагол to be в отрицательную форму Past Simple. Example : There weren’ t any tomatoes in the fridge. 1 . There (be) any flowers on the table ."}
+{"idx": 8, "title": "SciELO Brasil - Physical activity in periods of social distancing due to...", "date": "", "ddg_snippet": "A large majority of respondents (82.6%) had scores higher or equal to eight for the scale of anxiety, and 48.8% had scores above this cut-off for the scale of depression. Table 4 describes the association between HADS scores and selected variables.", "subpage_snippet": "", "source": "www.scielo.br", "link": "https://www.scielo.br/j/csc/a/pXxV8j7mbr3gv4LbLFKZB5K/?lang=en", "content": "A large majority of respondents (82.6%) had scores higher or equal to eight for the scale of anxiety, and 48.8% had scores above this cut-off for the scale of depression. Table 4 describes the association between HADS scores and selected variables."}
+{"idx": 9, "title": "Degradation modeling of polymer electrolyte membrane water...", "date": "", "ddg_snippet": "Table 4 . Cell-wide prognostics models.The control algorithm sought to maximize the profit of the electrolyzer over the course of a day , accounting for the local electricity price, the value of produced H2, and the cost of H2 storage.", "subpage_snippet": "", "source": "eprints.gla.ac.uk", "link": "https://eprints.gla.ac.uk/359718/2/359718.pdf", "content": "Table 4 . Cell-wide prognostics models.The control algorithm sought to maximize the profit of the electrolyzer over the course of a day , accounting for the local electricity price, the value of produced H2, and the cost of H2 storage."}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflict_in_Language_Models_gaotangli.github.io_World_Capital_Table_3.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflict_in_Language_Models_gaotangli.github.io_World_Capital_Table_3.jsonl
new file mode 100644
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+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Gaotang Li Yuzhong Chen Hanghang Tong. Abstract. Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v2", "content": "Gaotang Li Yuzhong Chen Hanghang Tong. Abstract. Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge ."}
+{"idx": 2, "title": "GaotangLi (Oliver Li ) · GitHub", "date": "", "ddg_snippet": "GaotangLi . github . io .Code for the paper \" Taming Knowledge Conflict in Language Models \".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GaotangLi", "content": "GaotangLi . github . io .Code for the paper \" Taming Knowledge Conflict in Language Models \"."}
+{"idx": 3, "title": "Taming Knowledge Conflicts in Language Models | OpenReview", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\"...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0cEZyhHEks&referrer=[the+profile+of+Hanghang+Tong](/profile?id=~Hanghang_Tong2)", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\"..."}
+{"idx": 4, "title": "(PDF) Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. Abstract.and World Capital as a concrete example in this section. Previous works analyzing model internals typically adhere. to two “locate-and-edit” principles (Xu et al.,2024)", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389894493_Taming_Knowledge_Conflicts_in_Language_Models", "content": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. Abstract.and World Capital as a concrete example in this section. Previous works analyzing model internals typically adhere. to two “locate-and-edit” principles (Xu et al.,2024)"}
+{"idx": 5, "title": "Knowledge Conflict - a gaotang Collection", "date": "", "ddg_snippet": "Parametric dataset related to the paper \" Taming Knowledge Conflict in Language Models \".", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/gaotang/knowledge-conflict-681408090438726d04c440ca", "content": "Parametric dataset related to the paper \" Taming Knowledge Conflict in Language Models \"."}
+{"idx": 6, "title": "Taming Knowledge Conflicts in", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Gaotang Li †, Yuzhong Chen‡, Hanghang Tong†. †University of Illinois Urbana-Champaign ‡ VISA Research Contact: gaotang 3 @illinois.edu.", "subpage_snippet": "", "source": "actionable-interpretability.github.io", "link": "https://actionable-interpretability.github.io/posters/ICML_25_JuICE_Poster+-+Gaotang+Li.pdf", "content": "Taming Knowledge Conflicts in Language Models . Gaotang Li †, Yuzhong Chen‡, Hanghang Tong†. †University of Illinois Urbana-Champaign ‡ VISA Research Contact: gaotang 3 @illinois.edu."}
+{"idx": 7, "title": "Github Repository for Top LLM Datasets | Analytics Vidhya", "date": "", "ddg_snippet": "Explore the Github Repository for LLM Datasets and transform your AI projects with quality data for model training.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/09/github-repository-for-top-llm-datasets/", "content": "Explore the Github Repository for LLM Datasets and transform your AI projects with quality data for model training."}
+{"idx": 8, "title": "Paper. io 2 World Conflict", "date": "", "ddg_snippet": "Paper. io 2 World Conflict . Defend your country's honor in this new mode! Capture as much territory as you can. Fight players from various countries.Welcome to the new exciting addition to the Paper. io 2 universe – the World Conflict mode.", "subpage_snippet": "", "source": "paperio.site", "link": "https://paperio.site/conflict/", "content": "Paper. io 2 World Conflict . Defend your country's honor in this new mode! Capture as much territory as you can. Fight players from various countries.Welcome to the new exciting addition to the Paper. io 2 universe – the World Conflict mode."}
+{"idx": 9, "title": "Gaotang Li @ gaotangli - Twitter Profile | Sotwe", "date": "", "ddg_snippet": "GaotangLi 's profile image. Download All. Share. Gaotang Li .Science of Language Models . Reasoning LMs. Joined November 2024.", "subpage_snippet": "", "source": "www.sotwe.com", "link": "https://www.sotwe.com/gaotangli", "content": "GaotangLi 's profile image. Download All. Share. Gaotang Li .Science of Language Models . Reasoning LMs. Joined November 2024."}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_JUICE_Gaotang_Li.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_JUICE_Gaotang_Li.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..32706acb635f5b3edee0c5219377cbe1226d124a
--- /dev/null
+++ b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_JUICE_Gaotang_Li.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models Images Taming Knowledge Conflict in Language Models Taming Knowledge Conflicts in Language Models - GitHub [PDF] Taming Knowledge Conflicts in Language Models ... Paper page - Taming Knowledge Conflicts in Language Models Taming Knowledge Conflicts in Language Models - papers.cool ICML Poster Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Mar 14, 2025 · View a PDF of the paper titled Taming Knowledge Conflicts in Language Models , by Gaotang Li and 2 other authors View all Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ... This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated. Mar 14, 2025 · This work proposes Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ... May 6, 2025 · Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art performance and robust generalization, achieving significant and consistent improvement across different domains under various conflict types. #1 Taming Knowledge Conflicts in Language Models [PDF 3] [Copy] [Kimi 1] [REL] Authors: Gaotang Li , Yuzhong Chen, Hanghang Tong Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or ... Language models frequently encounter \" knowledge conflicts ,\" where their pre-trained knowledge contradicts the information provided by specific contexts. These conflicts often arise in context-dependent systems, such as retrieval-augmented generation and tools integrated with language models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Mar 14, 2025 · View a PDF of the paper titled Taming Knowledge Conflicts in Language Models , by Gaotang Li and 2 other authors View all Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ... This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated. Mar 14, 2025 · This work proposes Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ... May 6, 2025 · Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art performance and robust generalization, achieving significant and consistent improvement across different domains under various conflict types. #1 Taming Knowledge Conflicts in Language Models [PDF 3] [Copy] [Kimi 1] [REL] Authors: Gaotang Li , Yuzhong Chen, Hanghang Tong Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or ... Language models frequently encounter \" knowledge conflicts ,\" where their pre-trained knowledge contradicts the information provided by specific contexts. These conflicts often arise in context-dependent systems, such as retrieval-augmented generation and tools integrated with language models ."}
+{"idx": 1, "title": "ICML Poster Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Language models frequently encounter \" knowledge conflicts ,\" where their pre-trained knowledge contradicts the information provided by specific contexts. These conflicts often arise in context-dependent systems, such as retrieval-augmented generation and tools integrated with language models .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46677", "content": "Language models frequently encounter \" knowledge conflicts ,\" where their pre-trained knowledge contradicts the information provided by specific contexts. These conflicts often arise in context-dependent systems, such as retrieval-augmented generation and tools integrated with language models ."}
+{"idx": 2, "title": "Taming Knowledge Conflict in Language Models", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ...", "subpage_snippet": "", "source": "gaotangli.github.io", "link": "https://gaotangli.github.io/project_page/Taming-Knowledge-Conflict/", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ..."}
+{"idx": 3, "title": "Taming Knowledge Conflicts in Language Models - GitHub", "date": "", "ddg_snippet": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GaotangLi/JUICE", "content": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated."}
+{"idx": 4, "title": "[PDF] Taming Knowledge Conflicts in Language Models ...", "date": "", "ddg_snippet": "Mar 14, 2025 · This work proposes Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Taming-Knowledge-Conflicts-in-Language-Models-Li-Chen/b7ba9df4eb239708cf48f25be87b5bceeca010e3", "content": "Mar 14, 2025 · This work proposes Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ..."}
+{"idx": 5, "title": "Paper page - Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "May 6, 2025 · Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art performance and robust generalization, achieving significant and consistent improvement across different domains under various conflict types.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.10996", "content": "May 6, 2025 · Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art performance and robust generalization, achieving significant and consistent improvement across different domains under various conflict types."}
+{"idx": 6, "title": "Taming Knowledge Conflicts in Language Models - papers.cool", "date": "", "ddg_snippet": "#1 Taming Knowledge Conflicts in Language Models [PDF 3] [Copy] [Kimi 1] [REL] Authors: Gaotang Li , Yuzhong Chen, Hanghang Tong Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2503.10996", "content": "#1 Taming Knowledge Conflicts in Language Models [PDF 3] [Copy] [Kimi 1] [REL] Authors: Gaotang Li , Yuzhong Chen, Hanghang Tong Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or ..."}
+{"idx": 7, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "by G Li — Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0cEZyhHEks", "content": "by G Li — Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or ..."}
+{"idx": 8, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "14 Mar 2025 — We evaluate JuICE in two distinct settings: enhancing parametric beliefs and enhancing contextual reliance. For the first setting, we use six ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v1", "content": "14 Mar 2025 — We evaluate JuICE in two distinct settings: enhancing parametric beliefs and enhancing contextual reliance. For the first setting, we use six ..."}
+{"idx": 9, "title": "[Literature Review] Taming Knowledge Conflicts in ...", "date": "", "ddg_snippet": "14 Mar 2025 — The paper titled \" Taming Knowledge Conflicts in Language Models \" by Gaotang Li and colleagues addresses significant issues related to knowledge conflicts in ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/taming-knowledge-conflicts-in-language-models", "content": "14 Mar 2025 — The paper titled \" Taming Knowledge Conflicts in Language Models \" by Gaotang Li and colleagues addresses significant issues related to knowledge conflicts in ..."}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_ResearchGate_PDF_Table_3_World_Capital.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_ResearchGate_PDF_Table_3_World_Capital.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b605d9cf200d90c10453e5a413a83803b0af6873
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Mar 14, 2025 · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Mar 14, 2025 · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ..."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models - arXiv.org", "date": "", "ddg_snippet": "Abstract Language Models (LMs) often encounter knowl-edge conflicts when parametric memory con-tradicts contextual knowledge . Previous works attribute this conflict to the interplay between “memory heads” and “context heads”, attention heads assumed to promote either memory or con-text exclusively. In this study, we go beyond this fundamental assumption by uncovering a criti-cal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10996", "content": "Abstract Language Models (LMs) often encounter knowl-edge conflicts when parametric memory con-tradicts contextual knowledge . Previous works attribute this conflict to the interplay between “memory heads” and “context heads”, attention heads assumed to promote either memory or con-text exclusively. In this study, we go beyond this fundamental assumption by uncovering a criti-cal ..."}
+{"idx": 2, "title": "[PDF] Taming Knowledge Conflicts in Language Models ...", "date": "", "ddg_snippet": "Mar 14, 2025 · This work proposes Just Run Twice (JuICE), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Taming-Knowledge-Conflicts-in-Language-Models-Li-Chen/b7ba9df4eb239708cf48f25be87b5bceeca010e3", "content": "Mar 14, 2025 · This work proposes Just Run Twice (JuICE), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ..."}
+{"idx": 3, "title": "Taming Knowledge Conflicts in Language Models - GitHub", "date": "", "ddg_snippet": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GaotangLi/JUICE", "content": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated."}
+{"idx": 4, "title": "ICML Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Poster in Workshop: Actionable Interpretability Taming Knowledge Conflicts in Language Models Gaotang Li · Yuzhong Chen · Hanghang Tong [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/49596", "content": "Poster in Workshop: Actionable Interpretability Taming Knowledge Conflicts in Language Models Gaotang Li · Yuzhong Chen · Hanghang Tong [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT"}
+{"idx": 5, "title": "Taming Knowledge Conflicts in Language Models - papers.cool", "date": "", "ddg_snippet": "#1 Taming Knowledge Conflicts in Language Models [ PDF 3 ] [Copy] [Kimi 1] [REL] Authors: Gaotang Li, Yuzhong Chen, Hanghang Tong Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2503.10996", "content": "#1 Taming Knowledge Conflicts in Language Models [ PDF 3 ] [Copy] [Kimi 1] [REL] Authors: Gaotang Li, Yuzhong Chen, Hanghang Tong Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or ..."}
+{"idx": 6, "title": "Resolving Knowledge Conflicts in Large Language Models", "date": "", "ddg_snippet": "It includes diverse and complex situations of knowledge conflict , knowl-edge from diverse entities and domains, two synthetic conflict creation methods, and settings with progressively increasing dificulty to reflect real-istic knowledge conflicts .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.00935", "content": "It includes diverse and complex situations of knowledge conflict , knowl-edge from diverse entities and domains, two synthetic conflict creation methods, and settings with progressively increasing dificulty to reflect real-istic knowledge conflicts ."}
+{"idx": 7, "title": "( PDF ) Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "PDF | Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge .Find, read and cite all the research you need on ResearchGate .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389894493_Taming_Knowledge_Conflicts_in_Language_Models", "content": "PDF | Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge .Find, read and cite all the research you need on ResearchGate ."}
+{"idx": 8, "title": "Taming Knowledge Conflicts in Language Models | OpenReview", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\"...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0cEZyhHEks&referrer=[the+profile+of+Hanghang+Tong](/profile?id=~Hanghang_Tong2)", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\"..."}
+{"idx": 9, "title": "From Internal Conflict to Contextual Adaptation of Language Models", "date": "", "ddg_snippet": "Knowledge Conflicts : The research highlights three primary sources of factual errors in LMs: fact popularity, temporality, and disputability. It emphasizes the need to evaluate models in the context of these knowledge conflicts to understand their behavior better .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-From-Internal-Conflict-clz1rdbcb3l4101ctnzw9ug6p", "content": "Knowledge Conflicts : The research highlights three primary sources of factual errors in LMs: fact popularity, temporality, and disputability. It emphasizes the need to evaluate models in the context of these knowledge conflicts to understand their behavior better ."}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_substitution-based_conflicts_coherence-based_conflicts.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_substitution-based_conflicts_coherence-based_conflicts.jsonl
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+{"idx": 0, "title": "LANE: Logic Alignment of Non-tuning Large Language Models and", "date": "", "ddg_snippet": "... into several subclasses based on the methods they employ, including factorization [ 58 , 44 , 9 , 6 ] , topic modeling [ 34 , 39 ] , graph [ 18 , 21 , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.02833v1", "content": "... into several subclasses based on the methods they employ, including factorization [ 58 , 44 , 9 , 6 ] , topic modeling [ 34 , 39 ] , graph [ 18 , 21 , ..."}
+{"idx": 1, "title": "NeurIPS 2024 Schedule", "date": "", "ddg_snippet": "Opening the Language Model Pipeline: A Tutorial on Data Preparation, Model Training, and Adaptation ... intrinsic functions that give fine-grained ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/calendar", "content": "Opening the Language Model Pipeline: A Tutorial on Data Preparation, Model Training, and Adaptation ... intrinsic functions that give fine-grained ..."}
+{"idx": 2, "title": "NeurIPS 2020 Papers", "date": "", "ddg_snippet": "Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion ... Models : Generative Temporal Difference Learning for ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2020/papers.html", "content": "Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion ... Models : Generative Temporal Difference Learning for ..."}
+{"idx": 3, "title": "Can LLMs Generate User Stories and Assess Their Quality?", "date": "", "ddg_snippet": "Large Language Models (LLMs) [ 7 , 8 , 9 ] demonstrated high-level performance in natural language processing tasks, and their use is becoming ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15157v1", "content": "Large Language Models (LLMs) [ 7 , 8 , 9 ] demonstrated high-level performance in natural language processing tasks, and their use is becoming ..."}
+{"idx": 4, "title": "New APPS: Art, Politics, Philosophy, Science: Catarina Dutilh", "date": "", "ddg_snippet": "In recent decades, the importance of taking into account the social aspects of knowledge became increasingly acknowledged in epistemology, leading to ...", "subpage_snippet": "", "source": "www.newappsblog.com", "link": "https://www.newappsblog.com/catarina-dutilh-novaes/", "content": "In recent decades, the importance of taking into account the social aspects of knowledge became increasingly acknowledged in epistemology, leading to ..."}
+{"idx": 5, "title": "GitHub - tomohideshibata/BERT-related-papers: BERT-related", "date": "", "ddg_snippet": "AMMUS : A Survey of Transformer- based Pretrained Models in Natural Language Processing ... in Natural Language Processing via Large Pre-Trained ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tomohideshibata/BERT-related-papers", "content": "AMMUS : A Survey of Transformer- based Pretrained Models in Natural Language Processing ... in Natural Language Processing via Large Pre-Trained ..."}
+{"idx": 6, "title": "Rorty, Richard | Internet Encyclopedia of Philosophy", "date": "", "ddg_snippet": "... away from an initial interest in linguistic philosophy as a way of finding a neutral standpoint from which to establish a strict science of language ...", "subpage_snippet": "", "source": "iep.utm.edu", "link": "https://iep.utm.edu/rorty/", "content": "... away from an initial interest in linguistic philosophy as a way of finding a neutral standpoint from which to establish a strict science of language ..."}
+{"idx": 7, "title": "complex business reasoning | Enlightened Conflict", "date": "", "ddg_snippet": "I should note language is always shared within context (words can take on different meaning in different environments) including organizational ...", "subpage_snippet": "", "source": "brucemctague.com", "link": "https://brucemctague.com/tag/complex-business-reasoning", "content": "I should note language is always shared within context (words can take on different meaning in different environments) including organizational ..."}
+{"idx": 8, "title": "What’s Decidable About Causally Consistent Shared Memory? |", "date": "", "ddg_snippet": "... consistency is one of the most fundamental consistency models weaker than sequential consistency, which is especially common and well studied in ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3505273", "content": "... consistency is one of the most fundamental consistency models weaker than sequential consistency, which is especially common and well studied in ..."}
+{"idx": 9, "title": "Neuroscience | We are all Interconnected even with the Universe", "date": "", "ddg_snippet": "... in neuroscience and biology indicates that besides some significant cognitive embellishments on the original phenomena, selectivity and choice is a ...", "subpage_snippet": "", "source": "rodger-ricketts.com", "link": "https://rodger-ricketts.com/tag/neuroscience/", "content": "... in neuroscience and biology indicates that besides some significant cognitive embellishments on the original phenomena, selectivity and choice is a ..."}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_substitution_conflict_coherent_conflict_definition.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_substitution_conflict_coherent_conflict_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3d5f37fa998650d931480f6f4b4da6a25f07f6bd
--- /dev/null
+++ b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_substitution_conflict_coherent_conflict_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "14 Mar 2025 — Taming Knowledge Conflicts in Language Models . Report issue for ... substitution conflict inputs, and coherent conflict inputs. We find ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v1", "content": "14 Mar 2025 — Taming Knowledge Conflicts in Language Models . Report issue for ... substitution conflict inputs, and coherent conflict inputs. We find ..."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "... conflict types. Fi- nally, we ... Taming Knowledge Conflicts in Language Models ... ( substitution ), and paragraph-level ( coherent ) conflicts (Sec.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0cEZyhHEks", "content": "... conflict types. Fi- nally, we ... Taming Knowledge Conflicts in Language Models ... ( substitution ), and paragraph-level ( coherent ) conflicts (Sec."}
+{"idx": 2, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "by G Li · 2025 — Our findings show that JUICE is highly robust. 20. Page 21. Taming Knowledge Conflicts in Language Models to variations in input prompt formats, consistently ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2503.10996", "content": "by G Li · 2025 — Our findings show that JUICE is highly robust. 20. Page 21. Taming Knowledge Conflicts in Language Models to variations in input prompt formats, consistently ..."}
+{"idx": 3, "title": "LANE: Logic Alignment of Non-tuning Large Language Models and", "date": "", "ddg_snippet": "In this paper, we propose an innovative explainable recommendation framework LANE that leverages large language models [ 5 , 10 , 46 ] requiring no ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.02833v1", "content": "In this paper, we propose an innovative explainable recommendation framework LANE that leverages large language models [ 5 , 10 , 46 ] requiring no ..."}
+{"idx": 4, "title": "complex business reasoning | Enlightened Conflict", "date": "", "ddg_snippet": "I should note language is always shared within context (words can take on different meaning in different environments) including organizational ...", "subpage_snippet": "", "source": "brucemctague.com", "link": "https://brucemctague.com/tag/complex-business-reasoning", "content": "I should note language is always shared within context (words can take on different meaning in different environments) including organizational ..."}
+{"idx": 5, "title": "Spotlight Posters", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Spotlight Poster.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/2025SpotlightPosters", "content": "Taming Knowledge Conflicts in Language Models . Spotlight Poster."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=selective+fact+forgetting", "content": "Taming Knowledge Conflicts in Language Models · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual ..."}
+{"idx": 7, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models · Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Taming Knowledge Conflicts in Language Models · Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models ..."}
+{"idx": 8, "title": "New APPS: Art, Politics, Philosophy, Science: Catarina Dutilh", "date": "", "ddg_snippet": "In recent decades, the importance of taking into account the social aspects of knowledge became increasingly acknowledged in epistemology, leading to ...", "subpage_snippet": "", "source": "www.newappsblog.com", "link": "https://www.newappsblog.com/catarina-dutilh-novaes/", "content": "In recent decades, the importance of taking into account the social aspects of knowledge became increasingly acknowledged in epistemology, leading to ..."}
+{"idx": 9, "title": "Rorty, Richard | Internet Encyclopedia of Philosophy", "date": "", "ddg_snippet": "... away from an initial interest in linguistic philosophy as a way of finding a neutral standpoint from which to establish a strict science of language ...", "subpage_snippet": "", "source": "iep.utm.edu", "link": "https://iep.utm.edu/rorty/", "content": "... away from an initial interest in linguistic philosophy as a way of finding a neutral standpoint from which to establish a strict science of language ..."}
diff --git a/data/sampled_jsons/Tanh_Demon_adaptive_temperature_tau_calculation_standard_deviation_variance_reward_estimates.jsonl b/data/sampled_jsons/Tanh_Demon_adaptive_temperature_tau_calculation_standard_deviation_variance_reward_estimates.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..571d07ac078e20c370eb617a7c0fb85379f06afe
--- /dev/null
+++ b/data/sampled_jsons/Tanh_Demon_adaptive_temperature_tau_calculation_standard_deviation_variance_reward_estimates.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Standard Deviation Calculator", "date": "", "ddg_snippet": "The calculator above computes population standard deviation and sample standard deviation , as well as confidence interval approximations. Population Standard Deviation The population standard deviation , the standard definition of σ, is used when an entire population can be measured, and is the square root of the variance of a given data set.", "subpage_snippet": "", "source": "www.calculator.net", "link": "https://www.calculator.net/standard-deviation-calculator.html", "content": "The calculator above computes population standard deviation and sample standard deviation , as well as confidence interval approximations. Population Standard Deviation The population standard deviation , the standard definition of σ, is used when an entire population can be measured, and is the square root of the variance of a given data set."}
+{"idx": 1, "title": "[2302.04775] Adap-||tau;$: Adaptively Modulating Embedding ... Variance aware reward smoothing for deep reinforcement ... Reward Estimation for Variance Reduction in Deep ... AdaptiveTanh — pina-mathlab 0.2.2.post2508 documentation Standard Deviation Calculator Training-free Diffusion Model Alignment with Sampling Demons - arXiv.… Training-free Diffusion Model Alignment with Sampling Demons - arXiv.… Training-free Diffusion Model Alignment with Sampling Demons - arXiv.… Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Feb 9, 2023 · Towards this end, we first make a comprehensive analyses of $\\ tau $ to fully understand its role on recommendation. We then accordingly develop an adaptive fine-grained strategy Adap-$\\ tau $ for the temperature with satisfying four desirable properties including adaptivity, personalized, efficiency and model-agnostic. Oct 11, 2021 · We show that the proposed method reduces the variance of rewards and mitigates the rewards drop problem without changing the formulation of the value function. Furthermore, the theoretical analysis of convergence of VAR is provided, which is derived from the γ -contraction operator and the fixed point attribute of the value function. Here, we propose a simple method for updating model-free RL algorithms to compensate for stochastic corrupted reward signals. We suggest learning an estimator for both the local expected reward and the value function – that is, using a direct estimate of rewards ^R(st) to update the dis-counted value function V (st) and policy (st), rather than the sampled rewards . See also Original reference: Godfrey, Luke B., and Michael S. Gashler. A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks. 2015 7th international joint conference on knowledge discovery, knowledge engineering and knowledge management (IC3K). Vol. 1. IEEE, 2015. DOI: arXiv preprint arXiv:1602.01321.. Jagtap, Ameya D ... The calculator above computes population standard deviation and sample standard deviation , as well as confidence interval approximations. Population Standard Deviation The population standard deviation , the standard definition of σ, is used when an entire population can be measured, and is the square root of the variance of a given data set. What reward functions does tanh demon use? We employ our Tanh Demon with various reward functions, such as Aes (LAION, 2023), ImageReward (IR)(Xu et al., 2023), PickScore (Pick)(Kirstain et al., 2023), HPSv2 (Wu et al., 2023), and a scaled sum (Ensemble) of Aes, IR, Pick, and HPSv2. Which method is better tanh-C or Boltzmann demon? Regarding reward queries, the Tanh Demon method outperforms Tanh-C, followed by the Boltzmann Demon method. Regarding execution time, however, Tanh-C is recommended over the Tanh Demon if computational time is limited. Appendix G Additional Results with Various Reward Functions. Why do we compare best-of-N and tanh with the ensemble reward? By comparing best-of-N and Tanh with the Ensemble reward, our method achieves superior performance on each objective using the Ensemble , demonstrating not only the ability to integrate a mixture of rewards but also generating a superior samples that outperforms all individual best samples selected by best-of-N methods. The base configuration is formulae-sequence 𝐾 16 𝛽 0.1 K=16,\\beta=0.1 italic_K = 16 , italic_β = 0.1, with an adaptive temperature 𝜏 \\ tau italic_τ for the Tanh Demon .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2302.04775", "content": "Feb 9, 2023 · Towards this end, we first make a comprehensive analyses of $\\ tau $ to fully understand its role on recommendation. We then accordingly develop an adaptive fine-grained strategy Adap-$\\ tau $ for the temperature with satisfying four desirable properties including adaptivity, personalized, efficiency and model-agnostic. Oct 11, 2021 · We show that the proposed method reduces the variance of rewards and mitigates the rewards drop problem without changing the formulation of the value function. Furthermore, the theoretical analysis of convergence of VAR is provided, which is derived from the γ -contraction operator and the fixed point attribute of the value function. Here, we propose a simple method for updating model-free RL algorithms to compensate for stochastic corrupted reward signals. We suggest learning an estimator for both the local expected reward and the value function – that is, using a direct estimate of rewards ^R(st) to update the dis-counted value function V (st) and policy (st), rather than the sampled rewards . See also Original reference: Godfrey, Luke B., and Michael S. Gashler. A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks. 2015 7th international joint conference on knowledge discovery, knowledge engineering and knowledge management (IC3K). Vol. 1. IEEE, 2015. DOI: arXiv preprint arXiv:1602.01321.. Jagtap, Ameya D ... The calculator above computes population standard deviation and sample standard deviation , as well as confidence interval approximations. Population Standard Deviation The population standard deviation , the standard definition of σ, is used when an entire population can be measured, and is the square root of the variance of a given data set. What reward functions does tanh demon use? We employ our Tanh Demon with various reward functions, such as Aes (LAION, 2023), ImageReward (IR)(Xu et al., 2023), PickScore (Pick)(Kirstain et al., 2023), HPSv2 (Wu et al., 2023), and a scaled sum (Ensemble) of Aes, IR, Pick, and HPSv2. Which method is better tanh-C or Boltzmann demon? Regarding reward queries, the Tanh Demon method outperforms Tanh-C, followed by the Boltzmann Demon method. Regarding execution time, however, Tanh-C is recommended over the Tanh Demon if computational time is limited. Appendix G Additional Results with Various Reward Functions. Why do we compare best-of-N and tanh with the ensemble reward? By comparing best-of-N and Tanh with the Ensemble reward, our method achieves superior performance on each objective using the Ensemble , demonstrating not only the ability to integrate a mixture of rewards but also generating a superior samples that outperforms all individual best samples selected by best-of-N methods. The base configuration is formulae-sequence 𝐾 16 𝛽 0.1 K=16,\\beta=0.1 italic_K = 16 , italic_β = 0.1, with an adaptive temperature 𝜏 \\ tau italic_τ for the Tanh Demon ."}
+{"idx": 2, "title": "Variance aware reward smoothing for deep reinforcement ...", "date": "", "ddg_snippet": "Oct 11, 2021 · We show that the proposed method reduces the variance of rewards and mitigates the rewards drop problem without changing the formulation of the value function. Furthermore, the theoretical analysis of convergence of VAR is provided, which is derived from the γ -contraction operator and the fixed point attribute of the value function.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231221009139", "content": "Oct 11, 2021 · We show that the proposed method reduces the variance of rewards and mitigates the rewards drop problem without changing the formulation of the value function. Furthermore, the theoretical analysis of convergence of VAR is provided, which is derived from the γ -contraction operator and the fixed point attribute of the value function."}
+{"idx": 3, "title": "Reward Estimation for Variance Reduction in Deep ...", "date": "", "ddg_snippet": "Here, we propose a simple method for updating model-free RL algorithms to compensate for stochastic corrupted reward signals. We suggest learning an estimator for both the local expected reward and the value function – that is, using a direct estimate of rewards ^R(st) to update the dis-counted value function V (st) and policy (st), rather than the sampled rewards .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v87/romoff18a/romoff18a.pdf", "content": "Here, we propose a simple method for updating model-free RL algorithms to compensate for stochastic corrupted reward signals. We suggest learning an estimator for both the local expected reward and the value function – that is, using a direct estimate of rewards ^R(st) to update the dis-counted value function V (st) and policy (st), rather than the sampled rewards ."}
+{"idx": 4, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "The base configuration is formulae-sequence 𝐾 16 𝛽 0.1 K=16,\\beta=0.1 italic_K = 16 , italic_β = 0.1, with an adaptive temperature 𝜏 \\ tau italic_τ for the Tanh Demon .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v2", "content": "The base configuration is formulae-sequence 𝐾 16 𝛽 0.1 K=16,\\beta=0.1 italic_K = 16 , italic_β = 0.1, with an adaptive temperature 𝜏 \\ tau italic_τ for the Tanh Demon ."}
+{"idx": 5, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "to the standard deviation of the estimations .Algorithm 2 Tanh Demon with Adaptive Temperature . 1: Input: A list of ODE reward estimate .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v1", "content": "to the standard deviation of the estimations .Algorithm 2 Tanh Demon with Adaptive Temperature . 1: Input: A list of ODE reward estimate ."}
+{"idx": 6, "title": "How To Calculate The Standard Deviation - YouTube", "date": "", "ddg_snippet": "This Statistics video tutorial explains how to calculate the standard deviation using 2 examples.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=IaTFpp-uzp0", "content": "This Statistics video tutorial explains how to calculate the standard deviation using 2 examples."}
+{"idx": 7, "title": "How to Calculate Standard Deviation : 12 Steps (with Pictures)", "date": "", "ddg_snippet": "Calculating the Standard Deviation . Download Article. Step 1 Find your variance figure.Go through finding the mean, variance and standard deviation again. This will allow you to check your answer.[14] X Research source.", "subpage_snippet": "", "source": "www.wikihow.com", "link": "https://www.wikihow.com/Calculate-Standard-Deviation", "content": "Calculating the Standard Deviation . Download Article. Step 1 Find your variance figure.Go through finding the mean, variance and standard deviation again. This will allow you to check your answer.[14] X Research source."}
+{"idx": 8, "title": "Standard Deviation - Formula | How to Calculate Standard Deviation ?", "date": "", "ddg_snippet": "Standard deviation is the degree of dispersion or the scatter of the data points relative to its mean. We have different standard deviation formulas to find the standard deviation for sample, population, grouped data, and ungrouped data.", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/data/standard-deviation/", "content": "Standard deviation is the degree of dispersion or the scatter of the data points relative to its mean. We have different standard deviation formulas to find the standard deviation for sample, population, grouped data, and ungrouped data."}
+{"idx": 9, "title": "Standard Deviation Calculator", "date": "", "ddg_snippet": "Use this calculator to easily calculate the standard deviation of a sample, or to estimate the population standard deviation based on a random sample from it. Standard deviation for binomial data.", "subpage_snippet": "", "source": "www.gigacalculator.com", "link": "https://www.gigacalculator.com/calculators/standard-deviation-calculator.php", "content": "Use this calculator to easily calculate the standard deviation of a sample, or to estimate the population standard deviation based on a random sample from it. Standard deviation for binomial data."}
diff --git a/data/sampled_jsons/TheWebConf_2024_accepted_papers.jsonl b/data/sampled_jsons/TheWebConf_2024_accepted_papers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3cb08eae9c51b9e191d51636e30ae370c60783de
--- /dev/null
+++ b/data/sampled_jsons/TheWebConf_2024_accepted_papers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Accepted Papers - Research Tracks - The Web Conf", "date": "", "ddg_snippet": "The submission versions of these accepted papers as well as their reviews can be found on OpenReview. The camera-ready versions can be found on ACM Digital Library. Free access in perpetuity is offered via SIGWEB. Intelligent Model Update Strategy for Sequential Recommendation", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/research-tracks/", "content": "The submission versions of these accepted papers as well as their reviews can be found on OpenReview. The camera-ready versions can be found on ACM Digital Library. Free access in perpetuity is offered via SIGWEB. Intelligent Model Update Strategy for Sequential Recommendation"}
+{"idx": 1, "title": "ACM TheWebConf 2024 Conference | OpenReview", "date": "", "ddg_snippet": "The Web Conference 2024 TheWebConf24 Singapore May 13 2024 https://www2024. thewebconf .org/ thewebconf24-pcchairs@acm.org", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=ACM.org/TheWebConf/2024/Conference", "content": "The Web Conference 2024 TheWebConf24 Singapore May 13 2024 https://www2024. thewebconf .org/ thewebconf24-pcchairs@acm.org"}
+{"idx": 2, "title": "Proceedings of the ACM Web Conference 2024 | ACM Conferences", "date": "", "ddg_snippet": "It is our great pleasure to welcome you to The ACM Web Conference 2024 held in person with virtual components on May 13-17, 2024 , in Singapore. It is the 33rd edition of a series of yearly international conferences on the future directions of the Web.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3589334", "content": "It is our great pleasure to welcome you to The ACM Web Conference 2024 held in person with virtual components on May 13-17, 2024 , in Singapore. It is the 33rd edition of a series of yearly international conferences on the future directions of the Web."}
+{"idx": 3, "title": "Accepted Papers", "date": "", "ddg_snippet": "Accepted Papers Research Tracks All Papers Social Network Analysis and Graph Algorithms User Modeling and Personalization Web Mining and Content Analysis", "subpage_snippet": "", "source": "archives.iw3c2.org", "link": "https://archives.iw3c2.org/www2023/program/accepted-papers/", "content": "Accepted Papers Research Tracks All Papers Social Network Analysis and Graph Algorithms User Modeling and Personalization Web Mining and Content Analysis"}
+{"idx": 4, "title": "Paper accepted at ACM The WebConf 2024 - Technische Universität Dresden", "date": "", "ddg_snippet": "Our paper \"From Files to Streams: Revisiting Web History and Exploring Potentials for Future Prospects\" has been accepted at ACM The Web Conference 2024 (formerly known as WWW). It will be presented in the Track History of the Web at this top-tier conference . Congrats Lucas!", "subpage_snippet": "", "source": "tu-dresden.de", "link": "https://tu-dresden.de/ing/informatik/sya/netd/about/news/paper-accepted-at-acm-the-webconf-2024", "content": "Our paper \"From Files to Streams: Revisiting Web History and Exploring Potentials for Future Prospects\" has been accepted at ACM The Web Conference 2024 (formerly known as WWW). It will be presented in the Track History of the Web at this top-tier conference . Congrats Lucas!"}
+{"idx": 5, "title": "Accepted Papers - Resource - The Web Conf", "date": "", "ddg_snippet": "Accepted Papers - Resource Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi and Tommaso Di Noia", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/resource/", "content": "Accepted Papers - Resource Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi and Tommaso Di Noia"}
+{"idx": 6, "title": "Accepted Papers - Demo - The Web Conf", "date": "", "ddg_snippet": "Accepted Papers - Demo Automating the Information Extraction from Semi-Structured Interview Transcripts", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/demo/", "content": "Accepted Papers - Demo Automating the Information Extraction from Semi-Structured Interview Transcripts"}
+{"idx": 7, "title": "Accepted Papers - Industry Track - The Web Conf", "date": "", "ddg_snippet": "Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/industry/", "content": "Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty"}
+{"idx": 8, "title": "Accepted Papers - TheWebConf 2022", "date": "", "ddg_snippet": "Web of Things, Ubiquitous and Mobile ComputingSpecial tracks:", "subpage_snippet": "", "source": "archives.iw3c2.org", "link": "https://archives.iw3c2.org/www2022/accepted-papers/", "content": "Web of Things, Ubiquitous and Mobile ComputingSpecial tracks:"}
+{"idx": 9, "title": "Accepted Papers - Short Papers - The Web Conf", "date": "", "ddg_snippet": "Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang and James Caverlee", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/short-papers/", "content": "Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang and James Caverlee"}
diff --git a/data/sampled_jsons/The_Illusion_of_State_in_State-Space_Models_Merrill_abstract_finite_state_machines_year_2024.jsonl b/data/sampled_jsons/The_Illusion_of_State_in_State-Space_Models_Merrill_abstract_finite_state_machines_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..183c313fb0b9c3bf522417528f99855e1cbc6e73
--- /dev/null
+++ b/data/sampled_jsons/The_Illusion_of_State_in_State-Space_Models_Merrill_abstract_finite_state_machines_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2404.08819] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "State -space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "State -space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 1, "title": "The Illusion of State in State-Space Models - Semantic Scholar", "date": "", "ddg_snippet": "Analysis of state -space models reveals that SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems. State -space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Illusion-of-State-in-State-Space-Models-Merrill-Petty/917479a7a72ee7c1fb320c14d770e30ef322ef28", "content": "Analysis of state -space models reveals that SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems. State -space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer ..."}
+{"idx": 2, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "State -space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/merrill24a.html", "content": "State -space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 3, "title": "The illusion of state in state-space models | Proceedings of the 41st ...", "date": "", "ddg_snippet": "State -space models (SSMs) have emerged as a potential alternative to transformers. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3693514", "content": "State -space models (SSMs) have emerged as a potential alternative to transformers. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent ..."}
+{"idx": 4, "title": "The Illusion of State in State-Space Models - OpenReview", "date": "", "ddg_snippet": "Abstract State -space models (SSMs) have emerged as a potential alternative to transformers. One theoret-ical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks. But do SSMs truly have an ad ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QZgo9JZpLq", "content": "Abstract State -space models (SSMs) have emerged as a potential alternative to transformers. One theoret-ical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks. But do SSMs truly have an ad ..."}
+{"idx": 5, "title": "The Illusion of State in State-Space Models - NSF Public Access", "date": "", "ddg_snippet": "State -space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10535878-illusion-state-state-space-models", "content": "State -space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 6, "title": "Will Merrill: The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Will Merrill : The Illusion of State in State -Space Models Formal Languages and Neural Networks Seminar 2.02K subscribers Subscribed", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=4-VXe1yPDjk", "content": "Will Merrill : The Illusion of State in State -Space Models Formal Languages and Neural Networks Seminar 2.02K subscribers Subscribed"}
+{"idx": 7, "title": "The Illusion of State in State-Space Models - NASA/ADS", "date": "", "ddg_snippet": "State -space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill and Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240408819M/abstract", "content": "State -space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill and Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 8, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Overview Examines the concept of \" state \" in state -space models, a widely used framework in machine learning and control theory Argues that the notion of \" state \" in these models is often an illusion, and the models may be better characterized as \"history-based\" rather than \" state -based\" Provides a new perspective on the foundations of state -space models and their limitations Plain English ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/illusion-state-state-space-models", "content": "Overview Examines the concept of \" state \" in state -space models, a widely used framework in machine learning and control theory Argues that the notion of \" state \" in these models is often an illusion, and the models may be better characterized as \"history-based\" rather than \" state -based\" Provides a new perspective on the foundations of state -space models and their limitations Plain English ..."}
+{"idx": 9, "title": "The Illusion of State in State-Space Models - arXiv.org", "date": "", "ddg_snippet": "Abstract State -space models (SSMs) have emerged as a po-tential alternative architecture for building large language models (LLMs) compared to the previ-ously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential com-putation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.08819v1", "content": "Abstract State -space models (SSMs) have emerged as a po-tential alternative architecture for building large language models (LLMs) compared to the previ-ously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential com-putation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to ..."}
diff --git a/data/sampled_jsons/The_Open_Images_Dataset_V4_Unified_Image_Classification,_Object_Detection,_and_Visual_Relationship_D_year_2018.jsonl b/data/sampled_jsons/The_Open_Images_Dataset_V4_Unified_Image_Classification,_Object_Detection,_and_Visual_Relationship_D_year_2018.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f6a23a5646a8a428ef882984da57dcfaafd6f0e
--- /dev/null
+++ b/data/sampled_jsons/The_Open_Images_Dataset_V4_Unified_Image_Classification,_Object_Detection,_and_Visual_Relationship_D_year_2018.jsonl
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+{"idx": 0, "title": "Unified image classification, object detection, and visual ...", "date": "", "ddg_snippet": "by A Kuznetsova · 2018 · Cited by 3478 — We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification , object detection and visual relationship detection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1811.00982", "content": "by A Kuznetsova · 2018 · Cited by 3478 — We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification , object detection and visual relationship detection."}
+{"idx": 1, "title": "The Open Images Dataset V4", "date": "", "ddg_snippet": "by A Kuznetsova · 2020 · Cited by 3478 — We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification , object detection and visual relationship detection.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-020-01316-z", "content": "by A Kuznetsova · 2020 · Cited by 3478 — We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification , object detection and visual relationship detection."}
+{"idx": 2, "title": "The Open Images Dataset V4", "date": "", "ddg_snippet": "by A Kuznetsova · 2018 · Cited by 3478 — Abstract We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification , ob- ject detection and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1811.00982", "content": "by A Kuznetsova · 2018 · Cited by 3478 — Abstract We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification , ob- ject detection and ..."}
+{"idx": 3, "title": "[PDF] The Open Images Dataset V4", "date": "", "ddg_snippet": "We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification , object detection and visual relationship detection ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Open-Images-Dataset-V4-Kuznetsova-Rom/5ac18d505ed6d10e8692cbb7d33f6852e6782692", "content": "We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification , object detection and visual relationship detection ..."}
+{"idx": 4, "title": "open_images_v4 | TensorFlow Datasets", "date": "", "ddg_snippet": "1 Jun 2024 — Description: Open Images is a dataset of ~9M images that have been annotated with image -level labels and object bounding boxes.", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/datasets/catalog/open_images_v4", "content": "1 Jun 2024 — Description: Open Images is a dataset of ~9M images that have been annotated with image -level labels and object bounding boxes."}
+{"idx": 5, "title": "Review: The Open Images Dataset V4 - Sik-Ho Tsang - Medium", "date": "", "ddg_snippet": "In this paper, Open Images V4 , is proposed, which is a dataset of 9.2M images with unified annotations for image classification , object ...", "subpage_snippet": "", "source": "sh-tsang.medium.com", "link": "https://sh-tsang.medium.com/review-the-open-images-dataset-v4-a0095b32b723", "content": "In this paper, Open Images V4 , is proposed, which is a dataset of 9.2M images with unified annotations for image classification , object ..."}
+{"idx": 6, "title": "[P] \"The Open Images Dataset V4: Unified ...", "date": "", "ddg_snippet": "\" The Open Images Dataset V4 : Unified image classification , object detection, and visual relationship detection at scale \", Kuznetsova et al ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/9wjcxd/p_the_open_images_dataset_v4_unified_image/", "content": "\" The Open Images Dataset V4 : Unified image classification , object detection, and visual relationship detection at scale \", Kuznetsova et al ..."}
+{"idx": 7, "title": "The Open Images Dataset V4", "date": "", "ddg_snippet": "by K Alina · 2020 · Cited by 3478 — We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification , object detection and visual relationship detection .", "subpage_snippet": "", "source": "search.proquest.com", "link": "https://search.proquest.com/openview/271b8e1da93f008ec48bde94801f5ce3/1?pq-origsite=gscholar&cbl=1456341", "content": "by K Alina · 2020 · Cited by 3478 — We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification , object detection and visual relationship detection ."}
+{"idx": 8, "title": "Evaluation results in the openimages v4 paper · Issue #81", "date": "", "ddg_snippet": "17 Dec 2018 — \" The Open Images Dataset V4 Unified image classification , object detection, and visual relationship detection at scale \". We are focusing the ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openimages/dataset/issues/81", "content": "17 Dec 2018 — \" The Open Images Dataset V4 Unified image classification , object detection, and visual relationship detection at scale \". We are focusing the ..."}
+{"idx": 9, "title": "Open Images V7 Dataset - Ultralytics YOLO Docs", "date": "", "ddg_snippet": "Provides a unified platform for image classification , object detection , relationship detection , instance segmentation, and multimodal image descriptions.", "subpage_snippet": "", "source": "docs.ultralytics.com", "link": "https://docs.ultralytics.com/datasets/detect/open-images-v7/", "content": "Provides a unified platform for image classification , object detection , relationship detection , instance segmentation, and multimodal image descriptions."}
diff --git a/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_2019_year_2019.jsonl b/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_2019_year_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cff338c23698c21d042d140b6737e8016d50f3f6
--- /dev/null
+++ b/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_2019_year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — [Submitted on 12 Sep 2019 ]. Title: The Randomized Midpoint Method for Log-Concave Sampling . Authors:Ruoqi Shen , Yin Tat Lee . View a PDF of the paper titled ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.05503", "content": "by R Shen · 2019 · Cited by 161 — [Submitted on 12 Sep 2019 ]. Title: The Randomized Midpoint Method for Log-Concave Sampling . Authors:Ruoqi Shen , Yin Tat Lee . View a PDF of the paper titled ..."}
+{"idx": 1, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — The Randomized Midpoint Method for Log-Concave Sampling . Part of Advances in ... Authors. Ruoqi Shen , Yin Tat Lee . Abstract. Sampling from log-concave ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/8483-the-randomized-midpoint-method-for-log-concave-sampling", "content": "by R Shen · 2019 · Cited by 161 — The Randomized Midpoint Method for Log-Concave Sampling . Part of Advances in ... Authors. Ruoqi Shen , Yin Tat Lee . Abstract. Sampling from log-concave ..."}
+{"idx": 2, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Paper.pdf", "content": "by R Shen · 2019 · Cited by 161 — Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning."}
+{"idx": 3, "title": "The randomized midpoint method for log-concave sampling", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 160 — The randomized midpoint method for log-concave sampling . AUTHORs: Ruoqi Shen .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3454287.3454475", "content": "by R Shen · 2019 · Cited by 160 — The randomized midpoint method for log-concave sampling . AUTHORs: Ruoqi Shen ."}
+{"idx": 4, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — The Randomized Midpoint Method for Log-Concave Sampling . Ruoqi Shen ... Journal of Machine Learning Research, 20(73):1–46, 2019 . [19] ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05503", "content": "by R Shen · 2019 · Cited by 161 — The Randomized Midpoint Method for Log-Concave Sampling . Ruoqi Shen ... Journal of Machine Learning Research, 20(73):1–46, 2019 . [19] ..."}
+{"idx": 5, "title": "[PDF] The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "The Randomized Midpoint Method for Log-Concave Sampling · Ruoqi Shen , Y. Lee · Published in Neural Information Processing… 12 September 2019 · Mathematics, ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Randomized-Midpoint-Method-for-Log-Concave-Shen-Lee/ba7e89968438f816cb6169c56533abec62034b93", "content": "The Randomized Midpoint Method for Log-Concave Sampling · Ruoqi Shen , Y. Lee · Published in Neural Information Processing… 12 September 2019 · Mathematics, ..."}
+{"idx": 6, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "The Randomized Midpoint Method for Log-Concave Sampling . Ruoqi Shen · Yin Tat Lee . [ Abstract ] [ Visit Track 4 Session 2 ]. [ Paper] [ Slides] [ Slides].", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2019/spotlight/15747", "content": "The Randomized Midpoint Method for Log-Concave Sampling . Ruoqi Shen · Yin Tat Lee . [ Abstract ] [ Visit Track 4 Session 2 ]. [ Paper] [ Slides] [ Slides]."}
+{"idx": 7, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "The Randomized Midpoint Method for Log-Concave Sampling . Ruoqi Shen ,Yin Tat Lee +1 moreUniversity of Washington ... 2019 ... 33rd Conference on Neural Information ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/the-randomized-midpoint-method-for-log-concave-sampling-4jwrm4tta7", "content": "The Randomized Midpoint Method for Log-Concave Sampling . Ruoqi Shen ,Yin Tat Lee +1 moreUniversity of Washington ... 2019 ... 33rd Conference on Neural Information ..."}
+{"idx": 8, "title": "LOG-CONCAVE SAMPLING ON COMPACT SUPPORTS", "date": "", "ddg_snippet": "by L Yu · Cited by 1 — Ruoqi Shen and Yin Tat Lee . The randomized midpoint method for log-concave sampling . Advances in Neural Information Processing Systems, 32, 2019 . Robert L ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LX9m5iWBun", "content": "by L Yu · Cited by 1 — Ruoqi Shen and Yin Tat Lee . The randomized midpoint method for log-concave sampling . Advances in Neural Information Processing Systems, 32, 2019 . Robert L ..."}
+{"idx": 9, "title": "Faster Diffusion Sampling with Randomized Midpoints", "date": "", "ddg_snippet": "by S Gupta — This paper considers diffusion sampling via the randomized midpoint method for log-concave sampling . Authors improve the existing dimension dependence for ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MT3aOfXIbY", "content": "by S Gupta — This paper considers diffusion sampling via the randomized midpoint method for log-concave sampling . Authors improve the existing dimension dependence for ..."}
diff --git a/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_methodology_approach_does_not_use_P.jsonl b/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_methodology_approach_does_not_use_P.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..25119321c34cef928c3de2080261e0a5f96fe87a
--- /dev/null
+++ b/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_methodology_approach_does_not_use_P.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hamiltonian Descent Algorithms for Optimization: Accelerated", "date": "", "ddg_snippet": "... the links between optimization and sampling in the opposite direction, by studying how to translate the Hamiltonian Monte Carlo ( HMC ), a classical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.12553v2", "content": "... the links between optimization and sampling in the opposite direction, by studying how to translate the Hamiltonian Monte Carlo ( HMC ), a classical ..."}
+{"idx": 1, "title": "The randomized midpoint method for log-concave sampling", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 160 — In our paper, we propose a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion (ULD). It can achieve ε · D error (in 2- ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3454287.3454475", "content": "by R Shen · 2019 · Cited by 160 — In our paper, we propose a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion (ULD). It can achieve ε · D error (in 2- ..."}
+{"idx": 2, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — The framework can be used to solve not only the log - concave sampling problem, but any problem that involves simulating (stochastic) differential equations. 1 ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Paper.pdf", "content": "by R Shen · 2019 · Cited by 161 — The framework can be used to solve not only the log - concave sampling problem, but any problem that involves simulating (stochastic) differential equations. 1 ..."}
+{"idx": 3, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — The general approach uses a MCMC-based algorithm that often includes two steps. The first step involves the choice of a Markov process with a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05503", "content": "by R Shen · 2019 · Cited by 161 — The general approach uses a MCMC-based algorithm that often includes two steps. The first step involves the choice of a Markov process with a ..."}
+{"idx": 4, "title": "Faster Diffusion Sampling with Randomized Midpoints", "date": "", "ddg_snippet": "by S Gupta · 2024 — In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log - concave sampling [SL19]. We prove that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.00924?", "content": "by S Gupta · 2024 — In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log - concave sampling [SL19]. We prove that ..."}
+{"idx": 5, "title": "Parallel Simulation for Log-concave Sampling and Score ...", "date": "", "ddg_snippet": "Our parallel Picard method for strongly log - concave sampling is summarized in Algorithm 1. ... The Randomized Midpoint Method for Log - Concave Sampling . Advances ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43916", "content": "Our parallel Picard method for strongly log - concave sampling is summarized in Algorithm 1. ... The Randomized Midpoint Method for Log - Concave Sampling . Advances ..."}
+{"idx": 6, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "The framework can be used to solve not only the log - concave sampling problem, but any problem that involves simulating (stochastic) differential equations.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/the-randomized-midpoint-method-for-log-concave-sampling-4jwrm4tta7", "content": "The framework can be used to solve not only the log - concave sampling problem, but any problem that involves simulating (stochastic) differential equations."}
+{"idx": 7, "title": "FASTER DIFFUSION SAMPLING WITH RANDOMIZED ...", "date": "", "ddg_snippet": "We begin by recalling Shen and Lee's random- ized midpoint method applied to approximate the underdamped Langevin process , for log - concave sampling in the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=yuIw1Na9ux&name=pdf", "content": "We begin by recalling Shen and Lee's random- ized midpoint method applied to approximate the underdamped Langevin process , for log - concave sampling in the ..."}
+{"idx": 8, "title": "Fast parallel sampling under isoperimetry", "date": "", "ddg_snippet": "by N Anari · 2024 · Cited by 14 — To illustrate, while accuracy in 2-Wasserstein distance can be achieved using eO(d1/3) gradient evaluations using a randomized midpoint algorithm ( Shen and Lee , ... 25 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/anari24a/anari24a.pdf", "content": "by N Anari · 2024 · Cited by 14 — To illustrate, while accuracy in 2-Wasserstein distance can be achieved using eO(d1/3) gradient evaluations using a randomized midpoint algorithm ( Shen and Lee , ... 25 pages"}
+{"idx": 9, "title": "Parallel Simulation for Log-concave Sampling and Score-based ...", "date": "", "ddg_snippet": "Shen , R. and Lee , Y. T. The Randomized Midpoint Method for Log - Concave Sampling . Advances in Neural Infor- mation Processing Systems, 32, 2019. Shih, A ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qtuxDy2qEB", "content": "Shen , R. and Lee , Y. T. The Randomized Midpoint Method for Log - Concave Sampling . Advances in Neural Infor- mation Processing Systems, 32, 2019. Shih, A ..."}
diff --git a/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_Unai_Fischer-Abaigar_Christoph_Kern_Juan_Carlos.jsonl b/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_Unai_Fischer-Abaigar_Christoph_Kern_Juan_Carlos.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a0576fa94227505da23f394098c3907bb4c18895
--- /dev/null
+++ b/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_Unai_Fischer-Abaigar_Christoph_Kern_Juan_Carlos.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "by U Fischer-Abaigar · 2025 — Title: The Value of Prediction in Identifying the Worst-Off . Authors: Unai Fischer - Abaigar , Christoph Kern , Juan Carlos Perdomo . View a PDF of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.19334", "content": "by U Fischer-Abaigar · 2025 — Title: The Value of Prediction in Identifying the Worst-Off . Authors: Unai Fischer - Abaigar , Christoph Kern , Juan Carlos Perdomo . View a PDF of ..."}
+{"idx": 1, "title": "The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "by U Fischer-Abaigar — The Value of Prediction in Identifying the Worst-Off . Download PDF. Unai Fischer - Abaigar , Christoph Kern , Juan Carlos Perdomo . Published: 01 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=26JsumCG0z", "content": "by U Fischer-Abaigar — The Value of Prediction in Identifying the Worst-Off . Download PDF. Unai Fischer - Abaigar , Christoph Kern , Juan Carlos Perdomo . Published: 01 ..."}
+{"idx": 2, "title": "The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "by U Fischer-Abaigar · 2025 — The Value of Prediction in Identifying the Worst-Off . Unai Fischer -Abaigar1,2. Christoph Kern1,2. Juan Carlos Perdomo3∗. 1LMU Munich. 2Munich Center for Machine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.19334?", "content": "by U Fischer-Abaigar · 2025 — The Value of Prediction in Identifying the Worst-Off . Unai Fischer -Abaigar1,2. Christoph Kern1,2. Juan Carlos Perdomo3∗. 1LMU Munich. 2Munich Center for Machine ..."}
+{"idx": 3, "title": "The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "The Value of Prediction in Identifying the Worst-Off . Unai Fischer Abaigar · Christoph Kern · Juan Perdomo . East Exhibition Hall A-B #E-800 ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46605", "content": "The Value of Prediction in Identifying the Worst-Off . Unai Fischer Abaigar · Christoph Kern · Juan Perdomo . East Exhibition Hall A-B #E-800 ..."}
+{"idx": 4, "title": "The Value of Prediction in Identifying the Worst-Off - ADS", "date": "", "ddg_snippet": "The Value of Prediction in Identifying the Worst-Off . Fischer - Abaigar , Unai ; ;; Kern , Christoph ; ;; Perdomo , Juan Carlos . Abstract. Machine learning is ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/arXiv:2501.19334", "content": "The Value of Prediction in Identifying the Worst-Off . Fischer - Abaigar , Unai ; ;; Kern , Christoph ; ;; Perdomo , Juan Carlos . Abstract. Machine learning is ..."}
+{"idx": 5, "title": "[Literature Review] The Value of Prediction in Identifying ...", "date": "", "ddg_snippet": "31 Jan 2025 — This page provides the most accurate and concise summary worldwide for the paper titled The Value of Prediction in Identifying the Worst-Off .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/the-value-of-prediction-in-identifying-the-worst-off", "content": "31 Jan 2025 — This page provides the most accurate and concise summary worldwide for the paper titled The Value of Prediction in Identifying the Worst-Off ."}
+{"idx": 6, "title": "The Value of Prediction in Identifying the Worst-Off | Unai ...", "date": "", "ddg_snippet": "Our paper “ The Value of Prediction in Identifying the Worst-Off ” has been accepted as a Spotlight Poster at [ICML] Int'l Conference on ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/unai-fa_the-value-of-prediction-in-identifying-the-activity-7329168130314813441-jfCG", "content": "Our paper “ The Value of Prediction in Identifying the Worst-Off ” has been accepted as a Spotlight Poster at [ICML] Int'l Conference on ..."}
+{"idx": 7, "title": "[ICML 2025] The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "\" The Value of Prediction in Identifying the Worst-Off \" is a significant and timely contribution to the field of AI for social good. By providing a rigorous ...", "subpage_snippet": "", "source": "arxiviq.substack.com", "link": "https://arxiviq.substack.com/p/icml-2025-the-value-of-prediction", "content": "\" The Value of Prediction in Identifying the Worst-Off \" is a significant and timely contribution to the field of AI for social good. By providing a rigorous ..."}
+{"idx": 8, "title": "Helping the Worst-Off: When Hiring More Case Workers Beats ...", "date": "", "ddg_snippet": "The findings, detailed in “ The Value of Prediction in Identifying the Worst-Off ,” challenge the current policy focus on perfecting ...", "subpage_snippet": "", "source": "nyudatascience.medium.com", "link": "https://nyudatascience.medium.com/helping-the-worst-off-when-hiring-more-case-workers-beats-building-better-ai-12e6f968de0b", "content": "The findings, detailed in “ The Value of Prediction in Identifying the Worst-Off ,” challenge the current policy focus on perfecting ..."}
+{"idx": 9, "title": "The value of prediction in identifying the worst-off", "date": "", "ddg_snippet": "27 Aug 2025 — ... Christoph Kern and Juan Carlos Perdomo won an ... The value of prediction in identifying the worst-off : Interview with Unai Fischer Abaigar .", "subpage_snippet": "", "source": "aitopics.org", "link": "https://aitopics.org/doc/news:9D1FA3F2", "content": "27 Aug 2025 — ... Christoph Kern and Juan Carlos Perdomo won an ... The value of prediction in identifying the worst-off : Interview with Unai Fischer Abaigar ."}
diff --git a/data/sampled_jsons/The_Web_Conference_2024_analysis_proceedings_Information_Retrieval_track_year_2024.jsonl b/data/sampled_jsons/The_Web_Conference_2024_analysis_proceedings_Information_Retrieval_track_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5436a060738de6ad84e8f5eb446387919a749ffb
--- /dev/null
+++ b/data/sampled_jsons/The_Web_Conference_2024_analysis_proceedings_Information_Retrieval_track_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International World Wide Web Conference 2024 ( WWW2024 | The", "date": "", "ddg_snippet": "We invite contributions to the research tracks of The Web Conference 2024 (formerly known as WWW). ... track is a forum for theoretical, empirical, ...", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/calls/research-tracks/", "content": "We invite contributions to the research tracks of The Web Conference 2024 (formerly known as WWW). ... track is a forum for theoretical, empirical, ..."}
+{"idx": 1, "title": "International World Wide Web Conference 2024 ( WWW2024 | The", "date": "", "ddg_snippet": "Breaking the Bot Barrier: Evaluating the Effectiveness of Adversarial AI Techniques Against Multi-Modal Defense Models", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/short-papers/", "content": "Breaking the Bot Barrier: Evaluating the Effectiveness of Adversarial AI Techniques Against Multi-Modal Defense Models"}
+{"idx": 2, "title": "International World Wide Web Conference 2024 ( WWW2024 | The", "date": "", "ddg_snippet": "NoteLLM: A Retrievable Large Language Model ... MS MARCO Web Search: A Large-scale Information -rich Web Dataset with Millions of Real Click Labels", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/industry/", "content": "NoteLLM: A Retrievable Large Language Model ... MS MARCO Web Search: A Large-scale Information -rich Web Dataset with Millions of Real Click Labels"}
+{"idx": 3, "title": "Prof. Dr. Marc Spaniol", "date": "", "ddg_snippet": "Spaniol CALVADOS: A Tool for the Semantic Analysis and Digestion of Web Contents Proceedings of the 16 th Extended Semantic Web Conference ( ESWC ...", "subpage_snippet": "", "source": "spaniol.users.greyc.fr", "link": "https://spaniol.users.greyc.fr/", "content": "Spaniol CALVADOS: A Tool for the Semantic Analysis and Digestion of Web Contents Proceedings of the 16 th Extended Semantic Web Conference ( ESWC ..."}
+{"idx": 4, "title": "LATTE: Learning Aligned Transactions and Textual Embeddings for", "date": "", "ddg_snippet": "The first reduces context length by summarizing user histories with general-purpose LLMs [ 21 , 20 ] , which risks losing domain-specific ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10021v2", "content": "The first reduces context length by summarizing user histories with general-purpose LLMs [ 21 , 20 ] , which risks losing domain-specific ..."}
+{"idx": 5, "title": "Deep Research: A Survey of Autonomous Research Agents", "date": "", "ddg_snippet": "We refer to this process as the Web Exploration module (see Section 4 ), which retrieves relevant information from diverse sources conditioned on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.12752v1", "content": "We refer to this process as the Web Exploration module (see Section 4 ), which retrieves relevant information from diverse sources conditioned on ..."}
+{"idx": 6, "title": "Hengshu Zhu's Homepage, 个人学术小站", "date": "", "ddg_snippet": "Hengshu Zhu is a Full Professor and the Deputy Director (Academic) at the Computer Network Information Center (CNIC) , Chinese Academy of Sciences ...", "subpage_snippet": "", "source": "www.zhuhengshu.com", "link": "http://www.zhuhengshu.com/", "content": "Hengshu Zhu is a Full Professor and the Deputy Director (Academic) at the Computer Network Information Center (CNIC) , Chinese Academy of Sciences ..."}
+{"idx": 7, "title": "Chuan Qin's Homepage - Homepage", "date": "", "ddg_snippet": "In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS- 2024 ) , 2024 .", "subpage_snippet": "", "source": "dylan-qin.github.io", "link": "https://dylan-qin.github.io/", "content": "In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS- 2024 ) , 2024 ."}
+{"idx": 8, "title": "Full Publication List", "date": "", "ddg_snippet": "The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'2023), 2023.", "subpage_snippet": "", "source": "www.cs.virginia.edu", "link": "https://www.cs.virginia.edu/~hw5x/publications.html", "content": "The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'2023), 2023."}
+{"idx": 9, "title": "Dr. Sven Hertling | Universität Mannheim", "date": "", "ddg_snippet": "... proceedings of the 18th ... In , CIKM '23 : Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (S.", "subpage_snippet": "", "source": "www.uni-mannheim.de", "link": "https://www.uni-mannheim.de/dws/people/professors/dr-sven-hertling/", "content": "... proceedings of the 18th ... In , CIKM '23 : Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (S."}
diff --git a/data/sampled_jsons/The_measurement_tasks_involved_in_evaluating_generative_AI_(GenAI)_systems_lack_sufficient_scientifi.jsonl b/data/sampled_jsons/The_measurement_tasks_involved_in_evaluating_generative_AI_(GenAI)_systems_lack_sufficient_scientifi.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a347578e9c02eb32c045049243300183f9492493
--- /dev/null
+++ b/data/sampled_jsons/The_measurement_tasks_involved_in_evaluating_generative_AI_(GenAI)_systems_lack_sufficient_scientifi.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00561] Position: Evaluating Generative AI Systems Is a Social...", "date": "", "ddg_snippet": "Abstract: The measurement tasks involved in evaluating generative AI ( GenAI ) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00561", "content": "Abstract: The measurement tasks involved in evaluating generative AI ( GenAI ) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024)."}
+{"idx": 1, "title": "Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · Cited by 10 — Abstract: The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1ZC4RNjqzU", "content": "by H Wallach · Cited by 10 — Abstract: The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been ..."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as “a tangle ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40182", "content": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as “a tangle ..."}
+{"idx": 3, "title": "Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · 2025 · Cited by 10 — The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2025arXiv250200561W/abstract", "content": "by H Wallach · 2025 · Cited by 10 — The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ..."}
+{"idx": 4, "title": "Matthew Vogel - Microsoft", "date": "", "ddg_snippet": "New York City Metropolitan Area · Microsoft ... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/mvogel930", "content": "New York City Metropolitan Area · Microsoft ... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ..."}
+{"idx": 5, "title": "Hanna Wallach (@hannawallach.bsky.social)", "date": "", "ddg_snippet": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ...", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/hannawallach/status/1934703250164379997", "content": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ..."}
+{"idx": 6, "title": "Hanna Wallach (@hannawallach.bsky.social)", "date": "", "ddg_snippet": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ...", "subpage_snippet": "", "source": "bsky.app", "link": "https://bsky.app/profile/hannawallach.bsky.social/post/3lrm6zuzsqc2b", "content": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ..."}
+{"idx": 7, "title": "Borhane Blili-Hamelin, PhD (@Borhane_B_H) / ...", "date": "", "ddg_snippet": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ...", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/borhane_b_h?lang=en", "content": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ..."}
+{"idx": 8, "title": "Ivo Emanuilov (@ivo@sigmoid.social) (@ivoemanuilov) ...", "date": "", "ddg_snippet": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ...", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/ivoemanuilov", "content": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ..."}
+{"idx": 9, "title": "ICML 2025 Wednesday 07/16", "date": "", "ddg_snippet": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/16", "content": "... The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor , leading to what has been described as \"a tangle ..."}
diff --git a/data/sampled_jsons/Theorem_3.1._Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_siteopenreview.net.jsonl b/data/sampled_jsons/Theorem_3.1._Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_siteopenreview.net.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a72f43857992972fe1d02eb7e3f20603037979c2
--- /dev/null
+++ b/data/sampled_jsons/Theorem_3.1._Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_siteopenreview.net.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "OLS (Pacchiano, 2024) Catoni -OFUL ( Theorem 3 .4). Function Type Linear. Linear Non-linear Non-linear.In other words, the theorem states that the regret of any contextual bandit algorithm scales with the square root of the sum of the variances of the rewards for its chosen actions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "OLS (Pacchiano, 2024) Catoni -OFUL ( Theorem 3 .4). Function Type Linear. Linear Non-linear Non-linear.In other words, the theorem states that the regret of any contextual bandit algorithm scales with the square root of the sum of the variances of the rewards for its chosen actions."}
+{"idx": 1, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "Table 1 : Comparisons of our regret bounds and computational complexity to previous best-known results for heavy - tailed linear bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "Table 1 : Comparisons of our regret bounds and computational complexity to previous best-known results for heavy - tailed linear bandits ."}
+{"idx": 2, "title": "Extended UCB Policies for Multi-Armed Bandit Problems", "date": "", "ddg_snippet": "... robust UCB generalizes Lattimore’s seminary work (for moments of orders p = 4 p=4 and q = 2 q=2 ) to arbitrarily chosen p > q > 1 p > q ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1112.1768v5", "content": "... robust UCB generalizes Lattimore’s seminary work (for moments of orders p = 4 p=4 and q = 2 q=2 ) to arbitrarily chosen p > q > 1 p > q ..."}
+{"idx": 3, "title": "Machine Learning Feb 2025", "date": "", "ddg_snippet": "Total of 681 entries : 1 -50 51-100 101-150 151-200 ... ... Title: Adversarial Robustness in Two-Stage Learning- to -Defer: Algorithms and Guarantees", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/stat.ML/2025-02", "content": "Total of 681 entries : 1 -50 51-100 101-150 151-200 ... ... Title: Adversarial Robustness in Two-Stage Learning- to -Defer: Algorithms and Guarantees"}
+{"idx": 4, "title": "ICML 2022 Papers", "date": "", "ddg_snippet": "... Helps to Overcome the Expressivity Limits of One -shot ... Representation Topology Divergence: A Method for Comparing Neural Network Representations.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/papers.html", "content": "... Helps to Overcome the Expressivity Limits of One -shot ... Representation Topology Divergence: A Method for Comparing Neural Network Representations."}
+{"idx": 5, "title": "Downloads", "date": "", "ddg_snippet": "Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2022", "content": "Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization"}
diff --git "a/data/sampled_jsons/Theorem_3.1_regret_\316\251_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl" "b/data/sampled_jsons/Theorem_3.1_regret_\316\251_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..a6fcc3a062983ae6f6a67fda8e15e26b15764b81
--- /dev/null
+++ "b/data/sampled_jsons/Theorem_3.1_regret_\316\251_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "OLS (Pacchiano, 2024) Catoni -OFUL ( Theorem 3 .4). Function Type Linear. Linear Non-linear Non-linear.In other words, the theorem states that the regret of any contextual bandit algorithm scales with the square root of the sum of the variances of the rewards for its chosen actions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "OLS (Pacchiano, 2024) Catoni -OFUL ( Theorem 3 .4). Function Type Linear. Linear Non-linear Non-linear.In other words, the theorem states that the regret of any contextual bandit algorithm scales with the square root of the sum of the variances of the rewards for its chosen actions."}
+{"idx": 1, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "Table 1 : Comparisons of our regret bounds and computational complexity to previous best-known results for heavy-tailed linear bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "Table 1 : Comparisons of our regret bounds and computational complexity to previous best-known results for heavy-tailed linear bandits ."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "... Ω ( d T ) lower bounds even in the linear setting with large action ... Q5: In Theorem 3.1 , the regret lower bound depends on the used policy. A5 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5IpVe9PH14&noteId=J3K6uYfoM5", "content": "... Ω ( d T ) lower bounds even in the linear setting with large action ... Q5: In Theorem 3.1 , the regret lower bound depends on the used policy. A5 ..."}
+{"idx": 3, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "The final regret is obtained by summing the regret for level l = l ... Proofs for the Known Variance Setting. B.1. Proof for the Lower Bound. Proof of Theorem 3.1 ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "The final regret is obtained by summing the regret for level l = l ... Proofs for the Known Variance Setting. B.1. Proof for the Lower Bound. Proof of Theorem 3.1 ..."}
+{"idx": 4, "title": "Extended UCB Policies for Multi-Armed Bandit Problems", "date": "", "ddg_snippet": "... robust UCB generalizes Lattimore’s seminary work (for moments of orders p = 4 p=4 and q = 2 q=2 ) to arbitrarily chosen p > q > 1 p > q ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1112.1768v5", "content": "... robust UCB generalizes Lattimore’s seminary work (for moments of orders p = 4 p=4 and q = 2 q=2 ) to arbitrarily chosen p > q > 1 p > q ..."}
+{"idx": 5, "title": "ICML 2022 Papers", "date": "", "ddg_snippet": "Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/papers.html", "content": "Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization"}
+{"idx": 6, "title": "Downloads", "date": "", "ddg_snippet": "Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2022", "content": "Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization"}
+{"idx": 7, "title": "Aaditya Ramdas' Webpage", "date": "", "ddg_snippet": "My goals are to improve my own (and eventually the field's) understanding of important problems, design creative algorithms for unsolved questions ...", "subpage_snippet": "", "source": "www.stat.cmu.edu", "link": "https://www.stat.cmu.edu/~aramdas/", "content": "My goals are to improve my own (and eventually the field's) understanding of important problems, design creative algorithms for unsolved questions ..."}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 9, "title": "Statistics and Machine Learning Working Group: List of Past", "date": "", "ddg_snippet": "14 Feb 2025 Off-Policy Evaluations of Linear Functionals: From Nonadaptive Semiparametric Efficiency to Adaptive Nonasymptotic Optimality Ojash ...", "subpage_snippet": "", "source": "statml.cs.cmu.edu", "link": "http://statml.cs.cmu.edu/blog/", "content": "14 Feb 2025 Off-Policy Evaluations of Linear Functionals: From Nonadaptive Semiparametric Efficiency to Adaptive Nonasymptotic Optimality Ojash ..."}
diff --git a/data/sampled_jsons/Theorem_3.4_upper_bound_Catoni_Contextual_Bandits_siteopenreview.net.jsonl b/data/sampled_jsons/Theorem_3.4_upper_bound_Catoni_Contextual_Bandits_siteopenreview.net.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1598253f2a456b85cfc99c09c8efd22616ad058e
--- /dev/null
+++ b/data/sampled_jsons/Theorem_3.4_upper_bound_Catoni_Contextual_Bandits_siteopenreview.net.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This theorem provides a variance-dependent upper bound when variances are unknown, which matches the upper bound when variances are observed ( Theorem 3 . 4 ) up to a slightly worse dependence on the eluder dimension.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "This theorem provides a variance-dependent upper bound when variances are unknown, which matches the upper bound when variances are observed ( Theorem 3 . 4 ) up to a slightly worse dependence on the eluder dimension."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "Theorem 4.2 achieves a variance-dependent regret bound that matches the known-variance case ( Theorem 3.4 ) up to a slightly worse dependence on the eluder ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5IpVe9PH14&noteId=J3K6uYfoM5", "content": "Theorem 4.2 achieves a variance-dependent regret bound that matches the known-variance case ( Theorem 3.4 ) up to a slightly worse dependence on the eluder ..."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/TmJvacopmV_Discrepancy_Minimization_Larsen_2023_computational_bottlenecks.jsonl b/data/sampled_jsons/TmJvacopmV_Discrepancy_Minimization_Larsen_2023_computational_bottlenecks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ee231f2c0f15afda8e2da02ae710006e752a08a
--- /dev/null
+++ b/data/sampled_jsons/TmJvacopmV_Discrepancy_Minimization_Larsen_2023_computational_bottlenecks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Discrepancy Minimization in Input-Sparsity Time", "date": "", "ddg_snippet": "Larsen , K. G. Fast discrepancy minimization with hereditary guarantees.( 2023 ) leverage discrepancy minimization for unsupervised graph matching by aligning predictions from classical solvers and neural models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TmJvacopmV", "content": "Larsen , K. G. Fast discrepancy minimization with hereditary guarantees.( 2023 ) leverage discrepancy minimization for unsupervised graph matching by aligning predictions from classical solvers and neural models."}
+{"idx": 1, "title": "Discrepancy Minimization in Input-Sparsity Time | OpenReview", "date": "", "ddg_snippet": "TL;DR: We give the algorithm for discrepancy minimization which runs in input-sparsity time.Our results nearly close the computational gap between real-valued and binary matrices, for which input-sparsity time coloring was recently obtained by [Jain, Sah and Sawhney, SODA 2023 ].", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=TmJvacopmV", "content": "TL;DR: We give the algorithm for discrepancy minimization which runs in input-sparsity time.Our results nearly close the computational gap between real-valued and binary matrices, for which input-sparsity time coloring was recently obtained by [Jain, Sah and Sawhney, SODA 2023 ]."}
+{"idx": 2, "title": "Fast Discrepancy Minimization with Hereditary Guarantees", "date": "", "ddg_snippet": "View a PDF of the paper titled Fast Discrepancy Minimization with Hereditary Guarantees, by Kasper Green Larsen .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.03268", "content": "View a PDF of the paper titled Fast Discrepancy Minimization with Hereditary Guarantees, by Kasper Green Larsen ."}
+{"idx": 3, "title": "(PDF) Discrepancy Minimization in Input-Sparsity Time", "date": "", "ddg_snippet": "Discrepancy minimization has various applications in combinatorics, computational geometry, de-. randomization, rounding of integer linear programming and approximation algorithms for NP-hard.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364690363_Discrepancy_Minimization_in_Input-Sparsity_Time", "content": "Discrepancy minimization has various applications in combinatorics, computational geometry, de-. randomization, rounding of integer linear programming and approximation algorithms for NP-hard."}
+{"idx": 4, "title": "Discrepancy minimization via a self-balancing walk | Proceedings of...", "date": "", "ddg_snippet": "2010. Constructive Algorithms for Discrepancy Minimization . In 51th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2010, October 23-26, 2010, Las Vegas, Nevada, USA.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3406325.3450994?cookieSet=1", "content": "2010. Constructive Algorithms for Discrepancy Minimization . In 51th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2010, October 23-26, 2010, Las Vegas, Nevada, USA."}
+{"idx": 5, "title": "Constructive Discrepancy Minimization with Hereditary... | DeepAI", "date": "", "ddg_snippet": "In discrepancy minimization problems, we are given a family of sets S = {S_1,...,S_m}, with each S_i ∈S a subset of some universe U = {u_1,...,u_n} of n elements. The goal is to find a coloring χ : U →{-1,+1} of the elements of U such that each set S ∈S is colored as evenly as possible.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/constructive-discrepancy-minimization-with-hereditary-l2-guarantees", "content": "In discrepancy minimization problems, we are given a family of sets S = {S_1,...,S_m}, with each S_i ∈S a subset of some universe U = {u_1,...,u_n} of n elements. The goal is to find a coloring χ : U →{-1,+1} of the elements of U such that each set S ∈S is colored as evenly as possible."}
+{"idx": 6, "title": "Constructive Discrepancy Minimization with Hereditary L2 Guarantees", "date": "", "ddg_snippet": "Kasper Green Larsen . 4 Department of Computer Science, Aarhus University, Denmark 5 larsen @cs.au.dk. 6 Abstract. 7 In discrepancy minimization problems, we are given a family of sets S = {S1, . . . , Sm}, with each.", "subpage_snippet": "", "source": "cs.au.dk", "link": "https://cs.au.dk/~larsen/papers/L2Discrepancy.pdf", "content": "Kasper Green Larsen . 4 Department of Computer Science, Aarhus University, Denmark 5 larsen @cs.au.dk. 6 Abstract. 7 In discrepancy minimization problems, we are given a family of sets S = {S1, . . . , Sm}, with each."}
+{"idx": 7, "title": "[PDF] Discrepancy minimization via... | Semantic Scholar", "date": "", "ddg_snippet": "We study discrepancy minimization for vectors in ℝn under various settings. The main result is the analysis of a new simple random process in high dimensions through a comparison argument.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Discrepancy-minimization-via-a-self-balancing-walk-Alweiss-Liu/bcfb4bfc8fee042d7322ee95cbcd5f64e24af5ba", "content": "We study discrepancy minimization for vectors in ℝn under various settings. The main result is the analysis of a new simple random process in high dimensions through a comparison argument."}
+{"idx": 8, "title": "(Open Access) Spherical discrepancy minimization and algorithmic...", "date": "", "ddg_snippet": "Inspired by the boolean discrepancy problem, we study the following optimization problem which we term Spherical Discrepancy : given m unit vectors v1,...,vm, find another unit vector x that minimizes maxi (x, vi).", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/spherical-discrepancy-minimization-and-algorithmic-lower-1djaj3lgn4", "content": "Inspired by the boolean discrepancy problem, we study the following optimization problem which we term Spherical Discrepancy : given m unit vectors v1,...,vm, find another unit vector x that minimizes maxi (x, vi)."}
+{"idx": 9, "title": "Streaming Algorithms for Discrepancy Minimization - Randorithms", "date": "", "ddg_snippet": "Minimizing discrepancy is a core part of several recent proposals for efficient machine learning and dataset summarization. Unfortunately, the problem is NP hard, and we are forced to use approximate solutions. Discrepancy Minimization via a Self-Balancing Walk.", "subpage_snippet": "", "source": "randorithms.com", "link": "https://randorithms.com/2021/09/05/Streaming-Discrepancy.html", "content": "Minimizing discrepancy is a core part of several recent proposals for efficient machine learning and dataset summarization. Unfortunately, the problem is NP hard, and we are forced to use approximate solutions. Discrepancy Minimization via a Self-Balancing Walk."}
diff --git a/data/sampled_jsons/TmJvacopmV_Discrepancy_Minimization_technical_overview_Larsen_bottlenecks.jsonl b/data/sampled_jsons/TmJvacopmV_Discrepancy_Minimization_technical_overview_Larsen_bottlenecks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2610d245392665ac48c5d1ed1955f2801051dac9
--- /dev/null
+++ b/data/sampled_jsons/TmJvacopmV_Discrepancy_Minimization_technical_overview_Larsen_bottlenecks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Discrepancy Minimization in Input-Sparsity Time - OpenReview", "date": "", "ddg_snippet": "We note that, in the case of binary matrices, the projection-free algorithm of Jain et al. (2023) avoids this bottleneck (and hence the nω term), but for real-valued matrices, all known discrepancy algorithms involve projections (Bansal, 2010; Lovett & Meka, 2015; Larsen , 2023; Rothvoss, 2017).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TmJvacopmV", "content": "We note that, in the case of binary matrices, the projection-free algorithm of Jain et al. (2023) avoids this bottleneck (and hence the nω term), but for real-valued matrices, all known discrepancy algorithms involve projections (Bansal, 2010; Lovett & Meka, 2015; Larsen , 2023; Rothvoss, 2017)."}
+{"idx": 1, "title": "Discrepancy Minimization in Input-Sparsity TimeA preliminary version of ...", "date": "", "ddg_snippet": "Algorithmic discrepancy theory. Sketching and Leverage score sampling. 2 Technical Overview 2.1 Overview and Barriers of Larsen's Algorithm 2.2 Our Techniques 2.2.1 Robust Analysis of Larsen's Algorithm Approximate norm-estimation suffices.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2210.12468v2", "content": "Algorithmic discrepancy theory. Sketching and Leverage score sampling. 2 Technical Overview 2.1 Overview and Barriers of Larsen's Algorithm 2.2 Our Techniques 2.2.1 Robust Analysis of Larsen's Algorithm Approximate norm-estimation suffices."}
+{"idx": 2, "title": "ICML 2025 5mins video: Discrepancy Minimization in Input ... - YouTube", "date": "", "ddg_snippet": "I am very excited to present our ICML spotlight work, Discrepancy Minimization in Input-Sparsity Time. This is a joint work with Yichuan Deng, Xiaoyu Li, and Omri Weinstein.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=TxQdaY3MP-A", "content": "I am very excited to present our ICML spotlight work, Discrepancy Minimization in Input-Sparsity Time. This is a joint work with Yichuan Deng, Xiaoyu Li, and Omri Weinstein."}
+{"idx": 3, "title": "ICML Poster Discrepancy Minimization in Input-Sparsity Time", "date": "", "ddg_snippet": "Mathematicians call the maximum imbalance across all subsets the discrepancy of the coloring. Discrepancy minimization is vital in areas ranging from computational geometry to data privacy, yet the best general-purpose algorithms were far too slow for today's large, sparse data sets, sometimes taking days to finish.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45157", "content": "Mathematicians call the maximum imbalance across all subsets the discrepancy of the coloring. Discrepancy minimization is vital in areas ranging from computational geometry to data privacy, yet the best general-purpose algorithms were far too slow for today's large, sparse data sets, sometimes taking days to finish."}
+{"idx": 4, "title": "PDF Discrepancy Minimization via Regularization", "date": "", "ddg_snippet": "Discrepancy /Vector Balancing problem: Given , un ∈ K ⊆ Rd, find x1, . . . , xn ∈ {±1} s.t. discrepancy ∥ ∑i xiui∥ is \"small\"", "subpage_snippet": "", "source": "lucaspesenti.github.io", "link": "https://lucaspesenti.github.io/Slides_SODA_2023.pdf", "content": "Discrepancy /Vector Balancing problem: Given , un ∈ K ⊆ Rd, find x1, . . . , xn ∈ {±1} s.t. discrepancy ∥ ∑i xiui∥ is \"small\""}
+{"idx": 5, "title": "co.combinatorics - Some question on Lovett-Meka Discrepancy ...", "date": "", "ddg_snippet": "I am reading the paper Constructive Discrepancy Minimization by Walking on The Edges which finds the discrepancy of a set system matching Spencer's bound, in randomised polynomial time. In short, ...", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/446546/some-question-on-lovett-meka-discrepancy-minimisation-algorithm", "content": "I am reading the paper Constructive Discrepancy Minimization by Walking on The Edges which finds the discrepancy of a set system matching Spencer's bound, in randomised polynomial time. In short, ..."}
+{"idx": 6, "title": "Discrepancy Minimization in Input-Sparsity Time", "date": "", "ddg_snippet": "TLDR: We give the algorithm for discrepancy minimization which runs in input-sparsity time.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/165249?from=subpath-search", "content": "TLDR: We give the algorithm for discrepancy minimization which runs in input-sparsity time."}
+{"idx": 7, "title": "Discrepancy Minimization in Input-Sparsity Time - arXiv.org", "date": "", "ddg_snippet": "A recent work by [ Larsen , SODA 2023] introduced a faster combinatorial alternative to Bansal's SDP algorithm for finding a coloring x ∈ {−1, 1}n that approximately minimizes the discrepancy disc(A, x) := |Ax|∞ of a real-valued m × n matrix A. Larsen's algorithm runs in eO(mn2) time compared to Bansal's approximation ratio in terms of the eO(mn4.5)-time algorithm, with a slightly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.12468v2", "content": "A recent work by [ Larsen , SODA 2023] introduced a faster combinatorial alternative to Bansal's SDP algorithm for finding a coloring x ∈ {−1, 1}n that approximately minimizes the discrepancy disc(A, x) := |Ax|∞ of a real-valued m × n matrix A. Larsen's algorithm runs in eO(mn2) time compared to Bansal's approximation ratio in terms of the eO(mn4.5)-time algorithm, with a slightly ..."}
+{"idx": 8, "title": "[1611.08752] Deterministic Discrepancy Minimization via the ...", "date": "", "ddg_snippet": "A well-known theorem of Spencer shows that any set system with n sets over n elements admits a coloring of discrepancy O(n−−√). While the original proof was non-constructive, recent progress brought polynomial time algorithms by Bansal, Lovett and Meka, and Rothvoss.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1611.08752", "content": "A well-known theorem of Spencer shows that any set system with n sets over n elements admits a coloring of discrepancy O(n−−√). While the original proof was non-constructive, recent progress brought polynomial time algorithms by Bansal, Lovett and Meka, and Rothvoss."}
+{"idx": 9, "title": "Discrepancy Minimization in Input-Sparsity Time,arXiv - CS - Data ...", "date": "", "ddg_snippet": "Larsen's algorithm runs in $\\widetilde {O} (mn^2)$ time compared to Bansal's $\\widetilde {O} (mn^ {4.5})$-time algorithm, at the price of a slightly weaker logarithmic approximation ratio in terms of the hereditary discrepancy of", "subpage_snippet": "", "source": "www.x-mol.com", "link": "https://www.x-mol.com/paper/1584961519860846592", "content": "Larsen's algorithm runs in $\\widetilde {O} (mn^2)$ time compared to Bansal's $\\widetilde {O} (mn^ {4.5})$-time algorithm, at the price of a slightly weaker logarithmic approximation ratio in terms of the hereditary discrepancy of"}
diff --git a/data/sampled_jsons/Tor_network_connectivity_metric_anomalous_circuit_detection_formula.jsonl b/data/sampled_jsons/Tor_network_connectivity_metric_anomalous_circuit_detection_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c490c20c60a3dd4220e7ce17c5d922bed621c921
--- /dev/null
+++ b/data/sampled_jsons/Tor_network_connectivity_metric_anomalous_circuit_detection_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via ...", "date": "", "ddg_snippet": "The Tor network , while offering anonymity through traffic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating traffic patterns between colluded entry and exit nodes in circuits . This paper presents a novel approach for detecting anomalous circuits in the Tor network , and for the first time provides a more comprehensive ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714767", "content": "The Tor network , while offering anonymity through traffic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating traffic patterns between colluded entry and exit nodes in circuits . This paper presents a novel approach for detecting anomalous circuits in the Tor network , and for the first time provides a more comprehensive ..."}
+{"idx": 1, "title": "PDF On Identifying Anomalies in Tor Usage with Applications in Detecting ...", "date": "", "ddg_snippet": "This work presents a theoretical contribution to network anomaly detection , a practical contribution in the form of an implemented tool for detecting anomalous events in Tor usage data, a resource in the form of a public dataset of detected anomalies in historical Tor trafic, and a practical analysis demonstrating the detection of real-world ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1507.05819.pdf", "content": "This work presents a theoretical contribution to network anomaly detection , a practical contribution in the form of an implemented tool for detecting anomalous events in Tor usage data, a resource in the form of a public dataset of detected anomalies in historical Tor trafic, and a practical analysis demonstrating the detection of real-world ..."}
+{"idx": 2, "title": "PDF TorFlow: Tor Network Analysis", "date": "", "ddg_snippet": "The Tor Control Port protocol used by TorCtl is a plaintext TCP-based protocol that provides well-formed information on Tor client status and events and optionally enables control over circuit construction and association of SOCKS streams to individual circuits .", "subpage_snippet": "", "source": "research.torproject.org", "link": "https://research.torproject.org/techreports/torflow-2009-08-07.pdf", "content": "The Tor Control Port protocol used by TorCtl is a plaintext TCP-based protocol that provides well-formed information on Tor client status and events and optionally enables control over circuit construction and association of SOCKS streams to individual circuits ."}
+{"idx": 3, "title": "通过异常电路检测揭露tor中的恶意同伙", "date": "", "ddg_snippet": "通过在真实 Tor 网络中部署自己的中间节点，修改节点源代码，记录电路段内的信元轨迹和相邻节点信息。 利用Tor协议层的流量特征，基于24种潜在的电路段类型设计电路段分类方法，从而辨别和收集出口电路中入口 - 出口对的信息。 识别出口电路并收集信息", "subpage_snippet": "", "source": "sechub.in", "link": "https://sechub.in/view/3027398", "content": "通过在真实 Tor 网络中部署自己的中间节点，修改节点源代码，记录电路段内的信元轨迹和相邻节点信息。 利用Tor协议层的流量特征，基于24种潜在的电路段类型设计电路段分类方法，从而辨别和收集出口电路中入口 - 出口对的信息。 识别出口电路并收集信息"}
+{"idx": 4, "title": "PDF Evaluation of Circuit Lifetimes in Tor - Springer", "date": "", "ddg_snippet": "Tor is a popular anonymity network which achieves its an-onymity by constructing paths over three Tor relays, so-called circuits . Multiple streams that correspond to TCP connections can be multiplexed over a single circuit .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-06975-8_9.pdf", "content": "Tor is a popular anonymity network which achieves its an-onymity by constructing paths over three Tor relays, so-called circuits . Multiple streams that correspond to TCP connections can be multiplexed over a single circuit ."}
+{"idx": 5, "title": "Detecting Malicious Users Behind Circuit-Based Anonymity Networks", "date": "", "ddg_snippet": "This project addresses the issue of detecting intruders from hiding behind privacy-protecting anonymity networks . The freely available Tor and the SOCKS proxy services have been popular tools that provide circuit -based anonymous connections to network users. However, recent security breaches reveal that SSH and HTTPS have been used to launch attacks by malicious users by taking advantage of ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9258912", "content": "This project addresses the issue of detecting intruders from hiding behind privacy-protecting anonymity networks . The freely available Tor and the SOCKS proxy services have been popular tools that provide circuit -based anonymous connections to network users. However, recent security breaches reveal that SSH and HTTPS have been used to launch attacks by malicious users by taking advantage of ..."}
+{"idx": 6, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via ...", "date": "", "ddg_snippet": "ABSTRACT The Tor network , while ofering anonymity through trafic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating trafic patterns between colluded Entry and Exit nodes in circuits . This paper presents a novel approach for detecting anomalous circuits in the Tor network , and for the first time provides a more comprehensive ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "ABSTRACT The Tor network , while ofering anonymity through trafic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating trafic patterns between colluded Entry and Exit nodes in circuits . This paper presents a novel approach for detecting anomalous circuits in the Tor network , and for the first time provides a more comprehensive ..."}
+{"idx": 7, "title": "Detecting Tor communication - Michael Edie", "date": "", "ddg_snippet": "Tor (The Onion Router) is an internet communication network built on privacy and anonymity. Much of the attention that Tor receives comes from the malicious segment of users that leverage the Tor network to conduct attacks while concealing their location.", "subpage_snippet": "", "source": "blog.edie.io", "link": "https://blog.edie.io/2020/12/31/detecting-tor-communication/", "content": "Tor (The Onion Router) is an internet communication network built on privacy and anonymity. Much of the attention that Tor receives comes from the malicious segment of users that leverage the Tor network to conduct attacks while concealing their location."}
+{"idx": 8, "title": "Welcome to Tor Metrics", "date": "", "ddg_snippet": "Welcome to Tor Metrics ! The Tor network is one of the largest deployed anonymity networks , consisting of thousands of volunteer-run relays and millions of users. Users, advocates, relay operators, and journalists can better understand the Tor network through data and analysis made available by Tor Metrics .", "subpage_snippet": "", "source": "metrics.torproject.org", "link": "https://metrics.torproject.org/", "content": "Welcome to Tor Metrics ! The Tor network is one of the largest deployed anonymity networks , consisting of thousands of volunteer-run relays and millions of users. Users, advocates, relay operators, and journalists can better understand the Tor network through data and analysis made available by Tor Metrics ."}
+{"idx": 9, "title": "PDF A Composite-Metric Based Path Selection Technique for the Tor Anonymity ...", "date": "", "ddg_snippet": "The Tor anonymous network has become quite popular with regular users on the Internet. In the Tor network , an anonymous path is created by selecting three relays through which the connection is redirected. Nevertheless, as the number of Tor users has increased substantially in recent years, the algorithm with which the relays are selected af-fects the performance provided by the Tor network ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1707.02520v1.pdf", "content": "The Tor anonymous network has become quite popular with regular users on the Internet. In the Tor network , an anonymous path is created by selecting three relays through which the connection is redirected. Nevertheless, as the number of Tor users has increased substantially in recent years, the algorithm with which the relays are selected af-fects the performance provided by the Tor network ..."}
diff --git a/data/sampled_jsons/Towards_realistic_semi-supervised_learning_Rizve_Shah_abstract.jsonl b/data/sampled_jsons/Towards_realistic_semi-supervised_learning_Rizve_Shah_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3dd0554f7e8fcd12b412385be4b45c132c0fb53
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Towards Realistic Semi-Supervised Learning - arXiv.org", "date": "", "ddg_snippet": "Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah @crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.02269", "content": "Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah @crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications."}
+{"idx": 1, "title": "Towards Realistic Semi-supervised Learning | SpringerLink (PDF) Towards Realistic Semi-Supervised Learning - ResearchGate Towards Realistic Semi-Supervised Learning - NASA/ADS Towards Realistic Semi-Supervised Learning Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi-Supervised Learning - Semantic Scholar", "date": "", "ddg_snippet": "In the following, we describe our experimental setup including dataset descriptions, implementation details, evaluation details, and specifics of our baselines. Datasets. We conduct experiments on four commonly used computer vision benchmark datasets: CIFAR-10 , CIFAR-100 , ImageNet-100 and Tiny ImageNet . The datasets are selected in increasing o... See full list on link.springer.com Standard Benchmark Datasets. We compare our method with existing literature on open-world SSL problem and other related approaches that have been modified for this problem in Table 1 and 2. On CIFAR-10 we observe that our proposed method outperforms ORCA on both seen and novel classes by 12.1% and 4.1%, respectively. Our proposed method also outp... See full list on link.springer.com To investigate the impact of different components, we conduct extensive ablation study on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. We report the results in Table 3. The first row depicts the performance of our proposed method without uncertainty-guided temperature scaling, and mixed pseudo-labeling. Here, we can see that our proposed method... See full list on link.springer.com Jul 5, 2022 · T ow ards Realistic Semi-Sup ervised Learning Mamshad Nayeem Rizv e , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Jul 5, 2022 · 5 July 2022 Mamshad Nayeem Rizve Navid Kardan M. Shah Re-assign community ArXivPDFHTML Abstract Is deep learning a real-world semi-supervised learning? Towards Realistic Semi-Supervised Learning Abstract. Deep learning is pushing the state-of-the-art in many com- puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved . What is semi supervised learning (SSL)? Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Can semi-supervised learning reduce the cost of annotated training data? However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. Are big self-supervised models strong semi-supervised learners? Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners . Advances in Neural Information Pro- cessing Systems 33 (2020) 4 Does the proposed method improve relative improvement compared to the second best method? To be specific, in overall, the proposed method achieves 50-100% relative improvement compared to the second best method UNO. Together, our previous results combined with these fine-grained results,showcasethee䩄ꃬcacyofourproposedmethodandindicateawiderap- plication for more practical settings. Mamshad Nayeem Rizve , Navid Kardan, Salman Khan, F. Khan, M. Shah Computer Science ECCV 2022 TLDR This work introduces OpenLDN that utilizes a pairwise similarity loss to discover novel classes that outperforms the current state-of-the-art methods on multiple popular classification benchmarks while providing a better accuracy/training time ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-19821-2_25", "content": "In the following, we describe our experimental setup including dataset descriptions, implementation details, evaluation details, and specifics of our baselines. Datasets. We conduct experiments on four commonly used computer vision benchmark datasets: CIFAR-10 , CIFAR-100 , ImageNet-100 and Tiny ImageNet . The datasets are selected in increasing o... See full list on link.springer.com Standard Benchmark Datasets. We compare our method with existing literature on open-world SSL problem and other related approaches that have been modified for this problem in Table 1 and 2. On CIFAR-10 we observe that our proposed method outperforms ORCA on both seen and novel classes by 12.1% and 4.1%, respectively. Our proposed method also outp... See full list on link.springer.com To investigate the impact of different components, we conduct extensive ablation study on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. We report the results in Table 3. The first row depicts the performance of our proposed method without uncertainty-guided temperature scaling, and mixed pseudo-labeling. Here, we can see that our proposed method... See full list on link.springer.com Jul 5, 2022 · T ow ards Realistic Semi-Sup ervised Learning Mamshad Nayeem Rizv e , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Jul 5, 2022 · 5 July 2022 Mamshad Nayeem Rizve Navid Kardan M. Shah Re-assign community ArXivPDFHTML Abstract Is deep learning a real-world semi-supervised learning? Towards Realistic Semi-Supervised Learning Abstract. Deep learning is pushing the state-of-the-art in many com- puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved . What is semi supervised learning (SSL)? Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Can semi-supervised learning reduce the cost of annotated training data? However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. Are big self-supervised models strong semi-supervised learners? Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners . Advances in Neural Information Pro- cessing Systems 33 (2020) 4 Does the proposed method improve relative improvement compared to the second best method? To be specific, in overall, the proposed method achieves 50-100% relative improvement compared to the second best method UNO. Together, our previous results combined with these fine-grained results,showcasethee䩄ꃬcacyofourproposedmethodandindicateawiderap- plication for more practical settings. Mamshad Nayeem Rizve , Navid Kardan, Salman Khan, F. Khan, M. Shah Computer Science ECCV 2022 TLDR This work introduces OpenLDN that utilizes a pairwise similarity loss to discover novel classes that outperforms the current state-of-the-art methods on multiple popular classification benchmarks while providing a better accuracy/training time ..."}
+{"idx": 2, "title": "(PDF) Towards Realistic Semi - Supervised Learning", "date": "", "ddg_snippet": "Towards Realistic Semi - Supervised Learning . Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah . Towards Realistic Semi - Supervised Learning 11. again our method outperforms all three methods on these fine-grained classifi", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361807923_Towards_Realistic_Semi-Supervised_Learning", "content": "Towards Realistic Semi - Supervised Learning . Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah . Towards Realistic Semi - Supervised Learning 11. again our method outperforms all three methods on these fine-grained classifi"}
+{"idx": 3, "title": "Towards Realistic Semi-supervised Learning | Computer Vision ...", "date": "", "ddg_snippet": "Open World Semi-supervised Learning Based on Multi-scale Enhanced Feature Web and Big Data Abstract Semi-supervised learning (SSL) is one of the main approaches to address the high cost of manual annotation in supervised learning. In recent years, SSL methods have effectively utilized consistency regularization on unlabeled data to improve ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1007/978-3-031-19821-2_25", "content": "Open World Semi-supervised Learning Based on Multi-scale Enhanced Feature Web and Big Data Abstract Semi-supervised learning (SSL) is one of the main approaches to address the high cost of manual annotation in supervised learning. In recent years, SSL methods have effectively utilized consistency regularization on unlabeled data to improve ..."}
+{"idx": 4, "title": "Towards Realistic Semi-Supervised Learning - NASA/ADS", "date": "", "ddg_snippet": "Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2022arXiv220702269N/abstract", "content": "Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ..."}
+{"idx": 5, "title": "Towards Realistic Semi-Supervised Learning", "date": "", "ddg_snippet": "Jul 5, 2022 · 5 July 2022 Mamshad Nayeem Rizve Navid Kardan M. Shah Re-assign community ArXivPDFHTML Abstract", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2207.02269", "content": "Jul 5, 2022 · 5 July 2022 Mamshad Nayeem Rizve Navid Kardan M. Shah Re-assign community ArXivPDFHTML Abstract"}
+{"idx": 6, "title": "Towards Realistic Semi-Supervised Learning - Semantic Scholar", "date": "", "ddg_snippet": "Mamshad Nayeem Rizve , Navid Kardan, Salman Khan, F. Khan, M. Shah Computer Science ECCV 2022 TLDR This work introduces OpenLDN that utilizes a pairwise similarity loss to discover novel classes that outperforms the current state-of-the-art methods on multiple popular classification benchmarks while providing a better accuracy/training time ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Towards-Realistic-Semi-Supervised-Learning-Rizve-Kardan/cce61de63d6691fce13bf1a1c8d231553df12f31/figure/0", "content": "Mamshad Nayeem Rizve , Navid Kardan, Salman Khan, F. Khan, M. Shah Computer Science ECCV 2022 TLDR This work introduces OpenLDN that utilizes a pairwise similarity loss to discover novel classes that outperforms the current state-of-the-art methods on multiple popular classification benchmarks while providing a better accuracy/training time ..."}
+{"idx": 7, "title": "Towards Realistic Semi - Supervised Learning | DeepAI", "date": "", "ddg_snippet": "Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/towards-realistic-semi-supervised-learning", "content": "Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data."}
+{"idx": 8, "title": "nayeemrizve/TRSSL: \" Towards Realistic Semi - Supervised Learning ...", "date": "", "ddg_snippet": "\" Towards Realistic Semi - Supervised Learning \" by Mamshad Nayeem Rizve , Navid Kardan, Mubarak Shah (ECCV 2022). Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nayeemrizve/TRSSL", "content": "\" Towards Realistic Semi - Supervised Learning \" by Mamshad Nayeem Rizve , Navid Kardan, Mubarak Shah (ECCV 2022). Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost."}
+{"idx": 9, "title": "Mamshad Nayeem Rizve - Google Akademik", "date": "", "ddg_snippet": "Towards Realistic Semi - Supervised Learning . MN Rizve , N Kardan, M Shah .2023. In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi - supervised learning . arXiv 2021.", "subpage_snippet": "", "source": "scholar.google.bg", "link": "https://scholar.google.bg/citations?user=kA8ZM5oAAAAJ&hl=tr", "content": "Towards Realistic Semi - Supervised Learning . MN Rizve , N Kardan, M Shah .2023. In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi - supervised learning . arXiv 2021."}
diff --git a/data/sampled_jsons/Towards_scientific_discovery_with_dictionary_learning_PCA_whitening_control_data_biological_variatio.jsonl b/data/sampled_jsons/Towards_scientific_discovery_with_dictionary_learning_PCA_whitening_control_data_biological_variatio.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..836c11239fc231a1d63090f41473149a3bb788ad
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+{"idx": 0, "title": "Towards scientific discovery with dictionary learning", "date": "", "ddg_snippet": "by K Donhauser · 2024 · Cited by 1 — Abstract page for arXiv paper 2412.16247: Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.16247", "content": "by K Donhauser · 2024 · Cited by 1 — Abstract page for arXiv paper 2412.16247: Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy"}
+{"idx": 1, "title": "Towards scientific discovery with dictionary learning", "date": "", "ddg_snippet": "by K Donhauser · Cited by 1 — Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models ... PCA whitening from a control dataset.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uDIiL89ViX", "content": "by K Donhauser · Cited by 1 — Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models ... PCA whitening from a control dataset."}
+{"idx": 2, "title": "Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models. Download PDF. Konstantin Donhauser ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fBn6om49Ur&noteId=A66YEyGgUV", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models. Download PDF. Konstantin Donhauser ..."}
+{"idx": 3, "title": "Extracting biological concepts from microscopy foundation ...", "date": "", "ddg_snippet": "11 Feb 2025 — Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models. Report issue for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16247v2", "content": "11 Feb 2025 — Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models. Report issue for ..."}
+{"idx": 4, "title": "Towards scientific discovery with dictionary learning", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models ... PCA Whitening for Biological Data .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2412.16247v3", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models ... PCA Whitening for Biological Data ."}
+{"idx": 5, "title": "Extracting biological concepts from microscopy foundation ...", "date": "", "ddg_snippet": "This page provides the most accurate and concise summary worldwide for the paper titled Towards scientific discovery with dictionary learning : Extracting ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/towards-scientific-discovery-with-dictionary-learning-extracting-biological-concepts-from-microscopy-foundation-models", "content": "This page provides the most accurate and concise summary worldwide for the paper titled Towards scientific discovery with dictionary learning : Extracting ..."}
+{"idx": 6, "title": "Towards scientific discovery with dictionary learning", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165789", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models."}
+{"idx": 7, "title": "GAN", "date": "", "ddg_snippet": "OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery arXiv_AI arXiv_AI GAN CNN Deep_ Learning ...", "subpage_snippet": "", "source": "amds123.github.io", "link": "https://amds123.github.io/gan/", "content": "OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery arXiv_AI arXiv_AI GAN CNN Deep_ Learning ..."}
+{"idx": 8, "title": "ICML 2023 Papers", "date": "", "ddg_snippet": "Meta-SAGE: Scale Meta- Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2023/papers.html", "content": "Meta-SAGE: Scale Meta- Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization"}
+{"idx": 9, "title": "Publikationen - Lehrstuhl für Datenverarbeitung", "date": "", "ddg_snippet": "... Christian; Heim, Dominik; Knopp, Martin; Hayden, Oliver; Diepold, Klaus: Towards Interpretable Classification of Leukocytes based on Deep Learning ...", "subpage_snippet": "", "source": "www.ce.cit.tum.de", "link": "https://www.ce.cit.tum.de/ldv/publikationen/", "content": "... Christian; Heim, Dominik; Knopp, Martin; Hayden, Oliver; Diepold, Klaus: Towards Interpretable Classification of Leukocytes based on Deep Learning ..."}
diff --git a/data/sampled_jsons/Tree_of_Thoughts_Deliberate_Problem_Solving_with_Large_Language_Models_Yao_et_al._abstract_year_2023.jsonl b/data/sampled_jsons/Tree_of_Thoughts_Deliberate_Problem_Solving_with_Large_Language_Models_Yao_et_al._abstract_year_2023.jsonl
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index 0000000000000000000000000000000000000000..4bf78c66e85bca5e29a4ecea1086ef034f63027f
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+{"idx": 0, "title": "Tree of Thoughts: Deliberate Problem Solving with Large", "date": "", "ddg_snippet": "We thus propose the Tree of Thoughts (ToT) framework for general problem solving with language models . ... large language models for problem - solving , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.10601v2", "content": "We thus propose the Tree of Thoughts (ToT) framework for general problem solving with language models . ... large language models for problem - solving , ..."}
+{"idx": 1, "title": "[2305.10601] Tree of Thoughts: Deliberate Problem Solving with", "date": "", "ddg_snippet": "View a PDF of the paper titled Tree of Thoughts : Deliberate Problem Solving with Large Language Models , by Shunyu Yao and 6 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.10601", "content": "View a PDF of the paper titled Tree of Thoughts : Deliberate Problem Solving with Large Language Models , by Shunyu Yao and 6 other authors"}
+{"idx": 2, "title": "Mixture of Reasonings: Teach Large Language Models to Reason", "date": "", "ddg_snippet": "Large language models (LLMs) have achieved remarkable success across diverse domains, largely due to advanced prompting techniques such as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.00606v1", "content": "Large language models (LLMs) have achieved remarkable success across diverse domains, largely due to advanced prompting techniques such as ..."}
+{"idx": 3, "title": "Evaluating the Ability of Large Language Models to Reason about", "date": "", "ddg_snippet": "... Large Reasoning Models (LRMs), in which the models have been trained to perform inference-time reasoning, had not yet emerged, though chain- of - thought ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12059v1", "content": "... Large Reasoning Models (LRMs), in which the models have been trained to perform inference-time reasoning, had not yet emerged, though chain- of - thought ..."}
+{"idx": 4, "title": "Understanding the Language Model to Solve the Symbolic", "date": "", "ddg_snippet": "Large language models have consistently struggled with complex reasoning tasks, such as mathematical problem - solving .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15302v3", "content": "Large language models have consistently struggled with complex reasoning tasks, such as mathematical problem - solving ."}
+{"idx": 5, "title": "(PDF) Tree of Thoughts: Deliberate Problem Solving with Large", "date": "", "ddg_snippet": "Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370869723_Tree_of_Thoughts_Deliberate_Problem_Solving_with_Large_Language_Models", "content": "Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left ..."}
+{"idx": 6, "title": "Thomas L. Griffiths | DeepAI", "date": "", "ddg_snippet": "Tree of Thoughts : Deliberate Problem Solving with Large Language Models Language models are increasingly being deployed for general problem solv ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/thomas-l-griffiths", "content": "Tree of Thoughts : Deliberate Problem Solving with Large Language Models Language models are increasingly being deployed for general problem solv ..."}
+{"idx": 7, "title": "Prompt Engineering Patterns :: Spring AI Reference", "date": "", "ddg_snippet": "Large language models are trained on vast corpora of text, allowing them to understand what tasks like \"translation,\" \"summarization,\" or ...", "subpage_snippet": "", "source": "docs.spring.io", "link": "https://docs.spring.io/spring-ai/reference/1.0/api/chat/prompt-engineering-patterns.html", "content": "Large language models are trained on vast corpora of text, allowing them to understand what tasks like \"translation,\" \"summarization,\" or ..."}
+{"idx": 8, "title": "Towards Reasoning Era: A Survey of Long Chain-of-Thought", "date": "", "ddg_snippet": "Inference scaling laws: An empirical analysis of compute-optimal inference for problem - solving with language models , Wu et al .,", "subpage_snippet": "", "source": "long-cot.github.io", "link": "https://long-cot.github.io/", "content": "Inference scaling laws: An empirical analysis of compute-optimal inference for problem - solving with language models , Wu et al .,"}
+{"idx": 9, "title": "GitHub - atfortes/Awesome-LLM-Reasoning: Reasoning in LLMs:", "date": "", "ddg_snippet": "... Solving using Reasoning of Large Language ... Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain- of - Thought Prompting.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/atfortes/Awesome-LLM-Reasoning", "content": "... Solving using Reasoning of Large Language ... Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain- of - Thought Prompting."}
diff --git a/data/sampled_jsons/Trockman_&_Kolter_2023_identity_initialization_abstract_year_2023.jsonl b/data/sampled_jsons/Trockman_&_Kolter_2023_identity_initialization_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f9a78d0aa547e000d947706b57e31cc0713dbcc
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+++ b/data/sampled_jsons/Trockman_&_Kolter_2023_identity_initialization_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.09828] Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.09828", "content": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity ."}
+{"idx": 1, "title": "PDF Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to ap-proximately the negative identity .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/trockman23a/trockman23a.pdf", "content": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to ap-proximately the negative identity ."}
+{"idx": 2, "title": "PDF CSD-Speaking-Skills-TROCKMAN-2023-11-07", "date": "", "ddg_snippet": "Mimetic initialization substantially reduces training time and increases final accuracy on various common benchmarks. Our technique enables us to almost close the gap between untrained and pre-trained Vision Transformers on small datasets like CIFAR-10, achieving up to a 6% gain in accuracy through initialization alone.", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/afs/cs.cmu.edu/Web/Posters/CSSpeakingSkills-AsherTrockman23.pdf", "content": "Mimetic initialization substantially reduces training time and increases final accuracy on various common benchmarks. Our technique enables us to almost close the gap between untrained and pre-trained Vision Transformers on small datasets like CIFAR-10, achieving up to a 6% gain in accuracy through initialization alone."}
+{"idx": 3, "title": "ICML Poster Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "Poster Mimetic Initialization of Self-Attention Layers Asher Trockman · Zico Kolter Exhibit Hall 1 #442 [ Abstract ]", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2023/poster/24304", "content": "Poster Mimetic Initialization of Self-Attention Layers Asher Trockman · Zico Kolter Exhibit Hall 1 #442 [ Abstract ]"}
+{"idx": 4, "title": "Mimetic initialization of self-attention layers | Proceedings of the ...", "date": "", "ddg_snippet": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3619843", "content": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity ."}
+{"idx": 5, "title": "Mimetic Initialization of Self-Attention Layers - Semantic Scholar", "date": "", "ddg_snippet": "Asher Trockman , J. Z. Kolter Published in International Conference on…16 May 2023 Computer Science TLDR", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Mimetic-Initialization-of-Self-Attention-Layers-Trockman-Kolter/26d7a95a029f91fd36d32bd5520a8cde691ffd0c/figure/0", "content": "Asher Trockman , J. Z. Kolter Published in International Conference on…16 May 2023 Computer Science TLDR"}
+{"idx": 6, "title": "\"Mimetic Initialization of Self-Attention Layers.\" - dblp", "date": "", "ddg_snippet": "Asher Trockman , J. Zico Kolter : Mimetic Initialization of Self-Attention Layers. ICML 2023 : 34456-34468", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/icml/TrockmanK23", "content": "Asher Trockman , J. Zico Kolter : Mimetic Initialization of Self-Attention Layers. ICML 2023 : 34456-34468"}
+{"idx": 7, "title": "Mimetic Initialization of Self-Attention Layers - ResearchGate", "date": "", "ddg_snippet": "Trockman , A., Willmott, D., and Kolter , J. Z. Understanding the covariance structure of convolutional filters. arXiv preprint arXiv:2210.03651, 2022.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370841580_Mimetic_Initialization_of_Self-Attention_Layers", "content": "Trockman , A., Willmott, D., and Kolter , J. Z. Understanding the covariance structure of convolutional filters. arXiv preprint arXiv:2210.03651, 2022."}
+{"idx": 8, "title": "Mimetic Initialization Helps State Space Models Learn to Recall", "date": "", "ddg_snippet": "Asher Trockman · Hrayr Harutyunyan · Zico Kolter · Sanjiv Kumar · Srinadh Bhojanapalli Keywords: [ Mamba ] [ SSM ] [ sequence models ] [ linear attention ] [ language models ] [ initialization ]", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/33067", "content": "Asher Trockman · Hrayr Harutyunyan · Zico Kolter · Sanjiv Kumar · Srinadh Bhojanapalli Keywords: [ Mamba ] [ SSM ] [ sequence models ] [ linear attention ] [ language models ] [ initialization ]"}
+{"idx": 9, "title": "Mimetic Initialization of Self-Attention Layers - PMLR", "date": "", "ddg_snippet": "%0 Conference Paper %T Mimetic Initialization of Self-Attention Layers %A Asher Trockman %A J Zico Kolter %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-trockman23a %I PMLR %P 34456--34468 %U ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/trockman23a.html", "content": "%0 Conference Paper %T Mimetic Initialization of Self-Attention Layers %A Asher Trockman %A J Zico Kolter %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-trockman23a %I PMLR %P 34456--34468 %U ..."}
diff --git a/data/sampled_jsons/Trockman_Kolter_2023_identity_initialization_paper_abstract_year_2023.jsonl b/data/sampled_jsons/Trockman_Kolter_2023_identity_initialization_paper_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..55fec8c4604225ae295587ac59c6d67cbf2a72cf
--- /dev/null
+++ b/data/sampled_jsons/Trockman_Kolter_2023_identity_initialization_paper_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.09828] Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "View a PDF of the paper titled Mimetic Initialization of Self-Attention Layers, by Asher Trockman and 1 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.09828", "content": "View a PDF of the paper titled Mimetic Initialization of Self-Attention Layers, by Asher Trockman and 1 other authors."}
+{"idx": 1, "title": "A Complete List of All Adversarial Example Papers", "date": "", "ddg_snippet": "Abstract : A continuously-updating list of all 1000+ papers posted to arXiv about adversarial examples.", "subpage_snippet": "", "source": "nicholas.carlini.com", "link": "https://nicholas.carlini.com/writing/2019/all-adversarial-example-papers.html", "content": "Abstract : A continuously-updating list of all 1000+ papers posted to arXiv about adversarial examples."}
+{"idx": 2, "title": "Asher Trockman", "date": "", "ddg_snippet": "Recently, I did a PhD at Carnegie Mellon University advised by Zico Kolter , where I worked on various topics in deep learning, including robustness, architecture design, initialization , and security.Summer 2023 .", "subpage_snippet": "", "source": "ashertrockman.com", "link": "https://ashertrockman.com/", "content": "Recently, I did a PhD at Carnegie Mellon University advised by Zico Kolter , where I worked on various topics in deep learning, including robustness, architecture design, initialization , and security.Summer 2023 ."}
+{"idx": 3, "title": "I Nitializing M odels with L arger o NES", "date": "", "ddg_snippet": "Mimetic initialization ( Trockman & Kolter , 2023 ) uses the diagonal properties. observed in the pretrained self-attention layer’s weights to initialize ViTs. We present results for.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=dyrGMhicMw", "content": "Mimetic initialization ( Trockman & Kolter , 2023 ) uses the diagonal properties. observed in the pretrained self-attention layer’s weights to initialize ViTs. We present results for."}
+{"idx": 4, "title": "Does structured visual representation help guide fine-tuning... | Medium", "date": "", "ddg_snippet": "Similarly, Trockman and Kolter ( 2023 ) showed that self-attention layers in ViTs identify patterns akin to those in pre-trained models, highlighting the role of well-structured layers in generalization. Trockman , A., & Kolter , J. Z. ( 2023 , July). Mimetic initialization of self-attention layers.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@christjan.bogoslovski/does-structured-visual-representation-help-guide-fine-tuning-decisions-0b3717ed8f92", "content": "Similarly, Trockman and Kolter ( 2023 ) showed that self-attention layers in ViTs identify patterns akin to those in pre-trained models, highlighting the role of well-structured layers in generalization. Trockman , A., & Kolter , J. Z. ( 2023 , July). Mimetic initialization of self-attention layers."}
+{"idx": 5, "title": "‘self-attention’ directory · Gwern.net", "date": "", "ddg_snippet": "“Mimetic Initialization of Self-Attention Layers ”, Trockman & Kolter 2023 .Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects.", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/transformer/attention/index", "content": "“Mimetic Initialization of Self-Attention Layers ”, Trockman & Kolter 2023 .Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects."}
+{"idx": 6, "title": "Simplifying Transformer Blocks: Related Work | HackerNoon", "date": "", "ddg_snippet": "Trockman & Kolter ( 2023 ) observe that the product of value and projection parameters often has a large identity component in trained transformers, and design an initialisation mimicking this to improve performance in standard transformers on small datasets.", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/simplifying-transformer-blocks-related-work", "content": "Trockman & Kolter ( 2023 ) observe that the product of value and projection parameters often has a large identity component in trained transformers, and design an initialisation mimicking this to improve performance in standard transformers on small datasets."}
+{"idx": 7, "title": "llm-research-summaries/training/inheritune-Smaller-Yet-More-Attentive...", "date": "", "ddg_snippet": "Recent works in model initialization : Trockman & Kolter ( 2023 ) and Xu et al. (2024). Synthetic attention patterns for initialization in vision settings have limited success in language models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cognitivetech/llm-research-summaries/blob/main/training/inheritune-Smaller-Yet-More-Attentive-Language-Models.md", "content": "Recent works in model initialization : Trockman & Kolter ( 2023 ) and Xu et al. (2024). Synthetic attention patterns for initialization in vision settings have limited success in language models."}
+{"idx": 8, "title": "Proceedings of the International Conference on Machine Learning 2022", "date": "", "ddg_snippet": "Following the setup in Trockman & Kolter (2021), we adopt KW-Large, ResNet9, WideResNet10-10 as the backbone architectures, and evaluate their robust accu-racy on CIFAR-10 with different designs of orthogonal con-volutions.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/su22a/su22a.pdf", "content": "Following the setup in Trockman & Kolter (2021), we adopt KW-Large, ResNet9, WideResNet10-10 as the backbone architectures, and evaluate their robust accu-racy on CIFAR-10 with different designs of orthogonal con-volutions."}
+{"idx": 9, "title": "SAMformer: Unlocking the Potential of Transformers in Time Series...", "date": "", "ddg_snippet": "( 2023 ) and Trockman & Kolter ( 2023 ): both works demonstrated the importance of diagonal patterns in attention matrices for signal propagation in transformers used in NLP and com-puter vision.", "subpage_snippet": "", "source": "helios2.mi.parisdescartes.fr", "link": "https://helios2.mi.parisdescartes.fr/~themisp/publications/icml24-samformer.pdf", "content": "( 2023 ) and Trockman & Kolter ( 2023 ): both works demonstrated the importance of diagonal patterns in attention matrices for signal propagation in transformers used in NLP and com-puter vision."}
diff --git a/data/sampled_jsons/Trockman_Kolter_2023_identity_matrix_initialization_transformer.jsonl b/data/sampled_jsons/Trockman_Kolter_2023_identity_matrix_initialization_transformer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7dacb62594cf4ad9ff066edc86deffa124494d39
--- /dev/null
+++ b/data/sampled_jsons/Trockman_Kolter_2023_identity_matrix_initialization_transformer.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Make Firefox your default browser | Firefox Help - Mozilla...", "date": "", "ddg_snippet": "May 14, 2024 · How to make Web links open in Firefox automatically by setting it as the default browser on your computer.", "subpage_snippet": "", "source": "support.mozilla.org", "link": "https://support.mozilla.org/en-US/kb/make-firefox-your-default-browser", "content": "May 14, 2024 · How to make Web links open in Firefox automatically by setting it as the default browser on your computer."}
+{"idx": 1, "title": "Choose your preferred PDF viewer in Firefox | Firefox Help", "date": "", "ddg_snippet": "Feb 27, 2025 · When Firefox is set as the default PDF viewer, it automatically opens PDF files in the built-in viewer. However, you can choose to use another application to view your downloaded PDF files. This article will guide you on how to open PDF files with a different viewer, set another viewer as the default, or configure Firefox to always ask which viewer to use.", "subpage_snippet": "", "source": "support.mozilla.org", "link": "https://support.mozilla.org/en-US/kb/choose-your-preferred-pdf-viewer-firefox", "content": "Feb 27, 2025 · When Firefox is set as the default PDF viewer, it automatically opens PDF files in the built-in viewer. However, you can choose to use another application to view your downloaded PDF files. This article will guide you on how to open PDF files with a different viewer, set another viewer as the default, or configure Firefox to always ask which viewer to use."}
+{"idx": 2, "title": "Install Thunderbird on Windows | Thunderbird Help - Mozilla...", "date": "", "ddg_snippet": "Apr 7, 2025 · This article describes how to install Thunderbird on Windows. If you are upgrading from a previous version of Thunderbird, see Update Thunderbird to the latest version.", "subpage_snippet": "", "source": "support.mozilla.org", "link": "https://support.mozilla.org/en-US/kb/installing-thunderbird-windows", "content": "Apr 7, 2025 · This article describes how to install Thunderbird on Windows. If you are upgrading from a previous version of Thunderbird, see Update Thunderbird to the latest version."}
+{"idx": 3, "title": "Thunderbird and Gmail | Thunderbird Help - Mozilla Support", "date": "", "ddg_snippet": "Dec 28, 2024 · Thunderbird can be set up to work seamlessly with Google's Gmail. Messages will be synchronized between your local version of Thunderbird and web-based Gmail.", "subpage_snippet": "", "source": "support.mozilla.org", "link": "https://support.mozilla.org/en-US/kb/thunderbird-and-gmail", "content": "Dec 28, 2024 · Thunderbird can be set up to work seamlessly with Google's Gmail. Messages will be synchronized between your local version of Thunderbird and web-based Gmail."}
+{"idx": 4, "title": "How to install Firefox on Windows | Firefox Help", "date": "", "ddg_snippet": "Aug 19, 2025 · This article explains how to install Firefox on Windows, either with a simple online installer from Mozilla or from the Microsoft Store. If you need a full, offline installer with custom options, see Custom installation of Firefox on Windows. To update Firefox from a previous version, see Update Firefox to the latest release.", "subpage_snippet": "", "source": "support.mozilla.org", "link": "https://support.mozilla.org/en-US/kb/how-download-and-install-firefox-windows", "content": "Aug 19, 2025 · This article explains how to install Firefox on Windows, either with a simple online installer from Mozilla or from the Microsoft Store. If you need a full, offline installer with custom options, see Custom installation of Firefox on Windows. To update Firefox from a previous version, see Update Firefox to the latest release."}
+{"idx": 5, "title": "Add or remove a search engine in Firefox | Firefox Help", "date": "", "ddg_snippet": "Jul 3, 2025 · Add search engines Found a website with a super useful search engine? Firefox makes it easy to add it to your search toolkit. Just follow the steps below: Add a search engine from the Search bar Firefox offers an optional Search bar. See Add the Search bar to your Firefox toolbar to add it to Firefox. Visit a website that offers an OpenSearch search engine (we'll use YouTube as an example ...", "subpage_snippet": "", "source": "support.mozilla.org", "link": "https://support.mozilla.org/en-US/kb/add-or-remove-search-engine-firefox", "content": "Jul 3, 2025 · Add search engines Found a website with a super useful search engine? Firefox makes it easy to add it to your search toolkit. Just follow the steps below: Add a search engine from the Search bar Firefox offers an optional Search bar. See Add the Search bar to your Firefox toolbar to add it to Firefox. Visit a website that offers an OpenSearch search engine (we'll use YouTube as an example ..."}
+{"idx": 6, "title": "Take screenshots in Firefox | Firefox Help - Mozilla Support", "date": "", "ddg_snippet": "Mar 5, 2025 · Screenshots is a tool in Firefox that allows you to save an image of all or parts of a web page.", "subpage_snippet": "", "source": "support.mozilla.org", "link": "https://support.mozilla.org/en-US/kb/firefox-screenshots", "content": "Mar 5, 2025 · Screenshots is a tool in Firefox that allows you to save an image of all or parts of a web page."}
+{"idx": 7, "title": "Search with the Firefox address bar | Firefox Help", "date": "", "ddg_snippet": "Aug 2, 2025 · The address bar makes it easier for you to find what you’re looking for. Enter search terms or a specific web address to get search suggestions, your top sites, bookmarks, history and search engines – all within the same field. You can also update Firefox or fix performance issues right from the search results. On occasion, you may see a tip from Firefox on how you can save time while ...", "subpage_snippet": "", "source": "support.mozilla.org", "link": "https://support.mozilla.org/en-US/kb/search-firefox-address-bar", "content": "Aug 2, 2025 · The address bar makes it easier for you to find what you’re looking for. Enter search terms or a specific web address to get search suggestions, your top sites, bookmarks, history and search engines – all within the same field. You can also update Firefox or fix performance issues right from the search results. On occasion, you may see a tip from Firefox on how you can save time while ..."}
+{"idx": 8, "title": "Manage Firefox with Microsoft Endpoint Manager (Intune)", "date": "", "ddg_snippet": "Mar 29, 2025 · Note: Microsoft has a new feature in preview that makes it much easier to import ADMX templates into Intune. We strongly recommend using it if you can. For more information, see Import custom ADMX and ADML administrative templates into Microsoft Intune.", "subpage_snippet": "", "source": "support.mozilla.org", "link": "https://support.mozilla.org/en-US/kb/managing-firefox-intune", "content": "Mar 29, 2025 · Note: Microsoft has a new feature in preview that makes it much easier to import ADMX templates into Intune. We strongly recommend using it if you can. For more information, see Import custom ADMX and ADML administrative templates into Microsoft Intune."}
+{"idx": 9, "title": "Move Thunderbird data to a new computer | Thunderbird Help", "date": "", "ddg_snippet": "Dec 30, 2024 · Thunderbird stores your data in a separate location, away from the program filesapplication, called your profile folder. To move your data, you can copy your profile folder to the equivalent location on your destination computer. Ensure both versions of Thunderbird are up to date or identical before attempting a transfer as otherwise it might fail.", "subpage_snippet": "", "source": "support.mozilla.org", "link": "https://support.mozilla.org/en-US/kb/moving-thunderbird-data-to-a-new-computer", "content": "Dec 30, 2024 · Thunderbird stores your data in a separate location, away from the program filesapplication, called your profile folder. To move your data, you can copy your profile folder to the equivalent location on your destination computer. Ensure both versions of Thunderbird are up to date or identical before attempting a transfer as otherwise it might fail."}
diff --git a/data/sampled_jsons/Trockman_Kolter_mimetic_initialization_identity_matrix_symmetric.jsonl b/data/sampled_jsons/Trockman_Kolter_mimetic_initialization_identity_matrix_symmetric.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b54eb1022c513d17f243124af8226780e5368923
--- /dev/null
+++ b/data/sampled_jsons/Trockman_Kolter_mimetic_initialization_identity_matrix_symmetric.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.09828] Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.09828", "content": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity ."}
+{"idx": 1, "title": "PDF CSD-Thesis-Oral-TROCKMAN-2025-03-31-display", "date": "", "ddg_snippet": "For SSMs, mimetic initialization substantially improves generalization abilities on synthetic language tasks like copying and associative recall. Overall, our findings suggest that the benefits of pre-training can be separated into two components: serving as a good initialization and storing transferable knowledge, with the former being simple ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/afs/.cs.cmu.edu/Web/Posters/CSThesis-AsherJamesTrockman25.pdf", "content": "For SSMs, mimetic initialization substantially improves generalization abilities on synthetic language tasks like copying and associative recall. Overall, our findings suggest that the benefits of pre-training can be separated into two components: serving as a good initialization and storing transferable knowledge, with the former being simple ..."}
+{"idx": 2, "title": "Mimetic Initialization of Self-Attention Layers - PMLR", "date": "", "ddg_snippet": "%0 Conference Paper %T Mimetic Initialization of Self-Attention Layers %A Asher Trockman %A J Zico Kolter %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-trockman23a %I PMLR %P 34456--34468 %U ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/trockman23a.html", "content": "%0 Conference Paper %T Mimetic Initialization of Self-Attention Layers %A Asher Trockman %A J Zico Kolter %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-trockman23a %I PMLR %P 34456--34468 %U ..."}
+{"idx": 3, "title": "Mimetic Initialization Helps State Space Models Learn to Recall", "date": "", "ddg_snippet": "Asher Trockman · Hrayr Harutyunyan · Zico Kolter · Sanjiv Kumar · Srinadh Bhojanapalli Keywords: [ Mamba ] [ SSM ] [ sequence models ] [ linear attention ] [ language models ] [ initialization ]", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/33067", "content": "Asher Trockman · Hrayr Harutyunyan · Zico Kolter · Sanjiv Kumar · Srinadh Bhojanapalli Keywords: [ Mamba ] [ SSM ] [ sequence models ] [ linear attention ] [ language models ] [ initialization ]"}
+{"idx": 4, "title": "Mimetic initialization of self-attention layers | Proceedings of the ...", "date": "", "ddg_snippet": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3619843", "content": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity ."}
+{"idx": 5, "title": "Mimetic Initialization Helps State Space Models Learn to Recall", "date": "", "ddg_snippet": "Mimetic Initialization Helps State Space Models Learn to Recall Asher Trockman , Hrayr Harutyunyan, J. Zico Kolter , Sanjiv Kumar, Srinadh Bhojanapalli", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.11135", "content": "Mimetic Initialization Helps State Space Models Learn to Recall Asher Trockman , Hrayr Harutyunyan, J. Zico Kolter , Sanjiv Kumar, Srinadh Bhojanapalli"}
+{"idx": 6, "title": "Mimetic Initialization Helps State Space Models Learn to Recall", "date": "", "ddg_snippet": "Mimetic Initialization Helps State Space Models Learn to Recall Asher Trockman , Hrayr Harutyunyan, J Zico Kolter , Sanjiv Kumar, Srinadh Bhojanapalli", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nerNr9fjfD", "content": "Mimetic Initialization Helps State Space Models Learn to Recall Asher Trockman , Hrayr Harutyunyan, J Zico Kolter , Sanjiv Kumar, Srinadh Bhojanapalli"}
+{"idx": 7, "title": "Mimetic Initialization of Self-Attention Layers - LOVO AI", "date": "", "ddg_snippet": "To address this problem, a paper written by Asher Trockman , J. Zico Kolter proposes mimetic initialization . As its name suggests, the self-attention weights of the transformers are initialized in a way that mimics those of their trained counterparts, allowing for a faster convergence", "subpage_snippet": "", "source": "lovo.ai", "link": "https://lovo.ai/post/mimetic-initialization-of-self-attention-layers", "content": "To address this problem, a paper written by Asher Trockman , J. Zico Kolter proposes mimetic initialization . As its name suggests, the self-attention weights of the transformers are initialized in a way that mimics those of their trained counterparts, allowing for a faster convergence"}
+{"idx": 8, "title": "Mimetic Initialization of Self-Attention Layers - Semantic Scholar", "date": "", "ddg_snippet": "This paper addresses the initialization problem of Vision Transformers by introducing a simple, yet highly innovative, initialization approach utilizing Discrete Cosine Transform (DCT) coefficients, and proposes a novel DCT-based compression technique for the attention function of Vision Transformers.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Mimetic-Initialization-of-Self-Attention-Layers-Trockman-Kolter/26d7a95a029f91fd36d32bd5520a8cde691ffd0c/figure/0", "content": "This paper addresses the initialization problem of Vision Transformers by introducing a simple, yet highly innovative, initialization approach utilizing Discrete Cosine Transform (DCT) coefficients, and proposes a novel DCT-based compression technique for the attention function of Vision Transformers."}
+{"idx": 9, "title": "PDF Mimetic Initialization for Deep Neural Networks", "date": "", "ddg_snippet": "For convolutional networks like ConvMixer and ConvNeXt, we observe improvements in accuracy and reductions in training time, even when convolutional filters are frozen (untrained) after initialization . For SSMs, mimetic initialization substantially improves generalization abilities on syn- thetic language tasks like copying and associative recall.", "subpage_snippet": "", "source": "csd.cs.cmu.edu", "link": "https://csd.cs.cmu.edu/sites/default/files/phd-thesis/CMU-CS-25-114.pdf", "content": "For convolutional networks like ConvMixer and ConvNeXt, we observe improvements in accuracy and reductions in training time, even when convolutional filters are frozen (untrained) after initialization . For SSMs, mimetic initialization substantially improves generalization abilities on syn- thetic language tasks like copying and associative recall."}
diff --git a/data/sampled_jsons/Ts-Attn_input_tensor_shape_Xin_Attn_inter_Attn_intra_dimensions_candidates_hidden_size.jsonl b/data/sampled_jsons/Ts-Attn_input_tensor_shape_Xin_Attn_inter_Attn_intra_dimensions_candidates_hidden_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85d005896f8992381812771bc38eea0e5d5c713a
--- /dev/null
+++ b/data/sampled_jsons/Ts-Attn_input_tensor_shape_Xin_Attn_inter_Attn_intra_dimensions_candidates_hidden_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention mask shape error - shape should be (1,1) - Stack Overflow", "date": "", "ddg_snippet": "Why should the shape be (1,1)? x has shape (1,100,10) - batch, context size, d_model. lookahead_mask has shape (100,100). Also going through this, realized not sure whether it 's ok to apply the attention mask after summing the embeddings with the positional embeddings.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/78108999/attention-mask-shape-error-shape-should-be-1-1", "content": "Why should the shape be (1,1)? x has shape (1,100,10) - batch, context size, d_model. lookahead_mask has shape (100,100). Also going through this, realized not sure whether it 's ok to apply the attention mask after summing the embeddings with the positional embeddings."}
+{"idx": 1, "title": "Custom 4D tensor caused shape mismatch error · Issue #35290 ...", "date": "", "ddg_snippet": "offset = 0 for i, (input_ids, attn_mask) in enumerate (zip (encoded [\"input_ids\"], encoded [\"attention_mask\"])): res [0, offset: offset + len (input_ids)] = torch. tensor (input_ids)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/huggingface/transformers/issues/35290", "content": "offset = 0 for i, (input_ids, attn_mask) in enumerate (zip (encoded [\"input_ids\"], encoded [\"attention_mask\"])): res [0, offset: offset + len (input_ids)] = torch. tensor (input_ids)"}
+{"idx": 2, "title": "Dimensions of attention mask - PyTorch Forums", "date": "", "ddg_snippet": "Well I am unsqueezing the third dimension so my tensor is [batch_size x seq_len x 1] sized, and send in a mask along with it with dimensions [batch_size*nheads, seq_len,seq_len]. This gets me the error: AssertionError: Expected attn_mask shape to be (5, 8, 8) but got torch.Size ( [40, 100, 100]) Note that here nheads is 5 batch size is 8 seq_le 100, so actually the wanted size is [nheads ...", "subpage_snippet": "", "source": "discuss.pytorch.org", "link": "https://discuss.pytorch.org/t/dimensions-of-attention-mask/190350", "content": "Well I am unsqueezing the third dimension so my tensor is [batch_size x seq_len x 1] sized, and send in a mask along with it with dimensions [batch_size*nheads, seq_len,seq_len]. This gets me the error: AssertionError: Expected attn_mask shape to be (5, 8, 8) but got torch.Size ( [40, 100, 100]) Note that here nheads is 5 batch size is 8 seq_le 100, so actually the wanted size is [nheads ..."}
+{"idx": 3, "title": "How to resolve the error regarding the 2D `attn_mask` shape in PyTorch ...", "date": "", "ddg_snippet": "The error you are encountering, which states that \"the shape of the 2D `attn_mask` is `torch.Size ( [77, 77])`, but should be ` (1, 1)`,\" typically arises in the c", "subpage_snippet": "", "source": "askai.glarity.app", "link": "https://askai.glarity.app/search/How-to-resolve-the-error-regarding-the-2D--attn-mask--shape-in-PyTorch", "content": "The error you are encountering, which states that \"the shape of the 2D `attn_mask` is `torch.Size ( [77, 77])`, but should be ` (1, 1)`,\" typically arises in the c"}
+{"idx": 4, "title": "The shape of the 2D attn_mask is torch.Size ( [77, 77]), but should be ...", "date": "", "ddg_snippet": "The shape of the 2D attn_mask is torch.Size ( [77, 77]), but should be (1, 1) #143 New issue Closed SPECT0R1A", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kijai/ComfyUI-SUPIR/issues/143", "content": "The shape of the 2D attn_mask is torch.Size ( [77, 77]), but should be (1, 1) #143 New issue Closed SPECT0R1A"}
+{"idx": 5, "title": "Attention mask shape error - shape should be (1,1) - PyTorch Forums", "date": "", "ddg_snippet": "RuntimeError: The shape of the 2D attn_mask is torch.Size ( [100, 100]), but should be (1, 1).", "subpage_snippet": "", "source": "discuss.pytorch.org", "link": "https://discuss.pytorch.org/t/attention-mask-shape-error-shape-should-be-1-1/197792", "content": "RuntimeError: The shape of the 2D attn_mask is torch.Size ( [100, 100]), but should be (1, 1)."}
+{"idx": 6, "title": "RuntimeError: Mask shape should match input shape", "date": "", "ddg_snippet": "hello，author，i have a question want to ask you. when i use clip.model.text_encode , the text. tensor . shape must is [77,77]?,meaning that the text must is consist of 77 sequences? the attn_mask could be changed? the attn_mask's tensor shape is [77,77],looks like a upper triangular matrix, but when ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mlfoundations/open_clip/issues/954", "content": "hello，author，i have a question want to ask you. when i use clip.model.text_encode , the text. tensor . shape must is [77,77]?,meaning that the text must is consist of 77 sequences? the attn_mask could be changed? the attn_mask's tensor shape is [77,77],looks like a upper triangular matrix, but when ..."}
+{"idx": 7, "title": "RuntimeError: The shape of the 2D attn_mask is torch.Size([77, 77 ...", "date": "", "ddg_snippet": "I encountered the problem \"RuntimeError: The shape of the 2D attn_mask is torch.Size ( [77, 77]), but should be (4, 4)\", which wasted half a day of my time, but I solved it, so I'm showing it here for the benefit of others who encounter the same problem. .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mlfoundations/open_clip/issues/937", "content": "I encountered the problem \"RuntimeError: The shape of the 2D attn_mask is torch.Size ( [77, 77]), but should be (4, 4)\", which wasted half a day of my time, but I solved it, so I'm showing it here for the benefit of others who encounter the same problem. ."}
+{"idx": 8, "title": "Qwen3 Fails w/4D Attn Mask when using FA2 #39608 - GitHub", "date": "", "ddg_snippet": "To pick up a draggable item, press the space bar. While dragging, use the arrow keys to move the item. Press space again to drop the item in its new position, or press escape to cancel.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/huggingface/transformers/issues/39608", "content": "To pick up a draggable item, press the space bar. While dragging, use the arrow keys to move the item. Press space again to drop the item in its new position, or press escape to cancel."}
+{"idx": 9, "title": "How to Implement Attention Layer in PyTorch? - Medium", "date": "", "ddg_snippet": "Shape Validation: Check that the output tensor and attention weights match the expected dimensions . Attention Weights Sum: Verify that the sum of attention weights across the last dimension is ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/biased-algorithms/how-to-implement-attention-layer-in-pytorch-4151c05bd9aa", "content": "Shape Validation: Check that the output tensor and attention weights match the expected dimensions . Attention Weights Sum: Verify that the sum of attention weights across the last dimension is ..."}
diff --git a/data/sampled_jsons/Tsamardinos_Brown_Aliferis_2006_max-min_hill-climbing_abstract_Bayesian_network_structure_learning.jsonl b/data/sampled_jsons/Tsamardinos_Brown_Aliferis_2006_max-min_hill-climbing_abstract_Bayesian_network_structure_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5ed35cdfb24df5d1b88b7bafae7cae3f04657c46
--- /dev/null
+++ b/data/sampled_jsons/Tsamardinos_Brown_Aliferis_2006_max-min_hill-climbing_abstract_Bayesian_network_structure_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Finding the optimal Bayesian network given a constraint graph", "date": "", "ddg_snippet": "... Moore & Wong, 2003 ), and hill - climbing ( Tsamardinos , Brown & Aliferis , 2006 ), typically attempt to efficiently identify a structure that ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-122/", "content": "... Moore & Wong, 2003 ), and hill - climbing ( Tsamardinos , Brown & Aliferis , 2006 ), typically attempt to efficiently identify a structure that ..."}
+{"idx": 1, "title": "Stable Structure Learning with HC-Stable and Tabu-Stable", "date": "", "ddg_snippet": "Many Bayesian Network structure learning algorithms are unstable, with the learned graph sensitive to arbitrary dataset artifacts, such as the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.01740v1", "content": "Many Bayesian Network structure learning algorithms are unstable, with the learned graph sensitive to arbitrary dataset artifacts, such as the ..."}
+{"idx": 2, "title": "Covariate Dependent Mixture of Bayesian Networks", "date": "", "ddg_snippet": "Learning the structure of a BN from data is complex; the cardinality of the discrete network search-space grows in a super-exponential fashion with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.05745v1", "content": "Learning the structure of a BN from data is complex; the cardinality of the discrete network search-space grows in a super-exponential fashion with ..."}
+{"idx": 3, "title": "Application of the Bayesian network theory in clinical trial", "date": "", "ddg_snippet": "... of using the Bayesian framework comes from the possibility of capturing a priori knowledge about a specific pattern by deriving the network structure ...", "subpage_snippet": "", "source": "www.msard-journal.com", "link": "https://www.msard-journal.com/article/S2211-0348(24)00045-2/fulltext", "content": "... of using the Bayesian framework comes from the possibility of capturing a priori knowledge about a specific pattern by deriving the network structure ..."}
+{"idx": 4, "title": "Learning Bayesian networks from big data with greedy search:", "date": "", "ddg_snippet": "Structure learning consists in finding the DAG \\(\\mathcal {G}\\) that encodes the dependence structure of the data, thus maximising \\({\\text {P ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11222-019-09857-1", "content": "Structure learning consists in finding the DAG \\(\\mathcal {G}\\) that encodes the dependence structure of the data, thus maximising \\({\\text {P ..."}
+{"idx": 5, "title": "NHESS - BN-FLEMOΔ: a Bayesian-network-based flood loss", "date": "", "ddg_snippet": "BN-FLEMO Δ : a Bayesian - network -based flood loss estimation model for adaptation planning in Ho Chi Minh City, Vietnam BN-FLEMO Δ : a Bayesian ...", "subpage_snippet": "", "source": "nhess.copernicus.org", "link": "https://nhess.copernicus.org/articles/25/2845/2025/", "content": "BN-FLEMO Δ : a Bayesian - network -based flood loss estimation model for adaptation planning in Ho Chi Minh City, Vietnam BN-FLEMO Δ : a Bayesian ..."}
+{"idx": 6, "title": "A Bayesian incorporated linear non-Gaussian acyclic model for", "date": "", "ddg_snippet": "Calhoun , Yu-Ping Wang; A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/netn/article/8/3/791/121183/A-Bayesian-incorporated-linear-non-Gaussian", "content": "Calhoun , Yu-Ping Wang; A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion ..."}
+{"idx": 7, "title": "The Econometrics of Bayesian Graphical Models: A Review With", "date": "", "ddg_snippet": "... network theory is critical to ... Sparse graphical vector autoregression: a Bayesian approach.Working Paper, Social Science Research Network .", "subpage_snippet": "", "source": "mpra.ub.uni-muenchen.de", "link": "https://mpra.ub.uni-muenchen.de/92634/", "content": "... network theory is critical to ... Sparse graphical vector autoregression: a Bayesian approach.Working Paper, Social Science Research Network ."}
+{"idx": 8, "title": "PEnBayes: A Multi-Layered Ensemble Approach for Learning", "date": "", "ddg_snippet": "Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples ... Cognitive Radio Networks", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/19/20/4400", "content": "Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples ... Cognitive Radio Networks"}
+{"idx": 9, "title": "US7117185B1 - Method, system, and apparatus for casual", "date": "", "ddg_snippet": "Bayesian Network analysis techniques are not able to work effectively upon larger data sets such as those derived from gene-expression array studies, ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US7117185B1/en", "content": "Bayesian Network analysis techniques are not able to work effectively upon larger data sets such as those derived from gene-expression array studies, ..."}
diff --git a/data/sampled_jsons/Tsamardinos_et_al._2006_Structural_Hamming_Distance_max-min_hill-climbing.jsonl b/data/sampled_jsons/Tsamardinos_et_al._2006_Structural_Hamming_Distance_max-min_hill-climbing.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..22925d1c38fb4147ab8b9d11a000b0952d506d0d
--- /dev/null
+++ b/data/sampled_jsons/Tsamardinos_et_al._2006_Structural_Hamming_Distance_max-min_hill-climbing.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) The Max-Min Hill-Climbing Bayesian Network ... - ResearchGate", "date": "", "ddg_snippet": "PDF | We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local... | Find, read and cite all the research you ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/220343713_The_Max-Min_Hill-Climbing_Bayesian_Network_Structure_Learning_Algorithm", "content": "PDF | We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local... | Find, read and cite all the research you ..."}
+{"idx": 1, "title": "The max-min hill-climbing Bayesian network structure ... - Springer", "date": "", "ddg_snippet": "We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-006-6889-7", "content": "We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our ..."}
+{"idx": 2, "title": "MMHC - The Max-Min Hill-Climbing Algorithm", "date": "", "ddg_snippet": "The complete Structural Hamming Distance results are also visually shown in 19 - 27. These figures are bar plots of the absolute timing results for each algorithm on a given network and sample size.", "subpage_snippet": "", "source": "pages.mtu.edu", "link": "https://pages.mtu.edu/~lebrown/supplements/mmhc_paper/mmhc_index.html", "content": "The complete Structural Hamming Distance results are also visually shown in 19 - 27. These figures are bar plots of the absolute timing results for each algorithm on a given network and sample size."}
+{"idx": 3, "title": "R: Structural Hamming distance between two partially oriented...", "date": "", "ddg_snippet": "Details The structural Hamming distance as proposed by Tsamardinos et al. ( 2006 ) is calculated and returned. The cases are listed below", "subpage_snippet": "", "source": "search.r-project.org", "link": "https://search.r-project.org/CRAN/refmans/MXM/html/shd.html", "content": "Details The structural Hamming distance as proposed by Tsamardinos et al. ( 2006 ) is calculated and returned. The cases are listed below"}
+{"idx": 4, "title": "max-min-hill-climbing-algorithm - GitHub", "date": "", "ddg_snippet": "The max-min hill-climbing Bayesian network structure learning algorithm, Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis, Mach Learn DOI 10.1007/s10994-006-6889-7 *This algorithm reconstructs Bayesian Networks from observational data. Therefore it first builds the skeleton of the DAG ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pespila/max-min-hill-climbing-algorithm", "content": "The max-min hill-climbing Bayesian network structure learning algorithm, Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis, Mach Learn DOI 10.1007/s10994-006-6889-7 *This algorithm reconstructs Bayesian Networks from observational data. Therefore it first builds the skeleton of the DAG ..."}
+{"idx": 5, "title": "The Max-Min Hill-Climbing Bayesian network structure learning algorithm.", "date": "", "ddg_snippet": "We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our ...", "subpage_snippet": "", "source": "psycnet.apa.org", "link": "https://psycnet.apa.org/record/2006-12885-002", "content": "We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our ..."}
+{"idx": 6, "title": "The max-min hill-climbing Bayesian network structure learning", "date": "", "ddg_snippet": "Mach Learn DOI 10.1007/s10994-006-6889-7 The max-min hill-climbing Bayesian network structure learning algorithm Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis Received: January 07, 2005 / Revised: December 21, 2005 / Accepted: December 22, 2005 / Published online: 28 March 2006 Springer Science + Business Media, Inc. 2006 Abstract We present a new algorithm for ...", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/18240467/the-max-min-hill-climbing-bayesian-network-structure-lear...", "content": "Mach Learn DOI 10.1007/s10994-006-6889-7 The max-min hill-climbing Bayesian network structure learning algorithm Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis Received: January 07, 2005 / Revised: December 21, 2005 / Accepted: December 22, 2005 / Published online: 28 March 2006 Springer Science + Business Media, Inc. 2006 Abstract We present a new algorithm for ..."}
+{"idx": 7, "title": "Tsamardinos, I., Brown, L.E. and Aliferis, C.F. (2006) The max-min hill ...", "date": "", "ddg_snippet": "Tsamardinos , I., Brown, L.E. and Aliferis, C.F. ( 2006 ) The max-min hill-climbing bayesian network structure learning algorithm. Machine Learning, 65 (1), 31-78.", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=38640", "content": "Tsamardinos , I., Brown, L.E. and Aliferis, C.F. ( 2006 ) The max-min hill-climbing bayesian network structure learning algorithm. Machine Learning, 65 (1), 31-78."}
+{"idx": 8, "title": "shd: Structural Hamming Distance in tzimiskes/causality: Examine The ...", "date": "", "ddg_snippet": "shd takes in patterns and calculates the structural hamming distance between them as defined in Tsamardinos et al ( 2006 ). DAGs will also work, as they will be converted into patterns.", "subpage_snippet": "", "source": "rdrr.io", "link": "https://rdrr.io/github/tzimiskes/causality/man/shd.html", "content": "shd takes in patterns and calculates the structural hamming distance between them as defined in Tsamardinos et al ( 2006 ). DAGs will also work, as they will be converted into patterns."}
+{"idx": 9, "title": "PDF The Max-Min Hill-Climbing Bayesian Network Structure ... - ResearchGate", "date": "", "ddg_snippet": "The Max-Min Hill-Climbing Algorithm In this section, we present the Max-Min Hill-Climbing algorithm (MM- HC) for learning the structure of a Bayesian network (Brown et al ., 2004).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Ioannis-Tsamardinos/publication/220343713_The_Max-Min_Hill-Climbing_Bayesian_Network_Structure_Learning_Algorithm/links/02e7e51e7c591b41a0000000/The-Max-Min-Hill-Climbing-Bayesian-Network-Structure-Learning-Algorithm.pdf", "content": "The Max-Min Hill-Climbing Algorithm In this section, we present the Max-Min Hill-Climbing algorithm (MM- HC) for learning the structure of a Bayesian network (Brown et al ., 2004)."}
diff --git a/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_Loihi_2_Jetson_Orin_Nano_latency_comparison.jsonl b/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_Loihi_2_Jetson_Orin_Nano_latency_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e029055e4ed8fb1003a74e11bbc4aa79276ee194
--- /dev/null
+++ b/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_Loihi_2_Jetson_Orin_Nano_latency_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.less energy than the Jetson Orin Nano (Token-by-token; Loihi 2 Fall-Through and Jetson Orin Nano Recurrent 1-step (b=1) in Table 1 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01330v1", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.less energy than the Jetson Orin Nano (Token-by-token; Loihi 2 Fall-Through and Jetson Orin Nano Recurrent 1-step (b=1) in Table 1 )."}
+{"idx": 1, "title": "(PDF) Accelerating Linear Recurrent Neural Networks for the Edge...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. Nano (Token-by-token; Loihi 2 Fall-Through and Jetson . Orin Nano Recurrent 1-step (b=1) in Table 1 ).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685255_Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. Nano (Token-by-token; Loihi 2 Fall-Through and Jetson . Orin Nano Recurrent 1-step (b=1) in Table 1 )."}
+{"idx": 2, "title": "LLM Leaderboard - Comparison of over 100 AI... | Artificial Analysis", "date": "", "ddg_snippet": "Comparison and ranking the performance of over 100 AI models (LLMs) across key metrics including intelligence, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others.", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/leaderboards/models", "content": "Comparison and ranking the performance of over 100 AI models (LLMs) across key metrics including intelligence, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others."}
+{"idx": 3, "title": "Introducing NVIDIA Jetson Orin ™ Nano Super: The... - YouTube", "date": "", "ddg_snippet": "The NVIDIA Jetson Orin ™ Nano Super Developer Kit’s performance, compact size, and low cost are redefining generative AI for small edge devices.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=S9L2WGf1KrM", "content": "The NVIDIA Jetson Orin ™ Nano Super Developer Kit’s performance, compact size, and low cost are redefining generative AI for small edge devices."}
+{"idx": 4, "title": "A Look at Loihi 2 - Intel - Neuromorphic Chip - Open Neuromorphic", "date": "", "ddg_snippet": "Loihi 2 is Intel's latest neuromorphic research chip, implementing spiking neural networks with programmable dynamics, modular connectivity, and optimizations for scale, speed, and efficiency.", "subpage_snippet": "", "source": "open-neuromorphic.org", "link": "https://open-neuromorphic.org/neuromorphic-computing/hardware/loihi-2-intel/", "content": "Loihi 2 is Intel's latest neuromorphic research chip, implementing spiking neural networks with programmable dynamics, modular connectivity, and optimizations for scale, speed, and efficiency."}
+{"idx": 5, "title": "Introduction to Recurrent Neural Networks - GeeksforGeeks", "date": "", "ddg_snippet": "Recurrent Neural Networks (RNNs) differ from regular neural networks in how they process information. While standard neural networks pass information in one direction i .e from input to output, RNNs feed information back into the network at each step.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/introduction-to-recurrent-neural-network/", "content": "Recurrent Neural Networks (RNNs) differ from regular neural networks in how they process information. While standard neural networks pass information in one direction i .e from input to output, RNNs feed information back into the network at each step."}
+{"idx": 6, "title": "Jetson orin , CUDA error: no kernel image is available... - vLLM Forums", "date": "", "ddg_snippet": "Docker image `vllm/vllm-openai:v0.9.0` doesn't work on 5090Can anyone help me ? Why is this not working? It used 😭", "subpage_snippet": "", "source": "discuss.vllm.ai", "link": "https://discuss.vllm.ai/t/jetson-orin-cuda-error-no-kernel-image-is-available-for-execution-on-the-device/321", "content": "Docker image `vllm/vllm-openai:v0.9.0` doesn't work on 5090Can anyone help me ? Why is this not working? It used 😭"}
+{"idx": 7, "title": "A Diagonal State Space Model", "date": "", "ddg_snippet": "\" Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity.\" arXiv preprint arXiv:2502.01330 (2025). - Go alternative routes such as MatMul-Free LLMs Abreu, Steven, et al.", "subpage_snippet": "", "source": "flagship.kip.uni-heidelberg.de", "link": "https://flagship.kip.uni-heidelberg.de/jss/HBPm?m=displayPresentation&mI=263&mEID=9685", "content": "\" Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity.\" arXiv preprint arXiv:2502.01330 (2025). - Go alternative routes such as MatMul-Free LLMs Abreu, Steven, et al."}
+{"idx": 8, "title": "N EUROMORPHIC", "date": "", "ddg_snippet": "Table 2 shows results for the comparison of the MatMul-free LLM on Loihi 2 and transformer-based LLMs on the NVIDIA Jetson Orin Nano , also including results for the MatMul-free LLM on an H100 GPU and the Transformer++ baseline from Zhu et al.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qaDM1R2nlm", "content": "Table 2 shows results for the comparison of the MatMul-free LLM on Loihi 2 and transformer-based LLMs on the NVIDIA Jetson Orin Nano , also including results for the MatMul-free LLM on an H100 GPU and the Transformer++ baseline from Zhu et al."}
+{"idx": 9, "title": "Deep Learning в иллюстрациях: Рекуррентные нейронные... / Хабр", "date": "", "ddg_snippet": "Рекуррентные нейронные сети ( Recurrent Neural Networks , RNN) — это уникальные модели, специально разработанные для решения задач обработки серии событий во времени или последовательных пространственных цепочек...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/otus/articles/886816/", "content": "Рекуррентные нейронные сети ( Recurrent Neural Networks , RNN) — это уникальные модели, специально разработанные для решения задач обработки серии событий во времени или последовательных пространственных цепочек..."}
diff --git a/data/sampled_jsons/UPGNET_NFR_layer_Equation_11_feature_selection_mechanism.jsonl b/data/sampled_jsons/UPGNET_NFR_layer_Equation_11_feature_selection_mechanism.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f48ed16aed887e93c53c6b380a269c76749041ee
--- /dev/null
+++ b/data/sampled_jsons/UPGNET_NFR_layer_Equation_11_feature_selection_mechanism.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention (machine learning) - Wikipedia", "date": "", "ddg_snippet": "Attentional Neural Networks introduced a learned feature selection mechanism using top-down cognitive modulation, showing how attention weights can highlight relevant inputs.[12].", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Attention_(machine_learning)", "content": "Attentional Neural Networks introduced a learned feature selection mechanism using top-down cognitive modulation, showing how attention weights can highlight relevant inputs.[12]."}
+{"idx": 1, "title": "Double layer (surface science) - Wikipedia", "date": "", "ddg_snippet": "Schematic of the electrical double layer in aqueous solution at the interface with a negatively-charged surface of a mineral solid. Blue + sphere: cations; red – spheres: anions. The number of cations is larger in the EDL close to the negatively-char...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Double_layer_(surface_science)", "content": "Schematic of the electrical double layer in aqueous solution at the interface with a negatively-charged surface of a mineral solid. Blue + sphere: cations; red – spheres: anions. The number of cations is larger in the EDL close to the negatively-char..."}
+{"idx": 2, "title": "(PDF) Sobol Sensitivity: A Strategy for Feature Selection", "date": "", "ddg_snippet": "Feature selection is a key. mechanism to reduce a large number of variables to relatively few.next algorithm utilizes this computational efficiency of the approach and calcu-. Sobol Sensitivity: a strategy for Feature Selection 11 .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/316618458_Sobol_Sensitivity_A_Strategy_for_Feature_Selection", "content": "Feature selection is a key. mechanism to reduce a large number of variables to relatively few.next algorithm utilizes this computational efficiency of the approach and calcu-. Sobol Sensitivity: a strategy for Feature Selection 11 ."}
+{"idx": 3, "title": "ICML Poster Going Deeper into Locally Differentially Private Graph...", "date": "", "ddg_snippet": "Based on the above analysis, UPGNet enhances utility by introducing two core layers : High-Order Aggregator (HOA) layer and the Node Feature Regularization ( NFR ) layer .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46579", "content": "Based on the above analysis, UPGNet enhances utility by introducing two core layers : High-Order Aggregator (HOA) layer and the Node Feature Regularization ( NFR ) layer ."}
+{"idx": 4, "title": "TabNet: Attentive Interpretable Tabular Learning", "date": "", "ddg_snippet": "Interpretability: TabNet's feature selection mechanism makes it easy to interpret which features are most important for the model's predictions. This can be done by analyzing the feature masks that are output by the attentive transformer.", "subpage_snippet": "", "source": "www.machinelearningexpedition.com", "link": "https://www.machinelearningexpedition.com/tabnet-tabular-neural-network/", "content": "Interpretability: TabNet's feature selection mechanism makes it easy to interpret which features are most important for the model's predictions. This can be done by analyzing the feature masks that are output by the attentive transformer."}
+{"idx": 5, "title": "classification - Chi-squared Vs Mutual information - Cross Validated", "date": "", "ddg_snippet": "Is chi-squared feature selection better than Mutual information based feature selection mechanism ?1,243 11 11 silver badges 21 21 bronze badges. $\\endgroup$.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/125660/chi-squared-vs-mutual-information", "content": "Is chi-squared feature selection better than Mutual information based feature selection mechanism ?1,243 11 11 silver badges 21 21 bronze badges. $\\endgroup$."}
+{"idx": 6, "title": "London Time to IST Converter - Convert London... - World Time Buddy", "date": "", "ddg_snippet": "Outlook / iCal Google Calendar Clipboard Gmail Link to this selection Event. +0. London BST. United Kingdom, England. 11 :45p.", "subpage_snippet": "", "source": "www.worldtimebuddy.com", "link": "https://www.worldtimebuddy.com/united-kingdom-england-london-to-ist", "content": "Outlook / iCal Google Calendar Clipboard Gmail Link to this selection Event. +0. London BST. United Kingdom, England. 11 :45p."}
+{"idx": 7, "title": "CMC | Free Full-Text | URL Phishing Detection Using Particle Swarm...", "date": "", "ddg_snippet": "The attributes selected using this feature selection mechanism are.The classification techniques exploited to analyze the better feature selection technique are Multi- layer Perceptron (MLP) and Random Tree.", "subpage_snippet": "", "source": "www.techscience.com", "link": "https://www.techscience.com/cmc/v73n3/49073/html", "content": "The attributes selected using this feature selection mechanism are.The classification techniques exploited to analyze the better feature selection technique are Multi- layer Perceptron (MLP) and Random Tree."}
+{"idx": 8, "title": "Regularization — Understanding L1 and L2 regularization for... | Medium", "date": "", "ddg_snippet": "So, this works well for feature selection in case we have a huge number of features . The L1 regularizer basically looks for the parameter vectors that minimize the norm of the parameter vector (the length of the vector).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/analytics-vidhya/regularization-understanding-l1-and-l2-regularization-for-deep-learning-a7b9e4a409bf", "content": "So, this works well for feature selection in case we have a huge number of features . The L1 regularizer basically looks for the parameter vectors that minimize the norm of the parameter vector (the length of the vector)."}
+{"idx": 9, "title": "Convolutional Neural Network: Text Classification Model", "date": "", "ddg_snippet": "Text classification mainly focus on three topics which includes: Feature Engineering: most used feature is the bag-of-words feature, some more multiplex feature are designed such as part-of-speech tags, tree kernels and noun phrases, [10] [ 11 ] Feature Selection ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1809.02479", "content": "Text classification mainly focus on three topics which includes: Feature Engineering: most used feature is the bag-of-words feature, some more multiplex feature are designed such as part-of-speech tags, tree kernels and noun phrases, [10] [ 11 ] Feature Selection ..."}
diff --git a/data/sampled_jsons/UVGS_Gaussian_Splatting_branched_mapping_layers_sitearxiv.org_year_2024.jsonl b/data/sampled_jsons/UVGS_Gaussian_Splatting_branched_mapping_layers_sitearxiv.org_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..843b24d9ea770772512908fde6e21a2fade8f61c
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Gaussian_Splatting_branched_mapping_layers_sitearxiv.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2502.01846v3 [cs.CV] 20 Mar 2025", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated supe- rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846", "content": "3D Gaussian Splatting (3DGS) has demonstrated supe- rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 1, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "3 Feb 2025 — Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v1", "content": "3 Feb 2025 — Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the ..."}
+{"idx": 2, "title": "[2502.01846] UVGS: Reimagining Unstructured 3D Gaussian ...", "date": "", "ddg_snippet": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images , with feature ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.01846", "content": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images , with feature ..."}
+{"idx": 3, "title": "UV Mapping Online Course - New Learner Offer Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Learn UV Mapping online at your own pace. Start today and improve your skills. Join millions of learners from around the world already learning on Udemy.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=MV8x-BzRwjTDkhhFePqvES4cmRqVwHpI0b4pXRbyAWzpfwPEIF29ad9LDI-_34m9j470Eblwiy8bWxNJe3gB_bI8yZPu2suocyYrlQrsQChfATBgRrCJLQbiPkyNmrFB._-UIxSu5bLvqNTh8Nkeomw&rut=21519491f8af9cab0acdfb46a339fcc03d7e9ae2bb6304a1c9ac656e05a056cf&u3=https://www.bing.com/aclick?ld=e8TkqNU0n4T5w5vKF65O-U5DVUCUyhYLm0w_EJ0VzLRXbyiVTazVXfPZjEgwvyKm7_Qvs74J3JS9DTXh-wzjcXJs5_6h5gCC350J4hKEVHMjsy3aCcTrNhd8hQZj624DlhO7XGX85eFqMMqLN6euG9JZ0e2mrE06W7N-8BO3XNgwaeGoqxDZtJ_FLN0I1GxOGHajK2_Q&u=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&rlid=34b631cb8b7b1834af6559183aba94eb&vqd=4-335505248288985732708655498621440573285&iurl={1}IG=FF9AF980100847E4963429B8C45A3B9B&CID=2CDB44456B486026243152346AF76125&ID=DevEx,5045.1", "content": "Learn UV Mapping online at your own pace. Start today and improve your skills. Join millions of learners from around the world already learning on Udemy."}
+{"idx": 4, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 5, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "To address these shortcomings, we introduce UV Gaussian Splatting ( UVGS ), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [43] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v2", "content": "To address these shortcomings, we introduce UV Gaussian Splatting ( UVGS ), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [43] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ..."}
+{"idx": 6, "title": "A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing ...", "date": "", "ddg_snippet": "Abstract—3D Gaussian Splatting (3DGS) has recently emerged as a powerful alternative to Neural Radiance Fields (NeRF) for 3D scene representation, offering high-fidelity photorealistic rendering with real-time performance. Beyond novel view synthesis, the explicit and compact nature of 3DGS enables a wide range of downstream applications that require geometric and semantic understanding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.09977", "content": "Abstract—3D Gaussian Splatting (3DGS) has recently emerged as a powerful alternative to Neural Radiance Fields (NeRF) for 3D scene representation, offering high-fidelity photorealistic rendering with real-time performance. Beyond novel view synthesis, the explicit and compact nature of 3DGS enables a wide range of downstream applications that require geometric and semantic understanding ..."}
+{"idx": 7, "title": "[2408.14823] LapisGS: Layered Progressive 3D Gaussian Splatting for ...", "date": "", "ddg_snippet": "View a PDF of the paper titled LapisGS: Layered Progressive 3D Gaussian Splatting for Adaptive Streaming, by Yuang Shi and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.14823", "content": "View a PDF of the paper titled LapisGS: Layered Progressive 3D Gaussian Splatting for Adaptive Streaming, by Yuang Shi and 3 other authors"}
+{"idx": 8, "title": "Gaussian Splatting as a Unified Representation for Autonomy in ...", "date": "", "ddg_snippet": "4. To address the challenges of mapping large-scale environments, we present a submapping framework for Gaussian splatting with particular considerations for autonomy. This framework enables efficient mapping and collision checking of Gaussians in expansive outdoor spaces, making our approach scalable to real-world applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.11794v1", "content": "4. To address the challenges of mapping large-scale environments, we present a submapping framework for Gaussian splatting with particular considerations for autonomy. This framework enables efficient mapping and collision checking of Gaussians in expansive outdoor spaces, making our approach scalable to real-world applications."}
+{"idx": 9, "title": "EdgeGaussians -- 3D Edge Mapping via Gaussian Splatting", "date": "", "ddg_snippet": "View a PDF of the paper titled EdgeGaussians -- 3D Edge Mapping via Gaussian Splatting , by Kunal Chelani and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.12886", "content": "View a PDF of the paper titled EdgeGaussians -- 3D Edge Mapping via Gaussian Splatting , by Kunal Chelani and 3 other authors"}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_UV_Mapping_spherical_coordinates_formula.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_UV_Mapping_spherical_coordinates_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8bcfa86c26c9bec279f4a895c6602283dd1ea1ad
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_UV_Mapping_spherical_coordinates_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UV mapping - Wikipedia", "date": "", "ddg_snippet": "The application of a texture in the UV space related to the effect in 3 D . A representation of the UV mapping of a cube. The flattened cube net may then be textured to texture the cube. UV mapping is the 3 D modeling process of projecting a 3 D model&ap...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/UV_mapping", "content": "The application of a texture in the UV space related to the effect in 3 D . A representation of the UV mapping of a cube. The flattened cube net may then be textured to texture the cube. UV mapping is the 3 D modeling process of projecting a 3 D model&ap..."}
+{"idx": 1, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v2", "content": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation."}
+{"idx": 2, "title": "(PDF) UVGS : Reimagining Unstructured 3 D Gaussian Splatting ...", "date": "", "ddg_snippet": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation."}
+{"idx": 3, "title": "UVGS", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .The Super UVGS can be converted to 3DGS object using inverse mapping network and inverse spherical projection.", "subpage_snippet": "", "source": "web.archive.org", "link": "https://web.archive.org/web/20250210041626/https://aashishrai3799.github.io/uvgs/", "content": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .The Super UVGS can be converted to 3DGS object using inverse mapping network and inverse spherical projection."}
+{"idx": 4, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Introduces spherical and cylindrical UV mapping for different object types. 3 D Gaussian Splatting is like painting a 3 D scene with tiny dots of color. This paper makes it better by using UV mapping - imagine unwrapping a 3 D object like a chocolate wrapper and painting on the flat surface.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/uvgs-reimagining-unstructured-3d-gaussian-splatting-using", "content": "Introduces spherical and cylindrical UV mapping for different object types. 3 D Gaussian Splatting is like painting a 3 D scene with tiny dots of color. This paper makes it better by using UV mapping - imagine unwrapping a 3 D object like a chocolate wrapper and painting on the flat surface."}
+{"idx": 5, "title": "Free 3 D Gaussian Splatting Tool | Polycam", "date": "", "ddg_snippet": "Gaussian splatting is an exciting new rendering technique that excels at creating lifelike, natural-looking 3 D scenes and subjects - think of a 3 D photograph. Learn more about splatting and try it out for yourself!", "subpage_snippet": "", "source": "poly.cam", "link": "https://poly.cam/tools/gaussian-splatting", "content": "Gaussian splatting is an exciting new rendering technique that excels at creating lifelike, natural-looking 3 D scenes and subjects - think of a 3 D photograph. Learn more about splatting and try it out for yourself!"}
+{"idx": 6, "title": "PlayCanvas Open Sources SOG: The WebP of Gaussian Splatting", "date": "", "ddg_snippet": "SuperSplat, the #1 platform publishing 3 D Gaussian Splats, has been updated to compress your scans with SOG. Since it provides around 2-3x the compression of Compressed PLY, your creations will load much faster and will load on more memory constrained devices.", "subpage_snippet": "", "source": "blog.playcanvas.com", "link": "https://blog.playcanvas.com/playcanvas-open-sources-sog-format-for-gaussian-splatting/", "content": "SuperSplat, the #1 platform publishing 3 D Gaussian Splats, has been updated to compress your scans with SOG. Since it provides around 2-3x the compression of Compressed PLY, your creations will load much faster and will load on more memory constrained devices."}
+{"idx": 7, "title": "What Is UV Mapping ? How It Makes 3 D Models Come to Life", "date": "", "ddg_snippet": "UV mapping in 3 D modeling projects a 2D image on the surface of a model to add more details. Learn the basic elements and software to make it possible.", "subpage_snippet": "", "source": "www.g2.com", "link": "https://www.g2.com/articles/uv-mapping", "content": "UV mapping in 3 D modeling projects a 2D image on the surface of a model to add more details. Learn the basic elements and software to make it possible."}
+{"idx": 8, "title": "UVGS Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=1uMuXsJScvY", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 9, "title": "GitHub - sailing-innocent/DailyArxiv: from https...", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .7 pages, 6 figures. Digital Twin Buildings: 3 D Modeling, GIS Integration, and Visual Descriptions Using Gaussian Splatting , ChatGPT/Deepseek, and Google Maps Platform.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sailing-innocent/DailyArxiv", "content": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .7 pages, 6 figures. Digital Twin Buildings: 3 D Modeling, GIS Integration, and Visual Descriptions Using Gaussian Splatting , ChatGPT/Deepseek, and Google Maps Platform."}
diff --git a/data/sampled_jsons/UVGS_spherical_mapping_formula_u_=_v_=_section_3.1.jsonl b/data/sampled_jsons/UVGS_spherical_mapping_formula_u_=_v_=_section_3.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..909cbaa0740b45368e9483d4c0fc6c110f2532e7
--- /dev/null
+++ b/data/sampled_jsons/UVGS_spherical_mapping_formula_u_=_v_=_section_3.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV...", "date": "", "ddg_snippet": "The Super UVGS is mapped back to UVGS through branched inverse mapping , which in turn can be reconstructed back to the 3DGS object through inverse spherical mapping .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846", "content": "The Super UVGS is mapped back to UVGS through branched inverse mapping , which in turn can be reconstructed back to the 3DGS object through inverse spherical mapping ."}
+{"idx": 1, "title": "Texture Mapping", "date": "", "ddg_snippet": "Once that test is passing, the next step is to tell your renderer how to map a 3D point (x, y, z) on the surface of sphere to a 2D point ( u , v ) on the flattened surface. You'll introduce a new function, spherical _ map (p), which returns the ( u , v ) pair corresponding to the given 3D point p.", "subpage_snippet": "", "source": "raytracerchallenge.com", "link": "http://raytracerchallenge.com/bonus/texture-mapping.html", "content": "Once that test is passing, the next step is to tell your renderer how to map a 3D point (x, y, z) on the surface of sphere to a 2D point ( u , v ) on the flattened surface. You'll introduce a new function, spherical _ map (p), which returns the ( u , v ) pair corresponding to the given 3D point p."}
+{"idx": 2, "title": "(PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using...", "date": "", "ddg_snippet": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation."}
+{"idx": 3, "title": "c++ - Spherical mapping calculation (UV) for point... - Stack Overflow", "date": "", "ddg_snippet": "I want to write an spherical mapping for my ray-tracer to generate UV-coordinates for the sphere . The texture applied with that mapping should looks like that: enter image description here. I have the sphere that is located at [0.0f,0.0f,0.0f] with the radius of 20.0f.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/33747876/spherical-mapping-calculation-uv-for-point-given-on-sphere-strange-u-values?rq=1", "content": "I want to write an spherical mapping for my ray-tracer to generate UV-coordinates for the sphere . The texture applied with that mapping should looks like that: enter image description here. I have the sphere that is located at [0.0f,0.0f,0.0f] with the radius of 20.0f."}
+{"idx": 4, "title": "UV Mapping Basics: A Quick Start Guide | Ahmad Merheb", "date": "", "ddg_snippet": "UV Mapping is the process of taking a 3D model and flattening it up and spreading it so we can have a 2D version of it in order to apply 2D textures directly on it.", "subpage_snippet": "", "source": "ahmadmerheb.com", "link": "https://ahmadmerheb.com/uv-mapping/", "content": "UV Mapping is the process of taking a 3D model and flattening it up and spreading it so we can have a 2D version of it in order to apply 2D textures directly on it."}
+{"idx": 5, "title": "Spherical , Cubic, and Parabolic Environment Mappings", "date": "", "ddg_snippet": "Sphere mapping utilizes 1 texture map , cubic mapping 6, and dual paraboloid mapping 2. The number of texture maps also affects how much an environment mapping technique distorts its environment.", "subpage_snippet": "", "source": "cgvr.informatik.uni-bremen.de", "link": "https://cgvr.informatik.uni-bremen.de/teaching/cg_literatur/Spherical,+Cubic,+and+Parabolic+Environment+Mappings.pdf", "content": "Sphere mapping utilizes 1 texture map , cubic mapping 6, and dual paraboloid mapping 2. The number of texture maps also affects how much an environment mapping technique distorts its environment."}
+{"idx": 6, "title": "Fast Spherical Mapping for Genus-0 Meshes", "date": "", "ddg_snippet": "However, for most spherical mapping methods, satisfying the non-overlapping requirement is still no gurantee, although this requirement is a critical component of any spherical mapping process.", "subpage_snippet": "", "source": "www.cs.uky.edu", "link": "https://www.cs.uky.edu/~cheng/PUBL/Paper_Spherical_Mapping.pdf", "content": "However, for most spherical mapping methods, satisfying the non-overlapping requirement is still no gurantee, although this requirement is a critical component of any spherical mapping process."}
+{"idx": 7, "title": "Texture coordinates and mapping techniques - Computer Graphics", "date": "", "ddg_snippet": "Spherical mapping maps the texture onto the surface of a sphere .By utilizing these mapping techniques and accurately assigning texture coordinates to each vertex, computer graphics artists and developers can bring their 3D models to life with detailed and realistic textures.", "subpage_snippet": "", "source": "noobtomaster.com", "link": "https://noobtomaster.com/computer-graphics/texture-coordinates-and-mapping-techniques/", "content": "Spherical mapping maps the texture onto the surface of a sphere .By utilizing these mapping techniques and accurately assigning texture coordinates to each vertex, computer graphics artists and developers can bring their 3D models to life with detailed and realistic textures."}
+{"idx": 8, "title": "\"Learning ray tracing in a week\" 5. Spherical texture mapping", "date": "", "ddg_snippet": "Spherical map mapping formula . In rectangular coordinates, for a picture with a width and height of nx*ny, the coordinates are.", "subpage_snippet": "", "source": "programmersought.com", "link": "https://programmersought.com/article/33695346424/", "content": "Spherical map mapping formula . In rectangular coordinates, for a picture with a width and height of nx*ny, the coordinates are."}
+{"idx": 9, "title": "Fast Spherical Mapping of Cortical Surface Meshes Using Deep...", "date": "", "ddg_snippet": "Typical spherical mapping processes can be divided into two steps: generation of an initial spherical mesh and distortion correction of the initial spherical mesh, as shown in Fig. 1. For the first step, a widely used method is to iteratively smooth and inflate the brain surface mesh until the...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10266716/", "content": "Typical spherical mapping processes can be divided into two steps: generation of an initial spherical mesh and distortion correction of the initial spherical mesh, as shown in Fig. 1. For the first step, a widely used method is to iteratively smooth and inflate the brain surface mesh until the..."}
diff --git a/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning_filetypepdf.jsonl b/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85e96803e6407ef362d3433156f17196087dadb1
--- /dev/null
+++ b/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning_filetypepdf.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "Unbiased Recommender Learning from Implicit Feedback via ...", "date": "", "ddg_snippet": "2023. Unbiased Pairwise Learning from Implicit Feed - back for Recommender Systems without Biased Variance Control.ACM, 551–556. 13. Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning . 10−1.", "subpage_snippet": "", "source": "zhouchenlin.github.io", "link": "https://zhouchenlin.github.io/Publications/2025-ICML-Unbiased.pdf", "content": "2023. Unbiased Pairwise Learning from Implicit Feed - back for Recommender Systems without Biased Variance Control.ACM, 551–556. 13. Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning . 10−1."}
+{"idx": 1, "title": "Unbiased Recommender Learning from Implicit Feedback via ...", "date": "", "ddg_snippet": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13&referrer=[the+profile+of+Licheng+Pan](/profile?id=~Licheng_Pan1)", "content": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback."}
+{"idx": 2, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning weighting, a methodology rooted in causal inference (Li et al., 2023b) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning weighting, a methodology rooted in causal inference (Li et al., 2023b) ..."}
+{"idx": 3, "title": "Downloads", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning . UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning . UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model."}
+{"idx": 4, "title": "Tianqiao Liu - Google Scholar", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning .Forty-second International Conference on Machine Learning, 0.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=HIuCuFkAAAAJ&hl=en", "content": "Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning .Forty-second International Conference on Machine Learning, 0."}
+{"idx": 5, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Universal_Transformers_'self-iteration'_'iterative_refinement'_'recurrence_over_depth'_Dehghani.jsonl b/data/sampled_jsons/Universal_Transformers_'self-iteration'_'iterative_refinement'_'recurrence_over_depth'_Dehghani.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cbcfb226dc9a8a400bdd0429b37788f2fc9d6e39
--- /dev/null
+++ b/data/sampled_jsons/Universal_Transformers_'self-iteration'_'iterative_refinement'_'recurrence_over_depth'_Dehghani.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A standard Transformer architecture, showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 T...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "Machine learningand data mining. v. t. e. A standard Transformer architecture, showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 T..."}
+{"idx": 1, "title": "Universal Transformers- Iterative Refinement for Better ...", "date": "", "ddg_snippet": "This seemingly small change makes a big difference: Universal Transformers can generalize better, capture algorithmic structure, and even approach Turing-completeness under certain assumptions.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jain.sm/universal-transformers-iterative-refinement-for-better-reasoning-a1fca43fea56", "content": "This seemingly small change makes a big difference: Universal Transformers can generalize better, capture algorithmic structure, and even approach Turing-completeness under certain assumptions."}
+{"idx": 2, "title": "UNIVERSAL TRANSFORMERS", "date": "", "ddg_snippet": "In each recurrent step, the Universal Transformer iteratively refines its representations for all symbols in the sequence in parallel using a self-attention mechanism (Parikh et al., 2016; Lin et al., 2017), followed by a transformation (shared across all positions and time-steps) consisting of a depth -wise separable convolution (Chollet, 2016 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1807.03819", "content": "In each recurrent step, the Universal Transformer iteratively refines its representations for all symbols in the sequence in parallel using a self-attention mechanism (Parikh et al., 2016; Lin et al., 2017), followed by a transformation (shared across all positions and time-steps) consisting of a depth -wise separable convolution (Chollet, 2016 ..."}
+{"idx": 3, "title": "US10740433B2 - Universal transformers - Google Patents", "date": "", "ddg_snippet": "Universal Transformers address, among others, the shortcomings described in the Background, above. Instead of the common sequence-aligned recurrence , the Universal Transformer is recurrent in depth , while employing self-attention to combine information from different parts of sequences.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US10740433B2/en", "content": "Universal Transformers address, among others, the shortcomings described in the Background, above. Instead of the common sequence-aligned recurrence , the Universal Transformer is recurrent in depth , while employing self-attention to combine information from different parts of sequences."}
+{"idx": 4, "title": "Universal transformer Hawkes process with adaptive recursive ...", "date": "", "ddg_snippet": "Oct 1, 2021 · Dehghani et al. point out that re-introduce recurrence calculation in transformer maybe promote the performance of transformer , which is called as universal transformer ( Dehghani et al., 2019), this model combines the advantage of transformer and RNN, organically combines the self-attention and recurrence learning mechanism.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197621002645", "content": "Oct 1, 2021 · Dehghani et al. point out that re-introduce recurrence calculation in transformer maybe promote the performance of transformer , which is called as universal transformer ( Dehghani et al., 2019), this model combines the advantage of transformer and RNN, organically combines the self-attention and recurrence learning mechanism."}
+{"idx": 5, "title": "Universal Transformers - Semantic Scholar", "date": "", "ddg_snippet": "Parallel-in-time Recurrence Universal Transformer iteratively refines its representations for all positions in the sequence in parallel. with a self-attention mechanism followed by a recurrent transformation", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/b28c/84b01e47a6c2badd6f258e0c467042271bbb.pdf", "content": "Parallel-in-time Recurrence Universal Transformer iteratively refines its representations for all positions in the sequence in parallel. with a self-attention mechanism followed by a recurrent transformation"}
+{"idx": 6, "title": "Universal Transformers | Request PDF - ResearchGate", "date": "", "ddg_snippet": "Jul 10, 2018 · Instead of recurring over the individual symbols of sequences like RNNs, the Universal Transformer repeatedly revises its representations of all symbols in the sequence with each recurrent step.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/326343192_Universal_Transformers", "content": "Jul 10, 2018 · Instead of recurring over the individual symbols of sequences like RNNs, the Universal Transformer repeatedly revises its representations of all symbols in the sequence with each recurrent step."}
+{"idx": 7, "title": "A Comparative of Looped, Universal, Recurrent Depth, and ...", "date": "", "ddg_snippet": "Jul 11, 2025 · The *Looped Transformer * aims to incorporate iterative characteristics into the Transformer architecture to effectively emulate iterative algorithms, particularly for data-fitting problems.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=xzJ3SLzsrZc", "content": "Jul 11, 2025 · The *Looped Transformer * aims to incorporate iterative characteristics into the Transformer architecture to effectively emulate iterative algorithms, particularly for data-fitting problems."}
+{"idx": 8, "title": "Learning to Reason with Transformers via Search Inductive Biases...", "date": "", "ddg_snippet": "search iteration , the Expansion Transformer (ET) receives the input tokens and search nodes expanded so far. Dehghani , M.; Gouws, S.; Vinyals, O.; Uszkoreit, J.; and Kaiser, L. 2019. Universal Transformers . In 7th Interna-tional Conference on Learning Representations.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04873693/document", "content": "search iteration , the Expansion Transformer (ET) receives the input tokens and search nodes expanded so far. Dehghani , M.; Gouws, S.; Vinyals, O.; Uszkoreit, J.; and Kaiser, L. 2019. Universal Transformers . In 7th Interna-tional Conference on Learning Representations."}
+{"idx": 9, "title": "andreamad8/ Universal - Transformer -Pytorch: Implementation of...", "date": "", "ddg_snippet": "Universal - Transformer -Pytorch copied to clipboard. Published 20 hours ago • verified publisher icon andreamad8.Simple and self -contained implementation of the Universal Transformer ( Dehghani , 2018) in Pytorch.", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/index.php/andreamad8/Universal-Transformer-Pytorch", "content": "Universal - Transformer -Pytorch copied to clipboard. Published 20 hours ago • verified publisher icon andreamad8.Simple and self -contained implementation of the Universal Transformer ( Dehghani , 2018) in Pytorch."}
diff --git a/data/sampled_jsons/Universal_Transformers_Mostafa_Dehghani_abstract_'Recurrent_neural_networks'_'self-attentive_recurre.jsonl b/data/sampled_jsons/Universal_Transformers_Mostafa_Dehghani_abstract_'Recurrent_neural_networks'_'self-attentive_recurre.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7cf3c87a2968207675621f7456388e050bcc801c
--- /dev/null
+++ b/data/sampled_jsons/Universal_Transformers_Mostafa_Dehghani_abstract_'Recurrent_neural_networks'_'self-attentive_recurre.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1807.03819] Universal Transformers", "date": "", "ddg_snippet": "We propose the Universal Transformer (UT), a parallel-in-time self - attentive recurrent sequence model which can be cast as a generalization of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1807.03819", "content": "We propose the Universal Transformer (UT), a parallel-in-time self - attentive recurrent sequence model which can be cast as a generalization of the ..."}
+{"idx": 1, "title": "Weight-Space Linear Recurrent Neural Networks", "date": "", "ddg_snippet": "... space features, sequence mixing via linear recurrence, and nonlinear self -decoding which enables the embedding of domain-specific inductive biases.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.01153v1", "content": "... space features, sequence mixing via linear recurrence, and nonlinear self -decoding which enables the embedding of domain-specific inductive biases."}
+{"idx": 2, "title": "Transformers are Expressive, But Are They Expressive Enough for", "date": "", "ddg_snippet": "... Transformers , several works ( Dehghani et al., 2018 ; Yun et al., 2020a ; Perez et al., 2021 ; Merrill and Sabharwal, 2023 ) have studied the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.15478v3", "content": "... Transformers , several works ( Dehghani et al., 2018 ; Yun et al., 2020a ; Perez et al., 2021 ; Merrill and Sabharwal, 2023 ) have studied the ..."}
+{"idx": 3, "title": "StagFormer: Time Staggering Decoder-only Transformers", "date": "", "ddg_snippet": "Block- Recurrent Transformers use cross-attention to introduce a per-layer recurrence mechanism into Transformer networks (Hutchins et al., 2022 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15665v2", "content": "Block- Recurrent Transformers use cross-attention to introduce a per-layer recurrence mechanism into Transformer networks (Hutchins et al., 2022 ) ."}
+{"idx": 4, "title": "‘self-attention’ directory · Gwern.net", "date": "", "ddg_snippet": "Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks As an Alternative to Attention Layers in Transformers ”, Bozic et al 2023", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/transformer/attention/index", "content": "Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks As an Alternative to Attention Layers in Transformers ”, Bozic et al 2023"}
+{"idx": 5, "title": "Transformers in Vision: A Survey | ACM Computing Surveys", "date": "", "ddg_snippet": "... Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3505244", "content": "... Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent ..."}
+{"idx": 6, "title": "Sequential Policy Gradient for Adaptive Hyperparameter", "date": "", "ddg_snippet": "Neural Architecture Search is an automated machine learning (AutoML) technique [ 65 , 38 , 47 , 22 , 54 ] designed to algorithmically discover neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15051v1", "content": "Neural Architecture Search is an automated machine learning (AutoML) technique [ 65 , 38 , 47 , 22 , 54 ] designed to algorithmically discover neural ..."}
+{"idx": 7, "title": "4 DNN Architectures – Machine Learning Systems", "date": "", "ddg_snippet": "Map fundamental neural network concepts to deep learning architectures (dense, spatial, temporal, attention-based).", "subpage_snippet": "", "source": "mlsysbook.ai", "link": "https://mlsysbook.ai/contents/core/dnn_architectures/dnn_architectures.html", "content": "Map fundamental neural network concepts to deep learning architectures (dense, spatial, temporal, attention-based)."}
+{"idx": 8, "title": "Scale Efficiently: Insights from Pre-training and Fine-tuning", "date": "", "ddg_snippet": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/354778166_Scale_Efficiently_Insights_from_Pre-training_and_Fine-tuning_Transformers", "content": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration."}
+{"idx": 9, "title": "Computer Vision Seminar", "date": "", "ddg_snippet": "Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani , Matthias Minderer, Georg ...", "subpage_snippet": "", "source": "cs.rice.edu", "link": "https://cs.rice.edu/~pc51/vislang_seminar/", "content": "Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani , Matthias Minderer, Georg ..."}
diff --git a/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_1_ResNet-50.jsonl b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_1_ResNet-50.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..731c16e9ec9d062384beddccb83aee6ae8dab4b5
--- /dev/null
+++ b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_1_ResNet-50.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "FLOW versus standard fine-tuning (FT) and relevant baselines for a ResNet-50 model pre-trained on ImageNet-1K (from Table 1 ). FLOW achieves the best average accuracy (between pre-training and target fine-tuning accuracies).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797", "content": "FLOW versus standard fine-tuning (FT) and relevant baselines for a ResNet-50 model pre-trained on ImageNet-1K (from Table 1 ). FLOW achieves the best average accuracy (between pre-training and target fine-tuning accuracies)."}
+{"idx": 1, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forget...", "date": "", "ddg_snippet": "This research paper talks about a problem in machine learning called \"catastrophic forgetting ,\" which happens when a computer model forgets what it learned before while trying to learn something n...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46655/paper", "content": "This research paper talks about a problem in machine learning called \"catastrophic forgetting ,\" which happens when a computer model forgets what it learned before while trying to learn something n..."}
+{"idx": 2, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Re-weight every sample of a target dataset using lp loss and temperature. Finetune the model using sample -wise weighted loss. One can finetune ResNet -18/ ResNet - 50 on 6 image classification datasets based on the following steps. To run full finetuning , you can run the following script:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sanyalsunny111/FLOW_finetuning", "content": "Re-weight every sample of a target dataset using lp loss and temperature. Finetune the model using sample -wise weighted loss. One can finetune ResNet -18/ ResNet - 50 on 6 image classification datasets based on the following steps. To run full finetuning , you can run the following script:"}
+{"idx": 3, "title": "Why Fine-Tuning Struggles with Forgetting in Machine Unlearning ...", "date": "", "ddg_snippet": "This analysis reveals that naive FT methods struggle with forgetting because the pretrained model retains information about the forgetting data, and the fine-tuning process has no impact on this retained information. To address this issue, we first propose a theoretical approach to mitigate the retention of forgetting data in the pretrained model.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=CGfWyU28Pd", "content": "This analysis reveals that naive FT methods struggle with forgetting because the pretrained model retains information about the forgetting data, and the fine-tuning process has no impact on this retained information. To address this issue, we first propose a theoretical approach to mitigate the retention of forgetting data in the pretrained model."}
+{"idx": 4, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as \"catastrophic forgetting \". This is especially an issue when one does not have access to the data and recipe used to develop the pre-trained model. Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02797", "content": "Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as \"catastrophic forgetting \". This is especially an issue when one does not have access to the data and recipe used to develop the pre-trained model. Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a ..."}
+{"idx": 5, "title": "Memory-Retaining Finetuning via Distillation - Apple Machine Learning ...", "date": "", "ddg_snippet": "We propose label annealing, a method that mitigates forgetting during finetuning without requiring access to the original pretraining data. Label annealing distills pretraining knowledge during finetuning by adding a KL divergence term in the loss function, regularizing the divergence between the finetuned model's predictions and those of the ...", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/memory-retaining", "content": "We propose label annealing, a method that mitigates forgetting during finetuning without requiring access to the original pretraining data. Label annealing distills pretraining knowledge during finetuning by adding a KL divergence term in the loss function, regularizing the divergence between the finetuned model's predictions and those of the ..."}
+{"idx": 6, "title": "(PDF) Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "This plot is for ResNet-50 on the Stanford cars dataset. FLOW's plot ( in red) is with τ = {10, 20, 30, 40, 50 } percentile of the per- sample losses.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388754394_Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting", "content": "This plot is for ResNet-50 on the Stanford cars dataset. FLOW's plot ( in red) is with τ = {10, 20, 30, 40, 50 } percentile of the per- sample losses."}
+{"idx": 7, "title": "微調整における簡単なサンプルの重量を増すことで忘却を緩和する（Upweighting Easy Samples in Fine-Tuning ...", "date": "", "ddg_snippet": "1 .どんなもの? 「 Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting 」という論文は、事前学習済みのモデルを他のタスクで微調整する際に発生する「破滅的忘却」という問題を緩和するための新しい手法を提案しています。", "subpage_snippet": "", "source": "aibr.jp", "link": "https://aibr.jp/2025/07/02/微調整における簡単なサンプルの重量を増すこと/", "content": "1 .どんなもの? 「 Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting 」という論文は、事前学習済みのモデルを他のタスクで微調整する際に発生する「破滅的忘却」という問題を緩和するための新しい手法を提案しています。"}
+{"idx": 8, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Overview Research examines how upweighting easy training samples reduces catastrophic forgetting during fine-tuning Introduces a new technique called Easy Sample Upweighting (ESU) Shows that focusing on simpler examples helps preserve model capabilities Demonstrates improved performance across multiple language tasks Validates findings through extensive experiments on different model ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/upweighting-easy-samples-fine-tuning-mitigates-forgetting", "content": "Overview Research examines how upweighting easy training samples reduces catastrophic forgetting during fine-tuning Introduces a new technique called Easy Sample Upweighting (ESU) Shows that focusing on simpler examples helps preserve model capabilities Demonstrates improved performance across multiple language tasks Validates findings through extensive experiments on different model ..."}
+{"idx": 9, "title": "arXiv:2502.02797v1 [cs.LG] 5 Feb 2025", "date": "", "ddg_snippet": "tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797v1", "content": "tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit"}
diff --git a/data/sampled_jsons/VACB_Unknown-Variance_OLS_reward_range_R_Catoni_contextual_bandits.jsonl b/data/sampled_jsons/VACB_Unknown-Variance_OLS_reward_range_R_Catoni_contextual_bandits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fd379eda524750289a01bcdcdf8dd3aa2ac57d17
--- /dev/null
+++ b/data/sampled_jsons/VACB_Unknown-Variance_OLS_reward_range_R_Catoni_contextual_bandits.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Bandits with Unknown Variance . Algorithm.variance-aware exploration from Zhao et al. (2023b) to propose Variance-Agnostic Catoni Bandit ( VACB ). in Algorithm 2, where we split the contexts {xt}t∈[T ] into L subsets according to their uncertainty. For each.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Bandits with Unknown Variance . Algorithm.variance-aware exploration from Zhao et al. (2023b) to propose Variance-Agnostic Catoni Bandit ( VACB ). in Algorithm 2, where we split the contexts {xt}t∈[T ] into L subsets according to their uncertainty. For each."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · Cited by 1 — Bandit ( VACB ) in Algorithm 2, where we split the contexts. {xt}t∈[T ] ... the reward range R . We address these challenges in the following three parts ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "by C Ye · Cited by 1 — Bandit ( VACB ) in Algorithm 2, where we split the contexts. {xt}t∈[T ] ... the reward range R . We address these challenges in the following three parts ..."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — [0, R ], and their regret scales polynomially with this reward range R . However, many practical scenarios naturally involve heavy-tailed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — [0, R ], and their regret scales polynomially with this reward range R . However, many practical scenarios naturally involve heavy-tailed ..."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/VBench_comprehensive_benchmark_suite_video_generative_models.jsonl b/data/sampled_jsons/VBench_comprehensive_benchmark_suite_video_generative_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e0457baab7609f945236907ad2fa2e7bafe8f53
--- /dev/null
+++ b/data/sampled_jsons/VBench_comprehensive_benchmark_suite_video_generative_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VBench: Comprehensive Benchmark Suite for Video Generative Models", "date": "", "ddg_snippet": "Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal evaluation system should provide insights to inform future developments of video generation. To this end, we present VBench ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.17982", "content": "Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal evaluation system should provide insights to inform future developments of video generation. To this end, we present VBench ..."}
+{"idx": 1, "title": "Vchitect/VBench: [CVPR2024 Highlight] VBench - GitHub", "date": "", "ddg_snippet": "We propose VBench , a comprehensive benchmark suite for video generative models . We design a comprehensive and hierarchical Evaluation Dimension Suite to decompose \" video generation quality\" into multiple well-defined dimensions to facilitate fine-grained and objective evaluation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Vchitect/VBench", "content": "We propose VBench , a comprehensive benchmark suite for video generative models . We design a comprehensive and hierarchical Evaluation Dimension Suite to decompose \" video generation quality\" into multiple well-defined dimensions to facilitate fine-grained and objective evaluation."}
+{"idx": 2, "title": "VBench: Comprehensive Benchmark Suite for Video Generative Models", "date": "", "ddg_snippet": "Video generation has witnessed significant advance-ments, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal eval-uation system should provide insights to inform future de-velopments of video generation. To this end, we present ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10657096", "content": "Video generation has witnessed significant advance-ments, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal eval-uation system should provide insights to inform future de-velopments of video generation. To this end, we present ..."}
+{"idx": 3, "title": "PDF VBench: Comprehensive Benchmark Suite for Video Generative Models", "date": "", "ddg_snippet": "Annotation Figure 1. Overview of VBench . We propose VBench , a comprehensive benchmark suite for video generative models . We design a com-prehensive and hierarchical Evaluation Dimension Suite to decompose \" video generation quality\" into multiple well-defined dimensions to facilitate fine-grained and objective evaluation. For each dimension and each content category, we carefully design a ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_VBench_Comprehensive_Benchmark_Suite_for_Video_Generative_Models_CVPR_2024_paper.pdf", "content": "Annotation Figure 1. Overview of VBench . We propose VBench , a comprehensive benchmark suite for video generative models . We design a com-prehensive and hierarchical Evaluation Dimension Suite to decompose \" video generation quality\" into multiple well-defined dimensions to facilitate fine-grained and objective evaluation. For each dimension and each content category, we carefully design a ..."}
+{"idx": 4, "title": "VBench-2.0: Advancing Video Generation Benchmark Suite for\\\\ Intrinsic ...", "date": "", "ddg_snippet": "Overview of VBench-2.0. (a) Scope of VBench-2.0. Video generative models have progressed from achieving superficial faithfulness in fundamental technical aspects such as pixel fidelity and basic prompt adherence, to addressing more complex challenges associated with intrinsic faithfulness, including commonsense reasoning, physics-based realism, human motion, and creative composition. While ...", "subpage_snippet": "", "source": "vchitect.github.io", "link": "https://vchitect.github.io/VBench-2.0-project/", "content": "Overview of VBench-2.0. (a) Scope of VBench-2.0. Video generative models have progressed from achieving superficial faithfulness in fundamental technical aspects such as pixel fidelity and basic prompt adherence, to addressing more complex challenges associated with intrinsic faithfulness, including commonsense reasoning, physics-based realism, human motion, and creative composition. While ..."}
+{"idx": 5, "title": "VBench: Comprehensive Benchmark Suite for Video Generative Models ...", "date": "", "ddg_snippet": "To this end, we present VBench , a comprehensive benchmark suite that dissects \" video generation quality\" into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kFCQpe3UyB", "content": "To this end, we present VBench , a comprehensive benchmark suite that dissects \" video generation quality\" into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods."}
+{"idx": 6, "title": "VBench++: Comprehensive and Versatile Benchmark Suite for Video ...", "date": "", "ddg_snippet": "The boom of video generation models requires a comprehensive evaluation system to inform their current capabilities and guide future developments, and VBench takes the initiative in providing a comprehensive benchmark suite for fine-grained and human-aligned evaluation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.13503v1", "content": "The boom of video generation models requires a comprehensive evaluation system to inform their current capabilities and guide future developments, and VBench takes the initiative in providing a comprehensive benchmark suite for fine-grained and human-aligned evaluation."}
+{"idx": 7, "title": "PDF VBench: Comprehensive Benchmark Suite for Video Generative Models", "date": "", "ddg_snippet": "The boom of video generation models requires a comprehensive evaluation system to inform their current ca-pabilities and guide future developments, and VBench takes the initiative in providing a comprehensive benchmark suite for fine-grained and human-aligned evaluation.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024//papers/Huang_VBench_Comprehensive_Benchmark_Suite_for_Video_Generative_Models_CVPR_2024_paper.pdf", "content": "The boom of video generation models requires a comprehensive evaluation system to inform their current ca-pabilities and guide future developments, and VBench takes the initiative in providing a comprehensive benchmark suite for fine-grained and human-aligned evaluation."}
+{"idx": 8, "title": "VBench: Comprehensive Benchmark Suite for Video Generative Models", "date": "", "ddg_snippet": "To this end, we present VBench , a comprehensive benchmark suite that dissects \" video generation quality\" into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2311.17982v1", "content": "To this end, we present VBench , a comprehensive benchmark suite that dissects \" video generation quality\" into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods."}
+{"idx": 9, "title": "VBench - a Vchitect Collection - Hugging Face", "date": "", "ddg_snippet": "VBench : Comprehensive Benchmark Suite for Video Generative Models ... VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models ... VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness ... Vchitect/VBench_human_annotation", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/Vchitect/vbench-673ed4de4edfc6d3b58333b7", "content": "VBench : Comprehensive Benchmark Suite for Video Generative Models ... VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models ... VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness ... Vchitect/VBench_human_annotation"}
diff --git a/data/sampled_jsons/VRSBench_A_Versatile_Vision-Language_Benchmark_Dataset_for_Remote_Sensing_Image_Understanding_PDF.jsonl b/data/sampled_jsons/VRSBench_A_Versatile_Vision-Language_Benchmark_Dataset_for_Remote_Sensing_Image_Understanding_PDF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..30b65db921d0491b657d862891c5ac7c1a34ef73
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_A_Versatile_Vision-Language_Benchmark_Dataset_for_Remote_Sensing_Image_Understanding_PDF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench : A Versatile Vision - Language Benchmark", "date": "", "ddg_snippet": "VRSBench : A Versatile Vision - Language Benchmark Dataset for Remote Sensing Image Understanding .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12384", "content": "VRSBench : A Versatile Vision - Language Benchmark Dataset for Remote Sensing Image Understanding ."}
+{"idx": 1, "title": "GitHub - lx709/ VRSBench", "date": "", "ddg_snippet": "VRSBench : A Versatile Vision - Language Benchmark Dataset for Remote Sensing Image Understanding .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "VRSBench : A Versatile Vision - Language Benchmark Dataset for Remote Sensing Image Understanding ."}
+{"idx": 2, "title": "xiang709/ VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "VRSBench is a Versatile Vision - Language Benchmark for Remote Sensing Image Understanding . It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "VRSBench is a Versatile Vision - Language Benchmark for Remote Sensing Image Understanding . It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 3, "title": "Introducing VRSBench : Advancing Remote Sensing Image Analysis", "date": "", "ddg_snippet": "Title: VRSBench : A Versatile Vision - Language Benchmark Dataset for Remote Sensing Image Understanding .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-27-introducing-vrsbench-advancing-remote-sensing-image-analysis--ak6l6or", "content": "Title: VRSBench : A Versatile Vision - Language Benchmark Dataset for Remote Sensing Image Understanding ."}
+{"idx": 4, "title": "VRSBench : A Versatile Vision - Language Benchmark Dataset for ...", "date": "", "ddg_snippet": "Although several vision - language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/vrsbench-versatile-vision-language-benchmark-dataset-remote", "content": "Although several vision - language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control."}
+{"idx": 5, "title": "( PDF ) VHM: Versatile and Honest Vision Language Model for...", "date": "", "ddg_snippet": "This paper develops a Versatile and Honest vision language Model (VHM) for remote sensing image analysis.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390715351_VHM_Versatile_and_Honest_Vision_Language_Model_for_Remote_Sensing_Image_Analysis", "content": "This paper develops a Versatile and Honest vision language Model (VHM) for remote sensing image analysis."}
+{"idx": 6, "title": "xiang709/REOBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "VRSBench / VRSBench _ Images _test/: Contains VRSBench test images organized by perturbation type. VRSBench / VRSBench _train.json: VRSBench training annotations in LLaVA-style JSON format.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/datasets/xiang709/REOBench", "content": "VRSBench / VRSBench _ Images _test/: Contains VRSBench test images organized by perturbation type. VRSBench / VRSBench _train.json: VRSBench training annotations in LLaVA-style JSON format."}
+{"idx": 7, "title": "VRSBench : A Versatile Vision - Language Benchmark Dataset for ...", "date": "", "ddg_snippet": "Although several vision - language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality...", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/VRSBench:-A-Versatile-Vision-Language-Benchmark-Dataset-for-Remote-Sensing-Image-Understanding-3acace4b-b237-41b4-8e9f-8a33709ddb23", "content": "Although several vision - language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality..."}
+{"idx": 8, "title": "lx709/ VRSBench | DeepWiki", "date": "", "ddg_snippet": "This document provides an overview of VRSBench , a comprehensive benchmark dataset for Vision - Language Models (VLMs) in remote sensing image understanding .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/lx709/VRSBench", "content": "This document provides an overview of VRSBench , a comprehensive benchmark dataset for Vision - Language Models (VLMs) in remote sensing image understanding ."}
+{"idx": 9, "title": "hf.qhduan.com/ datasets /tdujardin/summarized_ VRSBench /resolve...", "date": "", "ddg_snippet": "VRSBench dataset whose captions were shortened by LLaVa-v1.6-Mistral7B. --- Credits: VRSBench : A Versatile Vision - Language Benchmark Dataset for Remote Sensing Image Understanding .", "subpage_snippet": "", "source": "hf.qhduan.com", "link": "https://hf.qhduan.com/datasets/tdujardin/summarized_VRSBench/resolve/main/README.md?download=true", "content": "VRSBench dataset whose captions were shortened by LLaVa-v1.6-Mistral7B. --- Credits: VRSBench : A Versatile Vision - Language Benchmark Dataset for Remote Sensing Image Understanding ."}
diff --git a/data/sampled_jsons/VRSBench_Hugging_Face_image_resolution_year_2024.jsonl b/data/sampled_jsons/VRSBench_Hugging_Face_image_resolution_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8edec59c8ec80db891dfd0cef9f9d61f7711f098
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_Hugging_Face_image_resolution_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote ...", "date": "", "ddg_snippet": "In this study, to address these limitations, we introduce a novel versatile benchmark for vision-language understanding of remote sensing images . VRSBench comprises 29,614 images , each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v1", "content": "In this study, to address these limitations, we introduce a novel versatile benchmark for vision-language understanding of remote sensing images . VRSBench comprises 29,614 images , each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets."}
+{"idx": 1, "title": "VRSBench:", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 2, "title": "GitHub - lx709/VRSBench", "date": "", "ddg_snippet": "VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 3, "title": "README.md · xiang709/VRSBench at main - Hugging Face", "date": "", "ddg_snippet": "VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench/blob/main/README.md", "content": "VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 4, "title": "PDF VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote ...", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 5, "title": "VRSBench/README.md at main · lx709/VRSBench · GitHub", "date": "", "ddg_snippet": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench/blob/main/README.md", "content": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 6, "title": "xiang709/VRSBench at main - Hugging Face", "date": "", "ddg_snippet": "VRSBench like 9 Tasks: Visual Question Answering Text Generation Modalities: Image Languages: English Size: 10K<n<100K Tags: remote sensing, vision-language models License: cc-by-4. Dataset card Data Studio FilesFiles and versions Community 1 main VRSBench 1 contributor History:21 commits xiang709 Upload VRSBench_train.json 6c39918 verified9 ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench/tree/main", "content": "VRSBench like 9 Tasks: Visual Question Answering Text Generation Modalities: Image Languages: English Size: 10K<n<100K Tags: remote sensing, vision-language models License: cc-by-4. Dataset card Data Studio FilesFiles and versions Community 1 main VRSBench 1 contributor History:21 commits xiang709 Upload VRSBench_train.json 6c39918 verified9 ..."}
+{"idx": 7, "title": "PDF VRSBench: A Versatile Vision-Language - neurips.cc", "date": "", "ddg_snippet": "VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image , encapsulating intricate details and contextual relevance.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/97530.pdf", "content": "VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image , encapsulating intricate details and contextual relevance."}
+{"idx": 8, "title": "PDF Supplementary of VRSBench: A Versatile Benchmark for Vision Language ...", "date": "", "ddg_snippet": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ..."}
+{"idx": 9, "title": "xiang709/VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
diff --git a/data/sampled_jsons/VRSBench_Li_2024_image_size_resolution_remote_sensing.jsonl b/data/sampled_jsons/VRSBench_Li_2024_image_size_resolution_remote_sensing.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8ea4b7666de167ebb69b49879a984cf9a8bb3838
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_Li_2024_image_size_resolution_remote_sensing.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RSCC: A Large-Scale Remote Sensing Change Caption Dataset for", "date": "", "ddg_snippet": "... Remote Sensing Change Caption (RSCC) dataset, a large-scale benchmark comprising 62,315 pre-/post-disaster image pairs (spanning earthquakes, floods, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.01907v3", "content": "... Remote Sensing Change Caption (RSCC) dataset, a large-scale benchmark comprising 62,315 pre-/post-disaster image pairs (spanning earthquakes, floods, ..."}
+{"idx": 1, "title": "Paper Digest: NeurIPS 2024 Papers & Highlights –", "date": "", "ddg_snippet": "... Related Grants Related Venues Related Experts Related Code View Highlight : Similarly, when looking at a friend ’ s image , the interest lies in ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2024/10/neurips-2024-highlights/", "content": "... Related Grants Related Venues Related Experts Related Code View Highlight : Similarly, when looking at a friend ’ s image , the interest lies in ..."}
+{"idx": 2, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "9 Dec 2024 — Remote sensing images often feature very small objects (sometimes only 10 pixels) and require complex spatial reasoning from an overhead view.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/97530", "content": "9 Dec 2024 — Remote sensing images often feature very small objects (sometimes only 10 pixels) and require complex spatial reasoning from an overhead view."}
+{"idx": 3, "title": "Supplementary of VRSBench: A Versatile Benchmark for ...", "date": "", "ddg_snippet": "by XLJDM Elhoseiny — VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers,. 123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "by XLJDM Elhoseiny — VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers,. 123,221 visual question-answer pairs."}
+{"idx": 4, "title": "Enhancing Ultra High Resolution Remote Sensing Imagery ...", "date": "", "ddg_snippet": "12 Mar 2025 — In this paper, we introduce ImageRAG for RS, a framework to address the complexities of analyzing UHR remote sensing imagery with a little training requirement.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07688v2", "content": "12 Mar 2025 — In this paper, we introduce ImageRAG for RS, a framework to address the complexities of analyzing UHR remote sensing imagery with a little training requirement."}
+{"idx": 5, "title": "VRSBench", "date": "", "ddg_snippet": "by X Li · 2024 · Cited by 49 — Remote sensing images often feature very small objects (sometimes only 10 pixels) and require complex spatial reasoning from an overhead view.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12384?", "content": "by X Li · 2024 · Cited by 49 — Remote sensing images often feature very small objects (sometimes only 10 pixels) and require complex spatial reasoning from an overhead view."}
+{"idx": 6, "title": "xiang709/VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions ..."}
+{"idx": 7, "title": "Context-driven and sparse decoding for Remote Sensing ...", "date": "", "ddg_snippet": "by Y Zhao · 2025 · Cited by 3 — Remote Sensing Visual Grounding (RSVG) is an emerging multimodal RS task that involves grounding textual descriptions to specific objects in remote sensing ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1566253525003690", "content": "by Y Zhao · 2025 · Cited by 3 — Remote Sensing Visual Grounding (RSVG) is an emerging multimodal RS task that involves grounding textual descriptions to specific objects in remote sensing ..."}
+{"idx": 8, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "by X Li · Cited by 50 — We benchmark state-of-the-art models, including LLaVA-1.5, MiniGPT-v2, and. GeoChat, to demonstrate the potential of LVMs for remote sensing image understanding ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/97530.pdf", "content": "by X Li · Cited by 50 — We benchmark state-of-the-art models, including LLaVA-1.5, MiniGPT-v2, and. GeoChat, to demonstrate the potential of LVMs for remote sensing image understanding ..."}
+{"idx": 9, "title": "Enhancing Ultrahigh Resolution Remote Sensing Imagery ...", "date": "", "ddg_snippet": "Ultrahigh resolution (UHR) remote sensing imagery. (RSI) (e.g. 10,000 # 10,000 pixels) poses a significant challenge for current RS vision-language models ... 26 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6245518/8976286/11039502.pdf", "content": "Ultrahigh resolution (UHR) remote sensing imagery. (RSI) (e.g. 10,000 # 10,000 pixels) poses a significant challenge for current RS vision-language models ... 26 pages"}
diff --git a/data/sampled_jsons/VRSBench_Li_et_al._2024_remote_sensing_benchmark_image_size.jsonl b/data/sampled_jsons/VRSBench_Li_et_al._2024_remote_sensing_benchmark_image_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a66ecb77fcdbc9297687e36e4150ad40596bda53
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_Li_et_al._2024_remote_sensing_benchmark_image_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "YouTube - Wikipedia", "date": "", "ddg_snippet": "YouTube is an American online video sharing platform owned by Google. YouTube was founded on February 14, 2005, [7] by Chad Hurley, Jawed Karim, and Steve Chen, who were former employees of PayPal. Headquartered in San Bruno, California, it is the second-most-visited website in the world, after Google. In January 2024, YouTube had more than 2.7 billion monthly active users, who collectively ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/YouTube", "content": "YouTube is an American online video sharing platform owned by Google. YouTube was founded on February 14, 2005, [7] by Chad Hurley, Jawed Karim, and Steve Chen, who were former employees of PayPal. Headquartered in San Bruno, California, it is the second-most-visited website in the world, after Google. In January 2024, YouTube had more than 2.7 billion monthly active users, who collectively ..."}
+{"idx": 1, "title": "YouTube", "date": "", "ddg_snippet": "Share your videos with friends, family, and the world.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/", "content": "Share your videos with friends, family, and the world."}
+{"idx": 2, "title": "YouTube - YouTube", "date": "", "ddg_snippet": "YouTube 's Official Channel helps you discover what's new & trending globally. Watch must-see videos, from music to culture to Internet phenomena", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/youtube", "content": "YouTube 's Official Channel helps you discover what's new & trending globally. Watch must-see videos, from music to culture to Internet phenomena"}
+{"idx": 3, "title": "YouTube on the App Store", "date": "", "ddg_snippet": "Get the official YouTube app on iPhones and iPads. See what the world is watching -- from the hottest music videos to what’s popular in gaming, fashion, beauty, news, learning and more.", "subpage_snippet": "", "source": "apps.apple.com", "link": "https://apps.apple.com/us/app/youtube/id544007664", "content": "Get the official YouTube app on iPhones and iPads. See what the world is watching -- from the hottest music videos to what’s popular in gaming, fashion, beauty, news, learning and more."}
+{"idx": 4, "title": "YouTube - Apps on Google Play", "date": "", "ddg_snippet": "Get the official YouTube app on Android phones and tablets. See what the world is watching -- from the hottest music videos to what’s popular in gaming, fashion, beauty, news, learning and more....", "subpage_snippet": "", "source": "play.google.com", "link": "https://play.google.com/store/apps/details/?id=com.google.android.youtube&hl=en-US", "content": "Get the official YouTube app on Android phones and tablets. See what the world is watching -- from the hottest music videos to what’s popular in gaming, fashion, beauty, news, learning and more...."}
+{"idx": 5, "title": "Official YouTube Blog for Latest YouTube News & Insights", "date": "", "ddg_snippet": "Explore our official blog for the latest news about YouTube , creator and artist profiles, culture and trends analyses, and behind-the-scenes insights.", "subpage_snippet": "", "source": "blog.youtube", "link": "https://blog.youtube/", "content": "Explore our official blog for the latest news about YouTube , creator and artist profiles, culture and trends analyses, and behind-the-scenes insights."}
+{"idx": 6, "title": "YouTube Music", "date": "", "ddg_snippet": "With the YouTube Music app, enjoy over 100 million songs at your fingertips, plus albums, playlists, remixes, music videos, live performances, covers, and hard-to-find music you can’t get...", "subpage_snippet": "", "source": "music.youtube.com", "link": "https://music.youtube.com/", "content": "With the YouTube Music app, enjoy over 100 million songs at your fingertips, plus albums, playlists, remixes, music videos, live performances, covers, and hard-to-find music you can’t get..."}
+{"idx": 7, "title": "YouTube Help - Google Help", "date": "", "ddg_snippet": "Official YouTube Help Center where you can find tips and tutorials on using YouTube and other answers to frequently asked questions.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/youtube/?hl=en", "content": "Official YouTube Help Center where you can find tips and tutorials on using YouTube and other answers to frequently asked questions."}
+{"idx": 8, "title": "YouTube TV - Watch & DVR Live Sports, Shows & News", "date": "", "ddg_snippet": "YouTube TV is a live TV streaming service with major broadcast and popular cable networks. YouTube Premium gives you YouTube and YouTube Music ad-free, offline, and in the background.", "subpage_snippet": "", "source": "tv.youtube.com", "link": "https://tv.youtube.com/welcome/", "content": "YouTube TV is a live TV streaming service with major broadcast and popular cable networks. YouTube Premium gives you YouTube and YouTube Music ad-free, offline, and in the background."}
+{"idx": 9, "title": "Create an account on YouTube", "date": "", "ddg_snippet": "Create an account on YouTube To sign in to YouTube , you'll need to create a Google Account. A Google Account lets you use many YouTube features including Like, Subscribe, Watch Later, and...", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/youtube/answer/161805?hl=en&co=GENIE.Platform=Desktop", "content": "Create an account on YouTube To sign in to YouTube , you'll need to create a Google Account. A Google Account lets you use many YouTube features including Like, Subscribe, Watch Later, and..."}
diff --git a/data/sampled_jsons/VRSBench_arxiv2406.12384_PDF_image_size_resolution_year_2024.jsonl b/data/sampled_jsons/VRSBench_arxiv2406.12384_PDF_image_size_resolution_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..91be0396620ba1ff529ee3f46dcafbd0dfdda279
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_arxiv2406.12384_PDF_image_size_resolution_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench : A Versatile Vision-Language Benchmark", "date": "", "ddg_snippet": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. arXiv : 2406 . 12384 v2 [cs.CV] 11 Nov 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12384", "content": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. arXiv : 2406 . 12384 v2 [cs.CV] 11 Nov 2024."}
+{"idx": 1, "title": "[ 2406 . 12384 ] VRSBench : A Versatile Vision-Language Benchmark...", "date": "", "ddg_snippet": "arXiv : 2406 . 12384 (cs). [Submitted on 18 Jun 2024 (v1), last revised 11 Nov 2024 (this version, v2)].View a PDF of the paper titled VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding, by Xiang Li and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "arXiv : 2406 . 12384 (cs). [Submitted on 18 Jun 2024 (v1), last revised 11 Nov 2024 (this version, v2)].View a PDF of the paper titled VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding, by Xiang Li and 2 other authors."}
+{"idx": 2, "title": "Introducing VRSBench : Advancing Remote Sensing Image Analysis", "date": "", "ddg_snippet": "#Key Features of VRSBench . Human-Verified Annotations: Each image in the VRSBench dataset comes with detailed captions that describe what is in the image .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-27-introducing-vrsbench-advancing-remote-sensing-image-analysis--ak6l6or", "content": "#Key Features of VRSBench . Human-Verified Annotations: Each image in the VRSBench dataset comes with detailed captions that describe what is in the image ."}
+{"idx": 3, "title": "VRSBench : A Versatile Vision-Language Benchmark Dataset for...", "date": "", "ddg_snippet": "2406 . 12384 .• VRSBench contains a large collection of remote sensing images paired with natural language descriptions, providing a versatile benchmark for tasks like image captioning, visual question answering, and multimodal reasoning.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/vrsbench-versatile-vision-language-benchmark-dataset-remote", "content": "2406 . 12384 .• VRSBench contains a large collection of remote sensing images paired with natural language descriptions, providing a versatile benchmark for tasks like image captioning, visual question answering, and multimodal reasoning."}
+{"idx": 4, "title": "GitHub - lx709/ VRSBench", "date": "", "ddg_snippet": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding."}
+{"idx": 5, "title": "xiang709/ VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 6, "title": "VRSBench", "date": "", "ddg_snippet": "Table 2. Detailed image caption performance on VRSBench dataset. Avg_L denotes the average word length of generated captions.", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "Table 2. Detailed image caption performance on VRSBench dataset. Avg_L denotes the average word length of generated captions."}
+{"idx": 7, "title": "【2024年6月公開 Arxiv 論文ランキング】 2406.xxxxx # arXiv - Qiita", "date": "", "ddg_snippet": "arxiv 146. ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools. VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding.", "subpage_snippet": "", "source": "qiita.com", "link": "https://qiita.com/compassinai/items/df6bc866f1292c592d93", "content": "arxiv 146. ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools. VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding."}
+{"idx": 8, "title": "A Review of Visual Grounding on Remote Sensing Images", "date": "", "ddg_snippet": "Download PDF Download PDF with Cover Download XML Download Epub. Browse Figures.Li, X.; Ding, J.; Elhoseiny, M. VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. arXiv 2024, arXiv : 2406 . 12384 .", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2079-9292/14/14/2815", "content": "Download PDF Download PDF with Cover Download XML Download Epub. Browse Figures.Li, X.; Ding, J.; Elhoseiny, M. VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. arXiv 2024, arXiv : 2406 . 12384 ."}
+{"idx": 9, "title": "UrBench: A Comprehensive Benchmark for Evaluating Large", "date": "", "ddg_snippet": "For instance, geo-localization tasks require satellite-view imagery for spatial orientation and street-view imagery for detailed contexts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17267v3", "content": "For instance, geo-localization tasks require satellite-view imagery for spatial orientation and street-view imagery for detailed contexts."}
diff --git a/data/sampled_jsons/VRSBench_image_size_resolution_pixels.jsonl b/data/sampled_jsons/VRSBench_image_size_resolution_pixels.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b4c925a2db7a2bf928bae06f996f9ed27239bfbb
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_image_size_resolution_pixels.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Miami - Wikipedia", "date": "", "ddg_snippet": "Miami [b] is a coastal city in the U.S. state of Florida.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Miami", "content": "Miami [b] is a coastal city in the U.S. state of Florida."}
+{"idx": 1, "title": "Miami Florida - Discover Top Things to Do in Miami FL", "date": "", "ddg_snippet": "Miami is one of the world’s most popular vacation spots, with trendy nightlife, beaches, art galleries and world-class hotels. It's the magic city; come see for yourself.", "subpage_snippet": "", "source": "www.visitflorida.com", "link": "https://www.visitflorida.com/places-to-go/southeast/miami/", "content": "Miami is one of the world’s most popular vacation spots, with trendy nightlife, beaches, art galleries and world-class hotels. It's the magic city; come see for yourself."}
+{"idx": 2, "title": "THE 15 BEST Things to Do in Miami", "date": "", "ddg_snippet": "Beaches and water activities abound-but that's just the start. Do a food tour in Little Havana. Discover the art scene. For nightlife, hit Ocean Drive in South Beach. For personalised recommendations, try our AI trip-planning product. See what other travellers like to do, based on ratings and number of bookings.", "subpage_snippet": "", "source": "www.tripadvisor.ca", "link": "https://www.tripadvisor.ca/Attractions-g34438-Activities-Miami_Florida.html", "content": "Beaches and water activities abound-but that's just the start. Do a food tour in Little Havana. Discover the art scene. For nightlife, hit Ocean Drive in South Beach. For personalised recommendations, try our AI trip-planning product. See what other travellers like to do, based on ratings and number of bookings."}
+{"idx": 3, "title": "27 Fun Things to Do in Miami | U.S. News Travel", "date": "", "ddg_snippet": "Jul 31, 2025 · In this travel guide, Goldberg shares some of her favorite activities and tips to make the most of your Miami vacation. From the glamorous to the laid-back, Miami offers visitors a wide range...", "subpage_snippet": "", "source": "travel.usnews.com", "link": "https://travel.usnews.com/Miami_FL/Things_To_Do/", "content": "Jul 31, 2025 · In this travel guide, Goldberg shares some of her favorite activities and tips to make the most of your Miami vacation. From the glamorous to the laid-back, Miami offers visitors a wide range..."}
+{"idx": 4, "title": "The 14 best things to do in Miami - Lonely Planet", "date": "", "ddg_snippet": "Apr 29, 2025 · Miami is such fun to visit, from laid back beach days, to a robust art scene, delicious food and lush gardens. Plan your trip with these best things to do.", "subpage_snippet": "", "source": "www.lonelyplanet.com", "link": "https://www.lonelyplanet.com/articles/best-things-to-do-in-miami", "content": "Apr 29, 2025 · Miami is such fun to visit, from laid back beach days, to a robust art scene, delicious food and lush gardens. Plan your trip with these best things to do."}
+{"idx": 5, "title": "Greater Miami & Miami Beach Hotels, Restaurants & Things to do", "date": "", "ddg_snippet": "Discover the best things to do in Miami , from concerts and Miami Spice restaurant deals to Hispanic Heritage celebrations and sports, in our comprehensive guide.", "subpage_snippet": "", "source": "www.miamiandbeaches.com", "link": "https://www.miamiandbeaches.com/", "content": "Discover the best things to do in Miami , from concerts and Miami Spice restaurant deals to Hispanic Heritage celebrations and sports, in our comprehensive guide."}
+{"idx": 6, "title": "15 Best Things to Do for Your Miami Bucket List (2025)", "date": "", "ddg_snippet": "Caught between turquoise waters and electric-colored walls, Miami dishes out the kind of days that make regular life look boring. This city knows how to turn everything up – here’s your ultimate guide to the 15 most bucket list things to do in Miami .", "subpage_snippet": "", "source": "gowanderly.com", "link": "https://gowanderly.com/miami-bucket-list/", "content": "Caught between turquoise waters and electric-colored walls, Miami dishes out the kind of days that make regular life look boring. This city knows how to turn everything up – here’s your ultimate guide to the 15 most bucket list things to do in Miami ."}
+{"idx": 7, "title": "Miami Travel Guide: Things To Do & Vacation Ideas", "date": "", "ddg_snippet": "May 14, 2025 · Visit Miami for an unforgettable adventure. Discover the best hotels, restaurants and things to do with this highly curated Miami travel guide.", "subpage_snippet": "", "source": "www.travelandleisure.com", "link": "https://www.travelandleisure.com/travel-guide/miami", "content": "May 14, 2025 · Visit Miami for an unforgettable adventure. Discover the best hotels, restaurants and things to do with this highly curated Miami travel guide."}
+{"idx": 8, "title": "21 Best Miami Tourist Attractions, From Museums To Historical...", "date": "", "ddg_snippet": "Dec 10, 2024 · The best attractions in Miami are all in this list – from famous districts to Venetian pools and National Parks.", "subpage_snippet": "", "source": "www.timeout.com", "link": "https://www.timeout.com/miami/things-to-do/best-miami-attractions", "content": "Dec 10, 2024 · The best attractions in Miami are all in this list – from famous districts to Venetian pools and National Parks."}
+{"idx": 9, "title": "THE 15 BEST Things to Do in Miami (2025) - Must-See Attractions", "date": "", "ddg_snippet": "Book these experiences for a close-up look at Miami . These rankings are informed by Tripadvisor data—we consider traveler reviews, ratings, number of page views, and user location. 1. Wynwood Walls. At Wynwood Walls, you can view over 35 hand-sprayed murals and visit two street art galleries.", "subpage_snippet": "", "source": "www.tripadvisor.com", "link": "https://www.tripadvisor.com/Attractions-g34438-Activities-Miami_Florida.html", "content": "Book these experiences for a close-up look at Miami . These rankings are informed by Tripadvisor data—we consider traveler reviews, ratings, number of page views, and user location. 1. Wynwood Walls. At Wynwood Walls, you can view over 35 hand-sprayed murals and visit two street art galleries."}
diff --git a/data/sampled_jsons/VRSBench_remote_sensing_benchmark_image_resolution_Li_et_al._2024_year_2024.jsonl b/data/sampled_jsons/VRSBench_remote_sensing_benchmark_image_resolution_Li_et_al._2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..46b8e7a9ae491e9881fdb91f78ab273456f54bde
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_remote_sensing_benchmark_image_resolution_Li_et_al._2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "by X Li · 2024 · Cited by 50 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "by X Li · 2024 · Cited by 50 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ..."}
+{"idx": 1, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "9 Dec 2024 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/97530", "content": "9 Dec 2024 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ..."}
+{"idx": 2, "title": "Supplementary of VRSBench: A Versatile Benchmark for ...", "date": "", "ddg_snippet": "by XLJDM Elhoseiny — VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers,. 123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "by XLJDM Elhoseiny — VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers,. 123,221 visual question-answer pairs."}
+{"idx": 3, "title": "VRSBench: a versatile vision-language benchmark dataset for ...", "date": "", "ddg_snippet": "We introduce a new benchmark designed to advance the development of generalpurpose, large-scale vision-language models for remote sensing images .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738022", "content": "We introduce a new benchmark designed to advance the development of generalpurpose, large-scale vision-language models for remote sensing images ."}
+{"idx": 4, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "18 Jun 2024 — Our VRSBench dataset contains 29,614 remote sensing images , with high- quality human-verified annotations. It comprises 29,614 caption sentences, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v1", "content": "18 Jun 2024 — Our VRSBench dataset contains 29,614 remote sensing images , with high- quality human-verified annotations. It comprises 29,614 caption sentences, ..."}
+{"idx": 5, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset ...", "date": "", "ddg_snippet": "18 Jun 2024 — This paper introduces VRSBench , a versatile vision-language benchmark dataset for comprehensive remote sensing image understanding model ...", "subpage_snippet": "", "source": "liner.com", "link": "https://liner.com/review/vrsbench-a-versatile-visionlanguage-benchmark-dataset-for-remote-sensing-image", "content": "18 Jun 2024 — This paper introduces VRSBench , a versatile vision-language benchmark dataset for comprehensive remote sensing image understanding model ..."}
+{"idx": 6, "title": "A remote sensing vision language model and benchmark", "date": "", "ddg_snippet": "by Y Hu · 2025 · Cited by 174 — We build a high- quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the remote sensing field.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0924271625001352", "content": "by Y Hu · 2025 · Cited by 174 — We build a high- quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the remote sensing field."}
+{"idx": 7, "title": "NeurIPS 2024 Datasets Benchmarks 2024", "date": "", "ddg_snippet": "... Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/events/datasets-benchmarks-2024", "content": "... Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions ..."}
+{"idx": 8, "title": "Enhancing Ultrahigh Resolution Remote Sensing Imagery ...", "date": "", "ddg_snippet": "Ultrahigh resolution (UHR) remote sensing imagery. (RSI) (e.g. 10,000 # 10,000 pixels) poses a significant challenge for current RS vision-language models ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6245518/8976286/11039502.pdf", "content": "Ultrahigh resolution (UHR) remote sensing imagery. (RSI) (e.g. 10,000 # 10,000 pixels) poses a significant challenge for current RS vision-language models ..."}
+{"idx": 9, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "by F Wang · 2025 · Cited by 7 — In this paper, we introduce XLRS-Bench, a comprehen- sive benchmark for evaluating the perception and rea- soning capabilities of multimodal large language ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "by F Wang · 2025 · Cited by 7 — In this paper, we introduce XLRS-Bench, a comprehen- sive benchmark for evaluating the perception and rea- soning capabilities of multimodal large language ..."}
diff --git a/data/sampled_jsons/Van_Stekelenburg_and_Klandermans_2013_collective_action_theory_full_title.jsonl b/data/sampled_jsons/Van_Stekelenburg_and_Klandermans_2013_collective_action_theory_full_title.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4ef19b07d03af1b95f0d34b012df087b3de14ad6
--- /dev/null
+++ b/data/sampled_jsons/Van_Stekelenburg_and_Klandermans_2013_collective_action_theory_full_title.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Social movements and the dynamics of collective action.", "date": "", "ddg_snippet": "Citation Klandermans , B., & van Stekelenburg , J. ( 2013 ). Social movements and the dynamics of collective action . In L. Huddy, D. O. Sears, & J. S. Levy (Eds.), The ...", "subpage_snippet": "", "source": "psycnet.apa.org", "link": "https://psycnet.apa.org/record/2013-40838-023", "content": "Citation Klandermans , B., & van Stekelenburg , J. ( 2013 ). Social movements and the dynamics of collective action . In L. Huddy, D. O. Sears, & J. S. Levy (Eds.), The ..."}
+{"idx": 1, "title": "The social psychology of protest - Jacquelien van ...", "date": "", "ddg_snippet": "Mar 15, 2013 · Jacquelien van Stekelenburg is Associate Professor at the Sociology Department of the VU University Amsterdam, the Netherlands. She studies the social psychological dynamics of moderate and radical protest participation, with a special interest in group identification, emotions and ideologies as motivators for collective action . She co-authored (with Bert Klandermans ) ‘Individuals in ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/0011392113479314", "content": "Mar 15, 2013 · Jacquelien van Stekelenburg is Associate Professor at the Sociology Department of the VU University Amsterdam, the Netherlands. She studies the social psychological dynamics of moderate and radical protest participation, with a special interest in group identification, emotions and ideologies as motivators for collective action . She co-authored (with Bert Klandermans ) ‘Individuals in ..."}
+{"idx": 2, "title": "Collective Identity - Stekelenburg - Major Reference Works ...", "date": "", "ddg_snippet": "Sep 27, 2022 · In: The Future of Social Movement Research: Dynamics, Mechanisms, and Processes (ed. J. Stekelenburg , C.M. Roggeband, and B. Klandermans ), 37 – 57. Minneapolis : University of Minnesota Press. Van Stekelenburg , J. and Klandermans , B. (2017). Protesting youth: Collective and connective action participation compared.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470674871.wbespm039.pub2", "content": "Sep 27, 2022 · In: The Future of Social Movement Research: Dynamics, Mechanisms, and Processes (ed. J. Stekelenburg , C.M. Roggeband, and B. Klandermans ), 37 – 57. Minneapolis : University of Minnesota Press. Van Stekelenburg , J. and Klandermans , B. (2017). Protesting youth: Collective and connective action participation compared."}
+{"idx": 3, "title": "Collective Identity - Vrije Universiteit Amsterdam Ch 26 van Stekelenburg Gaidyte social movements and the ... The social psychology of protest - SAGE Journals The social psychology of protest - SAGE Journals (PDF) The Social Psychology of Protest - ResearchGate A Social Psychology of Protest: Individuals in Action ...", "date": "", "ddg_snippet": "Collective Identity J. van Stekelenburg Sociology Social Change and Conflict (SCC) The internet and social media play a pivotal role in creating networks that facilitate consensus formation and the formation of a collective identity, a topic we discuss later in the chapter (Earl & Kimport, 2011; Van Stekelenburg , Anikina, et al., 2013 ; van Stekelenburg & Klandermans , 2017b). Who is jacquelien Van Stekelenburg? Jacquelien van Stekelenburg is Associate Professor at the Sociology Department of the VU University Amsterdam, the Netherlands . She studies the social psychological dynamics of moderate and radical protest participation, with a special interest in group identification, emotions and ideologies as motivators for collective action. Who is Bert klandermans? Bert Klandermans is Professor in Applied Social Psychology at the VU University, Amsterdam, the Netherlands . He has published extensively on the social psychology of political protest and social movement participation. His Social Psychology of Protest appeared with Blackwell in 1997. How does klandermans define discrimination? ... Klandermans et al. (2008) understand discrimination as \"grievances (…) defined as a sense of indignation about the way authorities are treating a social or political problem\" (p. 994, with reference to Klandermans , 1997). Dec 7, 2023 · Jacquelien van Stekelenburg is Professor of Social Change and Conflict at Vrije Universiteit Amsterdam, the Netherlands. With a background in social psychology, she combines a social psychological approach with sociological insights.", "subpage_snippet": "", "source": "research.vu.nl", "link": "https://research.vu.nl/en/publications/collective-identity", "content": "Collective Identity J. van Stekelenburg Sociology Social Change and Conflict (SCC) The internet and social media play a pivotal role in creating networks that facilitate consensus formation and the formation of a collective identity, a topic we discuss later in the chapter (Earl & Kimport, 2011; Van Stekelenburg , Anikina, et al., 2013 ; van Stekelenburg & Klandermans , 2017b). Who is jacquelien Van Stekelenburg? Jacquelien van Stekelenburg is Associate Professor at the Sociology Department of the VU University Amsterdam, the Netherlands . She studies the social psychological dynamics of moderate and radical protest participation, with a special interest in group identification, emotions and ideologies as motivators for collective action. Who is Bert klandermans? Bert Klandermans is Professor in Applied Social Psychology at the VU University, Amsterdam, the Netherlands . He has published extensively on the social psychology of political protest and social movement participation. His Social Psychology of Protest appeared with Blackwell in 1997. How does klandermans define discrimination? ... Klandermans et al. (2008) understand discrimination as \"grievances (…) defined as a sense of indignation about the way authorities are treating a social or political problem\" (p. 994, with reference to Klandermans , 1997). Dec 7, 2023 · Jacquelien van Stekelenburg is Professor of Social Change and Conflict at Vrije Universiteit Amsterdam, the Netherlands. With a background in social psychology, she combines a social psychological approach with sociological insights."}
+{"idx": 4, "title": "Ch 26 van Stekelenburg Gaidyte social movements and the ...", "date": "", "ddg_snippet": "The internet and social media play a pivotal role in creating networks that facilitate consensus formation and the formation of a collective identity, a topic we discuss later in the chapter (Earl & Kimport, 2011; Van Stekelenburg , Anikina, et al., 2013 ; van Stekelenburg & Klandermans , 2017b).", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/115119854/Ch_26_van_Stekelenburg_Gaidyte_social_movements_and_the_dynamics_of_collective_action", "content": "The internet and social media play a pivotal role in creating networks that facilitate consensus formation and the formation of a collective identity, a topic we discuss later in the chapter (Earl & Kimport, 2011; Van Stekelenburg , Anikina, et al., 2013 ; van Stekelenburg & Klandermans , 2017b)."}
+{"idx": 5, "title": "A Social Psychology of Protest: Individuals in Action ...", "date": "", "ddg_snippet": "Dec 7, 2023 · Jacquelien van Stekelenburg is Professor of Social Change and Conflict at Vrije Universiteit Amsterdam, the Netherlands. With a background in social psychology, she combines a social psychological approach with sociological insights.", "subpage_snippet": "", "source": "books.google.com", "link": "https://books.google.com/books/about/A_Social_Psychology_of_Protest.html?id=5cjlEAAAQBAJ", "content": "Dec 7, 2023 · Jacquelien van Stekelenburg is Professor of Social Change and Conflict at Vrije Universiteit Amsterdam, the Netherlands. With a background in social psychology, she combines a social psychological approach with sociological insights."}
+{"idx": 6, "title": "social movements and the dynamics of collective action Jacquelien...", "date": "", "ddg_snippet": "Van Stekelenburg and colleagues ( 2013 ) investigate how, in a newly built neighborhood, demand for protest develops as a function of the development of formal, informal, and virtual networks. They argue that a collective is needed for consensus to emerge.", "subpage_snippet": "", "source": "research.vu.nl", "link": "https://research.vu.nl/ws/portalfiles/portal/298859540/Ch._26_van_Stekelenburg_Gaidyte_social_movements_and_the_dynamics_of_collective_action.docx", "content": "Van Stekelenburg and colleagues ( 2013 ) investigate how, in a newly built neighborhood, demand for protest develops as a function of the development of formal, informal, and virtual networks. They argue that a collective is needed for consensus to emerge."}
+{"idx": 7, "title": "Jacquelien van Stekelenburg - Google Akademik", "date": "", "ddg_snippet": "J van Stekelenburg Conny Roggeband Bert Klandermans . University of Minnesota Press, 2013 . Collective Identity. J van Stekelenburg . The Wiley-Blackwell Encyclopedia of Social and Political Movements, 219-225, 2013 .", "subpage_snippet": "", "source": "scholar.google.co.id", "link": "https://scholar.google.co.id/citations?user=fP-OzuoAAAAJ&hl=tr", "content": "J van Stekelenburg Conny Roggeband Bert Klandermans . University of Minnesota Press, 2013 . Collective Identity. J van Stekelenburg . The Wiley-Blackwell Encyclopedia of Social and Political Movements, 219-225, 2013 ."}
+{"idx": 8, "title": "social 14- collective action Flashcards | Quizlet", "date": "", "ddg_snippet": "- Integrative model: Social Identity Model of Collective Action (SIMCA, van Zomeren et al., 2008) - Extensions of SIMCA. - Alternative models: the dual pathway model of collective action (Sturmer and Simon, 2004).", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/595090444/social-14-collective-action-flash-cards/", "content": "- Integrative model: Social Identity Model of Collective Action (SIMCA, van Zomeren et al., 2008) - Extensions of SIMCA. - Alternative models: the dual pathway model of collective action (Sturmer and Simon, 2004)."}
+{"idx": 9, "title": "Protesting Youth: Collective and Connective Action Participation...", "date": "", "ddg_snippet": "Klandermans , B. & van Stekelenburg , J. ( 2013 ). Social movements and the dynamics of collective action . In L. HuddyD. O. SearsJ.The logic of collective action : Public goods and the theory of group . Cambridge, MA: Harvard University Press.", "subpage_snippet": "", "source": "econtent.hogrefe.com", "link": "https://econtent.hogrefe.com/doi/10.1027/2151-2604/a000300", "content": "Klandermans , B. & van Stekelenburg , J. ( 2013 ). Social movements and the dynamics of collective action . In L. HuddyD. O. SearsJ.The logic of collective action : Public goods and the theory of group . Cambridge, MA: Harvard University Press."}
diff --git "a/data/sampled_jsons/Var[\316\267_t^2F_t]_\342\211\244_c_\316\267__Var[\316\267_tF_t]_heavy-tailed_distributions_kurtosis_assumption.jsonl" "b/data/sampled_jsons/Var[\316\267_t^2F_t]_\342\211\244_c_\316\267__Var[\316\267_tF_t]_heavy-tailed_distributions_kurtosis_assumption.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..0eb62fe80429a4ace21fc9089f38d4562cd4aaf8
--- /dev/null
+++ "b/data/sampled_jsons/Var[\316\267_t^2F_t]_\342\211\244_c_\316\267__Var[\316\267_tF_t]_heavy-tailed_distributions_kurtosis_assumption.jsonl"
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+{"idx": 0, "title": "Normal distribution - Wikipedia", "date": "", "ddg_snippet": "The Gaussian distribution belongs to the family of stable distributions which are the attractors of sums of independent, identically distributed distributions whether or not the mean or variance is finite. Except for the Gaussian which is a limiting case...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Normal_distribution", "content": "The Gaussian distribution belongs to the family of stable distributions which are the attractors of sums of independent, identically distributed distributions whether or not the mean or variance is finite. Except for the Gaussian which is a limiting case..."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "Since Ii is the only term that depends on the heavy - tailed noise, it is approximated by the θ-robust Catoni estimator Catoniθ({Zi( f , f ′)}i∈[ t ]) in Lt( f , f ′). Then, we include all the f ∈ Ft −1 that have a small weighted squared loss to fˆt in the condence set Ft .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Since Ii is the only term that depends on the heavy - tailed noise, it is approximated by the θ-robust Catoni estimator Catoniθ({Zi( f , f ′)}i∈[ t ]) in Lt( f , f ′). Then, we include all the f ∈ Ft −1 that have a small weighted squared loss to fˆt in the condence set Ft ."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "Since Ii is the only term that depends on the heavy - tailed noise, it is approximated by the θ-robust Catoni estimator Catoniθ({Zi( f , f ′)}i∈[ t ]) in Lt( f , f ′). Then, we include all the f ∈ Ft −1 that have a small weighted squared loss to fˆt in the confidence set Ft .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "Since Ii is the only term that depends on the heavy - tailed noise, it is approximated by the θ-robust Catoni estimator Catoniθ({Zi( f , f ′)}i∈[ t ]) in Lt( f , f ′). Then, we include all the f ∈ Ft −1 that have a small weighted squared loss to fˆt in the confidence set Ft ."}
+{"idx": 3, "title": "Variance Gamma (VG) Model", "date": "", "ddg_snippet": "Ac-tual log price increments however display much greater kurtosis , due both to heavier tails and greater peakedness over the mean, which respectively increase the fourth moment and reduce the second moment relative to those of the normal.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/41233653.pdf", "content": "Ac-tual log price increments however display much greater kurtosis , due both to heavier tails and greater peakedness over the mean, which respectively increase the fourth moment and reduce the second moment relative to those of the normal."}
+{"idx": 4, "title": "Bayesian Analysis of Additive Factor Volatility Models with... | IntechOpen", "date": "", "ddg_snippet": "The distribution of financial series is heavier than the normal distribution . In addition, parameter estimates obtained in the presence of outliers are unreliable. Therefore, models that allow heavy - tailed distribution should be preferred for modelling high kurtosis .", "subpage_snippet": "", "source": "www.intechopen.com", "link": "https://www.intechopen.com/chapters/73559", "content": "The distribution of financial series is heavier than the normal distribution . In addition, parameter estimates obtained in the presence of outliers are unreliable. Therefore, models that allow heavy - tailed distribution should be preferred for modelling high kurtosis ."}
+{"idx": 5, "title": "DistributionTest — Library by Kabua — TradingView", "date": "", "ddg_snippet": "Student's t : Heavy - tailed distribution with degrees of freedom parameter - Heavy - tail VaR calculations, superior for extreme risk at >98.5% confidence levels. Laplace: Double exponential distribution with location and scale parameters...", "subpage_snippet": "", "source": "www.tradingview.com", "link": "https://www.tradingview.com/script/P67Sillr-DistributionTest/", "content": "Student's t : Heavy - tailed distribution with degrees of freedom parameter - Heavy - tail VaR calculations, superior for extreme risk at >98.5% confidence levels. Laplace: Double exponential distribution with location and scale parameters..."}
+{"idx": 6, "title": "Lecture 5a: ARCH Models", "date": "", "ddg_snippet": "Heavy - Tailed or Fat- Tailed Distribution .• Statistically heavy tail means kurtosis greater than 3 • The ARCH or GARCH model can capture part of the heavy tail • Even better, we can allow et to follow a distribution with tail heavier than the normal.", "subpage_snippet": "", "source": "www.fsb.miamioh.edu", "link": "https://www.fsb.miamioh.edu/lij14/672_s5.pdf", "content": "Heavy - Tailed or Fat- Tailed Distribution .• Statistically heavy tail means kurtosis greater than 3 • The ARCH or GARCH model can capture part of the heavy tail • Even better, we can allow et to follow a distribution with tail heavier than the normal."}
+{"idx": 7, "title": "Optimised use of independent component analysis for EEG signal...", "date": "", "ddg_snippet": "var ( ( t )) η =.Where var (.) denotes variance; EEG( t ) is the artefact-free EEG signal; EEGc( t ) is the EEG signal cleaned by the proposed methods and ( t ) is the cleaning error. The objective is to make the value of ( t ) as small as possible.", "subpage_snippet": "", "source": "etheses.bham.ac.uk", "link": "https://etheses.bham.ac.uk/id/eprint/7430/5/Zakeri17PhD.pdf", "content": "var ( ( t )) η =.Where var (.) denotes variance; EEG( t ) is the artefact-free EEG signal; EEGc( t ) is the EEG signal cleaned by the proposed methods and ( t ) is the cleaning error. The objective is to make the value of ( t ) as small as possible."}
+{"idx": 8, "title": "Asymmetric realized volatility risk", "date": "", "ddg_snippet": "For example, if ηt is assumed normal, the standard formula for the moment generating function of the normal distribution gives a conditional variance of exp(ψt + h 2 t / 2 ) and a kurtosis 3 exp( 2 h 2 t ). This analysis provides the ingredients for adequately modeling the empirically relevant ex ante.", "subpage_snippet": "", "source": "www.econstor.eu", "link": "https://www.econstor.eu/bitstream/10419/178547/1/jrfm-07-00080.pdf", "content": "For example, if ηt is assumed normal, the standard formula for the moment generating function of the normal distribution gives a conditional variance of exp(ψt + h 2 t / 2 ) and a kurtosis 3 exp( 2 h 2 t ). This analysis provides the ingredients for adequately modeling the empirically relevant ex ante."}
+{"idx": 9, "title": "On the point process of near-record values", "date": "", "ddg_snippet": "When the common distribution F of the observations is heavy - tailed , the total number of near-records along the whole sequence (Xn), denoted by η (∞), is shown to be nite.The rst one deals with heavy- and exponential- tailed distributions , while the second applies to light-tailed ones.", "subpage_snippet": "", "source": "repositorio.uchile.cl", "link": "https://repositorio.uchile.cl/bitstream/handle/2250/133049/On-the-point-process-of-near-record-values.pdf?sequence=1", "content": "When the common distribution F of the observations is heavy - tailed , the total number of near-records along the whole sequence (Xn), denoted by η (∞), is shown to be nite.The rst one deals with heavy- and exponential- tailed distributions , while the second applies to light-tailed ones."}
diff --git a/data/sampled_jsons/Vaswani_et_al._2017_Attention_Is_All_You_Need_abstract_arxiv_year_2017.jsonl b/data/sampled_jsons/Vaswani_et_al._2017_Attention_Is_All_You_Need_abstract_arxiv_year_2017.jsonl
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+{"idx": 1, "title": "Attention Is All You Need (Vaswani et al., ArXiv 2017) |", "date": "", "ddg_snippet": "Attention Is All You Need ( Vaswani et al ., ArXiv 2017 ) ... use a network that applies attention multiple times over both input and output (as it is ...", "subpage_snippet": "", "source": "jkk.name", "link": "http://jkk.name/reading-notes/old-blog/2017-10-20_onlyattention/", "content": "Attention Is All You Need ( Vaswani et al ., ArXiv 2017 ) ... use a network that applies attention multiple times over both input and output (as it is ..."}
+{"idx": 2, "title": "Abstractive Document Summarization with a Graph-Based", "date": "", "ddg_snippet": "Abstractive Document Summarization with a Graph-Based Attentional Neural Model (Tan et al ., ... Attention Is All You Need ( Vaswani et al ., ArXiv 2017 )", "subpage_snippet": "", "source": "jkk.name", "link": "http://jkk.name/reading-notes/old-blog/2017-11-29_abstractivesummarisation/", "content": "Abstractive Document Summarization with a Graph-Based Attentional Neural Model (Tan et al ., ... Attention Is All You Need ( Vaswani et al ., ArXiv 2017 )"}
+{"idx": 3, "title": "Abstractive Document Summarization with a Graph-Based", "date": "", "ddg_snippet": "Abstractive Document Summarization with a Graph-Based Attentional Neural Model (Tan et al ., ... Attention Is All You Need ( Vaswani et al ., ArXiv 2017 )", "subpage_snippet": "", "source": "jkk.name", "link": "https://jkk.name/reading-notes/old-blog/2017-11-29_abstractivesummarisation/", "content": "Abstractive Document Summarization with a Graph-Based Attentional Neural Model (Tan et al ., ... Attention Is All You Need ( Vaswani et al ., ArXiv 2017 )"}
+{"idx": 4, "title": "[1706.03762] Attention Is All You Need", "date": "", "ddg_snippet": "View a PDF of the paper titled Attention Is All You Need , by Ashish Vaswani and 7 other authors ... also connect the encoder and decoder through an ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "View a PDF of the paper titled Attention Is All You Need , by Ashish Vaswani and 7 other authors ... also connect the encoder and decoder through an ..."}
+{"idx": 5, "title": "The Abstraction and Reasoning Corpus: Conclusion &", "date": "", "ddg_snippet": "Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Zˇ´ıdek, A., Potapenko, A., et al .", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/the-abstraction-and-reasoning-corpus-conclusion-and-references", "content": "Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Zˇ´ıdek, A., Potapenko, A., et al ."}
+{"idx": 6, "title": "Recruiting: Masters | Jonathan K. Kummerfeld", "date": "", "ddg_snippet": "Abstractive Document Summarization with a Graph-Based Attentional Neural Model (Tan et al ., ... Attention Is All You Need ( Vaswani et al ., ArXiv 2017 )", "subpage_snippet": "", "source": "jkk.name", "link": "https://jkk.name/students/recruiting-ms/", "content": "Abstractive Document Summarization with a Graph-Based Attentional Neural Model (Tan et al ., ... Attention Is All You Need ( Vaswani et al ., ArXiv 2017 )"}
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+{"idx": 8, "title": "Enhancing early detection of cognitive decline in the elderly:", "date": "", "ddg_snippet": "If you don't remember your password, you can reset it by entering your email address and clicking the Reset Password button.", "subpage_snippet": "", "source": "www.thelancet.com", "link": "https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(24)00437-7/fulltext", "content": "If you don't remember your password, you can reset it by entering your email address and clicking the Reset Password button."}
+{"idx": 9, "title": "An Interpretable Ensemble of Graph and Language Models for", "date": "", "ddg_snippet": "Using the semantic information of queries and products along with the behavioral relationships between them, the goal is to classify the degree of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00923v1", "content": "Using the semantic information of queries and products along with the behavioral relationships between them, the goal is to classify the degree of ..."}
diff --git a/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_Text-to-Video_Retrieval_Section_4.3_sigmoid_InfoNCE.jsonl b/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_Text-to-Video_Retrieval_Section_4.3_sigmoid_InfoNCE.jsonl
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index 0000000000000000000000000000000000000000..6741ecb6672d7cb0c50d6167454c8f85bad128ca
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+{"idx": 0, "title": "Recurrence Meets Transformers for Universal Multimodal Retrieval", "date": "", "ddg_snippet": "... predominantly focused on unimodal retrieval , where queries and retrievable items belonged to the same modality, such as text or images [ 3 , 4 , 5 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08897v1", "content": "... predominantly focused on unimodal retrieval , where queries and retrievable items belonged to the same modality, such as text or images [ 3 , 4 , 5 ..."}
+{"idx": 1, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos ."}
+{"idx": 2, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094542", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial ..."}
+{"idx": 3, "title": "Exciting Research Alert: Video-ColBERT - A Breakthrough in Text-to ...", "date": "", "ddg_snippet": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim (MMS) operations on both ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/singhsidhukuldeep_exciting-research-alert-video-colbert-activity-7336927383549071360-eAP7", "content": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim (MMS) operations on both ..."}
+{"idx": 4, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to-video re-trieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video re-trieval , our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "Abstract In this work, we tackle the problem of text-to-video re-trieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video re-trieval , our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos ."}
+{"idx": 5, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos , and finds that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. In this work, we tackle the problem of text-to-video retrieval (T2VR).", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Video-ColBERT:-Contextualized-Late-Interaction-for-Reddy-Martin/bff2f91c763830a2d14dbbbeca150e92ede02323", "content": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos , and finds that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. In this work, we tackle the problem of text-to-video retrieval (T2VR)."}
+{"idx": 6, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "Abstract In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos ."}
+{"idx": 7, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT offers an advanced approach to text-to-video retrieval by refining interaction strategies and achieving competitive performance, opening new pathways for multimodal retrieval research.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2503.19009", "content": "Video-ColBERT offers an advanced approach to text-to-video retrieval by refining interaction strategies and achieving competitive performance, opening new pathways for multimodal retrieval research."}
+{"idx": 8, "title": "Recurrence-Enhanced Vision-and-Language Transformers for Robust", "date": "", "ddg_snippet": "Parallel efforts, including models like FLMR [ 31 ] , PreFLMR [ 32 ] , and the earlier ColBERT [ 23 ] , have explored a late - interaction ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01980v1", "content": "Parallel efforts, including models like FLMR [ 31 ] , PreFLMR [ 32 ] , and the earlier ColBERT [ 23 ] , have explored a late - interaction ..."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.html", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos ."}
diff --git a/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_PDF.jsonl b/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_PDF.jsonl
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+{"idx": 0, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ..."}
+{"idx": 1, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to-video re-trieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video re-trieval , our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "Abstract In this work, we tackle the problem of text-to-video re-trieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video re-trieval , our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos ."}
+{"idx": 2, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expan-sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong indi-vidual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "www.celsodemelo.net", "link": "http://www.celsodemelo.net/static/publications/Video_ColBERT_CVPR_2025_DistA.pdf", "content": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expan-sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong indi-vidual, yet compatible, representations for encoding video content."}
+{"idx": 3, "title": "GitHub - yogesh-iitj/Video-ColBERT", "date": "", "ddg_snippet": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to-video retrieval . Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yogesh-iitj/Video-ColBERT", "content": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to-video retrieval . Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ..."}
+{"idx": 4, "title": "[PDF] Video-ColBERT: Contextualized Late Interaction for Text-to-Video ...", "date": "", "ddg_snippet": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos , and finds that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Video-ColBERT:-Contextualized-Late-Interaction-for-Reddy-Martin/bff2f91c763830a2d14dbbbeca150e92ede02323", "content": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos , and finds that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in ..."}
+{"idx": 5, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094542", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial ..."}
+{"idx": 6, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009", "content": "Abstract In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained ..."}
+{"idx": 7, "title": "Video - ColBERT : Contextualized Late Interaction for Text - to - Video ...", "date": "", "ddg_snippet": "Text - to - video retrieval (T2VR) aims to address this by ranking large collections of videos based on their relevance to nat-ural language queries.Recently, ColPali [17] applied this to visual document retrieval with vision-language models for retrieving PDF files with textual queries.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.19009", "content": "Text - to - video retrieval (T2VR) aims to address this by ranking large collections of videos based on their relevance to nat-ural language queries.Recently, ColPali [17] applied this to visual document retrieval with vision-language models for retrieving PDF files with textual queries."}
+{"idx": 8, "title": "Video - ColBERT : Contextualized Late Interaction for Text - to - Video ...", "date": "", "ddg_snippet": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2503.19009", "content": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible..."}
+{"idx": 9, "title": "Video - ColBERT : Contextualized Late Interaction for Text - to - Video ...", "date": "", "ddg_snippet": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval@CVPR2025@CVF", "content": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual..."}
diff --git a/data/sampled_jsons/Video-ColBERT_Equation_5_final_similarity_MMSF_MMSv.jsonl b/data/sampled_jsons/Video-ColBERT_Equation_5_final_similarity_MMSF_MMSv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06c47bd9a6eddb72071983bc9458708513ad3994
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_Equation_5_final_similarity_MMSF_MMSv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "New Bidang Viral Videos Main Sama Kakak Abg Trending Top News...", "date": "", "ddg_snippet": "Ample storage options and expandable memory, perfect for downloading videos , music, or apps.Clear cache regularly so your downloads and playback run smoothly. Final Thoughts.", "subpage_snippet": "", "source": "senandungviral.baby", "link": "https://senandungviral.baby/bidang-viral-videos-main-sama-kakak-abg-trending-top-news-2025-on-the-spot/", "content": "Ample storage options and expandable memory, perfect for downloading videos , music, or apps.Clear cache regularly so your downloads and playback run smoothly. Final Thoughts."}
+{"idx": 1, "title": "Secret Things (2002) - TokyVideo", "date": "", "ddg_snippet": "Resume video . Secret Things (2002). 61. 5 k. Advertising. 5 .3k. Hoy llega a los cines “A todo tren: Destino Asturias” | Tráiler final 01:06.", "subpage_snippet": "", "source": "www.tokyvideo.com", "link": "https://www.tokyvideo.com/video/secret-things-2002", "content": "Resume video . Secret Things (2002). 61. 5 k. Advertising. 5 .3k. Hoy llega a los cines “A todo tren: Destino Asturias” | Tráiler final 01:06."}
+{"idx": 2, "title": "VK Video Downloader - Сохранить фото, музыку... | convert- video .com", "date": "", "ddg_snippet": "convert- video .com поможет вам скачать из ВК все ваши любимые видео, музыку и фото! С помощью этого инструмента вы сможете скачать из Вконтакте все виды аудио и видео бесплатно!", "subpage_snippet": "", "source": "convert-video.com", "link": "https://convert-video.com/ru/how-to-save-video-from-vk", "content": "convert- video .com поможет вам скачать из ВК все ваши любимые видео, музыку и фото! С помощью этого инструмента вы сможете скачать из Вконтакте все виды аудио и видео бесплатно!"}
+{"idx": 3, "title": "Ashes and Snow by Gregory Colbert", "date": "", "ddg_snippet": "Gregory Colbert ’s Ashes and Snow feature film captures extraordinary moments of contact between people and animals as seen through the lens of the artist’s…", "subpage_snippet": "", "source": "vimeo.com", "link": "https://vimeo.com/29498902", "content": "Gregory Colbert ’s Ashes and Snow feature film captures extraordinary moments of contact between people and animals as seen through the lens of the artist’s…"}
+{"idx": 4, "title": "Winlator Frost APK v 10.0 Final V 2 Free Download | Latest Version", "date": "", "ddg_snippet": "If you want to fix an old game, you can try the Environment Variables under Container Settings. Adding MESA_EXTENSION_MAX_YEAR=2003 setting might specifically help you with this. Final Words. Winlator Frost is an outstanding Windows emulator for Android.", "subpage_snippet": "", "source": "www.winlatordownload.com", "link": "https://www.winlatordownload.com/winlator-frost/", "content": "If you want to fix an old game, you can try the Environment Variables under Container Settings. Adding MESA_EXTENSION_MAX_YEAR=2003 setting might specifically help you with this. Final Words. Winlator Frost is an outstanding Windows emulator for Android."}
+{"idx": 5, "title": "Equation Solver: Step-by-Step Calculator - Wolfram|Alpha", "date": "", "ddg_snippet": "Free Equation Solver helps you to calculate linear, quadratic and polynomial systems of equations . Answers, graphs, roots, alternate forms.", "subpage_snippet": "", "source": "www.wolframalpha.com", "link": "https://www.wolframalpha.com/calculators/equation-solver-calculator", "content": "Free Equation Solver helps you to calculate linear, quadratic and polynomial systems of equations . Answers, graphs, roots, alternate forms."}
+{"idx": 6, "title": "AI Music Generator Online (Free, No Sign-Up, Royalty-free)", "date": "", "ddg_snippet": "short, 10 second audio clip. 8-bit retro arcade game video game, short victory fanfare, rising tones, finale . Similar to Super Mario Bros.", "subpage_snippet": "", "source": "aimusicgen.ai", "link": "https://aimusicgen.ai/", "content": "short, 10 second audio clip. 8-bit retro arcade game video game, short victory fanfare, rising tones, finale . Similar to Super Mario Bros."}
+{"idx": 7, "title": "Similarities Between Ossetian and Persian - YouTube", "date": "", "ddg_snippet": "In this video , we compare some of the similarities between Ossetian and Persian.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=PGbAUxO8et8", "content": "In this video , we compare some of the similarities between Ossetian and Persian."}
+{"idx": 8, "title": "Как Сделать Краба Из Магнита | TikTok", "date": "", "ddg_snippet": "TikTok video from ALEKSA (@esowitch): “14/23 , а вы собрали всю коллекцию? #brainrot #магнит #зверятыши”.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/как-сделать-краба-из-магнита", "content": "TikTok video from ALEKSA (@esowitch): “14/23 , а вы собрали всю коллекцию? #brainrot #магнит #зверятыши”."}
+{"idx": 9, "title": "Android TV Box - приложения для Андроид ТВ", "date": "", "ddg_snippet": "Video App ВК – это бесплатное приложение для ТВ-приставок, телевизоров и мобильных устройств на платформе Android для просмотра и скачивания видео.", "subpage_snippet": "", "source": "AndroidTVBox.ru", "link": "https://AndroidTVBox.ru/", "content": "Video App ВК – это бесплатное приложение для ТВ-приставок, телевизоров и мобильных устройств на платформе Android для просмотра и скачивания видео."}
diff --git a/data/sampled_jsons/Video-ColBERT_MSVD_R@1_SigLIP-B16_state-of-the-art_Table_1.jsonl b/data/sampled_jsons/Video-ColBERT_MSVD_R@1_SigLIP-B16_state-of-the-art_Table_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c4e41169d4b19e0d8960c609f93d3f06cd39a9a4
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_MSVD_R@1_SigLIP-B16_state-of-the-art_Table_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "24 Mar 2025 — For example, with SigLIP - B / 16 , Video - ColBERT sets a new state -of-the- art on MSRVTT, MSVD and VATEX. The results using CLIP4Clip with an ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "24 Mar 2025 — For example, with SigLIP - B / 16 , Video - ColBERT sets a new state -of-the- art on MSRVTT, MSVD and VATEX. The results using CLIP4Clip with an ..."}
+{"idx": 1, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "by A Reddy · 2025 · Cited by 5 — For example, with. SigLIP - B / 16 , VIDEO - COLBERT sets a new state -of-the- art on MSRVTT, MSVD and VATEX. The results using. CLIP4Clip with an upgraded SigLIP model ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "by A Reddy · 2025 · Cited by 5 — For example, with. SigLIP - B / 16 , VIDEO - COLBERT sets a new state -of-the- art on MSRVTT, MSVD and VATEX. The results using. CLIP4Clip with an upgraded SigLIP model ... 11 pages"}
+{"idx": 2, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "For example, with SigLIP - B / 16 , VIDEO - COLBERT sets a new state -of-the- art on MSRVTT, MSVD and VATEX. The results using CLIP4Clip with an upgraded SigLIP ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33381", "content": "For example, with SigLIP - B / 16 , VIDEO - COLBERT sets a new state -of-the- art on MSRVTT, MSVD and VATEX. The results using CLIP4Clip with an upgraded SigLIP ..."}
+{"idx": 3, "title": "Closing the Modality Gap for Mixed Modality Search", "date": "", "ddg_snippet": "25 Jul 2025 — We show that state -of-the- art contrastive models suffer from ranking bias and fusion failure due to the modality gap, and we propose a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.19054v1", "content": "25 Jul 2025 — We show that state -of-the- art contrastive models suffer from ranking bias and fusion failure due to the modality gap, and we propose a ..."}
+{"idx": 4, "title": "Video-R1: Reinforcing Video Reasoning in MLLMs - GitHub", "date": "", "ddg_snippet": "Feb 23, 2025 · Video -R1 significantly outperforms previous models across most benchmarks. Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video -R1-7B achieves a new state - of - the - art accuracy of 35.8%, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters. This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tulerfeng/Video-R1", "content": "Feb 23, 2025 · Video -R1 significantly outperforms previous models across most benchmarks. Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video -R1-7B achieves a new state - of - the - art accuracy of 35.8%, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters. This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the ..."}
+{"idx": 5, "title": "【EMNLP 2024 】Video-LLaVA: Learning United Visual ... - GitHub", "date": "", "ddg_snippet": "Video -LLaVA: Learning United Visual Representation by Alignment Before Projection If you like our project, please give us a star ⭐ on GitHub for latest update. 💡 I also have other video -language projects that may interest you . Open-Sora Plan: Open-Source Large Video Generation Model", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/PKU-YuanGroup/Video-LLaVA", "content": "Video -LLaVA: Learning United Visual Representation by Alignment Before Projection If you like our project, please give us a star ⭐ on GitHub for latest update. 💡 I also have other video -language projects that may interest you . Open-Sora Plan: Open-Source Large Video Generation Model"}
+{"idx": 6, "title": "Wan: Open and Advanced Large-Scale Video Generative Models", "date": "", "ddg_snippet": "Jul 28, 2025 · Wan: Open and Advanced Large-Scale Video Generative Models We are excited to introduce Wan2.2, a major upgrade to our foundational video models. With Wan2.2, we have focused on incorporating the following innovations: 👍 Effective MoE Architecture: Wan2.2 introduces a Mixture- of -Experts (MoE) architecture into video diffusion models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Wan-Video/Wan2.2", "content": "Jul 28, 2025 · Wan: Open and Advanced Large-Scale Video Generative Models We are excited to introduce Wan2.2, a major upgrade to our foundational video models. With Wan2.2, we have focused on incorporating the following innovations: 👍 Effective MoE Architecture: Wan2.2 introduces a Mixture- of -Experts (MoE) architecture into video diffusion models."}
+{"idx": 7, "title": "GitHub - k4yt3x/video2x: A machine learning-based video super ...", "date": "", "ddg_snippet": "A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, 2018. - k4yt3x/video2x", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/k4yt3x/video2x", "content": "A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, 2018. - k4yt3x/video2x"}
+{"idx": 8, "title": "hao-ai-lab/FastVideo - GitHub", "date": "", "ddg_snippet": "FastVideo is a unified post-training and inference framework for accelerated video generation. FastVideo features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hao-ai-lab/FastVideo", "content": "FastVideo is a unified post-training and inference framework for accelerated video generation. FastVideo features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be ..."}
+{"idx": 9, "title": "DepthAnything/Video-Depth-Anything - GitHub", "date": "", "ddg_snippet": "Jan 21, 2025 · ByteDance †Corresponding author This work presents Video Depth Anything based on Depth Anything V2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability. Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/DepthAnything/Video-Depth-Anything", "content": "Jan 21, 2025 · ByteDance †Corresponding author This work presents Video Depth Anything based on Depth Anything V2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability. Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth ..."}
diff --git a/data/sampled_jsons/Video-ColBERT_MSVD_dataset_frame_sampling_Section_5.2_33381.jsonl b/data/sampled_jsons/Video-ColBERT_MSVD_dataset_frame_sampling_Section_5.2_33381.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cb454f1cc800d33011898a56a5f882f324fc5d2a
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_MSVD_dataset_frame_sampling_Section_5.2_33381.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33381", "content": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss ..."}
+{"idx": 1, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos ."}
+{"idx": 2, "title": "msvd_r3d_video_feature_extraction.py - GitHub", "date": "", "ddg_snippet": "Video Captioning with PyTorch This project is a PyTorch implementation of a video captioning system based on the MSVD dataset . The goal is to generate natural language captions that describe the co...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ozan-git/videoCaptioningProject/blob/master/msvd_r3d_video_feature_extraction.py", "content": "Video Captioning with PyTorch This project is a PyTorch implementation of a video captioning system based on the MSVD dataset . The goal is to generate natural language captions that describe the co..."}
+{"idx": 3, "title": "Microsoft Video Description Dataset (MSVD) | Kaggle", "date": "", "ddg_snippet": "All videos have been converted into frames and splitted into training, val, test", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/steveandreasimmanuel/msvd-video-caption", "content": "All videos have been converted into frames and splitted into training, val, test"}
+{"idx": 4, "title": "Example video frames and captions from MSVD dataset", "date": "", "ddg_snippet": "Download scientific diagram | Example video frames and captions from MSVD dataset from publication: Video Description: Datasets & Evaluation Metrics | Rapid expansion and the novel phenomenon of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Example-video-frames-and-captions-from-MSVD-dataset_fig2_354175109", "content": "Download scientific diagram | Example video frames and captions from MSVD dataset from publication: Video Description: Datasets & Evaluation Metrics | Rapid expansion and the novel phenomenon of ..."}
+{"idx": 5, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to- video re-trieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video re-trieval, our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "Abstract In this work, we tackle the problem of text-to- video re-trieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video re-trieval, our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos ."}
+{"idx": 6, "title": "Subject-Oriented Video Captioning - arXiv.org", "date": "", "ddg_snippet": "To support this task, we construct two subject-oriented video captioning datasets based on two widely used video captioning datasets : MSVD and MSRVTT, by annotating subjects in each video for each caption. These datasets pave the way for future technique development.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.13330v1", "content": "To support this task, we construct two subject-oriented video captioning datasets based on two widely used video captioning datasets : MSVD and MSRVTT, by annotating subjects in each video for each caption. These datasets pave the way for future technique development."}
+{"idx": 7, "title": "Examples of 9 test videos from MSVD dataset and the top 1 retrieved...", "date": "", "ddg_snippet": "Download scientific diagram | Examples of 9 test videos from MSVD dataset and the top 1 retrieved captions by using a single video -text space and the fusion approach with our loss function.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Examples-of-9-test-videos-from-MSVD-dataset-and-the-top-1-retrieved-captions-by-using-a_fig5_330348236", "content": "Download scientific diagram | Examples of 9 test videos from MSVD dataset and the top 1 retrieved captions by using a single video -text space and the fusion approach with our loss function."}
+{"idx": 8, "title": "Video-Captioning Evaluation Metric for Segments (VEMS): A ... - Springer", "date": "", "ddg_snippet": "Thus VEMS evaluates captions at the segment level. A novel dataset structure called MSVD-S , a modified version of the MSVD dataset consisting of captions for multiple segments in a single video has also been proposed. Based on the MSVD-S dataset with weighted frames , VEMS captions to video in a frame -wise manner.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11042-023-17328-z", "content": "Thus VEMS evaluates captions at the segment level. A novel dataset structure called MSVD-S , a modified version of the MSVD dataset consisting of captions for multiple segments in a single video has also been proposed. Based on the MSVD-S dataset with weighted frames , VEMS captions to video in a frame -wise manner."}
+{"idx": 9, "title": "PDF MSR-VTT: A Large Video Description Dataset for Bridging Video and Language", "date": "", "ddg_snippet": "The dataset can also be utilized for video summarization if one can build the embedding between video frames and the words. Furthermore, emotion and action recognition could be integrated into existing framework to make the generated language more diverse and natural.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/cvpr16.msr-vtt.tmei_-1.pdf", "content": "The dataset can also be utilized for video summarization if one can build the embedding between video frames and the words. Furthermore, emotion and action recognition could be integrated into existing framework to make the generated language more diverse and natural."}
diff --git a/data/sampled_jsons/Video-ColBERT_MSVD_frame_sampling_Section_5.2_year_2023.jsonl b/data/sampled_jsons/Video-ColBERT_MSVD_frame_sampling_Section_5.2_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fe067fb46fe7e97f12f736ef815af1835b3e9850
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_MSVD_frame_sampling_Section_5.2_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "by A Reddy · 2025 · Cited by 5 — VIDEO - COLBERT incorporates a modification to the. MaxSim operation, MeanMaxSim (MMS), which replaces the summation with a mean to better accommodate variable. 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "by A Reddy · 2025 · Cited by 5 — VIDEO - COLBERT incorporates a modification to the. MaxSim operation, MeanMaxSim (MMS), which replaces the summation with a mean to better accommodate variable. 11 pages"}
+{"idx": 1, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33381", "content": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss ..."}
+{"idx": 2, "title": "Tencent Text-Video Retrieval: Hierarchical Cross-Modal ...", "date": "", "ddg_snippet": "by J Jiang · 2022 · Cited by 18 — With multi-level representations for video and text, hierarchical contrastive learning is designed to explore fine-grained cross-modal.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2204.03382", "content": "by J Jiang · 2022 · Cited by 18 — With multi-level representations for video and text, hierarchical contrastive learning is designed to explore fine-grained cross-modal."}
+{"idx": 3, "title": "Tencent Text-Video Retrieval: Hierarchical Cross-Modal ...", "date": "", "ddg_snippet": "by J Jiang · 2022 · Cited by 18 — ABSTRACT Text- Video Retrieval plays an important role in multi-modal understanding and has attracted increasing attention in recent years.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/6287639/6514899/09979153.pdf", "content": "by J Jiang · 2022 · Cited by 18 — ABSTRACT Text- Video Retrieval plays an important role in multi-modal understanding and has attracted increasing attention in recent years."}
+{"idx": 4, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for ...", "date": "", "ddg_snippet": "by Y Ma · 2022 · Cited by 381 — 1 Frame -level Representation. For a video ˆvi ∈ V, we first sam- ple video frames using the sampling rate of 1 frame per second (FPS).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.07285", "content": "by Y Ma · 2022 · Cited by 381 — 1 Frame -level Representation. For a video ˆvi ∈ V, we first sam- ple video frames using the sampling rate of 1 frame per second (FPS)."}
+{"idx": 5, "title": "Proceedings of the 31st International Conference on ...", "date": "", "ddg_snippet": "Discrete Subgraph Sampling for Interpretable Graph based Visual ... Existing video captioning models often fail to capture the nuanced semantics of videos ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2025.coling-main/", "content": "Discrete Subgraph Sampling for Interpretable Graph based Visual ... Existing video captioning models often fail to capture the nuanced semantics of videos ..."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "This paper proposes a novel framework utilizing multi-modal large language models (MLLMs) for referring video object segmentation (RefVOS).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=query+frames", "content": "This paper proposes a novel framework utilizing multi-modal large language models (MLLMs) for referring video object segmentation (RefVOS)."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "Experiments on MSR-VTT, MSVD , and AVSD show that our framework using question-based interaction significantly improves the performance of text-based video ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=few-shot+API+queries", "content": "Experiments on MSR-VTT, MSVD , and AVSD show that our framework using question-based interaction significantly improves the performance of text-based video ..."}
+{"idx": 8, "title": "Proceedings of the 2022 Conference on Empirical Methods ...", "date": "", "ddg_snippet": "Video -grounded Dialogue (VGD) aims to decode an answer sentence to a question regarding a given video and dialogue context . Despite the recent success of ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2022.emnlp-main/", "content": "Video -grounded Dialogue (VGD) aims to decode an answer sentence to a question regarding a given video and dialogue context . Despite the recent success of ..."}
+{"idx": 9, "title": "Advances in Cyber Security", "date": "", "ddg_snippet": "24 Aug 2021 — This volume contains the papers from the Third International Conference on Advances in CyberSecurity (ACeS 2021). The event was organized by ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-981-16-8059-5.pdf", "content": "24 Aug 2021 — This volume contains the papers from the Third International Conference on Advances in CyberSecurity (ACeS 2021). The event was organized by ..."}
diff --git a/data/sampled_jsons/Video-ColBERT_MSVD_frames_are_sampled.jsonl b/data/sampled_jsons/Video-ColBERT_MSVD_frames_are_sampled.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..32ba4cc0301a10e98a61f955cbc52ca448bda345
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_MSVD_frames_are_sampled.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "24 Mar 2025 — In line with previous work, we sample 12 frames for MSR-VTT, MSVD and VATEX, while using 64 frames for DiDeMo and ActivityNet. Report issue for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "24 Mar 2025 — In line with previous work, we sample 12 frames for MSR-VTT, MSVD and VATEX, while using 64 frames for DiDeMo and ActivityNet. Report issue for ..."}
+{"idx": 1, "title": "What Can Auxiliary Captions Do for Text-Video Retrieval?", "date": "", "ddg_snippet": "by W Wu · 2023 · Cited by 157 — We train the visual encoder to output F frame embeddings for a given video that samples F frames . Similarly, the query encoder returns W word embeddings and ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Cap4Video_What_Can_Auxiliary_Captions_Do_for_Text-Video_Retrieval_CVPR_2023_paper.pdf", "content": "by W Wu · 2023 · Cited by 157 — We train the visual encoder to output F frame embeddings for a given video that samples F frames . Similarly, the query encoder returns W word embeddings and ... 10 pages"}
+{"idx": 2, "title": "Zero-Shot Video Captioning by Evolving Pseudo-tokens", "date": "", "ddg_snippet": "by Y Tewel · 2022 · Cited by 43 — Adapting our model for video captioning begins by sampling three frames every second . To avoid repetition and capture diverse frames, we further subsample ... 14 pages", "subpage_snippet": "", "source": "papers.bmvc2023.org", "link": "https://papers.bmvc2023.org/0429.pdf", "content": "by Y Tewel · 2022 · Cited by 43 — Adapting our model for video captioning begins by sampling three frames every second . To avoid repetition and capture diverse frames, we further subsample ... 14 pages"}
+{"idx": 3, "title": "Cap4Video++: Enhancing Video Understanding With Auxiliary ...", "date": "", "ddg_snippet": "... video retrieval task. CLIPBERT offers an affordable pioneering approach to end-to-end training with a sparse frame sampling strategy. More recent works ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/07/10670217/206qdecAKty", "content": "... video retrieval task. CLIPBERT offers an affordable pioneering approach to end-to-end training with a sparse frame sampling strategy. More recent works ..."}
+{"idx": 4, "title": "Tencent Text-Video Retrieval: Hierarchical Cross-Modal ...", "date": "", "ddg_snippet": "by J Jiang · 2022 · Cited by 18 — For a video vi, we obtain two views v1 i and v2 i by randomly sampling its frames twice. We define two videos to be similar, if v1 i and v1. 11 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/6287639/10820123/09979153.pdf", "content": "by J Jiang · 2022 · Cited by 18 — For a video vi, we obtain two views v1 i and v2 i by randomly sampling its frames twice. We define two videos to be similar, if v1 i and v1. 11 pages"}
+{"idx": 5, "title": "CenterCLIP: Token Clustering for Efficient Text-Video Retrieval", "date": "", "ddg_snippet": "7 Jul 2022 — As the frame redundancy occurs mostly in consecutive frames , we divide videos into multiple segments and conduct segment-level clustering.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3477495.3531950", "content": "7 Jul 2022 — As the frame redundancy occurs mostly in consecutive frames , we divide videos into multiple segments and conduct segment-level clustering."}
+{"idx": 6, "title": "Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual", "date": "", "ddg_snippet": "... most current text-to- video retrieval systems are trained and evaluated on widely used datasets, such as MSR-VTT (Xu et al., 2016 ) and MSVD (Chen ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10202v1", "content": "... most current text-to- video retrieval systems are trained and evaluated on widely used datasets, such as MSR-VTT (Xu et al., 2016 ) and MSVD (Chen ..."}
+{"idx": 7, "title": "Bidirectional Likelihood Estimation with Multi-Modal Large ...", "date": "", "ddg_snippet": "31 Jul 2025 — The self-attention mechanism in our model is implemented under FlashAttention2 [66] and we sample 16 frames per video for all datasets. ... Video ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23284v1", "content": "31 Jul 2025 — The self-attention mechanism in our model is implemented under FlashAttention2 [66] and we sample 16 frames per video for all datasets. ... Video ..."}
+{"idx": 8, "title": "A Comprehensive Survey on Multimodal Retrieval- ...", "date": "", "ddg_snippet": "An empirical comparison of video frame sampling . 1267 methods for multi-modal rag retrieval. Preprint,. 1268. arXiv:2408.03340. 1269. Yasser Khalafaoui, Martino ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/371ce5eb217f4167e792883aeb958c38b5400e5c.pdf", "content": "An empirical comparison of video frame sampling . 1267 methods for multi-modal rag retrieval. Preprint,. 1268. arXiv:2408.03340. 1269. Yasser Khalafaoui, Martino ..."}
+{"idx": 9, "title": "Multi-level vision language interaction learning for cross ...", "date": "", "ddg_snippet": "by H Wang · 2025 — In this paper, we propose a novel framework that leverages the multi-stage grouping interaction to explore fine-grained knowledge.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1566253525005548", "content": "by H Wang · 2025 — In this paper, we propose a novel framework that leverages the multi-stage grouping interaction to explore fine-grained knowledge."}
diff --git a/data/sampled_jsons/Video-ColBERT_X-CLIP_do_not_perform_interaction_on_both_spatial_and_spatio-temporal_visual_features.jsonl b/data/sampled_jsons/Video-ColBERT_X-CLIP_do_not_perform_interaction_on_both_spatial_and_spatio-temporal_visual_features.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d08a4bc37b744ca083a26f46ab0a79037d1df98b
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_X-CLIP_do_not_perform_interaction_on_both_spatial_and_spatio-temporal_visual_features.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we introduced Video-ColBERT , a novel approach for text-to- video retrieval that uses efficient fine-grained interactions with both spatial and spatio-temporal visual features .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "In this work, we introduced Video-ColBERT , a novel approach for text-to- video retrieval that uses efficient fine-grained interactions with both spatial and spatio-temporal visual features ."}
+{"idx": 1, "title": "Spatial-temporal multi-scale interaction for few-shot video ...", "date": "", "ddg_snippet": "Multi-scale features are extracted from spatial - temporal representations, employing multi-scale interactions in a dual-branch architecture. Several state-of-the-art video summarization methods have been adapted to the few-shot paradigm for equitable evaluation, and several existing datasets have been refined for few-shot segmentation.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197624020426", "content": "Multi-scale features are extracted from spatial - temporal representations, employing multi-scale interactions in a dual-branch architecture. Several state-of-the-art video summarization methods have been adapted to the few-shot paradigm for equitable evaluation, and several existing datasets have been refined for few-shot segmentation."}
+{"idx": 2, "title": "Exploring Spatio-Temporal Graph Convolution for Video-Based Human ...", "date": "", "ddg_snippet": "In this paper, we propose a novel model, named Spatio-Temporal Interaction Graph Parsing Networks (STIGPN), for human-object interaction recognition in videos . STIGPN captures both spatial and temporal correlations simultaneously and thus can capture intra-frame and inter-frame dependencies efficiently and effectively.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10077416", "content": "In this paper, we propose a novel model, named Spatio-Temporal Interaction Graph Parsing Networks (STIGPN), for human-object interaction recognition in videos . STIGPN captures both spatial and temporal correlations simultaneously and thus can capture intra-frame and inter-frame dependencies efficiently and effectively."}
+{"idx": 3, "title": "PDF What When and Where? Self-Supervised Spatio-Temporal Grounding in ...", "date": "", "ddg_snippet": "Introduction Spatio-temporal grounding (STG) describes the challenging task of locating events in space and time within video data based on text referential expressions. Methods in this field usually rely on a combination of spatio-temporal bound-ing box annotation, together with a human-generated cap-tion, describing the visual content of the bounding box [23, 54], which limits their ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_What_When_and_Where_Self-Supervised_Spatio-Temporal_Grounding_in_Untrimmed_Multi-Action_CVPR_2024_paper.pdf", "content": "Introduction Spatio-temporal grounding (STG) describes the challenging task of locating events in space and time within video data based on text referential expressions. Methods in this field usually rely on a combination of spatio-temporal bound-ing box annotation, together with a human-generated cap-tion, describing the visual content of the bounding box [23, 54], which limits their ..."}
+{"idx": 4, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "On top of this modified interaction , VIDEO-COLBERT uses two MMS operations over both indepen-dent visual frame features and contextualized frame features to strengthen the fine-grained spatial and temporal interac-tion (Fig. 1).", "subpage_snippet": "", "source": "www.celsodemelo.net", "link": "https://www.celsodemelo.net/static/publications/Video_ColBERT_CVPR_2025_DistA.pdf", "content": "On top of this modified interaction , VIDEO-COLBERT uses two MMS operations over both indepen-dent visual frame features and contextualized frame features to strengthen the fine-grained spatial and temporal interac-tion (Fig. 1)."}
+{"idx": 5, "title": "Exciting Research Alert: Video-ColBERT - A Breakthrough in Text-to ...", "date": "", "ddg_snippet": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim (MMS) operations on both ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/singhsidhukuldeep_exciting-research-alert-video-colbert-activity-7336927383549071360-eAP7", "content": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim (MMS) operations on both ..."}
+{"idx": 6, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video ...", "date": "", "ddg_snippet": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.19009", "content": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content."}
+{"idx": 7, "title": "Spatial-temporal video grounding with cross-modal understanding and ...", "date": "", "ddg_snippet": "Toward this end, we contribute a transformer-based solution to address the spatial - temporal video grounding problem. Specifically, a transformer encoder is employed to fuse visual and textual spatial - temporal features , which is able to facilitate cross-modal information interaction and improve the representation of both modalities.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417425002726", "content": "Toward this end, we contribute a transformer-based solution to address the spatial - temporal video grounding problem. Specifically, a transformer encoder is employed to fuse visual and textual spatial - temporal features , which is able to facilitate cross-modal information interaction and improve the representation of both modalities."}
+{"idx": 8, "title": "Spatio-Temporal Context Prompting for Zero-Shot Action Detection", "date": "", "ddg_snippet": "Spatio-temporal action detection encompasses the tasks of localizing and classifying individual actions within a video . Recent works aim to enhance this process by incorporating interaction modeling, which captures the relationship between people and their surrounding context.", "subpage_snippet": "", "source": "webber2933.github.io", "link": "https://webber2933.github.io/ST-CLIP-project-page/", "content": "Spatio-temporal action detection encompasses the tasks of localizing and classifying individual actions within a video . Recent works aim to enhance this process by incorporating interaction modeling, which captures the relationship between people and their surrounding context."}
+{"idx": 9, "title": "arXiv:2503.19009v1 [cs.CV] 24 Mar 2025", "date": "", "ddg_snippet": "e CLIP [42] and SigLIP [63]) for T2VR. VIDEO-COLBERT (depicted in Fig. 2) has 3 main aspects: (i) fine-grained spa-tial and temporal interaction , performing MMS on both in-dependent frames and their contextualized representations, (ii) query and visual expansion tokens which allow for addi-tional information to be encoded for abstract queries ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.19009", "content": "e CLIP [42] and SigLIP [63]) for T2VR. VIDEO-COLBERT (depicted in Fig. 2) has 3 main aspects: (i) fine-grained spa-tial and temporal interaction , performing MMS on both in-dependent frames and their contextualized representations, (ii) query and visual expansion tokens which allow for addi-tional information to be encoded for abstract queries ..."}
diff --git a/data/sampled_jsons/WWW_'24_top-tier_papers_'Information_Retrieval'.jsonl b/data/sampled_jsons/WWW_'24_top-tier_papers_'Information_Retrieval'.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1cfe4cdc760cd5acc45f4ba7ae0958a3f00de9bc
--- /dev/null
+++ b/data/sampled_jsons/WWW_'24_top-tier_papers_'Information_Retrieval'.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning to rank - Wikipedia", "date": "", "ddg_snippet": "... top - k {\\displaystyle k} document retrieval and many heuristics were proposed in the literature to accelerate it, such as using a document's static ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Learning_to_rank", "content": "... top - k {\\displaystyle k} document retrieval and many heuristics were proposed in the literature to accelerate it, such as using a document's static ..."}
+{"idx": 1, "title": "Punched card - Wikipedia", "date": "", "ddg_snippet": "A punched card (also punch card 1 or punched-card 2 ) is a stiff paper -based medium used to store digital information via the presence or absence of ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Punched_card", "content": "A punched card (also punch card 1 or punched-card 2 ) is a stiff paper -based medium used to store digital information via the presence or absence of ..."}
+{"idx": 2, "title": "US10346453B2 - Multi-tiered information retrieval training -", "date": "", "ddg_snippet": "access is provided to the function of the first vector and the at least one other vector for use in multi-tiered information retrieval training.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US10346453B2/en", "content": "access is provided to the function of the first vector and the at least one other vector for use in multi-tiered information retrieval training."}
+{"idx": 3, "title": "Top 20 Publications in Databases and Information Systems -", "date": "", "ddg_snippet": "... on Information and Knowledge Management is a top - tier conference that covers a wide range of topics, including databases, information retrieval , and ...", "subpage_snippet": "", "source": "www.ilovephd.com", "link": "https://www.ilovephd.com/top-20-publications-in-databases-and-information-systems/", "content": "... on Information and Knowledge Management is a top - tier conference that covers a wide range of topics, including databases, information retrieval , and ..."}
+{"idx": 4, "title": "Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval", "date": "", "ddg_snippet": "TopicGraphRAG retrieves clusters of statements (i.e., topical groups) from the Summarization Tier , using entity relationships to connect thematically ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08074v1", "content": "TopicGraphRAG retrieves clusters of statements (i.e., topical groups) from the Summarization Tier , using entity relationships to connect thematically ..."}
+{"idx": 5, "title": "Xiangyu Zhao's Homepage", "date": "", "ddg_snippet": "He serves as the top - tier Artificial Intelligence and Data Science conference chair and organizer at WWW '25, ICICIP'25, ISNN'25, WAIM' 24 , NLPCC' 24 ...", "subpage_snippet": "", "source": "zhaoxyai.github.io", "link": "https://zhaoxyai.github.io/", "content": "He serves as the top - tier Artificial Intelligence and Data Science conference chair and organizer at WWW '25, ICICIP'25, ISNN'25, WAIM' 24 , NLPCC' 24 ..."}
+{"idx": 6, "title": "Deep Multimodal-Interactive Document Summarization Network and", "date": "", "ddg_snippet": "For more information , please refer to https:// www .mdpi.com/openaccess . ... A Feature Paper should be a substantial original Article that involves ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2624-6511/8/3/96", "content": "For more information , please refer to https:// www .mdpi.com/openaccess . ... A Feature Paper should be a substantial original Article that involves ..."}
+{"idx": 7, "title": "GitHub - Future-House/paper-qa: High accuracy RAG for answering", "date": "", "ddg_snippet": "PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs or text files, with a focus on the scientific literature.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Future-House/paper-qa", "content": "PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs or text files, with a focus on the scientific literature."}
+{"idx": 8, "title": "Xiang Wang, University of Science and Technology of China", "date": "", "ddg_snippet": "New!] 2024/02 Four papers are accepted by WWW ' 24 ! Big congrats to An Zhang, Junfeng Fang, Yuan Gao, Yongduo Sui and Other Collaborators! &emsp ...", "subpage_snippet": "", "source": "xiangwang1223.github.io", "link": "https://xiangwang1223.github.io/", "content": "New!] 2024/02 Four papers are accepted by WWW ' 24 ! Big congrats to An Zhang, Junfeng Fang, Yuan Gao, Yongduo Sui and Other Collaborators! &emsp ..."}
+{"idx": 9, "title": "Why TPC is not enough: An analysis of the Amazon Redshift fleet", "date": "", "ddg_snippet": "In this paper , we summarize well-known, confirm suspected, and unearth novel discrepancies between TPC-H/DS and actual work-loads using empirical ...", "subpage_snippet": "", "source": "www.amazon.science", "link": "https://www.amazon.science/publications/why-tpc-is-not-enough-an-analysis-of-the-amazon-redshift-fleet", "content": "In this paper , we summarize well-known, confirm suspected, and unearth novel discrepancies between TPC-H/DS and actual work-loads using empirical ..."}
diff --git a/data/sampled_jsons/WWW_2024_Information_Retrieval_papers_count_statistics_year_2024.jsonl b/data/sampled_jsons/WWW_2024_Information_Retrieval_papers_count_statistics_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a66bdc732338295aa824a3af2a2f754e41b451c5
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_Information_Retrieval_papers_count_statistics_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "US8972378B2 - Formulating global statistics for distributed", "date": "", "ddg_snippet": "G06F16/20 — Information retrieval ; Database ... All information for optimization, including statistics , is available in a single location.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US8972378B2/en", "content": "G06F16/20 — Information retrieval ; Database ... All information for optimization, including statistics , is available in a single location."}
+{"idx": 1, "title": "Ricardo BAEZA-YATES | Professor (Full) | PhD in Computer", "date": "", "ddg_snippet": "Providing the latest information retrieval techniques, this guide discusses Information Retrieval data structures and algorithms, including ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Ricardo-Baeza-Yates", "content": "Providing the latest information retrieval techniques, this guide discusses Information Retrieval data structures and algorithms, including ..."}
+{"idx": 2, "title": "(PDF) Measuring how computer science research translates into", "date": "", "ddg_snippet": "A wider gap between the distributions in each plot indicates a greater ability of papers going into patents and repositories to attract more academic ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/393764135_Measuring_how_computer_science_research_translates_into_innovation_and_development", "content": "A wider gap between the distributions in each plot indicates a greater ability of papers going into patents and repositories to attract more academic ..."}
+{"idx": 3, "title": "Academics: David C. Anastasiu", "date": "", "ddg_snippet": "Outstanding Paper Reviewer award, 2017, 26th ACM International Conference on Information and Knowledge Management, CIKM'17, Pan Pacific, Singapore.", "subpage_snippet": "", "source": "davidanastasiu.net", "link": "https://davidanastasiu.net/academic.php", "content": "Outstanding Paper Reviewer award, 2017, 26th ACM International Conference on Information and Knowledge Management, CIKM'17, Pan Pacific, Singapore."}
+{"idx": 4, "title": "Cédric | Nicolas Audebert", "date": "", "ddg_snippet": "In Proceedings of the 25th International Society for Music Information Retrieval Conference , San Francisco, United States, 2024 .", "subpage_snippet": "", "source": "cedric.cnam.fr", "link": "http://cedric.cnam.fr/lab/en/author/audebern/", "content": "In Proceedings of the 25th International Society for Music Information Retrieval Conference , San Francisco, United States, 2024 ."}
+{"idx": 5, "title": "Details of a Researcher - SATO, Toshio", "date": "", "ddg_snippet": "The data was downloaded from Scopus API in November 14, 2024 , via http://api.elsevier.com and http:// www .scopus.com .", "subpage_snippet": "", "source": "w-rdb.waseda.jp", "link": "https://w-rdb.waseda.jp/html/100001984_en.html", "content": "The data was downloaded from Scopus API in November 14, 2024 , via http://api.elsevier.com and http:// www .scopus.com ."}
+{"idx": 6, "title": "US8352452B2 - Methods and apparatus for employing usage", "date": "", "ddg_snippet": "These usage statistics may include, for example, the number of visitors to the document (perhaps over a period of time), the frequency with which the ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US8352452B2/en", "content": "These usage statistics may include, for example, the number of visitors to the document (perhaps over a period of time), the frequency with which the ..."}
+{"idx": 7, "title": "Publications Using Public Datasets", "date": "", "ddg_snippet": "In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp.", "subpage_snippet": "", "source": "www.etrialstestbed.org", "link": "https://www.etrialstestbed.org/resources/featured-studies/dataset-papers", "content": "In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp."}
+{"idx": 8, "title": "Journal of Medical Internet Research - Crawling the German", "date": "", "ddg_snippet": "As desired, the computed statistical data allows for determining major information hubs and important content providers on the GHW.", "subpage_snippet": "", "source": "www.jmir.org", "link": "https://www.jmir.org/2020/7/e17853/", "content": "As desired, the computed statistical data allows for determining major information hubs and important content providers on the GHW."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/WWW_2024_Web_Conference_2024_proceedings_accepted_papers_list_year_2024.jsonl b/data/sampled_jsons/WWW_2024_Web_Conference_2024_proceedings_accepted_papers_list_year_2024.jsonl
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index 0000000000000000000000000000000000000000..70c09d501f7973fffa975871a86a2ff1db83e378
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_Web_Conference_2024_proceedings_accepted_papers_list_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International World Wide Web Conference 2024 ( WWW 2024 )", "date": "", "ddg_snippet": "Seoul ToT 2024 Press Release. Accepted Papers - Resource. Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation.", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/resource/", "content": "Seoul ToT 2024 Press Release. Accepted Papers - Resource. Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation."}
+{"idx": 1, "title": "Proceedings of the ACM Web Conference 2024 | ACM Conferences", "date": "", "ddg_snippet": "Sections. WWW '24: Proceedings of the ACM Web Conference 2024 .It is our great pleasure to welcome you to The ACM Web Conference 2024 held in person with virtual components on May 13-17, 2024 , in Singapore.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3589334?cookieSet=1", "content": "Sections. WWW '24: Proceedings of the ACM Web Conference 2024 .It is our great pleasure to welcome you to The ACM Web Conference 2024 held in person with virtual components on May 13-17, 2024 , in Singapore."}
+{"idx": 2, "title": "International World Wide Web Conference 2024 ( WWW 2024 )", "date": "", "ddg_snippet": "Important Dates Paper submission: February 5, 2024 Notification to authors: March 4, 2024 Each accepted paper will be included in the Companion Proceedings of the Web Conference ...", "subpage_snippet": "", "source": "archives.iw3c2.org", "link": "https://archives.iw3c2.org/www2024/calls/short-papers/", "content": "Important Dates Paper submission: February 5, 2024 Notification to authors: March 4, 2024 Each accepted paper will be included in the Companion Proceedings of the Web Conference ..."}
+{"idx": 3, "title": "dblp: Bibliographic content of WWW 2024", "date": "", "ddg_snippet": "33rd WWW 2024 : Singapore. export records of this page. first 1000 hits onlyResearch Track: Social Networks, Social Media, and Society. Research Track: Systems and Infrastructure for Web , Mobile, and WoT.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/db/conf/www/www2024.html", "content": "33rd WWW 2024 : Singapore. export records of this page. first 1000 hits onlyResearch Track: Social Networks, Social Media, and Society. Research Track: Systems and Infrastructure for Web , Mobile, and WoT."}
+{"idx": 4, "title": "Social-AI-Studio/Mod_HATE: Official repository for WWW '24 paper ...", "date": "", "ddg_snippet": "@inproceedings{DBLP:conf/www/0002L024, author = {Rui Cao and. Roy Ka{-}Wei Lee and. Jing Jiang}, title = {Modularized Networks for Few-shot Hateful Meme Detection}, booktitle = { Proceedings of the {ACM} on Web Conference 2024 , { WWW } 2024 , Singapore, May 13-17, 2024}...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Social-AI-Studio/Mod_HATE", "content": "@inproceedings{DBLP:conf/www/0002L024, author = {Rui Cao and. Roy Ka{-}Wei Lee and. Jing Jiang}, title = {Modularized Networks for Few-shot Hateful Meme Detection}, booktitle = { Proceedings of the {ACM} on Web Conference 2024 , { WWW } 2024 , Singapore, May 13-17, 2024}..."}
+{"idx": 5, "title": "Two papers accepted at The Web Conference 2024 | News", "date": "", "ddg_snippet": "Two papers from the Artificial Intelligence Institute were recently accepted at The Web Conference 2024 , the premier venue to present and discuss progress in research, development, standards, and applications of the topics related to the Web.", "subpage_snippet": "", "source": "www.ki.uni-stuttgart.de", "link": "https://www.ki.uni-stuttgart.de/institute/news/Two-papers-accepted-at-The-Web-Conference-2024/", "content": "Two papers from the Artificial Intelligence Institute were recently accepted at The Web Conference 2024 , the premier venue to present and discuss progress in research, development, standards, and applications of the topics related to the Web."}
+{"idx": 6, "title": "Paper Accepted at WWW 2024 – IRLab", "date": "", "ddg_snippet": "A paper co-authored by IRLab-ers has been accepted for publication at The Web Conference 2024 ( WWW 2024 ): Yifei Yuan, Clemencia Siro, Mohammad Aliannejadi, Maarten de Rijke, Wai Lam.", "subpage_snippet": "", "source": "irlab.science.uva.nl", "link": "https://irlab.science.uva.nl/2024/01/26/one-papers-accepted-at-www-2024/", "content": "A paper co-authored by IRLab-ers has been accepted for publication at The Web Conference 2024 ( WWW 2024 ): Yifei Yuan, Clemencia Siro, Mohammad Aliannejadi, Maarten de Rijke, Wai Lam."}
+{"idx": 7, "title": "The ACM Web Conference 2024 Report", "date": "", "ddg_snippet": "The 33rd edition of The Web Conference was successfully held from May 13 to 17, 2024 at Resorts World Convention Centre, Singapore.Report on the 33rd The ACM Web Conference ( WWW 2024 ). March 2025 · ACM SIGIR Forum.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385127669_The_ACM_Web_Conference_2024_Report", "content": "The 33rd edition of The Web Conference was successfully held from May 13 to 17, 2024 at Resorts World Convention Centre, Singapore.Report on the 33rd The ACM Web Conference ( WWW 2024 ). March 2025 · ACM SIGIR Forum."}
+{"idx": 8, "title": "Research on Position Bias Estimation to be Presented at ACM Web ...", "date": "", "ddg_snippet": "The conference will be held from 13-17 May 2024 in Singapore.", "subpage_snippet": "", "source": "rit.rakuten.com", "link": "https://rit.rakuten.com/news/2024/research-on-position-bias-estimation-to-be-presented-at-acm-web-conference-2024/", "content": "The conference will be held from 13-17 May 2024 in Singapore."}
+{"idx": 9, "title": "ConferenceList/ Web -conferences / Twitter", "date": "", "ddg_snippet": "The ACM Web Conference 2024 will be held in Singapore from May 13 to 17, 2024 . Save the date!Under this link, Springer has made the proceedings available open access for 4 weeks. The link can also be found on the program overview and the list of all accepted papers .", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/i/lists/861823746264498176", "content": "The ACM Web Conference 2024 will be held in Singapore from May 13 to 17, 2024 . Save the date!Under this link, Springer has made the proceedings available open access for 4 weeks. The link can also be found on the program overview and the list of all accepted papers ."}
diff --git a/data/sampled_jsons/WWW_2024_accepted_papers_information_retrieval_statistics_year_2024.jsonl b/data/sampled_jsons/WWW_2024_accepted_papers_information_retrieval_statistics_year_2024.jsonl
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index 0000000000000000000000000000000000000000..0ee6c485fda3b18bd9d45293129cbc2e2dc22b3f
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_accepted_papers_information_retrieval_statistics_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "WWW 2024 Accepted Paper List", "date": "", "ddg_snippet": "WWW 2024 Accepted Paper List. Home » WWW Paper List » WWW 2024 Accepted ... Scalable and Effective Generative Information Retrieval -, Hansi Zeng;Chen ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/www-paper-list/www-2024-paper-list/", "content": "WWW 2024 Accepted Paper List. Home » WWW Paper List » WWW 2024 Accepted ... Scalable and Effective Generative Information Retrieval -, Hansi Zeng;Chen ..."}
+{"idx": 1, "title": "Information Retrieval Meets Large Language Models Workshop", "date": "", "ddg_snippet": "This workshop is dedicated to exploring how LLMs can enhance information retrieval algorithms, introducing a new era of data processing and analysis. ... WWW 2024 ...", "subpage_snippet": "", "source": "irmeetsllm.github.io", "link": "https://irmeetsllm.github.io/", "content": "This workshop is dedicated to exploring how LLMs can enhance information retrieval algorithms, introducing a new era of data processing and analysis. ... WWW 2024 ..."}
+{"idx": 2, "title": "Retrieval Augmented Generation Evaluation in the Era of ...", "date": "", "ddg_snippet": "by A Gan · 2025 · Cited by 4 — RAG is a cross-disciplinary system founded on traditional re- search fields including information retrieval (IR) and natu- ral language ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.14891", "content": "by A Gan · 2025 · Cited by 4 — RAG is a cross-disciplinary system founded on traditional re- search fields including information retrieval (IR) and natu- ral language ..."}
+{"idx": 3, "title": "Paper Digest: ICML 2024 Papers & Highlights", "date": "", "ddg_snippet": "12 Jun 2024 — ... accepted papers , and generated one highlight sentence (typically the ... Information Retrieval · MachineLearning · Math · Networking · NLP ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2024/06/icml-2024-highlights/", "content": "12 Jun 2024 — ... accepted papers , and generated one highlight sentence (typically the ... Information Retrieval · MachineLearning · Math · Networking · NLP ..."}
+{"idx": 4, "title": "Computer Science Mar 2024", "date": "", "ddg_snippet": "... Information Retrieval (cs.IR); Machine Learning (cs.LG); Audio and Speech ... Title: A collection of the accepted papers for the Human-Centric Representation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2024-03?skip=3980&show=2000", "content": "... Information Retrieval (cs.IR); Machine Learning (cs.LG); Audio and Speech ... Title: A collection of the accepted papers for the Human-Centric Representation ..."}
+{"idx": 5, "title": "Advances in Information Retrieval", "date": "", "ddg_snippet": "6 Apr 2025 — The accepted papers cover the state of the art in information retrieval ... WWW 2024 , Singapore, Singapore, 13–17 May 2024, pp. 1268–. 1271 ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-88720-8.pdf", "content": "6 Apr 2025 — The accepted papers cover the state of the art in information retrieval ... WWW 2024 , Singapore, Singapore, 13–17 May 2024, pp. 1268–. 1271 ..."}
+{"idx": 6, "title": "The 1𝑠𝑡 NIP@IR Workshop on New Interaction Paradigms ...", "date": "", "ddg_snippet": "by Y Zhou · 2025 — in information retrieval research. By spotlighting new interaction ... reviewers, and accepted papers will be selected based on originality,.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3726302.3730364", "content": "by Y Zhou · 2025 — in information retrieval research. By spotlighting new interaction ... reviewers, and accepted papers will be selected based on originality,."}
+{"idx": 7, "title": "Jiabin Tang' Homepage - Homepage", "date": "", "ddg_snippet": "of Special Interest Group on Information Retrieval (SIGIR), 2024. (Top-2 Most Cited Paper: 2 / 159 Accepted Papers ) ... of The Web Conference ( WWW), 2024 .", "subpage_snippet": "", "source": "tjb-tech.github.io", "link": "https://tjb-tech.github.io/", "content": "of Special Interest Group on Information Retrieval (SIGIR), 2024. (Top-2 Most Cited Paper: 2 / 159 Accepted Papers ) ... of The Web Conference ( WWW), 2024 ."}
+{"idx": 8, "title": "The Second Workshop on Trustworthy Learning on Graphs", "date": "", "ddg_snippet": "by J He · 2024 — and data mining, thus it is a good fit for WWW 2024 . Meanwhile ... across AI, data mining, and information retrieval (e.g., ICML, AAAI,.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3589335.3641305", "content": "by J He · 2024 — and data mining, thus it is a good fit for WWW 2024 . Meanwhile ... across AI, data mining, and information retrieval (e.g., ICML, AAAI,."}
+{"idx": 9, "title": "2504.14891v1 | PDF | Accuracy And Precision", "date": "", "ddg_snippet": "ternal information retrieval ... the collection of the papers since 2022 autumn with keywords porary RAG evaluation. about RAG in the accepted papers ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/902420829/2504-14891v1", "content": "ternal information retrieval ... the collection of the papers since 2022 autumn with keywords porary RAG evaluation. about RAG in the accepted papers ..."}
diff --git a/data/sampled_jsons/WWW_2024_blockchain_reinforcement_learning_accepted_papers.jsonl b/data/sampled_jsons/WWW_2024_blockchain_reinforcement_learning_accepted_papers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5167b06daa263b78cd5c7065c5fbdb20c20c74d9
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_blockchain_reinforcement_learning_accepted_papers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Proceedings of the ACM Web Conference 2024", "date": "", "ddg_snippet": "13 May 2024 — SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement · Pengze Li,; Mingxuan Song ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3589334?tocHeading=heading9", "content": "13 May 2024 — SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement · Pengze Li,; Mingxuan Song ..."}
+{"idx": 1, "title": "Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "by P Li · Cited by 21 — SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement. WWW, 2024 , Singapore. 2.3 State Redistribution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=8oczaP1YKD", "content": "by P Li · Cited by 21 — SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement. WWW, 2024 , Singapore. 2.3 State Redistribution."}
+{"idx": 2, "title": "Computer Science Apr 2024", "date": "", "ddg_snippet": "Title: SIR-RL: Reinforcement Learning for Optimized Policy Control during ... Comments: 4 pages, accepted by WWW 2024 Short Track. Subjects: Information ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2024-04?skip=3275&show=2000", "content": "Title: SIR-RL: Reinforcement Learning for Optimized Policy Control during ... Comments: 4 pages, accepted by WWW 2024 Short Track. Subjects: Information ..."}
+{"idx": 3, "title": "FLock: Robust and Privacy-Preserving Federated Learning ...", "date": "", "ddg_snippet": "22 Apr 2025 — Numerous works have explored security and privacy protection in FL, as well as its integration with blockchain technology. ... In WWW 2024 ( ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714666", "content": "22 Apr 2025 — Numerous works have explored security and privacy protection in FL, as well as its integration with blockchain technology. ... In WWW 2024 ( ..."}
+{"idx": 4, "title": "SPRING: Improving the Throughput of Sharding Blockchain via ...", "date": "", "ddg_snippet": "SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement ... WWW 2024 EveryoneRevisionsBibTeXCC BY-SA 4.0.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=VHnOB7oDng", "content": "SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement ... WWW 2024 EveryoneRevisionsBibTeXCC BY-SA 4.0."}
+{"idx": 5, "title": "List of Accepted Papers - IGARSS 2024", "date": "", "ddg_snippet": "Blockchain -based Fine-grained Access Control for Space Resource Sharing and Management ... Differentiable SAR Renderer Embedded Reinforcement Learning for ...", "subpage_snippet": "", "source": "www.2024.ieeeigarss.org", "link": "https://www.2024.ieeeigarss.org/papers/accepted_papers.php", "content": "Blockchain -based Fine-grained Access Control for Space Resource Sharing and Management ... Differentiable SAR Renderer Embedded Reinforcement Learning for ..."}
+{"idx": 6, "title": "Cryptography and Security Feb 2024", "date": "", "ddg_snippet": "Title: Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques ... Comments: Accepted by WWW 2024 as a Short Research ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CR/2024-02?skip=100&show=500", "content": "Title: Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques ... Comments: Accepted by WWW 2024 as a Short Research ..."}
+{"idx": 7, "title": "Federated Learning Resources | Awesome-FL - GitHub Pages", "date": "", "ddg_snippet": "Federated Reinforcement Learning with Environment Heterogeneity. PKU ... 2024/05/20 - add WWW 2024 papers ; 2024/05/12 - add AISTATS, EuroSys 2024 ...", "subpage_snippet": "", "source": "youngfish42.github.io", "link": "https://youngfish42.github.io/Awesome-FL/", "content": "Federated Reinforcement Learning with Environment Heterogeneity. PKU ... 2024/05/20 - add WWW 2024 papers ; 2024/05/12 - add AISTATS, EuroSys 2024 ..."}
+{"idx": 8, "title": "Gang Huang (0000-0001-8393-6469)", "date": "", "ddg_snippet": "Advancing Web 3.0: Making Smart Contracts Smarter on Blockchain . WWW 2024 - Proceedings of the ACM Web Conference ... Reinforcement Learning for Multiple Electric ...", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0000-0001-8393-6469", "content": "Advancing Web 3.0: Making Smart Contracts Smarter on Blockchain . WWW 2024 - Proceedings of the ACM Web Conference ... Reinforcement Learning for Multiple Electric ..."}
+{"idx": 9, "title": "Publications | Research Departments", "date": "", "ddg_snippet": "August 6, 2020 / Blockchain 2020 - The 3rd IEEE International Conference on Blockchain , Rhodes Island, Greece ... reinforcement learning (DRL) which are ...", "subpage_snippet": "", "source": "www.nec-labs.com", "link": "https://www.nec-labs.com/publications/", "content": "August 6, 2020 / Blockchain 2020 - The 3rd IEEE International Conference on Blockchain , Rhodes Island, Greece ... reinforcement learning (DRL) which are ..."}
diff --git a/data/sampled_jsons/WWW_2024_paper_classification_top-tier_ranking_system_categories_year_2024.jsonl b/data/sampled_jsons/WWW_2024_paper_classification_top-tier_ranking_system_categories_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5a811ebe1c8fa55305097e471b47fd529e24a0d7
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_paper_classification_top-tier_ranking_system_categories_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "College and university rankings", "date": "", "ddg_snippet": "Aggregate Ranking of Top Universities In 2024, ARTU ranked 471 universities and featured the Top 400 for publication, with MIT securing first place, followed ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/College_and_university_rankings", "content": "Aggregate Ranking of Top Universities In 2024, ARTU ranked 471 universities and featured the Top 400 for publication, with MIT securing first place, followed ..."}
+{"idx": 1, "title": "SJR : Scientific Journal Rankings", "date": "", "ddg_snippet": "International Scientific Journal & Country RankingAgricultural and Biological Sciences (miscellaneous) Biochemistry, Genetics and Molecular Biology (miscellaneous) Business, Management and Accounting (miscellaneous) Economics, Econometrics and Finance (miscellaneous) Organizational Behavior and Human Resource Management Pharmacology, Toxicology and Pharmaceutics (miscellaneous) Physical ...", "subpage_snippet": "", "source": "www.scimagojr.com", "link": "https://www.scimagojr.com/journalrank.php", "content": "International Scientific Journal & Country RankingAgricultural and Biological Sciences (miscellaneous) Biochemistry, Genetics and Molecular Biology (miscellaneous) Business, Management and Accounting (miscellaneous) Economics, Econometrics and Finance (miscellaneous) Organizational Behavior and Human Resource Management Pharmacology, Toxicology and Pharmaceutics (miscellaneous) Physical ..."}
+{"idx": 2, "title": "ABS Journal Ranking 2024 - Journal Ranking Portal ABDC Journal Quality List - Australian Business Deans Council ShanghaiRanking'sGlobalRankingofAcademicSubjects(2024 ... List of Q1 journals Clarivate Reveals World's Leading and Trusted Journals with ... ABDC Journal Quality List - Australian Business Deans Council Clarivate Reveals World's Leading and Trusted Journals with the 202… Clarivate Reveals World's Leading and Trusted Journals with the 202… Academic Journal Guide 2024 available now | Chartered ...", "date": "", "ddg_snippet": "ABS Journal Ranking 2024 Journalranking.org is a platform dedicated to share useful information and advice for academics looking to publish in top journals. We have launched the page with a user-friendly, easily searchable copy of the ABS Journal Ranking 2024 list to help established or early-career researchers worldwide. See full list on journalranking.org Journal search tool Use the search tool to see which journals are currently ranked in the ABDC Journal Quality List. The search tool is current to the most recent update. No ranking changes have been made since 2022. Downloads ABDC Journal Quality List (xls file, includes 2022, 2019, 2016, 2013… ShanghaiRanking'sGlobalRankingofAcademicSubjects( 2024 ) TopJournallist ShanghaiRanking's Global Ranking of Academic Subjects ( 2024 ) Top Journal list Each subject category of journals is divided into four quartiles: Q1, Q2, Q3, Q4. Q1 is occupied by the top 25% of journals. Jun 20, 2024 · Adds Emerging Sources Citation Index™ for a simplified and unified category view LONDON, June 20, 2024 /PRNewswire/ -- Clarivate Plc (NYSE:CLVT), a leading global provider of transformative intelligence, today released the 2024 update to the Journal Citation Reports™ (JCR™). The reports provide an essential and comprehensive resource of high-quality journals, ranked by field to enable ... Are there any ranking changes in the ABDC journal quality list? No ranking changes have been made since 2022. ABDC Journal Quality List (xls file, includes 2022, 2019, 2016, 2013 and 2010 lists: see corresponding sheets). NB: no ranking changes have been made since 2022. Stakeholder submissions for the 2025 Journal Quality List review have closed. What changes have been made to Journal Rankings? Changes to journal rankings include the addition of the Emerging Sources Citation Index . Only journals that have met the rigorous quality standards for inclusion in the Web of Science Core Collection™ are featured within the Journal Citation Reports, to ensure that users can confidently rely on the information and descriptive data provided. What's new in the 2024 JCR? Key highlights for the 2024 release: The JCR has been enhanced to provide an easier and more complete user experience. It includes the integration of journals from the Emerging Sources Citation Index (ESCI) in the new unified category rankings. Oct 30, 2024 · The Chartered ABS is delighted to publish the 2024 edition of the Academic Journal Guide (AJG 2024 ) – your guide to the wide range of journals across the academic fields of business and management studies. Alongside the 2024 journal ratings, this edition includes a number of innovations.", "subpage_snippet": "", "source": "journalranking.org", "link": "https://journalranking.org/", "content": "ABS Journal Ranking 2024 Journalranking.org is a platform dedicated to share useful information and advice for academics looking to publish in top journals. We have launched the page with a user-friendly, easily searchable copy of the ABS Journal Ranking 2024 list to help established or early-career researchers worldwide. See full list on journalranking.org Journal search tool Use the search tool to see which journals are currently ranked in the ABDC Journal Quality List. The search tool is current to the most recent update. No ranking changes have been made since 2022. Downloads ABDC Journal Quality List (xls file, includes 2022, 2019, 2016, 2013… ShanghaiRanking'sGlobalRankingofAcademicSubjects( 2024 ) TopJournallist ShanghaiRanking's Global Ranking of Academic Subjects ( 2024 ) Top Journal list Each subject category of journals is divided into four quartiles: Q1, Q2, Q3, Q4. Q1 is occupied by the top 25% of journals. Jun 20, 2024 · Adds Emerging Sources Citation Index™ for a simplified and unified category view LONDON, June 20, 2024 /PRNewswire/ -- Clarivate Plc (NYSE:CLVT), a leading global provider of transformative intelligence, today released the 2024 update to the Journal Citation Reports™ (JCR™). The reports provide an essential and comprehensive resource of high-quality journals, ranked by field to enable ... Are there any ranking changes in the ABDC journal quality list? No ranking changes have been made since 2022. ABDC Journal Quality List (xls file, includes 2022, 2019, 2016, 2013 and 2010 lists: see corresponding sheets). NB: no ranking changes have been made since 2022. Stakeholder submissions for the 2025 Journal Quality List review have closed. What changes have been made to Journal Rankings? Changes to journal rankings include the addition of the Emerging Sources Citation Index . Only journals that have met the rigorous quality standards for inclusion in the Web of Science Core Collection™ are featured within the Journal Citation Reports, to ensure that users can confidently rely on the information and descriptive data provided. What's new in the 2024 JCR? Key highlights for the 2024 release: The JCR has been enhanced to provide an easier and more complete user experience. It includes the integration of journals from the Emerging Sources Citation Index (ESCI) in the new unified category rankings. Oct 30, 2024 · The Chartered ABS is delighted to publish the 2024 edition of the Academic Journal Guide (AJG 2024 ) – your guide to the wide range of journals across the academic fields of business and management studies. Alongside the 2024 journal ratings, this edition includes a number of innovations."}
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+{"idx": 4, "title": "Academic Journal Guide 2024 available now | Chartered ...", "date": "", "ddg_snippet": "Oct 30, 2024 · The Chartered ABS is delighted to publish the 2024 edition of the Academic Journal Guide (AJG 2024 ) – your guide to the wide range of journals across the academic fields of business and management studies. Alongside the 2024 journal ratings, this edition includes a number of innovations.", "subpage_snippet": "", "source": "charteredabs.org", "link": "https://charteredabs.org/insights/news/academic-journal-guide-2024-available-now", "content": "Oct 30, 2024 · The Chartered ABS is delighted to publish the 2024 edition of the Academic Journal Guide (AJG 2024 ) – your guide to the wide range of journals across the academic fields of business and management studies. Alongside the 2024 journal ratings, this edition includes a number of innovations."}
+{"idx": 5, "title": "College Ranking Systems: A Methodological Review", "date": "", "ddg_snippet": "by S Barari · 2024 — For example, the Forbes 2024 Top . Colleges list has one category score for 'return on investment' and one category score for 'academic success' and the latter ... 62 pages", "subpage_snippet": "", "source": "www.norc.org", "link": "https://www.norc.org/content/dam/norc-org/pdf2024/college-rankings-review.pdf", "content": "by S Barari · 2024 — For example, the Forbes 2024 Top . Colleges list has one category score for 'return on investment' and one category score for 'academic success' and the latter ... 62 pages"}
+{"idx": 6, "title": "Carnegie Classification of Institutions of Higher Education®", "date": "", "ddg_snippet": "The Carnegie Classification of Institutions of Higher Education is the nation's leading framework for categorizing diverse US higher education institutions.", "subpage_snippet": "", "source": "carnegieclassifications.acenet.edu", "link": "https://carnegieclassifications.acenet.edu/", "content": "The Carnegie Classification of Institutions of Higher Education is the nation's leading framework for categorizing diverse US higher education institutions."}
+{"idx": 7, "title": "QS World University Rankings", "date": "", "ddg_snippet": "Explore global leading institutions by region, subject & location ranked as per 6 ranking indicators by QS World University Rankings.", "subpage_snippet": "", "source": "www.topuniversities.com", "link": "https://www.topuniversities.com/university-rankings", "content": "Explore global leading institutions by region, subject & location ranked as per 6 ranking indicators by QS World University Rankings."}
+{"idx": 8, "title": "How U.S. News Calculated the 2025 Best Colleges Rankings", "date": "", "ddg_snippet": "23 Sept 2024 — Each ranking has ranking factors – outlined below – for which each eligible school was scored on its underlying data. These scores were ...", "subpage_snippet": "", "source": "www.usnews.com", "link": "https://www.usnews.com/education/best-colleges/articles/how-us-news-calculated-the-rankings", "content": "23 Sept 2024 — Each ranking has ranking factors – outlined below – for which each eligible school was scored on its underlying data. These scores were ..."}
+{"idx": 9, "title": "Journal Citation Reports: Quartile rankings and other metrics", "date": "", "ddg_snippet": "In Journal Citation Reports, we provide quartile rankings based on rank for the Journal Impact Factor. In Journal Citation Reports, quartiles are defined as ...", "subpage_snippet": "", "source": "support.clarivate.com", "link": "https://support.clarivate.com/ScientificandAcademicResearch/s/article/Journal-Citation-Reports-Quartile-rankings-and-other-metrics", "content": "In Journal Citation Reports, we provide quartile rankings based on rank for the Journal Impact Factor. In Journal Citation Reports, quartiles are defined as ..."}
diff --git a/data/sampled_jsons/WWW_2024_proceedings_announcement_year_2024.jsonl b/data/sampled_jsons/WWW_2024_proceedings_announcement_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c6ea22dd3cfa05cb1c7f24161c6aec9a2717a6e2
--- /dev/null
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+{"idx": 0, "title": "Proceedings - Scte 2024", "date": "", "ddg_snippet": "Announcement of SCTE 2024 proceeding in Solid State Sciences Special issue title: Chemistry and solid state physics of compounds and materials based on d and f electron elements", "subpage_snippet": "", "source": "www.scte2024.org", "link": "https://www.scte2024.org/proceedings/", "content": "Announcement of SCTE 2024 proceeding in Solid State Sciences Special issue title: Chemistry and solid state physics of compounds and materials based on d and f electron elements"}
+{"idx": 1, "title": "Proceedings | Announcements - MDPI", "date": "", "ddg_snippet": "Coming Together for Science 2024 MDPI Conference Summary Throughout 2024 , the MDPI Conference Team connected with scientific communities by organizing nine in-person events and 26 virtual conferences.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/journal/proceedings/announcements/10114", "content": "Coming Together for Science 2024 MDPI Conference Summary Throughout 2024 , the MDPI Conference Team connected with scientific communities by organizing nine in-person events and 26 virtual conferences."}
+{"idx": 2, "title": "Proceedings of the 61st ACM/IEEE Design Automation Conference | ACM ...", "date": "", "ddg_snippet": "Publisher: Association for Computing Machinery New York NY United States Conference: DAC '24: 61st ACM/IEEE Design Automation Conference San Francisco CA USA June 23 - 27, 2024 ISBN: 979-8-4007-0601-1 Published:", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3649329", "content": "Publisher: Association for Computing Machinery New York NY United States Conference: DAC '24: 61st ACM/IEEE Design Automation Conference San Francisco CA USA June 23 - 27, 2024 ISBN: 979-8-4007-0601-1 Published:"}
+{"idx": 3, "title": "IMPACT 2024 Proceedings", "date": "", "ddg_snippet": "Explore the proceedings and key highlights of IMPACT 2024 , showcasing innovative ideas and advancements in various fields.", "subpage_snippet": "", "source": "ingway.s3.amazonaws.com", "link": "https://ingway.s3.amazonaws.com/impact/2024/mobile/index.html", "content": "Explore the proceedings and key highlights of IMPACT 2024 , showcasing innovative ideas and advancements in various fields."}
+{"idx": 4, "title": "2024 AOM Annual Meeting Proceedings including Best Papers", "date": "", "ddg_snippet": "2024 AOM Annual Meeting Proceedings including Best Papers", "subpage_snippet": "", "source": "my.aom.org", "link": "https://my.aom.org/program2024/proceedings.aspx", "content": "2024 AOM Annual Meeting Proceedings including Best Papers"}
+{"idx": 5, "title": "NIME 2024 Paper Proceedings - Announcements - NIME Forum", "date": "", "ddg_snippet": "Dear All, NIME 2024 Paper Proceedings have been published here. Do not hesitate to open an issue on the NIME-bibliography GitHub repository anytime you encounter errors in the archived proceedings (including previous y…", "subpage_snippet": "", "source": "forum.nime.org", "link": "https://forum.nime.org/t/nime-2024-paper-proceedings/574", "content": "Dear All, NIME 2024 Paper Proceedings have been published here. Do not hesitate to open an issue on the NIME-bibliography GitHub repository anytime you encounter errors in the archived proceedings (including previous y…"}
+{"idx": 6, "title": "Proceedings Preview | ISLS Annual Meeting 2024", "date": "", "ddg_snippet": "The following is a preview of the ISLS 2024 Proceedings , the ICLS 2024 Proceedings , and the CSCL 2024 Proceedings . The final versions will be uploaded shortly.", "subpage_snippet": "", "source": "2024.isls.org", "link": "https://2024.isls.org/2024/06/proceedings-preview/", "content": "The following is a preview of the ISLS 2024 Proceedings , the ICLS 2024 Proceedings , and the CSCL 2024 Proceedings . The final versions will be uploaded shortly."}
+{"idx": 7, "title": "Proceedings | 2024 - Browse Conferences - MDPI", "date": "", "ddg_snippet": "Proceedings , an international, peer-reviewed Open Access journal.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2504-3900/year/2024", "content": "Proceedings , an international, peer-reviewed Open Access journal."}
+{"idx": 8, "title": "Proceedings of the 2024 USENIX Conference on Usenix Annual Technical ...", "date": "", "ddg_snippet": "Index Terms Proceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference Applied computing General and reference Document types", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.5555/3691992", "content": "Index Terms Proceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference Applied computing General and reference Document types"}
+{"idx": 9, "title": "Program: Oral and Poster Abstracts (66th ASH Annual Meeting)", "date": "", "ddg_snippet": "619. Acute Myeloid Leukemias: Disease Burden and Minimal Residual Disease in Prognosis and Treatment: Measurable Residual Disease in AML in 2024 and Beyond 621. Lymphomas: Translational - Molecular and Genetic: Molecular Profiling and Targets in Aggressive Lymphomas 623.", "subpage_snippet": "", "source": "ash.confex.com", "link": "https://ash.confex.com/ash/2024/webprogram/COP.html", "content": "619. Acute Myeloid Leukemias: Disease Burden and Minimal Residual Disease in Prognosis and Treatment: Measurable Residual Disease in AML in 2024 and Beyond 621. Lymphomas: Translational - Molecular and Genetic: Molecular Profiling and Targets in Aggressive Lymphomas 623."}
diff --git a/data/sampled_jsons/WWW_2024_top_tier_papers_blockchain_reinforcement_learning.jsonl b/data/sampled_jsons/WWW_2024_top_tier_papers_blockchain_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90a0980ebf7247fbfaf9792cd3d066a9d2195e2d
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+{"idx": 0, "title": "Reinforcement learning with smart contracts on blockchains", "date": "", "ddg_snippet": "by TT Davarakis · 2023 · Cited by 13 — In this work we propose a combination of Machine Learning and in particular Reinforcement Learning (RL) and Imitation Learning (IL) with Blockchain .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0167739X23002406", "content": "by TT Davarakis · 2023 · Cited by 13 — In this work we propose a combination of Machine Learning and in particular Reinforcement Learning (RL) and Imitation Learning (IL) with Blockchain ."}
+{"idx": 1, "title": "Sustainable broadcasting in Blockchain Network with ...", "date": "", "ddg_snippet": "22 Jul 2024 — In this paper , we follow the second avenue and propose an efficient approach based on reinforcement learning that improves the block ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.15616v1", "content": "22 Jul 2024 — In this paper , we follow the second avenue and propose an efficient approach based on reinforcement learning that improves the block ..."}
+{"idx": 2, "title": "Recent Reinforcement Learning and Blockchain Based ...", "date": "", "ddg_snippet": "In this paper , we provide a summary of research efforts made in the past few years, from 2018 to 2021, addressing security issues using RL and blockchain ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/s11277-023-10664-1", "content": "In this paper , we provide a summary of research efforts made in the past few years, from 2018 to 2021, addressing security issues using RL and blockchain ..."}
+{"idx": 3, "title": "Prism blockchain enabled Internet of Things with deep ...", "date": "", "ddg_snippet": "by DS Gadiraju · 2024 · Cited by 3 — This paper presents a Deep Reinforcement Learning (DRL) based Internet of Things (IoT)-enabled Prism blockchain . The recent advancements in the field of IoT ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2096720924000186", "content": "by DS Gadiraju · 2024 · Cited by 3 — This paper presents a Deep Reinforcement Learning (DRL) based Internet of Things (IoT)-enabled Prism blockchain . The recent advancements in the field of IoT ..."}
+{"idx": 4, "title": "Meta-Learning Reinforcement Learning for Crypto-Return ...", "date": "", "ddg_snippet": "11 Sept 2025 — In this paper , we present Meta-RL- Crypto , a unified transformer-based architecture that unifies meta-learning and reinforcement learning (RL) to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09751v1", "content": "11 Sept 2025 — In this paper , we present Meta-RL- Crypto , a unified transformer-based architecture that unifies meta-learning and reinforcement learning (RL) to ..."}
+{"idx": 5, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers , paper visualization, competitions, datasets & benchmarks.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers , paper visualization, competitions, datasets & benchmarks."}
+{"idx": 6, "title": "Data privacy model using blockchain reinforcement federated ...", "date": "", "ddg_snippet": "by C Dhasaratha · 2024 · Cited by 40 — This paper explores the strategy for combining these three technologies that can revolutionise ML in Internet of Medical Things (IoMT). 1.2 ...", "subpage_snippet": "", "source": "ietresearch.onlinelibrary.wiley.com", "link": "https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.12287", "content": "by C Dhasaratha · 2024 · Cited by 40 — This paper explores the strategy for combining these three technologies that can revolutionise ML in Internet of Medical Things (IoMT). 1.2 ..."}
+{"idx": 7, "title": "Trends in quantum reinforcement learning: State‐of‐the‐ ...", "date": "", "ddg_snippet": "by S Park · 2024 · Cited by 10 — This paper presents the basic quantum reinforcement learning theory and its applications to various engineering problems.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.4218/etrij.2024-0153", "content": "by S Park · 2024 · Cited by 10 — This paper presents the basic quantum reinforcement learning theory and its applications to various engineering problems."}
+{"idx": 8, "title": "Machine Learning for Blockchain Data Analysis: Progress and ...", "date": "", "ddg_snippet": "by P Azad · Cited by 14 — We examine the state-of-the-art solutions, applications, and future directions associated with leveraging machine learning for blockchain data analysis.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3728474", "content": "by P Azad · Cited by 14 — We examine the state-of-the-art solutions, applications, and future directions associated with leveraging machine learning for blockchain data analysis."}
+{"idx": 9, "title": "Design of an improved model using federated learning and ...", "date": "", "ddg_snippet": "by R Vijay Anand · 2025 · Cited by 3 — This paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-83564-4", "content": "by R Vijay Anand · 2025 · Cited by 3 — This paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency"}
diff --git a/data/sampled_jsons/Wallach_Evaluating_Generative_AI_a_lot_of_work_computer_science_history_justification.jsonl b/data/sampled_jsons/Wallach_Evaluating_Generative_AI_a_lot_of_work_computer_science_history_justification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..96cab117d8a85ceb447d1109e6abeae233f365ce
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+++ b/data/sampled_jsons/Wallach_Evaluating_Generative_AI_a_lot_of_work_computer_science_history_justification.jsonl
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+{"idx": 0, "title": "5 Steps to AI Transformation - AI and Automation Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "The AI and workflow automation moment is now. Leaders are harnessing their true potential. Download the whitepaper now and create your AI -powered, autonomous enterprise in 2025.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=pega.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=dFd2gwUT30MF9FjW40C7XUM48H6H-T90y7y3P3eVvzx8Va0ed_44QLIIDXtc4YJ8uWmZ4ByWOimqw3GOnk9J0LY5rY6vMspfqeGnEjJNNVwZJ-0EfHQe0qxSq0G6nxlr.bsxulUyirvzZobTMrE2itw&rut=fde090714e453276d19463b8fe01cb94999e43329620db8fe29d54629847d825&u3=https://www.bing.com/aclick?ld=e8ffy-lowYL4LrXor8j1xh4jVUCUycB9qbUgC9IgvD9DahDkYqg2Pz6B3vxkDffx4acFPTjtBLVZ6Dig8jXE9JnWWcxh1_3GoJ6FkhhpEyWouCS5Q0Z6o_f404tD6LKB-boASP7TXsNeoY7jDsV2nL8tIgKbzc4_Ldw8DTNxwuWNNc0swBDNI5QSlPFAk99HFJhcLQZw&u=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&rlid=aab9e0f1ef8d11de3e6f8821e5c792c6&vqd=4-142932888527713685838953925772656452099&iurl={1}IG=A4BD134C7D18418AA3F78261A03FA0DE&CID=2E813624A18C6AF527DE204AA0716B75&ID=DevEx,5039.1", "content": "The AI and workflow automation moment is now. Leaders are harnessing their true potential. Download the whitepaper now and create your AI -powered, autonomous enterprise in 2025."}
+{"idx": 1, "title": "2025 Accepted Papers - ACM Symposium on Computer Science &", "date": "", "ddg_snippet": "... historical and doctrinal response of copyright law to new technologies, this Article offers a new analytical framework for determining liability for ...", "subpage_snippet": "", "source": "computersciencelaw.org", "link": "https://computersciencelaw.org/2025-accepted-papers/", "content": "... historical and doctrinal response of copyright law to new technologies, this Article offers a new analytical framework for determining liability for ..."}
+{"idx": 2, "title": "Toward Holistic Evaluation of Recommender Systems Powered by", "date": "", "ddg_snippet": "First, we categorize the evaluation challenges of Gen-RecSys into two groups: (i) existing concerns that are exacerbated by generative outputs (e.g ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.06667v2", "content": "First, we categorize the evaluation challenges of Gen-RecSys into two groups: (i) existing concerns that are exacerbated by generative outputs (e.g ..."}
+{"idx": 3, "title": "$11M AI safety research program launched - Future of Life", "date": "", "ddg_snippet": "... applicants worldwide, will research a host of questions in computer science , law, policy, economics, and other fields relevant to coming advances in ...", "subpage_snippet": "", "source": "futureoflife.org", "link": "https://futureoflife.org/ai/11m-ai-safety-research-program-launched/", "content": "... applicants worldwide, will research a host of questions in computer science , law, policy, economics, and other fields relevant to coming advances in ..."}
+{"idx": 4, "title": "2015 AI Safety Grant Program - Future of Life Institute", "date": "", "ddg_snippet": "We are also collecting case studies of abrupt technological progress to aid in evaluating the probability of discontinuities in AI progress.", "subpage_snippet": "", "source": "www.futureof.biz", "link": "http://www.futureof.biz/index-172.html", "content": "We are also collecting case studies of abrupt technological progress to aid in evaluating the probability of discontinuities in AI progress."}
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+{"idx": 6, "title": "The Future of Moral Machines « On the Human", "date": "", "ddg_snippet": "Many Singularitarians assume a lot , not the least of which is that intelligence is fundamentally a computational process.", "subpage_snippet": "", "source": "nationalhumanitiescenter.org", "link": "https://nationalhumanitiescenter.org/on-the-human/2011/12/the-future-of-moral-machines/", "content": "Many Singularitarians assume a lot , not the least of which is that intelligence is fundamentally a computational process."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 8, "title": "Philosophy of Technology (Stanford Encyclopedia of Philosophy)", "date": "", "ddg_snippet": "The entry starts with a brief historical overview, then continues with a presentation of the themes on which modern analytic philosophy of technology ...", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/technology/", "content": "The entry starts with a brief historical overview, then continues with a presentation of the themes on which modern analytic philosophy of technology ..."}
+{"idx": 9, "title": "Ethics of Artificial Intelligence and Robotics (Stanford", "date": "", "ddg_snippet": "The notion of “ artificial intelligence ” ( AI ) is understood broadly as any kind of artificial computational system that shows ...", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/ethics-ai/", "content": "The notion of “ artificial intelligence ” ( AI ) is understood broadly as any kind of artificial computational system that shows ..."}
diff --git a/data/sampled_jsons/Wallach_Evaluating_Generative_AI_framework_criticism_defense_computer_science_history_development_year_2024.jsonl b/data/sampled_jsons/Wallach_Evaluating_Generative_AI_framework_criticism_defense_computer_science_history_development_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..208536ae38b6757bf97f4fba1a21b57bfc3b18cb
--- /dev/null
+++ b/data/sampled_jsons/Wallach_Evaluating_Generative_AI_framework_criticism_defense_computer_science_history_development_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Toward an Evaluation Science for Generative AI Systems", "date": "", "ddg_snippet": "In this piece, we advocate for maturing an evaluation science for generative AI systems. While generative AI creates unique challenges for system safety engineering and measurement science , the field can draw valuable insights from the development of safety evaluation practices in other fields, including transportation, aerospace, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.05336", "content": "In this piece, we advocate for maturing an evaluation science for generative AI systems. While generative AI creates unique challenges for system safety engineering and measurement science , the field can draw valuable insights from the development of safety evaluation practices in other fields, including transportation, aerospace, and ..."}
+{"idx": 1, "title": "Position: Evaluating Generative AI Syste...", "date": "", "ddg_snippet": "The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems. We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework , grounded in measurement theory from the social sciences , extends the work of ...", "subpage_snippet": "", "source": "axi.lims.ac.uk", "link": "https://axi.lims.ac.uk/paper/2502.00561", "content": "The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems. We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework , grounded in measurement theory from the social sciences , extends the work of ..."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "It is argued that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for evaluating GenAI systems. The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as\"a tangle of sloppy tests [and] apples-to-oranges comparisons\"(Roose, 2024 ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Position:-Evaluating-Generative-AI-Systems-is-a-Wallach-Desai/7018e806ce1efe53b8ae923c48be411286deb092", "content": "It is argued that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for evaluating GenAI systems. The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as\"a tangle of sloppy tests [and] apples-to-oranges comparisons\"(Roose, 2024 ..."}
+{"idx": 3, "title": "NeurIPS Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Oral in Workshop: Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI Evaluating Generative AI Systems is a Social Science Measurement Challenge Hanna Wallach · Meera Desai · Nicholas Pangakis · A. Feder Cooper · Angelina Wang · Solon Barocas · Alexandra Chouldechova · Chad Atalla · Su Lin Blodgett · Emily Corvi · Alex Dow · Jean Garcia ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/104194", "content": "Oral in Workshop: Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI Evaluating Generative AI Systems is a Social Science Measurement Challenge Hanna Wallach · Meera Desai · Nicholas Pangakis · A. Feder Cooper · Angelina Wang · Solon Barocas · Alexandra Chouldechova · Chad Atalla · Su Lin Blodgett · Emily Corvi · Alex Dow · Jean Garcia ..."}
+{"idx": 4, "title": "PDF Evaluating Generative AI Systems is a Social Science Measurement Challenge", "date": "", "ddg_snippet": "1 Measurement and its Role in Evaluating GenAI Systems Evaluating an ML system means making evaluative judgements about that system's capabilities, impacts, opportunities, and risks in order to facilitate decisions like whether it should be used for a particular purpose, whether it should be deployed in a particular context, or even whether it should be redesigned. However, we cannot make ...", "subpage_snippet": "", "source": "evaleval.github.io", "link": "https://evaleval.github.io/2024workshop/accepted_papers/EvalEval_24_Wallach.pdf", "content": "1 Measurement and its Role in Evaluating GenAI Systems Evaluating an ML system means making evaluative judgements about that system's capabilities, impacts, opportunities, and risks in order to facilitate decisions like whether it should be used for a particular purpose, whether it should be deployed in a particular context, or even whether it should be redesigned. However, we cannot make ..."}
+{"idx": 5, "title": "PDF Dimensions of Generative AI Evaluation Design", "date": "", "ddg_snippet": "Evaluating the capabilities and risks of generative AI (GenAI) models and systems is crucial for their successful development , deployment, and adoption. Despite this, many would likely agree with New York Times columnist Kevin Roose's recent characterization of the current state of GenAI evaluation as \"a mess—a tangle of sloppy tests, apples-to-oranges comparisons and self-serving hype ...", "subpage_snippet": "", "source": "evalevalai.com", "link": "https://evalevalai.com/2024workshop/accepted_papers/EvalEval_24_Dow.pdf", "content": "Evaluating the capabilities and risks of generative AI (GenAI) models and systems is crucial for their successful development , deployment, and adoption. Despite this, many would likely agree with New York Times columnist Kevin Roose's recent characterization of the current state of GenAI evaluation as \"a mess—a tangle of sloppy tests, apples-to-oranges comparisons and self-serving hype ..."}
+{"idx": 6, "title": "Toward an Evaluation Science for Generative AI Systems - ADS", "date": "", "ddg_snippet": "In this piece, we advocate for maturing an evaluation science for generative AI systems. While generative AI creates unique challenges for system safety engineering and measurement science , the field can draw valuable insights from the development of safety evaluation practices in other fields, including transportation, aerospace, and ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2025arXiv250305336W/abstract", "content": "In this piece, we advocate for maturing an evaluation science for generative AI systems. While generative AI creates unique challenges for system safety engineering and measurement science , the field can draw valuable insights from the development of safety evaluation practices in other fields, including transportation, aerospace, and ..."}
+{"idx": 7, "title": "A Shared Standard for Valid Measurement of Generative AI Systems ...", "date": "", "ddg_snippet": "The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems. We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework , grounded in measurement theory from the social […]", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/a-shared-standard-for-valid-measurement-of-generative-ai-systems-capabilities-risks-and-impacts/", "content": "The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems. We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework , grounded in measurement theory from the social […]"}
+{"idx": 8, "title": "Generative artificial intelligence: a historical perspective", "date": "", "ddg_snippet": "ABSTRACT Generative artificial intelligence (GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes while providing insights that might not be possible with traditional tools. To stimulate future research, this work provides a brief summary of the ongoing and historical developments in GAI over the past 70 years. The achievements ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/nsr/article/12/5/nwaf050/8029900", "content": "ABSTRACT Generative artificial intelligence (GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes while providing insights that might not be possible with traditional tools. To stimulate future research, this work provides a brief summary of the ongoing and historical developments in GAI over the past 70 years. The achievements ..."}
+{"idx": 9, "title": "[2411.10939] Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences . With this in mind, we present a framework , grounded in measurement theory from the social ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.10939", "content": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences . With this in mind, we present a framework , grounded in measurement theory from the social ..."}
diff --git "a/data/sampled_jsons/Wampler_Andersen_Herbst_Lee_Popovi\304\207_Character_Animation_Two-Player_Adversarial_Games_abstract_2010.jsonl" "b/data/sampled_jsons/Wampler_Andersen_Herbst_Lee_Popovi\304\207_Character_Animation_Two-Player_Adversarial_Games_abstract_2010.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..0ece967f902d8343dc62dc75d483af2f13667943
--- /dev/null
+++ "b/data/sampled_jsons/Wampler_Andersen_Herbst_Lee_Popovi\304\207_Character_Animation_Two-Player_Adversarial_Games_abstract_2010.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Character animation in two-player adversarial games | ACM ...", "date": "", "ddg_snippet": "Jul 2, 2010 · The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game. ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/1805964.1805970", "content": "Jul 2, 2010 · The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game. ..."}
+{"idx": 1, "title": "Character animation in two-player adversarial games", "date": "", "ddg_snippet": "The approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ both long-term planning and an intelligent use of randomness, exhibiting nuanced strategies based on unpredictability. The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Character-animation-in-two-player-adversarial-games-Wampler-Andersen/aab9ee829c871af99ca5c91dbf594ad9e0674a79", "content": "The approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ both long-term planning and an intelligent use of randomness, exhibiting nuanced strategies based on unpredictability. The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games ..."}
+{"idx": 2, "title": "Zoran Popović", "date": "", "ddg_snippet": "Six games produced in the 10-week Games Capstone class make it big in the wild: two independent reviews ( 1 , 2 ), second best game of the week, and ...", "subpage_snippet": "", "source": "homes.cs.washington.edu", "link": "https://homes.cs.washington.edu/~zoran/", "content": "Six games produced in the 10-week Games Capstone class make it big in the wild: two independent reviews ( 1 , 2 ), second best game of the week, and ..."}
+{"idx": 3, "title": "Adversarial Character Animation | PDF | Basis (Linear Algebra ...", "date": "", "ddg_snippet": "Character Animation in Two-Player Adversarial Games KEVIN WAMPLER, ERIK ANDERSEN , EVAN HERBST , YONGJOON LEE , and ZORAN POPOVIC University of Washington The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/332112195/Adversarial-Character-Animation", "content": "Character Animation in Two-Player Adversarial Games KEVIN WAMPLER, ERIK ANDERSEN , EVAN HERBST , YONGJOON LEE , and ZORAN POPOVIC University of Washington The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such ..."}
+{"idx": 4, "title": "Sci-Hub | Character animation in two-player adversarial games ...", "date": "", "ddg_snippet": "Sci-Hub | Character animation in two-player adversarial games . ACM Transactions on Graphics, 29 (3), 1–13 | 10.1145/1805964.1805970 hubto open science ↓ save", "subpage_snippet": "", "source": "sci-hub.st", "link": "https://sci-hub.st/10.1145/1805964.1805970", "content": "Sci-Hub | Character animation in two-player adversarial games . ACM Transactions on Graphics, 29 (3), 1–13 | 10.1145/1805964.1805970 hubto open science ↓ save"}
+{"idx": 5, "title": "Character animation in two-player adversarial games - 百度学术", "date": "", "ddg_snippet": "The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game. Our approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ ...", "subpage_snippet": "", "source": "xueshu.baidu.com", "link": "https://xueshu.baidu.com/usercenter/paper/show?paperid=19acb930c84762c35ee65e6cef54bb96&site=xueshu_se", "content": "The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game. Our approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ ..."}
+{"idx": 6, "title": "Kevin Wampler", "date": "", "ddg_snippet": "I am a research engineer in the Creative Technologies Lab at Adobe. I primarily do software development on new computer graphics technologies, but I also occasionally do some computer graphics research, primarily in animation and geometry. Previously I was a Ph.D. student in computer graphics at the University of Washington under Zoran Popović .", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "http://www.kevinwampler.com/homepage/index.html", "content": "I am a research engineer in the Creative Technologies Lab at Adobe. I primarily do software development on new computer graphics technologies, but I also occasionally do some computer graphics research, primarily in animation and geometry. Previously I was a Ph.D. student in computer graphics at the University of Washington under Zoran Popović ."}
+{"idx": 7, "title": "dblp: Character animation in two - player adversarial games .", "date": "", "ddg_snippet": "Kevin Wampler , Erik Andersen , Evan Herbst , Yongjoon Lee , Zoran Popovic : Character animation in two - player adversarial games . ACM Trans.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/tog/WamplerAHLP10.html", "content": "Kevin Wampler , Erik Andersen , Evan Herbst , Yongjoon Lee , Zoran Popovic : Character animation in two - player adversarial games . ACM Trans."}
+{"idx": 8, "title": "CSC 2521: Physics-Based Character Animation calendar", "date": "", "ddg_snippet": "SIGGRAPH 2010 . [web] [Huixuan]. K. Wampler , E. Andersen , E. Herbst , Y. Lee , Z. Popović . Character Animation in Two - Player Adversarial Games .", "subpage_snippet": "", "source": "www.dgp.toronto.edu", "link": "https://www.dgp.toronto.edu/~hertzman/courses/csc2521/fall_2010/calendar.html", "content": "SIGGRAPH 2010 . [web] [Huixuan]. K. Wampler , E. Andersen , E. Herbst , Y. Lee , Z. Popović . Character Animation in Two - Player Adversarial Games ."}
+{"idx": 9, "title": "dblp: List of computer science publications by Evan Herbst", "date": "", "ddg_snippet": "2010 – 2019. FAQ.Kevin Wampler , Erik Andersen , Evan Herbst , Yongjoon Lee , Zoran Popovic : Character animation in two - player adversarial games . ACM Trans.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/03/1066.html", "content": "2010 – 2019. FAQ.Kevin Wampler , Erik Andersen , Evan Herbst , Yongjoon Lee , Zoran Popovic : Character animation in two - player adversarial games . ACM Trans."}
diff --git a/data/sampled_jsons/Wampler_et_al._2010.jsonl b/data/sampled_jsons/Wampler_et_al._2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c7e2273d2a4c5b110684d11752be20a41d408d37
--- /dev/null
+++ b/data/sampled_jsons/Wampler_et_al._2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Publications of Charles W. Wampler", "date": "", "ddg_snippet": "Wampler , et al .,“Revealing the mechanism behind sudden capacity loss in lithium metal batteries,” J. Electrochem. Soc., 170 100528. DOI:10.1149/1945-7111 ...", "subpage_snippet": "", "source": "www3.nd.edu", "link": "https://www3.nd.edu/~cwample1/publist.pdf", "content": "Wampler , et al .,“Revealing the mechanism behind sudden capacity loss in lithium metal batteries,” J. Electrochem. Soc., 170 100528. DOI:10.1149/1945-7111 ..."}
+{"idx": 1, "title": "Reply to Wampler et al.: Deforestation and biodiversity loss ...", "date": "", "ddg_snippet": "by SB Hedges · 2019 · Cited by 1 — PJ Wampler , A Tarter, R Bailis, K Sander, W Sun, Discussion of forest definitions and tree cover estimates for Haiti. Proc Natl Acad Sci USA 116, 5202–5203 ( ...", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/abs/10.1073/pnas.1901879116", "content": "by SB Hedges · 2019 · Cited by 1 — PJ Wampler , A Tarter, R Bailis, K Sander, W Sun, Discussion of forest definitions and tree cover estimates for Haiti. Proc Natl Acad Sci USA 116, 5202–5203 ( ..."}
+{"idx": 2, "title": "Evaluating the Effect of Participatory Democracy on Well- ...", "date": "", "ddg_snippet": "by C Boulding · 2010 · Cited by 319 — Participatory budgeting is no magic bullet, though (Boulding & Wampler , 2010 ). Simple adoption of this model of public budgeting does not translate into ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0305750X09000953", "content": "by C Boulding · 2010 · Cited by 319 — Participatory budgeting is no magic bullet, though (Boulding & Wampler , 2010 ). Simple adoption of this model of public budgeting does not translate into ..."}
+{"idx": 3, "title": "\"Should I Stay or Should I Go? Explaining Why Most Mexican ...", "date": "", "ddg_snippet": "by B Wampler · 2010 · Cited by 41 — This paper analyzes why some Mexican immigrants, especially undocumented residents, plan to remain permanently in the United States, ...", "subpage_snippet": "", "source": "scholarworks.boisestate.edu", "link": "https://scholarworks.boisestate.edu/polsci_facpubs/11/", "content": "by B Wampler · 2010 · Cited by 41 — This paper analyzes why some Mexican immigrants, especially undocumented residents, plan to remain permanently in the United States, ..."}
+{"idx": 4, "title": "\"Participatory Governance\" in", "date": "", "ddg_snippet": "by SL McNULTY · 2015 · Cited by 29 — Boulding, C., & Wampler , B. ( 2010 ). Voice, votes, and resources: Evaluating the effect of participatory democracy on well-being. World Development, 38(1) ... 14 pages", "subpage_snippet": "", "source": "emergingtrends.stanford.edu", "link": "https://emergingtrends.stanford.edu/files/original/29891fc7b357b1a93fec6791d4bb22e2793de70c.pdf", "content": "by SL McNULTY · 2015 · Cited by 29 — Boulding, C., & Wampler , B. ( 2010 ). Voice, votes, and resources: Evaluating the effect of participatory democracy on well-being. World Development, 38(1) ... 14 pages"}
+{"idx": 5, "title": "Civil Society Activism and Participatory Governance in Brazil", "date": "", "ddg_snippet": "by B Wampler · 2012 · Cited by 113 — Wampler B., Avritzer L. (2005) 'The Spread of Participatory Democracy in Brazil: From Radical Democracy to Good Government', Journal of Latin ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/abs/10.1111/j.1467-9248.2011.00912.x", "content": "by B Wampler · 2012 · Cited by 113 — Wampler B., Avritzer L. (2005) 'The Spread of Participatory Democracy in Brazil: From Radical Democracy to Good Government', Journal of Latin ..."}
+{"idx": 6, "title": "Does Participatory Governance Matter", "date": "", "ddg_snippet": "21 May 2010 — Research on Brazil's participatory budgeting shows that a significant percent- age of the population does attend participatory budget meetings, ... 49 pages", "subpage_snippet": "", "source": "www.wilsoncenter.org", "link": "https://www.wilsoncenter.org/sites/default/files/media/documents/publication/CUSP_110108_Participatory+Gov.pdf", "content": "21 May 2010 — Research on Brazil's participatory budgeting shows that a significant percent- age of the population does attend participatory budget meetings, ... 49 pages"}
+{"idx": 7, "title": "Participatory budgeting and the pattern of local government ...", "date": "", "ddg_snippet": "by D Lee · 2023 · Cited by 15 — In a study of 220 Brazilian cities, Boulding and Wampler ( 2010 ) similarly found that the adoption of participatory budgeting redirects budget priorities ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0176268022000465", "content": "by D Lee · 2023 · Cited by 15 — In a study of 220 Brazilian cities, Boulding and Wampler ( 2010 ) similarly found that the adoption of participatory budgeting redirects budget priorities ..."}
+{"idx": 8, "title": "Democracy at Work: Moving Beyond Elections to Improve ...", "date": "", "ddg_snippet": "by M TOUCHTON · 2017 · Cited by 114 — How does democracy work to improve well-being? In this article, we disentangle the component parts of democratic practice—elections, ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/american-political-science-review/article/democracy-at-work-moving-beyond-elections-to-improve-wellbeing/638A2418416825139D164AFD929C8F74", "content": "by M TOUCHTON · 2017 · Cited by 114 — How does democracy work to improve well-being? In this article, we disentangle the component parts of democratic practice—elections, ..."}
+{"idx": 9, "title": "Participation and the Poor: Social Accountability Institutions ...", "date": "", "ddg_snippet": "by M Touchton · 2015 · Cited by 9 — Proponents argue that democratic practices such as competitive elections, checks and balances, and protection of individual rights contribute to government's ...", "subpage_snippet": "", "source": "scholarworks.boisestate.edu", "link": "https://scholarworks.boisestate.edu/polsci_facpubs/163/", "content": "by M Touchton · 2015 · Cited by 9 — Proponents argue that democratic practices such as competitive elections, checks and balances, and protection of individual rights contribute to government's ..."}
diff --git a/data/sampled_jsons/Wampler_et_al._2010_Character_animation_in_two-player_adversarial_games_abstract_year_2010.jsonl b/data/sampled_jsons/Wampler_et_al._2010_Character_animation_in_two-player_adversarial_games_abstract_year_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..61db9eaad27ad22455cb9c970c9c59eaa97a3b10
--- /dev/null
+++ b/data/sampled_jsons/Wampler_et_al._2010_Character_animation_in_two-player_adversarial_games_abstract_year_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Character Animation in Two - Player Adversarial Games", "date": "", "ddg_snippet": "Abstract . The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game .", "subpage_snippet": "", "source": "grail.cs.washington.edu", "link": "https://grail.cs.washington.edu/projects/adversarial-control/tog/", "content": "Abstract . The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game ."}
+{"idx": 1, "title": "[PDF] Character animation in two - player adversarial games", "date": "", "ddg_snippet": "The approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ both long-term planning and an intelligent use of randomness, exhibiting nuanced strategies based on unpredictability.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Character-animation-in-two-player-adversarial-games-Wampler-Andersen/aab9ee829c871af99ca5c91dbf594ad9e0674a79", "content": "The approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ both long-term planning and an intelligent use of randomness, exhibiting nuanced strategies based on unpredictability."}
+{"idx": 2, "title": "dblp: Character animation in two - player adversarial games .", "date": "", "ddg_snippet": "Kevin Wampler et al . ( 2010 ). top.Kevin Wampler , Erik Andersen, Evan Herbst, Yongjoon Lee, Zoran Popovic: Character animation in two - player adversarial games .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/tog/WamplerAHLP10.html", "content": "Kevin Wampler et al . ( 2010 ). top.Kevin Wampler , Erik Andersen, Evan Herbst, Yongjoon Lee, Zoran Popovic: Character animation in two - player adversarial games ."}
+{"idx": 3, "title": "Character Animation in Two - Player Adversarial Games", "date": "", "ddg_snippet": "Wampler et al . [12] proposed a framework for planning two characters ' continuous motions in real time. Their method also requires that the attacker's motion be generated using the same method, and avoidance motion for any attack is not realized. ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/220184094_Character_Animation_in_Two-Player_Adversarial_Games", "content": "Wampler et al . [12] proposed a framework for planning two characters ' continuous motions in real time. Their method also requires that the attacker's motion be generated using the same method, and avoidance motion for any attack is not realized. ..."}
+{"idx": 4, "title": "Character animation in two - player adversarial games - Peeref", "date": "", "ddg_snippet": "Kevin Wampler Erik Andersen Evan Herbst Yongjoon Lee Zoran Popović.Online. 2010 -07-01.", "subpage_snippet": "", "source": "www.peeref.com", "link": "https://www.peeref.com/works/6185520", "content": "Kevin Wampler Erik Andersen Evan Herbst Yongjoon Lee Zoran Popović.Online. 2010 -07-01."}
+{"idx": 5, "title": "Formalizing Feint Actions, and Example Studies in Two - Player Games", "date": "", "ddg_snippet": "[ Wampler et al ., 2010 ] animates Feint actions as a proof of the capability to construct nuanced game strategies with unpredictability, in which Feint actions are treated the same as other actions. 2010 . Character animation in two - player adversarial games .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07931v1", "content": "[ Wampler et al ., 2010 ] animates Feint actions as a proof of the capability to construct nuanced game strategies with unpredictability, in which Feint actions are treated the same as other actions. 2010 . Character animation in two - player adversarial games ."}
+{"idx": 6, "title": "Kevin Wampler", "date": "", "ddg_snippet": "Character Animation in Two - Player Adversarial Games .Kevin Wampler , Daichi Sasaki, Li Zhang, Zoran Popović. SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation .", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "https://www.kevinwampler.com/", "content": "Character Animation in Two - Player Adversarial Games .Kevin Wampler , Daichi Sasaki, Li Zhang, Zoran Popović. SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation ."}
+{"idx": 7, "title": "CSC 2521: Physics-Based Character Animation calendar", "date": "", "ddg_snippet": "SIGGRAPH 2010 [web] [Huixuan] Background: Pratt et al . Virtual Model Control: An Intuitive Approach for Bipedal Locomotion. IJRR 2001. [web]. Character Animation in Two - Player Adversarial Games .", "subpage_snippet": "", "source": "www.dgp.toronto.edu", "link": "https://www.dgp.toronto.edu/~hertzman/courses/csc2521/fall_2010/calendar.html", "content": "SIGGRAPH 2010 [web] [Huixuan] Background: Pratt et al . Virtual Model Control: An Intuitive Approach for Bipedal Locomotion. IJRR 2001. [web]. Character Animation in Two - Player Adversarial Games ."}
+{"idx": 8, "title": "Adaptive motion synthesis for virtual characters : a survey | The Visual...", "date": "", "ddg_snippet": "Abstract . Character motion synthesis is the process of artificially generating natural motion for a virtual character . Wampler , K., Andersen, E., Herbst, E., Lee, Y., Popović, Z.: Character animation in two - player adversarial games . ACM Trans. Graph. 29, 26:1–26:13 ( 2010 ).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00371-014-0943-4", "content": "Abstract . Character motion synthesis is the process of artificially generating natural motion for a virtual character . Wampler , K., Andersen, E., Herbst, E., Lee, Y., Popović, Z.: Character animation in two - player adversarial games . ACM Trans. Graph. 29, 26:1–26:13 ( 2010 )."}
+{"idx": 9, "title": "Character Animation in Two - Player Adversarial Games - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=VIqeuCTpcv8", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
diff --git a/data/sampled_jsons/Wampler_et_al._2010_feint_behaviors_abstract.jsonl b/data/sampled_jsons/Wampler_et_al._2010_feint_behaviors_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c2fcf6a32e94eaf6c822543d2e55400f32d873d3
--- /dev/null
+++ b/data/sampled_jsons/Wampler_et_al._2010_feint_behaviors_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reply to Wampler et al .: Deforestation and biodiversity loss should not...", "date": "", "ddg_snippet": "Wampler et al . (3) refer to our exclusion of other forest types, but these types were not relevant because the bulk of biodiversity is in the primary forest.Those extinct species will not reappear if denuded mountaintops are reforested. Although not mentioned by Wampler et al .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6431194/", "content": "Wampler et al . (3) refer to our exclusion of other forest types, but these types were not relevant because the bulk of biodiversity is in the primary forest.Those extinct species will not reappear if denuded mountaintops are reforested. Although not mentioned by Wampler et al ."}
+{"idx": 1, "title": "Modeling wildfire effects on streamflow in the Cascade Mountains...", "date": "", "ddg_snippet": "K.A. Wampler et al . here, calibrating for both streamflow and sediment increases confidence that the underlying physical processes are being correctly represented in the model.", "subpage_snippet": "", "source": "fews.forestry.oregonstate.edu", "link": "https://fews.forestry.oregonstate.edu/publications/Wampler_JoH_2023.pdf", "content": "K.A. Wampler et al . here, calibrating for both streamflow and sediment increases confidence that the underlying physical processes are being correctly represented in the model."}
+{"idx": 2, "title": "Оцени свою фигуру онлайн - профессиональный анализ... | GymBot", "date": "", "ddg_snippet": "WHO Technical Report on Waist Circumference and Waist-Hip Ratio (2008)Dixson B.J. et al . ( 2010 ). Male preferences for female waist-to-hip ratio", "subpage_snippet": "", "source": "gymbot.ru", "link": "https://gymbot.ru/figure_assessment_calculator", "content": "WHO Technical Report on Waist Circumference and Waist-Hip Ratio (2008)Dixson B.J. et al . ( 2010 ). Male preferences for female waist-to-hip ratio"}
+{"idx": 3, "title": "US7753645.pdf", "date": "", "ddg_snippet": "(12) United States Patent. Wampler et al .13, 2010 . (54) rotary blood pump with opposing spindle magnets and contoured housing. (75) Inventors: Richard K. Wampler , Loomis, CA (US); David M. Lancisi, Folsom, CA.", "subpage_snippet": "", "source": "patentimages.storage.googleapis.com", "link": "https://patentimages.storage.googleapis.com/pdfs/US7753645.pdf", "content": "(12) United States Patent. Wampler et al .13, 2010 . (54) rotary blood pump with opposing spindle magnets and contoured housing. (75) Inventors: Richard K. Wampler , Loomis, CA (US); David M. Lancisi, Folsom, CA."}
+{"idx": 4, "title": "RU2799473C1 - Учебно-тренировочное средство... - Google Patents", "date": "", "ddg_snippet": "Wampler et al . 2006. Training lessons learned and confirmed from military training research. 2010 -09-10. Комплексный классный тренажер для подготовки специалистов артиллерийских подразделений (варианты). Kosenko et al .", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/RU2799473C1/ru", "content": "Wampler et al . 2006. Training lessons learned and confirmed from military training research. 2010 -09-10. Комплексный классный тренажер для подготовки специалистов артиллерийских подразделений (варианты). Kosenko et al ."}
+{"idx": 5, "title": "AMP: Adversarial Motion Priors for Stylized Physics-Based Character...", "date": "", "ddg_snippet": "2002, 2010 b]. Given a motion dataset, controllers can be constructed to select an appropriate motion clip to play back for a particular scenario [Agrawal and van de Panne 2016; Safonova and Hodgins 2007; Treuille et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2104.02180", "content": "2002, 2010 b]. Given a motion dataset, controllers can be constructed to select an appropriate motion clip to play back for a particular scenario [Agrawal and van de Panne 2016; Safonova and Hodgins 2007; Treuille et al ."}
+{"idx": 6, "title": "BG - The influence of burn severity on dissolved organic carbon...", "date": "", "ddg_snippet": "Katie A. Wampler et al . Abstract . Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds.", "subpage_snippet": "", "source": "bg.copernicus.org", "link": "https://bg.copernicus.org/articles/21/3093/2024/", "content": "Katie A. Wampler et al . Abstract . Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds."}
+{"idx": 7, "title": "Recommendations for Collective Training for the Battle Management...", "date": "", "ddg_snippet": "Wampler et al . (2006) highlight that the trainer should be able to observe the trainee during important tasks, and that this capability usually needs to be incorporated into the system design, as it is often too expensive to modify the system after purchase.", "subpage_snippet": "", "source": "www.dst.defence.gov.au", "link": "https://www.dst.defence.gov.au/sites/default/files/publications/documents/DSTO-TR-2685+PR.pdf", "content": "Wampler et al . (2006) highlight that the trainer should be able to observe the trainee during important tasks, and that this capability usually needs to be incorporated into the system design, as it is often too expensive to modify the system after purchase."}
+{"idx": 8, "title": "Scala autoboxing and Java Map - Stack Overflow", "date": "", "ddg_snippet": "edited Dec 16, 2010 at 16:48. Vasil Remeniuk's user avatar.Commented Dec 16, 2010 at 18:06. I cannot find anything on it in any of my Scala books (Odersky et al , Wampler et al , or Loverdos et al ).", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/4462762/scala-autoboxing-and-java-map", "content": "edited Dec 16, 2010 at 16:48. Vasil Remeniuk's user avatar.Commented Dec 16, 2010 at 18:06. I cannot find anything on it in any of my Scala books (Odersky et al , Wampler et al , or Loverdos et al )."}
+{"idx": 9, "title": "Telegram: View @homemade3", "date": "", "ddg_snippet": "If you have Telegram, you can view and join Homemade right away.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/homemade3", "content": "If you have Telegram, you can view and join Homemade right away."}
diff --git a/data/sampled_jsons/Waymo_Open_Dataset_2020_Sun_dataset_size_frames_statistics_breakdown_year_2020.jsonl b/data/sampled_jsons/Waymo_Open_Dataset_2020_Sun_dataset_size_frames_statistics_breakdown_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fbad36a345ebd002df9eff610401cfc1a69b082c
--- /dev/null
+++ b/data/sampled_jsons/Waymo_Open_Dataset_2020_Sun_dataset_size_frames_statistics_breakdown_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Best Machine Learning Datasets", "date": "", "ddg_snippet": "We will discuss what to look for in a dataset , provide an overview of the most popular datasets this year, share successful case studies, and even ...", "subpage_snippet": "", "source": "pyimagesearch.com", "link": "https://pyimagesearch.com/2023/07/31/best-machine-learning-datasets/", "content": "We will discuss what to look for in a dataset , provide an overview of the most popular datasets this year, share successful case studies, and even ..."}
+{"idx": 1, "title": "Scalability in Perception for Autonomous Driving: Waymo Open ...", "date": "", "ddg_snippet": "All of the coordinate systems follow the right hand rule, and the dataset contains all information needed to transform data between any two frames within a run segment.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Scalability_in_Perception_for_Autonomous_Driving_Waymo_Open_Dataset_CVPR_2020_paper.pdf", "content": "All of the coordinate systems follow the right hand rule, and the dataset contains all information needed to transform data between any two frames within a run segment."}
+{"idx": 2, "title": "Scalability in Perception for Autonomous Driving: Waymo Open ...", "date": "", "ddg_snippet": "Dec 10, 2019 · We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames . Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1912.04838", "content": "Dec 10, 2019 · We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames . Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks."}
+{"idx": 3, "title": "waymo-research/waymo-open-dataset - GitHub Meet the Winners of the Waymo Open Dataset Challenges Scalability in Perception for Autonomous Driving: Waymo Open ... CVPR 2020 Open Access Repository waymo -research/ waymo-open-dataset - GitHub Meet the Winners of the Waymo Open Dataset Challenges waymo -research/ waymo-open-dataset - GitHub Scalability in Perception for Autonomous Driving : Waymo Open Dataset Scalability in Perception for Autonomous Driving : Waymo Open Dataset Scalability in Perception for Autonomous Driving : Waymo Open Dataset About – Waymo Open Dataset", "date": "", "ddg_snippet": "We have released the Waymo Open Dataset publicly to aid the research community in making advancements in machine perception and autonomous driving technology. The Waymo Open Dataset is composed of two datasets - the Perception dataset with high resolution sensor data and labels for 2,030 scenes, and the Motion dataset with object trajectories and corresponding 3D maps for 103,354 scenes. See full list on github.com This code repository (excluding src/waymo_open_dataset/wdl_limited folder) is licensed under the Apache License, Version 2.0. The code appearing in src/waymo_open_dataset/wdl_limited is licensed under terms appearing therein. The Waymo Open Dataset itself is licensed under separate terms. Please visit https:// waymo .com/ open /terms/ for details. Code... See full list on github.com We released v1.6.1 version of the pip package with fixes for the WOSAC metrics: •Fixing a bug in validity checking for collision and offroad. See full list on github.com We released a large-scale object-centric asset dataset containing over 1.2M images and lidar observations of two major categories (vehicles and pedestrians) from the Perception Dataset (v2.0.0). •Extracted perception objects from multi-sensor data: all five cameras and the top lidar. •Lidar features include 3D point cloud sequences that support 3D object shape reconstruction. We additionally provide refined box pose through point cloud shape registration for all vehicle objects. •Camera features include sequences of camera patches from the most_visible_camera, projected lidar returns on the corresponding camera, per-pixel camera rays information, and auto-labeled 2D panoptic segmentation that supports object NeRF reconstruction. See full list on github.com This major update includes supporting code to four challenges at waymo .com/ open , and dataset updates to both the Perception and Motion Datasets. v2.0.0 of the Perception Dataset •Introduced the dataset in modular format, enabling users to selectively download only the components they need. •Includes all features in v1.4.2 of the Perception Dataset except maps. •Added a tutorial and supporting code. v1.4.2 of the Perception Dataset See full list on github.com We released v1.4.1 of the Perception dataset . •Improved the quality of the 2D video panoptic segmentation labels. See full list on github.com We released v1.4.0 of the Perception dataset . •Added 2D video panoptic segmentation labels and supporting code. See full list on github.com •Released a tutorial for the 3D Camera-Only Detection Challenge. •Added support for computing 3D-LET-APL in Python metrics ops. See Compute Metrics in the tutorial. See full list on github.com We released v1.3.2 of the Perception dataset to improve the quality and accuracy of the labels. •Updated 3D semantic segmentation labels, for better temporal consistency and to fix mislabeled points. •Updated 2D key point labels to fix image cropping issues. •Added num_top_lidar_points_in_box in dataset .proto for the 3D Camera-Only Detection Challenge. See full list on github.com We released v1.3.1 of the Perception dataset to support the 2022 Challenges and have updated this repository accordingly. •Added metrics (LET-3D-APL and LET-3D-AP) for the 3D Camera-Only Detection Challenge. •Added 80 segments of 20-second camera imagery, as a test set for the 3D Camera-Only Detection Challenge. •Added z-axis speed and acceleration in lidar label metadata. •Fixed some inconsistencies in projected_lidar_labels in dataset .proto. •Updated the default configuration for the Occupancy and Flow Challenge, switching from aggregate waypoints to subsampled waypoints. See full list on github.com Jul 13, 2020 · In March 2020 , we launched the Waymo Open Dataset Challenges, inviting researchers to build and test their machine learning models using Waymo’ s diverse self-driving dataset. The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data . Exist. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames . Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. What datasets are included in the Waymo open dataset? The Waymo Open Dataset includes three datasets: The Perception dataset , with high resolution sensor data and labels for various tasks. The Motion dataset , with object trajectories and corresponding 3D maps for 103,354 scenes. The End-To-End Driving dataset , with camera data and high-level commands. What is the Waymo open dataset challenge? The Waymo Open Dataset is a great contribution to the research community, as it allows us to work on a real world problem without having an autonomous car with expensive sensors .” While the official Waymo Open Dataset Challenges have come to an end, our leaderboards will remain open to new submissions. What license does Waymo open dataset use? This code repository (excluding src/waymo_open_dataset/wdl_limited folder) is licensed under the Apache License , Version 2.0. The code appearing in src/waymo_open_dataset/wdl_limited is licensed under terms appearing therein. The Waymo Open Dataset itself is licensed under separate terms. How diverse is a cam-Era+lidar dataset? It is 15x more diverse than the largest cam-era+LiDAR dataset available based on our proposed geo-graphical coverage metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames . Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. Why is a large-scale dataset important for AU-tonomous driving research? High-quality , large-scale datasets are crucial for au-tonomous driving research. There have been an increasing number of efforts in releasing datasets to the community in recent years. Most autonomous driving systems fuse sensor readings from multiple sensors, including cameras, LiDAR, radar, GPS, wheel odometry, and IMUs. How is the dataset organized? Our dataset is organized into sequences , each 20 seconds long with multiple sensors producing data sampled at 10Hz. Additionally, every object in the dataset is annotated with a unique identifier that is consistent across each sequence. Here's what's currently included in our unique Waymo Open Dataset. We plan to continue growing this dataset, so make sure to continue visiting the page for our most recent updates.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/waymo-research/waymo-open-dataset", "content": "We have released the Waymo Open Dataset publicly to aid the research community in making advancements in machine perception and autonomous driving technology. The Waymo Open Dataset is composed of two datasets - the Perception dataset with high resolution sensor data and labels for 2,030 scenes, and the Motion dataset with object trajectories and corresponding 3D maps for 103,354 scenes. See full list on github.com This code repository (excluding src/waymo_open_dataset/wdl_limited folder) is licensed under the Apache License, Version 2.0. The code appearing in src/waymo_open_dataset/wdl_limited is licensed under terms appearing therein. The Waymo Open Dataset itself is licensed under separate terms. Please visit https:// waymo .com/ open /terms/ for details. Code... See full list on github.com We released v1.6.1 version of the pip package with fixes for the WOSAC metrics: •Fixing a bug in validity checking for collision and offroad. See full list on github.com We released a large-scale object-centric asset dataset containing over 1.2M images and lidar observations of two major categories (vehicles and pedestrians) from the Perception Dataset (v2.0.0). •Extracted perception objects from multi-sensor data: all five cameras and the top lidar. •Lidar features include 3D point cloud sequences that support 3D object shape reconstruction. We additionally provide refined box pose through point cloud shape registration for all vehicle objects. •Camera features include sequences of camera patches from the most_visible_camera, projected lidar returns on the corresponding camera, per-pixel camera rays information, and auto-labeled 2D panoptic segmentation that supports object NeRF reconstruction. See full list on github.com This major update includes supporting code to four challenges at waymo .com/ open , and dataset updates to both the Perception and Motion Datasets. v2.0.0 of the Perception Dataset •Introduced the dataset in modular format, enabling users to selectively download only the components they need. •Includes all features in v1.4.2 of the Perception Dataset except maps. •Added a tutorial and supporting code. v1.4.2 of the Perception Dataset See full list on github.com We released v1.4.1 of the Perception dataset . •Improved the quality of the 2D video panoptic segmentation labels. See full list on github.com We released v1.4.0 of the Perception dataset . •Added 2D video panoptic segmentation labels and supporting code. See full list on github.com •Released a tutorial for the 3D Camera-Only Detection Challenge. •Added support for computing 3D-LET-APL in Python metrics ops. See Compute Metrics in the tutorial. See full list on github.com We released v1.3.2 of the Perception dataset to improve the quality and accuracy of the labels. •Updated 3D semantic segmentation labels, for better temporal consistency and to fix mislabeled points. •Updated 2D key point labels to fix image cropping issues. •Added num_top_lidar_points_in_box in dataset .proto for the 3D Camera-Only Detection Challenge. See full list on github.com We released v1.3.1 of the Perception dataset to support the 2022 Challenges and have updated this repository accordingly. •Added metrics (LET-3D-APL and LET-3D-AP) for the 3D Camera-Only Detection Challenge. •Added 80 segments of 20-second camera imagery, as a test set for the 3D Camera-Only Detection Challenge. •Added z-axis speed and acceleration in lidar label metadata. •Fixed some inconsistencies in projected_lidar_labels in dataset .proto. •Updated the default configuration for the Occupancy and Flow Challenge, switching from aggregate waypoints to subsampled waypoints. See full list on github.com Jul 13, 2020 · In March 2020 , we launched the Waymo Open Dataset Challenges, inviting researchers to build and test their machine learning models using Waymo’ s diverse self-driving dataset. The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data . Exist. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames . Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. What datasets are included in the Waymo open dataset? The Waymo Open Dataset includes three datasets: The Perception dataset , with high resolution sensor data and labels for various tasks. The Motion dataset , with object trajectories and corresponding 3D maps for 103,354 scenes. The End-To-End Driving dataset , with camera data and high-level commands. What is the Waymo open dataset challenge? The Waymo Open Dataset is a great contribution to the research community, as it allows us to work on a real world problem without having an autonomous car with expensive sensors .” While the official Waymo Open Dataset Challenges have come to an end, our leaderboards will remain open to new submissions. What license does Waymo open dataset use? This code repository (excluding src/waymo_open_dataset/wdl_limited folder) is licensed under the Apache License , Version 2.0. The code appearing in src/waymo_open_dataset/wdl_limited is licensed under terms appearing therein. The Waymo Open Dataset itself is licensed under separate terms. How diverse is a cam-Era+lidar dataset? It is 15x more diverse than the largest cam-era+LiDAR dataset available based on our proposed geo-graphical coverage metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames . Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. Why is a large-scale dataset important for AU-tonomous driving research? High-quality , large-scale datasets are crucial for au-tonomous driving research. There have been an increasing number of efforts in releasing datasets to the community in recent years. Most autonomous driving systems fuse sensor readings from multiple sensors, including cameras, LiDAR, radar, GPS, wheel odometry, and IMUs. How is the dataset organized? Our dataset is organized into sequences , each 20 seconds long with multiple sensors producing data sampled at 10Hz. Additionally, every object in the dataset is annotated with a unique identifier that is consistent across each sequence. Here's what's currently included in our unique Waymo Open Dataset. We plan to continue growing this dataset, so make sure to continue visiting the page for our most recent updates."}
+{"idx": 4, "title": "Meet the Winners of the Waymo Open Dataset Challenges", "date": "", "ddg_snippet": "Jul 13, 2020 · In March 2020 , we launched the Waymo Open Dataset Challenges, inviting researchers to build and test their machine learning models using Waymo’ s diverse self-driving dataset.", "subpage_snippet": "", "source": "waymo.com", "link": "https://waymo.com/blog/2020/07/opendataset-challenge-winners-2020/", "content": "Jul 13, 2020 · In March 2020 , we launched the Waymo Open Dataset Challenges, inviting researchers to build and test their machine learning models using Waymo’ s diverse self-driving dataset."}
+{"idx": 5, "title": "CVPR 2020 Open Access Repository", "date": "", "ddg_snippet": "We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames . Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Scalability_in_Perception_for_Autonomous_Driving_Waymo_Open_Dataset_CVPR_2020_paper.html", "content": "We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames . Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks."}
+{"idx": 6, "title": "About – Waymo Open Dataset", "date": "", "ddg_snippet": "Here's what's currently included in our unique Waymo Open Dataset. We plan to continue growing this dataset, so make sure to continue visiting the page for our most recent updates.", "subpage_snippet": "", "source": "waymo.com", "link": "https://waymo.com/open/about/", "content": "Here's what's currently included in our unique Waymo Open Dataset. We plan to continue growing this dataset, so make sure to continue visiting the page for our most recent updates."}
+{"idx": 7, "title": "Scalability in Perception for Autonomous Driving: Waymo Open ...", "date": "", "ddg_snippet": "The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data . Exist.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9156973", "content": "The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data . Exist."}
+{"idx": 8, "title": "Scalable Scene Flow from Point Clouds in the Real World - arXiv", "date": "", "ddg_snippet": "We hope that the resulting We extend the Waymo Open Dataset (WOD) to con- dataset presented in this paper may open the opportunity for struct a large ...", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/scalable-scene-flow-from-point-clouds-in-the-real-world-4828761", "content": "We hope that the resulting We extend the Waymo Open Dataset (WOD) to con- dataset presented in this paper may open the opportunity for struct a large ..."}
+{"idx": 9, "title": "LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for", "date": "", "ddg_snippet": "For example, the KITTI dataset [ 24 ] and the Waymo Open Dataset [ 21 ] adopt 3D IoU as the main matching function.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2206.07705v2", "content": "For example, the KITTI dataset [ 24 ] and the Waymo Open Dataset [ 21 ] adopt 3D IoU as the main matching function."}
diff --git a/data/sampled_jsons/Waymo_Open_Dataset_Sun_et_al_2020_original_paper_frames_year_2020.jsonl b/data/sampled_jsons/Waymo_Open_Dataset_Sun_et_al_2020_original_paper_frames_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f258844039a891eda7ba54cd59fb955ce6dd27e
--- /dev/null
+++ b/data/sampled_jsons/Waymo_Open_Dataset_Sun_et_al_2020_original_paper_frames_year_2020.jsonl
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+{"idx": 0, "title": "Scalability in Perception for Autonomous Driving: Waymo ...", "date": "", "ddg_snippet": "by P Sun · 2020 · Cited by 4205 — This section describes the coordinate systems used in the dataset . All of the coordinate systems follow the right hand rule, and the dataset contains all ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Scalability_in_Perception_for_Autonomous_Driving_Waymo_Open_Dataset_CVPR_2020_paper.pdf", "content": "by P Sun · 2020 · Cited by 4205 — This section describes the coordinate systems used in the dataset . All of the coordinate systems follow the right hand rule, and the dataset contains all ..."}
+{"idx": 1, "title": "Processing, assessing, and enhancing the Waymo ...", "date": "", "ddg_snippet": "by X Hu · 2022 · Cited by 134 — This paper aims to comprehensively and systematically process and assess one of the AV-oriented open datasets, i.e., Waymo Open Dataset , with a focus on car ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0968090X21004769", "content": "by X Hu · 2022 · Cited by 134 — This paper aims to comprehensively and systematically process and assess one of the AV-oriented open datasets, i.e., Waymo Open Dataset , with a focus on car ..."}
+{"idx": 2, "title": "arXiv:1912.04838v7 [cs.CV] 12 May 2020", "date": "", "ddg_snippet": "by P Sun · 2019 · Cited by 4205 — Scalability in Perception for Autonomous Driving: Waymo Open Dataset ... We trained the model on single frame of sensor data with all ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1912.04838", "content": "by P Sun · 2019 · Cited by 4205 — Scalability in Perception for Autonomous Driving: Waymo Open Dataset ... We trained the model on single frame of sensor data with all ..."}
+{"idx": 3, "title": "The Waymo Open Sim Agents Challenge", "date": "", "ddg_snippet": "by N Montali · Cited by 79 — Simulation with realistic, interactive agents represents a key task for autonomous vehicle software development. In this work, we introduce the Waymo Open ...", "subpage_snippet": "", "source": "ml4ad.github.io", "link": "https://ml4ad.github.io/files/papers2023/The+Waymo+Open+Sim+Agents+Challenge.pdf", "content": "by N Montali · Cited by 79 — Simulation with realistic, interactive agents represents a key task for autonomous vehicle software development. In this work, we introduce the Waymo Open ..."}
+{"idx": 4, "title": "Scalability in Perception for Autonomous Driving", "date": "", "ddg_snippet": "by P Sun · 2019 · Cited by 4199 — Access Paper : View a PDF of the paper titled Scalability in Perception for Autonomous Driving: Waymo Open Dataset , by Pei Sun and 24 other ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1912.04838", "content": "by P Sun · 2019 · Cited by 4199 — Access Paper : View a PDF of the paper titled Scalability in Perception for Autonomous Driving: Waymo Open Dataset , by Pei Sun and 24 other ..."}
+{"idx": 5, "title": "A survey on 3D object detection in real time for ...", "date": "", "ddg_snippet": "by M Contreras · 2024 · Cited by 12 — This paper reviews the state-of-the-art 3D object detection techniques that utilizes monocular and stereo vision for reliable detection in urban settings.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10950960/", "content": "by M Contreras · 2024 · Cited by 12 — This paper reviews the state-of-the-art 3D object detection techniques that utilizes monocular and stereo vision for reliable detection in urban settings."}
+{"idx": 6, "title": "Investigating alternative explanations for shorter time ...", "date": "", "ddg_snippet": "by Y Jiao · 2024 · Cited by 10 — One (Hu et al ., 2022) is extracted from Waymo open data ( Sun et al ., 2020 ), and the other (Li et al ., 2023) is extracted from Lyft open data (Houston et al ., ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0968090X24001943", "content": "by Y Jiao · 2024 · Cited by 10 — One (Hu et al ., 2022) is extracted from Waymo open data ( Sun et al ., 2020 ), and the other (Li et al ., 2023) is extracted from Lyft open data (Houston et al ., ..."}
+{"idx": 7, "title": "Do You Remember the Future? Weak-to-Strong ...", "date": "", "ddg_snippet": "by A Gambashidze — Figure 4: Comparison of object-complete and original frames ,. Waymo Open dataset . in Table 1 and Table 2, respectively. We also show visual side-by-side ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/1001.pdf", "content": "by A Gambashidze — Figure 4: Comparison of object-complete and original frames ,. Waymo Open dataset . in Table 1 and Table 2, respectively. We also show visual side-by-side ..."}
+{"idx": 8, "title": "A New Literature Review of 3D Object Detection on ...", "date": "", "ddg_snippet": "The annotations in the Waymo open dataset categorize objects into four groups: cars, pedestrians, cyclists, and signs. Many recent research papers have started ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1613/jair.1.15961?download=true", "content": "The annotations in the Waymo open dataset categorize objects into four groups: cars, pedestrians, cyclists, and signs. Many recent research papers have started ..."}
+{"idx": 9, "title": "Obstacle Detection for Autonomous Driving Using Semantic ...", "date": "", "ddg_snippet": "by C Aranguren — In this project, we investigate three paradigms for obstacle detection on the Waymo Open . Dataset : a semantic segmentation model, an instance detec- tion model, ...", "subpage_snippet": "", "source": "cs231n.stanford.edu", "link": "https://cs231n.stanford.edu/papers/text_file_840565585-CS231N+(3).pdf", "content": "by C Aranguren — In this project, we investigate three paradigms for obstacle detection on the Waymo Open . Dataset : a semantic segmentation model, an instance detec- tion model, ..."}
diff --git a/data/sampled_jsons/Waymo_Open_Dataset_paper_Sun_et_al_2020.jsonl b/data/sampled_jsons/Waymo_Open_Dataset_paper_Sun_et_al_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..35ac6e41e38066740680214835bacaabf01f19a5
--- /dev/null
+++ b/data/sampled_jsons/Waymo_Open_Dataset_paper_Sun_et_al_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scalability in Perception for Autonomous Driving: Waymo ...", "date": "", "ddg_snippet": "by P Sun · 2020 · Cited by 4199 — We recorded all the sensor data of our dataset using an industrial-strength sensor suite consisting of multiple high- resolution cameras and multiple high- ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Scalability_in_Perception_for_Autonomous_Driving_Waymo_Open_Dataset_CVPR_2020_paper.pdf", "content": "by P Sun · 2020 · Cited by 4199 — We recorded all the sensor data of our dataset using an industrial-strength sensor suite consisting of multiple high- resolution cameras and multiple high- ..."}
+{"idx": 1, "title": "Waymo Open Dataset - CVPR 2020 Open Access Repository", "date": "", "ddg_snippet": "by P Sun · 2020 · Cited by 4199 — Scalability in Perception for Autonomous Driving: Waymo Open Dataset . Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Scalability_in_Perception_for_Autonomous_Driving_Waymo_Open_Dataset_CVPR_2020_paper.html", "content": "by P Sun · 2020 · Cited by 4199 — Scalability in Perception for Autonomous Driving: Waymo Open Dataset . Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul ..."}
+{"idx": 2, "title": "Processing, assessing, and enhancing the Waymo ...", "date": "", "ddg_snippet": "by X Hu · 2022 · Cited by 134 — This paper aims to comprehensively and systematically process and assess one of the AV-oriented open datasets, i.e., Waymo Open Dataset , with a focus on car ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0968090X21004769", "content": "by X Hu · 2022 · Cited by 134 — This paper aims to comprehensively and systematically process and assess one of the AV-oriented open datasets, i.e., Waymo Open Dataset , with a focus on car ..."}
+{"idx": 3, "title": "Waymo Open Dataset", "date": "", "ddg_snippet": "The Waymo Open Dataset is a collection of datasets and evaluation code that we have released publicly to aid the research community.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/waymo-research/waymo-open-dataset", "content": "The Waymo Open Dataset is a collection of datasets and evaluation code that we have released publicly to aid the research community."}
+{"idx": 4, "title": "A unified longitudinal trajectory dataset for automated vehicle", "date": "", "ddg_snippet": "by H Zhou · 2024 · Cited by 23 — This study addresses these challenges by creating a Unified longitudinal trajectory dataset for AVs (Ultra-AV) to analyze their microscopic longitudinal ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41597-024-03795-y", "content": "by H Zhou · 2024 · Cited by 23 — This study addresses these challenges by creating a Unified longitudinal trajectory dataset for AVs (Ultra-AV) to analyze their microscopic longitudinal ..."}
+{"idx": 5, "title": "Aerial Perception and Driven Behavior Dataset", "date": "", "ddg_snippet": "by G Zhang · 2024 — (2017), the Waymo Open dataset from Sun et al. (2020 ), and the Waymo Open Motion dataset from Ettinger et al. ... The rest of this paper is organized as follows.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v222/zhang24d/zhang24d.pdf", "content": "by G Zhang · 2024 — (2017), the Waymo Open dataset from Sun et al. (2020 ), and the Waymo Open Motion dataset from Ettinger et al. ... The rest of this paper is organized as follows."}
+{"idx": 6, "title": "The Waymo Open Sim Agents Challenge", "date": "", "ddg_snippet": "The goal of the challenge is to stimulate the design of realistic simulators that can be used to evaluate and train a behavior model for autonomous driving.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.12032v4", "content": "The goal of the challenge is to stimulate the design of realistic simulators that can be used to evaluate and train a behavior model for autonomous driving."}
+{"idx": 7, "title": "Autonomous vehicle lane-changing dynamics and impact ...", "date": "", "ddg_snippet": "by Y Ali · 2025 — This study uses the perception dataset of approximately 103,354 segments ( Sun et al ., 2020 ). Note that one segment (also referred to as a scenario) corresponds ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2213665725000193", "content": "by Y Ali · 2025 — This study uses the perception dataset of approximately 103,354 segments ( Sun et al ., 2020 ). Note that one segment (also referred to as a scenario) corresponds ..."}
+{"idx": 8, "title": "A survey on 3D object detection in real time for ...", "date": "", "ddg_snippet": "by M Contreras · 2024 · Cited by 12 — This paper reviews the state-of-the-art 3D object detection techniques that utilizes monocular and stereo vision for reliable detection in urban settings.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10950960/", "content": "by M Contreras · 2024 · Cited by 12 — This paper reviews the state-of-the-art 3D object detection techniques that utilizes monocular and stereo vision for reliable detection in urban settings."}
+{"idx": 9, "title": "A survey of decision-making and planning methods for self- ...", "date": "", "ddg_snippet": "by J Hu · 2025 · Cited by 4 — The Waymo (Sun et al., 2020) Open Dataset is a large-scale and high-quality dataset containing LiDAR and camera data with detailed ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2025.1451923/full", "content": "by J Hu · 2025 · Cited by 4 — The Waymo (Sun et al., 2020) Open Dataset is a large-scale and high-quality dataset containing LiDAR and camera data with detailed ..."}
diff --git a/data/sampled_jsons/WcuXvn3HVk_AERO_Enhancing_Sharding_Blockchain_Section_5.1_experimental_setup_nodes_year_2024.jsonl b/data/sampled_jsons/WcuXvn3HVk_AERO_Enhancing_Sharding_Blockchain_Section_5.1_experimental_setup_nodes_year_2024.jsonl
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+{"idx": 2, "title": "Previsión meteorológica de tres días para Cuencamé , Durango ,...", "date": "", "ddg_snippet": "Consiga su previsión del tiempo de 3 días para Cuencamé, Durango, México. Máximas y mínimas, RealFeel, precipitaciones, radar y todo lo que necesita saber para estar preparado para el día, el...", "subpage_snippet": "", "source": "www.accuweather.com", "link": "https://www.accuweather.com/es/mx/cuencamé/232590/weather-forecast/232590", "content": "Consiga su previsión del tiempo de 3 días para Cuencamé, Durango, México. Máximas y mínimas, RealFeel, precipitaciones, radar y todo lo que necesita saber para estar preparado para el día, el..."}
+{"idx": 3, "title": "Clima en Cuencamé | Tiempo | Pronóstico | ¿Va a llover?", "date": "", "ddg_snippet": "Pronóstico del tiempo en Cuencamé . Averigua si lloverá, a qué hora va a llover, y la temperatura y nubosidad por horas en Cuencamé para hoy y los próximos 9 días.", "subpage_snippet": "", "source": "mx.meteosolana.net", "link": "https://mx.meteosolana.net/clima-por-municipios-en-durango/clima-en-cuencame", "content": "Pronóstico del tiempo en Cuencamé . Averigua si lloverá, a qué hora va a llover, y la temperatura y nubosidad por horas en Cuencamé para hoy y los próximos 9 días."}
+{"idx": 4, "title": "El Tiempo en Cuencame . Predicción a 14 días - Meteored", "date": "", "ddg_snippet": "Sep 18, 2025 · El Tiempo en Cuencame - Previsión meteorológica para los próximos 14 días. El pronóstico del tiempo más actualizado en Cuencame : temperatura, lluvia, viento, etc.", "subpage_snippet": "", "source": "www.tiempo.com", "link": "https://www.tiempo.com/cuencame.htm", "content": "Sep 18, 2025 · El Tiempo en Cuencame - Previsión meteorológica para los próximos 14 días. El pronóstico del tiempo más actualizado en Cuencame : temperatura, lluvia, viento, etc."}
+{"idx": 5, "title": "Clima Cuencamé para los próxímos 7 días", "date": "", "ddg_snippet": "Conozca el pronóstico del tiempo para Cuencamé ahora y cómo va a evolucionar el tiempo en los próximos 7 días. Puede encontrar información detallada sobre: velocidad del viento, dirección del viento, presión, temperatura, humedad y visibilidad en Cuencamé", "subpage_snippet": "", "source": "www.climamexico.mx", "link": "https://www.climamexico.mx/mexico/durango/clima-cuencame/pronostico-7-dias", "content": "Conozca el pronóstico del tiempo para Cuencamé ahora y cómo va a evolucionar el tiempo en los próximos 7 días. Puede encontrar información detallada sobre: velocidad del viento, dirección del viento, presión, temperatura, humedad y visibilidad en Cuencamé"}
+{"idx": 6, "title": "Tiempo en Cuencame . Clima a 14 días - Meteored", "date": "", "ddg_snippet": "May 20, 2025 · Tiempo en Cuencame - Pronóstico del tiempo a 14 días. Los datos sobre el Tiempo, temperatura, velocidad del viento, la humedad, la cota de nieve, presión, etc.", "subpage_snippet": "", "source": "www.meteored.do", "link": "https://www.meteored.do/tiempo-en_Cuencame-America+Norte-Mexico-Durango--1-21923.html", "content": "May 20, 2025 · Tiempo en Cuencame - Pronóstico del tiempo a 14 días. Los datos sobre el Tiempo, temperatura, velocidad del viento, la humedad, la cota de nieve, presión, etc."}
+{"idx": 7, "title": "Clima en Cuencame por horas - Meteored", "date": "", "ddg_snippet": "Sep 16, 2025 · Clima en Cuencame por hora con el estado del tiempo a 14 días. Los datos hora a hora sobre el tiempo, temperatura, velocidad del viento, la humedad, la cota de nieve, presión, etc. Cuencame pronóstico por horas.", "subpage_snippet": "", "source": "www.meteored.mx", "link": "https://www.meteored.mx/cuencame/por-hora", "content": "Sep 16, 2025 · Clima en Cuencame por hora con el estado del tiempo a 14 días. Los datos hora a hora sobre el tiempo, temperatura, velocidad del viento, la humedad, la cota de nieve, presión, etc. Cuencame pronóstico por horas."}
+{"idx": 8, "title": "Tiempo cada hora en Cuencamé , Durango , México | AccuWeather", "date": "", "ddg_snippet": "Consiga la previsión local por hora para Cuencamé , Durango , México, incluida la temperatura, RealFeel y la probabilidad de precipitaciones.", "subpage_snippet": "", "source": "www.accuweather.com", "link": "https://www.accuweather.com/es/mx/cuencamé/232590/hourly-weather-forecast/232590", "content": "Consiga la previsión local por hora para Cuencamé , Durango , México, incluida la temperatura, RealFeel y la probabilidad de precipitaciones."}
+{"idx": 9, "title": "Pronóstico del tiempo y condiciones meteorológicas para Cuencamé ...", "date": "", "ddg_snippet": "Pronóstico del tiempo en Cuencamé , Durango para hoy y esta noche, condiciones meteorológicas y radar Doppler de The Weather Channel y weather.com", "subpage_snippet": "", "source": "weather.com", "link": "https://weather.com/es-MX/tiempo/hoy/l/Cuencamé+Durango?canonicalCityId=a4689b8d35af014011d6c4c72bc275b0", "content": "Pronóstico del tiempo en Cuencamé , Durango para hoy y esta noche, condiciones meteorológicas y radar Doppler de The Weather Channel y weather.com"}
diff --git a/data/sampled_jsons/What_Limits_Virtual_Agent_Application_OmniBench_A_Scalable_Multi-Dimensional_Benchmark_for_Essential.jsonl b/data/sampled_jsons/What_Limits_Virtual_Agent_Application_OmniBench_A_Scalable_Multi-Dimensional_Benchmark_for_Essential.jsonl
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index 0000000000000000000000000000000000000000..77ab4ca38f8eff7da693632d7157006c488c4a0a
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+{"idx": 3, "title": "OmniBench ...", "date": "", "ddg_snippet": "论文名称： What Limits Virtual Agent Application ?Self-improving Agent 25篇. LLM4computing 1篇.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_42540492/article/details/149117606", "content": "论文名称： What Limits Virtual Agent Application ?Self-improving Agent 25篇. LLM4computing 1篇."}
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+{"idx": 5, "title": "OmniBench", "date": "", "ddg_snippet": "A Scalable Multi - Dimensional Benchmark of Essential Virtual Agent Capabilities .arithmetic reasoning Comparison of virtual agent benchmarks across environment, task, and evaluation dimensions.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "A Scalable Multi - Dimensional Benchmark of Essential Virtual Agent Capabilities .arithmetic reasoning Comparison of virtual agent benchmarks across environment, task, and evaluation dimensions."}
+{"idx": 6, "title": "antgroup repositories · GitHub", "date": "", "ddg_snippet": "OmniBench : A Scalable Multi - Dimensional Benchmark for Essential Virtual Agent Capabilities \". benchmark cross-platform graph. + 3. virtual-agents automated-pipeline multidimension.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/orgs/antgroup/repositories", "content": "OmniBench : A Scalable Multi - Dimensional Benchmark for Essential Virtual Agent Capabilities \". benchmark cross-platform graph. + 3. virtual-agents automated-pipeline multidimension."}
+{"idx": 7, "title": "ICML.2025 | Cool Papers - Immersive Paper Discovery", "date": "", "ddg_snippet": "#12 What Limits Virtual Agent Application ? OmniBench : A Scalable Multi - Dimensional Benchmark for Essential Virtual Agent Capabilities .", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/ICML.2025", "content": "#12 What Limits Virtual Agent Application ? OmniBench : A Scalable Multi - Dimensional Benchmark for Essential Virtual Agent Capabilities ."}
+{"idx": 8, "title": "Arxiver", "date": "", "ddg_snippet": "What Limits Virtual Agent Application ?In response to these challenges, we introduce OmniBench , a self-generating, cross-platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition.", "subpage_snippet": "", "source": "arxiver.lazybrains.com", "link": "http://arxiver.lazybrains.com/author/1031083", "content": "What Limits Virtual Agent Application ?In response to these challenges, we introduce OmniBench , a self-generating, cross-platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition."}
+{"idx": 9, "title": "ICML 2025 Review Controversies Spark Academic Debate | CSPaper", "date": "", "ddg_snippet": "What Limits Virtual Agent Application ? OmniBench : A Scalable Multi - Dimensional Benchmark for Essential Virtual Agent Capabilities Introduces a broad benchmark for virtual agents’ key capabilities.", "subpage_snippet": "", "source": "cspaper.org", "link": "https://cspaper.org/topic/62/icml-2025-review-controversies-spark-academic-debate", "content": "What Limits Virtual Agent Application ? OmniBench : A Scalable Multi - Dimensional Benchmark for Essential Virtual Agent Capabilities Introduces a broad benchmark for virtual agents’ key capabilities."}
diff --git a/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_6_layer_OR_6-layer_transformer_configuration_experimental_s.jsonl b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_6_layer_OR_6-layer_transformer_configuration_experimental_s.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ee69e73e60aae58af94b661dcbd9f3c3fd6ee21
--- /dev/null
+++ b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_6_layer_OR_6-layer_transformer_configuration_experimental_s.jsonl
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+{"idx": 0, "title": "WHAT DO INDUSTRIAL ENGINEERS NEED TO KNOW ABOUT FUNCTIONAL ...", "date": "", "ddg_snippet": "Engineers designing systems for industrial and factory applications are adopting functional safety design practices to reduce the risk of system failures that could result in costly damage or injury. In addition, adhering to functional safety processes can help expand a manufacturer’s market and customer base worldwide. Protecting systems’ operators from the potential for major injury is ...", "subpage_snippet": "", "source": "www.sensata.com", "link": "https://www.sensata.com/sites/default/files/a/sensata-industrial-engineers-functional-safety-white-paper.pdf", "content": "Engineers designing systems for industrial and factory applications are adopting functional safety design practices to reduce the risk of system failures that could result in costly damage or injury. In addition, adhering to functional safety processes can help expand a manufacturer’s market and customer base worldwide. Protecting systems’ operators from the potential for major injury is ..."}
+{"idx": 1, "title": "Six Sigma and Change Management for Sustainable Success DeFIANT: A degradation-aware large model paradigm for ... What is Six Sigma? Definition, Methodology and Tools 6M Root Cause Analysis in Lean Six Sigma. Everything to Know 6M Root Cause Analysis in Lean Six Sigma. Everything to Know 6M Root Cause Analysis in Lean Six Sigma. Everything to Know The Ultimate Guide to FMEA: What it is and How to Use it", "date": "", "ddg_snippet": "Oct 1, 2024 · Developed in the 1980s, Six Sigma focuses on process improvement, enabling organizations to identify and minimize errors that impede performance, ultimately boosting efficiency. This approach can help achieve key objectives such as increasing customer satisfaction, reducing costs, and enhancing profitability. Sep 1, 2025 · Based on this balance between performance and deployment cost, we select a 6-layer frozen backbone as the optimal architecture. The finding reinforces our hypothesis that long-horizon degradation forecasting benefits most from a moderate-depth representation reservoir rather than brute-force overparameterization. Jan 9, 2020 · Six Sigma is a data-driven methodology that provides tools and techniques for making business processes more effective and efficient. Is 6ms a good framework for process improvement? While originally developed for manufacturing environments, the ubiquity and flexibility of the 6Ms framework enable adaptation across functions. Nonetheless, its ability to handle complexity at scale remains a cornerstone application for process improvement practitioners. What is a 6ms approach? Before exploring the versatile 6Ms approach, review foundational concepts and alternative causal analysis techniques. This iterative interrogation method identifies root causes by repeatedly asking “Why?” 5 times. Each answer forms the basis of the next why question until the source is revealed. What are 6ms & how do they work? The 6Ms will trace inconsistencies back to supplier QA programs, handling, inspection methods, storage conditions, and formulation changes that bleed impacts. Frontline personnel directly or indirectly influence production, whether through operation, maintenance, or quality assurance. In a world that constantly strives for quality, dependability, and customer satisfaction, preempting errors before they occur is a business imperative. Enter FMEA—Failure Mode and Effects Analysis—a structured approach for identifying potential failures in a product, process, or system, and taking proactive steps to mitigate them. This analytical method is a cornerstone in industries like ...", "subpage_snippet": "", "source": "www.prosci.com", "link": "https://www.prosci.com/blog/six-sigma", "content": "Oct 1, 2024 · Developed in the 1980s, Six Sigma focuses on process improvement, enabling organizations to identify and minimize errors that impede performance, ultimately boosting efficiency. This approach can help achieve key objectives such as increasing customer satisfaction, reducing costs, and enhancing profitability. Sep 1, 2025 · Based on this balance between performance and deployment cost, we select a 6-layer frozen backbone as the optimal architecture. The finding reinforces our hypothesis that long-horizon degradation forecasting benefits most from a moderate-depth representation reservoir rather than brute-force overparameterization. Jan 9, 2020 · Six Sigma is a data-driven methodology that provides tools and techniques for making business processes more effective and efficient. Is 6ms a good framework for process improvement? While originally developed for manufacturing environments, the ubiquity and flexibility of the 6Ms framework enable adaptation across functions. Nonetheless, its ability to handle complexity at scale remains a cornerstone application for process improvement practitioners. What is a 6ms approach? Before exploring the versatile 6Ms approach, review foundational concepts and alternative causal analysis techniques. This iterative interrogation method identifies root causes by repeatedly asking “Why?” 5 times. Each answer forms the basis of the next why question until the source is revealed. What are 6ms & how do they work? The 6Ms will trace inconsistencies back to supplier QA programs, handling, inspection methods, storage conditions, and formulation changes that bleed impacts. Frontline personnel directly or indirectly influence production, whether through operation, maintenance, or quality assurance. In a world that constantly strives for quality, dependability, and customer satisfaction, preempting errors before they occur is a business imperative. Enter FMEA—Failure Mode and Effects Analysis—a structured approach for identifying potential failures in a product, process, or system, and taking proactive steps to mitigate them. This analytical method is a cornerstone in industries like ..."}
+{"idx": 2, "title": "What is Six Sigma? Definition, Methodology and Tools", "date": "", "ddg_snippet": "Jan 9, 2020 · Six Sigma is a data-driven methodology that provides tools and techniques for making business processes more effective and efficient.", "subpage_snippet": "", "source": "www.sixsigmadaily.com", "link": "https://www.sixsigmadaily.com/what-is-six-sigma/", "content": "Jan 9, 2020 · Six Sigma is a data-driven methodology that provides tools and techniques for making business processes more effective and efficient."}
+{"idx": 3, "title": "The Ultimate Guide to FMEA: What it is and How to Use it", "date": "", "ddg_snippet": "In a world that constantly strives for quality, dependability, and customer satisfaction, preempting errors before they occur is a business imperative. Enter FMEA—Failure Mode and Effects Analysis—a structured approach for identifying potential failures in a product, process, or system, and taking proactive steps to mitigate them. This analytical method is a cornerstone in industries like ...", "subpage_snippet": "", "source": "www.learnleansigma.com", "link": "https://www.learnleansigma.com/error-proofing/how-to-complete-fmea/", "content": "In a world that constantly strives for quality, dependability, and customer satisfaction, preempting errors before they occur is a business imperative. Enter FMEA—Failure Mode and Effects Analysis—a structured approach for identifying potential failures in a product, process, or system, and taking proactive steps to mitigate them. This analytical method is a cornerstone in industries like ..."}
+{"idx": 4, "title": "What Makes and Breaks Safety Fine - tuning ? Mechanistic Study", "date": "", "ddg_snippet": "Layer 6 ( Safety fine - tuning ).This makes sense as, for unsafe samples, the variation in the preferred output strings in safety fine - tuning dataset is much less compared to that of safe samples: e.g., preferred outputs for unsafe samples are generally ‘NULL’, ‘I can’t assist’, etc.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BS2CbUkJpy", "content": "Layer 6 ( Safety fine - tuning ).This makes sense as, for unsafe samples, the variation in the preferred output strings in safety fine - tuning dataset is much less compared to that of safe samples: e.g., preferred outputs for unsafe samples are generally ‘NULL’, ‘I can’t assist’, etc."}
+{"idx": 5, "title": "What Makes and Breaks Safety Fine - tuning ? Mechanistic Study", "date": "", "ddg_snippet": "Layer 6 ( Safety fine - tuning ).This makes sense as, for unsafe samples, the variation in the preferred output strings in safety fine - tuning dataset is much less compared to that of safe samples: e.g., preferred outputs for unsafe samples are generally ‘NULL’, ‘I can’t assist’, etc.", "subpage_snippet": "", "source": "ora.ox.ac.uk", "link": "https://ora.ox.ac.uk/objects/uuid:9757f000-486f-48dc-84b1-1ab9d4db09ee/files/rbz60cx627", "content": "Layer 6 ( Safety fine - tuning ).This makes sense as, for unsafe samples, the variation in the preferred output strings in safety fine - tuning dataset is much less compared to that of safe samples: e.g., preferred outputs for unsafe samples are generally ‘NULL’, ‘I can’t assist’, etc."}
+{"idx": 6, "title": "Harmful Fine-tuning Attacks and Defenses for Large ...", "date": "", "ddg_snippet": "by T Huang · 2024 · Cited by 55 — Safety alignment done by SFT makes the model slightly resistant to the fine-tuning attack compared to non-aligned model, but still the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.18169", "content": "by T Huang · 2024 · Cited by 55 — Safety alignment done by SFT makes the model slightly resistant to the fine-tuning attack compared to non-aligned model, but still the ..."}
+{"idx": 7, "title": "The Comprehensive Guide to Fine-tuning LLM | by Sunil Rao", "date": "", "ddg_snippet": "Fine-tuning is the process of taking a pre-trained language model (a large neural network that has learned general language patterns from a massive dataset)", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science-collective/comprehensive-guide-to-fine-tuning-llm-4a8fd4d0e0af", "content": "Fine-tuning is the process of taking a pre-trained language model (a large neural network that has learned general language patterns from a massive dataset)"}
+{"idx": 8, "title": "A structured review of advanced LLM fine-tuning techniques", "date": "", "ddg_snippet": "by S Pratap · 2025 · Cited by 3 — In this study, we will be reviewing the types of techniques developed, their impacts and benefits concerning performance and resource usage along with the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2949719125000202", "content": "by S Pratap · 2025 · Cited by 3 — In this study, we will be reviewing the types of techniques developed, their impacts and benefits concerning performance and resource usage along with the ..."}
+{"idx": 9, "title": "Towards Understanding Fine-Tuning Mechanisms of LLMs ...", "date": "", "ddg_snippet": "Fine-tuning significantly improves the performance of Large Language Models (LLMs), yet its underlying mechanisms remain poorly understood.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46507", "content": "Fine-tuning significantly improves the performance of Large Language Models (LLMs), yet its underlying mechanisms remain poorly understood."}
diff --git "a/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_\316\267M_\316\267S_5e-5_1e-5_learning_rates.jsonl" "b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_\316\267M_\316\267S_5e-5_1e-5_learning_rates.jsonl"
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index 0000000000000000000000000000000000000000..4efbfc67d674d9ca3bc69b8f9133404fb3d7a39e
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+{"idx": 0, "title": "Compression Machinery for Oil and Gas 978-0-12-814683-5", "date": "", "ddg_snippet": "Typical compression equipment consists of both reciprocating and centrifugal compressors that are used to inject the gas into the storage formation and to boost ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/compression-machinery-for-oil-and-gas-978-0-12-814683-5.html", "content": "Typical compression equipment consists of both reciprocating and centrifugal compressors that are used to inject the gas into the storage formation and to boost ..."}
+{"idx": 1, "title": "Genetic and Evolutionary Computation – GECCO 2004", "date": "", "ddg_snippet": "A total of 460 papers were submitted to GECCO 2004. After a rigorous double-blind reviewing process, 230 papers were accepted for full publication and oral ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/b98643.pdf", "content": "A total of 460 papers were submitted to GECCO 2004. After a rigorous double-blind reviewing process, 230 papers were accepted for full publication and oral ..."}
+{"idx": 2, "title": "PhDTesis ECasati | PDF | Applied And Interdisciplinary ...", "date": "", "ddg_snippet": "This document is the introduction to Emiliano Casati's 2014 doctoral thesis on new concepts for organic Rankine cycle power systems.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/706989216/PhDTesis-ECasati", "content": "This document is the introduction to Emiliano Casati's 2014 doctoral thesis on new concepts for organic Rankine cycle power systems."}
+{"idx": 3, "title": "(Food Preservation Technology Series) Jorge Welti ...", "date": "", "ddg_snippet": "(Food preservation technology series) Jorge Welti-Chanes, Jose Miguel Aguilera - Engineering and food for the 21st century-CRC Press (2002).pdf - Free ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/398421310/Food-preservation-technology-series-Jorge-Welti-Chanes-Jose-Miguel-Aguilera-Engineering-and-food-for-the-21st-century-CRC-Press-2002-pdf", "content": "(Food preservation technology series) Jorge Welti-Chanes, Jose Miguel Aguilera - Engineering and food for the 21st century-CRC Press (2002).pdf - Free ..."}
+{"idx": 4, "title": "International Workshop, MRCS 2006, Istanbul, Turkey ...", "date": "", "ddg_snippet": "Multimedia Content Representation, Classification and Security: International Workshop, MRCS 2006, Istanbul, Turkey, September 11-13, 2006, Proceedings.", "subpage_snippet": "", "source": "epdf.pub", "link": "https://epdf.pub/multimedia-content-representation-classification-and-security-international-work.html", "content": "Multimedia Content Representation, Classification and Security: International Workshop, MRCS 2006, Istanbul, Turkey, September 11-13, 2006, Proceedings."}
+{"idx": 5, "title": "Heat Pumps In Chemical Process Industry [PDF]", "date": "", "ddg_snippet": "Reader friendly resources namely relevant equations, diagrams, figures and references that reflect the current and upcoming heat pump technologies, will be of ...", "subpage_snippet": "", "source": "vdoc.pub", "link": "https://vdoc.pub/documents/heat-pumps-in-chemical-process-industry-o24of28njm80", "content": "Reader friendly resources namely relevant equations, diagrams, figures and references that reflect the current and upcoming heat pump technologies, will be of ..."}
+{"idx": 6, "title": "Dictionary of Hydromechanics", "date": "", "ddg_snippet": "by L Benedetti — If the failure rate decreases over time, then k < 1 . If the failure rate is constant over time, then k = 1 . If the failure rate increases ...", "subpage_snippet": "", "source": "www.ittc.info", "link": "https://www.ittc.info/media/3998/2014-ittc-harmonizeddictionary_mfbj_12-8-14final.pdf", "content": "by L Benedetti — If the failure rate decreases over time, then k < 1 . If the failure rate is constant over time, then k = 1 . If the failure rate increases ..."}
+{"idx": 7, "title": "Dictionary of Hydromechanics", "date": "", "ddg_snippet": "9 Dec 2017 — If the failure rate decreases over time, then k < 1 . If the failure rate is constant over time, then k = 1 . If the failure rate increases ...", "subpage_snippet": "", "source": "ittc.info", "link": "https://ittc.info/media/7939/2017-ittc-dictionary.pdf", "content": "9 Dec 2017 — If the failure rate decreases over time, then k < 1 . If the failure rate is constant over time, then k = 1 . If the failure rate increases ..."}
+{"idx": 8, "title": "Chemical Process Design Computer-Aided Case Studies", "date": "", "ddg_snippet": "This research paper discusses the integration of dynamic simulation in the early stages of chemical process design, emphasizing its role in supporting design ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/11311882/Chemical_Process_Design_Computer_Aided_Case_Studies", "content": "This research paper discusses the integration of dynamic simulation in the early stages of chemical process design, emphasizing its role in supporting design ..."}
+{"idx": 9, "title": "Gas Turbines For Electric Power Generation", "date": "", "ddg_snippet": "This very well-written book covers the theoretical basics of thermodynamics as well as components such as the compressor, the combustor, the turbine, the whole ...", "subpage_snippet": "", "source": "pdfcoffee.com", "link": "https://pdfcoffee.com/gas-turbines-for-electric-power-generation-3-pdf-free.html", "content": "This very well-written book covers the theoretical basics of thermodynamics as well as components such as the compressor, the combustor, the turbine, the whole ..."}
diff --git a/data/sampled_jsons/Winter_et_al._sybilhunter_Tor_abstract.jsonl b/data/sampled_jsons/Winter_et_al._sybilhunter_Tor_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f1262d863cd53145db402ba5799a477fff1b1ef0
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+++ b/data/sampled_jsons/Winter_et_al._sybilhunter_Tor_abstract.jsonl
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+{"idx": 0, "title": "GitHub - NullHypothesis/ sybilhunter : Hunting for Sybils and...", "date": "", "ddg_snippet": "Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NullHypothesis/sybilhunter", "content": "Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data."}
+{"idx": 1, "title": "Identifying and characterizing Sybils in the Tor network | Request PDF", "date": "", "ddg_snippet": "Abstract . Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks.P. Winter [23], have discussed different Sybil attacks in Tor network and as per their analysis, it is found that manual verification is required for analyzing the Sybil frequently.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/301854678_Identifying_and_characterizing_Sybils_in_the_Tor_network", "content": "Abstract . Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks.P. Winter [23], have discussed different Sybil attacks in Tor network and as per their analysis, it is found that manual verification is required for analyzing the Sybil frequently."}
+{"idx": 2, "title": "Impact Analysis of Sybil Attacks in the Tor", "date": "", "ddg_snippet": "Jansen et al . evalu-ated the Tor simulator, Shadow with Tornettools [10,38], concluding that Shadow accurately models Tor ’s behavior and scales efficiently. Winter , P., Ensafi, R., Loesing, K., Feamster, N.: Identifying and Characterizing. Sybils in the Tor Network.", "subpage_snippet": "", "source": "se.informatik.uni-wuerzburg.de", "link": "https://se.informatik.uni-wuerzburg.de/secure-software-systems-group/staff0/alexandra-dmitrienko/?tx_extbibsonomycsl_publicationlist[action]=download&tx_extbibsonomycsl_publicationlist[controller]=Document&tx_extbibsonomycsl_publicationlist[fileName]=sybil_tor_WNDSS_camera_ready.pdf&tx_extbibsonomycsl_publicationlist[intraHash]=d95611676b7a299a279022a0880a9243&tx_extbibsonomycsl_publicationlist[userName]=sssgroup&cHash=1694f875b8ff2734c4c6c59cea0c7e7d", "content": "Jansen et al . evalu-ated the Tor simulator, Shadow with Tornettools [10,38], concluding that Shadow accurately models Tor ’s behavior and scales efficiently. Winter , P., Ensafi, R., Loesing, K., Feamster, N.: Identifying and Characterizing. Sybils in the Tor Network."}
+{"idx": 3, "title": "Ученые помогают Tor Project бороться с sybil -атаками — Хакер", "date": "", "ddg_snippet": "Теперь, объединив усилия с группой ученых, разработчики Tor Project создали инструмент, получивший название Sybilhunter . Он позволяет обнаруживать различные типы sybil -атак и распознавать в сети Tor вредоносные ноды.", "subpage_snippet": "", "source": "xakep.ru", "link": "https://xakep.ru/2016/02/29/sybilhunter/", "content": "Теперь, объединив усилия с группой ученых, разработчики Tor Project создали инструмент, получивший название Sybilhunter . Он позволяет обнаруживать различные типы sybil -атак и распознавать в сети Tor вредоносные ноды."}
+{"idx": 4, "title": "Carley E. Winter et al . Comparative Biochemistry and Physiology Part...", "date": "", "ddg_snippet": "Sublethal disruption to ion regulation persisted following an acute salt exposure. CaCO3 mitigated mortality amid a salt exposure. Abstract Salmonids spawn in freshwater streams including those in urban areas that are impacted by human activities.", "subpage_snippet": "", "source": "zoology.ubc.ca", "link": "https://zoology.ubc.ca/article/publication/carley-e-winter-et-al-comparative-biochemistry-and-physiology-part-c", "content": "Sublethal disruption to ion regulation persisted following an acute salt exposure. CaCO3 mitigated mortality amid a salt exposure. Abstract Salmonids spawn in freshwater streams including those in urban areas that are impacted by human activities."}
+{"idx": 5, "title": "ZXAD: High-volume Attack Mitigation for Tor", "date": "", "ddg_snippet": "ABSTRACT . The Tor anonymity network is often abused by attackers to (anony-mously) convey attack traffic. These attacks abuse Tor exit relays (i.e., the relays through which traffic exits Tor ) by making it appear the attack originates there; as a result...", "subpage_snippet": "", "source": "cypherpunks.ca", "link": "https://cypherpunks.ca/~iang/pubs/zxad-wpes21.pdf", "content": "ABSTRACT . The Tor anonymity network is often abused by attackers to (anony-mously) convey attack traffic. These attacks abuse Tor exit relays (i.e., the relays through which traffic exits Tor ) by making it appear the attack originates there; as a result..."}
+{"idx": 6, "title": "[ tor -talk] Tor Weekly News — June 3rd, 2015", "date": "", "ddg_snippet": "Philipp Winter added functionality [23] to sybilhunter [24], the tool for detecting attempts to take control of a large part of the Tor network, that produces a visualization of similarities between relay descriptors.", "subpage_snippet": "", "source": "lists.torproject.org", "link": "https://lists.torproject.org/pipermail/tor-talk/2015-June/038057.html", "content": "Philipp Winter added functionality [23] to sybilhunter [24], the tool for detecting attempts to take control of a large part of the Tor network, that produces a visualization of similarities between relay descriptors."}
+{"idx": 7, "title": "ExitSniffer: Towards Comprehensive Security Analysis of... | SpringerLink", "date": "", "ddg_snippet": "Winter , P., et al .: Spoiled onions: exposing malicious tor exit relays. In: De Cristofaro, E., Murdoch, S.J. (eds.)Identifying and characterizing Sybils in the Tor network.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-16-9229-1_6", "content": "Winter , P., et al .: Spoiled onions: exposing malicious tor exit relays. In: De Cristofaro, E., Murdoch, S.J. (eds.)Identifying and characterizing Sybils in the Tor network."}
+{"idx": 8, "title": "Honey Onions: a Framework for Characterizing and", "date": "", "ddg_snippet": "Winter et al . [29] expose malicious exit nodes by de-veloping two exit relay scanners, one for credential snifng and one for active man-in-the-middle (MITM) attacks.For example, Biryukov et al . [32], document their ndings on probing the network topology and connectivity of Tor relays.", "subpage_snippet": "", "source": "www.khoury.northeastern.edu", "link": "https://www.khoury.northeastern.edu/home/amirali/publications/HOnion_CNS_2016.pdf", "content": "Winter et al . [29] expose malicious exit nodes by de-veloping two exit relay scanners, one for credential snifng and one for active man-in-the-middle (MITM) attacks.For example, Biryukov et al . [32], document their ndings on probing the network topology and connectivity of Tor relays."}
+{"idx": 9, "title": "Cyber Security", "date": "", "ddg_snippet": "Sybilhunter integrates the functions of exitmap tool and adds HTML and HTTP injection detection.According to the work of Zhao Zhang et al ., some websites have implemented IP blocking on Tor exit nodes [13].", "subpage_snippet": "", "source": "library.oapen.org", "link": "https://library.oapen.org/bitstream/handle/20.500.12657/52870/978-981-16-9229-1.pdf?sequence=1&isAllowed=y", "content": "Sybilhunter integrates the functions of exitmap tool and adds HTML and HTTP injection detection.According to the work of Zhao Zhang et al ., some websites have implemented IP blocking on Tor exit nodes [13]."}
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new file mode 100644
index 0000000000000000000000000000000000000000..082fd30676d90454e903fdd34e87b687cd795e99
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Witness Complexes in Computational Topology", "date": "", "ddg_snippet": "Introduction to Witness Complexes Witness complexes are a fundamental concept in computational topology, playing a crucial role in simplifying complex data analysis. In this article, we will explore the definition, motivation, historical context, and importance of witness complexes in computational topology. Definition and Motivation A witness complex is a simplicial complex constructed from a ...", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/witness-complexes-computational-topology", "content": "Introduction to Witness Complexes Witness complexes are a fundamental concept in computational topology, playing a crucial role in simplifying complex data analysis. In this article, we will explore the definition, motivation, historical context, and importance of witness complexes in computational topology. Definition and Motivation A witness complex is a simplicial complex constructed from a ..."}
+{"idx": 1, "title": "(PDF) Topological estimation using witness complexes", "date": "", "ddg_snippet": "We obtain smaller complexes by choosing a set of `landmark'points from our data set, and then constructing a \" witness complex \" on this set using ideas motivated by the usual Delaunay complex in ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/248117036_Topological_estimation_using_witness_complexes", "content": "We obtain smaller complexes by choosing a set of `landmark'points from our data set, and then constructing a \" witness complex \" on this set using ideas motivated by the usual Delaunay complex in ..."}
+{"idx": 2, "title": "Topological Data Analysis with -net Induced Lazy Witness Complex", "date": "", "ddg_snippet": "We empirically and comparatively show that the size of the -net landmarks constructed by the algorithms varies inversely with and agrees with the known bound on the size of -net [20]. We empirically and comparatively validate our claim on the topological ap-proximation quality of the lazy witness complex induced by the -net landmarks (Section 6).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1906.06122", "content": "We empirically and comparatively show that the size of the -net landmarks constructed by the algorithms varies inversely with and agrees with the known bound on the size of -net [20]. We empirically and comparatively validate our claim on the topological ap-proximation quality of the lazy witness complex induced by the -net landmarks (Section 6)."}
+{"idx": 3, "title": "PDF Reconstruction Using Witness Complexes - Inria", "date": "", "ddg_snippet": "The advantage of the CW(L) latter complex is that it can be stored as a subcomplex of D(L), which allows to speed-up the (reverse) ·-nearest landmarks queries in practice.", "subpage_snippet": "", "source": "geometrica.saclay.inria.fr", "link": "https://geometrica.saclay.inria.fr/team/Steve.Oudot/papers/go-ruwc-07/go-ruwc-07.pdf", "content": "The advantage of the CW(L) latter complex is that it can be stored as a subcomplex of D(L), which allows to speed-up the (reverse) ·-nearest landmarks queries in practice."}
+{"idx": 4, "title": "Reconstruction Using Witness Complexes - PMC", "date": "", "ddg_snippet": "The fact that (L) is an abstract simplicial complex means that a simplex belongs to the complex only if all its faces do. By the so-called Weak Witness Theorem [20], we have (L) ⊆ (L), which implies that (L) is an embedded simplicial complex . In the sequel, L will be referred to as the set of landmarks , and W as the set of witnesses.", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3105630/", "content": "The fact that (L) is an abstract simplicial complex means that a simplex belongs to the complex only if all its faces do. By the so-called Weak Witness Theorem [20], we have (L) ⊆ (L), which implies that (L) is an embedded simplicial complex . In the sequel, L will be referred to as the set of landmarks , and W as the set of witnesses."}
+{"idx": 5, "title": "PDF Topological estimation using witness complexes", "date": "", "ddg_snippet": "\" Witness complexes (+ persistence algorithm!) lead to a rapid, accurate and well-motivated method for estimating the topology of a point-cloud data set.\" The definitions depend only on having a distance function.", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/07c7/72ccea386a7141dec71dd0d6a1e638dfc1f9.pdf", "content": "\" Witness complexes (+ persistence algorithm!) lead to a rapid, accurate and well-motivated method for estimating the topology of a point-cloud data set.\" The definitions depend only on having a distance function."}
+{"idx": 6, "title": "Witness Complexes - knowledge.deck.no", "date": "", "ddg_snippet": "Witness complexes are a type of simplicial complex which offer a fast and memory-efficient way to approximate the topology of large data sets.", "subpage_snippet": "", "source": "knowledge.deck.no", "link": "https://knowledge.deck.no/mathematics/topology/topological-data-analysis/witness-complexes", "content": "Witness complexes are a type of simplicial complex which offer a fast and memory-efficient way to approximate the topology of large data sets."}
+{"idx": 7, "title": "Computes a Witness Complex for a given set of landmarks and ... - GitHub", "date": "", "ddg_snippet": "Example import torch from witnesscomplex. simplicial_complex import WitnessComplex witnesses = torch. randn ((100, 2)) landmarks = witnesses [: 20,:] #computation of filtration values of 1-simplicies (recommended if that is the only thing needed) wc1 = WitnessComplex ( landmarks , witnesses) wc1. compute_metric_optimized (n_jobs=2)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MrBellamonte/WitnessComplex", "content": "Example import torch from witnesscomplex. simplicial_complex import WitnessComplex witnesses = torch. randn ((100, 2)) landmarks = witnesses [: 20,:] #computation of filtration values of 1-simplicies (recommended if that is the only thing needed) wc1 = WitnessComplex ( landmarks , witnesses) wc1. compute_metric_optimized (n_jobs=2)"}
+{"idx": 8, "title": "Mastering Witness Complex for Topological Insights", "date": "", "ddg_snippet": "Computational efficiency: The Witness Complex can be computed efficiently, even for large datasets, as it only requires computing the closest landmark points for each data point.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/mastering-witness-complex-topological-insights", "content": "Computational efficiency: The Witness Complex can be computed efficiently, even for large datasets, as it only requires computing the closest landmark points for each data point."}
+{"idx": 9, "title": "PDF Topological Data Analysis with -net Induced Lazy Witness Complex - Springer", "date": "", "ddg_snippet": "The enthusiasm that followed the initial successes of topological data analysis was curbed by the computational cost of constructing such simplicial representations. The lazy witness complex is a computationally feasible approximation of the underlying topological structure of a point cloud.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-27618-8_28.pdf", "content": "The enthusiasm that followed the initial successes of topological data analysis was curbed by the computational cost of constructing such simplicial representations. The lazy witness complex is a computationally feasible approximation of the underlying topological structure of a point cloud."}
diff --git a/data/sampled_jsons/Witness_complex_computational_efficiency_sparse_approximation_topological_data_analysis.jsonl b/data/sampled_jsons/Witness_complex_computational_efficiency_sparse_approximation_topological_data_analysis.jsonl
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index 0000000000000000000000000000000000000000..1f596bb4777707503c62449b500755d3f664ce6c
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "net Induced Lazy Witness Complex for Efficient Topological ...", "date": "", "ddg_snippet": "Outline Background on Topological data analysis (TDA) Computational Issues in TDA and approximate simplicial representations ε-net: Old approach in new application. Our proposal: ε-net induced lazy witness complex and approximation guarantees. Our algorithms. Questions.", "subpage_snippet": "", "source": "toggled.github.io", "link": "https://toggled.github.io/naheed/assets/pdf/Moscow_Beijing_Seminar_epsilon_net_May25_2022.pdf", "content": "Outline Background on Topological data analysis (TDA) Computational Issues in TDA and approximate simplicial representations ε-net: Old approach in new application. Our proposal: ε-net induced lazy witness complex and approximation guarantees. Our algorithms. Questions."}
+{"idx": 1, "title": "Topological Data Analyses of Time Series Using Witness Complexes", "date": "", "ddg_snippet": "Nov 16, 2019 · We introduce a topological membership test for sliding windows of time series data that uses a sparse simplicial complex - the witness complex - to model the data and assess its performance across a range of model parameters affecting computational efficiency .", "subpage_snippet": "", "source": "scholar.colorado.edu", "link": "https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/mc87pq327", "content": "Nov 16, 2019 · We introduce a topological membership test for sliding windows of time series data that uses a sparse simplicial complex - the witness complex - to model the data and assess its performance across a range of model parameters affecting computational efficiency ."}
+{"idx": 2, "title": "Topological Data Analysis with ||epsi;$-net Induced Lazy Witness ... net Induced Lazy Witness Complex for Efficient Topological ... Witness Complexes in Computational Topology Topological Data Analysis with -net Induced Lazy Witness Complex Topological Data Analyses of Time Series Using Witness Complexes Effective data reduction algorithm for topological data analysis arXiv:2009.13071v1 [cs.CG] 25 Sep 2020", "date": "", "ddg_snippet": "Jun 14, 2019 · The enthusiasm that followed the initial successes of topological data analysis was curbed by the computational cost of constructing such simplicial representations. The lazy witness complex is a computationally feasible approximation of the underlying topological structure of a point cloud. Outline Background on Topological data analysis (TDA) Computational Issues in TDA and approximate simplicial representations ε-net: Old approach in new application. Our proposal: ε-net induced lazy witness complex and approximation guarantees. Our algorithms. Questions. Jun 15, 2025 · Importance in Computational Topology Witness complexes are essential in computational topology because they provide a way to simplify complex data while preserving its topological features. This is particularly useful in data analysis and visualization, where the goal is to identify patterns and structures in the data . As we discussed in Sect. 3, topological data analysis of a dataset begins with the computation of simplicial complex representations. Though Vietoris-Rips is the best possible approximation of the Cech ˇ complex , it incurs an exponential computational cost with respect to the size of the point cloud. Thus, lazy wit -ness complex is often used as a p... See full list on link.springer.com We implement the pipeline illustrated in Fig. 1 to empirically validate our theo-retical claims and also the effectiveness, efficiency , and stability of the algorithms that construct -net landmarks compared to that of the random and maxmin algorithms. We test and evaluate these algorithms on two synthetic point cloud datasets, namely Torus and Tang... See full list on link.springer.com We use the notion of -net to capture bounds on the loss of the topological features of the induced lazy witness complex . We prove that -net is an - approximation to the original point cloud and the lazy witness complex induced by -net is a 3- approximation to the Vietoris-Rips complex on the landmarks for values of filtration parameter beyond 2 . Suc... See full list on link.springer.com Nov 16, 2019 · We introduce a topological membership test for sliding windows of time series data that uses a sparse simplicial complex - the witness complex - to model the data and assess its performance across a range of model parameters affecting computational efficiency . Jun 15, 2025 · Recent advances in computational science have led to a data explosion in the scientific community exploring complex natural phenomena. In particular, high-dimensional and complex simulations are being implemented in various fields using high-performance computing technology. However, these datasets, especially biological data and astronomical data , are often so high-dimensional that they ... Simplicial Complex . Topological data analysis computes the topological features of a dataset, such as persistent homology classes, by computing the topo-logical objects called simplicial complex . A simplicial complex is constructed using simplices. Formally, a k-simplex is the convex-hull of (k + 1) data points. For instance, a 0-simplex [v0] is a single point, a 1-simplex [v0v1] is an edge ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1906.06122", "content": "Jun 14, 2019 · The enthusiasm that followed the initial successes of topological data analysis was curbed by the computational cost of constructing such simplicial representations. The lazy witness complex is a computationally feasible approximation of the underlying topological structure of a point cloud. Outline Background on Topological data analysis (TDA) Computational Issues in TDA and approximate simplicial representations ε-net: Old approach in new application. Our proposal: ε-net induced lazy witness complex and approximation guarantees. Our algorithms. Questions. Jun 15, 2025 · Importance in Computational Topology Witness complexes are essential in computational topology because they provide a way to simplify complex data while preserving its topological features. This is particularly useful in data analysis and visualization, where the goal is to identify patterns and structures in the data . As we discussed in Sect. 3, topological data analysis of a dataset begins with the computation of simplicial complex representations. Though Vietoris-Rips is the best possible approximation of the Cech ˇ complex , it incurs an exponential computational cost with respect to the size of the point cloud. Thus, lazy wit -ness complex is often used as a p... See full list on link.springer.com We implement the pipeline illustrated in Fig. 1 to empirically validate our theo-retical claims and also the effectiveness, efficiency , and stability of the algorithms that construct -net landmarks compared to that of the random and maxmin algorithms. We test and evaluate these algorithms on two synthetic point cloud datasets, namely Torus and Tang... See full list on link.springer.com We use the notion of -net to capture bounds on the loss of the topological features of the induced lazy witness complex . We prove that -net is an - approximation to the original point cloud and the lazy witness complex induced by -net is a 3- approximation to the Vietoris-Rips complex on the landmarks for values of filtration parameter beyond 2 . Suc... See full list on link.springer.com Nov 16, 2019 · We introduce a topological membership test for sliding windows of time series data that uses a sparse simplicial complex - the witness complex - to model the data and assess its performance across a range of model parameters affecting computational efficiency . Jun 15, 2025 · Recent advances in computational science have led to a data explosion in the scientific community exploring complex natural phenomena. In particular, high-dimensional and complex simulations are being implemented in various fields using high-performance computing technology. However, these datasets, especially biological data and astronomical data , are often so high-dimensional that they ... Simplicial Complex . Topological data analysis computes the topological features of a dataset, such as persistent homology classes, by computing the topo-logical objects called simplicial complex . A simplicial complex is constructed using simplices. Formally, a k-simplex is the convex-hull of (k + 1) data points. For instance, a 0-simplex [v0] is a single point, a 1-simplex [v0v1] is an edge ..."}
+{"idx": 3, "title": "Witness Complexes in Computational Topology", "date": "", "ddg_snippet": "Jun 15, 2025 · Importance in Computational Topology Witness complexes are essential in computational topology because they provide a way to simplify complex data while preserving its topological features. This is particularly useful in data analysis and visualization, where the goal is to identify patterns and structures in the data .", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/witness-complexes-computational-topology", "content": "Jun 15, 2025 · Importance in Computational Topology Witness complexes are essential in computational topology because they provide a way to simplify complex data while preserving its topological features. This is particularly useful in data analysis and visualization, where the goal is to identify patterns and structures in the data ."}
+{"idx": 4, "title": "Topological Data Analysis with -net Induced Lazy Witness Complex Topological Data Analyses of Time Series Using Witness Complexes Effective data reduction algorithm for topological data analysis arXiv:2009.13071v1 [cs.CG] 25 Sep 2020", "date": "", "ddg_snippet": "As we discussed in Sect. 3, topological data analysis of a dataset begins with the computation of simplicial complex representations. Though Vietoris-Rips is the best possible approximation of the Cech ˇ complex , it incurs an exponential computational cost with respect to the size of the point cloud. Thus, lazy wit -ness complex is often used as a p... See full list on link.springer.com We implement the pipeline illustrated in Fig. 1 to empirically validate our theo-retical claims and also the effectiveness, efficiency , and stability of the algorithms that construct -net landmarks compared to that of the random and maxmin algorithms. We test and evaluate these algorithms on two synthetic point cloud datasets, namely Torus and Tang... See full list on link.springer.com We use the notion of -net to capture bounds on the loss of the topological features of the induced lazy witness complex . We prove that -net is an - approximation to the original point cloud and the lazy witness complex induced by -net is a 3- approximation to the Vietoris-Rips complex on the landmarks for values of filtration parameter beyond 2 . Suc... See full list on link.springer.com Nov 16, 2019 · We introduce a topological membership test for sliding windows of time series data that uses a sparse simplicial complex - the witness complex - to model the data and assess its performance across a range of model parameters affecting computational efficiency . Jun 15, 2025 · Recent advances in computational science have led to a data explosion in the scientific community exploring complex natural phenomena. In particular, high-dimensional and complex simulations are being implemented in various fields using high-performance computing technology. However, these datasets, especially biological data and astronomical data , are often so high-dimensional that they ... Simplicial Complex . Topological data analysis computes the topological features of a dataset, such as persistent homology classes, by computing the topo-logical objects called simplicial complex . A simplicial complex is constructed using simplices. Formally, a k-simplex is the convex-hull of (k + 1) data points. For instance, a 0-simplex [v0] is a single point, a 1-simplex [v0v1] is an edge ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-27618-8_28.pdf", "content": "As we discussed in Sect. 3, topological data analysis of a dataset begins with the computation of simplicial complex representations. Though Vietoris-Rips is the best possible approximation of the Cech ˇ complex , it incurs an exponential computational cost with respect to the size of the point cloud. Thus, lazy wit -ness complex is often used as a p... See full list on link.springer.com We implement the pipeline illustrated in Fig. 1 to empirically validate our theo-retical claims and also the effectiveness, efficiency , and stability of the algorithms that construct -net landmarks compared to that of the random and maxmin algorithms. We test and evaluate these algorithms on two synthetic point cloud datasets, namely Torus and Tang... See full list on link.springer.com We use the notion of -net to capture bounds on the loss of the topological features of the induced lazy witness complex . We prove that -net is an - approximation to the original point cloud and the lazy witness complex induced by -net is a 3- approximation to the Vietoris-Rips complex on the landmarks for values of filtration parameter beyond 2 . Suc... See full list on link.springer.com Nov 16, 2019 · We introduce a topological membership test for sliding windows of time series data that uses a sparse simplicial complex - the witness complex - to model the data and assess its performance across a range of model parameters affecting computational efficiency . Jun 15, 2025 · Recent advances in computational science have led to a data explosion in the scientific community exploring complex natural phenomena. In particular, high-dimensional and complex simulations are being implemented in various fields using high-performance computing technology. However, these datasets, especially biological data and astronomical data , are often so high-dimensional that they ... Simplicial Complex . Topological data analysis computes the topological features of a dataset, such as persistent homology classes, by computing the topo-logical objects called simplicial complex . A simplicial complex is constructed using simplices. Formally, a k-simplex is the convex-hull of (k + 1) data points. For instance, a 0-simplex [v0] is a single point, a 1-simplex [v0v1] is an edge ..."}
+{"idx": 5, "title": "Effective data reduction algorithm for topological data analysis", "date": "", "ddg_snippet": "Jun 15, 2025 · Recent advances in computational science have led to a data explosion in the scientific community exploring complex natural phenomena. In particular, high-dimensional and complex simulations are being implemented in various fields using high-performance computing technology. However, these datasets, especially biological data and astronomical data , are often so high-dimensional that they ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0096300325000293", "content": "Jun 15, 2025 · Recent advances in computational science have led to a data explosion in the scientific community exploring complex natural phenomena. In particular, high-dimensional and complex simulations are being implemented in various fields using high-performance computing technology. However, these datasets, especially biological data and astronomical data , are often so high-dimensional that they ..."}
+{"idx": 6, "title": "(PDF) Topological estimation using witness complexes", "date": "", "ddg_snippet": "witness complexes in topological data analysis .The Witness Complex was chosen for its efficiency and scalability, as it constructs a simplicial complex using landmark points, reducing computational load compared to methods like the Vietoris-Rips and Čech complexes .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/248117036_Topological_estimation_using_witness_complexes", "content": "witness complexes in topological data analysis .The Witness Complex was chosen for its efficiency and scalability, as it constructs a simplicial complex using landmark points, reducing computational load compared to methods like the Vietoris-Rips and Čech complexes ."}
+{"idx": 7, "title": "Topological Data Analysis", "date": "", "ddg_snippet": "Topological Data Analysis with -net Induced Lazy Witness Complex . Topological data analysis computes and analyses topological features of gener-ally high-dimensional and possibly noisy data sets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1906.06122", "content": "Topological Data Analysis with -net Induced Lazy Witness Complex . Topological data analysis computes and analyses topological features of gener-ally high-dimensional and possibly noisy data sets."}
+{"idx": 8, "title": "Frontiers | An Introduction to Topological Data Analysis : Fundamental...", "date": "", "ddg_snippet": "Topological data analysis (tda) is a recent and fast-growing field providing a set of new topological and geometric tools to infer relevant features for possibly complex data . It proposes new well-founded mathematical theories and computational tools that can be used independently or in...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.667963/full", "content": "Topological data analysis (tda) is a recent and fast-growing field providing a set of new topological and geometric tools to infer relevant features for possibly complex data . It proposes new well-founded mathematical theories and computational tools that can be used independently or in..."}
+{"idx": 9, "title": "Topological data analysis | Elementary Algebraic... | Fiveable", "date": "", "ddg_snippet": "Topological Data Analysis . (TDA) uses topology to extract insights from complex datasets. It focuses on the shape and structure of data , measuring how features persist across different scales.", "subpage_snippet": "", "source": "library.fiveable.me", "link": "https://library.fiveable.me/elementary-algebraic-topology/unit-13/topological-data-analysis/study-guide/5nZpW0Z6ylSpMIKM", "content": "Topological Data Analysis . (TDA) uses topology to extract insights from complex datasets. It focuses on the shape and structure of data , measuring how features persist across different scales."}
diff --git a/data/sampled_jsons/Wolpert_No_Free_Lunch_theorems_learning_algorithms.jsonl b/data/sampled_jsons/Wolpert_No_Free_Lunch_theorems_learning_algorithms.jsonl
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index 0000000000000000000000000000000000000000..11741d832eacc9b2777ab27707c8d02de18f66fa
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No free lunch theorem - Wikipedia", "date": "", "ddg_snippet": "In mathematical folklore, the \" no free lunch \" (NFL) theorem (sometimes pluralized) of David Wolpert and William Macready, alludes to the saying \" no such thing as a free lunch \", that is, there are no easy shortcuts to success. It appeared in the 1997 \" No Free Lunch Theorems for Optimization\". [1] Wolpert had previously derived no free lunch theorems for machine learning (statistical inference ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/No_free_lunch_theorem", "content": "In mathematical folklore, the \" no free lunch \" (NFL) theorem (sometimes pluralized) of David Wolpert and William Macready, alludes to the saying \" no such thing as a free lunch \", that is, there are no easy shortcuts to success. It appeared in the 1997 \" No Free Lunch Theorems for Optimization\". [1] Wolpert had previously derived no free lunch theorems for machine learning (statistical inference ..."}
+{"idx": 1, "title": "No Free Lunch Theorems", "date": "", "ddg_snippet": "No Free Lunch Theorems Broadly speaking, there are two no free lunch theorems . One for supervised machine learning ( Wolpert 1996) and one for search/optimization ( Wolpert and Macready 1997). For an overview of the ( no ) free lunch and associated theorems , see David Wolpert's What does dinner cost?", "subpage_snippet": "", "source": "no-free-lunch.org", "link": "http://no-free-lunch.org/", "content": "No Free Lunch Theorems Broadly speaking, there are two no free lunch theorems . One for supervised machine learning ( Wolpert 1996) and one for search/optimization ( Wolpert and Macready 1997). For an overview of the ( no ) free lunch and associated theorems , see David Wolpert's What does dinner cost?"}
+{"idx": 2, "title": "The no-free-lunch theorems of supervised learning | Synthese", "date": "", "ddg_snippet": "The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as purely data ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11229-021-03233-1", "content": "The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as purely data ..."}
+{"idx": 3, "title": "PDF No Free Lunch Theorems For Optimization - Evolutionary Computation ...", "date": "", "ddg_snippet": "Abstract— A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm , any elevated performance over one class of problems is offset by perfor- mance over another class. These theorems result in a geometric interpretation of ...", "subpage_snippet": "", "source": "www.cs.ubc.ca", "link": "https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf", "content": "Abstract— A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm , any elevated performance over one class of problems is offset by perfor- mance over another class. These theorems result in a geometric interpretation of ..."}
+{"idx": 4, "title": "The no-free-lunch theorems of supervised learning - JSTOR", "date": "", "ddg_snippet": "Abstract The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as ...", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/48692436", "content": "Abstract The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as ..."}
+{"idx": 5, "title": "PDF What the No Free Lunch Theorems Really Mean; How to Improve Search ...", "date": "", "ddg_snippet": "What the No Free Lunch Theorems Really Mean; How to Improve Search Algorithms David H. Wolpert SFI WORKING PAPER: 2012-10-017 SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute.", "subpage_snippet": "", "source": "sfi-edu.s3.amazonaws.com", "link": "https://sfi-edu.s3.amazonaws.com/sfi-edu/production/uploads/sfi-com/dev/uploads/filer/33/44/33440e97-fe46-4827-a1eb-a27196e1c49a/12-10-017.pdf", "content": "What the No Free Lunch Theorems Really Mean; How to Improve Search Algorithms David H. Wolpert SFI WORKING PAPER: 2012-10-017 SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute."}
+{"idx": 6, "title": "No Free Lunch Theorem: No Universal Machine Learning Algorithm", "date": "", "ddg_snippet": "Overview The \" No Free Lunch Theorem \" (NFL) is a foundational concept in the field of optimization and machine learning , introduced by David Wolpert and William Macready in the 1990s ( Wolpert , D. H., & Macready, W. G. , 1997). It essentially states that no one algorithm is universally better than others when averaged across all possible problems. This means that if an algorithm performs ...", "subpage_snippet": "", "source": "artificium.us", "link": "http://artificium.us/lessons/03.ml/l-3-106-nfl-theorem/l-3-106.html", "content": "Overview The \" No Free Lunch Theorem \" (NFL) is a foundational concept in the field of optimization and machine learning , introduced by David Wolpert and William Macready in the 1990s ( Wolpert , D. H., & Macready, W. G. , 1997). It essentially states that no one algorithm is universally better than others when averaged across all possible problems. This means that if an algorithm performs ..."}
+{"idx": 7, "title": "No Free Lunch Theorem and Its Foundational Implications for Algorithm ...", "date": "", "ddg_snippet": "The No Free Lunch (NFL) theorem is a sobering reminder that algorithmic excellence is conditional. Across the total universe of problems, every learning or optimization method performs equally ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@adnanmasood/no-free-lunch-theorem-and-its-foundational-implications-for-algorithm-selection-in-artificial-5fc49c218d76", "content": "The No Free Lunch (NFL) theorem is a sobering reminder that algorithmic excellence is conditional. Across the total universe of problems, every learning or optimization method performs equally ..."}
+{"idx": 8, "title": "No-Free-Lunch Theorems in Combinatorial Algorithms", "date": "", "ddg_snippet": "Introduction to No-Free-Lunch Theorems The No-Free-Lunch (NFL) Theorems , introduced by David Wolpert and William Macready in the late 1990s, are a fundamental concept in the field of optimization and machine learning . These theorems have far-reaching implications for combinatorial algorithms , stating that no single algorithm can outperform all others across all possible problems. Definition ...", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/no-free-lunch-theorems-combinatorial-algorithms", "content": "Introduction to No-Free-Lunch Theorems The No-Free-Lunch (NFL) Theorems , introduced by David Wolpert and William Macready in the late 1990s, are a fundamental concept in the field of optimization and machine learning . These theorems have far-reaching implications for combinatorial algorithms , stating that no single algorithm can outperform all others across all possible problems. Definition ..."}
+{"idx": 9, "title": "arXiv:2007.10928v1 [cs.LG] 21 Jul 2020", "date": "", "ddg_snippet": "David H. Wolpert Abstract The No Free Lunch theorems prove that under a uniform distribution over induction problems (search problems or learning problems), all induction algorithms performequally. As I discuss in this chapter, the importance of the theorems arises by using them to analyze scenarios involvingnon-uniform distributions, and to compare different algorithms , without any assumption ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2007.10928", "content": "David H. Wolpert Abstract The No Free Lunch theorems prove that under a uniform distribution over induction problems (search problems or learning problems), all induction algorithms performequally. As I discuss in this chapter, the importance of the theorems arises by using them to analyze scenarios involvingnon-uniform distributions, and to compare different algorithms , without any assumption ..."}
diff --git a/data/sampled_jsons/X-CLIP_frame-word_video-sentence_cross-grained_interaction_spatial_temporal_features_year_2022.jsonl b/data/sampled_jsons/X-CLIP_frame-word_video-sentence_cross-grained_interaction_spatial_temporal_features_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5aa1135eaeb47c144751b300c24e18dceb5806a9
--- /dev/null
+++ b/data/sampled_jsons/X-CLIP_frame-word_video-sentence_cross-grained_interaction_spatial_temporal_features_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DropletVideo: A Dataset and Approach to Explore Integral", "date": "", "ddg_snippet": "Temporal Consistency : Ensuring smooth transitions between frames that adhere to physical principles, enabling the video to progress in a plausible ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06053v1", "content": "Temporal Consistency : Ensuring smooth transitions between frames that adhere to physical principles, enabling the video to progress in a plausible ..."}
+{"idx": 1, "title": "Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding", "date": "", "ddg_snippet": "... grained temporal grounding : (1) Models like Video -ChatGPT (Maaz et al., 2024b ) , P-LLaVA (Xu et al., 2024a ) , and Video -LLAMA (Zhang et al., ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03290v2", "content": "... grained temporal grounding : (1) Models like Video -ChatGPT (Maaz et al., 2024b ) , P-LLaVA (Xu et al., 2024a ) , and Video -LLAMA (Zhang et al., ..."}
+{"idx": 2, "title": "Repeating Words for Video-Language Retrieval with", "date": "", "ddg_snippet": "Specifically, during the training stage, the framework contains two coarse- grained objectives and one fine- grained objective, i.e., the Video -Caption ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14812v1", "content": "Specifically, during the training stage, the framework contains two coarse- grained objectives and one fine- grained objective, i.e., the Video -Caption ..."}
+{"idx": 3, "title": "From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained", "date": "", "ddg_snippet": "Second, clip -level pooling on the video side merges all frames —expressive and neutral alike—into a single representation, allowing irrelevant ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11892v1", "content": "Second, clip -level pooling on the video side merges all frames —expressive and neutral alike—into a single representation, allowing irrelevant ..."}
+{"idx": 4, "title": "CN113869154A - A video actor segmentation method based on", "date": "", "ddg_snippet": "... cross -modal attention modules, uses clip -level visual features to roughly pay attention to the information words of language queries, and then uses ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/CN113869154A/en", "content": "... cross -modal attention modules, uses clip -level visual features to roughly pay attention to the information words of language queries, and then uses ..."}
+{"idx": 5, "title": "The Past, Present, and Future of Video Understanding", "date": "", "ddg_snippet": "... be done; there is so much more potential yet to be realized when it comes to extracting engaging clips within videos or even creating new interactive ...", "subpage_snippet": "", "source": "www.twelvelabs.io", "link": "https://www.twelvelabs.io/blog/the-past-present-and-future-of-video-understanding-applications", "content": "... be done; there is so much more potential yet to be realized when it comes to extracting engaging clips within videos or even creating new interactive ..."}
+{"idx": 6, "title": "Unleashing the Potential of Multimodal LLMs for Zero-Shot", "date": "", "ddg_snippet": "Spatio- Temporal Video Grounding (STVG) aims to localize a target object in a video both spatially and temporally, given an input text query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15178v1", "content": "Spatio- Temporal Video Grounding (STVG) aims to localize a target object in a video both spatially and temporally, given an input text query."}
+{"idx": 7, "title": "CVPR 2023 Schedule", "date": "", "ddg_snippet": "... Workshop and Challenge on Long-form Video ... 4D Hand Object Interaction : Geometric Understanding and Applications in Dexterous Manipulation", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/calendar", "content": "... Workshop and Challenge on Long-form Video ... 4D Hand Object Interaction : Geometric Understanding and Applications in Dexterous Manipulation"}
+{"idx": 8, "title": "CVPR 2023 Papers", "date": "", "ddg_snippet": "X3KD: Knowledge Distillation Across Modalities, Tasks and ... CLIPPING: Distilling CLIP -Based Models With a Student Base for Video -Language Retrieval", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/papers.html", "content": "X3KD: Knowledge Distillation Across Modalities, Tasks and ... CLIPPING: Distilling CLIP -Based Models With a Student Base for Video -Language Retrieval"}
+{"idx": 9, "title": "CVPR 2023 Papers", "date": "", "ddg_snippet": "X3KD: Knowledge Distillation Across Modalities, Tasks and ... CLIPPING: Distilling CLIP -Based Models With a Student Base for Video -Language Retrieval", "subpage_snippet": "", "source": "cvpr2023.thecvf.com", "link": "https://cvpr2023.thecvf.com/virtual/2023/papers.html", "content": "X3KD: Knowledge Distillation Across Modalities, Tasks and ... CLIPPING: Distilling CLIP -Based Models With a Student Base for Video -Language Retrieval"}
diff --git a/data/sampled_jsons/XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_Sensin.jsonl b/data/sampled_jsons/XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_Sensin.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8bc14baba50c43c72708d4f51e180cc0d83b91c1
--- /dev/null
+++ b/data/sampled_jsons/XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_Sensin.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) XLRS - Bench : Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "Ultra - High - Resolution Remote Sensing Imagery ?Figure 6. Example of XLRS - Bench in English. XLRS - Bench focuses on large -size ultra - high - resolution remote sensing imagery , integrat-. ing over 10 multimodal perception and reasoning tasks within the same image.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390354847_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_Sensing_Imagery", "content": "Ultra - High - Resolution Remote Sensing Imagery ?Figure 6. Example of XLRS - Bench in English. XLRS - Bench focuses on large -size ultra - high - resolution remote sensing imagery , integrat-. ing over 10 multimodal perception and reasoning tasks within the same image."}
+{"idx": 1, "title": "XLRS - Bench : Could Your Multimodal LLMs Understand Extremely ...", "date": "", "ddg_snippet": "XLRS - Bench focuses on extremely large ultra - high - resolution RS imagery , integrating over 10 multimodal vision-language perception and reasoning tasks within the same image.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "XLRS - Bench focuses on extremely large ultra - high - resolution RS imagery , integrating over 10 multimodal vision-language perception and reasoning tasks within the same image."}
+{"idx": 2, "title": "XLRS - Bench : Could Your Multimodal LLMs Understand Extremely ...", "date": "", "ddg_snippet": "XLRS - Bench boasts the largest average image size (8500$\\times$8500) observed thus far, with all evaluation samples meticulously annotated manually, assisted by a novel semi-automatic captioner on ultra - high - resolution RS images.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ThgEfn5CSU&referrer=[the+profile+of+Maosong+Sun](/profile?id=~Maosong_Sun1)", "content": "XLRS - Bench boasts the largest average image size (8500$\\times$8500) observed thus far, with all evaluation samples meticulously annotated manually, assisted by a novel semi-automatic captioner on ultra - high - resolution RS images."}
+{"idx": 3, "title": "XLRS - Bench", "date": "", "ddg_snippet": "XLRS - Bench focuses on large -size ultra - high - resolution remote sensing imagery , integrating over 10 multimodal perception and reasoning tasks within the same image. Benchmark Statistics.", "subpage_snippet": "", "source": "xlrs-bench.github.io", "link": "https://xlrs-bench.github.io/home_page.html", "content": "XLRS - Bench focuses on large -size ultra - high - resolution remote sensing imagery , integrating over 10 multimodal perception and reasoning tasks within the same image. Benchmark Statistics."}
+{"idx": 4, "title": "LLaVA : Large Language and Vision Assistant Explained | Encord", "date": "", "ddg_snippet": "LLaVA, short for Large Language and Vision Assistant, is one of the pioneering multimodal models.Here are the performance improvements LLaVA - NeXT has over LLaVA - 1 . 5", "subpage_snippet": "", "source": "encord.com", "link": "https://encord.com/blog/llava-large-language-vision-assistant/", "content": "LLaVA, short for Large Language and Vision Assistant, is one of the pioneering multimodal models.Here are the performance improvements LLaVA - NeXT has over LLaVA - 1 . 5"}
+{"idx": 5, "title": "GitHub - Jack-bo1220/Awesome- Remote - Sensing -Foundation-Models", "date": "", "ddg_snippet": "XLRS - Bench : Could Your Multimodal LLMs Understand Extremely Large Ultra - High - Resolution Remote Sensing Imagery ? CVPR2025. Paper.", "subpage_snippet": "", "source": "www.hubp.de", "link": "https://www.hubp.de/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models", "content": "XLRS - Bench : Could Your Multimodal LLMs Understand Extremely Large Ultra - High - Resolution Remote Sensing Imagery ? CVPR2025. Paper."}
+{"idx": 6, "title": "Comparing multi - modal LLMs using Go", "date": "", "ddg_snippet": "Comparing OpenAI’s GPT-4V, Google’s Imagen and Llava - 1 . 5 multi - modal LLMs using Go. Llava ( Large Language and Vision Assistant) is an open source multi - modal LLM that can understand and follow instructions based on image and language inputs.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/comparing-multi-modal-llms-using-go-4c9a58503692", "content": "Comparing OpenAI’s GPT-4V, Google’s Imagen and Llava - 1 . 5 multi - modal LLMs using Go. Llava ( Large Language and Vision Assistant) is an open source multi - modal LLM that can understand and follow instructions based on image and language inputs."}
+{"idx": 7, "title": "Setting Up LLaVA /BakLLaVA with vLLM: Backend... - PyImageSearch", "date": "", "ddg_snippet": "Multimodal LLMs (e.g., LLaVA and BakLLaVA) are unlocking powerful new capabilities — from answering questions about images to reasoning over documents and complex visual scenes.", "subpage_snippet": "", "source": "pyimagesearch.com", "link": "https://pyimagesearch.com/2025/09/22/setting-up-llava-bakllava-with-vllm-backend-and-api-integration/", "content": "Multimodal LLMs (e.g., LLaVA and BakLLaVA) are unlocking powerful new capabilities — from answering questions about images to reasoning over documents and complex visual scenes."}
+{"idx": 8, "title": "Enhancing Ultra High Resolution Remote Sensing Imagery Analysis...", "date": "", "ddg_snippet": "Sources of ultra - high resolution remote sensing imagery . Methods for collecting relevant visual contexts.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-Enhancing-Ultra-High-cm3gd90p5mwx3019jfbh0t0fr", "content": "Sources of ultra - high resolution remote sensing imagery . Methods for collecting relevant visual contexts."}
+{"idx": 9, "title": "MoE- LLaVA Outperforms LLaVA - 1 . 5 -7B with Only... - DigiAlps LTD", "date": "", "ddg_snippet": "Performance Evaluation: MoE- LLaVA vs LLaVA - 1 . 5 -7B. In extensive experiments, this model has demonstrated remarkable capabilities in visual understanding and the ability to reduce hallucinations in model outputs.", "subpage_snippet": "", "source": "digialps.com", "link": "https://digialps.com/moe-llava-outperforms-llava-1-5-7b-with-only-3b-parameters/", "content": "Performance Evaluation: MoE- LLaVA vs LLaVA - 1 . 5 -7B. In extensive experiments, this model has demonstrated remarkable capabilities in visual understanding and the ability to reduce hallucinations in model outputs."}
diff --git a/data/sampled_jsons/XLRS-Bench_multimodal_LLMs_remote_sensing_spatiotemporal_limitations.jsonl b/data/sampled_jsons/XLRS-Bench_multimodal_LLMs_remote_sensing_spatiotemporal_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0946dc7a06a028904472da82e65d2eac656498ef
--- /dev/null
+++ b/data/sampled_jsons/XLRS-Bench_multimodal_LLMs_remote_sensing_spatiotemporal_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.23771] XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "The astonishing breakthrough of multimodal large language models (MLLMs) has necessitated new benchmarks to quantitatively assess their capabilities, reveal their limitations , and indicate future research directions. However, this is challenging in the context of remote sensing (RS), since the imagery features ultra-high resolution that incorporates extremely complex semantic relationships ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.23771", "content": "The astonishing breakthrough of multimodal large language models (MLLMs) has necessitated new benchmarks to quantitatively assess their capabilities, reveal their limitations , and indicate future research directions. However, this is challenging in the context of remote sensing (RS), since the imagery features ultra-high resolution that incorporates extremely complex semantic relationships ..."}
+{"idx": 1, "title": "GitHub - AI9Stars/XLRS-Bench: [CVPR 2025 HIghlight] XLRS-Bench: ould ...", "date": "", "ddg_snippet": "[CVPR 2025 HIghlight] XLRS - Bench : ould Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery? - AI9Stars/ XLRS - Bench", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars/XLRS-Bench", "content": "[CVPR 2025 HIghlight] XLRS - Bench : ould Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery? - AI9Stars/ XLRS - Bench"}
+{"idx": 2, "title": "XLRS-Bench", "date": "", "ddg_snippet": "XLRS - Bench focuses on large-size ultra-high-resolution remote sensing imagery, integrating over 10 multimodal perception and reasoning tasks within the same image.", "subpage_snippet": "", "source": "xlrs-bench.github.io", "link": "https://xlrs-bench.github.io/home_page.html", "content": "XLRS - Bench focuses on large-size ultra-high-resolution remote sensing imagery, integrating over 10 multimodal perception and reasoning tasks within the same image."}
+{"idx": 3, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra ...", "date": "", "ddg_snippet": "The astonishing breakthrough of multimodal large language models (MLLMs) has necessitated new benchmarks to quantitatively assess their capabilities, reveal their limitations , and indicate future research directions. However, this is challenging in the context of remote sensing (RS), since the imagery features ultra-high resolution that incorporates extremely complex semantic relationships ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11093712", "content": "The astonishing breakthrough of multimodal large language models (MLLMs) has necessitated new benchmarks to quantitatively assess their capabilities, reveal their limitations , and indicate future research directions. However, this is challenging in the context of remote sensing (RS), since the imagery features ultra-high resolution that incorporates extremely complex semantic relationships ..."}
+{"idx": 4, "title": "Paper page - XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "The results of both general and RS-focused MLLMs on XLRS - Bench indicate that further efforts are needed for real-world RS applications. We have open-sourced XLRS - Bench to support further research in developing more powerful MLLMs for remote sensing . View arXiv page View PDF Add to collection", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.23771", "content": "The results of both general and RS-focused MLLMs on XLRS - Bench indicate that further efforts are needed for real-world RS applications. We have open-sourced XLRS - Bench to support further research in developing more powerful MLLMs for remote sensing . View arXiv page View PDF Add to collection"}
+{"idx": 5, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra ...", "date": "", "ddg_snippet": "In this paper, we introduce XLRS - Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of multimodal large language models (MLLMs) in ultra-high-resolution remote sensing (RS) scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.23771v1", "content": "In this paper, we introduce XLRS - Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of multimodal large language models (MLLMs) in ultra-high-resolution remote sensing (RS) scenarios."}
+{"idx": 6, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ... - ResearchGate", "date": "", "ddg_snippet": "We have open-sourced XLRS - Bench to support further research in developing more powerful MLLMs for remote sensing .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390354847_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_Sensing_Imagery", "content": "We have open-sourced XLRS - Bench to support further research in developing more powerful MLLMs for remote sensing ."}
+{"idx": 7, "title": "PDF XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra ...", "date": "", "ddg_snippet": "In this paper, we introduce XLRS - Bench , a comprehen-sive benchmark for evaluating the perception and rea-soning capabilities of multimodal large language models (MLLMs) in ultra-high-resolution remote sensing (RS) sce-narios.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "In this paper, we introduce XLRS - Bench , a comprehen-sive benchmark for evaluating the perception and rea-soning capabilities of multimodal large language models (MLLMs) in ultra-high-resolution remote sensing (RS) sce-narios."}
+{"idx": 8, "title": "XLRS-Bench: Welcome", "date": "", "ddg_snippet": "Benchmarking Multimodal LLMs in Real-World Ultra-High-Resolution Remote Sensing with Human-Verified Annotations", "subpage_snippet": "", "source": "xlrs-bench.github.io", "link": "https://xlrs-bench.github.io/", "content": "Benchmarking Multimodal LLMs in Real-World Ultra-High-Resolution Remote Sensing with Human-Verified Annotations"}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "The astonishing breakthrough of multimodal large language models (MLLMs) has necessitated new benchmarks to quantitatively assess their capabilities, reveal their limitations , and indicate future research directions. However, this is challenging in the context of remote sensing (RS), since the imagery features ultra-high resolution that incorporates extremely complex semantic relationships ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.html", "content": "The astonishing breakthrough of multimodal large language models (MLLMs) has necessitated new benchmarks to quantitatively assess their capabilities, reveal their limitations , and indicate future research directions. However, this is challenging in the context of remote sensing (RS), since the imagery features ultra-high resolution that incorporates extremely complex semantic relationships ..."}
diff --git a/data/sampled_jsons/XLRS-Bench_paper_Table_2_Qwen2-VL_Chinese_English_Avg_score_difference.jsonl b/data/sampled_jsons/XLRS-Bench_paper_Table_2_Qwen2-VL_Chinese_English_Avg_score_difference.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c4e7cb75741cca555441bf2b26200a32cc585d6
--- /dev/null
+++ b/data/sampled_jsons/XLRS-Bench_paper_Table_2_Qwen2-VL_Chinese_English_Avg_score_difference.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Qwen - Wikipedia", "date": "", "ddg_snippet": "Qwen is a family of large language models developed by Chinese company Alibaba Cloud. In July 2024, it was ranked as the top Chinese language model in some benchmarks and third globally behind the top models of Anthropic and OpenAI. Models.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Qwen", "content": "Qwen is a family of large language models developed by Chinese company Alibaba Cloud. In July 2024, it was ranked as the top Chinese language model in some benchmarks and third globally behind the top models of Anthropic and OpenAI. Models."}
+{"idx": 1, "title": "(PDF) XLRS - Bench : Could Your Multimodal LLMs Understand...", "date": "", "ddg_snippet": "Qwen 2 - VL excels in both. English and Chinese proficiency, outperforming both proFigure 6. Example of XLRS - Bench in English . XLRS - Bench focuses on large-size ultra-high-resolution remote sensing imagery, integrat", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390354847_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_Sensing_Imagery", "content": "Qwen 2 - VL excels in both. English and Chinese proficiency, outperforming both proFigure 6. Example of XLRS - Bench in English . XLRS - Bench focuses on large-size ultra-high-resolution remote sensing imagery, integrat"}
+{"idx": 2, "title": "Chinese | SEAL Leaderboard", "date": "", "ddg_snippet": "Chinese . Last updated: July 23, 2025. Qwen 2 72B Instruct.", "subpage_snippet": "", "source": "scale.com", "link": "https://scale.com/leaderboard/chinese", "content": "Chinese . Last updated: July 23, 2025. Qwen 2 72B Instruct."}
+{"idx": 3, "title": "Qwen 2 .5 VL ! Qwen 2 .5 VL ! Qwen 2 .5 VL ! | Qwen", "date": "", "ddg_snippet": "Capable of visual localization in different formats: Qwen 2 .5- VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.Please give their names in Chinese and English . Qwen 2 .5- VL . Sure!", "subpage_snippet": "", "source": "qwenlm.github.io", "link": "https://qwenlm.github.io/blog/qwen2.5-vl/", "content": "Capable of visual localization in different formats: Qwen 2 .5- VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.Please give their names in Chinese and English . Qwen 2 .5- VL . Sure!"}
+{"idx": 4, "title": "Comparing LLMs for Test Synthesis. Part 2", "date": "", "ddg_snippet": "Explyt benchmarked Claude Sonnet 4, Qwen3-235B, and Devstral against OpenAI’s o4-mini and GPT-4.1. See how commercial and open LLMs perform in test synthesis.", "subpage_snippet": "", "source": "explyt.ai", "link": "https://explyt.ai/en/blog/llm-choise-2", "content": "Explyt benchmarked Claude Sonnet 4, Qwen3-235B, and Devstral against OpenAI’s o4-mini and GPT-4.1. See how commercial and open LLMs perform in test synthesis."}
+{"idx": 5, "title": "Нейросеть для работы с файлами (pdf, word, excel). Анализ.", "date": "", "ddg_snippet": "Qwen 2 .5 72B Instruct. Stable Diffusion XL.", "subpage_snippet": "", "source": "smartbuddy.ru", "link": "https://smartbuddy.ru/ai/files", "content": "Qwen 2 .5 72B Instruct. Stable Diffusion XL."}
+{"idx": 6, "title": "Vision Arena | LMArena", "date": "", "ddg_snippet": "qwen 2 .5- vl -72b-instruct.Confidence Intervals on Model Strength (via Bootstrapping). Battle Count for Each Combination of Models (without Ties). Average Win Rate Against All Other Models (Uniform Sampling and No Ties).", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/leaderboard/vision", "content": "qwen 2 .5- vl -72b-instruct.Confidence Intervals on Model Strength (via Bootstrapping). Battle Count for Each Combination of Models (without Ties). Average Win Rate Against All Other Models (Uniform Sampling and No Ties)."}
+{"idx": 7, "title": "GitHub - QwenLM/ Qwen 2 .5- VL : Qwen 2 .5- VL is the multimodal large...", "date": "", "ddg_snippet": "In the past five months since Qwen 2 - VL 's release, numerous developers have built new models on the Qwen 2 - VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/QwenLM/Qwen2.5-VL", "content": "In the past five months since Qwen 2 - VL 's release, numerous developers have built new models on the Qwen 2 - VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models."}
+{"idx": 8, "title": "Qwen 2 - VL - 2 B - a Hugging Face Space by MaziyarPanahi", "date": "", "ddg_snippet": "/ Qwen 2 - VL - 2 B. like.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/MaziyarPanahi/Qwen2-VL-2B", "content": "/ Qwen 2 - VL - 2 B. like."}
+{"idx": 9, "title": "togetherai/ qwen 2 - vl -72b-instruct", "date": "", "ddg_snippet": "togetherai. qwen 2 - vl -72b-instruct.Preview. YAML. together Qwen 2 - VL (72B) Instruct model icon.", "subpage_snippet": "", "source": "hub.continue.dev", "link": "https://hub.continue.dev/togetherai/qwen2-vl-72b-instruct", "content": "togetherai. qwen 2 - vl -72b-instruct.Preview. YAML. together Qwen 2 - VL (72B) Instruct model icon."}
diff --git a/data/sampled_jsons/YOpa6dTrpt_Pedestrian_Motion_Reconstruction_Section_4.3_LiDAR_modality_improvement.jsonl b/data/sampled_jsons/YOpa6dTrpt_Pedestrian_Motion_Reconstruction_Section_4.3_LiDAR_modality_improvement.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b89045b6fa50ebb23d522beab6ff2512756231c1
--- /dev/null
+++ b/data/sampled_jsons/YOpa6dTrpt_Pedestrian_Motion_Reconstruction_Section_4.3_LiDAR_modality_improvement.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed ...", "date": "", "ddg_snippet": "This data provides a rich foundation for modeling pedestrian intent through multi-view and multi-modal insights. We also conduct comprehensive benchmark assessments across different modalities to thoroughly evaluate pedestrian motion reconstruction methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YOpa6dTrpt", "content": "This data provides a rich foundation for modeling pedestrian intent through multi-view and multi-modal insights. We also conduct comprehensive benchmark assessments across different modalities to thoroughly evaluate pedestrian motion reconstruction methods."}
+{"idx": 1, "title": "P M Reconstruction: a Large Scale Benchmark Via Mixed Reality Rendering ...", "date": "", "ddg_snippet": "4.3 LIDAR In addition to RGB images, LiDAR point clouds are a widely used modality in pedestrian motion reconstruction , offering greater range and suitability for night scenes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=YOpa6dTrpt", "content": "4.3 LIDAR In addition to RGB images, LiDAR point clouds are a widely used modality in pedestrian motion reconstruction , offering greater range and suitability for night scenes."}
+{"idx": 2, "title": "Waymo-3DSkelMo: A Multi-Agent 3D Skeletal Motion Dataset for Pedestrian ...", "date": "", "ddg_snippet": "Waymo-3DSkelMo: A high-quality 3D Multi-pedestrian motion dataset created using human motion and shape priors from LiDAR range images in the Waymo perception dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.09404v1", "content": "Waymo-3DSkelMo: A high-quality 3D Multi-pedestrian motion dataset created using human motion and shape priors from LiDAR range images in the Waymo perception dataset."}
+{"idx": 3, "title": "Three-dimensional reconstruction using SFM for actual pedestrian ...", "date": "", "ddg_snippet": "Finally, some works use a LiDAR to perform classification of three-dimensional data. In Liu et al. (2019b), the authors present a pedestrian detection system using template matching. As a first step, they segment the ground and filter the points in grid cells whose height difference does not correspond to humans.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417422020243", "content": "Finally, some works use a LiDAR to perform classification of three-dimensional data. In Liu et al. (2019b), the authors present a pedestrian detection system using template matching. As a first step, they segment the ground and filter the points in grid cells whose height difference does not correspond to humans."}
+{"idx": 4, "title": "LiDAR-Based Dense Pedestrian Detection and Tracking - MDPI", "date": "", "ddg_snippet": "The problem of LiDAR -based PDT suffers from the complex gathering movements and the phenomenon of self- and inter-object occlusions. In this paper, the detection and tracking of dense pedestrians using three-dimensional (3D) real-measured LiDAR point clouds in surveillance applications is studied.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/12/4/1799", "content": "The problem of LiDAR -based PDT suffers from the complex gathering movements and the phenomenon of self- and inter-object occlusions. In this paper, the detection and tracking of dense pedestrians using three-dimensional (3D) real-measured LiDAR point clouds in surveillance applications is studied."}
+{"idx": 5, "title": "Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed ...", "date": "", "ddg_snippet": "Explore the Pedestrian Motion Reconstruction dataset, focusing on pedestrian behavior reconstruction using mixed reality rendering with diverse perspectives and modalities.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/29263", "content": "Explore the Pedestrian Motion Reconstruction dataset, focusing on pedestrian behavior reconstruction using mixed reality rendering with diverse perspectives and modalities."}
+{"idx": 6, "title": "Human Motion Predicition, Reconstruction, and Generation", "date": "", "ddg_snippet": "Abstract This report reviews recent advancements in human motion prediction, reconstruction , and generation. Human motion prediction focuses on forecasting future poses and movements from historical data, addressing challenges like nonlinear dynamics, occlusions, and motion style variations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.15956v1", "content": "Abstract This report reviews recent advancements in human motion prediction, reconstruction , and generation. Human motion prediction focuses on forecasting future poses and movements from historical data, addressing challenges like nonlinear dynamics, occlusions, and motion style variations."}
+{"idx": 7, "title": "VR Pedestrian Flow Simulator by Real-time 3D Motion Reconstruction for ...", "date": "", "ddg_snippet": "In this paper, we propose a VR system in which can generate pedestrian flows including the whole-body motion of each NPC, and also allows a user to experience generated pedestrian flows by implementing the collision avoidance effect between the user and NPCs.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-45770-8_27", "content": "In this paper, we propose a VR system in which can generate pedestrian flows including the whole-body motion of each NPC, and also allows a user to experience generated pedestrian flows by implementing the collision avoidance effect between the user and NPCs."}
+{"idx": 8, "title": "PMRDataset/README.md at main · coding-rachal/PMRDataset", "date": "", "ddg_snippet": "Pedestrian Motion Reconstruction : A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities Official code respository for the paper \"Pedestrian Motion Reconstruction : A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/coding-rachal/PMRDataset/blob/main/README.md", "content": "Pedestrian Motion Reconstruction : A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities Official code respository for the paper \"Pedestrian Motion Reconstruction : A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities\"."}
+{"idx": 9, "title": "Controllable instance synthesis with hierarchical regularization for ...", "date": "", "ddg_snippet": "Generative Adversarial Network (GAN)-based pedestrian image synthesis opens up the possibility of using synthesized data with sufficient diversity to cover unseen variations, which facilitates semi-supervised pedestrian detection. To improve synthesized image quality and diversity in the semi-supervised setting, we propose a Hierarchical Regularized GAN (HiR-GAN), which allows a controllable ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224016023", "content": "Generative Adversarial Network (GAN)-based pedestrian image synthesis opens up the possibility of using synthesized data with sufficient diversity to cover unseen variations, which facilitates semi-supervised pedestrian detection. To improve synthesized image quality and diversity in the semi-supervised setting, we propose a Hierarchical Regularized GAN (HiR-GAN), which allows a controllable ..."}
diff --git a/data/sampled_jsons/Yao_et_al_2024c_Multiview_Causal_Representation_Learning_assumptions.jsonl b/data/sampled_jsons/Yao_et_al_2024c_Multiview_Causal_Representation_Learning_assumptions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6685a26f87a24e05cc66634e3dafa4aa06e17f68
--- /dev/null
+++ b/data/sampled_jsons/Yao_et_al_2024c_Multiview_Causal_Representation_Learning_assumptions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi - View Causal Representation Learning ... | ServiceNow Research", "date": "", "ddg_snippet": "Multi - View Causal Representation Learning with Partial Observability.Overall, we find that access to multiple partial views enables to identify a more fine-grained representation , under the generally milder assumption of partial observability.", "subpage_snippet": "", "source": "www.servicenow.com", "link": "https://www.servicenow.com/research/publication/dingling-yao-mult-iclr2024.html", "content": "Multi - View Causal Representation Learning with Partial Observability.Overall, we find that access to multiple partial views enables to identify a more fine-grained representation , under the generally milder assumption of partial observability."}
+{"idx": 1, "title": "Multi - View Causal Representation Learning with Partial Observability", "date": "", "ddg_snippet": "2024. Conference Paper. al . ei. Author(s): Yao , D. and Xu, D. and Lachapelle, S. and Magliacane, S. and Taslakian, P. and Martius, G. and von Kügelgen, J. and Locatello, F. Book Title: Proceedings of the Twelfth International Conference on Learning Representations (ICLR). Year", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/publications/yaoetal24", "content": "2024. Conference Paper. al . ei. Author(s): Yao , D. and Xu, D. and Lachapelle, S. and Magliacane, S. and Taslakian, P. and Martius, G. and von Kügelgen, J. and Locatello, F. Book Title: Proceedings of the Twelfth International Conference on Learning Representations (ICLR). Year"}
+{"idx": 2, "title": "ICLR Poster Multi - View Causal Representation Learning with Partial...", "date": "", "ddg_snippet": "Login. Select Year: (2024).Our general framework and theoretical results unify and extend several previous work on multi - view nonlinear ICA, disentanglement, and causal representation learning .", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/poster/18749", "content": "Login. Select Year: (2024).Our general framework and theoretical results unify and extend several previous work on multi - view nonlinear ICA, disentanglement, and causal representation learning ."}
+{"idx": 3, "title": "Marrying Causal Representation Learning with", "date": "", "ddg_snippet": "Multi - view causal representation learning with partial observability.Following Yao et al . [71, Lemma D.3], we conclude that both g(x)S and g(x˜)S can only depend on information about the shared partition about the ground truth parameter θS. In other words", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/83eb339ed42297658fa24b5cec939285-Paper-Conference.pdf", "content": "Multi - view causal representation learning with partial observability.Following Yao et al . [71, Lemma D.3], we conclude that both g(x)S and g(x˜)S can only depend on information about the shared partition about the ground truth parameter θS. In other words"}
+{"idx": 4, "title": "Causal Representation Learning from Multi -modal Biomedical...", "date": "", "ddg_snippet": "Multimodal representation learning (Zhang et al ., 2020; Manzoor et al ., 2023) refers to the process of learning representations from multiple data modalities (e.g., text, image, audio) for specific tasks.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11952583/", "content": "Multimodal representation learning (Zhang et al ., 2020; Manzoor et al ., 2023) refers to the process of learning representations from multiple data modalities (e.g., text, image, audio) for specific tasks."}
+{"idx": 5, "title": "Unifying Causal Representation Learning with the", "date": "", "ddg_snippet": "Identifying the causal graph. Related Works. Multiview Causal Representation Learning .[19] directly average the learned representations from paired data g(x1), g(x2) on the shared coordinates before forwarding them to the decoder; Ahuja et al .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LaU3p8Pj0D", "content": "Identifying the causal graph. Related Works. Multiview Causal Representation Learning .[19] directly average the learned representations from paired data g(x1), g(x2) on the shared coordinates before forwarding them to the decoder; Ahuja et al ."}
+{"idx": 6, "title": "(PDF) Unifying Causal Representation Learning with the Invariance...", "date": "", "ddg_snippet": "Causal representation learning (Schölkopf et al .,2021) posits that many real-world high-dimensional perceptual. data can be described through a simplified latent structure specified by a few interpretable low-dimensional. causally -related variables.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383752883_Unifying_Causal_Representation_Learning_with_the_Invariance_Principle", "content": "Causal representation learning (Schölkopf et al .,2021) posits that many real-world high-dimensional perceptual. data can be described through a simplified latent structure specified by a few interpretable low-dimensional. causally -related variables."}
+{"idx": 7, "title": "CaRiNG: Learning Temporal Causal Representation under...", "date": "", "ddg_snippet": "(Hälvä et al ., 2021; Klindt et al ., 2020; Yao et al ., 2022b, a; Lachapelle et al ., 2022) further extend this nonlinear ICA framework into scenarios of the time-delayed dynamical systems, which allows the temporal transitions among the latent variables.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.14535v2", "content": "(Hälvä et al ., 2021; Klindt et al ., 2020; Yao et al ., 2022b, a; Lachapelle et al ., 2022) further extend this nonlinear ICA framework into scenarios of the time-delayed dynamical systems, which allows the temporal transitions among the latent variables."}
+{"idx": 8, "title": "Towards the Causal Complete Cause of Multi -Modal Representation ...", "date": "", "ddg_snippet": "To learn representations of causal variable for MML that is with both causal sufficiency and necessity, based on (Pearl, 2009; Yang et al ., 2024 ), we introduce the concept of the probability of Causal Complete Cause ( ) as follows", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46215/paper", "content": "To learn representations of causal variable for MML that is with both causal sufficiency and necessity, based on (Pearl, 2009; Yang et al ., 2024 ), we introduce the concept of the probability of Causal Complete Cause ( ) as follows"}
+{"idx": 9, "title": "Causal Representation Learning Made Identifiable by Grouping", "date": "", "ddg_snippet": "MVCRL Multi - view CRL (MVCRL; Yao et al . (2023)) is a CRL framework targeting multi - view data, based on alignments of the (embeddings of) intersections of the latent variables (called content) between views (for the sake of consistency, we hereafter call views as groups).", "subpage_snippet": "", "source": "helda.helsinki.fi", "link": "https://helda.helsinki.fi/server/api/core/bitstreams/b0c84266-835c-49d0-bc45-fdf94b494e04/content", "content": "MVCRL Multi - view CRL (MVCRL; Yao et al . (2023)) is a CRL framework targeting multi - view data, based on alignments of the (embeddings of) intersections of the latent variables (called content) between views (for the sake of consistency, we hereafter call views as groups)."}
diff --git a/data/sampled_jsons/Yao_multiview_causal_representation_learning_partial_observability_non-Gaussian.jsonl b/data/sampled_jsons/Yao_multiview_causal_representation_learning_partial_observability_non-Gaussian.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..81e514ad75754acf6c07728690a53d47834f56bf
--- /dev/null
+++ b/data/sampled_jsons/Yao_multiview_causal_representation_learning_partial_observability_non-Gaussian.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi-View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "by D Yao · Cited by 54 — This paper provides a unified framework for several identifiability results in observational multi-view causal representation learning under partial ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E8IhOxNREv", "content": "by D Yao · Cited by 54 — This paper provides a unified framework for several identifiability results in observational multi-view causal representation learning under partial ..."}
+{"idx": 1, "title": "MULTI-VIEW CAUSAL REPRESENTATION LEARNING", "date": "", "ddg_snippet": "by D Yao · Cited by 54 — This paper provides a unified framework for several identifiability results in observational multi-view causal representation learning under partial ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=OGtnhKQJms", "content": "by D Yao · Cited by 54 — This paper provides a unified framework for several identifiability results in observational multi-view causal representation learning under partial ..."}
+{"idx": 2, "title": "publications | Dingling YAO - GitHub Pages", "date": "", "ddg_snippet": "Causal representation learning (CRL) aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, ...", "subpage_snippet": "", "source": "ddcoan.github.io", "link": "https://ddcoan.github.io/publications/", "content": "Causal representation learning (CRL) aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, ..."}
+{"idx": 3, "title": "A Sparsity Principle for Partially Observable Causal ...", "date": "", "ddg_snippet": "13 Mar 2024 — Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.08335v1", "content": "13 Mar 2024 — Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent ..."}
+{"idx": 4, "title": "Marrying Causal Representation Learning with Dynamical ...", "date": "", "ddg_snippet": "9 Dec 2024 — Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95516", "content": "9 Dec 2024 — Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements."}
+{"idx": 5, "title": "A sparsity principle for partially observable causal ...", "date": "", "ddg_snippet": "by D Xu · 2024 · Cited by 22 — Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3694354", "content": "by D Xu · 2024 · Cited by 22 — Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent ..."}
+{"idx": 6, "title": "Score-based Causal Representation Learning: Linear and ...", "date": "", "ddg_snippet": "by B Varici · 2025 · Cited by 4 — Yao et al. (2024) generalizes the multi-view approach via a unified framework, which also allows partial observability with nonlinear transforms. We also note ... 90 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume26/24-0194/24-0194.pdf", "content": "by B Varici · 2025 · Cited by 4 — Yao et al. (2024) generalizes the multi-view approach via a unified framework, which also allows partial observability with nonlinear transforms. We also note ... 90 pages"}
+{"idx": 7, "title": "Causal Representation Learning from General Environments ...", "date": "", "ddg_snippet": "by I Ng — Causal representation learning aims to re- cover the latent causal variables and their causal relations, typically represented by di-. 23 pages", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v258/main/assets/ng25a/ng25a.pdf", "content": "by I Ng — Causal representation learning aims to re- cover the latent causal variables and their causal relations, typically represented by di-. 23 pages"}
+{"idx": 8, "title": "Causal Representation Learning from Multi-modal ...", "date": "", "ddg_snippet": "by Y Sun · 2025 · Cited by 6 — In this work, we aim to develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11952583/", "content": "by Y Sun · 2025 · Cited by 6 — In this work, we aim to develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical ..."}
+{"idx": 9, "title": "Max Planck Institute for Intelligent Systems, Tübingen", "date": "", "ddg_snippet": "This paper provides a unified framework for several identifiability results in observational multi-view causal representation learning under partial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.04056v2", "content": "This paper provides a unified framework for several identifiability results in observational multi-view causal representation learning under partial ..."}
diff --git a/data/sampled_jsons/Yicheng_Pan_Hierarchical_Overlapping_Clustering_time_complexity_Algorithm_2_page_7.jsonl b/data/sampled_jsons/Yicheng_Pan_Hierarchical_Overlapping_Clustering_time_complexity_Algorithm_2_page_7.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1d6a9fe20283450a503809f90609dd4951ec4a30
--- /dev/null
+++ b/data/sampled_jsons/Yicheng_Pan_Hierarchical_Overlapping_Clustering_time_complexity_Algorithm_2_page_7.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function ...", "date": "", "ddg_snippet": "In this section, we verify by experiments the effectiveness and scalability of the speed-up version of Algorithm 2 , which demonstrates the validity of our cost function as well.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "In this section, we verify by experiments the effectiveness and scalability of the speed-up version of Algorithm 2 , which demonstrates the validity of our cost function as well."}
+{"idx": 1, "title": "HIERARCHICAL OVERLAPPING CLUSTERING FUNCTION ALGORITHM AND ...", "date": "", "ddg_snippet": "HOC graph. We prove it formally in Appendix B.6. 419 Time complexity . The runtime of Algorithm 2 consists of three parts: the recurs ve division, merging 420 identical nodes and removing redundant edges. In the division step,", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "HOC graph. We prove it formally in Appendix B.6. 419 Time complexity . The runtime of Algorithm 2 consists of three parts: the recurs ve division, merging 420 identical nodes and removing redundant edges. In the division step,"}
+{"idx": 2, "title": "Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs Hierarchical Clustering: Objective Functions and Algorithms Cost-effective hierarchical clustering with local density ...", "date": "", "ddg_snippet": "This paper presents two eficient hierarchical clus - tering (HC) algorithms with respect to Dasgupta’s cost function. For any input graph G with a clear cluster -structure, our designed algorithms run in nearly-linear time in the input size of G, and re-turn an O(1)-approximate HC tree with respect to Dasgupta’s cost function. We compare the perfor-ma... See full list on proceedings.mlr.press e={u,v}∈E where u ∨ v is the lowest common ancestor of u and v in T . Sometimes, it is convenient to consider the cost of an edge e = {u, v} ∈ E in T as costG(e) ≜ we ·|leaves(T [u∨v])|. Trees that achieve a better hierarchical clustering have a lower cost, and the objective of HC is to construct trees with the lowest cost based on the following co... See full list on proceedings.mlr.press i=1 be a partition of V . We say that the vertex and edge-weighted graph H = ([k], ([k] ), W∗, w∗) is a contraction of 2 G with respect to A if for every i, j ∈ [k] we have that W∗(i, j) = w(Ai, Aj) and for every i ∈ [k] we have w∗(i) = We denote the contraction of G with respect |Ai|. to A as G/A. Note that contracted graphs are vertex-weighted, i... See full list on proceedings.mlr.press This section presents our hierarchical clustering algorithms for well-clustered graphs. It consists of two subsections, each of which corresponds to one algorithm . See full list on proceedings.mlr.press Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Aug 1, 2024 · This situation is alleviated using Algorithm 2 , which was presented in Section 4.3. This algorithm merges small clusters into larger clusters and thus decreases the probability of an equal local density of nodes, as proven below.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/laenen23a/laenen23a.pdf", "content": "This paper presents two eficient hierarchical clus - tering (HC) algorithms with respect to Dasgupta’s cost function. For any input graph G with a clear cluster -structure, our designed algorithms run in nearly-linear time in the input size of G, and re-turn an O(1)-approximate HC tree with respect to Dasgupta’s cost function. We compare the perfor-ma... See full list on proceedings.mlr.press e={u,v}∈E where u ∨ v is the lowest common ancestor of u and v in T . Sometimes, it is convenient to consider the cost of an edge e = {u, v} ∈ E in T as costG(e) ≜ we ·|leaves(T [u∨v])|. Trees that achieve a better hierarchical clustering have a lower cost, and the objective of HC is to construct trees with the lowest cost based on the following co... See full list on proceedings.mlr.press i=1 be a partition of V . We say that the vertex and edge-weighted graph H = ([k], ([k] ), W∗, w∗) is a contraction of 2 G with respect to A if for every i, j ∈ [k] we have that W∗(i, j) = w(Ai, Aj) and for every i ∈ [k] we have w∗(i) = We denote the contraction of G with respect |Ai|. to A as G/A. Note that contracted graphs are vertex-weighted, i... See full list on proceedings.mlr.press This section presents our hierarchical clustering algorithms for well-clustered graphs. It consists of two subsections, each of which corresponds to one algorithm . See full list on proceedings.mlr.press Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Aug 1, 2024 · This situation is alleviated using Algorithm 2 , which was presented in Section 4.3. This algorithm merges small clusters into larger clusters and thus decreases the probability of an equal local density of nodes, as proven below."}
+{"idx": 3, "title": "Hierarchical Clustering: Objective Functions and Algorithms", "date": "", "ddg_snippet": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/fullHtml/10.1145/3321386", "content": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity."}
+{"idx": 4, "title": "Cost-effective hierarchical clustering with local density ...", "date": "", "ddg_snippet": "Aug 1, 2024 · This situation is alleviated using Algorithm 2 , which was presented in Section 4.3. This algorithm merges small clusters into larger clusters and thus decreases the probability of an equal local density of nodes, as proven below.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0020025524007254", "content": "Aug 1, 2024 · This situation is alleviated using Algorithm 2 , which was presented in Section 4.3. This algorithm merges small clusters into larger clusters and thus decreases the probability of an equal local density of nodes, as proven below."}
+{"idx": 5, "title": "\"Design and Use of Anatomical Atlases for Radiotherapy\"", "date": "", "ddg_snippet": "64 Chapter 4. Ephemeral Clustering. parameter free architecture, which implements normalized full-text hierarchical overlapping clustering .", "subpage_snippet": "", "source": "dias.users.greyc.fr", "link": "https://dias.users.greyc.fr/publications/Thesis-HDR.pdf", "content": "64 Chapter 4. Ephemeral Clustering. parameter free architecture, which implements normalized full-text hierarchical overlapping clustering ."}
+{"idx": 6, "title": "Rapport de stage", "date": "", "ddg_snippet": "• Hierarchical overlapping clustering methods: The concept of the hierarchical methods is to limit the disharmony between the initial dissimilarities trough the examined dataset and the ones that the hierarchical structure generates.", "subpage_snippet": "", "source": "www.larodec.com", "link": "https://www.larodec.com/download/file/fid/630", "content": "• Hierarchical overlapping clustering methods: The concept of the hierarchical methods is to limit the disharmony between the initial dissimilarities trough the examined dataset and the ones that the hierarchical structure generates."}
+{"idx": 7, "title": "G O arXiv:2306.09950v1 [cs.DS] 16 Jun 2023", "date": "", "ddg_snippet": "In comparison to the previous algorithms for hierarchical clustering on well-structured graphs [CAKMT17, MS21], the advantages of our algorithm are its simplicity and nearly-linear time complexity , which is optimal up to a poly-logarithmic factor.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.09950.pdf", "content": "In comparison to the previous algorithms for hierarchical clustering on well-structured graphs [CAKMT17, MS21], the advantages of our algorithm are its simplicity and nearly-linear time complexity , which is optimal up to a poly-logarithmic factor."}
+{"idx": 8, "title": "Most Influential AAAI Papers (2023-04) – Paper Digest", "date": "", "ddg_snippet": "In this paper, we explore the idea of nesting basic local transformers on non- overlapping image blocks and aggregating them in a hierarchical way.", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2023/04/most-influential-aaai-papers-2023-04/", "content": "In this paper, we explore the idea of nesting basic local transformers on non- overlapping image blocks and aggregating them in a hierarchical way."}
+{"idx": 9, "title": "Enhancing Dual Network Based Semi-Supervised Medical Image", "date": "", "ddg_snippet": "... comparisons between our method and recent semi-supervised approaches on the LA, Pancreas, and BraTS-2019 datasets using 10% labeled data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13084v1", "content": "... comparisons between our method and recent semi-supervised approaches on the LA, Pancreas, and BraTS-2019 datasets using 10% labeled data."}
diff --git a/data/sampled_jsons/Yixuan_Yao_Ming_Yang_Zixia_Liu_conn(a,b)_equation_8_abnormal_behavioral_pattern.jsonl b/data/sampled_jsons/Yixuan_Yao_Ming_Yang_Zixia_Liu_conn(a,b)_equation_8_abnormal_behavioral_pattern.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f3d4643f5bf0363737ba2bcac2fdfdba8e87a523
--- /dev/null
+++ b/data/sampled_jsons/Yixuan_Yao_Ming_Yang_Zixia_Liu_conn(a,b)_equation_8_abnormal_behavioral_pattern.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Yao Ming - Wikipedia", "date": "", "ddg_snippet": "Yao Ming is a Chinese basketball executive and former professional player. He played for the Shanghai Sharks of the Chinese Basketball Association and the Houston Rockets of the National Basketball Association.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Yao_Ming", "content": "Yao Ming is a Chinese basketball executive and former professional player. He played for the Shanghai Sharks of the Chinese Basketball Association and the Houston Rockets of the National Basketball Association."}
+{"idx": 1, "title": "YangMing Marine Transport Corp.", "date": "", "ddg_snippet": "Yang Ming Marine Transport Corporation ( Yang Ming ) have observed our core value of 'Teamwork, Innovation, Honesty and Pragmatism' in enhancing our transportation expertise and service quality. 陽明海運股份有限公司全球員工秉持”團隊、創新、誠信、務實”之價值理念，專注於全球化...", "subpage_snippet": "", "source": "e-solution.yangming.com", "link": "https://e-solution.yangming.com/e-service/track_trace/track_trace_cargo_tracking.aspx", "content": "Yang Ming Marine Transport Corporation ( Yang Ming ) have observed our core value of 'Teamwork, Innovation, Honesty and Pragmatism' in enhancing our transportation expertise and service quality. 陽明海運股份有限公司全球員工秉持”團隊、創新、誠信、務實”之價值理念，專注於全球化..."}
+{"idx": 2, "title": "Купить Материнская плата MSI MPG B 550 GAMING PLUS...", "date": "", "ddg_snippet": "Характеристики, цена MSI MPG B 550 GAMING PLUS | 1679842.", "subpage_snippet": "", "source": "www.dns-shop.ru", "link": "https://www.dns-shop.ru/product/232aa9f9b9a11b80/materinskaa-plata-msi-mpg-b550-gaming-plus/", "content": "Характеристики, цена MSI MPG B 550 GAMING PLUS | 1679842."}
+{"idx": 3, "title": "Do Not Trust What They Tell: Exposing Malicious... | OpenReview", "date": "", "ddg_snippet": "Our method strategically utilizes modified Middle nodes to capture traffic data, followed by a novel circuit classification based on traffic patterns to pinpoint concerned circuits. Yixuan Yao , Ming Yang , Zixia Liu , Kai Dong, Xiaodan-Gu, Chunmian Wang.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qcnePVejeV", "content": "Our method strategically utilizes modified Middle nodes to capture traffic data, followed by a novel circuit classification based on traffic patterns to pinpoint concerned circuits. Yixuan Yao , Ming Yang , Zixia Liu , Kai Dong, Xiaodan-Gu, Chunmian Wang."}
+{"idx": 4, "title": "ORCID", "date": "", "ddg_snippet": "Contributors : Yixuan Yao ; Ming Yang ; Zixia Liu ; Kai Dong; Xiaodan Gu; Chunmian Wang. Show more detail. Source : check_circle.Contributors : Kaizheng Liu ; Ming Yang ; Zhen Ling; Yue Zhang; Chongqing Lei; Junzhou Luo; Xinwen Fu.", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0000-0002-8209-1000", "content": "Contributors : Yixuan Yao ; Ming Yang ; Zixia Liu ; Kai Dong; Xiaodan Gu; Chunmian Wang. Show more detail. Source : check_circle.Contributors : Kaizheng Liu ; Ming Yang ; Zhen Ling; Yue Zhang; Chongqing Lei; Junzhou Luo; Xinwen Fu."}
+{"idx": 5, "title": "Nano Banana: Free Online AI Image Editor", "date": "", "ddg_snippet": "Zhang Yixuan . @E-commerce Designer. Liu Siqi. @Beauty Influencer.", "subpage_snippet": "", "source": "www.nano-banana.com", "link": "https://www.nano-banana.com/", "content": "Zhang Yixuan . @E-commerce Designer. Liu Siqi. @Beauty Influencer."}
+{"idx": 6, "title": "dblp: Bibliographic content of WWW 2025", "date": "", "ddg_snippet": "Yixuan Yao , Ming Yang , Zixia Liu , Kai Dong , Xiaodan Gu , Chunmian Wang : Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection. 2959-2968.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/db/conf/www/www2025.html", "content": "Yixuan Yao , Ming Yang , Zixia Liu , Kai Dong , Xiaodan Gu , Chunmian Wang : Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection. 2959-2968."}
+{"idx": 7, "title": "Сознание без мистики: эволюция, архитектуры, квалиа / Хабр", "date": "", "ddg_snippet": "[32] Bannert M.M., Bartels A. Human V4 activity patterns predict behavioral performance in imagery of object color.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/949414/", "content": "[32] Bannert M.M., Bartels A. Human V4 activity patterns predict behavioral performance in imagery of object color."}
+{"idx": 8, "title": "Паромы из Краби в Пхукет от THB 840 Сент. 2025", "date": "", "ddg_snippet": "Speed boats looked very old. Other than that, all ok. Краби в Пхукет, Паром Быстроходный катер, Koh Yao Sun Smile (Koh Yao Sun Smile), 20 нояб. 2021 г.", "subpage_snippet": "", "source": "12go.co", "link": "https://12go.co/ru/ferry/krabi/phuket", "content": "Speed boats looked very old. Other than that, all ok. Краби в Пхукет, Паром Быстроходный катер, Koh Yao Sun Smile (Koh Yao Sun Smile), 20 нояб. 2021 г."}
+{"idx": 9, "title": "Ювелирные украшения TOUS — купить ювелирные изделия Тоус...", "date": "", "ddg_snippet": "A b C D e f g h j k L M n o p r s t u V w y z а б в г и л м р с ф.La Nordica (82). Lena Artemeva (9). LIU JO (224).", "subpage_snippet": "", "source": "www.AllTime.ru", "link": "https://www.AllTime.ru/jewellary/filter/brand:tous/", "content": "A b C D e f g h j k L M n o p r s t u V w y z а б в г и л м р с ф.La Nordica (82). Lena Artemeva (9). LIU JO (224)."}
diff --git a/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_Equation_7_RA2C_reverse_A2C.jsonl b/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_Equation_7_RA2C_reverse_A2C.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..579d3ad142769f2e198220d7d26fb048fcd172dc
--- /dev/null
+++ b/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_Equation_7_RA2C_reverse_A2C.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "The Symmetric Reinforcement Learning (SRL) loss Lsrl con-sists of two parts like SCE ( Equation 6): the original actor loss Lrl ( A2C or PPO) and the corresponding reverse RL loss Lrev ( RA2C or RPPO).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=YjBrt82S3v", "content": "The Symmetric Reinforcement Learning (SRL) loss Lsrl con-sists of two parts like SCE ( Equation 6): the original actor loss Lrl ( A2C or PPO) and the corresponding reverse RL loss Lrev ( RA2C or RPPO)."}
+{"idx": 1, "title": "Symmetry-Informed Reinforcement Learning and its Application to Low ...", "date": "", "ddg_snippet": "Symmetry is ubiquitous in nature, physics, and mathematics. However, a classical symmetry-agnostic reinforcement learning (RL) approach cannot guarantee to respect symmetry. Researchers have shown that if the symmetry of a system cannot be respected, the performance of a symmetry-agnostic RL approach can be inhibited. To this end, this article develops a generally applicable neural network (NN ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10054413", "content": "Symmetry is ubiquitous in nature, physics, and mathematics. However, a classical symmetry-agnostic reinforcement learning (RL) approach cannot guarantee to respect symmetry. Researchers have shown that if the symmetry of a system cannot be respected, the performance of a symmetry-agnostic RL approach can be inhibited. To this end, this article develops a generally applicable neural network (NN ..."}
+{"idx": 2, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "The Symmetric Reinforcement Learning (SRL) loss L srl consists of two parts like SCE ( Equation 6): the original RL loss L rl ( A2C or PPO) and the corresponding reverse RL loss L rev ( RA2C or RPPO).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17618v2", "content": "The Symmetric Reinforcement Learning (SRL) loss L srl consists of two parts like SCE ( Equation 6): the original RL loss L rl ( A2C or PPO) and the corresponding reverse RL loss L rev ( RA2C or RPPO)."}
+{"idx": 3, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "The symmetric RL loss function is a novel approach that makes reinforcement learning more resistant to noisy feedback. It works by modifying how the AI calculates its learning errors, similar to methods used in supervised learning . The process involves: 1) Analyzing both positive and negative feedback symmetrically, 2) Reducing the impact of outlier feedback signals, and 3) Maintaining ...", "subpage_snippet": "", "source": "www.promptlayer.com", "link": "https://www.promptlayer.com/research-papers/symmetric-reinforcement-learning-loss-for-robust-learning-on-diverse-tasks-and-model-scales", "content": "The symmetric RL loss function is a novel approach that makes reinforcement learning more resistant to noisy feedback. It works by modifying how the AI calculates its learning errors, similar to methods used in supervised learning . The process involves: 1) Analyzing both positive and negative feedback symmetrically, 2) Reducing the impact of outlier feedback signals, and 3) Maintaining ..."}
+{"idx": 4, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss ."}
+{"idx": 5, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "In this work, we enhance the stability of the RL training procedure by adapting reverse cross-entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9oq0iY2Jxx", "content": "In this work, we enhance the stability of the RL training procedure by adapting reverse cross-entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 6, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "and PPO. We define a symmetric RL loss , whose fundamental working mechanism aligns with the robust loss function of supervised learning [Wang et al., 2019], to make the RL learning procedure more robust for A2C", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618", "content": "and PPO. We define a symmetric RL loss , whose fundamental working mechanism aligns with the robust loss function of supervised learning [Wang et al., 2019], to make the RL learning procedure more robust for A2C"}
+{"idx": 7, "title": "GitHub - shashacks/Symmetric_RL", "date": "", "ddg_snippet": "To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_RL", "content": "To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss ."}
+{"idx": 8, "title": "GitHub - shashacks/Symmetric_tril", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_tril", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance."}
+{"idx": 9, "title": "Symmetric_RL/README.md at master · shashacks/Symmetric_RL", "date": "", "ddg_snippet": "To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_RL/blob/master/README.md", "content": "To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss ."}
diff --git a/data/sampled_jsons/YuMEUNNpeb_Figure_1_60%_evaluation_data_constructed_benchmarks.jsonl b/data/sampled_jsons/YuMEUNNpeb_Figure_1_60%_evaluation_data_constructed_benchmarks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..88e5c59e3389e49047c0622fc5f09c3206b4a27a
--- /dev/null
+++ b/data/sampled_jsons/YuMEUNNpeb_Figure_1_60%_evaluation_data_constructed_benchmarks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Medical Large Language Model Benchmarks Should Prioritize ...", "date": "", "ddg_snippet": "To put these ideas into practice, we use real-world clinical data in proof-of-concept experiments to evaluate popular medical LLM benchmarks and report significant gaps in their construct validity. Finally, we outline a vision for a new ecosystem of medical LLM evaluation centered around the creation of valid benchmarks .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YuMEUNNpeb", "content": "To put these ideas into practice, we use real-world clinical data in proof-of-concept experiments to evaluate popular medical LLM benchmarks and report significant gaps in their construct validity. Finally, we outline a vision for a new ecosystem of medical LLM evaluation centered around the creation of valid benchmarks ."}
+{"idx": 1, "title": "Medical Large Language Model Benchmarks Should", "date": "", "ddg_snippet": "Figure 1 : Overview of evaluation datasets for medical LLMs. We analyzed the evaluation datasets used in the 100 most cited papers on medical LLMs over the past 5 years. The majority ( 60 %) of studies assess models on public benchmarks constructed based on medical exams, while 40% rely on (private or public access) real-world hospital data .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10694v1", "content": "Figure 1 : Overview of evaluation datasets for medical LLMs. We analyzed the evaluation datasets used in the 100 most cited papers on medical LLMs over the past 5 years. The majority ( 60 %) of studies assess models on public benchmarks constructed based on medical exams, while 40% rely on (private or public access) real-world hospital data ."}
+{"idx": 2, "title": "GitHub - leobeeson/llm_benchmarks: A collection of benchmarks and ...", "date": "", "ddg_snippet": "We extensively evaluate the performance of LLM-Eval on various benchmark datasets, demonstrating its effectiveness, efficiency, and adaptability compared to state-of-the-art evaluation methods.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/leobeeson/llm_benchmarks", "content": "We extensively evaluate the performance of LLM-Eval on various benchmark datasets, demonstrating its effectiveness, efficiency, and adaptability compared to state-of-the-art evaluation methods."}
+{"idx": 3, "title": "BetterBench | Assessing AI Benchmarks, Uncovering Issues, and ...", "date": "", "ddg_snippet": "The problem Benchmarks are widely used to measure attributes like fairness, safety, or general capabilities, compare model performances, track progress, and identify weaknesses of AI systems. However, the quality of these benchmarks varies significantly depending on their design and usability. Poor quality benchmarks can lead to misleading comparisons and inaccurate assessments of AI models ...", "subpage_snippet": "", "source": "betterbench.stanford.edu", "link": "https://betterbench.stanford.edu/", "content": "The problem Benchmarks are widely used to measure attributes like fairness, safety, or general capabilities, compare model performances, track progress, and identify weaknesses of AI systems. However, the quality of these benchmarks varies significantly depending on their design and usability. Poor quality benchmarks can lead to misleading comparisons and inaccurate assessments of AI models ..."}
+{"idx": 4, "title": "DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation", "date": "", "ddg_snippet": "We notice that not every function can directly convert to benchmark data suitable for LLM evaluation . Therefore, to facilitate the automatic construction of code benchmarks , we impose three criteria on the candidate subject.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/34811/36966", "content": "We notice that not every function can directly convert to benchmark data suitable for LLM evaluation . Therefore, to facilitate the automatic construction of code benchmarks , we impose three criteria on the candidate subject."}
+{"idx": 5, "title": "PDF ChatBench: From Static Benchmarks to Human-AI Evaluation", "date": "", "ddg_snippet": "As a result, it is difcult to directly compare results from standard benchmarks to real-world interactions or to understand how incorporating interactions changes evaluation insights. Here, we seek to bring these lines of research closer together by directly converting benchmarks into user-AI conversations.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1262.pdf", "content": "As a result, it is difcult to directly compare results from standard benchmarks to real-world interactions or to understand how incorporating interactions changes evaluation insights. Here, we seek to bring these lines of research closer together by directly converting benchmarks into user-AI conversations."}
+{"idx": 6, "title": "AI Benchmarking Dashboard | Epoch AI", "date": "", "ddg_snippet": "Our database of benchmark results, featuring the performance of leading AI models on challenging tasks. It includes results from benchmarks evaluated internally by Epoch AI as well as data collected from external sources. The dashboard tracks AI progress over time, and correlates benchmark scores with key factors like compute or model accessibility.", "subpage_snippet": "", "source": "epoch.ai", "link": "https://epoch.ai/benchmarks", "content": "Our database of benchmark results, featuring the performance of leading AI models on challenging tasks. It includes results from benchmarks evaluated internally by Epoch AI as well as data collected from external sources. The dashboard tracks AI progress over time, and correlates benchmark scores with key factors like compute or model accessibility."}
+{"idx": 7, "title": "An Introduction to LLM Benchmarking - Confident AI", "date": "", "ddg_snippet": "For now, here is a brief overview: Figure 1 : A simplified taxonomy of different metrics used in LLM evaluation The above figures try to simplify the taxonomy of the classification of different types of metrics used in LLM evaluation . A metric can also be a composition of different atomic/granular metrics.", "subpage_snippet": "", "source": "www.confident-ai.com", "link": "https://www.confident-ai.com/blog/the-current-state-of-benchmarking-llms", "content": "For now, here is a brief overview: Figure 1 : A simplified taxonomy of different metrics used in LLM evaluation The above figures try to simplify the taxonomy of the classification of different types of metrics used in LLM evaluation . A metric can also be a composition of different atomic/granular metrics."}
+{"idx": 8, "title": "PDF TAIL: A Toolkit for Automatic and Realistic Long-Context Large Language ...", "date": "", "ddg_snippet": "To bridge this gap, we propose TAIL, an automatic toolkit for creating realistic evaluation benchmarks and assessing the per- formance of long-context LLMs. With TAIL, users can customize the building of a long- context, document-grounded QA benchmark and obtain visualized performance metrics of evaluated models.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-demo.21.pdf", "content": "To bridge this gap, we propose TAIL, an automatic toolkit for creating realistic evaluation benchmarks and assessing the per- formance of long-context LLMs. With TAIL, users can customize the building of a long- context, document-grounded QA benchmark and obtain visualized performance metrics of evaluated models."}
+{"idx": 9, "title": "PDF Measuring how benchmark assessments affect student achievement - ed", "date": "", "ddg_snippet": "That finding might, however, reflect limita-tions in the data rather than the inefective-ness of benchmark assessments. First, data are lacking on what benchmark assessment practices comparison schools may be using, because the study examined the impact of a particular structured benchmark -ing program.", "subpage_snippet": "", "source": "files.eric.ed.gov", "link": "https://files.eric.ed.gov/fulltext/ED499792.pdf", "content": "That finding might, however, reflect limita-tions in the data rather than the inefective-ness of benchmark assessments. First, data are lacking on what benchmark assessment practices comparison schools may be using, because the study examined the impact of a particular structured benchmark -ing program."}
diff --git a/data/sampled_jsons/Yu_EventPS_CVPR_3D_printed_MAE.jsonl b/data/sampled_jsons/Yu_EventPS_CVPR_3D_printed_MAE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..74dcdb53034f69f83c834cdd8b20dcbec20dbe33
--- /dev/null
+++ b/data/sampled_jsons/Yu_EventPS_CVPR_3D_printed_MAE.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10655016", "content": "Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
+{"idx": 2, "title": "CVPR 2024最佳论文分享┆EventPS: 基于事件相机的实时光度立体视觉", "date": "", "ddg_snippet": "文章浏览阅读3.1k次，点赞15次，收藏16次。本文介绍了CVPR 2024的最佳论文提名，该论文利用事件相机的独特属性，实现了实时光度立体视觉。该算法在传统和深度学习领域均取得成功。配合高速转台数据采集和GPU优化，算法实现了每秒超30帧的实时表面法线重建。_eventps: real-time photometric stereo using an ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/audyxiao001/article/details/140520624", "content": "文章浏览阅读3.1k次，点赞15次，收藏16次。本文介绍了CVPR 2024的最佳论文提名，该论文利用事件相机的独特属性，实现了实时光度立体视觉。该算法在传统和深度学习领域均取得成功。配合高速转台数据采集和GPU优化，算法实现了每秒超30帧的实时表面法线重建。_eventps: real-time photometric stereo using an ..."}
+{"idx": 3, "title": "Talk｜北京大学于博涵：EventPS - 基于事件相机的实时光度立体视觉 - 知乎", "date": "", "ddg_snippet": "EventPS 项目主页： ybh1998.space/ eventps 项目主页包含了数据、代码、装置的3D打印文件，以及CVPR现场15分钟Oral录像。本项目得到了国家自然科学基金重点项目的资助。 实验室主页： camera.pku.edu.cn/ 北京大学相机智能实验室是由施柏鑫老师带领的团队，致力于 计算摄像学 研究。实验室承担了科技创新2030 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/711985667", "content": "EventPS 项目主页： ybh1998.space/ eventps 项目主页包含了数据、代码、装置的3D打印文件，以及CVPR现场15分钟Oral录像。本项目得到了国家自然科学基金重点项目的资助。 实验室主页： camera.pku.edu.cn/ 北京大学相机智能实验室是由施柏鑫老师带领的团队，致力于 计算摄像学 研究。实验室承担了科技创新2030 ..."}
+{"idx": 4, "title": "CVPR 2024 Open Access Repository", "date": "", "ddg_snippet": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency."}
+{"idx": 5, "title": "Track: Orals 3C Medical and Physics-based vision - cvpr.thecvf.com", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/session/32097", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency."}
+{"idx": 6, "title": "GitHub - ZrrSkywalker/I2P-MAE: [CVPR 2023] Learning 3D Representations ...", "date": "", "ddg_snippet": "[ CVPR 2023] Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders - ZrrSkywalker/I2P- MAE", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ZrrSkywalker/I2P-MAE", "content": "[ CVPR 2023] Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders - ZrrSkywalker/I2P- MAE"}
+{"idx": 7, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ..."}
+{"idx": 8, "title": "Eventps: Real-time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency.", "subpage_snippet": "", "source": "prophesee-prod.euregion.site", "link": "https://prophesee-prod.euregion.site/2024/07/05/eventps-real-time-photometric-stereo-event-camera/", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency."}
+{"idx": 9, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "The processed 3D object files are at data/{blobs,sculpture}_processed/. To render the training and evaluation data, make sure to have a working LibreDR server and worker, and run the following command:", "subpage_snippet": "", "source": "codeberg.org", "link": "https://codeberg.org/ybh1998/EventPS", "content": "The processed 3D object files are at data/{blobs,sculpture}_processed/. To render the training and evaluation data, make sure to have a working LibreDR server and worker, and run the following command:"}
diff --git a/data/sampled_jsons/Zhang_et_al._error_rate_scaling_law_dataset_size_pre-training_year_2022.jsonl b/data/sampled_jsons/Zhang_et_al._error_rate_scaling_law_dataset_size_pre-training_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e7bb6b726b0943f8705d577db91923c144b02a74
--- /dev/null
+++ b/data/sampled_jsons/Zhang_et_al._error_rate_scaling_law_dataset_size_pre-training_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law", "date": "", "ddg_snippet": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down."}
+{"idx": 1, "title": "Scaling Laws of Synthetic Data for Language Models", "date": "", "ddg_snippet": "26 Mar 2025 — We systematically investigate the scaling laws of synthetic data by introducing SynthLLM, a scalable framework that transforms pre - training corpora into ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19551v2", "content": "26 Mar 2025 — We systematically investigate the scaling laws of synthetic data by introducing SynthLLM, a scalable framework that transforms pre - training corpora into ..."}
+{"idx": 2, "title": "Scaling Laws of Synthetic Data for Language Models", "date": "", "ddg_snippet": "by Z Qin · 2025 · Cited by 8 — Their findings indicate that fine-tuning on pre -trained models exhibits more data efficient scaling behavior compared to training models from ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.19551", "content": "by Z Qin · 2025 · Cited by 8 — Their findings indicate that fine-tuning on pre -trained models exhibits more data efficient scaling behavior compared to training models from ..."}
+{"idx": 3, "title": "Scaling Laws for BERT in Low-Resource Settings", "date": "", "ddg_snippet": "by G Urbizu · 2023 · Cited by 5 — The relationship between the size of the pre - training corpus and the performance of the lan- guage model in NLU tasks has been addressed in the ... 19 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.findings-acl.492.pdf", "content": "by G Urbizu · 2023 · Cited by 5 — The relationship between the size of the pre - training corpus and the performance of the lan- guage model in NLU tasks has been addressed in the ... 19 pages"}
+{"idx": 4, "title": "WHEN SCALING MEETS LLM FINETUNING", "date": "", "ddg_snippet": "by B Zhang · Cited by 214 — While LLM model size and pretraining data size show similar impact on the pretraining scaling following the optimal scaling under a computational budget ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5HCnKDeTws", "content": "by B Zhang · Cited by 214 — While LLM model size and pretraining data size show similar impact on the pretraining scaling following the optimal scaling under a computational budget ..."}
+{"idx": 5, "title": "On Data Scaling in Masked Image Modeling - CVF Open Access", "date": "", "ddg_snippet": "by Z Xie · 2023 · Cited by 86 — Scaling properties have been one of the central issues in self-supervised pre - training , especially the data scalabil- ity, which has successfully motivated ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_On_Data_Scaling_in_Masked_Image_Modeling_CVPR_2023_paper.pdf", "content": "by Z Xie · 2023 · Cited by 86 — Scaling properties have been one of the central issues in self-supervised pre - training , especially the data scalabil- ity, which has successfully motivated ..."}
+{"idx": 6, "title": "Observational Scaling Laws and the Predictability of ...", "date": "", "ddg_snippet": "by Y Ruan · 2024 · Cited by 51 — Our scaling law precisely predicts the GPT-4 performance using weaker models (sub GPT-3.5) and identifies programming capabilities as driving agent performance.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/1cded4f97cf5f01a284c574110b7e3b9-Paper-Conference.pdf", "content": "by Y Ruan · 2024 · Cited by 51 — Our scaling law precisely predicts the GPT-4 performance using weaker models (sub GPT-3.5) and identifies programming capabilities as driving agent performance."}
+{"idx": 7, "title": "How Does Critical Batch Size Scale in Pre-training?", "date": "", "ddg_snippet": "by H Zhang · Cited by 19 — Our results demonstrate that CBS scales primarily with data size rather than model size , a finding we justify theoretically through the analysis of infinite- ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JCiF03qnmi", "content": "by H Zhang · Cited by 19 — Our results demonstrate that CBS scales primarily with data size rather than model size , a finding we justify theoretically through the analysis of infinite- ..."}
+{"idx": 8, "title": "How Does Critical Batch Size Scale in Pre-training ...", "date": "", "ddg_snippet": "Scaling laws offer a solution : they let us extrapolate from smaller-scale experiments, providing insights that guide decisions without the ...", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/how-does-critical-batch-size-scale-in-pre-training-decoupling-data-and-model-size/", "content": "Scaling laws offer a solution : they let us extrapolate from smaller-scale experiments, providing insights that guide decisions without the ..."}
+{"idx": 9, "title": "Scaling Law for Document-Level Neural Machine Translation", "date": "", "ddg_snippet": "by Z Zhuocheng · 2023 · Cited by 6 — In this paper, we carry out an in-depth analysis of the influence among three factors on translation quality: model scale , data scale , and ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.findings-emnlp.556.pdf", "content": "by Z Zhuocheng · 2023 · Cited by 6 — In this paper, we carry out an in-depth analysis of the influence among three factors on translation quality: model scale , data scale , and ..."}
diff --git a/data/sampled_jsons/Zhenhong_Zhou_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety.jsonl b/data/sampled_jsons/Zhenhong_Zhou_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1a5b605102309a1c0a581f21a57c94d40d6791b5
--- /dev/null
+++ b/data/sampled_jsons/Zhenhong_Zhou_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.13708", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
+{"idx": 1, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=h0Ak8A5yqw", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
+{"idx": 2, "title": "(PDF) On the Role of Attention Heads in Large Language Model ...", "date": "", "ddg_snippet": "Large language model safety . Zhenhong Zhou 1, Haiyang Yu1, Xinghua Zhang1, Rongwu Xu3, Fei Huang1, Kun Wang2,Yang Liu4,Junfeng Fang2∗,Yongbin Li1∗. 1Alibaba Group, 2University of Science and Technology of China", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety", "content": "Large language model safety . Zhenhong Zhou 1, Haiyang Yu1, Xinghua Zhang1, Rongwu Xu3, Fei Huang1, Kun Wang2,Yang Liu4,Junfeng Fang2∗,Yongbin Li1∗. 1Alibaba Group, 2University of Science and Technology of China"}
+{"idx": 3, "title": "ICLR 2025 On the Role of Attention Heads in Large Language ...", "date": "", "ddg_snippet": "Abstract: Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that when safety ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/oral/31798", "content": "Abstract: Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that when safety ..."}
+{"idx": 4, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Ablating a single safety head allows aligned model (e.g., Llama-2-7b-chat) to respond to 16 times more harmful queries, while only modifying 0.006% of the parameters, in contrast to the ~ 5% modification required in previous studies.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/on-the-role-of-attention-heads-in-large", "content": "Ablating a single safety head allows aligned model (e.g., Llama-2-7b-chat) to respond to 16 times more harmful queries, while only modifying 0.006% of the parameters, in contrast to the ~ 5% modification required in previous studies."}
+{"idx": 5, "title": "Paper page - On the Role of Attention Heads in Large Language ...", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2410.13708", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
+{"idx": 6, "title": "Zhenhong Zhou - Google Akademik", "date": "", "ddg_snippet": "Large Language Model AI Safety LLM Safety .Z Zhou , J Xiang, C Chen, S Su. Proceedings of the AAAI Conference on Artificial Intelligence 38 (17), 19741 …, 2024.", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=6TuPwzMAAAAJ&hl=tr", "content": "Large Language Model AI Safety LLM Safety .Z Zhou , J Xiang, C Chen, S Su. Proceedings of the AAAI Conference on Artificial Intelligence 38 (17), 19741 …, 2024."}
+{"idx": 7, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/role-attention-heads-large-language-model-safety", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
+{"idx": 8, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "View recent discussion. Abstract: Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2410.13708v1", "content": "View recent discussion. Abstract: Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
+{"idx": 9, "title": "GitHub - ydyjya/SafetyHeadAttribution", "date": "", "ddg_snippet": "On the Role of Attention Heads in Large Language Model Safety . Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ydyjya/SafetyHeadAttribution", "content": "On the Role of Attention Heads in Large Language Model Safety . Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
diff --git a/data/sampled_jsons/Zhou_conditional_density_estimation_GANs_2022_abstract_year_2022.jsonl b/data/sampled_jsons/Zhou_conditional_density_estimation_GANs_2022_abstract_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..17d7fb1e200ea9cfa389abb0ba939a603c518e5e
--- /dev/null
+++ b/data/sampled_jsons/Zhou_conditional_density_estimation_GANs_2022_abstract_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2022.1.10 Vision papers — Eye On AI", "date": "", "ddg_snippet": "Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling by Fakai Wang et al", "subpage_snippet": "", "source": "www.eye-on.ai", "link": "https://www.eye-on.ai/ai-research-watch-papers/2022/1/11/2022110-vision-papers", "content": "Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling by Fakai Wang et al"}
+{"idx": 1, "title": "A Systematic Methodology for Parasitic Capacitance Estimation", "date": "", "ddg_snippet": "Recently, some of the well-known EV manufacturers have utilized higher-voltage batteries to benefit from lower current, higher power density , and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/369963991_A_Systematic_Methodology_for_Parasitic_Capacitance_Estimation_and_Validation_of_Multi-Chip_Modules", "content": "Recently, some of the well-known EV manufacturers have utilized higher-voltage batteries to benefit from lower current, higher power density , and ..."}
+{"idx": 2, "title": "Dr. Feng Li – { computing, forecasting and learning with", "date": "", "ddg_snippet": "... Wang, Haichao Chen, Yilan Wu, Yueyuan Xu, Gangyue Wu, Yang Zhang, Lvfu He, Jianping Zhang, Fangxia Zhang, Xuehan Qian, Xiuhong Zhang, Lianhong Zhou ...", "subpage_snippet": "", "source": "feng.li", "link": "https://feng.li/", "content": "... Wang, Haichao Chen, Yilan Wu, Yueyuan Xu, Gangyue Wu, Yang Zhang, Lvfu He, Jianping Zhang, Fangxia Zhang, Xuehan Qian, Xiuhong Zhang, Lianhong Zhou ..."}
+{"idx": 3, "title": "FINALLY: fast and universal speech enhancement with studio-like", "date": "", "ddg_snippet": "... GANs ) for speech enhancement and theoretically show that GANs are naturally inclined to seek the point of maximum density within the conditional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05920v3", "content": "... GANs ) for speech enhancement and theoretically show that GANs are naturally inclined to seek the point of maximum density within the conditional ..."}
+{"idx": 4, "title": "Simulation-Based Inference with Quantile Regression", "date": "", "ddg_snippet": "The cornerstone of most contemporary SBI methods is some form of conditional density estimator, which is used to approximate the likelihood, the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.02413v2", "content": "The cornerstone of most contemporary SBI methods is some form of conditional density estimator, which is used to approximate the likelihood, the ..."}
+{"idx": 5, "title": "Generative Modeling | Climate Change AI", "date": "", "ddg_snippet": "Abstract : Satellite-based estimates of greenhouse gas (GHG) properties from observations of reflected solar spectra are integral for understanding ...", "subpage_snippet": "", "source": "www.climatechange.ai", "link": "https://www.climatechange.ai/subject_areas/generative_modeling", "content": "Abstract : Satellite-based estimates of greenhouse gas (GHG) properties from observations of reflected solar spectra are integral for understanding ..."}
+{"idx": 6, "title": "Volume 132 Issue 4 | Journal of Applied Physics | AIP Publishing", "date": "", "ddg_snippet": "View article titled, Machine learning estimation of magnetic parameters and classification of magnetic vortex states", "subpage_snippet": "", "source": "pubs.aip.org", "link": "https://pubs.aip.org/aip/jap/issue/132/4", "content": "View article titled, Machine learning estimation of magnetic parameters and classification of magnetic vortex states"}
+{"idx": 7, "title": "石油实验地质", "date": "", "ddg_snippet": "Abstract : There is great potential for developing deep shale gas resources, but the engineering geological conditions are relatively poorer, making ...", "subpage_snippet": "", "source": "www.sysydz.net", "link": "https://www.sysydz.net/en/article/2023/6", "content": "Abstract : There is great potential for developing deep shale gas resources, but the engineering geological conditions are relatively poorer, making ..."}
+{"idx": 8, "title": "Deep Learning in Characteristics-Sorted Factor Models | Request", "date": "", "ddg_snippet": "This paper introduces Factor- GAN , an innovative framework that utilizes Generative Adversarial Networks ( GAN ) technology for factor investing.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372602423_Deep_Learning_in_Characteristics-Sorted_Factor_Models", "content": "This paper introduces Factor- GAN , an innovative framework that utilizes Generative Adversarial Networks ( GAN ) technology for factor investing."}
+{"idx": 9, "title": "Tap Maize Yield Productivity in China: A Meta-Analysis of", "date": "", "ddg_snippet": "For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2073-4395/15/4/861", "content": "For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ..."}
diff --git a/data/sampled_jsons/Zhou_et_al_2024_attention_heads_safety_Section_5.1_mechanistic_analysis_undifferentiated_attention.jsonl b/data/sampled_jsons/Zhou_et_al_2024_attention_heads_safety_Section_5.1_mechanistic_analysis_undifferentiated_attention.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c4bcf37f51af78b88e023cdb08646ca8f94d46c
--- /dev/null
+++ b/data/sampled_jsons/Zhou_et_al_2024_attention_heads_safety_Section_5.1_mechanistic_analysis_undifferentiated_attention.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "parallel multi- head attention mechanism , allowing the signal for key tokens to be amplified and less important tokens to be diminished.There are variants, described in the following section . By convention, we write all vectors as row vectors.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "parallel multi- head attention mechanism , allowing the signal for key tokens to be amplified and less important tokens to be diminished.There are variants, described in the following section . By convention, we write all vectors as row vectors."}
+{"idx": 1, "title": "Figure 12: Mean attention entropy by layer / head . Higher values...", "date": "", "ddg_snippet": "... (Dai et al ., 2024 ) and Qwen1.5-MoE-A2.7B (Team, 2024 ), which are illustrated in Figure 2, Figure 10 and Figure 11 in Appendix B.2. Theoretical insights into how attention mechanisms compute correlations between tokens using the inner product of query (Q) and key (K)...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Mean-attention-entropy-by-layer-head-Higher-values-indicate-more-diffuse-attention_fig12_335781217", "content": "... (Dai et al ., 2024 ) and Qwen1.5-MoE-A2.7B (Team, 2024 ), which are illustrated in Figure 2, Figure 10 and Figure 11 in Appendix B.2. Theoretical insights into how attention mechanisms compute correlations between tokens using the inner product of query (Q) and key (K)..."}
+{"idx": 2, "title": "Open Problems in Mechanistic Interpretability", "date": "", "ddg_snippet": "Interpreting individual attention heads does not fare better than interpreting individual neurons, as attention heads also exhibit polysemanticity (Janiak et al ., 2023) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.16496v1", "content": "Interpreting individual attention heads does not fare better than interpreting individual neurons, as attention heads also exhibit polysemanticity (Janiak et al ., 2023) ."}
+{"idx": 3, "title": "【DL輪読会】 On The Role of Attention Heads in Large Language...", "date": "", "ddg_snippet": "例：Retrieval Head , Induction Head – 本研究：攻撃 × 解釈（ Attention Head ）の交差点上にあたる研究 3.– Undifferentiated Attention > Scaling Contribution 12.", "subpage_snippet": "", "source": "www.docswell.com", "link": "https://www.docswell.com/s/DeepLearning2023/Z3GEV8-2024-11-21-153626", "content": "例：Retrieval Head , Induction Head – 本研究：攻撃 × 解釈（ Attention Head ）の交差点上にあたる研究 3.– Undifferentiated Attention > Scaling Contribution 12."}
+{"idx": 4, "title": "GitHub - sunnyhuma171/ Attention - Mechanism -arXiv-daily:...", "date": "", "ddg_snippet": "Unraveling Safety Attention Heads in Large Vision-Language Models. Ziwei Zheng et . al . 2501.02029. null. 2025-01-02. RealDiffFusionNet: Neural Controlled Differential Equation Informed Multi- Head Attention Fusion Networks for Disease Progression Modeling Using Real-World Data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sunnyhuma171/Attention-Mechanism-arXiv-daily", "content": "Unraveling Safety Attention Heads in Large Vision-Language Models. Ziwei Zheng et . al . 2501.02029. null. 2025-01-02. RealDiffFusionNet: Neural Controlled Differential Equation Informed Multi- Head Attention Fusion Networks for Disease Progression Modeling Using Real-World Data."}
+{"idx": 5, "title": "Mechanistic Understanding and Mitigation of... - ACL Anthology", "date": "", "ddg_snippet": "@inproceedings{yu- etal - 2024 - mechanistic , title = \" Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations\", author = \"Yu, Lei and.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.466/", "content": "@inproceedings{yu- etal - 2024 - mechanistic , title = \" Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations\", author = \"Yu, Lei and."}
+{"idx": 6, "title": "A Primer on the Inner Workings of... | Read Paper on Bytez", "date": "", "ddg_snippet": "5 . 1 .1 attention heads with interpretable attention weights patterns.(2023)’s analysis on zero-shot settings (Lv et al ., 2024 ) and finds specific attention heads “passing” the argument from the context (Poland), but also promoting the capital cities (Warsaw).", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2405.00208/paper", "content": "5 . 1 .1 attention heads with interpretable attention weights patterns.(2023)’s analysis on zero-shot settings (Lv et al ., 2024 ) and finds specific attention heads “passing” the argument from the context (Poland), but also promoting the capital cities (Warsaw)."}
+{"idx": 7, "title": "Video understanding with", "date": "", "ddg_snippet": "( Zhou et al ., 2018) presented the Cloud-based Visual Surveillance System (CVSS), a paradigm that effectively utilizes cloud computing resources for robust storage solutions, real-time video transcoding, advanced intelligent analytics, and timely dissemination of alerts and notifications.", "subpage_snippet": "", "source": "cerv.aut.ac.nz", "link": "https://cerv.aut.ac.nz/wp-content/uploads/2025/09/Annis-thesiswyan.pdf", "content": "( Zhou et al ., 2018) presented the Cloud-based Visual Surveillance System (CVSS), a paradigm that effectively utilizes cloud computing resources for robust storage solutions, real-time video transcoding, advanced intelligent analytics, and timely dissemination of alerts and notifications."}
+{"idx": 8, "title": "Охота за 'спящими' кошельками: как инвестбанки пытается... / Хабр", "date": "", "ddg_snippet": "Möser M. et al . Resurrecting Address Clustering in Bitcoin, 2021. Ron D., Shamir A. Quantitative Analysis of the Full Bitcoin Transaction Graph. BlockSci — open-source blockchain analysis platform.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/949028/", "content": "Möser M. et al . Resurrecting Address Clustering in Bitcoin, 2021. Ron D., Shamir A. Quantitative Analysis of the Full Bitcoin Transaction Graph. BlockSci — open-source blockchain analysis platform."}
+{"idx": 9, "title": "The influence of China’s low-carbon city pilot policy on the green...", "date": "", "ddg_snippet": "This analysis follows the approach of Koltai et al . (2022), involving the construction and visualization of time-specific dummy variables to estimate regression coefficients.", "subpage_snippet": "", "source": "www.elsevier.es", "link": "https://www.elsevier.es/es-revista-journal-innovation-knowledge-376-articulo-the-influence-chinas-low-carbon-city-S2444569X25001593", "content": "This analysis follows the approach of Koltai et al . (2022), involving the construction and visualization of time-specific dummy variables to estimate regression coefficients."}
diff --git a/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_Olah_2020_abstract.jsonl b/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_Olah_2020_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8b18a51cf247c8ee29eb82c9c99c5c8230bb289b
--- /dev/null
+++ b/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_Olah_2020_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mechanistic interpretability - Wikipedia", "date": "", "ddg_snippet": "In March 2020 , Olah and the OpenAI Clarity team published Zoom In : An Introduction to Circuits , which outlined an approach inspired by neuroscience and cellular biology.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Mechanistic_interpretability", "content": "In March 2020 , Olah and the OpenAI Clarity team published Zoom In : An Introduction to Circuits , which outlined an approach inspired by neuroscience and cellular biology."}
+{"idx": 1, "title": "Zoom In : An Introduction to Circuits", "date": "", "ddg_snippet": "... Introduction Olah et al. ( 2020 ) make the case for mechanistic interpretability as the study of a new kind of system:In this view, neural networks are an object of empirical investigation, perhaps similar to an organism in biology.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/339841165_Zoom_In_An_Introduction_to_Circuits", "content": "... Introduction Olah et al. ( 2020 ) make the case for mechanistic interpretability as the study of a new kind of system:In this view, neural networks are an object of empirical investigation, perhaps similar to an organism in biology."}
+{"idx": 2, "title": "Zoom In : An Introduction to Circuits — LessWrong", "date": "", "ddg_snippet": "“ Zoom In ” provides lots of in-depth justification and examples for each of these claims which I will mostly leave to the actual article. Some highlights, however: How do convolutional neural networks (CNNs) detect dogs in an orientation-invariant way?", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/MG4ZjWQDrdpgeu8wG/zoom-in-an-introduction-to-circuits", "content": "“ Zoom In ” provides lots of in-depth justification and examples for each of these claims which I will mostly leave to the actual article. Some highlights, however: How do convolutional neural networks (CNNs) detect dogs in an orientation-invariant way?"}
+{"idx": 3, "title": "Zoom In : An Introduction to Circuits . Published by OpenAI.", "date": "", "ddg_snippet": "March 10, 2020 . Zoom In : An Introduction to Circuits . By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.", "subpage_snippet": "", "source": "blog.biocomm.ai", "link": "https://blog.biocomm.ai/2020/03/10/zoom-in-an-introduction-to-circuits-published-by-openai-march-10-2020/", "content": "March 10, 2020 . Zoom In : An Introduction to Circuits . By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks."}
+{"idx": 4, "title": "Thread: Circuits", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits . Authors.The thread is organized through the open #circuits channel on the Distill slack. Articles can be suggested there, and will be included at the discretion of previous authors in the thread, or in the case of disagreement by an uninvolved editor.", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/", "content": "Zoom In : An Introduction to Circuits . Authors.The thread is organized through the open #circuits channel on the Distill slack. Articles can be suggested there, and will be included at the discretion of previous authors in the thread, or in the case of disagreement by an uninvolved editor."}
+{"idx": 5, "title": "Zoom In : An Introduction to Circuits", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits . Zoom In : An Introduction to Circuits . By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.", "subpage_snippet": "", "source": "archive.md", "link": "https://archive.md/2023.02.19-114338/https://distill.pub/2020/circuits/zoom-in/", "content": "Zoom In : An Introduction to Circuits . Zoom In : An Introduction to Circuits . By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks."}
+{"idx": 6, "title": "Distill: Zoom in on Circuits | Dynamically Typed", "date": "", "ddg_snippet": "Chris Olah et al. wrote a fascinating new Distill article about “circuits” in convolutional neural networks. The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry( 2020 ) on Distill: Zoom In : An Introduction to Circuits .", "subpage_snippet": "", "source": "dynamicallytyped.com", "link": "https://dynamicallytyped.com/stories/2020/distill-zoom-in-on-circuits/", "content": "Chris Olah et al. wrote a fascinating new Distill article about “circuits” in convolutional neural networks. The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry( 2020 ) on Distill: Zoom In : An Introduction to Circuits ."}
+{"idx": 7, "title": "Zoom In : An Introduction to Circuits | Nick’s Notes", "date": "", "ddg_snippet": "Circuits - Features are connected by weights, forming circuits . A “circuit” is a computational subgraph of a neural network. It consists of a set of features, and the weighted edges that go between them in the original network.", "subpage_snippet": "", "source": "www.nickjalbert.com", "link": "https://www.nickjalbert.com/reading/2020/03/27/zoom-in-an-introduction-to-circuits.html", "content": "Circuits - Features are connected by weights, forming circuits . A “circuit” is a computational subgraph of a neural network. It consists of a set of features, and the weighted edges that go between them in the original network."}
+{"idx": 8, "title": "distillpub/post--circuits- zoom - in : Zoom In : An Introduction to Circuits", "date": "", "ddg_snippet": "distillpub / post--circuits- zoom - in Public. Notifications You must be signed in to change notification settings. Fork 9.View all files. About. Zoom In : An Introduction to Circuits .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/distillpub/post--circuits-zoom-in", "content": "distillpub / post--circuits- zoom - in Public. Notifications You must be signed in to change notification settings. Fork 9.View all files. About. Zoom In : An Introduction to Circuits ."}
+{"idx": 9, "title": "Zoom In An Introduction to Circuits | Arshia Soltani Moakhar", "date": "", "ddg_snippet": "Investigate Vision Circuits by Studying the Connections between Neurons.There are two separate paths in inception for detecting dog heads. The first one detects left oriented heads, while the second one detects right oriented heads.", "subpage_snippet": "", "source": "ckodser.github.io", "link": "https://ckodser.github.io/summaries/Zoom_In_An_Introduction_to_Circuits/", "content": "Investigate Vision Circuits by Studying the Connections between Neurons.There are two separate paths in inception for detecting dog heads. The first one detects left oriented heads, while the second one detects right oriented heads."}
diff --git a/data/sampled_jsons/Zou_et_al._2023a_RepE_acronym.jsonl b/data/sampled_jsons/Zou_et_al._2023a_RepE_acronym.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e3ed314360aa694fb8b8152b82e55760c5be5611
--- /dev/null
+++ b/data/sampled_jsons/Zou_et_al._2023a_RepE_acronym.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub Pages - Andy Zou", "date": "", "ddg_snippet": "RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs).", "subpage_snippet": "", "source": "andyzoujm.github.io", "link": "https://andyzoujm.github.io/", "content": "RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs)."}
+{"idx": 1, "title": "repe · PyPI", "date": "", "ddg_snippet": "Representation EngineeringRepresentation Engineering ( RepE ) This is the official repository for \"Representation Engineering: A Top-Down Approach to AI Transparency\" by Andy Zou , Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart ...", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/repe/", "content": "Representation EngineeringRepresentation Engineering ( RepE ) This is the official repository for \"Representation Engineering: A Top-Down Approach to AI Transparency\" by Andy Zou , Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart ..."}
+{"idx": 2, "title": "Representation Engineering (RepE) - GitHub", "date": "", "ddg_snippet": "In this paper, we introduce and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/andyzoujm/representation-engineering", "content": "In this paper, we introduce and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level ..."}
+{"idx": 3, "title": "Zou Et Al. (2023) | PDF", "date": "", "ddg_snippet": "Zou et al. (2023) - Free download as PDF File (.pdf), Text File (.txt) or read online for free.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/646469918/Zou-et-al-2023", "content": "Zou et al. (2023) - Free download as PDF File (.pdf), Text File (.txt) or read online for free."}
+{"idx": 4, "title": "[2310.01405] Representation Engineering: A Top-Down Approach to AI ...", "date": "", "ddg_snippet": "In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.01405", "content": "In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level ..."}
+{"idx": 5, "title": "Representation Engineering: A Top-Down Approach to AI Transparency", "date": "", "ddg_snippet": "Representation Engineering ( RepE ) Representation engineering ( RepE ) is a top-down approach to transparency research that treats representations as the fundamental unit of analysis, aiming to understand and control representations of high-level cognitive phenomena in neural networks.", "subpage_snippet": "", "source": "montrealethics.ai", "link": "https://montrealethics.ai/representation-engineering-a-top-down-approach-to-ai-transparency/", "content": "Representation Engineering ( RepE ) Representation engineering ( RepE ) is a top-down approach to transparency research that treats representations as the fundamental unit of analysis, aiming to understand and control representations of high-level cognitive phenomena in neural networks."}
+{"idx": 6, "title": "chrisliu298/awesome-representation-engineering - GitHub", "date": "", "ddg_snippet": "This repository tracks the latest research on representation engineering ( RepE ), which was originally introduced by Zou et al. (2023). The goal is to offer a comprehensive list of papers and resources relevant to the topic. Work that falls under the umbrella of representation engineering are also ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chrisliu298/awesome-representation-engineering", "content": "This repository tracks the latest research on representation engineering ( RepE ), which was originally introduced by Zou et al. (2023). The goal is to offer a comprehensive list of papers and resources relevant to the topic. Work that falls under the umbrella of representation engineering are also ..."}
+{"idx": 7, "title": "Representation Engineering: A Top-Down Approach to AI Transparency", "date": "", "ddg_snippet": "Abstract We identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.01405", "content": "Abstract We identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep ..."}
+{"idx": 8, "title": "Taxonomy, Opportunities, and Challenges of Representation Engineering ...", "date": "", "ddg_snippet": "Initial work on Activation Steering (Turner et al ., 2024; Li et al ., 2023a ) was built on the assumption that concepts are represented as linear directions in the activation space of LLMs (Park et al ., 2024b). These methods focused on the difference in activations for inputs that are positive or negative with regards to the concept.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.19649v2", "content": "Initial work on Activation Steering (Turner et al ., 2024; Li et al ., 2023a ) was built on the assumption that concepts are represented as linear directions in the activation space of LLMs (Park et al ., 2024b). These methods focused on the difference in activations for inputs that are positive or negative with regards to the concept."}
+{"idx": 9, "title": "(PDF) NLPBench: Evaluating Large Language Models on Solving NLP", "date": "", "ddg_snippet": "the LLM might not always recall accurate ... different NLP categories and (2) an ev aluation of problem-solving abilities from a human expert’ s", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/374170865_NLPBench_Evaluating_Large_Language_Models_on_Solving_NLP_Problems", "content": "the LLM might not always recall accurate ... different NLP categories and (2) an ev aluation of problem-solving abilities from a human expert’ s"}
diff --git a/data/sampled_jsons/a_lot_of_work_measurement_theory_social_science_computer_science_early_days_arxiv_2502.00561.jsonl b/data/sampled_jsons/a_lot_of_work_measurement_theory_social_science_computer_science_early_days_arxiv_2502.00561.jsonl
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index 0000000000000000000000000000000000000000..e1c3268229085a1ea779d694141cf689988380fb
--- /dev/null
+++ b/data/sampled_jsons/a_lot_of_work_measurement_theory_social_science_computer_science_early_days_arxiv_2502.00561.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "by H Wallach · 2025 · Cited by 12 — We present a four-level framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00561", "content": "by H Wallach · 2025 · Cited by 12 — We present a four-level framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and ..."}
+{"idx": 1, "title": "Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "6 Jun 2025 — We present a four-level framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "6 Jun 2025 — We present a four-level framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, ..."}
+{"idx": 2, "title": "A Validity-Centered Framework for AI Evaluation", "date": "", "ddg_snippet": "by O Salaudeen · 2025 · Cited by 5 — Position: Evaluating generative ai systems is a social science measurement challenge. arXiv preprint arXiv : 2502.00561 ,. 2025. Yuxuan Wan ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.10573", "content": "by O Salaudeen · 2025 · Cited by 5 — Position: Evaluating generative ai systems is a social science measurement challenge. arXiv preprint arXiv : 2502.00561 ,. 2025. Yuxuan Wan ..."}
+{"idx": 3, "title": "Social Sycophancy: A Broader Understanding of LLM ...", "date": "", "ddg_snippet": "20 May 2025 — We introduce a richer theory of social sycophancy in LLMs, characterizing sycophancy as the excessive preservation of a user's face.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13995v1", "content": "20 May 2025 — We introduce a richer theory of social sycophancy in LLMs, characterizing sycophancy as the excessive preservation of a user's face."}
+{"idx": 4, "title": "Methodological Challenges in Agentic Evaluations of AI ...", "date": "", "ddg_snippet": "by K Wei — Our hope is to improve the state of agentic evaluations of AI systems, systematize the methodological work in this domain, and con- tribute to the establishment ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ZhSKG8IslC", "content": "by K Wei — Our hope is to improve the state of agentic evaluations of AI systems, systematize the methodological work in this domain, and con- tribute to the establishment ..."}
+{"idx": 5, "title": "Against 'softmaxing' culture", "date": "", "ddg_snippet": "28 Jun 2025 — This position paper argues that machine learning (ML) and human- computer interaction (HCI) approaches to evaluation are limited.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.22968v1", "content": "28 Jun 2025 — This position paper argues that machine learning (ML) and human- computer interaction (HCI) approaches to evaluation are limited."}
+{"idx": 6, "title": "Human Learning about AI", "date": "", "ddg_snippet": "12 Feb 2025 — , “Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge,” arXiv preprint arXiv : 2502.00561 , 2025. Wang et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.05408v2", "content": "12 Feb 2025 — , “Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge,” arXiv preprint arXiv : 2502.00561 , 2025. Wang et al ..."}
+{"idx": 7, "title": "Large Language Model Psychometrics: A Systematic ...", "date": "", "ddg_snippet": "13 May 2025 — Position: Evaluating generative ai systems is a social science measurement challenge. arXiv preprint arXiv : 2502.00561 , 2025. Wan and Chang ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.08245v1", "content": "13 May 2025 — Position: Evaluating generative ai systems is a social science measurement challenge. arXiv preprint arXiv : 2502.00561 , 2025. Wan and Chang ..."}
+{"idx": 8, "title": "Industry Aspirations vs. User Realities with AI Agent Software", "date": "", "ddg_snippet": "3 days ago — Position: Evaluating generative ai systems is a social science measurement challenge. arXiv preprint arXiv : 2502.00561 (2025). Whitten and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14528v1", "content": "3 days ago — Position: Evaluating generative ai systems is a social science measurement challenge. arXiv preprint arXiv : 2502.00561 (2025). Whitten and ..."}
+{"idx": 9, "title": "Large language model psychometrics: A systematic review ...", "date": "", "ddg_snippet": "Position: Evaluating generative ai systems is a social science measurement challenge. arXiv preprint arXiv : 2502.00561 , 2025. Wan and Chang [2024] ↑ Yixin ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.08245v2", "content": "Position: Evaluating generative ai systems is a social science measurement challenge. arXiv preprint arXiv : 2502.00561 , 2025. Wan and Chang [2024] ↑ Yixin ..."}
diff --git a/data/sampled_jsons/abstract_Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_arxiv_2011.13456_year_2021.jsonl b/data/sampled_jsons/abstract_Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_arxiv_2011.13456_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0ebb9fc234305582306ca333ba4614ef86c461cc
--- /dev/null
+++ b/data/sampled_jsons/abstract_Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_arxiv_2011.13456_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2011.13456] Score-Based Generative Modeling through Stochastic", "date": "", "ddg_snippet": "View a PDF of the paper titled Score - Based Generative Modeling through Stochastic Differential Equations , by Yang Song and 4 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456", "content": "View a PDF of the paper titled Score - Based Generative Modeling through Stochastic Differential Equations , by Yang Song and 4 other authors"}
+{"idx": 1, "title": "The Information Dynamics of Generative Diffusion", "date": "", "ddg_snippet": "... based on stochastic differential equations (SDEs) was formulated in (Song et al., 2021 ; Rombach et al., 2022 ) and is central to the modern ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19897v1", "content": "... based on stochastic differential equations (SDEs) was formulated in (Song et al., 2021 ; Rombach et al., 2022 ) and is central to the modern ..."}
+{"idx": 2, "title": "SOLVING ILL-CONDITIONED POLYNOMIAL EQUATIONS USING SCORE-BASED", "date": "", "ddg_snippet": "Score - based stochastic differential equations ( score -SDE) [ 21 ] is a popular diffusion model that makes use of the Stein score , i.e., the gradient ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11397v1", "content": "Score - based stochastic differential equations ( score -SDE) [ 21 ] is a popular diffusion model that makes use of the Stein score , i.e., the gradient ..."}
+{"idx": 3, "title": "A Group Symmetric Stochastic Differential Equation Model for", "date": "", "ddg_snippet": "SE(3)-equivariant and reflection-antisymmetric) stochastic differential equation models to generate the 3D geometries from 2D topologies, and vice ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18407v2", "content": "SE(3)-equivariant and reflection-antisymmetric) stochastic differential equation models to generate the 3D geometries from 2D topologies, and vice ..."}
+{"idx": 4, "title": "Lattice Random Walk Discretisations of Stochastic Differential", "date": "", "ddg_snippet": "Stochastic differential equations (SDEs) are a powerful tool for modelling a wide range of phenomena across physics, finance, biology, and machine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20883v1", "content": "Stochastic differential equations (SDEs) are a powerful tool for modelling a wide range of phenomena across physics, finance, biology, and machine ..."}
+{"idx": 5, "title": "Stochastic Karras VE", "date": "", "ddg_snippet": "... Based Generative Models .” ... Score - based generative modeling through stochastic differential equations .” https:// arxiv .org/abs/ 2011 . 13456", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/diffusers/v0.5.1/en/api/pipelines/stochastic_karras_ve", "content": "... Based Generative Models .” ... Score - based generative modeling through stochastic differential equations .” https:// arxiv .org/abs/ 2011 . 13456"}
+{"idx": 6, "title": "GitHub - yang-song/score_sde_pytorch: PyTorch implementation", "date": "", "ddg_snippet": "... framework that generalizes and improves previous work on score - based generative models through the lens of stochastic differential equations (SDEs).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yang-song/score_sde_pytorch", "content": "... framework that generalizes and improves previous work on score - based generative models through the lens of stochastic differential equations (SDEs)."}
+{"idx": 7, "title": "GitHub - yang-song/score_sde: Official code for Score-Based", "date": "", "ddg_snippet": "... framework that generalizes and improves previous work on score - based generative models through the lens of stochastic differential equations (SDEs).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yang-song/score_sde", "content": "... framework that generalizes and improves previous work on score - based generative models through the lens of stochastic differential equations (SDEs)."}
+{"idx": 8, "title": "google/ncsnpp-ffhq-1024 · Hugging Face", "date": "", "ddg_snippet": "Score - Based Generative Modeling through Stochastic Differential ... Paper : Score - Based Generative Modeling through Stochastic Differential Equations", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/google/ncsnpp-ffhq-1024", "content": "Score - Based Generative Modeling through Stochastic Differential ... Paper : Score - Based Generative Modeling through Stochastic Differential Equations"}
+{"idx": 9, "title": "[Distributed w/ TorchTitan] FLUX is Here: Experience Diffusion", "date": "", "ddg_snippet": "... modeled as a stochastic process, where each step adds a small amount of noise to the data, and the model learns to reverse this process to generate ...", "subpage_snippet": "", "source": "discuss.pytorch.org", "link": "https://discuss.pytorch.org/t/distributed-w-torchtitan-flux-is-here-experience-diffusion-model-training-on-torchtitan/221119", "content": "... modeled as a stochastic process, where each step adds a small amount of noise to the data, and the model learns to reverse this process to generate ..."}
diff --git a/data/sampled_jsons/abstract_of_'Distilling_the_knowledge_in_a_neural_network'_by_Hinton_(2015).jsonl b/data/sampled_jsons/abstract_of_'Distilling_the_knowledge_in_a_neural_network'_by_Hinton_(2015).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ecca0f4a1bbba4e581199a318fc1e2dbc1c01fd9
--- /dev/null
+++ b/data/sampled_jsons/abstract_of_'Distilling_the_knowledge_in_a_neural_network'_by_Hinton_(2015).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "History of artificial neural networks - Wikipedia", "date": "", "ddg_snippet": "The paper argued that several abstract models of neural networks (some learning, some not learning) have the same computational power as Turing ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/History_of_artificial_neural_networks", "content": "The paper argued that several abstract models of neural networks (some learning, some not learning) have the same computational power as Turing ..."}
+{"idx": 1, "title": "Geoffrey Hinton - Distilling the Knowledge in a Neural Network", "date": "", "ddg_snippet": "Home ML Papers Geoffrey Hinton - Distilling the Knowledge in a Neural Network ( 2015 ) ... Distilling the knowledge in a neural network .\" arXiv preprint ...", "subpage_snippet": "", "source": "www.tomrochette.com", "link": "https://www.tomrochette.com/machine-learning/papers/geoffrey-hinton-distilling-the-knowledge-in-a-neural-network", "content": "Home ML Papers Geoffrey Hinton - Distilling the Knowledge in a Neural Network ( 2015 ) ... Distilling the knowledge in a neural network .\" arXiv preprint ..."}
+{"idx": 2, "title": "[1503.02531] Distilling the Knowledge in a Neural Network", "date": "", "ddg_snippet": "View a PDF of the paper titled Distilling the Knowledge in a Neural Network , by Geoffrey Hinton and 2 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1503.02531", "content": "View a PDF of the paper titled Distilling the Knowledge in a Neural Network , by Geoffrey Hinton and 2 other authors"}
+{"idx": 3, "title": "Distilling the Knowledge in a Neural Network — All Things", "date": "", "ddg_snippet": "The authors demonstrated that a distilled model is capable of attaining similar accuracy to an ensemble of deep models with respect to MNIST and ...", "subpage_snippet": "", "source": "allthingsphi.com", "link": "https://allthingsphi.com/blog/2016/11/11/distilling-the-knowledge-in-a-neural-network.html", "content": "The authors demonstrated that a distilled model is capable of attaining similar accuracy to an ensemble of deep models with respect to MNIST and ..."}
+{"idx": 4, "title": "Distilling the Knowledge in a Neural Network « Another", "date": "", "ddg_snippet": "Distilling the Knowledge in a Neural Network by Geoffrey Hinton , Oriol Vinyals , Jeff Dean. ... achieve some surprising results on MNIST and we show ...", "subpage_snippet": "", "source": "tm.durusau.net", "link": "http://tm.durusau.net/?p=61104", "content": "Distilling the Knowledge in a Neural Network by Geoffrey Hinton , Oriol Vinyals , Jeff Dean. ... achieve some surprising results on MNIST and we show ..."}
+{"idx": 5, "title": "What is Knowledge distillation? | IBM", "date": "", "ddg_snippet": "In the seminal 2015 paper, “ Distilling the Knowledge in a Neural Network ,” Hinton et al proposed to circumvent these limitations by dividing ...", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/topics/knowledge-distillation", "content": "In the seminal 2015 paper, “ Distilling the Knowledge in a Neural Network ,” Hinton et al proposed to circumvent these limitations by dividing ..."}
+{"idx": 6, "title": "Geoffrey Hinton's Capsule Networks: A Novel Approach to", "date": "", "ddg_snippet": "While overcoming the shortcomings of traditional neural networks , Hinton has also been devoted to understanding deep neural networks .", "subpage_snippet": "", "source": "www.alibabacloud.com", "link": "https://www.alibabacloud.com/blog/geoffrey-hintons-capsule-networks-a-novel-approach-to-deep-learning_370957", "content": "While overcoming the shortcomings of traditional neural networks , Hinton has also been devoted to understanding deep neural networks ."}
+{"idx": 7, "title": "Neural network (machine learning) - WikiMili, The Best", "date": "", "ddg_snippet": "Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict starfish and ...", "subpage_snippet": "", "source": "wikimili.com", "link": "https://wikimili.com/en/Neural_network_(machine_learning)", "content": "Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict starfish and ..."}
+{"idx": 8, "title": "US10726326B2 - Learning of neural network - Google Patents", "date": "", "ddg_snippet": "the method includes calculating a plurality of projection parameter sets by analyzing one or more training data, in which the plurality of the ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US10726326B2/en", "content": "the method includes calculating a plurality of projection parameter sets by analyzing one or more training data, in which the plurality of the ..."}
+{"idx": 9, "title": "Label Smoothing++: Enhanced Label Regularization for Training", "date": "", "ddg_snippet": "Knowledge distillation is considered a form of label regularization that involves generating targets from a larger network ( the Teacher) and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.05307v1", "content": "Knowledge distillation is considered a form of label regularization that involves generating targets from a larger network ( the Teacher) and ..."}
diff --git a/data/sampled_jsons/alternative_loss_functions_for_PPO_in_RLHF_before_2025.jsonl b/data/sampled_jsons/alternative_loss_functions_for_PPO_in_RLHF_before_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..12c3447eafc8d405b7fe55b25d693da6fa895277
--- /dev/null
+++ b/data/sampled_jsons/alternative_loss_functions_for_PPO_in_RLHF_before_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "5 PPO Variants for Enhancing RLHF Performance - apxml.com", "date": "", "ddg_snippet": "Discover 5 Proximal Policy Optimization ( PPO ) variants designed to elevate your Reinforcement Learning from Human Feedback ( RLHF ) pipelines. This technical guide explains how these modifications address common PPO limitations, leading to better LLM alignment and performance.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/posts/ppo-variants-for-enhancing-rlhf-performance", "content": "Discover 5 Proximal Policy Optimization ( PPO ) variants designed to elevate your Reinforcement Learning from Human Feedback ( RLHF ) pipelines. This technical guide explains how these modifications address common PPO limitations, leading to better LLM alignment and performance."}
+{"idx": 1, "title": "A vision researcher's guide to some RL stuff: PPO & GRPO", "date": "", "ddg_snippet": "This is a deep dive into Proximal Policy Optimization ( PPO ), which is one of the most popular algorithm used in RLHF for LLMs, as well as Group Relative Policy Optimization (GRPO) proposed by the DeepSeek folks, and there's also a quick summary of the tricks I find impressive in the DeepSeek R1 tech report in the end.", "subpage_snippet": "", "source": "yugeten.github.io", "link": "https://yugeten.github.io/posts/2025/01/ppogrpo/", "content": "This is a deep dive into Proximal Policy Optimization ( PPO ), which is one of the most popular algorithm used in RLHF for LLMs, as well as Group Relative Policy Optimization (GRPO) proposed by the DeepSeek folks, and there's also a quick summary of the tricks I find impressive in the DeepSeek R1 tech report in the end."}
+{"idx": 2, "title": "A Beginner's Guide to Tuning LLMs with RLHF and PPO", "date": "", "ddg_snippet": "Alternatives to PPO in RLHF While Proximal Policy Optimization ( PPO ) is a go-to algorithm for Reinforcement Learning from Human Feedback ( RLHF ) due to its stability and effectiveness, it's not ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@tam.tamanna18/a-beginners-guide-to-tuning-llms-with-rlhf-and-ppo-ea96f9c43165", "content": "Alternatives to PPO in RLHF While Proximal Policy Optimization ( PPO ) is a go-to algorithm for Reinforcement Learning from Human Feedback ( RLHF ) due to its stability and effectiveness, it's not ..."}
+{"idx": 3, "title": "DPO Meets PPO: Reinforced Token Optimization for RLHF", "date": "", "ddg_snippet": "Figure 1: In the MDP framework of RLHF , RTOuses DPO to derive a token-level reward function and then applies PPO to enhance it. This approach is significantly different from the traditional RLHF process, which employs PPO to improve sentence-level rewards under the bandit framework of RLHF .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.18922v4", "content": "Figure 1: In the MDP framework of RLHF , RTOuses DPO to derive a token-level reward function and then applies PPO to enhance it. This approach is significantly different from the traditional RLHF process, which employs PPO to improve sentence-level rewards under the bandit framework of RLHF ."}
+{"idx": 4, "title": "LLM Optimization: Optimizing AI with GRPO, PPO, and DPO", "date": "", "ddg_snippet": "The introduction of RLHF (Reinforcement Learning with Human Feedback) revolutionized LLMs, enabling models like ChatGPT and DeepSeek to optimize responses based on user feedback. However, standard PPO -based RLHF faced inefficiencies, requiring costly reward modeling.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/02/llm-optimization/", "content": "The introduction of RLHF (Reinforcement Learning with Human Feedback) revolutionized LLMs, enabling models like ChatGPT and DeepSeek to optimize responses based on user feedback. However, standard PPO -based RLHF faced inefficiencies, requiring costly reward modeling."}
+{"idx": 5, "title": "Proximal Policy Optimization (PPO): The Key to LLM Alignment", "date": "", "ddg_snippet": "Here, we will see this concept used in the definition of TRPO, as well as to explain the intuition behind PPO's update rule. Additionally, most implementations of RLHF add an extra KL divergence term to their loss function , which helps to prevent reward hacking and ensures updates to the language model's policy are not too large.", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/proximal-policy-optimization-ppo", "content": "Here, we will see this concept used in the definition of TRPO, as well as to explain the intuition behind PPO's update rule. Additionally, most implementations of RLHF add an extra KL divergence term to their loss function , which helps to prevent reward hacking and ensures updates to the language model's policy are not too large."}
+{"idx": 6, "title": "Fine Tuning Beyond SFT - On PPO, DPO and RLHF", "date": "", "ddg_snippet": "PPO is used to finetune the baseline LLM based on rewards by the reward model. PPO is designed to be more stable and efficient than traditional policy gradient methods. DPO is an alternative to use PPO within RLHF . It simplifies the fine-tuning process by directly optimizing the LLM based on the human ratings, without training a reward model.", "subpage_snippet": "", "source": "heinzermch.github.io", "link": "https://heinzermch.github.io/posts/on-rlhf-dpo-and-ppo/", "content": "PPO is used to finetune the baseline LLM based on rewards by the reward model. PPO is designed to be more stable and efficient than traditional policy gradient methods. DPO is an alternative to use PPO within RLHF . It simplifies the fine-tuning process by directly optimizing the LLM based on the human ratings, without training a reward model."}
+{"idx": 7, "title": "Navigating the RLHF Landscape: From Policy Gradients to PPO, GAE, and ...", "date": "", "ddg_snippet": "Welcome to this blog post! If you're keen on exploring Reinforcement Learning from Human Feedback ( RLHF ) in large language models (LLMs) and want to understand the process from the ground up—from basic policy gradient methods and the classic REINFORCE algorithm, through the derivation of Proximal Policy Optimization ( PPO ) using clipping objectives and Generalized Advantage Estimation (GAE ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/NormalUhr/rlhf-pipeline", "content": "Welcome to this blog post! If you're keen on exploring Reinforcement Learning from Human Feedback ( RLHF ) in large language models (LLMs) and want to understand the process from the ground up—from basic policy gradient methods and the classic REINFORCE algorithm, through the derivation of Proximal Policy Optimization ( PPO ) using clipping objectives and Generalized Advantage Estimation (GAE ..."}
+{"idx": 8, "title": "Policy Optimization with RLHF — PPO/DPO/ORPO - Medium", "date": "", "ddg_snippet": "Unlike PPO , DPO avoids complex normalization terms that can be difficult to optimize. Instead, DPO uses an updated or re-parameterized cross-entropy loss function that incorporates preference data.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sulbha.jindal/policy-optimization-with-rlhf-ppo-dpo-orpo-d65d075d99f3", "content": "Unlike PPO , DPO avoids complex normalization terms that can be difficult to optimize. Instead, DPO uses an updated or re-parameterized cross-entropy loss function that incorporates preference data."}
+{"idx": 9, "title": "Fine-Tuning LLMs in 2025: RLHF PPO DPO and TRL for ML Engineers ...", "date": "", "ddg_snippet": "Because of its increased complexity, RLHF requires more effort to set up and more computing power to run. Stability and Control: Since RLHF uses an objective that isn't a simple fixed loss function , training can become unstable if it isn't managed carefully. The model may try to manipulate the reward model or deviate from human-like language.", "subpage_snippet": "", "source": "pub.towardsai.net", "link": "https://pub.towardsai.net/why-fine-tuning-is-the-secret-sauce-for-ml-engineers-in-2025-98b752439a83", "content": "Because of its increased complexity, RLHF requires more effort to set up and more computing power to run. Stability and Control: Since RLHF uses an objective that isn't a simple fixed loss function , training can become unstable if it isn't managed carefully. The model may try to manipulate the reward model or deviate from human-like language."}
diff --git a/data/sampled_jsons/alternative_loss_functions_for_PPO_in_RLHF_for_robustness_published_before_2025.jsonl b/data/sampled_jsons/alternative_loss_functions_for_PPO_in_RLHF_for_robustness_published_before_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..09b4e77ec847cdf99a8a4584c8b5776dd7755491
--- /dev/null
+++ b/data/sampled_jsons/alternative_loss_functions_for_PPO_in_RLHF_for_robustness_published_before_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "5 PPO Variants for Enhancing RLHF Performance - apxml.com", "date": "", "ddg_snippet": "Discover 5 Proximal Policy Optimization ( PPO ) variants designed to elevate your Reinforcement Learning from Human Feedback ( RLHF ) pipelines. This technical guide explains how these modifications address common PPO limitations, leading to better LLM alignment and performance.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/posts/ppo-variants-for-enhancing-rlhf-performance", "content": "Discover 5 Proximal Policy Optimization ( PPO ) variants designed to elevate your Reinforcement Learning from Human Feedback ( RLHF ) pipelines. This technical guide explains how these modifications address common PPO limitations, leading to better LLM alignment and performance."}
+{"idx": 1, "title": "A vision researcher's guide to some RL stuff: PPO & GRPO", "date": "", "ddg_snippet": "This is a deep dive into Proximal Policy Optimization ( PPO ), which is one of the most popular algorithm used in RLHF for LLMs, as well as Group Relative Policy Optimization (GRPO) proposed by the DeepSeek folks, and there's also a quick summary of the tricks I find impressive in the DeepSeek R1 tech report in the end.", "subpage_snippet": "", "source": "yugeten.github.io", "link": "https://yugeten.github.io/posts/2025/01/ppogrpo/", "content": "This is a deep dive into Proximal Policy Optimization ( PPO ), which is one of the most popular algorithm used in RLHF for LLMs, as well as Group Relative Policy Optimization (GRPO) proposed by the DeepSeek folks, and there's also a quick summary of the tricks I find impressive in the DeepSeek R1 tech report in the end."}
+{"idx": 2, "title": "Policy Optimization with RLHF — PPO/DPO/ORPO - Medium", "date": "", "ddg_snippet": "Unlike PPO , DPO avoids complex normalization terms that can be difficult to optimize. Instead, DPO uses an updated or re-parameterized cross-entropy loss function that incorporates preference data.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sulbha.jindal/policy-optimization-with-rlhf-ppo-dpo-orpo-d65d075d99f3", "content": "Unlike PPO , DPO avoids complex normalization terms that can be difficult to optimize. Instead, DPO uses an updated or re-parameterized cross-entropy loss function that incorporates preference data."}
+{"idx": 3, "title": "Secrets of RLHF in Large Language Models Part I: PPO", "date": "", "ddg_snippet": "The diagram then illustrates the computation of various loss functions employed in PPO , signifying the iterative nature of the learning process and the policy updates derived from these losses.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2307.04964", "content": "The diagram then illustrates the computation of various loss functions employed in PPO , signifying the iterative nature of the learning process and the policy updates derived from these losses."}
+{"idx": 4, "title": "Direct Preference Optimization (DPO) | OpenRLHF/OpenRLHF | DeepWiki", "date": "", "ddg_snippet": "Direct Preference Optimization (DPO) Relevant source files This document covers Direct Preference Optimization (DPO), an alternative alignment method that directly trains language models on preference data without requiring a separate reward model. For information about traditional RLHF training with reward models, see PPO Training System. For reward model training, see Reward Modeling (RM ...", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/OpenRLHF/OpenRLHF/4.3-proximal-policy-optimization-(ppo)", "content": "Direct Preference Optimization (DPO) Relevant source files This document covers Direct Preference Optimization (DPO), an alternative alignment method that directly trains language models on preference data without requiring a separate reward model. For information about traditional RLHF training with reward models, see PPO Training System. For reward model training, see Reward Modeling (RM ..."}
+{"idx": 5, "title": "raghavc/LLM-RLHF-Tuning-with-PPO-and-DPO - GitHub", "date": "", "ddg_snippet": "Comprehensive toolkit for Reinforcement Learning from Human Feedback ( RLHF ) training, featuring instruction fine-tuning, reward model training, and support for PPO and DPO algorithms with various configurations for the Alpaca, LLaMA, and LLaMA2 models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/raghavc/LLM-RLHF-Tuning-with-PPO-and-DPO", "content": "Comprehensive toolkit for Reinforcement Learning from Human Feedback ( RLHF ) training, featuring instruction fine-tuning, reward model training, and support for PPO and DPO algorithms with various configurations for the Alpaca, LLaMA, and LLaMA2 models."}
+{"idx": 6, "title": "Fine Tuning Beyond SFT - On PPO, DPO and RLHF", "date": "", "ddg_snippet": "PPO is used to finetune the baseline LLM based on rewards by the reward model. PPO is designed to be more stable and efficient than traditional policy gradient methods. DPO is an alternative to use PPO within RLHF . It simplifies the fine-tuning process by directly optimizing the LLM based on the human ratings, without training a reward model.", "subpage_snippet": "", "source": "heinzermch.github.io", "link": "https://heinzermch.github.io/posts/on-rlhf-dpo-and-ppo/", "content": "PPO is used to finetune the baseline LLM based on rewards by the reward model. PPO is designed to be more stable and efficient than traditional policy gradient methods. DPO is an alternative to use PPO within RLHF . It simplifies the fine-tuning process by directly optimizing the LLM based on the human ratings, without training a reward model."}
+{"idx": 7, "title": "Asynchronous RLHF: Faster and More Efficient Off-Policy RL for...", "date": "", "ddg_snippet": "I especially appreciate the experiments comparing various RLHF loss functions and scaling behaviors related to off-policyness in Section 3. These experiments not only enrich this paper but also validate concurrent works on applying DPO to off-policy data and advocating for on-policy PPO training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FhTAG591Ve", "content": "I especially appreciate the experiments comparing various RLHF loss functions and scaling behaviors related to off-policyness in Section 3. These experiments not only enrich this paper but also validate concurrent works on applying DPO to off-policy data and advocating for on-policy PPO training."}
+{"idx": 8, "title": "REINFORCE++: An Efficient RLHF Algorithm with Robustness to Both Prompt ...", "date": "", "ddg_snippet": "To address these challenges, we propose REINFORCE++, a novel REINFORCE-based method that eliminates the critic model from PPO and uses the global advantage normalization. This approach is unbiased and prevents overfitting to specific training prompts and demonstrates robustness across both Bradley-Terry and rule-based reward models. Notably, REINFORCE++ eliminates the need for prompt set ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.03262", "content": "To address these challenges, we propose REINFORCE++, a novel REINFORCE-based method that eliminates the critic model from PPO and uses the global advantage normalization. This approach is unbiased and prevents overfitting to specific training prompts and demonstrates robustness across both Bradley-Terry and rule-based reward models. Notably, REINFORCE++ eliminates the need for prompt set ..."}
+{"idx": 9, "title": "Calculating Advantages and Returns in PPO - apxml.com", "date": "", "ddg_snippet": "It is a standard practice to normalize the advantages across a batch before using them in the PPO loss calculation. This involves subtracting the mean and dividing by the standard deviation of the advantages within the batch, which helps stabilize training by preventing excessively large policy updates.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/rlhf-reinforcement-learning-human-feedback/chapter-4-rl-ppo-fine-tuning/ppo-advantages-returns", "content": "It is a standard practice to normalize the advantages across a batch before using them in the PPO loss calculation. This involves subtracting the mean and dividing by the standard deviation of the advantages within the batch, which helps stabilize training by preventing excessively large policy updates."}
diff --git a/data/sampled_jsons/arXiv2502.00136_methodology_Section_3.2_behavioral_emotional_mapping.jsonl b/data/sampled_jsons/arXiv2502.00136_methodology_Section_3.2_behavioral_emotional_mapping.jsonl
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+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2 .4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2 .4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "Log in to arXiv | arXiv e-print repository", "date": "", "ddg_snippet": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/login", "content": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy."}
+{"idx": 2, "title": "YOLOv12: Attention-Centric Real-Time Object Detectors - arXiv.org", "date": "", "ddg_snippet": "Feb 18, 2025 · Abstract page for arXiv paper 2502 .12524: YOLOv12: Attention-Centric Real-Time Object Detectors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12524", "content": "Feb 18, 2025 · Abstract page for arXiv paper 2502 .12524: YOLOv12: Attention-Centric Real-Time Object Detectors"}
+{"idx": 3, "title": "Computer Science - arXiv.org", "date": "", "ddg_snippet": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/archive/cs", "content": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:"}
+{"idx": 4, "title": "[2508.10104] DINOv3 - arXiv.org", "date": "", "ddg_snippet": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.10104", "content": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ..."}
+{"idx": 5, "title": "[2212.10156] Planning-oriented Autonomous Driving - arXiv.org", "date": "", "ddg_snippet": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.10156", "content": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ..."}
+{"idx": 6, "title": "[1706.03762] Attention Is All You Need - arXiv.org", "date": "", "ddg_snippet": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ..."}
+{"idx": 7, "title": "[2506.01844] SmolVLA: A Vision-Language-Action Model for ... -...", "date": "", "ddg_snippet": "Jun 2 , 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.01844", "content": "Jun 2 , 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics"}
+{"idx": 8, "title": "[2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in...", "date": "", "ddg_snippet": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.12948", "content": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning"}
+{"idx": 9, "title": "[2410.10762] AFlow: Automating Agentic Workflow Generation -...", "date": "", "ddg_snippet": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10762", "content": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation"}
diff --git a/data/sampled_jsons/arXiv2502.00561_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge.jsonl b/data/sampled_jsons/arXiv2502.00561_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge.jsonl
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+{"idx": 0, "title": "[ 2502 . 00561 ] Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "Computer Science > Computers and Society . arXiv : 2502 . 00561 (cs).View a PDF of the paper titled Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge , by Hanna Wallach and 19 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00561", "content": "Computer Science > Computers and Society . arXiv : 2502 . 00561 (cs).View a PDF of the paper titled Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge , by Hanna Wallach and 19 other authors."}
+{"idx": 1, "title": "Social Sycophancy: A Broader Understanding of LLM Sycophancy", "date": "", "ddg_snippet": "We introduce ELEPHANT , 1 1 1 E valuation of L LMs as E xcessive syco PHANT s a framework for automatically measuring social sycophancy of a given ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13995v1", "content": "We introduce ELEPHANT , 1 1 1 E valuation of L LMs as E xcessive syco PHANT s a framework for automatically measuring social sycophancy of a given ..."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "arXiv : 2502 . 00561 v2 [cs.CY] 6 Jun 2025.Although GenAI systems are increasingly widely deployed, the current state of GenAI evaluations leaves much to be desired. We take the position that evaluating GenAI systems is a social science measurement challenge .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561", "content": "arXiv : 2502 . 00561 v2 [cs.CY] 6 Jun 2025.Although GenAI systems are increasingly widely deployed, the current state of GenAI evaluations leaves much to be desired. We take the position that evaluating GenAI systems is a social science measurement challenge ."}
+{"idx": 3, "title": "Position: Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "Generative AI , Capabilities, Behaviors, Impacts, Evaluation , Measurement , Measurement Theory, Social Sciences , Validity. 1 Evaluating GenAI Systems .We take the position that evaluating GenAI systems is a social science measurement challenge .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "Generative AI , Capabilities, Behaviors, Impacts, Evaluation , Measurement , Measurement Theory, Social Sciences , Validity. 1 Evaluating GenAI Systems .We take the position that evaluating GenAI systems is a social science measurement challenge ."}
+{"idx": 4, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "generative AI systems that borrow from how social scientists measure human behaviors and abilities. Think of current AI evaluation like using a ruler made of rubber - it stretches and bends, giving different measurements each time.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/evaluating-generative-ai-systems-is-social-science", "content": "generative AI systems that borrow from how social scientists measure human behaviors and abilities. Think of current AI evaluation like using a ruler made of rubber - it stretches and bends, giving different measurements each time."}
+{"idx": 5, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/?locale=ko-kr", "content": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult."}
+{"idx": 6, "title": "NeurIPS Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that evaluating generative AI (GenAI) systems is challenging , as concepts related to their capabilities (e.g., intelligence, reasoning) and risks (e.g., stereotyping, anthropomorphism) are especially...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/104194", "content": "Across academia, industry, and government, there is an increasing awareness that evaluating generative AI (GenAI) systems is challenging , as concepts related to their capabilities (e.g., intelligence, reasoning) and risks (e.g., stereotyping, anthropomorphism) are especially..."}
+{"idx": 7, "title": "Downes.ca ~ Stephen's Web ~ Evaluating Generative AI Systems is ...", "date": "", "ddg_snippet": "The argument in this short paper (6 page PDF) is that \" measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social sciences \" and thus \"the ML community would benefit from learning from and drawing on the social ...", "subpage_snippet": "", "source": "www.downes.ca", "link": "https://www.downes.ca/post/77850", "content": "The argument in this short paper (6 page PDF) is that \" measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social sciences \" and thus \"the ML community would benefit from learning from and drawing on the social ..."}
+{"idx": 8, "title": "(PDF) A Shared Standard for Valid Measurement of Generative AI ...", "date": "", "ddg_snippet": "Evaluating Generative AI Systems is a Social Science Measurement Challenge .tasks involving LLM-based systems ; and (4) challenges specific to measuring representational harms.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386419617_A_Shared_Standard_for_Valid_Measurement_of_Generative_AI_Systems'_Capabilities_Risks_and_Impacts", "content": "Evaluating Generative AI Systems is a Social Science Measurement Challenge .tasks involving LLM-based systems ; and (4) challenges specific to measuring representational harms."}
+{"idx": 9, "title": "Towards Interactive Evaluations for Interaction Harms in Human- AI ...", "date": "", "ddg_snippet": "2025. Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge . arXiv preprint arXiv : 2502 . 00561 . Wang, A.; Morgenstern, J.; and Dickerson, J. P. 2025. Large language models that replace human participants can harm fully misportray and flatten...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "2025. Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge . arXiv preprint arXiv : 2502 . 00561 . Wang, A.; Morgenstern, J.; and Dickerson, J. P. 2025. Large language models that replace human participants can harm fully misportray and flatten..."}
diff --git a/data/sampled_jsons/arXiv_2305.18290_Direct_Preference_Optimization_abstract_language_model_year_2023.jsonl b/data/sampled_jsons/arXiv_2305.18290_Direct_Preference_Optimization_abstract_language_model_year_2023.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2305.18290 : Direct Preference Optimization : Your Language Model is Secretly a Reward Model", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "Abstract page for arXiv paper 2305.18290 : Direct Preference Optimization : Your Language Model is Secretly a Reward Model"}
+{"idx": 1, "title": "PDF Direct Preference Optimization(DPO)", "date": "", "ddg_snippet": "R. Rafailov, K. Lee, J. Ba, and M. Zhao, \" Direct Preference Optimization : Your Language Model is Secretly a Reward Model ,\" arXiv preprint arXiv:2305.18290 , 2023.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~cmaddis/courses/csc2541_w25/presentations/mu_cao_dpo.pdf", "content": "R. Rafailov, K. Lee, J. Ba, and M. Zhao, \" Direct Preference Optimization : Your Language Model is Secretly a Reward Model ,\" arXiv preprint arXiv:2305.18290 , 2023."}
+{"idx": 2, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning."}
+{"idx": 3, "title": "Direct preference optimization | Proceedings of the 37th International ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3666122.3668460", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or performing significant hyperparameter tuning."}
+{"idx": 4, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "A new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form is introduced, allowing us to solve the standard RLHF problem with only a simple classification loss. While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Direct-Preference-Optimization:-Your-Language-Model-Rafailov-Sharma/0d1c76d45afa012ded7ab741194baf142117c495", "content": "A new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form is introduced, allowing us to solve the standard RLHF problem with only a simple classification loss. While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due ..."}
+{"idx": 5, "title": "PDF Direct Preference Optimization: Your Language Model is ... - NeurIPS", "date": "", "ddg_snippet": "Abstract While large-scale unsupervised language models (LMs) learn broad world knowl-edge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/a85b405ed65c6477a4fe8302b5e06ce7-Paper-Conference.pdf", "content": "Abstract While large-scale unsupervised language models (LMs) learn broad world knowl-edge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with ..."}
+{"idx": 6, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "Join the discussion on this paper pageDirect Preference Optimization : Your Language Model is Secretly a Reward Model", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2305.18290", "content": "Join the discussion on this paper pageDirect Preference Optimization : Your Language Model is Secretly a Reward Model"}
+{"idx": 7, "title": "Abstract Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "Abstract While large-scale unsupervised language models (LMs) learn broad world knowl-edge and some reasoning skills, achieving precise control of their behavior is dificult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.18290v3", "content": "Abstract While large-scale unsupervised language models (LMs) learn broad world knowl-edge and some reasoning skills, achieving precise control of their behavior is dificult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with ..."}
+{"idx": 8, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is sta-ble, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or per-forming significant hyperparameter tuning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=53HUHMvQLQ", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is sta-ble, performant, and computationally lightweight, eliminating the need for fitting a reward model , sampling from the LM during fine-tuning, or per-forming significant hyperparameter tuning."}
+{"idx": 9, "title": "PDF Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "Each transcript ends with a pair of responses generated by a large (although unknown) language model along with a preference label denoting the human-preferred response. In this setting, no pre-trained SFT model is available; we therefore fine-tune an off-the-shelf language model on only the preferred completions to form the SFT model .评估。", "subpage_snippet": "", "source": "public.agent-matrix.com", "link": "https://public.agent-matrix.com/publish/shared/Paper/DPO.pdf", "content": "Each transcript ends with a pair of responses generated by a large (although unknown) language model along with a preference label denoting the human-preferred response. In this setting, no pre-trained SFT model is available; we therefore fine-tune an off-the-shelf language model on only the preferred completions to form the SFT model .评估。"}
diff --git a/data/sampled_jsons/arXiv_2501.02376_Section_G_IP-Adapter_CLIP_encoding_failure_analysis.jsonl b/data/sampled_jsons/arXiv_2501.02376_Section_G_IP-Adapter_CLIP_encoding_failure_analysis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f9c03371da45cefafe619b1ec8ab47faea821e09
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2501.02376_Section_G_IP-Adapter_CLIP_encoding_failure_analysis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2501.08816] IDEA: Image Description Enhanced CLIP-Adapter", "date": "", "ddg_snippet": "Jan 15, 2025 · However, current studies primarily focus on either prompt learning for text or adapter tuning for vision, without fully exploiting the complementary information and correlations among image-text pairs. In this paper, we propose an Image Description Enhanced CLIP - Adapter (IDEA) method to adapt CLIP to few-shot image classification tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.08816", "content": "Jan 15, 2025 · However, current studies primarily focus on either prompt learning for text or adapter tuning for vision, without fully exploiting the complementary information and correlations among image-text pairs. In this paper, we propose an Image Description Enhanced CLIP - Adapter (IDEA) method to adapt CLIP to few-shot image classification tasks."}
+{"idx": 1, "title": "h94/IP-Adapter · CLIP ViT Origin - Hugging Face", "date": "", "ddg_snippet": "Sep 13, 2023 · What is the origin of the CLIP Vision model weights? Are they copied from another HF repo?", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/h94/IP-Adapter/discussions/4", "content": "Sep 13, 2023 · What is the origin of the CLIP Vision model weights? Are they copied from another HF repo?"}
+{"idx": 2, "title": "IP-Adapter/README.md at main · tencent-ailab/IP-Adapter", "date": "", "ddg_snippet": "Dec 20, 2023 · An IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fine-tuned image prompt model. IP-Adapter can be generalized not only to other custom models fine-tuned from the same base model, but also to controllable generation using existing controllable tools.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tencent-ailab/IP-Adapter/blob/main/README.md", "content": "Dec 20, 2023 · An IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fine-tuned image prompt model. IP-Adapter can be generalized not only to other custom models fine-tuned from the same base model, but also to controllable generation using existing controllable tools."}
+{"idx": 3, "title": "[2308.06721] IP-Adapter: Text Compatible Image Prompt Adapter ... Diving into Mitigating Hallucinations from a Vision ... Generalizable Origin Identification for Text-Guided Image-to ... arXiv.org e-Print archive", "date": "", "ddg_snippet": "Aug 13, 2023 · The key design of our IP-Adapter is decoupled cross-attention mechanism that separates cross-attention layers for text features and image features. Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model. The aggregated expert representation is denoted as Y, which shares the same dimensional-ity as both Zi and the CLIP patch token IP . The final visual representation ˆI is obtained by com-bining the expert features with the original CLIP representation through a residual-style connection. Text-guided image-to-image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading … arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2308.06721", "content": "Aug 13, 2023 · The key design of our IP-Adapter is decoupled cross-attention mechanism that separates cross-attention layers for text features and image features. Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model. The aggregated expert representation is denoted as Y, which shares the same dimensional-ity as both Zi and the CLIP patch token IP . The final visual representation ˆI is obtained by com-bining the expert features with the original CLIP representation through a residual-style connection. Text-guided image-to-image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading … arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 4, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 5, "title": "IP - Adapters : All you need to know - Stable Diffusion Art", "date": "", "ddg_snippet": "The original IP - adapter uses the CLIP image encoder to extract features from the reference image.", "subpage_snippet": "", "source": "stable-diffusion-art.com", "link": "https://stable-diffusion-art.com/ip-adapter/", "content": "The original IP - adapter uses the CLIP image encoder to extract features from the reference image."}
+{"idx": 6, "title": "IP Adapter installation with Workflow (A1111/ForgeUI/ComfyUI)", "date": "", "ddg_snippet": "Preprocessor: Ip Adapter Clip SDXL. Model: IP Adapter adapter_xl. IP Adapter can be used with Stable Diffusion XL or stable Diffusion 1.5 base models. Many people have issues using it, although you can raise your issues in IP AdpaterV2 issues section or IP AdpaterV1 issues section .", "subpage_snippet": "", "source": "www.stablediffusiontutorials.com", "link": "https://www.stablediffusiontutorials.com/2024/04/ip-adapter.html", "content": "Preprocessor: Ip Adapter Clip SDXL. Model: IP Adapter adapter_xl. IP Adapter can be used with Stable Diffusion XL or stable Diffusion 1.5 base models. Many people have issues using it, although you can raise your issues in IP AdpaterV2 issues section or IP AdpaterV1 issues section ."}
+{"idx": 7, "title": "How to Enhance Image Generation with IP - Adapter ... - Next Diffusion", "date": "", "ddg_snippet": "4. Implementing IP - Adapter Models in Image Generation. 5. Using IP - adapter (txt2img).In the ControlNet interface, import an image (e. g ., a woman sitting on a motorcycle), activate ControlNet, and select the appropriate model (e. g ., ip - adapter _sd15).", "subpage_snippet": "", "source": "www.nextdiffusion.ai", "link": "https://www.nextdiffusion.ai/tutorials/how-to-use-ip-adapter-models-for-image-prompting-a1111", "content": "4. Implementing IP - Adapter Models in Image Generation. 5. Using IP - adapter (txt2img).In the ControlNet interface, import an image (e. g ., a woman sitting on a motorcycle), activate ControlNet, and select the appropriate model (e. g ., ip - adapter _sd15)."}
+{"idx": 8, "title": "Обзоры препринтов научных статей «astro-ph/ arxiv .org» за... / Хабр", "date": "", "ddg_snippet": "обзор arxiv :2506.20236 Получение прямых изображений землеподобных планет в высоком разрешении (Direct High-Resolution Imaging of Earth-Like Exoplanets)Authors: Slava G . TuryshevComments: 47 pages, 1 table.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948996/", "content": "обзор arxiv :2506.20236 Получение прямых изображений землеподобных планет в высоком разрешении (Direct High-Resolution Imaging of Earth-Like Exoplanets)Authors: Slava G . TuryshevComments: 47 pages, 1 table."}
+{"idx": 9, "title": "Encoding Failure : Real-Life Examples Explained", "date": "", "ddg_snippet": "Discover the phenomenon of encoding failure , its causes, effects on memory retention, and strategies to improve attention for better recall in daily life.", "subpage_snippet": "", "source": "examples-of.net", "link": "https://examples-of.net/encoding-failure/", "content": "Discover the phenomenon of encoding failure , its causes, effects on memory retention, and strategies to improve attention for better recall in daily life."}
diff --git a/data/sampled_jsons/arXiv_Hierarchical_Overlapping_Clustering_on_Graphs_Pan_Chen.jsonl b/data/sampled_jsons/arXiv_Hierarchical_Overlapping_Clustering_on_Graphs_Pan_Chen.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..950d8edd6051f18c90cb409e9a4acf1dc25afebb
--- /dev/null
+++ b/data/sampled_jsons/arXiv_Hierarchical_Overlapping_Clustering_on_Graphs_Pan_Chen.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval", "date": "", "ddg_snippet": "Building on these insights, we propose a Hierarchical Lexical Graph (HLG) framework to address the gap between surface-level similarity and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08074v1", "content": "Building on these insights, we propose a Hierarchical Lexical Graph (HLG) framework to address the gap between surface-level similarity and ..."}
+{"idx": 1, "title": "Richard J. Chen | Publications", "date": "", "ddg_snippet": "... Georg and Mahmood, Faisal}, journal = { arXiv preprint arXiv :TBD}, year = {2023}, arxiv = {TBD}, abbr = {chen2023general.jpg}, selected = {true} }", "subpage_snippet": "", "source": "richarizardd.me", "link": "http://richarizardd.me/publications/", "content": "... Georg and Mahmood, Faisal}, journal = { arXiv preprint arXiv :TBD}, year = {2023}, arxiv = {TBD}, abbr = {chen2023general.jpg}, selected = {true} }"}
+{"idx": 2, "title": "Most Influential ArXiv (Databases) Papers (2025-03 Version)", "date": "", "ddg_snippet": "Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases IF:3 Related Papers Related Patents Related Grants Related Venues ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2025/03/most-influential-arxiv-databases-papers-2025-03-version/", "content": "Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases IF:3 Related Papers Related Patents Related Grants Related Venues ..."}
+{"idx": 3, "title": "Distributed, Parallel, and Cluster Computing Mar 2023", "date": "", "ddg_snippet": "arXiv admin note: substantial text overlap with arXiv :1306.3075 by other authors; substantial text overlap with arXiv :1910.05786 by other authors ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.DC/2023-03", "content": "arXiv admin note: substantial text overlap with arXiv :1306.3075 by other authors; substantial text overlap with arXiv :1910.05786 by other authors ..."}
+{"idx": 4, "title": "Social and Information Networks Dec 2024", "date": "", "ddg_snippet": "Title: Provably Extending PageRank-based Local Clustering Algorithm to Weighted Directed Graphs with Self-Loops and to Hypergraphs", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.SI/2024-12", "content": "Title: Provably Extending PageRank-based Local Clustering Algorithm to Weighted Directed Graphs with Self-Loops and to Hypergraphs"}
+{"idx": 5, "title": "Community Discovery Methods for Complex Networks", "date": "", "ddg_snippet": "Andrea Lancichinetti, Santo Fortunato, Janos Kertesz, \"Detecting the overlapping and hierarchical community structure of complex networks\", arxiv ...", "subpage_snippet": "", "source": "bactra.org", "link": "http://bactra.org/notebooks/community-discovery.html", "content": "Andrea Lancichinetti, Santo Fortunato, Janos Kertesz, \"Detecting the overlapping and hierarchical community structure of complex networks\", arxiv ..."}
+{"idx": 6, "title": "Sheng Li", "date": "", "ddg_snippet": "It utilizes a cross-lingual embedding clustering method to construct a hierarchical Softmax (H-Softmax) decoder, which enables similar tokens across ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Sheng+Li", "content": "It utilizes a cross-lingual embedding clustering method to construct a hierarchical Softmax (H-Softmax) decoder, which enables similar tokens across ..."}
+{"idx": 7, "title": "Machine Learning Mar 2023", "date": "", "ddg_snippet": "Siqi Li , Yilin Ning , Marcus Eng Hock Ong , Bibhas Chakraborty , Chuan Hong , Feng Xie , Han Yuan , Mingxuan Liu , Daniel M.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.LG/2023-03", "content": "Siqi Li , Yilin Ning , Marcus Eng Hock Ong , Bibhas Chakraborty , Chuan Hong , Feng Xie , Han Yuan , Mingxuan Liu , Daniel M."}
+{"idx": 8, "title": "Attention Beyond Neighborhoods: Reviving Transformer for Graph", "date": "", "ddg_snippet": "... on graph topology than conventional Transformers and effectively alleviates the over-globalization issue, making AGCN particularly suitable for graph ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15024v1", "content": "... on graph topology than conventional Transformers and effectively alleviates the over-globalization issue, making AGCN particularly suitable for graph ..."}
+{"idx": 9, "title": "David Gleich - Publications", "date": "", "ddg_snippet": "... discovery is a fundamental primitive in graph and hypergraph analysis which among other applications has been used for real-time story detection on ...", "subpage_snippet": "", "source": "www.cs.purdue.edu", "link": "https://www.cs.purdue.edu/homes/dgleich/publications.html", "content": "... discovery is a fundamental primitive in graph and hypergraph analysis which among other applications has been used for real-time story detection on ..."}
diff --git a/data/sampled_jsons/arXiv_Machine_Learning_meets_Algebraic_Combinatorics.jsonl b/data/sampled_jsons/arXiv_Machine_Learning_meets_Algebraic_Combinatorics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2d3cb53f72d7c5fdb9e21013e394c9865867b0cd
--- /dev/null
+++ b/data/sampled_jsons/arXiv_Machine_Learning_meets_Algebraic_Combinatorics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics : A Suite of...", "date": "", "ddg_snippet": "Machine Learning meets Algebraic Combinatoric. with an associated question(s). Our collection includes both open problems (e.g., the combinatorial interpretation of Schubert polynomial structure constants) and classic prob-lems whose solution is a major result in the field (e.g...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "Machine Learning meets Algebraic Combinatoric. with an associated question(s). Our collection includes both open problems (e.g., the combinatorial interpretation of Schubert polynomial structure constants) and classic prob-lems whose solution is a major result in the field (e.g..."}
+{"idx": 1, "title": "Paper page - Machine Learning meets Algebraic Combinatorics ...", "date": "", "ddg_snippet": "Papers. arxiv :2503.06366. Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.06366", "content": "Papers. arxiv :2503.06366. Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics."}
+{"idx": 2, "title": "Machine Learning meets Algebraic Combinatorics ... | alphaXiv", "date": "", "ddg_snippet": "We describe all nine datasets, the different ways machine learning models can be applied to them (e.g., training with narrow models followed by interpretability analysis or program synthesis with LLMs), and discuss some of the challenges involved in designing datasets like these.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/ja/overview/2503.06366v1", "content": "We describe all nine datasets, the different ways machine learning models can be applied to them (e.g., training with narrow models followed by interpretability analysis or program synthesis with LLMs), and discuss some of the challenges involved in designing datasets like these."}
+{"idx": 3, "title": "Combinatorial Algebra Meets Algebraic Combinatorics at Dalhousie", "date": "", "ddg_snippet": "CAAC is a unique conference centered around continually evolving interactions between combinatorial algebra and algebraic combinatorics , and featuring work done by junior people in the field.", "subpage_snippet": "", "source": "aarms.math.ca", "link": "https://aarms.math.ca/event/combinatorial-algebra-meets-algebraic-combinatorics-at-dalhousie/", "content": "CAAC is a unique conference centered around continually evolving interactions between combinatorial algebra and algebraic combinatorics , and featuring work done by junior people in the field."}
+{"idx": 4, "title": "Machine Learning meets Algebraic Combinatorics ... | OpenReview", "date": "", "ddg_snippet": "Keywords : Algebraic combinatorics , AI for Math, Datasets. TL;DR : We introduce a collection of datasets for machine learning that target both open and foundational results in algebraic combinatorics .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KQ1gI5qzAf", "content": "Keywords : Algebraic combinatorics , AI for Math, Datasets. TL;DR : We introduce a collection of datasets for machine learning that target both open and foundational results in algebraic combinatorics ."}
+{"idx": 5, "title": "Combinatorial Algebra Meets Algebraic Combinatorics", "date": "", "ddg_snippet": "Fourth Mini-workshop: Algebraic Combinatorics meets Inverse Systems. Part of the CRM Theme Semester: Recent Advances in Combinatorics . January 19--21, 2007, CRM, Montreal.", "subpage_snippet": "", "source": "www.mathstat.dal.ca", "link": "https://www.mathstat.dal.ca/~faridi/research/inverse_systems/workshop.html", "content": "Fourth Mini-workshop: Algebraic Combinatorics meets Inverse Systems. Part of the CRM Theme Semester: Recent Advances in Combinatorics . January 19--21, 2007, CRM, Montreal."}
+{"idx": 6, "title": "Combinatorial Algebra meets Algebraic Combinatorics - Annual...", "date": "", "ddg_snippet": "Teaching and Learning at McMaster.The 15th Annual Meeting of the Combinatorial Algebra meets Algebraic Combinatorics will take place at McMaster University, January 26-28, 2018.", "subpage_snippet": "", "source": "math.mcmaster.ca", "link": "https://math.mcmaster.ca/combinatorial-algebra-meets-algebraic-combinatorics-annual-meeting/", "content": "Teaching and Learning at McMaster.The 15th Annual Meeting of the Combinatorial Algebra meets Algebraic Combinatorics will take place at McMaster University, January 26-28, 2018."}
+{"idx": 7, "title": "2009: Combinatorial Algebra meets Algebraic Combinatorics", "date": "", "ddg_snippet": "This meeting is a continuation of five previous annual workshops focusing on the interplay between commutative algebra (particularly, resolutions and inverse systems) and algebraic combinatorics (the representation theory of symmetric groups).", "subpage_snippet": "", "source": "www.mun.ca", "link": "https://www.mun.ca/aac/workshops/past-workshops/2009-ii/", "content": "This meeting is a continuation of five previous annual workshops focusing on the interplay between commutative algebra (particularly, resolutions and inverse systems) and algebraic combinatorics (the representation theory of symmetric groups)."}
+{"idx": 8, "title": "Combinatorial Algebra meets Algebraic Combinatorics ...", "date": "", "ddg_snippet": "The purpose of this conference is to encourage unique collaborations between combinatorial algebraists and algebraic combinatorics .The meeting will take place starting in the afternoon of Friday, January 25th, 2013 and end early in the afternoon on Sunday, January 27th, 2013.", "subpage_snippet": "", "source": "garsia.math.yorku.ca", "link": "https://garsia.math.yorku.ca/CAAC_2013/", "content": "The purpose of this conference is to encourage unique collaborations between combinatorial algebraists and algebraic combinatorics .The meeting will take place starting in the afternoon of Friday, January 25th, 2013 and end early in the afternoon on Sunday, January 27th, 2013."}
+{"idx": 9, "title": "Combinatorial Algebra Meets Algebraic Combinatorics (CAAC) 2025", "date": "", "ddg_snippet": "These conferences also provided opportunities for graduate students, postdoctoral fellows, and early-career researchers to present their work, learn about new research directions in related fields, and establish future collaborations.", "subpage_snippet": "", "source": "www1.fields.utoronto.ca", "link": "https://www1.fields.utoronto.ca/activities/24-25/CAAC2025", "content": "These conferences also provided opportunities for graduate students, postdoctoral fellows, and early-career researchers to present their work, learn about new research directions in related fields, and establish future collaborations."}
diff --git a/data/sampled_jsons/arxiv.org_2504.11786_methodology_section_equation_5_lambda_m_disease_matching.jsonl b/data/sampled_jsons/arxiv.org_2504.11786_methodology_section_equation_5_lambda_m_disease_matching.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f978fe7d264708b19654c0e895cf87b771352bba
--- /dev/null
+++ b/data/sampled_jsons/arxiv.org_2504.11786_methodology_section_equation_5_lambda_m_disease_matching.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DART: Disease -aware Image-Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "In this study, we propose a Disease -aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation (DART) framework.", "subpage_snippet": "", "source": "paperreading.club", "link": "https://paperreading.club/page?id=300010", "content": "In this study, we propose a Disease -aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation (DART) framework."}
+{"idx": 1, "title": "Writing Equations of Ellipses In Standard Form and... - YouTube", "date": "", "ddg_snippet": "This algebra video tutorial explains how to write the equation of an ellipse in standard form as well as how to graph the ellipse when in standard form.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=OPSCKXXvWiM", "content": "This algebra video tutorial explains how to write the equation of an ellipse in standard form as well as how to graph the ellipse when in standard form."}
+{"idx": 2, "title": "ArXiv . org — Рувики: Интернет-энциклопедия", "date": "", "ddg_snippet": "arXiv . org — электронный архив с открытым доступом для научных статей и препринтов по физике, математике, астрономии, информатике, биологии, электротехнике, статистике, финансовой математике и экономике.", "subpage_snippet": "", "source": "ru.ruwiki.ru", "link": "https://ru.ruwiki.ru/wiki/ArXiv.org", "content": "arXiv . org — электронный архив с открытым доступом для научных статей и препринтов по физике, математике, астрономии, информатике, биологии, электротехнике, статистике, финансовой математике и экономике."}
+{"idx": 3, "title": "Обзоры препринтов научных статей «astro-ph/ arxiv . org » за... / Хабр", "date": "", "ddg_snippet": "Выпуск 445. Ежемесячный обзор научных статей в области астрофизики от профессора МГУ Сергея Попова. Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv . org .", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948996/", "content": "Выпуск 445. Ежемесячный обзор научных статей в области астрофизики от профессора МГУ Сергея Попова. Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv . org ."}
+{"idx": 4, "title": "Stefan-Boltzmann Law - GeeksforGeeks", "date": "", "ddg_snippet": "R is Universal Gas Constant which is equal to 8.3144598 J per mole per K (J x mol-1 x K-1).Putting all the results in equation (1).", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/physics/stefan-boltzmann-law/", "content": "R is Universal Gas Constant which is equal to 8.3144598 J per mole per K (J x mol-1 x K-1).Putting all the results in equation (1)."}
+{"idx": 5, "title": "arXiv . org e-Print archive", "date": "", "ddg_snippet": "Condensed Matter (cond-mat new, recent, search) Disordered Systems and Neural Networks; Materials Science; Mesoscale and Nanoscale Physics; Other Condensed Matter; Quantum Gases; Soft Condensed Matter; Statistical Mechanics; Strongly Correlated Electrons; Superconductivity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "Condensed Matter (cond-mat new, recent, search) Disordered Systems and Neural Networks; Materials Science; Mesoscale and Nanoscale Physics; Other Condensed Matter; Quantum Gases; Soft Condensed Matter; Statistical Mechanics; Strongly Correlated Electrons; Superconductivity."}
+{"idx": 6, "title": "Are all sins equal to God? Is all sin the same? | GotQuestions. org", "date": "", "ddg_snippet": "Unjustified anger, lust, and adultery are all sins, and we need to take them all seriously. Now that we’ve established the general rule that all sins are equal to God by nature, we can add some refinements.", "subpage_snippet": "", "source": "www.gotquestions.org", "link": "https://www.gotquestions.org/sins-equal.html", "content": "Unjustified anger, lust, and adultery are all sins, and we need to take them all seriously. Now that we’ve established the general rule that all sins are equal to God by nature, we can add some refinements."}
+{"idx": 7, "title": "Journal of Medical Internet Research - Conversational Agents...", "date": "", "ddg_snippet": "Studies of CAs for self-management of chronic disease operate within a multidisciplinary domain: self-management originates from (behavioral) psychology and CAs stem from intervention technology, while diseases are typically studied within the biomedical context.", "subpage_snippet": "", "source": "www.jmir.org", "link": "https://www.jmir.org/2025/1/e72309/", "content": "Studies of CAs for self-management of chronic disease operate within a multidisciplinary domain: self-management originates from (behavioral) psychology and CAs stem from intervention technology, while diseases are typically studied within the biomedical context."}
+{"idx": 8, "title": "Problem 4 What happens to the diffraction ... [FREE SOLUTION] | Vaia", "date": "", "ddg_snippet": "The angular position of the minima in a diffraction pattern is a measurable quantity that can reveal details about the light and the slit it passes through. For a single-slit setup, the minima occur at specific angles that can be mathematically described by the equation \\(\\theta = \\frac{ m \\ lambda }...", "subpage_snippet": "", "source": "www.vaia.com", "link": "https://www.vaia.com/en-us/textbooks/physics/university-physics-3-edition/chapter-4/problem-4-what-happens-to-the-diffraction-pattern-of-a-singl/", "content": "The angular position of the minima in a diffraction pattern is a measurable quantity that can reveal details about the light and the slit it passes through. For a single-slit setup, the minima occur at specific angles that can be mathematically described by the equation \\(\\theta = \\frac{ m \\ lambda }..."}
+{"idx": 9, "title": "Application of the nudging technique to produce initial states for the...", "date": "", "ddg_snippet": "Section .G. Marchuk, Numerical Methods in Weather Prediction(Academic Press, New York-London, 1974). doi 10.1016/B978-0-12-470650-7.X5001-4. E. Hairer and G. Wanner, Solving Ordinary Differential Equations II.", "subpage_snippet": "", "source": "en.num-meth.ru", "link": "https://en.num-meth.ru/index.php/journal/article/view/1436", "content": "Section .G. Marchuk, Numerical Methods in Weather Prediction(Academic Press, New York-London, 1974). doi 10.1016/B978-0-12-470650-7.X5001-4. E. Hairer and G. Wanner, Solving Ordinary Differential Equations II."}
diff --git a/data/sampled_jsons/arxiv.orgabs2403.01633_abstract.jsonl b/data/sampled_jsons/arxiv.orgabs2403.01633_abstract.jsonl
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index 0000000000000000000000000000000000000000..190e41cf5364cb6bc1b70d5c88f306c43b0a2b78
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orgabs2403.01633_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "Computer Science - arXiv.org", "date": "", "ddg_snippet": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/archive/cs", "content": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:"}
+{"idx": 2, "title": "Log in to arXiv | arXiv e-print repository", "date": "", "ddg_snippet": "Log in to arXiv . org The arXiv Privacy Policy has changed. By continuing to use arxiv . org , you are agreeing to the privacy policy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/login", "content": "Log in to arXiv . org The arXiv Privacy Policy has changed. By continuing to use arxiv . org , you are agreeing to the privacy policy."}
+{"idx": 3, "title": "YOLOv12: Attention-Centric Real-Time Object Detectors - arXiv.org", "date": "", "ddg_snippet": "Feb 18, 2025 · Abstract page for arXiv paper 2502.12524: YOLOv12: Attention-Centric Real-Time Object Detectors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12524", "content": "Feb 18, 2025 · Abstract page for arXiv paper 2502.12524: YOLOv12: Attention-Centric Real-Time Object Detectors"}
+{"idx": 4, "title": "[2508.10104] DINOv3 - arXiv.org", "date": "", "ddg_snippet": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.10104", "content": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ..."}
+{"idx": 5, "title": "[2212.10156] Planning-oriented Autonomous Driving - arXiv.org", "date": "", "ddg_snippet": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.10156", "content": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ..."}
+{"idx": 6, "title": "[1706.03762] Attention Is All You Need - arXiv.org", "date": "", "ddg_snippet": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ..."}
+{"idx": 7, "title": "[2506.01844] SmolVLA: A Vision-Language-Action Model for ... -...", "date": "", "ddg_snippet": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.01844", "content": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics"}
+{"idx": 8, "title": "[2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in...", "date": "", "ddg_snippet": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.12948", "content": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning"}
+{"idx": 9, "title": "[2410.10762] AFlow: Automating Agentic Workflow Generation -...", "date": "", "ddg_snippet": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10762", "content": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation"}
diff --git a/data/sampled_jsons/arxiv.orgabs2407.01511_abstract.jsonl b/data/sampled_jsons/arxiv.orgabs2407.01511_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c35b8393287362c6ddd6d8d9e38d8de2982a586d
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orgabs2407.01511_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2407.01511] CRAB: Cross-environment Agent Benchmark for", "date": "", "ddg_snippet": "Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.01511", "content": "Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data ..."}
+{"idx": 1, "title": "Cosmology from a joint analysis of second and third order shear", "date": "", "ddg_snippet": "Abstract ... The above original shape catalog contains more than 35 million galaxies covering 433 deg 2 {\\rm deg}^{2} .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14019v1", "content": "Abstract ... The above original shape catalog contains more than 35 million galaxies covering 433 deg 2 {\\rm deg}^{2} ."}
+{"idx": 2, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal Language", "date": "", "ddg_snippet": "Abstract : Recently, spearheaded by the CAMEL-AI community, a pioneer in open-source multi-agent ... Paper link: https:// arxiv . org / abs / 2407 . 01511", "subpage_snippet": "", "source": "www.camel-ai.org", "link": "https://www.camel-ai.org/blogs/crab-cross-platform-agent-benchmark", "content": "Abstract : Recently, spearheaded by the CAMEL-AI community, a pioneer in open-source multi-agent ... Paper link: https:// arxiv . org / abs / 2407 . 01511"}
+{"idx": 3, "title": "Out-of-Distribution Recovery with Object-Centric Keypoint", "date": "", "ddg_snippet": "Abstract ... Paper organization Section II presents an overview of the existing works.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.03294v4", "content": "Abstract ... Paper organization Section II presents an overview of the existing works."}
+{"idx": 4, "title": "UAD: Unsupervised Affordance Distillation for Generalization in", "date": "", "ddg_snippet": "Vision-language models (VLMs) have demonstrated the ability to internalize world knowledge by pretraining on large-scale image-text datasets [ 10 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.09284v2", "content": "Vision-language models (VLMs) have demonstrated the ability to internalize world knowledge by pretraining on large-scale image-text datasets [ 10 ..."}
+{"idx": 5, "title": "AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid", "date": "", "ddg_snippet": "... experiments both in simulation and the real world with teleoperation and autonomous results showing the effectiveness of our method and ablations of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.03738v1", "content": "... experiments both in simulation and the real world with teleoperation and autonomous results showing the effectiveness of our method and ablations of ..."}
+{"idx": 6, "title": "ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat", "date": "", "ddg_snippet": "... LLMs from a knowledge memorization perspective [ 56 , 22 ] , instead of targeting the security investigation and reasoning ability of LLM agents.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14201v1", "content": "... LLMs from a knowledge memorization perspective [ 56 , 22 ] , instead of targeting the security investigation and reasoning ability of LLM agents."}
+{"idx": 7, "title": "Aligning large language models with radiologists by", "date": "", "ddg_snippet": "On the other hand, it has been shown that RLAIF outperformed RLHF in harmless task, demonstrating the ability to enhance supervised fine-tuning (SFT ...", "subpage_snippet": "", "source": "www.ejradiology.com", "link": "https://www.ejradiology.com/article/S0720-048X(25)00070-1/fulltext", "content": "On the other hand, it has been shown that RLAIF outperformed RLHF in harmless task, demonstrating the ability to enhance supervised fine-tuning (SFT ..."}
+{"idx": 8, "title": "COMMA: A Communicative Multimodal Multi-Agent Benchmark", "date": "", "ddg_snippet": "Our benchmark draws inspiration from the foundational principles of intelligence, often defined as the ability to learn from experience, adapt to the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07553v3", "content": "Our benchmark draws inspiration from the foundational principles of intelligence, often defined as the ability to learn from experience, adapt to the ..."}
+{"idx": 9, "title": "Securing Large Language Models: Addressing Bias,", "date": "", "ddg_snippet": "It starts with concerns about misinformation and hallucination in LLM outputs, followed by studies on built-in biases and strategies for bias ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.08087v2", "content": "It starts with concerns about misinformation and hallucination in LLM outputs, followed by studies on built-in biases and strategies for bias ..."}
diff --git a/data/sampled_jsons/arxiv.orghtml2405.17618v3_Equation_7_RA2C.jsonl b/data/sampled_jsons/arxiv.orghtml2405.17618v3_Equation_7_RA2C.jsonl
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index 0000000000000000000000000000000000000000..0c1fcf1f643d3bcbde19e97254444697921341bf
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orghtml2405.17618v3_Equation_7_RA2C.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv."}
+{"idx": 1, "title": "Life span of solutions to a semilinear parabolic equation on locally ...", "date": "", "ddg_snippet": "Using these methods, we establish the estimates and asymptotic behaviour of the life span of solutions to a semilinear heat equation with initial data $\\lambda\\psi (x)$ for different scales of $\\lambda$ on $G$ under some different conditions.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240518173H/abstract", "content": "Using these methods, we establish the estimates and asymptotic behaviour of the life span of solutions to a semilinear heat equation with initial data $\\lambda\\psi (x)$ for different scales of $\\lambda$ on $G$ under some different conditions."}
+{"idx": 2, "title": "Equation error · Issue #1394 · arXiv/html_feedback · GitHub", "date": "", "ddg_snippet": "Description In the equation (1.1), >< in bra and ket are lost (Optional:) Please add any files, screenshots, or other information here. No response (Required) What is this issue most closely relate...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/arXiv/html_feedback/issues/1394", "content": "Description In the equation (1.1), >< in bra and ket are lost (Optional:) Please add any files, screenshots, or other information here. No response (Required) What is this issue most closely relate..."}
+{"idx": 3, "title": "Pilot project to render HTML5 from arXiv LaTeX sources", "date": "", "ddg_snippet": "O1: Develop a cloud-native service that provides HTML renderings from LaTeX source submitted to arXiv, leveraging LaTeXML. O2: Demonstrate the feasibility and value of the service by providing it on an experimental basis to arXiv authors, with links to HTML on the public abstract page.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/arXiv/zzzArchived_arxiv-readability", "content": "O1: Develop a cloud-native service that provides HTML renderings from LaTeX source submitted to arXiv, leveraging LaTeXML. O2: Demonstrate the feasibility and value of the service by providing it on an experimental basis to arXiv authors, with links to HTML on the public abstract page."}
+{"idx": 4, "title": "Soliton resolution for the energy-critical nonlinear heat equation in ...", "date": "", "ddg_snippet": "We establish the Soliton Resolution Conjecture for the radial critical non-linear heat equation in dimension $D\\geq 3.$ Thus, every finite energy solution resolves, continuously in time, into a finite superposition of asymptotically decoupled copies of the ground state and free radiation.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240506005A/abstract", "content": "We establish the Soliton Resolution Conjecture for the radial critical non-linear heat equation in dimension $D\\geq 3.$ Thus, every finite energy solution resolves, continuously in time, into a finite superposition of asymptotically decoupled copies of the ground state and free radiation."}
+{"idx": 5, "title": "Reflections to set-theoretic solutions of the Yang-Baxter equation", "date": "", "ddg_snippet": "The main aim of this paper is to determine reflections to bijective and non-degenerate solutions of the Yang-Baxter equation , by exploring their connections with their derived solutions. This is motivated by a recent description of left non-degenerate solutions in terms of a family of automorphisms of their associated left rack. In some cases, we show that the study of reflections for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.19105", "content": "The main aim of this paper is to determine reflections to bijective and non-degenerate solutions of the Yang-Baxter equation , by exploring their connections with their derived solutions. This is motivated by a recent description of left non-degenerate solutions in terms of a family of automorphisms of their associated left rack. In some cases, we show that the study of reflections for ..."}
+{"idx": 6, "title": "(PDF) The Dirac Equation and the Majorana Dirac Equation - ResearchGate", "date": "", "ddg_snippet": "We rewrite the 1 + 1 Dirac equation in light cone coordinates in two significant forms, and solve them exactly using the classical calculus of finite differences.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/344197681_The_Dirac_Equation_and_the_Majorana_Dirac_Equation", "content": "We rewrite the 1 + 1 Dirac equation in light cone coordinates in two significant forms, and solve them exactly using the classical calculus of finite differences."}
+{"idx": 7, "title": "HTML papers on arXiv: why it's important, and how we made it happen", "date": "", "ddg_snippet": "In the current iteration of the HTML render, we do not display author affiliations, footnotes, or mathematical equations due to the difficulty of extracting these pieces of information from the PDF.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.08954v1", "content": "In the current iteration of the HTML render, we do not display author affiliations, footnotes, or mathematical equations due to the difficulty of extracting these pieces of information from the PDF."}
+{"idx": 8, "title": "On computing quantum waves exactly from classical action", "date": "", "ddg_snippet": "We show that the Schrödinger equation in quantum mechanics can be solved exactly based only on classical least action and classical density. Most quantum mechanics problems have classical versions which involve multiple least action solutions. These extremal action paths may stem from spatial inequality constraints (as in the double slit experiment), from singularities in the Hamiltonian (as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.06328", "content": "We show that the Schrödinger equation in quantum mechanics can be solved exactly based only on classical least action and classical density. Most quantum mechanics problems have classical versions which involve multiple least action solutions. These extremal action paths may stem from spatial inequality constraints (as in the double slit experiment), from singularities in the Hamiltonian (as ..."}
+{"idx": 9, "title": "Existence and uniqueness of solutions in the Lipschitz space of a ...", "date": "", "ddg_snippet": "In this paper, we examine the solvability of a functional equation in a Lipschitz space. As an application, we use our result to determine the existence and uniqueness of solutions to an equation describing a specific type of choice behavior model for the learning process of the paradise fish. Finally, we present some concrete examples where, using numerical techniques, we obtain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.12345", "content": "In this paper, we examine the solvability of a functional equation in a Lipschitz space. As an application, we use our result to determine the existence and uniqueness of solutions to an equation describing a specific type of choice behavior model for the learning process of the paradise fish. Finally, we present some concrete examples where, using numerical techniques, we obtain ..."}
diff --git a/data/sampled_jsons/arxiv.orghtml2406.14532v1_spurious_correlations.jsonl b/data/sampled_jsons/arxiv.orghtml2406.14532v1_spurious_correlations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2b1efcf26f4ecf8fedffea0b083f9ced1197085d
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orghtml2406.14532v1_spurious_correlations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Supplement to ``Are Discoveries Spurious ? Distributions of Maximum...", "date": "", "ddg_snippet": "2 Spurious correlation , conditions, and notation3 Distributions of maximum spurious correlations 4 Extension to sparse linear models", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1502.04237", "content": "2 Spurious correlation , conditions, and notation3 Distributions of maximum spurious correlations 4 Extension to sparse linear models"}
+{"idx": 1, "title": "Identifying Spurious Correlations Early in Training", "date": "", "ddg_snippet": "Keywords: spurious correlations , simplicity bias. Extended Abstract. The simplicity bias of gradient-based training algorithms towards learning simpler solutions has been suggested as a...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=t3LBgI4Uw2", "content": "Keywords: spurious correlations , simplicity bias. Extended Abstract. The simplicity bias of gradient-based training algorithms towards learning simpler solutions has been suggested as a..."}
+{"idx": 2, "title": "(PDF) Severing Spurious Correlations with Data Pruning", "date": "", "ddg_snippet": "DOI:10.48550/ arXiv .2503.18258.Deep neural networks have been shown to learn and rely on spurious correlations present in the data that they are trained on.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390142841_Severing_Spurious_Correlations_with_Data_Pruning", "content": "DOI:10.48550/ arXiv .2503.18258.Deep neural networks have been shown to learn and rely on spurious correlations present in the data that they are trained on."}
+{"idx": 3, "title": "(PDF) Investigating Spurious Correlations in Vision Models Using...", "date": "", "ddg_snippet": "Vision models often rely on spurious correlations , patterns in the data that are not intrinsic to the task but are nonetheless exploited by the model. These correlations can lead to biases...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/142997035/Investigating_Spurious_Correlations_in_Vision_Models_Using_Counterfactual_Images", "content": "Vision models often rely on spurious correlations , patterns in the data that are not intrinsic to the task but are nonetheless exploited by the model. These correlations can lead to biases..."}
+{"idx": 4, "title": "Spurious correlation , unit roots and cointegration", "date": "", "ddg_snippet": "I learned about the spurious regression problem during a course at the Booth school of business.x,y. is their correlation . Equivalently, we can fit a univariate linear regression to the data", "subpage_snippet": "", "source": "ericjanofsky.com", "link": "https://ericjanofsky.com/2016/03/01/spurious-correlation-unit-roots-and-cointegration/", "content": "I learned about the spurious regression problem during a course at the Booth school of business.x,y. is their correlation . Equivalently, we can fit a univariate linear regression to the data"}
+{"idx": 5, "title": "Spurious Correlations – Nicholas Cage, Tangled Bedsheets, and...", "date": "", "ddg_snippet": "According to Wikipedia, a spurious correlation is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or...", "subpage_snippet": "", "source": "jborden.com", "link": "https://jborden.com/2021/05/01/spurious-correlations-nicholas-cage-tangled-bedsheets-and-venomous-spiders/", "content": "According to Wikipedia, a spurious correlation is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or..."}
+{"idx": 6, "title": "Spurious correlations : How Alaskan pollock predict... | Deep Sea News", "date": "", "ddg_snippet": "Ok, cue that little voice in your head from first-year statistics that echoes infinitum: “ correlation is not causation”. This appears to be an example of the dreaded spurious ...", "subpage_snippet": "", "source": "deepseanews.com", "link": "https://deepseanews.com/2015/01/spurious-correlations-how-alaskan-pollock-predict-a-seahawks-super-bowl-win/", "content": "Ok, cue that little voice in your head from first-year statistics that echoes infinitum: “ correlation is not causation”. This appears to be an example of the dreaded spurious ..."}
+{"idx": 7, "title": "What are some examples of spurious correlations ? - Glarity", "date": "", "ddg_snippet": "The correlation is spurious ; the lurking variable is temperature[6]. 2. Master's degrees versus box office revenue. The correlation between the two variables is spurious [7].", "subpage_snippet": "", "source": "askai.glarity.app", "link": "https://askai.glarity.app/search/What-are-some-examples-of-spurious-correlations", "content": "The correlation is spurious ; the lurking variable is temperature[6]. 2. Master's degrees versus box office revenue. The correlation between the two variables is spurious [7]."}
+{"idx": 8, "title": "11. 1 Spurious Correlations & Ex6 - смотреть видео онлайн от...", "date": "", "ddg_snippet": "11. 1 Spurious Correlations & Ex6.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/971ab1f1ab60bef3529eb52976f70f16/", "content": "11. 1 Spurious Correlations & Ex6."}
+{"idx": 9, "title": "Beware Spurious Correlations", "date": "", "ddg_snippet": "We all know the truism “ Correlation doesn’t imply causation,” but when we see lines sloping together, bars rising together, or points on a scatterplot clustering, the data practically begs us to...", "subpage_snippet": "", "source": "hbr.org", "link": "https://hbr.org/2015/06/beware-spurious-correlations", "content": "We all know the truism “ Correlation doesn’t imply causation,” but when we see lines sloping together, bars rising together, or points on a scatterplot clustering, the data practically begs us to..."}
diff --git a/data/sampled_jsons/arxiv.orghtml2411.02959v2_Table_3_ablation_Prune-Embed.jsonl b/data/sampled_jsons/arxiv.orghtml2411.02959v2_Table_3_ablation_Prune-Embed.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e53870a0fa4405ed6f2563aa9f6077e397e1a56
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orghtml2411.02959v2_Table_3_ablation_Prune-Embed.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG, including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v2", "content": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG, including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen)."}
+{"idx": 1, "title": "[2411.02959] HtmlRAG: HTML is Better Than Plain Text for ... [2411.02959] HtmlRAG: HTML is Better Than Plain Text for ... arXiv.org e-Print archive Paper page - HtmlRAG: HTML is Better Than Plain Text for ... mav23/HTML-Pruner-Llama-1B-GGUF · Hugging Face QuantFactory/HTML-Pruner-Llama-1B-GGUF · Hugging Face", "date": "", "ddg_snippet": "Nov 5, 2024 · Abstract page for arXiv paper 2411 .02959: HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems We conduct ablation studies to demonstrate the effectiveness of each component in HtmlRAG, including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen). arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Nov 5, 2024 · Plain Text (128k), Markdown (128k), and HtmlRAG w/o Prune (128k) are long-context reference after rule-base cleaning (refer to Table 2 for end-to-end results). HTML 's socre is lightly lower due to extra HTML tags occupying tokens. like 0 Transformers GGUF English conversational arxiv : 2411 .02959 License:apache-2.0 Model card FilesFiles and versions xet Community Train Deploy Use this model Model Information 📦 Installation 📖 User Guide 🧹 HTML Cleaning 🌲 Build Block Tree ️ Prune HTML Blocks with Embedding Model ️ Prune HTML Blocks with Generative Model ... Two-Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps, both of which are conducted on the block tree structure. The first pruning step uses a embedding model to calculate scores for blocks, while the second step uses a path generative model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "Nov 5, 2024 · Abstract page for arXiv paper 2411 .02959: HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems We conduct ablation studies to demonstrate the effectiveness of each component in HtmlRAG, including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen). arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Nov 5, 2024 · Plain Text (128k), Markdown (128k), and HtmlRAG w/o Prune (128k) are long-context reference after rule-base cleaning (refer to Table 2 for end-to-end results). HTML 's socre is lightly lower due to extra HTML tags occupying tokens. like 0 Transformers GGUF English conversational arxiv : 2411 .02959 License:apache-2.0 Model card FilesFiles and versions xet Community Train Deploy Use this model Model Information 📦 Installation 📖 User Guide 🧹 HTML Cleaning 🌲 Build Block Tree ️ Prune HTML Blocks with Embedding Model ️ Prune HTML Blocks with Generative Model ... Two-Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps, both of which are conducted on the block tree structure. The first pruning step uses a embedding model to calculate scores for blocks, while the second step uses a path generative model."}
+{"idx": 2, "title": "[2411.02959] HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "We conduct ablation studies to demonstrate the effectiveness of each component in HtmlRAG, including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2411.02959", "content": "We conduct ablation studies to demonstrate the effectiveness of each component in HtmlRAG, including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen)."}
+{"idx": 3, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 4, "title": "Paper page - HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "Nov 5, 2024 · Plain Text (128k), Markdown (128k), and HtmlRAG w/o Prune (128k) are long-context reference after rule-base cleaning (refer to Table 2 for end-to-end results). HTML 's socre is lightly lower due to extra HTML tags occupying tokens.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "Nov 5, 2024 · Plain Text (128k), Markdown (128k), and HtmlRAG w/o Prune (128k) are long-context reference after rule-base cleaning (refer to Table 2 for end-to-end results). HTML 's socre is lightly lower due to extra HTML tags occupying tokens."}
+{"idx": 5, "title": "mav23/HTML-Pruner-Llama-1B-GGUF · Hugging Face", "date": "", "ddg_snippet": "like 0 Transformers GGUF English conversational arxiv : 2411 .02959 License:apache-2.0 Model card FilesFiles and versions xet Community Train Deploy Use this model Model Information 📦 Installation 📖 User Guide 🧹 HTML Cleaning 🌲 Build Block Tree ️ Prune HTML Blocks with Embedding Model ️ Prune HTML Blocks with Generative Model ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/mav23/HTML-Pruner-Llama-1B-GGUF", "content": "like 0 Transformers GGUF English conversational arxiv : 2411 .02959 License:apache-2.0 Model card FilesFiles and versions xet Community Train Deploy Use this model Model Information 📦 Installation 📖 User Guide 🧹 HTML Cleaning 🌲 Build Block Tree ️ Prune HTML Blocks with Embedding Model ️ Prune HTML Blocks with Generative Model ..."}
+{"idx": 6, "title": "QuantFactory/HTML-Pruner-Llama-1B-GGUF · Hugging Face", "date": "", "ddg_snippet": "Two-Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps, both of which are conducted on the block tree structure. The first pruning step uses a embedding model to calculate scores for blocks, while the second step uses a path generative model.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/QuantFactory/HTML-Pruner-Llama-1B-GGUF", "content": "Two-Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps, both of which are conducted on the block tree structure. The first pruning step uses a embedding model to calculate scores for blocks, while the second step uses a path generative model."}
+{"idx": 7, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "The embedding -based HTML pruning algorithm is lightweight but effective. It adapts to the HTML format better compared to plain-text-based refiners. Table 3 . Ablation studies for HtmlRAG.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v2", "content": "The embedding -based HTML pruning algorithm is lightweight but effective. It adapts to the HTML format better compared to plain-text-based refiners. Table 3 . Ablation studies for HtmlRAG."}
+{"idx": 8, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "This table presents the ablation study results for the HtmlRAG model. It shows the impact of removing key components of the model, such as the block tree structure, the text embedding -based pruning , and the generative model-based pruning .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2411.02959/", "content": "This table presents the ablation study results for the HtmlRAG model. It shows the impact of removing key components of the model, such as the block tree structure, the text embedding -based pruning , and the generative model-based pruning ."}
+{"idx": 9, "title": "Embedding Functions - Chroma Docs", "date": "", "ddg_snippet": "Chroma provides lightweight wrappers around popular embedding providers, making it easy to use them in your apps. You can set an embedding function when you create a Chroma collection, to be automatically used when adding and querying data, or you can call them directly yourself.", "subpage_snippet": "", "source": "docs.trychroma.com", "link": "https://docs.trychroma.com/docs/embeddings/embedding-functions", "content": "Chroma provides lightweight wrappers around popular embedding providers, making it easy to use them in your apps. You can set an embedding function when you create a Chroma collection, to be automatically used when adding and querying data, or you can call them directly yourself."}
diff --git a/data/sampled_jsons/arxiv1611.01393_hierarchical_overlapping_clustering_cost_function_optimization.jsonl b/data/sampled_jsons/arxiv1611.01393_hierarchical_overlapping_clustering_cost_function_optimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..552376347ad38866fb96d66a8f1ec57f0d298de2
--- /dev/null
+++ b/data/sampled_jsons/arxiv1611.01393_hierarchical_overlapping_clustering_cost_function_optimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cluster analysis - Wikipedia", "date": "", "ddg_snippet": "Hierarchical clustering : objects that belong to a child cluster also belong to the parent cluster . Subspace clustering : while an overlapping clustering , within a uniquely defined subspace, clusters are not expected to overlap .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cluster_analysis", "content": "Hierarchical clustering : objects that belong to a child cluster also belong to the parent cluster . Subspace clustering : while an overlapping clustering , within a uniquely defined subspace, clusters are not expected to overlap ."}
+{"idx": 1, "title": "Hierarchical Overlapping Clustering of", "date": "", "ddg_snippet": "Hierarchical Overlapping Clustering of Network Data Using Cut Metrics. Fernando Gama, Santiago Segarra, and Alejandro Ribeiro. arXiv : 1611 . 01393 v1 [cs.SI] 4 Nov 2016.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1611.01393v1", "content": "Hierarchical Overlapping Clustering of Network Data Using Cut Metrics. Fernando Gama, Santiago Segarra, and Alejandro Ribeiro. arXiv : 1611 . 01393 v1 [cs.SI] 4 Nov 2016."}
+{"idx": 2, "title": "Hierarchical Overlapping Clustering of Network Data Using Cut...", "date": "", "ddg_snippet": "DOI:10.48550/ arXiv . 1611 . 01393 .Furthermore, the so-called overlapping function is presented as a tool for gaining insights about the data by identifying meaningful resolutions of the obtained hierarchical structure.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386690815_Hierarchical_Overlapping_Clustering_of_Network_Data_Using_Cut_Metrics", "content": "DOI:10.48550/ arXiv . 1611 . 01393 .Furthermore, the so-called overlapping function is presented as a tool for gaining insights about the data by identifying meaningful resolutions of the obtained hierarchical structure."}
+{"idx": 3, "title": "An Overlapping Clustering Approach with Correlation... | SpringerLink", "date": "", "ddg_snippet": "Overlapping clustering works on the hypothesis that one object belongs to one or more clusters . It tolerates intersection among clusters and discovers overlapping information hidden in observed data as well.Process. Netw. 1–13 (2016, submitted). arXiv : 1611 . 01393 v1 [cs.SI] 4 Nov 2016.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-319-60837-2_49", "content": "Overlapping clustering works on the hypothesis that one object belongs to one or more clusters . It tolerates intersection among clusters and discovers overlapping information hidden in observed data as well.Process. Netw. 1–13 (2016, submitted). arXiv : 1611 . 01393 v1 [cs.SI] 4 Nov 2016."}
+{"idx": 4, "title": "hierarchical - clustering · GitHub Topics · GitHub", "date": "", "ddg_snippet": "Implementations of cost - function optimization and hierarchical clustering techniques, along with evaluations and visualizations in reduced-dimensional spaces.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/hierarchical-clustering?l=matlab", "content": "Implementations of cost - function optimization and hierarchical clustering techniques, along with evaluations and visualizations in reduced-dimensional spaces."}
+{"idx": 5, "title": "machinelearningmastery.com/tour-of- optimization -algorithms", "date": "", "ddg_snippet": "How to Choose an Optimization Algorithm - Machine Learning …", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/tour-of-optimization-algorithms/", "content": "How to Choose an Optimization Algorithm - Machine Learning …"}
+{"idx": 6, "title": "Clustering Visualizer is a Web Application for visualizing of Machine...", "date": "", "ddg_snippet": "Clustering Visualizer is a Web Application for visualizing of Machine Learning Clustering Algorithms.", "subpage_snippet": "", "source": "clustering-visualizer.web.app", "link": "https://clustering-visualizer.web.app/", "content": "Clustering Visualizer is a Web Application for visualizing of Machine Learning Clustering Algorithms."}
+{"idx": 7, "title": "Example: Plot Hierarchical Clustering Dendrogram... - TypeError", "date": "", "ddg_snippet": "This example plots the corresponding dendrogram of a hierarchical clustering using AgglomerativeClustering and the dendrogram method available in scipy.", "subpage_snippet": "", "source": "www.typeerror.org", "link": "https://www.typeerror.org/docs/scikit_learn/auto_examples/cluster/plot_agglomerative_dendrogram", "content": "This example plots the corresponding dendrogram of a hierarchical clustering using AgglomerativeClustering and the dendrogram method available in scipy."}
+{"idx": 8, "title": "Case Study: How a sports site owner used Content Optimization to...", "date": "", "ddg_snippet": "Lauren Lauren Fitzpatrick. Summary. Emil Shour acquired a sports site with only 14 posts but steady traffic. He used Content Optimization to create almost 200 posts in less than a year. Overall pageviews and monthly ad earnings increased by 300% year-over-year.", "subpage_snippet": "", "source": "raptive.com", "link": "https://raptive.com/blog/case-study-how-a-sports-site-owner-used-content-optimization-to-increase-pageviews-and-revenue-by-300/", "content": "Lauren Lauren Fitzpatrick. Summary. Emil Shour acquired a sports site with only 14 posts but steady traffic. He used Content Optimization to create almost 200 posts in less than a year. Overall pageviews and monthly ad earnings increased by 300% year-over-year."}
+{"idx": 9, "title": "Fernando Gama, Santiago Segarra, and Alejandro Ribeiro", "date": "", "ddg_snippet": "Hierarchical Overlapping Clustering of Network Data Using Cut Metrics. Fernando Gama, Santiago Segarra, and Alejandro Ribeiro. arXiv : 1611 . 01393 v2 [cs.SI] 12 Dec 2017. Abstract—A novel method to obtain hierarchical and overlap -ping clusters from network data – i.e., a set of nodes...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1611.01393", "content": "Hierarchical Overlapping Clustering of Network Data Using Cut Metrics. Fernando Gama, Santiago Segarra, and Alejandro Ribeiro. arXiv : 1611 . 01393 v2 [cs.SI] 12 Dec 2017. Abstract—A novel method to obtain hierarchical and overlap -ping clusters from network data – i.e., a set of nodes..."}
diff --git a/data/sampled_jsons/arxiv_2303.17651_Self-Refine_abstract.jsonl b/data/sampled_jsons/arxiv_2303.17651_Self-Refine_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ed5324d010f70452daecbf57cfa907e712a98bb6
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2303.17651_Self-Refine_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2303.17651] Self-Refine: Iterative Refinement with", "date": "", "ddg_snippet": "Motivated by how humans refine their written text, we introduce Self - Refine , an approach for improving initial outputs from LLMs through iterative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.17651", "content": "Motivated by how humans refine their written text, we introduce Self - Refine , an approach for improving initial outputs from LLMs through iterative ..."}
+{"idx": 1, "title": "SELF: Self-Evolution with Language Feedback", "date": "", "ddg_snippet": "Secondly, the learning of meta-skills, specifically self -feedback and self -refinement, is crucial not only for equipping the model with self ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00533v4", "content": "Secondly, the learning of meta-skills, specifically self -feedback and self -refinement, is crucial not only for equipping the model with self ..."}
+{"idx": 2, "title": "Unleashing the Emergent Cognitive Synergy in Large Language", "date": "", "ddg_snippet": "Recent advancements, such as Chain-of-Thought (CoT) prompting (Wei et al., 2023 ; Kojima et al., 2022 ) and Self -refinement (Madaan et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.05300v4", "content": "Recent advancements, such as Chain-of-Thought (CoT) prompting (Wei et al., 2023 ; Kojima et al., 2022 ) and Self -refinement (Madaan et al ..."}
+{"idx": 3, "title": "Refining Financial Consumer Complaints through Multi-Scale", "date": "", "ddg_snippet": "To obtain better quality of output texts, several works have explored the “ self - refining ” and “ self -correcting” abilities of LLMs, where they ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09903v1", "content": "To obtain better quality of output texts, several works have explored the “ self - refining ” and “ self -correcting” abilities of LLMs, where they ..."}
+{"idx": 4, "title": "LLM driven Text-to-Table Generation through Sub-Tasks Guidance", "date": "", "ddg_snippet": "... some of these challenges via two key components: (1) Explicitly guiding LLMs via custom sub-tasks and (2) Refining table output by leveraging self ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08653v1", "content": "... some of these challenges via two key components: (1) Explicitly guiding LLMs via custom sub-tasks and (2) Refining table output by leveraging self ..."}
+{"idx": 5, "title": "Design Patterns for Building Agentic Workflows | Diego", "date": "", "ddg_snippet": "This pattern draws inspiration from self -reflection , a common approach in problem-solving that involves introspective examination of thoughts ...", "subpage_snippet": "", "source": "tech.diegocarpintero.com", "link": "https://tech.diegocarpintero.com/blog/agentic-shift-design-patterns-for-building-ai-systems", "content": "This pattern draws inspiration from self -reflection , a common approach in problem-solving that involves introspective examination of thoughts ..."}
+{"idx": 6, "title": "Further Explorations on the Use of Large Language Models for", "date": "", "ddg_snippet": "Self - refine : Iterative refinement with self -feedback. arXiv preprint, https:// arxiv .org/abs/ 2303 . 17651 [Date of Access: December 12, 2023].", "subpage_snippet": "", "source": "www.qualitative-research.net", "link": "https://www.qualitative-research.net/index.php/fqs/article/view/4196", "content": "Self - refine : Iterative refinement with self -feedback. arXiv preprint, https:// arxiv .org/abs/ 2303 . 17651 [Date of Access: December 12, 2023]."}
+{"idx": 7, "title": "RL4F: Generating Natural Language Feedback with Reinforcement", "date": "", "ddg_snippet": "Self -debiasing does not rely on manually curated word lists, nor does it require any training data or changes to the model’s parameters.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370775704_RL4F_Generating_Natural_Language_Feedback_with_Reinforcement_Learning_for_Repairing_Model_Outputs", "content": "Self -debiasing does not rely on manually curated word lists, nor does it require any training data or changes to the model’s parameters."}
+{"idx": 8, "title": "Generative Agents: Interactive Simulacra of Human Behavior |", "date": "", "ddg_snippet": "Generative Agents: Interactive Simulacra of Human Behavior ( arxiv .org ) ... Intelligence is a tool of the human self , not the self .", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=35517649", "content": "Generative Agents: Interactive Simulacra of Human Behavior ( arxiv .org ) ... Intelligence is a tool of the human self , not the self ."}
+{"idx": 9, "title": "ACL2023レポート − LLMの動向を中心に - Speaker Deck", "date": "", "ddg_snippet": "NLPコロキウム：Unsupervised Abstractive Summarization Based on Tree-Structured Topic Guidance and Rate-Distortion Theory", "subpage_snippet": "", "source": "speakerdeck.com", "link": "https://speakerdeck.com/misonuma/acl2023repoto-llmnodong-xiang-wozhong-xin-ni", "content": "NLPコロキウム：Unsupervised Abstractive Summarization Based on Tree-Structured Topic Guidance and Rate-Distortion Theory"}
diff --git a/data/sampled_jsons/arxiv_2403.09040.jsonl b/data/sampled_jsons/arxiv_2403.09040.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..56ec6bdbcbb4cf0bbed05d09925d88d442a3c28f
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2403.09040.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "Log in to arXiv | arXiv e-print repository", "date": "", "ddg_snippet": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/login", "content": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy."}
+{"idx": 2, "title": "YOLOv12: Attention-Centric Real-Time Object Detectors - arXiv.org", "date": "", "ddg_snippet": "Feb 18, 2025 · Abstract page for arXiv paper 2502.12524: YOLOv12: Attention-Centric Real-Time Object Detectors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12524", "content": "Feb 18, 2025 · Abstract page for arXiv paper 2502.12524: YOLOv12: Attention-Centric Real-Time Object Detectors"}
+{"idx": 3, "title": "Computer Science - arXiv.org", "date": "", "ddg_snippet": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/archive/cs", "content": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:"}
+{"idx": 4, "title": "[2508.10104] DINOv3 - arXiv.org", "date": "", "ddg_snippet": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.10104", "content": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ..."}
+{"idx": 5, "title": "[2212.10156] Planning-oriented Autonomous Driving - arXiv.org", "date": "", "ddg_snippet": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.10156", "content": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ..."}
+{"idx": 6, "title": "[1706.03762] Attention Is All You Need - arXiv.org", "date": "", "ddg_snippet": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ..."}
+{"idx": 7, "title": "[2506.01844] SmolVLA: A Vision-Language-Action Model for ... -...", "date": "", "ddg_snippet": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.01844", "content": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics"}
+{"idx": 8, "title": "[2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in...", "date": "", "ddg_snippet": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.12948", "content": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning"}
+{"idx": 9, "title": "[2410.10762] AFlow: Automating Agentic Workflow Generation -...", "date": "", "ddg_snippet": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10762", "content": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation"}
diff --git a/data/sampled_jsons/arxiv_2404.08819_abstract.jsonl b/data/sampled_jsons/arxiv_2404.08819_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f4395c9c6121715d926c1d22ea2735a16fc1f468
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2404.08819_abstract.jsonl
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+{"idx": 0, "title": "[2404.08819] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Abstract : State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "Abstract : State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the ..."}
+{"idx": 1, "title": "Formal Languages and Automata Theory Apr 2024", "date": "", "ddg_snippet": "22] arXiv : 2404 .01219 (cross-list from cs.RO) [ pdf , html , other ] ... 32] arXiv : 2404 . 08819 (cross-list from cs.LG) [ pdf , html , other ]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.FL/2024-04", "content": "22] arXiv : 2404 .01219 (cross-list from cs.RO) [ pdf , html , other ] ... 32] arXiv : 2404 . 08819 (cross-list from cs.LG) [ pdf , html , other ]"}
+{"idx": 2, "title": "Tightening constraints on primordial oscillations with latest", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.17276v1", "content": "... Abstract"}
+{"idx": 3, "title": "EpochCore: Digital Hardware Accelerator For Structured", "date": "", "ddg_snippet": "... Abstract ... Arxiv [ 23 ]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21394v1", "content": "... Abstract ... Arxiv [ 23 ]"}
+{"idx": 4, "title": "A Survey of Calibration Process for Black-Box LLMs", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.12767v1", "content": "... Abstract"}
+{"idx": 5, "title": "Héctor Socas Navarro – Coffee Break: Señal y Ruido. La", "date": "", "ddg_snippet": "... arxiv .org/abs/2408.09089 https://iopscience.iop.org/article/10.3847/1538-4357/ad87f4/meta https:// arxiv .org/abs/2409.11332 ...", "subpage_snippet": "", "source": "www.museosdetenerife.org", "link": "https://www.museosdetenerife.org/coffeebreak/?author=2", "content": "... arxiv .org/abs/2408.09089 https://iopscience.iop.org/article/10.3847/1538-4357/ad87f4/meta https:// arxiv .org/abs/2409.11332 ..."}
+{"idx": 6, "title": "Coffee Break: Señal y Ruido. La tertulia científica semanal", "date": "", "ddg_snippet": "... arxiv .org/abs/2408.09089 https://iopscience.iop.org/article/10.3847/1538-4357/ad87f4/meta https:// arxiv .org/abs/2409.11332 ...", "subpage_snippet": "", "source": "www.museosdetenerife.org", "link": "https://www.museosdetenerife.org/coffeebreak/", "content": "... arxiv .org/abs/2408.09089 https://iopscience.iop.org/article/10.3847/1538-4357/ad87f4/meta https:// arxiv .org/abs/2409.11332 ..."}
+{"idx": 7, "title": "AI in Digital Marketing: Why LLMs Excel at Some Tasks but Fail", "date": "", "ddg_snippet": "... customer patterns to broader strategic planning, we encounter AI ’ s limits in abstract ... ICML 2024 – https:// arxiv .org/abs/ 2404 . 08819", "subpage_snippet": "", "source": "vesivanov.com", "link": "https://vesivanov.com/ai-in-digital-marketing/", "content": "... customer patterns to broader strategic planning, we encounter AI ’ s limits in abstract ... ICML 2024 – https:// arxiv .org/abs/ 2404 . 08819"}
+{"idx": 8, "title": "How LLMs Really Work - Ves Ivanov", "date": "", "ddg_snippet": "Modern LLMs repeat this process dozens to hundreds of times, each time refining and abstracting the internal representations.", "subpage_snippet": "", "source": "vesivanov.com", "link": "https://vesivanov.com/how-llms-work/", "content": "Modern LLMs repeat this process dozens to hundreds of times, each time refining and abstracting the internal representations."}
+{"idx": 9, "title": "Agent Identity Evals: Measuring Agentic Identity", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.17257v1", "content": "... Abstract"}
diff --git a/data/sampled_jsons/arxiv_2405.16218_abstract_full_text_year_2024.jsonl b/data/sampled_jsons/arxiv_2405.16218_abstract_full_text_year_2024.jsonl
new file mode 100644
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+{"idx": 0, "title": "[ 2405 . 16218 ] On the Optimal Time Complexities in Decentralized...", "date": "", "ddg_snippet": "arXiv : 2405 . 16218 (math). [Submitted on 25 May 2024 (v1), last revised 2 Nov 2024 (this version, v2)]. Title:On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.16218", "content": "arXiv : 2405 . 16218 (math). [Submitted on 25 May 2024 (v1), last revised 2 Nov 2024 (this version, v2)]. Title:On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization."}
+{"idx": 1, "title": "microsoft/BiomedNLP-BiomedBERT-base-uncased- abstract - fulltext ...", "date": "", "ddg_snippet": "MSR BiomedBERT ( abstracts + full text ).You can either adopt the new model name \"microsoft/BiomedNLP-BiomedBERT-base-uncased- abstract - fulltext \" or update your transformers library to version 4.22+ if you need to refer to the old name.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext", "content": "MSR BiomedBERT ( abstracts + full text ).You can either adopt the new model name \"microsoft/BiomedNLP-BiomedBERT-base-uncased- abstract - fulltext \" or update your transformers library to version 4.22+ if you need to refer to the old name."}
+{"idx": 2, "title": "AnySummary summarizes any text , audio or video file using AI.", "date": "", "ddg_snippet": "AnySummary summarizes any text , audio or video file using AI.", "subpage_snippet": "", "source": "www.anysummary.app", "link": "https://www.anysummary.app/", "content": "AnySummary summarizes any text , audio or video file using AI."}
+{"idx": 3, "title": "Исправить ошибки в тексте — Грамматика... | UltraText", "date": "", "ddg_snippet": "Грамматика онлайн на UltraText.ru поможет вам легко и быстро исправить ошибки в тексте онлайн. Проверить орфографию онлайн просто: загрузите текст и получите мгновенные результаты. Наш сервис обеспечивает проверку и автоматическое исправление...", "subpage_snippet": "", "source": "ultratext.ru", "link": "https://ultratext.ru/check-text", "content": "Грамматика онлайн на UltraText.ru поможет вам легко и быстро исправить ошибки в тексте онлайн. Проверить орфографию онлайн просто: загрузите текст и получите мгновенные результаты. Наш сервис обеспечивает проверку и автоматическое исправление..."}
+{"idx": 4, "title": "Сервис перефразирования, бесплатный синонимайзер | Retext.AI", "date": "", "ddg_snippet": "Retext.AI – лучший сервис перефразирования онлайн, умный синонимайзер, бесплатная нейросеть для текста. Автоматический рерайт! Увеличение оригинальности без потери смысла!", "subpage_snippet": "", "source": "retext.ai", "link": "https://retext.ai/ru", "content": "Retext.AI – лучший сервис перефразирования онлайн, умный синонимайзер, бесплатная нейросеть для текста. Автоматический рерайт! Увеличение оригинальности без потери смысла!"}
+{"idx": 5, "title": "Free Image Background Remover | Adobe Express", "date": "", "ddg_snippet": "Use the collection of free design assets including text templates, icons, shapes, and more to endlessly customize your image.", "subpage_snippet": "", "source": "www.adobe.com", "link": "https://www.adobe.com/express/feature/image/remove-background", "content": "Use the collection of free design assets including text templates, icons, shapes, and more to endlessly customize your image."}
+{"idx": 6, "title": "medrxiv.org/content/10.1101/2020.03.22.20040758v3", "date": "", "ddg_snippet": "Abstract der Studie in medRxiv 2020.", "subpage_snippet": "", "source": "www.medrxiv.org", "link": "https://www.medrxiv.org/content/10.1101/2020.03.22.20040758v3", "content": "Abstract der Studie in medRxiv 2020."}
+{"idx": 7, "title": "Temporal topological operators with picture fuzzy multifunctions", "date": "", "ddg_snippet": "Abstract . Full Text (HTML). Download PDF.Show full outline. A correction on. Conflict of interest.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/doi/10.3934/math.2025973", "content": "Abstract . Full Text (HTML). Download PDF.Show full outline. A correction on. Conflict of interest."}
+{"idx": 8, "title": "American Journal of Medical Sciences and Medicine", "date": "", "ddg_snippet": "Science and Education Publishing, publisher of open access journals in the scientific, technical and medical fields. Read full text articles or submit your research for publishing.", "subpage_snippet": "", "source": "www.sciepub.com", "link": "https://www.sciepub.com/journal/AJMSM", "content": "Science and Education Publishing, publisher of open access journals in the scientific, technical and medical fields. Read full text articles or submit your research for publishing."}
+{"idx": 9, "title": "Novel Retina-like Optoelectronic Sensors Based on Two-Dimensional...", "date": "", "ddg_snippet": "ABSTRACT . Artificial intelligence technology is in a stage of continuous development, intelligent perception as a key link of artificial intelligence, the corresponding sensing technology has been initially mature. However, the information acquired by sensors when sensing external information...", "subpage_snippet": "", "source": "the-innovation.org", "link": "https://the-innovation.org/article/doi/10.59717/j.xinn-energy.2025.100112", "content": "ABSTRACT . Artificial intelligence technology is in a stage of continuous development, intelligent perception as a key link of artificial intelligence, the corresponding sensing technology has been initially mature. However, the information acquired by sensors when sensing external information..."}
diff --git a/data/sampled_jsons/arxiv_2410.09543_Boltzmann_Aligned_Inverse_Folding_hardware_configuration_experimental_setup_GPU.jsonl b/data/sampled_jsons/arxiv_2410.09543_Boltzmann_Aligned_Inverse_Folding_hardware_configuration_experimental_setup_GPU.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..754df4acca9d04e333d0697c06a5e36e4806853d
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+{"idx": 0, "title": "[ 2410 . 09543 ] Boltzmann - Aligned Inverse Folding Model as...", "date": "", "ddg_snippet": "View a PDF of the paper titled Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions, by Xiaoran Jiao and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "View a PDF of the paper titled Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions, by Xiaoran Jiao and 5 other authors."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 2, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsuper-vised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coeficients of 0.3201 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the ∆∆G thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsuper-vised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coeficients of 0.3201 ..."}
+{"idx": 3, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "论文原名： 《 BOLTZMANN - ALIGNED INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL EFFECTS ON PROTEIN-PROTEIN INTERACTIONS》", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "论文原名： 《 BOLTZMANN - ALIGNED INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL EFFECTS ON PROTEIN-PROTEIN INTERACTIONS》"}
+{"idx": 4, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values."}
+{"idx": 5, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 6, "title": "GPU Requirements Guide for DeepSeek Models (V3, All Variants)", "date": "", "ddg_snippet": "Learn the GPU , VRAM, and hardware requirements for training and running DeepSeek models, including all model variants.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/posts/system-requirements-deepseek-models", "content": "Learn the GPU , VRAM, and hardware requirements for training and running DeepSeek models, including all model variants."}
+{"idx": 7, "title": "Bottleneck Calculator - Check CPU GPU Compatibility", "date": "", "ddg_snippet": "Easily check for CPU and GPU bottlenecks in your PC setup . Enter your system specifications below and see how well your components work together.", "subpage_snippet": "", "source": "pc-builds.com", "link": "https://pc-builds.com/bottleneck-calculator/", "content": "Easily check for CPU and GPU bottlenecks in your PC setup . Enter your system specifications below and see how well your components work together."}
+{"idx": 8, "title": "How to use a GPU in VirtualBox", "date": "", "ddg_snippet": "The main culprit here is VirtualBox's lackluster support for GPU hardware acceleration. By default, VirtualBox can dedicate a maximum of only 128MB of video memory to your guest OS, which was fine in 2005 but isn’t enough for the current workflow.", "subpage_snippet": "", "source": "www.xda-developers.com", "link": "https://www.xda-developers.com/how-use-gpu-virtualbox/", "content": "The main culprit here is VirtualBox's lackluster support for GPU hardware acceleration. By default, VirtualBox can dedicate a maximum of only 128MB of video memory to your guest OS, which was fine in 2005 but isn’t enough for the current workflow."}
+{"idx": 9, "title": "How to Enable Hardware -Accelerated GPU Scheduling in Windows 10...", "date": "", "ddg_snippet": "Should You Enable Hardware -Accelerated GPU Scheduling? What You'll Need to Make This Feature Work.On Windows 11, navigate to Settings > System > Display > Graphics > Change Default Graphics Settings and enable \" Hardware -Accelerated GPU Scheduling.\"", "subpage_snippet": "", "source": "www.howtogeek.com", "link": "https://www.howtogeek.com/756935/how-to-enable-hardware-accelerated-gpu-scheduling-in-windows-11/", "content": "Should You Enable Hardware -Accelerated GPU Scheduling? What You'll Need to Make This Feature Work.On Windows 11, navigate to Settings > System > Display > Graphics > Change Default Graphics Settings and enable \" Hardware -Accelerated GPU Scheduling.\""}
diff --git a/data/sampled_jsons/arxiv_2503.19009.jsonl b/data/sampled_jsons/arxiv_2503.19009.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2d1725bd8cb8153d984a011066ee87559fdb489b
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2503.19009.jsonl
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+{"idx": 0, "title": "arXiv - Wikipedia", "date": "", "ddg_snippet": "arXiv (pronounced as \"archive\"—the X represents the Greek letter chi χ ) [1] is an open-access repository of electronic preprints and postprints (known as e-prints) approved for posting after moderation, but not peer reviewed. It consists of scientific papers in the fields of mathematics, physics, astronomy, electrical engineering, computer science, quantitative biology, statistics ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/ArXiv", "content": "arXiv (pronounced as \"archive\"—the X represents the Greek letter chi χ ) [1] is an open-access repository of electronic preprints and postprints (known as e-prints) approved for posting after moderation, but not peer reviewed. It consists of scientific papers in the fields of mathematics, physics, astronomy, electrical engineering, computer science, quantitative biology, statistics ..."}
+{"idx": 1, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv ."}
+{"idx": 2, "title": "Finding Articles - arXiv info", "date": "", "ddg_snippet": "All arXiv submissions are assigned a unique identifier of the form yymm.nnnnn (or arch-ive/yymmnnn for older submissions). To retrieve the abstract page a paper simply enter the identifier in the \" Search or Article-id \" box in the top right of most pages.", "subpage_snippet": "", "source": "info.arxiv.org", "link": "https://info.arxiv.org/help/find/index.html", "content": "All arXiv submissions are assigned a unique identifier of the form yymm.nnnnn (or arch-ive/yymmnnn for older submissions). To retrieve the abstract page a paper simply enter the identifier in the \" Search or Article-id \" box in the top right of most pages."}
+{"idx": 3, "title": "GitHub - Xuchen-Li/llm-arxiv-daily: Automatically update arXiv papers ...", "date": "", "ddg_snippet": "Automatically update arXiv papers about LLM Reasoning, LLM Evaluation, LLM & MLLM and Video Understanding using Github Actions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xuchen-Li/llm-arxiv-daily", "content": "Automatically update arXiv papers about LLM Reasoning, LLM Evaluation, LLM & MLLM and Video Understanding using Github Actions."}
+{"idx": 4, "title": "arXiv Xplorer", "date": "", "ddg_snippet": "Thank you to arXiv , bioRxiv and medRxiv for their open access interoperability. Built by ttumiel with OpenAI's Embeddings.", "subpage_snippet": "", "source": "arxivxplorer.com", "link": "https://arxivxplorer.com/", "content": "Thank you to arXiv , bioRxiv and medRxiv for their open access interoperability. Built by ttumiel with OpenAI's Embeddings."}
+{"idx": 5, "title": "[2503.19009] Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text-video retrieval, our approach, Video-ColBERT, introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos. Video-ColBERT is built upon 3 main components: a fine-grained spatial and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text-video retrieval, our approach, Video-ColBERT, introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos. Video-ColBERT is built upon 3 main components: a fine-grained spatial and ..."}
+{"idx": 6, "title": "arXiv:2503.19009v1 [cs.CV] 24 Mar 2025", "date": "", "ddg_snippet": "1. Introduction With an ever-increasing amount of video data being gener-ated and stored daily, the need for effective and eficient re-trieval methods has become more pressing than ever. Text-to-video retrieval (T2VR) aims to address this by ranking large collections of videos based on their relevance to nat-ural language queries. However, the task remains challeng-ing due to the inherent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.19009", "content": "1. Introduction With an ever-increasing amount of video data being gener-ated and stored daily, the need for effective and eficient re-trieval methods has become more pressing than ever. Text-to-video retrieval (T2VR) aims to address this by ranking large collections of videos based on their relevance to nat-ural language queries. However, the task remains challeng-ing due to the inherent ..."}
+{"idx": 7, "title": "Mathematics - arXiv.org", "date": "", "ddg_snippet": "math.MP is an alias for math-ph. Articles in this category focus on areas of research that illustrate the application of mathematics to problems in physics, develop mathematical methods for such applications, or provide mathematically rigorous formulations of existing physical theories. Submissions to math-ph should be of interest to both physically oriented mathematicians and mathematically ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/archive/math/", "content": "math.MP is an alias for math-ph. Articles in this category focus on areas of research that illustrate the application of mathematics to problems in physics, develop mathematical methods for such applications, or provide mathematically rigorous formulations of existing physical theories. Submissions to math-ph should be of interest to both physically oriented mathematicians and mathematically ..."}
+{"idx": 8, "title": "Computer Science - arXiv.org", "date": "", "ddg_snippet": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/archive/cs", "content": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:"}
+{"idx": 9, "title": "\"Video-ColBERT: Contextualized Late Interaction for Text-to ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2503-19009", "content": "Bibliographic details on Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval."}
diff --git a/data/sampled_jsons/arxiv_2506.06866_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning.jsonl b/data/sampled_jsons/arxiv_2506.06866_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning.jsonl
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index 0000000000000000000000000000000000000000..68207a139ad4383086dcc930fb37cc3c57cf1b61
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by D Lee · 2025 — Abstract:Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "by D Lee · 2025 — Abstract:Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore ..."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by D Lee · 2025 — We evaluate SAFE across standard benchmark tasks in image classification and large language model post-training pruning , and compare with ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.06866", "content": "by D Lee · 2025 — We evaluate SAFE across standard benchmark tasks in image classification and large language model post-training pruning , and compare with ..."}
+{"idx": 2, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v2", "content": "Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time ."}
+{"idx": 3, "title": "Computer Science Jun 2025", "date": "", "ddg_snippet": "[3728] arXiv:2506.06866 [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-06?skip=3725&show=500", "content": "[3728] arXiv:2506.06866 [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon ..."}
+{"idx": 4, "title": "Artificial Intelligence Jun 2025", "date": "", "ddg_snippet": "[1788] arXiv:2506.06866 (cross-list from cs.LG) [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.AI/2025-06?skip=1775&show=25", "content": "[1788] arXiv:2506.06866 (cross-list from cs.LG) [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee ..."}
+{"idx": 5, "title": "Week Ending 6.8.2025 — Eye On AI", "date": "", "ddg_snippet": "... sparse (few connections) and flat (stable to perturbations). The SAFE framework formulates pruning as a constrained optimization problem that explicitly ...", "subpage_snippet": "", "source": "www.eye-on.ai", "link": "https://www.eye-on.ai/ai-articles/e6n7f8m6dc4a3aw-kysfw-p3bpn-gj8zp-p9mmz-b34zn-brxry-mr228-48slx-mew99-4724k-te62h-8ejzm-5lzzt-t3nh2-stb98-5fs7c-l7tsn-p6jbs-szxbt-pf6w4-pyb9z-8sfh4", "content": "... sparse (few connections) and flat (stable to perturbations). The SAFE framework formulates pruning as a constrained optimization problem that explicitly ..."}
+{"idx": 6, "title": "Machine Learning", "date": "", "ddg_snippet": "[675] arXiv:2506.06866 [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "http://www.arxiv.org/list/cs.LG/recent?skip=667&show=25", "content": "[675] arXiv:2506.06866 [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon ..."}
+{"idx": 7, "title": "Artificial Intelligence Jun 2025", "date": "", "ddg_snippet": "[1788] arXiv:2506.06866 (cross-list from cs.LG) [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.AI/2025-06?skip=1075&show=2000", "content": "[1788] arXiv:2506.06866 (cross-list from cs.LG) [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee ..."}
+{"idx": 8, "title": "Machine Learning Jun 2025", "date": "", "ddg_snippet": "1 Jun 2025 — [777] arXiv:2506.06866 [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee, Jinseok ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.LG/2025-06?skip=25&show=2000", "content": "1 Jun 2025 — [777] arXiv:2506.06866 [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee, Jinseok ..."}
+{"idx": 9, "title": "Artificial Intelligence Jun 2025", "date": "", "ddg_snippet": "11 Jun 2025 — [1788] arXiv:2506.06866 (cross-list from cs.LG) [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.AI/2025-06?skip=1575&show=1000", "content": "11 Jun 2025 — [1788] arXiv:2506.06866 (cross-list from cs.LG) [pdf, html, other]. Title: SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop ..."}
diff --git a/data/sampled_jsons/arxiv_Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them_Shen_Lee.jsonl b/data/sampled_jsons/arxiv_Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them_Shen_Lee.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..54e4504d70722eb35d0c7aed212be2124bb7f0cd
--- /dev/null
+++ b/data/sampled_jsons/arxiv_Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them_Shen_Lee.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Minimizer of the Sum of Two Strongly Convex Functions", "date": "", "ddg_snippet": "by K Kuwaranancharoen · 2023 · Cited by 3 — The primary goal in this context is to solve the decentralized optimization objective of minimizing the sum of cost functions associated with regular (non- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.13134", "content": "by K Kuwaranancharoen · 2023 · Cited by 3 — The primary goal in this context is to solve the decentralized optimization objective of minimizing the sum of cost functions associated with regular (non- ..."}
+{"idx": 1, "title": "arXiv:2105.14153v2 [math.OC] 29 Nov 2021", "date": "", "ddg_snippet": "by X Shen · 2021 · Cited by 3 — Our story starts with a well studied problem, minimizing a convex function that is the sum of two convex functions with different access methods ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2105.14153", "content": "by X Shen · 2021 · Cited by 3 — Our story starts with a well studied problem, minimizing a convex function that is the sum of two convex functions with different access methods ..."}
+{"idx": 2, "title": "Minimizing Layerwise Activation Norm Improves ...", "date": "", "ddg_snippet": "by M Yashwanth · 2024 · Cited by 3 — We apply our proposed flatness-constrained optimization to the existing FL techniques and obtain significant improvements, thereby establishing new state-of-the ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2024/papers/Yashwanth_Minimizing_Layerwise_Activation_Norm_Improves_Generalization_in_Federated_Learning_WACV_2024_paper.pdf", "content": "by M Yashwanth · 2024 · Cited by 3 — We apply our proposed flatness-constrained optimization to the existing FL techniques and obtain significant improvements, thereby establishing new state-of-the ..."}
+{"idx": 3, "title": "arXiv:2310.08424v2 [math.OC] 15 Jun 2024", "date": "", "ddg_snippet": "by T He · 2023 · Cited by 7 — As shown in [44], minimizing the sum of a linear fractional function and a linear function over {0, 1 }n is NP-hard. We relate this hardness ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.08424", "content": "by T He · 2023 · Cited by 7 — As shown in [44], minimizing the sum of a linear fractional function and a linear function over {0, 1 }n is NP-hard. We relate this hardness ..."}
+{"idx": 4, "title": "Bregman Proximal Linearized ADMM for Minimizing ...", "date": "", "ddg_snippet": "by TN Pham · 2024 · Cited by 6 — In this paper, we develop a splitting algorithm incorporating Bregman distances to solve a broad class of linearly constrained composite optimization problems.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10957-024-02539-7", "content": "by TN Pham · 2024 · Cited by 6 — In this paper, we develop a splitting algorithm incorporating Bregman distances to solve a broad class of linearly constrained composite optimization problems."}
+{"idx": 5, "title": "Integrated Offline and Online Learning to Solve a Large ...", "date": "", "ddg_snippet": "by A Liu · 2025 — In this paper, we consider the entire class of single -machine scheduling problems with a min- sum objective that is non- decreasing in job ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.04253?", "content": "by A Liu · 2025 — In this paper, we consider the entire class of single -machine scheduling problems with a min- sum objective that is non- decreasing in job ..."}
+{"idx": 6, "title": "Reducing Variance of Stochastic Optimization for ...", "date": "", "ddg_snippet": "15 Jul 2025 — To improve the convergence rate by mitigating the high variance associated with the existing unbiased loss function , we propose a novel ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45762", "content": "15 Jul 2025 — To improve the convergence rate by mitigating the high variance associated with the existing unbiased loss function , we propose a novel ..."}
+{"idx": 7, "title": "EinDecomp: Decomposition of Declaratively-Specified Machine ...", "date": "", "ddg_snippet": "ABSTRACT. We consider the problem of automatic parallelism in high-perform- ance, tensor-based systems. Our focus is on intra-operator paral-.", "subpage_snippet": "", "source": "vldb.org", "link": "https://vldb.org/pvldb/vol18/p2240-bourgeois.pdf", "content": "ABSTRACT. We consider the problem of automatic parallelism in high-perform- ance, tensor-based systems. Our focus is on intra-operator paral-."}
+{"idx": 8, "title": "AutoML: A systematic review on automated machine ...", "date": "", "ddg_snippet": "by I Salehin · 2024 · Cited by 183 — The descent step minimizes the function with respect to a set of parameters while the ascent step maximizes the function with respect to another set of ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2949715923000604", "content": "by I Salehin · 2024 · Cited by 183 — The descent step minimizes the function with respect to a set of parameters while the ascent step maximizes the function with respect to another set of ..."}
+{"idx": 9, "title": "SHARPNESS-AWARE MINIMIZATION ENHANCES ...", "date": "", "ddg_snippet": "by JM Springer · Cited by 7 — ABSTRACT. Sharpness-Aware Minimization (SAM) has emerged as a promising alternative op- timizer to stochastic gradient descent (SGD).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=3xDaj4pRna", "content": "by JM Springer · Cited by 7 — ABSTRACT. Sharpness-Aware Minimization (SAM) has emerged as a promising alternative op- timizer to stochastic gradient descent (SGD)."}
diff --git a/data/sampled_jsons/arxiv_Taming_Knowledge_Conflicts_in_Language_Models_JUICE.jsonl b/data/sampled_jsons/arxiv_Taming_Knowledge_Conflicts_in_Language_Models_JUICE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6cce215a7077fd1d33e38f723dd65f3925b98134
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models - arXiv.org", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ..."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models - GitHub", "date": "", "ddg_snippet": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GaotangLi/JUICE", "content": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated."}
+{"idx": 2, "title": "Taming Knowledge Conflicts in Language Models - Semantic Scholar", "date": "", "ddg_snippet": "This work proposes Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Taming-Knowledge-Conflicts-in-Language-Models-Li-Chen/b7ba9df4eb239708cf48f25be87b5bceeca010e3", "content": "This work proposes Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ..."}
+{"idx": 3, "title": "Paper page - Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Finally, we theoretically analyze knowledge conflict and the superposition of contextual information and parametric memory in attention heads, which further elucidates the effectiveness of JUICE in these settings. View arXiv page View PDF Add to collection", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.10996", "content": "Finally, we theoretically analyze knowledge conflict and the superposition of contextual information and parametric memory in attention heads, which further elucidates the effectiveness of JUICE in these settings. View arXiv page View PDF Add to collection"}
+{"idx": 4, "title": "ICML Poster Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Language models frequently encounter \" knowledge conflicts ,\" where their pre-trained knowledge contradicts the information provided by specific contexts. These conflicts often arise in context-dependent systems, such as retrieval-augmented generation and tools integrated with language models .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46677", "content": "Language models frequently encounter \" knowledge conflicts ,\" where their pre-trained knowledge contradicts the information provided by specific contexts. These conflicts often arise in context-dependent systems, such as retrieval-augmented generation and tools integrated with language models ."}
+{"idx": 5, "title": "Taming Knowledge Conflict in Language Models", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ...", "subpage_snippet": "", "source": "gaotangli.github.io", "link": "https://gaotangli.github.io/project_page/Taming-Knowledge-Conflict/", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ..."}
+{"idx": 6, "title": "Taming Knowledge Conflicts in Language Models - papers.cool", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2503.10996", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ..."}
+{"idx": 7, "title": "Taming Knowledge Conflicts in Language Models | AI Research Paper Details", "date": "", "ddg_snippet": "Conclusion This research represents an important step forward in making language models more reliable and trustworthy. By developing methods to identify and handle knowledge conflicts , the researchers address one of the fundamental challenges of current AI systems.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/taming-knowledge-conflicts-language-models", "content": "Conclusion This research represents an important step forward in making language models more reliable and trustworthy. By developing methods to identify and handle knowledge conflicts , the researchers address one of the fundamental challenges of current AI systems."}
+{"idx": 8, "title": "Taming Knowledge Conflicts in Language Models - arXiv.org", "date": "", "ddg_snippet": "Abstract Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v1", "content": "Abstract Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon ..."}
+{"idx": 9, "title": "Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting ...", "date": "", "ddg_snippet": "Cutting Off the Head Ends the Conflict : A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models . In Findings of the Association for Computational Linguistics: ACL 2024, pages 1193-1215, Bangkok, Thailand. Association for Computational Linguistics. Cite (Informal):", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.70/", "content": "Cutting Off the Head Ends the Conflict : A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models . In Findings of the Association for Computational Linguistics: ACL 2024, pages 1193-1215, Bangkok, Thailand. Association for Computational Linguistics. Cite (Informal):"}
diff --git a/data/sampled_jsons/attention_head_ablation_methods_comparison_safety_mechanism_feature_extraction.jsonl b/data/sampled_jsons/attention_head_ablation_methods_comparison_safety_mechanism_feature_extraction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..57e5e4f5d3fbb674e5522889a99a7a708ffd1820
--- /dev/null
+++ b/data/sampled_jsons/attention_head_ablation_methods_comparison_safety_mechanism_feature_extraction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Head Gating: A Framework for Interpreting Roles of", "date": "", "ddg_snippet": "We present causal head gating (CHG), a scalable method for interpreting the functional roles of attention heads in transformer models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13737v1", "content": "We present causal head gating (CHG), a scalable method for interpreting the functional roles of attention heads in transformer models."}
+{"idx": 1, "title": "Rapid Vehicle Trajectory Prediction Based on Multi-Attention", "date": "", "ddg_snippet": "We propose an agent-agent interaction information extraction module based on a sparse graph attention mechanism , which enables efficient aggregation ...", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202411.0644/v1", "content": "We propose an agent-agent interaction information extraction module based on a sparse graph attention mechanism , which enables efficient aggregation ..."}
+{"idx": 2, "title": "Integrating attention mechanism and multi-scale feature", "date": "", "ddg_snippet": "By enhancing the spatial attention mechanism , this network enables the model to understand the spatial distribution of the feature 's pose more ...", "subpage_snippet": "", "source": "filmsizlerle.com", "link": "https://filmsizlerle.com/article/integrating-attention-mechanism-and-multi-scale-feature-extraction-for-fall-detection", "content": "By enhancing the spatial attention mechanism , this network enables the model to understand the spatial distribution of the feature 's pose more ..."}
+{"idx": 3, "title": "SCATrans: semantic cross-attention transformer for drug–drug", "date": "", "ddg_snippet": "... existing DDIP methods , it makes use of multimodal biomedical data, BiGRU, and Cross- Attention to extract the local–global context semantic feature ...", "subpage_snippet": "", "source": "bmcbioinformatics.biomedcentral.com", "link": "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-025-06165-6", "content": "... existing DDIP methods , it makes use of multimodal biomedical data, BiGRU, and Cross- Attention to extract the local–global context semantic feature ..."}
+{"idx": 4, "title": "A Crowded Object Counting System with Self-Attention Mechanism", "date": "", "ddg_snippet": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/24/20/6612", "content": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ..."}
+{"idx": 5, "title": "CrackSCF: Lightweight Cascaded Fusion Network for Robust and", "date": "", "ddg_snippet": "We also propose a lightweight Long-range Dependency Extractor (LDE), which uses a lightweight deformable attention mechanism to effectively capture ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.12815v4", "content": "We also propose a lightweight Long-range Dependency Extractor (LDE), which uses a lightweight deformable attention mechanism to effectively capture ..."}
+{"idx": 6, "title": "SMA: Who Said That? Auditing Membership Leakage in", "date": "", "ddg_snippet": "To address the environmental constraints of semi-black-box auditing, we further design an attribution estimation mechanism based on zero-order ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.09105v2", "content": "To address the environmental constraints of semi-black-box auditing, we further design an attribution estimation mechanism based on zero-order ..."}
+{"idx": 7, "title": "CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for", "date": "", "ddg_snippet": "A lightweight, layered routing mechanism is developed, integrating convolutional operations, self- attention , and multi-layer perceptrons.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.07838v1", "content": "A lightweight, layered routing mechanism is developed, integrating convolutional operations, self- attention , and multi-layer perceptrons."}
+{"idx": 8, "title": "Beyond I’m Sorry, I Can’t: Dissecting Large-Language-Model", "date": "", "ddg_snippet": "... features we identify as critical to refusal re‑activate whenever a distinct precursor is removed, implying a safety ‑critical network of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09708v1", "content": "... features we identify as critical to refusal re‑activate whenever a distinct precursor is removed, implying a safety ‑critical network of ..."}
+{"idx": 9, "title": "Appearance-Based Gaze Estimation Method Using Static", "date": "", "ddg_snippet": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7390/11/3/686", "content": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ..."}
diff --git a/data/sampled_jsons/attention_heads_large_language_model_safety_arXiv.jsonl b/data/sampled_jsons/attention_heads_large_language_model_safety_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6abf1cde76c3b4c829daa5b6af461f7976ce811
--- /dev/null
+++ b/data/sampled_jsons/attention_heads_large_language_model_safety_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.13708", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
+{"idx": 2, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. Attention heads of large language models : A survey. arXiv preprint arXiv :2409.03752, 2024c.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v2", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. Attention heads of large language models : A survey. arXiv preprint arXiv :2409.03752, 2024c."}
+{"idx": 3, "title": "(PDF) On the Role of Attention Heads in Large Language Model ...", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
+{"idx": 4, "title": "GitHub - IAAR-Shanghai/Awesome- Attention - Heads : An awesome...", "date": "", "ddg_snippet": "Attention Heads of Large Language Models : A Survey (Awesome Attention Heads ). Important. About this repo.With the development of Large Language Model (LLMs), their underlying network structure, the Transformer, is being extensively studied.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IAAR-Shanghai/Awesome-Attention-Heads", "content": "Attention Heads of Large Language Models : A Survey (Awesome Attention Heads ). Important. About this repo.With the development of Large Language Model (LLMs), their underlying network structure, the Transformer, is being extensively studied."}
+{"idx": 5, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Keywords: interpretability, large language model , multi- head attention , safety , harmful content.Abstract: Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=h0Ak8A5yqw", "content": "Keywords: interpretability, large language model , multi- head attention , safety , harmful content.Abstract: Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
+{"idx": 6, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/role-attention-heads-large-language-model-safety", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
+{"idx": 7, "title": "Paper page - On the Role of Attention Heads in Large Language ...", "date": "", "ddg_snippet": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.Cite arxiv .org/abs/2410.13708 in a model README.md to link it from this page.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2410.13708", "content": "Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.Cite arxiv .org/abs/2410.13708 in a model README.md to link it from this page."}
+{"idx": 8, "title": "Attention Heads of Large Language Models : A Survey", "date": "", "ddg_snippet": "Large Language Models (LLMs) excel in various tasks but remain largely black-box systems. Their development relies heavily on data-driven approaches, limiting performance enhancement through changes in internal architecture and reasoning pathways.", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/attention-heads-of-large-language", "content": "Large Language Models (LLMs) excel in various tasks but remain largely black-box systems. Their development relies heavily on data-driven approaches, limiting performance enhancement through changes in internal architecture and reasoning pathways."}
+{"idx": 9, "title": "Attention Mechanism In Large Language Models | Restackio", "date": "", "ddg_snippet": "arxiv .org. Mechanistic interpretability of large language models with applications to the financial services industry.Multi- Head Attention . The attention mechanism is fundamentally based on learnable key, query, and value vectors.", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/large-language-models-answer-attention-mechanism-cat-ai", "content": "arxiv .org. Mechanistic interpretability of large language models with applications to the financial services industry.Multi- Head Attention . The attention mechanism is fundamentally based on learnable key, query, and value vectors."}
diff --git a/data/sampled_jsons/capture_the_flag_CTF_challenges_vs_real_world_vulnerabilities_cybersecurity.jsonl b/data/sampled_jsons/capture_the_flag_CTF_challenges_vs_real_world_vulnerabilities_cybersecurity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a149d87c563b0265adf4b1e4f4e330f88750f189
--- /dev/null
+++ b/data/sampled_jsons/capture_the_flag_CTF_challenges_vs_real_world_vulnerabilities_cybersecurity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Difference between CTF and real world vulnerabilities", "date": "", "ddg_snippet": "Conclusion Capture the Flag events and real-world vulnerabilities are both important ways to test and improve cybersecurity measures. While CTF events provide a controlled environment for participants to test their skills, real-world vulnerabilities offer insight into the vulnerabilities that exist in live systems.", "subpage_snippet": "", "source": "hacklido.com", "link": "https://hacklido.com/blog/367-difference-between-ctf-and-real-world-vulnerabilities", "content": "Conclusion Capture the Flag events and real-world vulnerabilities are both important ways to test and improve cybersecurity measures. While CTF events provide a controlled environment for participants to test their skills, real-world vulnerabilities offer insight into the vulnerabilities that exist in live systems."}
+{"idx": 1, "title": "Capture the Flag: What you should know about cybersecurity CTFs", "date": "", "ddg_snippet": "Capture the Flag ( CTF ) competitions are surging in popularity among cybersecurity enthusiasts, students, and professionals alike. They're a hands-on way to deepen expertise, connect with peers, and explore real-world scenarios in a simulated environment. But what exactly is a CTF—and why should it be on your radar? What is Capture the Flag ?", "subpage_snippet": "", "source": "fieldeffect.com", "link": "https://fieldeffect.com/blog/capture-the-flag-cybersecurity", "content": "Capture the Flag ( CTF ) competitions are surging in popularity among cybersecurity enthusiasts, students, and professionals alike. They're a hands-on way to deepen expertise, connect with peers, and explore real-world scenarios in a simulated environment. But what exactly is a CTF—and why should it be on your radar? What is Capture the Flag ?"}
+{"idx": 2, "title": "Why Is Capture the Flag (CTF) Important in Cyber Security?", "date": "", "ddg_snippet": "What is Capture the Flag ( CTF ) in cyber security, and why is it a crucial practice? Learn how CTFs build ethical hacking skills, boost teamwork, and enhance threat detection.", "subpage_snippet": "", "source": "www.eccouncil.org", "link": "https://www.eccouncil.org/cybersecurity-exchange/ethical-hacking/capture-the-flag-ctf-cybersecurity/", "content": "What is Capture the Flag ( CTF ) in cyber security, and why is it a crucial practice? Learn how CTFs build ethical hacking skills, boost teamwork, and enhance threat detection."}
+{"idx": 3, "title": "CTFs vs Real life scenarios : r/cybersecurity", "date": "", "ddg_snippet": "The difference is contrived vs real world scenarios , managing a small set of known vulnerable targets vs a large set with an unknown number of ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/cybersecurity/comments/zj590l/ctfs_vs_real_life_scenarios/", "content": "The difference is contrived vs real world scenarios , managing a small set of known vulnerable targets vs a large set with an unknown number of ..."}
+{"idx": 4, "title": "What's CTF? Capture The Flag Competitions for ...", "date": "", "ddg_snippet": "22 Jan 2025 — Capture the Flag (CTF) is a cybersecurity competition. In CTF, participants search for vulnerabilities in technology systems to discover hidden “flags”.", "subpage_snippet": "", "source": "www.splunk.com", "link": "https://www.splunk.com/en_us/blog/learn/capture-the-flag-ctf.html", "content": "22 Jan 2025 — Capture the Flag (CTF) is a cybersecurity competition. In CTF, participants search for vulnerabilities in technology systems to discover hidden “flags”."}
+{"idx": 5, "title": "Capture The Flag: Honing the Edge in Cybersecurity", "date": "", "ddg_snippet": "2 Aug 2024 — Unlike structured training or certifications, CTFs present unpredictable challenges that mimic the uncertainty of real-world cybersecurity ...", "subpage_snippet": "", "source": "reflare.com", "link": "https://reflare.com/research/capture-the-flag-honing-the-edge-in-cybersecurity", "content": "2 Aug 2024 — Unlike structured training or certifications, CTFs present unpredictable challenges that mimic the uncertainty of real-world cybersecurity ..."}
+{"idx": 6, "title": "Types of CTF challenges", "date": "", "ddg_snippet": "Capture the Flag (CTF) challenges in cybersecurity often come in various types, each designed to test different sets of hacking/security-evading skills.", "subpage_snippet": "", "source": "snyk.io", "link": "https://snyk.io/articles/ctf/ctf-types/", "content": "Capture the Flag (CTF) challenges in cybersecurity often come in various types, each designed to test different sets of hacking/security-evading skills."}
+{"idx": 7, "title": "Beginner's Guide to CTF: How to Get Started in ...", "date": "", "ddg_snippet": "5 Jun 2025 — CTF stands for Capture The Flag , a game rooted in cybersecurity where participants solve challenges to find hidden “flags.” These flags are ...", "subpage_snippet": "", "source": "securiumsolutions.com", "link": "https://securiumsolutions.com/beginners-guide-to-ctf/", "content": "5 Jun 2025 — CTF stands for Capture The Flag , a game rooted in cybersecurity where participants solve challenges to find hidden “flags.” These flags are ..."}
+{"idx": 8, "title": "What CTFs Don't Teach You About Real-World Pentesting ...", "date": "", "ddg_snippet": "In a CTF, there's always a flag — somewhere. You just have to find it. In real-life pentests, there's no guarantee anything is vulnerable.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@cybercom0101/from-ctf-to-real-life-what-ctfs-dont-teach-you-about-real-world-pentesting-be7aca26b393", "content": "In a CTF, there's always a flag — somewhere. You just have to find it. In real-life pentests, there's no guarantee anything is vulnerable."}
+{"idx": 9, "title": "Why Capture the Flag (CTF) Events Are Essential for Cybersecurity ...", "date": "", "ddg_snippet": "CTF competitions are gamified cybersecurity challenges where participants solve problems to \" capture the flag .\" These challenges replicate real-world scenarios, such as identifying security vulnerabilities , analyzing network traffic, and recovering sensitive information from compromised systems.", "subpage_snippet": "", "source": "www.cyberyami.com", "link": "https://www.cyberyami.com/blogs/why-capture-the-flag-ctf-events-are-essential-for-cybersecurity-skills", "content": "CTF competitions are gamified cybersecurity challenges where participants solve problems to \" capture the flag .\" These challenges replicate real-world scenarios, such as identifying security vulnerabilities , analyzing network traffic, and recovering sensitive information from compromised systems."}
diff --git a/data/sampled_jsons/causal_modeling_climate_activism_reddit_92_percent_activation_three_subreddits_year_2024.jsonl b/data/sampled_jsons/causal_modeling_climate_activism_reddit_92_percent_activation_three_subreddits_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf1d375d64dd36915db07385e2bcc280492365a7
--- /dev/null
+++ b/data/sampled_jsons/causal_modeling_climate_activism_reddit_92_percent_activation_three_subreddits_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Climate Change Frames and Emotional Responses on Reddit", "date": "", "ddg_snippet": "by II Villanueva · 2021 · Cited by 8 — Six subreddits were selected because they represent the spectrum of climate change opinions. They are classified into 3 groups – climate believers, climate ...", "subpage_snippet": "", "source": "scholarworks.uark.edu", "link": "https://scholarworks.uark.edu/cgi/viewcontent.cgi?article=5626&context=etd", "content": "by II Villanueva · 2021 · Cited by 8 — Six subreddits were selected because they represent the spectrum of climate change opinions. They are classified into 3 groups – climate believers, climate ..."}
+{"idx": 1, "title": "Exploring the Rise of a Deviant Culture in a Misogynist ...", "date": "", "ddg_snippet": "by Y Shi · 2024 · Cited by 3 — This article presents a study of the Reddit incel community, active from mid-2016 to its ban in late 2017, which evolved from a self-help forum to a hub for ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/23780231241272681", "content": "by Y Shi · 2024 · Cited by 3 — This article presents a study of the Reddit incel community, active from mid-2016 to its ban in late 2017, which evolved from a self-help forum to a hub for ..."}
+{"idx": 2, "title": "A Social Media Based Examination of the Effects of ...", "date": "", "ddg_snippet": "by K Saha · 2018 · Cited by 73 — We propose a causal analysis framework to examine the effects of these counseling recommendations after student deaths. We leverage a dataset from 174 Reddit ...", "subpage_snippet": "", "source": "koustuv.com", "link": "https://koustuv.com/papers/ICWSM18_CounselingRecommendation.pdf", "content": "by K Saha · 2018 · Cited by 73 — We propose a causal analysis framework to examine the effects of these counseling recommendations after student deaths. We leverage a dataset from 174 Reddit ..."}
+{"idx": 3, "title": "The Digital Fallout of Major Criminal Incidents", "date": "", "ddg_snippet": "by JM Scott · 2024 — Reddit posts and 5 billion comments generated in nearly 3 million subreddits (Baumgartner et al., 2020). A somewhat limited version of this dataset is also ...", "subpage_snippet": "", "source": "academicworks.cuny.edu", "link": "https://academicworks.cuny.edu/cgi/viewcontent.cgi?article=6838&context=gc_etds", "content": "by JM Scott · 2024 — Reddit posts and 5 billion comments generated in nearly 3 million subreddits (Baumgartner et al., 2020). A somewhat limited version of this dataset is also ..."}
+{"idx": 4, "title": "Findings of the Association for Computational Linguistics", "date": "", "ddg_snippet": "by Y Al-Onaizan · 2024 · Cited by 22 — Specifically, we focus on modeling the causal relations that lead directly from utterances earlier in the dialogue to the emergence of the probing question ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2024.findings-emnlp/", "content": "by Y Al-Onaizan · 2024 · Cited by 22 — Specifically, we focus on modeling the causal relations that lead directly from utterances earlier in the dialogue to the emergence of the probing question ..."}
+{"idx": 5, "title": "large-scale investigation of everyday moral dilemmas", "date": "", "ddg_snippet": "by DA Yudkin · 2025 · Cited by 4 — While science has discovered much about moral decision-making ( 3 , 4), surprisingly little is known about everyday moral dilemmas. This is partly ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/pnasnexus/article/4/5/pgaf119/8124984", "content": "by DA Yudkin · 2025 · Cited by 4 — While science has discovered much about moral decision-making ( 3 , 4), surprisingly little is known about everyday moral dilemmas. This is partly ..."}
+{"idx": 6, "title": "Seeking in Cycles: How Users Leverage Personal ...", "date": "", "ddg_snippet": "11 May 2024 — We present an interview study (n = 17) of participants who use online platforms to seek information about their mental illnesses.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3613904.3641894", "content": "11 May 2024 — We present an interview study (n = 17) of participants who use online platforms to seek information about their mental illnesses."}
+{"idx": 7, "title": "[D] General negative sentiment surrounding “AI”", "date": "", "ddg_snippet": "“AI” has increasingly been carrying an existential fearful/negative connotation to the general (nontechnical) public.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/1agj5y1/d_general_negative_sentiment_surrounding_ai/", "content": "“AI” has increasingly been carrying an existential fearful/negative connotation to the general (nontechnical) public."}
+{"idx": 8, "title": "Y Social: an LLM-powered Social Media Digital Twin", "date": "", "ddg_snippet": "1 Aug 2024 — In the case of social media, a digital twin such as Y provides a powerful tool for researchers to simulate and understand complex online ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.00818", "content": "1 Aug 2024 — In the case of social media, a digital twin such as Y provides a powerful tool for researchers to simulate and understand complex online ..."}
+{"idx": 9, "title": "Using Machine Learning to Improve Lead Times in the ...", "date": "", "ddg_snippet": "by D Kilroy · 2022 · Cited by 27 — Therefore, in this paper we propose two keyphrase extraction algorithms for obtain- ing customer needs from two diverse collections of text.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/6287639/9668973/09749255.pdf", "content": "by D Kilroy · 2022 · Cited by 27 — Therefore, in this paper we propose two keyphrase extraction algorithms for obtain- ing customer needs from two diverse collections of text."}
diff --git a/data/sampled_jsons/causal_representation_learning_approaches_assumptions.jsonl b/data/sampled_jsons/causal_representation_learning_approaches_assumptions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a339809d602ea8633ef424617765cf8682cc0194
--- /dev/null
+++ b/data/sampled_jsons/causal_representation_learning_approaches_assumptions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unifying Causal Representation Learning with the", "date": "", "ddg_snippet": "Causal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predict-ing the effect of new interventions or more robust classication.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LaU3p8Pj0D", "content": "Causal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predict-ing the effect of new interventions or more robust classication."}
+{"idx": 1, "title": "Marrying Causal Representation Learning with", "date": "", "ddg_snippet": "We remark that the causal representation learning schemes mainly differ in the latent regularizers, specified by the assumptions and settings. Therefore, we provide a more extensive summary of different causal representation learning approaches and their corresponding latent...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.13888", "content": "We remark that the causal representation learning schemes mainly differ in the latent regularizers, specified by the assumptions and settings. Therefore, we provide a more extensive summary of different causal representation learning approaches and their corresponding latent..."}
+{"idx": 2, "title": "Causal Representation Learning | Empirical Inference – Max Planck...", "date": "", "ddg_snippet": "The goal of causal representation learning is to learn representations from data that recover the underlying latent causal variables and their structure. Such a representation can then be used for planning, to predict the effect of interventions, or for out-of-distribution generalization.", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/ei/research_fields/causal-representation-learning", "content": "The goal of causal representation learning is to learn representations from data that recover the underlying latent causal variables and their structure. Such a representation can then be used for planning, to predict the effect of interventions, or for out-of-distribution generalization."}
+{"idx": 3, "title": "Causal Representations", "date": "", "ddg_snippet": "In this work, we relax this assumption and provide a novel identifiability result for causal representation learning that allows for multiple variables to be targeted by an intervention within one environment. Our approach hinges on a general assumption on the coverage and...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v236/bing24a/bing24a.pdf", "content": "In this work, we relax this assumption and provide a novel identifiability result for causal representation learning that allows for multiple variables to be targeted by an intervention within one environment. Our approach hinges on a general assumption on the coverage and..."}
+{"idx": 4, "title": "(PDF) Unifying Causal Representation Learning with the Invariance...", "date": "", "ddg_snippet": "Causal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect of new interventions or more robust classification.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383752883_Unifying_Causal_Representation_Learning_with_the_Invariance_Principle", "content": "Causal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect of new interventions or more robust classification."}
+{"idx": 5, "title": "Causal Representation Learning from Multiple Distributions...", "date": "", "ddg_snippet": "The authors provide theoretical analysis to show that under certain assumptions , the learned representation will converge to the true causal representation as the number of environments increases. They also demonstrate the effectiveness of their approach through...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/causal-representation-learning-from-multiple-distributions-general", "content": "The authors provide theoretical analysis to show that under certain assumptions , the learned representation will converge to the true causal representation as the number of environments increases. They also demonstrate the effectiveness of their approach through..."}
+{"idx": 6, "title": "Unifying Causal Representation Learning with... | Papers With Code", "date": "", "ddg_snippet": "Causal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect of new interventions or more robust classification.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/unifying-causal-representation-learning-with", "content": "Causal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect of new interventions or more robust classification."}
+{"idx": 7, "title": "BISCUIT: Causal Representation Learning from Binary... | alphaXiv", "date": "", "ddg_snippet": "Causal representation learning aims to discover latent causal variables and their relationships from high-dimensional observations.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2306.09643v1", "content": "Causal representation learning aims to discover latent causal variables and their relationships from high-dimensional observations."}
+{"idx": 8, "title": "Causal Representation Learning from Multi-modal Biomedical...", "date": "", "ddg_snippet": "Causal representation learning via counterfactual intervention. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3234–3242, 2024c.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11952583/", "content": "Causal representation learning via counterfactual intervention. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3234–3242, 2024c."}
+{"idx": 9, "title": "Causal Representation Learning Made Identifiable by Grouping", "date": "", "ddg_snippet": "Causal Representation Learning Made Identiable by Grouping of Observational Variables.3 just represents the factorization of the joint distribution and does not incorporate any causal directional assumptions between variables.", "subpage_snippet": "", "source": "helda.helsinki.fi", "link": "https://helda.helsinki.fi/server/api/core/bitstreams/b0c84266-835c-49d0-bc45-fdf94b494e04/content", "content": "Causal Representation Learning Made Identiable by Grouping of Observational Variables.3 just represents the factorization of the joint distribution and does not incorporate any causal directional assumptions between variables."}
diff --git a/data/sampled_jsons/citesd2aGLPSpFz_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System.jsonl b/data/sampled_jsons/citesd2aGLPSpFz_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System.jsonl
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index 0000000000000000000000000000000000000000..430cc773df24b29e4165b35739a18648b1211011
--- /dev/null
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+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple ... GitHub - simonbing/CRLSanityCheck Toward Causal Representation Learning - IEEE Xplore Sanity Checking Causal Representation Learning on a Simple ... Sanity Checking Causal Representation Learning on a Simple ... Sanity Checking Causal Representation Learning on a Simple ... Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "Feb 27, 2025 · We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. Official code repository for the paper Sanity Checking Causal Representation Learning on a Simple Real - World System (2025) by Juan L. Gamella*, Simon Bing* and Jakob Runge. Feb 26, 2021 · In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This paper by Gamella, Bing, and Runge addresses this fundamental limitation by introducing the first real - world physical testbed for CRL evaluation: a carefully controlled optical system called a \"light tunnel.\" Oral Sanity Checking Causal Representation Learning on a Simple Real - World System Juan L. Gamella · Simon Bing · Jakob Runge West Ballroom D [ Abstract ] [ Visit Oral 3E Causality and Domain Generalization ] Wed 16 Jul 10:45 a.m. — 11 a.m. PDT Poster presentation: Sanity Checking Causal Representation Learning on a Simple Real-World System This work evaluates methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work, and finds that they all fail to recover the underlying causal factors. Feb 27, 2025 · This work provides important insights into the practical limitations of causal representation learning . The findings suggest that significant improvements are needed before these methods can be reliably applied to real - world problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20099", "content": "Feb 27, 2025 · We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. Official code repository for the paper Sanity Checking Causal Representation Learning on a Simple Real - World System (2025) by Juan L. Gamella*, Simon Bing* and Jakob Runge. Feb 26, 2021 · In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This paper by Gamella, Bing, and Runge addresses this fundamental limitation by introducing the first real - world physical testbed for CRL evaluation: a carefully controlled optical system called a \"light tunnel.\" Oral Sanity Checking Causal Representation Learning on a Simple Real - World System Juan L. Gamella · Simon Bing · Jakob Runge West Ballroom D [ Abstract ] [ Visit Oral 3E Causality and Domain Generalization ] Wed 16 Jul 10:45 a.m. — 11 a.m. PDT Poster presentation: Sanity Checking Causal Representation Learning on a Simple Real-World System This work evaluates methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work, and finds that they all fail to recover the underlying causal factors. Feb 27, 2025 · This work provides important insights into the practical limitations of causal representation learning . The findings suggest that significant improvements are needed before these methods can be reliably applied to real - world problems."}
+{"idx": 1, "title": "GitHub - simonbing/CRLSanityCheck", "date": "", "ddg_snippet": "Official code repository for the paper Sanity Checking Causal Representation Learning on a Simple Real - World System (2025) by Juan L. Gamella*, Simon Bing* and Jakob Runge.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/simonbing/CRLSanityCheck", "content": "Official code repository for the paper Sanity Checking Causal Representation Learning on a Simple Real - World System (2025) by Juan L. Gamella*, Simon Bing* and Jakob Runge."}
+{"idx": 2, "title": "Toward Causal Representation Learning - IEEE Xplore", "date": "", "ddg_snippet": "Feb 26, 2021 · In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9363924", "content": "Feb 26, 2021 · In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research."}
+{"idx": 3, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "This paper by Gamella, Bing, and Runge addresses this fundamental limitation by introducing the first real - world physical testbed for CRL evaluation: a carefully controlled optical system called a \"light tunnel.\"", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.20099v2", "content": "This paper by Gamella, Bing, and Runge addresses this fundamental limitation by introducing the first real - world physical testbed for CRL evaluation: a carefully controlled optical system called a \"light tunnel.\""}
+{"idx": 4, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "Oral Sanity Checking Causal Representation Learning on a Simple Real - World System Juan L. Gamella · Simon Bing · Jakob Runge West Ballroom D [ Abstract ] [ Visit Oral 3E Causality and Domain Generalization ] Wed 16 Jul 10:45 a.m. — 11 a.m. PDT Poster presentation: Sanity Checking Causal Representation Learning on a Simple Real-World System", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47207", "content": "Oral Sanity Checking Causal Representation Learning on a Simple Real - World System Juan L. Gamella · Simon Bing · Jakob Runge West Ballroom D [ Abstract ] [ Visit Oral 3E Causality and Domain Generalization ] Wed 16 Jul 10:45 a.m. — 11 a.m. PDT Poster presentation: Sanity Checking Causal Representation Learning on a Simple Real-World System"}
+{"idx": 5, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "This work evaluates methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work, and finds that they all fail to recover the underlying causal factors.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Sanity-Checking-Causal-Representation-Learning-on-a-Gamella-Bing/638e050573f438f77583f2b210c2d5da0f1b4ca7", "content": "This work evaluates methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work, and finds that they all fail to recover the underlying causal factors."}
+{"idx": 6, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "Feb 27, 2025 · This work provides important insights into the practical limitations of causal representation learning . The findings suggest that significant improvements are needed before these methods can be reliably applied to real - world problems.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/sanity-checking-causal-representation-learning-simple-real", "content": "Feb 27, 2025 · This work provides important insights into the practical limitations of causal representation learning . The findings suggest that significant improvements are needed before these methods can be reliably applied to real - world problems."}
+{"idx": 7, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44652/paper", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work."}
+{"idx": 8, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Sanity-Checking-Causal-Representation-Learning-on-a-Simple-Real-World-System-ea56ed1c-68a5-4a60-8384-8b1132108289", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work."}
+{"idx": 9, "title": "ICML Poster Sanity Checking Causal Representation Learning on ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process."}
diff --git a/data/sampled_jsons/comparison_of_Rubin_potential_outcomes_and_Pearl_response_functions.jsonl b/data/sampled_jsons/comparison_of_Rubin_potential_outcomes_and_Pearl_response_functions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f60bc43d47ed9372fe9f091c7f6e22b7ffb385e
--- /dev/null
+++ b/data/sampled_jsons/comparison_of_Rubin_potential_outcomes_and_Pearl_response_functions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Rubin causal model - Wikipedia", "date": "", "ddg_snippet": "The Rubin causal model, also known as the Neyman– Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes , named after Donald Rubin . The name \" Rubin causal model\" was fi...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Rubin_causal_model", "content": "The Rubin causal model, also known as the Neyman– Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes , named after Donald Rubin . The name \" Rubin causal model\" was fi..."}
+{"idx": 1, "title": "[2309.05997v4] A clarification on the links between potential ...", "date": "", "ddg_snippet": "... potential - outcome framework of Rubin to be formally equivalent, and therefore interchangeably uses do-interventions and the potential - outcome subscript notation to write counterfactual outcomes .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2309.05997v4", "content": "... potential - outcome framework of Rubin to be formally equivalent, and therefore interchangeably uses do-interventions and the potential - outcome subscript notation to write counterfactual outcomes ."}
+{"idx": 2, "title": "Comparing Causal Frameworks: Potential Outcomes , Structural...", "date": "", "ddg_snippet": "Comparing Rubin and Pearl 's Causal Modeling Frameworks: A Commentary on Markus (2021). Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371871955_Comparing_Causal_Frameworks_Potential_Outcomes_Structural_Models_Graphs_and_Abstractions", "content": "Comparing Rubin and Pearl 's Causal Modeling Frameworks: A Commentary on Markus (2021). Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics."}
+{"idx": 3, "title": "expected value - Baffled by Rubin 's Potential Outcomes RE: What...", "date": "", "ddg_snippet": "The potential outcomes and the causal effect are random variables.Under the Rubin potential outcome model, we consider these cases through separate random variables called the \" potential outcomes \", which are Y0∼F0.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/627779/baffled-by-rubins-potential-outcomes-re-what-would-have-happened", "content": "The potential outcomes and the causal effect are random variables.Under the Rubin potential outcome model, we consider these cases through separate random variables called the \" potential outcomes \", which are Y0∼F0."}
+{"idx": 4, "title": "Causality without potential outcomes and the dynamic... | DeepAI", "date": "", "ddg_snippet": "Several approaches to causal inference from observational studies have been proposed. Since the proposal of Rubin (1974) many works have developed a counterfactual approach to causality, statistically formalized by potential outcomes .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/causality-without-potential-outcomes-and-the-dynamic-approach", "content": "Several approaches to causal inference from observational studies have been proposed. Since the proposal of Rubin (1974) many works have developed a counterfactual approach to causality, statistically formalized by potential outcomes ."}
+{"idx": 5, "title": "Reproduce Section 8.10 in Imbens and Rubin (2005) with Stan", "date": "", "ddg_snippet": "Model 4: with covariates, potential outcomes are independent, different variances, using a zero-inflated lognormal distribution. We will examine each one in detail below.", "subpage_snippet": "", "source": "zenn.dev", "link": "https://zenn.dev/kmat/articles/d4f0038aa6da9c", "content": "Model 4: with covariates, potential outcomes are independent, different variances, using a zero-inflated lognormal distribution. We will examine each one in detail below."}
+{"idx": 6, "title": "Latent potential outcomes : An analysis of the effects of programs...", "date": "", "ddg_snippet": "The model can be cast in the hidden Markov literature and is formulated as an extension of Rubin 's causal model based on potential versions of discrete time-varying latent variables.", "subpage_snippet": "", "source": "boa.unimib.it", "link": "https://boa.unimib.it/handle/10281/433238", "content": "The model can be cast in the hidden Markov literature and is formulated as an extension of Rubin 's causal model based on potential versions of discrete time-varying latent variables."}
+{"idx": 7, "title": "Introduction tocausalinference april02_2020 | PPTX", "date": "", "ddg_snippet": "2. The Rubin causal model and potential outcomes framework for defining average treatment effects. 3. Propensity score theory and how propensity scores can create balanced groups to estimate causal effects.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/introduction-tocausalinference-april022020/231300235", "content": "2. The Rubin causal model and potential outcomes framework for defining average treatment effects. 3. Propensity score theory and how propensity scores can create balanced groups to estimate causal effects."}
+{"idx": 8, "title": "Open Lab Notebook", "date": "", "ddg_snippet": "Potential Outcomes Model for Defining Effects Caused by a Treatment. Definitions. These are due to Neyman 1923, Rubin 1974.Yi (0) denotes the potential outcome if control is applied. The causal effect of treatment compared to control for unit. ii. i can be expressed as.", "subpage_snippet": "", "source": "open-lab-notebook.glitch.me", "link": "https://open-lab-notebook.glitch.me/posts/stats_700/Lecture01/", "content": "Potential Outcomes Model for Defining Effects Caused by a Treatment. Definitions. These are due to Neyman 1923, Rubin 1974.Yi (0) denotes the potential outcome if control is applied. The causal effect of treatment compared to control for unit. ii. i can be expressed as."}
+{"idx": 9, "title": "00001.tif", "date": "", "ddg_snippet": "Existence of potential responses . The first problem is whether it makes sense to consider the joint distribution or a function of two variables that cannot physically exist simultaneously such as Y 1 and Y°. Rubin assumes th at this is indeed plausible.", "subpage_snippet": "", "source": "discovery.ucl.ac.uk", "link": "https://discovery.ucl.ac.uk/id/eprint/1445505/1/U592829.pdf", "content": "Existence of potential responses . The first problem is whether it makes sense to consider the joint distribution or a function of two variables that cannot physically exist simultaneously such as Y 1 and Y°. Rubin assumes th at this is indeed plausible."}
diff --git a/data/sampled_jsons/computer_science_has_a_long_history_standards_measurement_Wallach_evaluating_generative_AI.jsonl b/data/sampled_jsons/computer_science_has_a_long_history_standards_measurement_Wallach_evaluating_generative_AI.jsonl
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index 0000000000000000000000000000000000000000..9fe44a607a14a2b8c16a28230343131358545dd4
--- /dev/null
+++ b/data/sampled_jsons/computer_science_has_a_long_history_standards_measurement_Wallach_evaluating_generative_AI.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Shared Standard for Valid Measurement of Generative AI ...", "date": "", "ddg_snippet": "by A Chouldechova · 2024 · Cited by 5 — We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.01934", "content": "by A Chouldechova · 2024 · Cited by 5 — We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing."}
+{"idx": 1, "title": "Generative artificial intelligence: a systematic review and ...", "date": "", "ddg_snippet": "by SS Sengar · 2024 · Cited by 262 — This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI with a detailed discussion of ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11042-024-20016-1", "content": "by SS Sengar · 2024 · Cited by 262 — This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI with a detailed discussion of ..."}
+{"idx": 2, "title": "Toward an evaluation science for generative AI systems", "date": "", "ddg_snippet": "7 Mar 2025 — We present three key lessons: evaluation metrics must be applicable to real-world performance, metrics must be iteratively refined, and evaluation institutions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05336v1", "content": "7 Mar 2025 — We present three key lessons: evaluation metrics must be applicable to real-world performance, metrics must be iteratively refined, and evaluation institutions ..."}
+{"idx": 3, "title": "Benchmark suites instead of leaderboards for evaluating AI ...", "date": "", "ddg_snippet": "by A Wang · 2024 · Cited by 14 — Benchmarks and leaderboards are commonly used to track the fairness impacts of artificial intelligence ( AI ) models.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11573903/", "content": "by A Wang · 2024 · Cited by 14 — Benchmarks and leaderboards are commonly used to track the fairness impacts of artificial intelligence ( AI ) models."}
+{"idx": 4, "title": "Evaluating AI-Assisted Creative Ideation: A Crossover ...", "date": "", "ddg_snippet": "by R Baltà-Salvador · 2025 — This view advocates for educational strategies that help students develop divergent thinking and encourage creative exploration within ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S187118712500207X", "content": "by R Baltà-Salvador · 2025 — This view advocates for educational strategies that help students develop divergent thinking and encourage creative exploration within ..."}
+{"idx": 5, "title": "A Survey of AI-Generated Content (AIGC) - ACM Digital Library", "date": "", "ddg_snippet": "Generative models have a long history in artificial intelligence , dating back to the 1950s with the development of Hidden Markov Models (HMMs) [111] and ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3704262", "content": "Generative models have a long history in artificial intelligence , dating back to the 1950s with the development of Hidden Markov Models (HMMs) [111] and ..."}
+{"idx": 6, "title": "Towards Interactive Evaluations for Interaction Harms in ...", "date": "", "ddg_snippet": "23 Jun 2025 — Safety evaluations of generative AI systems build on a rich history ... Position: Evaluating Generative AI Systems is a Social Science Measurement ...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "23 Jun 2025 — Safety evaluations of generative AI systems build on a rich history ... Position: Evaluating Generative AI Systems is a Social Science Measurement ..."}
+{"idx": 7, "title": "Assessing deep learning: a work program for the ...", "date": "", "ddg_snippet": "by J Segessenmann · 2025 · Cited by 18 — Although the confusion of human beings with machines, and especially computers , has a long history [65,66,67,68], notable recent achievements in ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s43681-023-00408-z", "content": "by J Segessenmann · 2025 · Cited by 18 — Although the confusion of human beings with machines, and especially computers , has a long history [65,66,67,68], notable recent achievements in ..."}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 9, "title": "Unpacking the Gap in Human-Centered Evaluations of AI ...", "date": "", "ddg_snippet": "by A Khullar · 2025 — We argue for assessing broader achievements enabled through AI's use when conducting human-centered evaluations of AI .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3706598.3713278", "content": "by A Khullar · 2025 — We argue for assessing broader achievements enabled through AI's use when conducting human-centered evaluations of AI ."}
diff --git a/data/sampled_jsons/computer_vision_GUI_automation_text_to_action_mapping.jsonl b/data/sampled_jsons/computer_vision_GUI_automation_text_to_action_mapping.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..15191e16501d6c83ea2d7d3a17aae4fdbf8c871b
--- /dev/null
+++ b/data/sampled_jsons/computer_vision_GUI_automation_text_to_action_mapping.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF ShowUI: One Vision-Language-Action Model for GUI Visual Agent", "date": "", "ddg_snippet": "We introduced ShowUI, a vision -language- action model for GUI visual agents that addresses key challenges in UI visual and action modeling, and instruction-tuning data cu-rations.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_ShowUI_One_Vision-Language-Action_Model_for_GUI_Visual_Agent_CVPR_2025_paper.pdf", "content": "We introduced ShowUI, a vision -language- action model for GUI visual agents that addresses key challenges in UI visual and action modeling, and instruction-tuning data cu-rations."}
+{"idx": 1, "title": "OmniParser: Vision Based GUI Agent - LearnOpenCV", "date": "", "ddg_snippet": "The rapid advancement of Vision -Language Models (VLMs) has significantly improved the ability of AI systems to interact with graphical user interfaces ( GUIs ). However, existing models often struggle with action grounding. Models like GPT-4V cannot accurately map actions to specific UI elements across diverse applications and operating systems. To bridge this gap, Microsoft OmniParser ...", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/omniparser-vision-based-gui-agent/", "content": "The rapid advancement of Vision -Language Models (VLMs) has significantly improved the ability of AI systems to interact with graphical user interfaces ( GUIs ). However, existing models often struggle with action grounding. Models like GPT-4V cannot accurately map actions to specific UI elements across diverse applications and operating systems. To bridge this gap, Microsoft OmniParser ..."}
+{"idx": 2, "title": "ShowUI: Advanced Open-Source Vision-Language-Action Model for GUI", "date": "", "ddg_snippet": "Interleaved Vision -Language- Action Streaming: Different GUI actions across the platform are shown by organizing them in a JSON format, while providing documentation using the system prompt, which ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aimonks/showui-advanced-open-source-vision-language-action-model-for-gui-aea8c8f0223a", "content": "Interleaved Vision -Language- Action Streaming: Different GUI actions across the platform are shown by organizing them in a JSON format, while providing documentation using the system prompt, which ..."}
+{"idx": 3, "title": "Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction", "date": "", "ddg_snippet": "AGUVIS is a unified pure vision -based framework for autonomous GUI agents that can operate across various platforms (web, desktop, mobile). Unlike previous approaches that rely on textual representations, AGUVIS leverages unified purely vision -based observations and a consistent action space to ensure better generalization across different ...", "subpage_snippet": "", "source": "aguvis-project.github.io", "link": "https://aguvis-project.github.io/", "content": "AGUVIS is a unified pure vision -based framework for autonomous GUI agents that can operate across various platforms (web, desktop, mobile). Unlike previous approaches that rely on textual representations, AGUVIS leverages unified purely vision -based observations and a consistent action space to ensure better generalization across different ..."}
+{"idx": 4, "title": "ShowUI: A Vision-Language-Action Model for GUI Visual Agents that ...", "date": "", "ddg_snippet": "Early attempts at GUI automation focused on language-based agents that relied on closed-source, API-based Large Language Models like GPT-4. These initial approaches primarily utilized text -rich metadata such as HTML inputs and accessibility trees to perform navigation and related tasks.", "subpage_snippet": "", "source": "www.aiinteliigence.com", "link": "https://www.aiinteliigence.com/2024/12/02/showui-a-vision-language-action-model-for-gui-visual-agents-that-addresses-key-challenges-in-ui-visual-and-action-modeling/", "content": "Early attempts at GUI automation focused on language-based agents that relied on closed-source, API-based Large Language Models like GPT-4. These initial approaches primarily utilized text -rich metadata such as HTML inputs and accessibility trees to perform navigation and related tasks."}
+{"idx": 5, "title": "ShowUI: One Vision-Language-Action Model for GUI Visual Agent", "date": "", "ddg_snippet": "Early efforts in GUI automation have primarily focused on developing language agents [12, 47, 55] that rely on closed-source, API-based LLMs like GPT-4 [32]. These agents leverage text -rich metadata like HTML inputs or accessibility trees to perform navigation and other tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.17465v1", "content": "Early efforts in GUI automation have primarily focused on developing language agents [12, 47, 55] that rely on closed-source, API-based LLMs like GPT-4 [32]. These agents leverage text -rich metadata like HTML inputs or accessibility trees to perform navigation and other tasks."}
+{"idx": 6, "title": "ShowUI: One Vision-Language-Action Model for Generalist GUI Agent", "date": "", "ddg_snippet": "We developed ShowUI, a Vision -Language- Action (VLA) model for GUI automation across plat-forms such as websites, desktops, and mobile devices. We provide a clear pathway for building VLA models on top of existing VLMs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=UXdxYnkJtX", "content": "We developed ShowUI, a Vision -Language- Action (VLA) model for GUI automation across plat-forms such as websites, desktops, and mobile devices. We provide a clear pathway for building VLA models on top of existing VLMs."}
+{"idx": 7, "title": "PDF AssistGUI: Task-Oriented PC Graphical User Interface Automation", "date": "", "ddg_snippet": "Subsequent ablation analysis of different components within our agent framework revealed limitations of current methods when it comes to intricate GUI automation tasks. These insights lead us to suggest future directions for improvement in GUI understanding and action generation for PC GUI applications.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Gao_AssistGUI_Task-Oriented_PC_Graphical_User_Interface_Automation_CVPR_2024_paper.pdf", "content": "Subsequent ablation analysis of different components within our agent framework revealed limitations of current methods when it comes to intricate GUI automation tasks. These insights lead us to suggest future directions for improvement in GUI understanding and action generation for PC GUI applications."}
+{"idx": 8, "title": "GitHub - showlab/Awesome-GUI-Agent: A curated list of papers and ...", "date": "", "ddg_snippet": "💻 A curated list of papers and resources for multi-modal Graphical User Interface ( GUI ) agents. - showlab/Awesome- GUI -Agent", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/showlab/Awesome-GUI-Agent", "content": "💻 A curated list of papers and resources for multi-modal Graphical User Interface ( GUI ) agents. - showlab/Awesome- GUI -Agent"}
+{"idx": 9, "title": "GitHub - a-real-ai/pywinassistant: The first open-source Artificial ...", "date": "", "ddg_snippet": "By visualizing interface contents to dynamically simulate and plan actions over abstract GUI semantic dimensions, concepts, and differentials, PyWinAssistant redefines computer vision automation , enabling high-efficiency visual processing at a fraction of traditional computational costs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/a-real-ai/pywinassistant", "content": "By visualizing interface contents to dynamically simulate and plan actions over abstract GUI semantic dimensions, concepts, and differentials, PyWinAssistant redefines computer vision automation , enabling high-efficiency visual processing at a fraction of traditional computational costs."}
diff --git a/data/sampled_jsons/concept_bottleneck_model_limitation_foundation_model_dependency_2024_2025_year_2024.jsonl b/data/sampled_jsons/concept_bottleneck_model_limitation_foundation_model_dependency_2024_2025_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3f7388f660d595fb1b4a6667f3105f9f66e7518b
--- /dev/null
+++ b/data/sampled_jsons/concept_bottleneck_model_limitation_foundation_model_dependency_2024_2025_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "If Concept Bottlenecks are the Question, are Foundation ...", "date": "", "ddg_snippet": "Apr 28, 2025 · Concept Bottleneck Models (CBMs) are neural networks designed to conjoin high performance with ante-hoc interpretability. CBMs work by first mapping inputs (e.g., images) to high-level concepts (e.g., visible objects and their properties) and then use these to solve a downstream task (e.g., tagging or scoring an image) in an interpretable manner. Their performance and interpretability, however ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.19774", "content": "Apr 28, 2025 · Concept Bottleneck Models (CBMs) are neural networks designed to conjoin high performance with ante-hoc interpretability. CBMs work by first mapping inputs (e.g., images) to high-level concepts (e.g., visible objects and their properties) and then use these to solve a downstream task (e.g., tagging or scoring an image) in an interpretable manner. Their performance and interpretability, however ..."}
+{"idx": 1, "title": "Hybrid Concept Bottleneck Models - CVF Open Access", "date": "", "ddg_snippet": "In this work, we propose the Hybrid Concept Bottleneck Model (HybridCBM), specifically designed to address the challenges of incomplete concept representation, predefined concept bank dependence, and scalability issue by dynam-ically discovering new concepts directly from visual repre-sentations.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Hybrid_Concept_Bottleneck_Models_CVPR_2025_paper.pdf", "content": "In this work, we propose the Hybrid Concept Bottleneck Model (HybridCBM), specifically designed to address the challenges of incomplete concept representation, predefined concept bank dependence, and scalability issue by dynam-ically discovering new concepts directly from visual repre-sentations."}
+{"idx": 2, "title": "kkzhang95/Awesome_Concept_Bottleneck_Models - GitHub", "date": "", "ddg_snippet": "Awesome_ Concept _ Bottleneck _Models A comprehensive survey of Concept Bottleneck Models (CBM). CBMs typically involve a layer preceding the final fully connected classifier, where each neuron corresponds to a concept that can be interpreted by humans. CBMs also show advantages in improving accuracy through human intervention during testing.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kkzhang95/Awesome_Concept_Bottleneck_Models", "content": "Awesome_ Concept _ Bottleneck _Models A comprehensive survey of Concept Bottleneck Models (CBM). CBMs typically involve a layer preceding the final fully connected classifier, where each neuron corresponds to a concept that can be interpreted by humans. CBMs also show advantages in improving accuracy through human intervention during testing."}
+{"idx": 3, "title": "Selective Concept Bottleneck Models Without Predefined Concepts", "date": "", "ddg_snippet": "Toalleviatethis, recentmethodsutilizedlargelanguagemodelstoautomat- ically generate class-specific concept descriptions and learned mappings from a pretrained black-box model ’s raw features to these concepts using vision-language models. However, theseapproachesassumepriorknowledgeofwhichconceptstheblack-boxmodelhaslearned.", "subpage_snippet": "", "source": "lmb.informatik.uni-freiburg.de", "link": "https://lmb.informatik.uni-freiburg.de/Publications/2025/SAB25/paper-ucbm.pdf", "content": "Toalleviatethis, recentmethodsutilizedlargelanguagemodelstoautomat- ically generate class-specific concept descriptions and learned mappings from a pretrained black-box model ’s raw features to these concepts using vision-language models. However, theseapproachesassumepriorknowledgeofwhichconceptstheblack-boxmodelhaslearned."}
+{"idx": 4, "title": "CONDA: Adaptive Concept Bottleneck for Foundation Models ...", "date": "", "ddg_snippet": "Then we propose an adaptive concept bottleneck frameworkto address these failure modes, that dynamically adapts the concept -vector bankand the prediction layer based solely on unlabeled data from the target domain,without access to the source dataset.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/df334022279996b07e0870a629c18857-Abstract-Conference.html", "content": "Then we propose an adaptive concept bottleneck frameworkto address these failure modes, that dynamically adapts the concept -vector bankand the prediction layer based solely on unlabeled data from the target domain,without access to the source dataset."}
+{"idx": 5, "title": "Graph Concept Bottleneck Models - OpenReview", "date": "", "ddg_snippet": "Sep 25, 2024 · To mitigate this limitation , we propose **Graph CBMs**: a new variant of CBM that facilitates concept relationships by constructing latent concept graphs, which can be combined with CBMs to enhance model performance while retaining their interpretability.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qPH7lAyQgV", "content": "Sep 25, 2024 · To mitigate this limitation , we propose **Graph CBMs**: a new variant of CBM that facilitates concept relationships by constructing latent concept graphs, which can be combined with CBMs to enhance model performance while retaining their interpretability."}
+{"idx": 6, "title": "If Concept Bottlenecks are the Question, are Foundation ...", "date": "", "ddg_snippet": "[25] Jean Feng et al. Bayesian concept bottleneck models with llm priors. arXiv:2410.15555, 2024 . [26] Hidde Fokkema et al. Sample-efficient learning of concepts with theoretical guarantees: from data to concepts without interventions. arXiv:2502.06536, 2025 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.19774v2", "content": "[25] Jean Feng et al. Bayesian concept bottleneck models with llm priors. arXiv:2410.15555, 2024 . [26] Hidde Fokkema et al. Sample-efficient learning of concepts with theoretical guarantees: from data to concepts without interventions. arXiv:2502.06536, 2025 ."}
+{"idx": 7, "title": "[2405.15476] Editable Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a humanunderstandable concept layer.arXiv:2405.15476 (cs). [Submitted on 24 May 2024 (v1), last revised 1 Feb 2025 (this version, v3)].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.15476", "content": "Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a humanunderstandable concept layer.arXiv:2405.15476 (cs). [Submitted on 24 May 2024 (v1), last revised 1 Feb 2025 (this version, v3)]."}
+{"idx": 8, "title": "If Concept Bottlenecks are the Question, are Foundation Models the...", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) are neural networks designed to conjoin high performance with ante-hoc interpretability. CBMs work by first mapping inputs (e.g., images) to high-level concepts (e.g., visible objects and their properties) and then use these to solve a downstream task (e.g...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2504.19774", "content": "Concept Bottleneck Models (CBMs) are neural networks designed to conjoin high performance with ante-hoc interpretability. CBMs work by first mapping inputs (e.g., images) to high-level concepts (e.g., visible objects and their properties) and then use these to solve a downstream task (e.g..."}
+{"idx": 9, "title": "Bottleneck Calculator - Check CPU GPU Compatibility", "date": "", "ddg_snippet": "Find and fix CPU or GPU bottlenecks in your PC. Check if your PC will bottleneck , optimize performance and game frame rate.", "subpage_snippet": "", "source": "pc-builds.com", "link": "https://pc-builds.com/bottleneck-calculator/", "content": "Find and fix CPU or GPU bottlenecks in your PC. Check if your PC will bottleneck , optimize performance and game frame rate."}
diff --git a/data/sampled_jsons/concept_bottleneck_models_learn_concepts_without_annotations_year_2024.jsonl b/data/sampled_jsons/concept_bottleneck_models_learn_concepts_without_annotations_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8300a1ee07f06f9157eace5ed5b8d96f410ea61d
--- /dev/null
+++ b/data/sampled_jsons/concept_bottleneck_models_learn_concepts_without_annotations_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Concept Bottleneck Models Without Predefined Concepts", "date": "", "ddg_snippet": "However, these approaches predefine a set of concepts , assuming which concepts a black-box model encodes in its representations. In this work, we eliminate this assumption by leveraging unsupervised concept discovery to automatically extract concepts without human annotations or a predefined set of concepts .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.03921", "content": "However, these approaches predefine a set of concepts , assuming which concepts a black-box model encodes in its representations. In this work, we eliminate this assumption by leveraging unsupervised concept discovery to automatically extract concepts without human annotations or a predefined set of concepts ."}
+{"idx": 1, "title": "PDF Selective Concept Bottleneck Models Without Predefined Concepts", "date": "", "ddg_snippet": "These approaches alleviate the need for costly concept annotations by leveraging language models , like GPT-3 (Brown et al.,2020), to automatically generate class-specific descriptions and vision-language models,likeCLIP(Radfordetal.,2021),tolearnamappingfromablack-boxmodel'suninterpretablefea- tures to these concepts .", "subpage_snippet": "", "source": "lmb.informatik.uni-freiburg.de", "link": "https://lmb.informatik.uni-freiburg.de/Publications/2025/SAB25/paper-ucbm.pdf", "content": "These approaches alleviate the need for costly concept annotations by leveraging language models , like GPT-3 (Brown et al.,2020), to automatically generate class-specific descriptions and vision-language models,likeCLIP(Radfordetal.,2021),tolearnamappingfromablack-boxmodel'suninterpretablefea- tures to these concepts ."}
+{"idx": 2, "title": "Label-Free Concept Bottleneck Models (ICLR 2023): A New ... - Medium", "date": "", "ddg_snippet": "The Contribution of LF-CBMs The key contribution of the Label-Free Concept Bottleneck Model is its ability to learn concepts without requiring labeled annotations . This is achieved by leveraging ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ml_dl_explained/label-free-concept-bottleneck-models-iclr-2023-a-new-paradigm-for-explainable-ai-222336762fc7", "content": "The Contribution of LF-CBMs The key contribution of the Label-Free Concept Bottleneck Model is its ability to learn concepts without requiring labeled annotations . This is achieved by leveraging ..."}
+{"idx": 3, "title": "PDF Incremental Residual Concept Bottleneck Models - CVF Open Access", "date": "", "ddg_snippet": "Multimodal pre-trained models can match visual representations with textual concept em-beddings, allowing for obtaining the interpretable concept bottleneck without the expertise concept annotations . Re-cent research has focused on the concept bank establish-ment and the high-quality concept selection.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Shang_Incremental_Residual_Concept_Bottleneck_Models_CVPR_2024_paper.pdf", "content": "Multimodal pre-trained models can match visual representations with textual concept em-beddings, allowing for obtaining the interpretable concept bottleneck without the expertise concept annotations . Re-cent research has focused on the concept bank establish-ment and the high-quality concept selection."}
+{"idx": 4, "title": "PDF Concept Bottleneck Models", "date": "", "ddg_snippet": "We revisit the classic idea of first predicting concepts that are provided at train-ing time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a/koh20a.pdf", "content": "We revisit the classic idea of first predicting concepts that are provided at train-ing time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction."}
+{"idx": 5, "title": "Concept Bottleneck Models Without Predefined Concepts", "date": "", "ddg_snippet": "In this work, we eliminate this assumption by leveraging unsupervised concept discovery to automatically extract concepts without human annotations or a predefined set of concepts .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382064909_Concept_Bottleneck_Models_Without_Predefined_Concepts", "content": "In this work, we eliminate this assumption by leveraging unsupervised concept discovery to automatically extract concepts without human annotations or a predefined set of concepts ."}
+{"idx": 6, "title": "Selective Concept Bottleneck Models Without Predefined Concepts", "date": "", "ddg_snippet": "ABSTRACT Concept -based models like Concept Bottleneck Models (CBMs) have garnered significant interest for improving model interpretability by first predicting human-understandable concepts before mapping them to the output classes. Early ap-proaches required costly concept annotations .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=uuvujfQXZy", "content": "ABSTRACT Concept -based models like Concept Bottleneck Models (CBMs) have garnered significant interest for improving model interpretability by first predicting human-understandable concepts before mapping them to the output classes. Early ap-proaches required costly concept annotations ."}
+{"idx": 7, "title": "Selective Concept Bottleneck Models Without Predefined Concepts", "date": "", "ddg_snippet": "Concept -based models like Concept Bottleneck Models (CBMs) have garnered significant interest for improving model interpretability by first predicting human-understandable concepts before mapping them to the output classes. Early approaches required costly concept annotations .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Selective-Concept-Bottleneck-Models-Without-Schrodi-Schur/1ae701fedad1955b3e061bac42bc24c07bb670a6", "content": "Concept -based models like Concept Bottleneck Models (CBMs) have garnered significant interest for improving model interpretability by first predicting human-understandable concepts before mapping them to the output classes. Early approaches required costly concept annotations ."}
+{"idx": 8, "title": "Zero-shot Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "However, they re-quire target task training to learn input-to- concept and concept -to-label mappings, incurring target dataset collections and training resources. In this paper, we present zero-shot concept bottleneck models (Z-CBMs), which predict concepts and labels in a fully zero-shot manner without train-ing neural networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.09018", "content": "However, they re-quire target task training to learn input-to- concept and concept -to-label mappings, incurring target dataset collections and training resources. In this paper, we present zero-shot concept bottleneck models (Z-CBMs), which predict concepts and labels in a fully zero-shot manner without train-ing neural networks."}
+{"idx": 9, "title": "Concept Bottleneck Model With Additional Unsupervised Concepts", "date": "", "ddg_snippet": "By seamlessly training these two types of concepts while reducing the amount of computation, we can obtain both supervised and unsupervised concepts simultaneously, even for large-sized images. We refer to the proposed model as the concept bottleneck model with additional unsupervised concepts (CBM-AUC).", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9758745", "content": "By seamlessly training these two types of concepts while reducing the amount of computation, we can obtain both supervised and unsupervised concepts simultaneously, even for large-sized images. We refer to the proposed model as the concept bottleneck model with additional unsupervised concepts (CBM-AUC)."}
diff --git a/data/sampled_jsons/conf(i,k)_formula_distributed_machine_learning_adaptive_task_allocation.jsonl b/data/sampled_jsons/conf(i,k)_formula_distributed_machine_learning_adaptive_task_allocation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48ce286230c7fdb5d87d4cd8f2931722c10bf953
--- /dev/null
+++ b/data/sampled_jsons/conf(i,k)_formula_distributed_machine_learning_adaptive_task_allocation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "In this paper, we propose ATA( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "May 1, 2025 · The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "May 1, 2025 · The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times.", "subpage_snippet": "", "source": "repository.kaust.edu.sa", "link": "https://repository.kaust.edu.sa/bitstreams/884560fb-34b9-42e5-a5ee-88334b809ebb/download", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times."}
+{"idx": 3, "title": "Utilizing machine learning algorithms for task allocation in ...", "date": "", "ddg_snippet": "Nov 15, 2024 · The proposed method for task allocation aims to support task distribution in distributed software development. It examines aspects of the task allocation context to identify requirements for decision making.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2405844024159579", "content": "Nov 15, 2024 · The proposed method for task allocation aims to support task distribution in distributed software development. It examines aspects of the task allocation context to identify requirements for decision making."}
+{"idx": 4, "title": "Adaptive Task Allocation for Heterogeneous Multi-Robot Teams ...", "date": "", "ddg_snippet": "May 31, 2020 · For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9197283", "content": "May 31, 2020 · For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions ..."}
+{"idx": 5, "title": "Adaptive Allocation of Computing Resources for Multiple ...", "date": "", "ddg_snippet": "In such a situation, the whole hardware resources can be fully utilized for that task . However, in a real system, it is usual to have several learning tasks running in the same node. So in this paper, we propose an adaptive allocation of computing resources for multiple learning tasks , with the knowledge of current learning phase for each task .", "subpage_snippet": "", "source": "proceedings-of-deim.github.io", "link": "https://proceedings-of-deim.github.io/DEIM2021/papers/E14-4.pdf", "content": "In such a situation, the whole hardware resources can be fully utilized for that task . However, in a real system, it is usual to have several learning tasks running in the same node. So in this paper, we propose an adaptive allocation of computing resources for multiple learning tasks , with the knowledge of current learning phase for each task ."}
+{"idx": 6, "title": "[2502.00775] ATA: Adaptive Task Allocation for Efficient ...", "date": "", "ddg_snippet": "Feb 2, 2025 · Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "Feb 2, 2025 · Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ..."}
+{"idx": 7, "title": "The Importance of Adaptive Task Allocation in Distributed Robotics", "date": "", "ddg_snippet": "Learn about the significance of adaptive task allocation , a method that dynamically assigns tasks based on real-time data and environmental conditions.", "subpage_snippet": "", "source": "diversedaily.com", "link": "https://diversedaily.com/the-importance-of-adaptive-task-allocation-in-distributed-robotics/", "content": "Learn about the significance of adaptive task allocation , a method that dynamically assigns tasks based on real-time data and environmental conditions."}
+{"idx": 8, "title": "(PDF) Task Allocation for Asynchronous Mobile Edge Learning with...", "date": "", "ddg_snippet": "Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/346555823_Task_Allocation_for_Asynchronous_Mobile_Edge_Learning_with_Delay_and_Energy_Constraints", "content": "Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning ."}
+{"idx": 9, "title": "Edge Computing Solutions for Distributed Machine Learning (2022)", "date": "", "ddg_snippet": "In this paper, we analyze distributed machine learning algorithms and how they should be adapted to run at the network edge and, if needed, cooperate with the cloud to ensure low latency, energy savings, privacy preserving and scalability.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/edge-computing-solutions-for-distributed-machine-learning-2sdvvlai", "content": "In this paper, we analyze distributed machine learning algorithms and how they should be adapted to run at the network edge and, if needed, cooperate with the cloud to ensure low latency, energy savings, privacy preserving and scalability."}
diff --git a/data/sampled_jsons/consequential_validity_AI_evaluation_judge_LLM_measurements.jsonl b/data/sampled_jsons/consequential_validity_AI_evaluation_judge_LLM_measurements.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1bb7184f64e970bc4f59e36fc063628a21999128
--- /dev/null
+++ b/data/sampled_jsons/consequential_validity_AI_evaluation_judge_LLM_measurements.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How LLM -as-a- judge is Calculated - Galileo", "date": "", "ddg_snippet": "LLM -as- Judge Metrics use large language models as judges to assess AI system performance.This page includes: How LLM -as- judge evaluation works step-by-step. Different types of metrics and their applications.", "subpage_snippet": "", "source": "v2docs.galileo.ai", "link": "https://v2docs.galileo.ai/concepts/metrics/how-llm-as-judge-metrics-are-calculated", "content": "LLM -as- Judge Metrics use large language models as judges to assess AI system performance.This page includes: How LLM -as- judge evaluation works step-by-step. Different types of metrics and their applications."}
+{"idx": 1, "title": "LLM -as-a- Judge Simply Explained: The Complete... - Confident AI", "date": "", "ddg_snippet": "Using LLM Judges as Scorers in Metrics. LLM -as-a- judge can be used as an automated grader/scorer for your chosen evaluation metric. To get startedConfident AI : The DeepEval LLM Evaluation Platform.", "subpage_snippet": "", "source": "www.confident-ai.com", "link": "https://www.confident-ai.com/blog/why-llm-as-a-judge-is-the-best-llm-evaluation-method", "content": "Using LLM Judges as Scorers in Metrics. LLM -as-a- judge can be used as an automated grader/scorer for your chosen evaluation metric. To get startedConfident AI : The DeepEval LLM Evaluation Platform."}
+{"idx": 2, "title": "LLM -as-a- judge : a complete guide to using LLMs for evaluations", "date": "", "ddg_snippet": "LLM -as-a- judge is a common technique to evaluate LLM -powered products. In this guide, we’ll cover how it works, how to build an LLM evaluator and craft good prompts, and what are the alternatives to LLM evaluations .", "subpage_snippet": "", "source": "www.evidentlyai.com", "link": "https://www.evidentlyai.com/llm-guide/llm-as-a-judge", "content": "LLM -as-a- judge is a common technique to evaluate LLM -powered products. In this guide, we’ll cover how it works, how to build an LLM evaluator and craft good prompts, and what are the alternatives to LLM evaluations ."}
+{"idx": 3, "title": "How to Evaluate LLMs?. From Benchmarks to Business... | Medium", "date": "", "ddg_snippet": "Evaluating an LLM Means Evaluating Its Usage and Risks First. Evaluating AI systems requires considering the risks tied to their use — a core principle of the European AI Act [9]. This approach is independent of the technology itself.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/capgemini-invent-lab/how-to-evaluate-llms-d5f0ab5b35b0", "content": "Evaluating an LLM Means Evaluating Its Usage and Risks First. Evaluating AI systems requires considering the risks tied to their use — a core principle of the European AI Act [9]. This approach is independent of the technology itself."}
+{"idx": 4, "title": "LLM -as-a- Judge : Intro & Overview to LLM -Based App Evaluation", "date": "", "ddg_snippet": "LLM -as-a- judge stands as the most reliable option for now due to its scalability and consistency in the evaluation process, and is transforming how developers evaluate and refine their AI applications. To get started implementing LLM -as-a- Judge in your LLM evaluations , try Opik, Comet’s...", "subpage_snippet": "", "source": "www.comet.com", "link": "https://www.comet.com/site/blog/llm-as-a-judge/", "content": "LLM -as-a- judge stands as the most reliable option for now due to its scalability and consistency in the evaluation process, and is transforming how developers evaluate and refine their AI applications. To get started implementing LLM -as-a- Judge in your LLM evaluations , try Opik, Comet’s..."}
+{"idx": 5, "title": "What is AI -as-a- Judge and Why It Matters", "date": "", "ddg_snippet": "Why AI -as-a- Judge Matters. You may hesitate first to rely on an LLM to judge another model’s responses. After all, if the model can generate mistakes, why should it be trusted to identify them? The answer lies in how the evaluation is framed.", "subpage_snippet": "", "source": "www.hyperstack.cloud", "link": "https://www.hyperstack.cloud/blog/thought-leadership/what-is-ai-as-a-judge-and-why-it-matters", "content": "Why AI -as-a- Judge Matters. You may hesitate first to rely on an LLM to judge another model’s responses. After all, if the model can generate mistakes, why should it be trusted to identify them? The answer lies in how the evaluation is framed."}
+{"idx": 6, "title": "Evaluating the Effectiveness of LLM - Evaluators (aka LLM -as- Judge )", "date": "", "ddg_snippet": "Judging LLM -as-a- Judge with MT-Bench and Chatbot Arena evaluates the performance of strong LLMs, such as gpt-4, on evaluating chatbot responses to open-ended questions. The authors introduced two new benchmarks.", "subpage_snippet": "", "source": "eugeneyan.com", "link": "https://eugeneyan.com/writing/llm-evaluators/", "content": "Judging LLM -as-a- Judge with MT-Bench and Chatbot Arena evaluates the performance of strong LLMs, such as gpt-4, on evaluating chatbot responses to open-ended questions. The authors introduced two new benchmarks."}
+{"idx": 7, "title": "AI Evaluation Systems Are Measuring the Wrong Things", "date": "", "ddg_snippet": "The latest evolution in AI evaluation uses large language models to evaluate other LLMs, promising more nuanced assessment than traditional metrics. Popular platforms like Chatbot Arena, LMSys, and automated evaluation services have made LLM -as-a- judge approaches standard practice.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/ai-evaluation-systems-measuring-wrong-things-andrew-dillon-blykf", "content": "The latest evolution in AI evaluation uses large language models to evaluate other LLMs, promising more nuanced assessment than traditional metrics. Popular platforms like Chatbot Arena, LMSys, and automated evaluation services have made LLM -as-a- judge approaches standard practice."}
+{"idx": 8, "title": "Local LLM -as- judge evaluation with lm-buddy, Prometheus and...", "date": "", "ddg_snippet": "Prometheus evaluation as an LM Buddy job reads an input dataset that contains prompts to be evaluated , sends those prompts to the LLM judge served via vLLM, and outputs another version of the dataset which is augmented with Prometheus’ feedback and scores.", "subpage_snippet": "", "source": "blog.mozilla.ai", "link": "https://blog.mozilla.ai/local-llm-as-judge-evaluation-with-lm-buddy-prometheus-and-llamafile/", "content": "Prometheus evaluation as an LM Buddy job reads an input dataset that contains prompts to be evaluated , sends those prompts to the LLM judge served via vLLM, and outputs another version of the dataset which is augmented with Prometheus’ feedback and scores."}
+{"idx": 9, "title": "Evaluating LLM -Generated Versus Human-Authored Responses in...", "date": "", "ddg_snippet": "LLM -as-a- judge is a promising paradigm to simulate the depth and granularity of human evaluation Zheng et al. (2023) . This approach typically prompts an LLM to perform either point-wise scoring or pairwise comparisons Li et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.17694v1", "content": "LLM -as-a- judge is a promising paradigm to simulate the depth and granularity of human evaluation Zheng et al. (2023) . This approach typically prompts an LLM to perform either point-wise scoring or pairwise comparisons Li et al."}
diff --git a/data/sampled_jsons/contrastive_CRL_Gaussian_distribution_independent_Buchholz_et_al._Yao_et_al._smooth_distribution.jsonl b/data/sampled_jsons/contrastive_CRL_Gaussian_distribution_independent_Buchholz_et_al._Yao_et_al._smooth_distribution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b01fb5350efe2d13e9c05c7e899b6b8fdcdaf2f4
--- /dev/null
+++ b/data/sampled_jsons/contrastive_CRL_Gaussian_distribution_independent_Buchholz_et_al._Yao_et_al._smooth_distribution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "27 Feb 2025 — We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v1", "content": "27 Feb 2025 — We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work."}
+{"idx": 1, "title": "Score-based Causal Representation Learning: Linear and ...", "date": "", "ddg_snippet": "by B Varici · 2025 · Cited by 4 — The study in ( Buchholz et al ., 2023) focuses on linear Gaussian latent models and extends the results in (Squires et al ., 2023) to prove identifiability for ... 90 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume26/24-0194/24-0194.pdf", "content": "by B Varici · 2025 · Cited by 4 — The study in ( Buchholz et al ., 2023) focuses on linear Gaussian latent models and extends the results in (Squires et al ., 2023) to prove identifiability for ... 90 pages"}
+{"idx": 2, "title": "A Survey on Causal Generative Modeling", "date": "", "ddg_snippet": "23 May 2024 — Causal models offer several beneficial properties to deep generative models, such as distribution shift robustness, fairness, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.11011v2", "content": "23 May 2024 — Causal models offer several beneficial properties to deep generative models, such as distribution shift robustness, fairness, and ..."}
+{"idx": 3, "title": "NeurIPS Poster Learning Linear Causal Representations ...", "date": "", "ddg_snippet": "We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/70383", "content": "We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian ."}
+{"idx": 4, "title": "A Survey on Causal Generative Modeling", "date": "", "ddg_snippet": "by A Komanduri · Cited by 19 — Causal models offer several beneficial properties to deep generative models, such as distribution shift robustness, fairness, and interpretability (Scholkopf et ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=PUpZXvNqmb", "content": "by A Komanduri · Cited by 19 — Causal models offer several beneficial properties to deep generative models, such as distribution shift robustness, fairness, and interpretability (Scholkopf et ..."}
+{"idx": 5, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "As an underlying causal model, CCRL assumes a linear structural causal model (SCM) with additive Gaussian noise ( Buchholz et al ., 2023, Assumption 2). The ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "As an underlying causal model, CCRL assumes a linear structural causal model (SCM) with additive Gaussian noise ( Buchholz et al ., 2023, Assumption 2). The ..."}
+{"idx": 6, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "As an underlying causal model, CCRL as- sumes a linear structural causal model (SCM) with addi- tive Gaussian noise ( Buchholz et al ., 2023, Assumption 2).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f49eb15381f8ea150aedc07d26d294f4b7c91ada.pdf", "content": "As an underlying causal model, CCRL as- sumes a linear structural causal model (SCM) with addi- tive Gaussian noise ( Buchholz et al ., 2023, Assumption 2)."}
+{"idx": 7, "title": "Identifying Linearly-Mixed Causal Representations from Multi ...", "date": "", "ddg_snippet": "by S Bing · 2024 · Cited by 17 — Recent works that provide such identifiability guarantees either restrict the underlying structural causal model (Lachapelle et al ., 2022; Buchholz et al ., 2023 ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v236/bing24a/bing24a.pdf", "content": "by S Bing · 2024 · Cited by 17 — Recent works that provide such identifiability guarantees either restrict the underlying structural causal model (Lachapelle et al ., 2022; Buchholz et al ., 2023 ..."}
+{"idx": 8, "title": "Causal Representation Learning Made Identifiable by ...", "date": "", "ddg_snippet": "by H Morioka · Cited by 22 — A topic of great current interest is Causal Repre- sentation Learning ( CRL ), whose goal is to learn a causal model for hidden features in a data-driven.", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/morioka24a/morioka24a.pdf", "content": "by H Morioka · Cited by 22 — A topic of great current interest is Causal Repre- sentation Learning ( CRL ), whose goal is to learn a causal model for hidden features in a data-driven."}
+{"idx": 9, "title": "Causal Representation Learning from Multi-modal ...", "date": "", "ddg_snippet": "by Y Sun · 2025 · Cited by 6 — Recently, a growing body of CRL research has investigated multimodal distributions ( Yao et al ., 2023; Morioka & Hyvarinen, 2023; 2024; Daunhawer et al ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11952583/", "content": "by Y Sun · 2025 · Cited by 6 — Recently, a growing body of CRL research has investigated multimodal distributions ( Yao et al ., 2023; Morioka & Hyvarinen, 2023; 2024; Daunhawer et al ..."}
diff --git a/data/sampled_jsons/correlation_engagement_participation_rich_subreddits_Causal_Modeling_of_Climate_Activism_on_Reddit.jsonl b/data/sampled_jsons/correlation_engagement_participation_rich_subreddits_Causal_Modeling_of_Climate_Activism_on_Reddit.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9863c8b6db71b190a794db62cc3860adb1d1665d
--- /dev/null
+++ b/data/sampled_jsons/correlation_engagement_participation_rich_subreddits_Causal_Modeling_of_Climate_Activism_on_Reddit.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "between the engagement on Reddit and the participation in ‘female’ and ‘ rich ’ subreddits , respectively (Appendix E), and highlights the importance of developing a comprehensive causal model. All the other effects maintain the same signs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "between the engagement on Reddit and the participation in ‘female’ and ‘ rich ’ subreddits , respectively (Appendix E), and highlights the importance of developing a comprehensive causal model. All the other effects maintain the same signs."}
+{"idx": 1, "title": "(PDF) Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Conversely, research on public engagement with climate issues. has largely concentrated on external climate -related events and. their correlation with social media responses. participation in activist subreddits , we consider the overall Reddit . engagement of a user.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929273_Causal_Modeling_of_Climate_Activism_on_Reddit", "content": "Conversely, research on public engagement with climate issues. has largely concentrated on external climate -related events and. their correlation with social media responses. participation in activist subreddits , we consider the overall Reddit . engagement of a user."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — This effect is indeed confounded by the positive correlation of 0.38 and 0.42 between the engagement on Reddit and the participation in 'female' and 'rich'.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — This effect is indeed confounded by the positive correlation of 0.38 and 0.42 between the engagement on Reddit and the participation in 'female' and 'rich'."}
+{"idx": 3, "title": "Greater engagement with anti-masturbation groups linked to", "date": "", "ddg_snippet": "Welcome to r/science ! This is a heavily moderated subreddit in order to keep the discussion on science. ... contact the moderators of this subreddit ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/science/comments/11pny81/greater_engagement_with_antimasturbation_groups/", "content": "Welcome to r/science ! This is a heavily moderated subreddit in order to keep the discussion on science. ... contact the moderators of this subreddit ..."}
+{"idx": 4, "title": "Science AMA Series: Hi Reddit! We’re Dr Rhys Hester and Dr", "date": "", "ddg_snippet": "... global study finds that economic inequality on a social level cannot be explained by bad choices among the poor nor by good decisions among the rich .", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/science/comments/4ea9gm/science_ama_series_hi_reddit_were_dr_rhys_hester/", "content": "... global study finds that economic inequality on a social level cannot be explained by bad choices among the poor nor by good decisions among the rich ."}
+{"idx": 5, "title": "Kristina LERMAN | University of Southern California, Los", "date": "", "ddg_snippet": "The rich and dynamic information environment of social media provides researchers, policymakers, and entrepreneurs with opportunities to learn about ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Kristina-Lerman", "content": "The rich and dynamic information environment of social media provides researchers, policymakers, and entrepreneurs with opportunities to learn about ..."}
+{"idx": 6, "title": "Rada Mihalcea - ACL Anthology", "date": "", "ddg_snippet": "This paper introduces a causal formulation for bias measurement in generative language models . ... To improve LMM model performance on ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/r/rada-mihalcea/", "content": "This paper introduces a causal formulation for bias measurement in generative language models . ... To improve LMM model performance on ..."}
+{"idx": 7, "title": "tailcalled's Comments - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "It’s a fact about reality : there are truths of the matter about which “plans”—sequences of interventions on a causal model of the universe ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/users/tailcalled?show=comments&sort=top", "content": "It’s a fact about reality : there are truths of the matter about which “plans”—sequences of interventions on a causal model of the universe ..."}
+{"idx": 8, "title": "Estimating community feedback effect on topic choice in social", "date": "", "ddg_snippet": "... our work seeks to quantitatively model the role of social feedback for one particular type of behavior, specifically topic choice, there is rich and ...", "subpage_snippet": "", "source": "epjdatascience.springeropen.com", "link": "https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-020-00243-w", "content": "... our work seeks to quantitatively model the role of social feedback for one particular type of behavior, specifically topic choice, there is rich and ..."}
+{"idx": 9, "title": "Integrated or Segregated? User Behavior Change after", "date": "", "ddg_snippet": "... use a regression model to inspect the association between receiving a cross-party reply in r/news and participating more in out-/in-party subreddits ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.04923v1", "content": "... use a regression model to inspect the association between receiving a cross-party reply in r/news and participating more in out-/in-party subreddits ..."}
diff --git a/data/sampled_jsons/cost_function_hierarchical_overlapping_clustering_graph_siteacm.org_OR_siteieee.org_OR_sitespringer._year_2023.jsonl b/data/sampled_jsons/cost_function_hierarchical_overlapping_clustering_graph_siteacm.org_OR_siteieee.org_OR_sitespringer._year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..02dad1f8411f177dba4949e5abb0d6c69019301e
--- /dev/null
+++ b/data/sampled_jsons/cost_function_hierarchical_overlapping_clustering_graph_siteacm.org_OR_siteieee.org_OR_sitespringer._year_2023.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "Overlapping Hierarchical Clustering (OHC) | SpringerLink", "date": "", "ddg_snippet": "Apr 22, 2020 · In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-44584-3_21", "content": "Apr 22, 2020 · In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering."}
+{"idx": 1, "title": "Overlapping clustering: A review - IEEE Xplore", "date": "", "ddg_snippet": "Sep 1, 2016 · Data Clustering or unsupervised classification is one of the main research area in Data Mining. Partitioning Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard (crisp) partitioning techniques where each object is assigned to one cluster. Other algorithms utilise overlapping techniques where an object may belong to one or more clusters ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/7555988", "content": "Sep 1, 2016 · Data Clustering or unsupervised classification is one of the main research area in Data Mining. Partitioning Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard (crisp) partitioning techniques where each object is assigned to one cluster. Other algorithms utilise overlapping techniques where an object may belong to one or more clusters ..."}
+{"idx": 2, "title": "Finding Overlapping Clusters in a Highly Connected Graph from ...", "date": "", "ddg_snippet": "Dec 13, 2020 · The two popular quality graph metrics, conductance and coverage, are selected to evaluate the overlapping clustering. Two examples of highly connected graphs are chosen for demonstration. The proposed approach is able to discover overlapping clusters from a given specific difference density.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9378329", "content": "Dec 13, 2020 · The two popular quality graph metrics, conductance and coverage, are selected to evaluate the overlapping clustering. Two examples of highly connected graphs are chosen for demonstration. The proposed approach is able to discover overlapping clusters from a given specific difference density."}
+{"idx": 3, "title": "An Overlapping Clustering Approach with Correlation Weight", "date": "", "ddg_snippet": "Jun 22, 2017 · Thereafter researches on overlapping clustering are mostly on the basis of hierarchical model [3, 4] and graph theory [5, 6]. With the evolution of Internet and information technology, overlapping clustering has set off a research boom in academia once again.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-319-60837-2_49", "content": "Jun 22, 2017 · Thereafter researches on overlapping clustering are mostly on the basis of hierarchical model [3, 4] and graph theory [5, 6]. With the evolution of Internet and information technology, overlapping clustering has set off a research boom in academia once again."}
+{"idx": 4, "title": "Overlapping Hierarchical Clustering (OHC) - Springer", "date": "", "ddg_snippet": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and com-bines the advantages of hierarchies with the precision of a less arbitrary clustering.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-44584-3_21.pdf", "content": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and com-bines the advantages of hierarchies with the precision of a less arbitrary clustering."}
+{"idx": 5, "title": "Hierarchical Overlapping Clustering of Network Data Using Cut ...", "date": "", "ddg_snippet": "May 24, 2017 · We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric. Furthermore, the so-called overlapping function is presented as a tool for gaining insights about the data by identifying meaningful resolutions of the obtained hierarchical structure.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/7933249", "content": "May 24, 2017 · We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric. Furthermore, the so-called overlapping function is presented as a tool for gaining insights about the data by identifying meaningful resolutions of the obtained hierarchical structure."}
+{"idx": 6, "title": "Non-Exhaustive, Overlapping Clustering - IEEE Xplore", "date": "", "ddg_snippet": "Aug 6, 2018 · Traditional clustering algorithms, such as K-Means, output a clustering that is disjoint and exhaustive, i.e., every single data point is assigned to exactly one cluster. However, in many real-world datasets, clusters can overlap and there are often outliers that do not belong to any cluster. While this is a well-recognized problem, most existing algorithms address either overlap or outlier ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/8425790", "content": "Aug 6, 2018 · Traditional clustering algorithms, such as K-Means, output a clustering that is disjoint and exhaustive, i.e., every single data point is assigned to exactly one cluster. However, in many real-world datasets, clusters can overlap and there are often outliers that do not belong to any cluster. While this is a well-recognized problem, most existing algorithms address either overlap or outlier ..."}
diff --git a/data/sampled_jsons/crowdsourced_action_sequences_Mind2Web_year_2023.jsonl b/data/sampled_jsons/crowdsourced_action_sequences_Mind2Web_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..27b320b65842402a4414ff7983bd0bb4dc86adfc
--- /dev/null
+++ b/data/sampled_jsons/crowdsourced_action_sequences_Mind2Web_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ShowUI : Un modèle Vision-Langage- Action pour un Agent Visuel...", "date": "", "ddg_snippet": "ShowUI: One Vision-Language- Action Model for GUI Visual Agent.Navigation experiments across web Mind 2 Web , mobile AITW, and online MiniWob environments further underscore the effectiveness and potential of our model in advancing GUI visual agents.", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/fr/dashboard/paper/16120350-bd74-462b-b603-e7e733402c4e", "content": "ShowUI: One Vision-Language- Action Model for GUI Visual Agent.Navigation experiments across web Mind 2 Web , mobile AITW, and online MiniWob environments further underscore the effectiveness and potential of our model in advancing GUI visual agents."}
+{"idx": 1, "title": "Large Action Models: From Inception to Implementation", "date": "", "ddg_snippet": "Mind 2 Web (Deng et al., 2024) is the first dataset developed for web agents that follow natural language instructions to complete complex tasks across diverse websites. It includes task descriptions, action sequences , and webpage snapshots, offering rich data for training and testing...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=bYdKtf0Q31", "content": "Mind 2 Web (Deng et al., 2024) is the first dataset developed for web agents that follow natural language instructions to complete complex tasks across diverse websites. It includes task descriptions, action sequences , and webpage snapshots, offering rich data for training and testing..."}
+{"idx": 2, "title": "Manus: General AI agent that bridges mind and action", "date": "", "ddg_snippet": "Manus is a general AI agent that turns your thoughts into actions . It excels at various tasks in work and life, getting everything done while you rest.", "subpage_snippet": "", "source": "manus.im", "link": "https://manus.im/", "content": "Manus is a general AI agent that turns your thoughts into actions . It excels at various tasks in work and life, getting everything done while you rest."}
+{"idx": 3, "title": "The Importance of Contextualization of Crowdsourced Active Speed...", "date": "", "ddg_snippet": "Crowdsourced active network measurements have emerged as a powerful tool to map fixed broadband access more accurately. These “speed tests” provide a critical snapshot of the network state from the vantage point of the end users.", "subpage_snippet": "", "source": "sites.cs.ucsb.edu", "link": "https://sites.cs.ucsb.edu/~arpitgupta/pdfs/speedtest.pdf", "content": "Crowdsourced active network measurements have emerged as a powerful tool to map fixed broadband access more accurately. These “speed tests” provide a critical snapshot of the network state from the vantage point of the end users."}
+{"idx": 4, "title": "Sharing Llama-3-8B-Web, an action model designed for browsing the...", "date": "", "ddg_snippet": "The site owner hides the web page description.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/1caw3ad/sharing_llama38bweb_an_action_model_designed_for/", "content": "The site owner hides the web page description."}
+{"idx": 5, "title": "OSU-NLP-Group/ Mind 2 Web - Githubissues", "date": "", "ddg_snippet": "OSU-NLP-Group / Mind 2 Web . [NeurIPS'23 Spotlight] \" Mind 2 Web : Towards a Generalist Agent for the Web\".You will see two files: results_*.json with the evaluation metrics, and scores_*.pkl with the prediction scores which can be used for the action prediction module.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/OSU-NLP-Group/Mind2Web/readme", "content": "OSU-NLP-Group / Mind 2 Web . [NeurIPS'23 Spotlight] \" Mind 2 Web : Towards a Generalist Agent for the Web\".You will see two files: results_*.json with the evaluation metrics, and scores_*.pkl with the prediction scores which can be used for the action prediction module."}
+{"idx": 6, "title": "The Hidden Dangers of Browsing AI Agents", "date": "", "ddg_snippet": "Mind 2 Web is an offline dataset of web tasks (compiled as textual descriptions and human action traces) on which agents are evaluated by how well they can follow a “golden” action sequence for each task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.13076", "content": "Mind 2 Web is an offline dataset of web tasks (compiled as textual descriptions and human action traces) on which agents are evaluated by how well they can follow a “golden” action sequence for each task."}
+{"idx": 7, "title": "BLAST: Basic Local Alignment Search Tool", "date": "", "ddg_snippet": "The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance of matches. BLAST can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families.", "subpage_snippet": "", "source": "blast.ncbi.nlm.nih.gov", "link": "https://blast.ncbi.nlm.nih.gov/", "content": "The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance of matches. BLAST can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families."}
+{"idx": 8, "title": "Microsoft releases Magma multimodal AI agent base model with UI and...", "date": "", "ddg_snippet": "Magma's core technology is Set-of-Mark (SoM), which allows artificial intelligence to accurately understand the interactive elements in the image by marking operable objects, such as UI buttons or robotic arms, and then make appropriate actions .", "subpage_snippet": "", "source": "inf.news", "link": "https://inf.news/en/tech/7a6b5c3fdb0657c6850479f29aab1f75.html", "content": "Magma's core technology is Set-of-Mark (SoM), which allows artificial intelligence to accurately understand the interactive elements in the image by marking operable objects, such as UI buttons or robotic arms, and then make appropriate actions ."}
+{"idx": 9, "title": "lumos Understanding processing of Mind 2 Web dataset for Lumos...", "date": "", "ddg_snippet": "For my work I need a mapping of the Lumos grounding steps (that is the user msgs in the Lumos dataset) to the html_source code found in Mind 2 Web . Happy to receive and guidance or advice and thanks for the great open-source work!", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/allenai/lumos/issues/5", "content": "For my work I need a mapping of the Lumos grounding steps (that is the user msgs in the Lumos dataset) to the html_source code found in Mind 2 Web . Happy to receive and guidance or advice and thanks for the great open-source work!"}
diff --git a/data/sampled_jsons/currying_Gaussian_processes_before2024.jsonl b/data/sampled_jsons/currying_Gaussian_processes_before2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ef1a97b59afc6f767d37fa52dccfb1a45637ca13
--- /dev/null
+++ b/data/sampled_jsons/currying_Gaussian_processes_before2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be interpreted as a probabilistic version of the concept of currying from functional programming, yielding a function-valued ( Gaussian )...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be interpreted as a probabilistic version of the concept of currying from functional programming, yielding a function-valued ( Gaussian )..."}
+{"idx": 1, "title": "(PDF) Linearization Turns Neural Operators into Function-Valued...", "date": "", "ddg_snippet": "June 2024 . currying to construct function-valued Gaussian processes from neural operators with Gaussian weight. posteriors. We discuss related work in Section 4 and showcase the efficacy on different PDE datasets.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes", "content": "June 2024 . currying to construct function-valued Gaussian processes from neural operators with Gaussian weight. posteriors. We discuss related work in Section 4 and showcase the efficacy on different PDE datasets."}
+{"idx": 2, "title": "A Tour of TensorFlow Probability - Colab", "date": "", "ddg_snippet": "Gaussian Processes . ↳ 2 celdas ocultas. kernel = tfp.math.psd_kernels.ExponentiatedQuadratic().tf.reduce_sum(rv_y.log_prob(y), axis=-1)). # Create our unnormalized target density by currying x and y from the joint. def unnormalized_posterior(w): return joint_log_prob(w, xs, ys).", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/A_Tour_of_TensorFlow_Probability.ipynb?hl=es", "content": "Gaussian Processes . ↳ 2 celdas ocultas. kernel = tfp.math.psd_kernels.ExponentiatedQuadratic().tf.reduce_sum(rv_y.log_prob(y), axis=-1)). # Create our unnormalized target density by currying x and y from the joint. def unnormalized_posterior(w): return joint_log_prob(w, xs, ys)."}
+{"idx": 3, "title": "Modeling the COVID-19 incorporating oil futures | Research", "date": "", "ddg_snippet": "Furthermore, Matveeva and Leonenko [ 13 ] utilized a Gaussian process in a regression model to simulate the COVID-19 spread, highlighting in this way ...", "subpage_snippet": "", "source": "www.researchsquare.com", "link": "https://www.researchsquare.com/article/rs-4997929/v1", "content": "Furthermore, Matveeva and Leonenko [ 13 ] utilized a Gaussian process in a regression model to simulate the COVID-19 spread, highlighting in this way ..."}
+{"idx": 4, "title": "Grand Design Spiral Arms in the Compact, Embedded", "date": "", "ddg_snippet": "Spiral arms observed in one tracer do not necessarily have counterparts in others, suggesting that multiple spiral arm formation processes may be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06433v1", "content": "Spiral arms observed in one tracer do not necessarily have counterparts in others, suggesting that multiple spiral arm formation processes may be ..."}
+{"idx": 5, "title": "Start from the End: A Framework for Computational Policy", "date": "", "ddg_snippet": "Before , during, and after waves of epidemic spread, policymakers and stakeholders have used mathematical models of disease transmission to forecast ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10870v1", "content": "Before , during, and after waves of epidemic spread, policymakers and stakeholders have used mathematical models of disease transmission to forecast ..."}
+{"idx": 6, "title": "David Duvenaud", "date": "", "ddg_snippet": "... Neural Information Processing Systems , 2024 ... Bowman, Ethan Perez, Roger Grosse, David Duvenaud Neural Information Processing Systems , 2024 .", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "http://www.cs.toronto.edu/~duvenaud/", "content": "... Neural Information Processing Systems , 2024 ... Bowman, Ethan Perez, Roger Grosse, David Duvenaud Neural Information Processing Systems , 2024 ."}
+{"idx": 7, "title": "David Duvenaud", "date": "", "ddg_snippet": "... Neural Information Processing Systems , 2024 ... Bowman, Ethan Perez, Roger Grosse, David Duvenaud Neural Information Processing Systems , 2024 .", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~duvenaud/", "content": "... Neural Information Processing Systems , 2024 ... Bowman, Ethan Perez, Roger Grosse, David Duvenaud Neural Information Processing Systems , 2024 ."}
+{"idx": 8, "title": "react hooks - Typescript generic parameter typing for curried", "date": "", "ddg_snippet": "Trying to type a curried function which takes a functor first and then an object ... Why Gaussian Process Regression (GPR) is non-parametric?", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/70431363/typescript-generic-parameter-typing-for-curried-function", "content": "Trying to type a curried function which takes a functor first and then an object ... Why Gaussian Process Regression (GPR) is non-parametric?"}
+{"idx": 9, "title": "ASCL.net - Browsing Codes", "date": "", "ddg_snippet": "Juliet also fits transiting and non-transiting multi-planetary systems, and Gaussian Processes (GPs) which might share hyperparameters between the ...", "subpage_snippet": "", "source": "ascl.net", "link": "http://ascl.net/code/all/page/8/limit/250/order/title/listmode/full/dir/asc", "content": "Juliet also fits transiting and non-transiting multi-planetary systems, and Gaussian Processes (GPs) which might share hyperparameters between the ..."}
diff --git a/data/sampled_jsons/currying_functional_programming_Gaussian_processes_neural_operators.jsonl b/data/sampled_jsons/currying_functional_programming_Gaussian_processes_neural_operators.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0ba492e5125ba24b43230de025e38aaea83399cb
--- /dev/null
+++ b/data/sampled_jsons/currying_functional_programming_Gaussian_processes_neural_operators.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gaussian process - Wikipedia", "date": "", "ddg_snippet": "In probability theory and statistics, a Gaussian process is a stochastic process , such that every finite collection of those random variables has a multivariate normal distribution. The distribution of a Gaussian process is the joint distribution of ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Gaussian_process", "content": "In probability theory and statistics, a Gaussian process is a stochastic process , such that every finite collection of those random variables has a multivariate normal distribution. The distribution of a Gaussian process is the joint distribution of ..."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function -Valued Gaussian ...", "date": "", "ddg_snippet": "We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function -valued Gaussian processes . Our approach can be interpreted as a probabilistic analogue of the concept of currying from functional programming and...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2406.05072", "content": "We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function -valued Gaussian processes . Our approach can be interpreted as a probabilistic analogue of the concept of currying from functional programming and..."}
+{"idx": 2, "title": "Linearization Turns Neural Operators into Function -Valued Gaussian ...", "date": "", "ddg_snippet": "Neural operators generalize neural networks to learn mappings between function spaces from data.We show that this can be interpreted as a probabilistic version of the concept of currying from functional programming , yielding a function -valued ( Gaussian ) random process belief.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Linearization-Turns-Neural-Operators-into-Function-Valued-Gaussian-Processes-ce500c51-36f5-4336-a4a2-f8d35011ebeb", "content": "Neural operators generalize neural networks to learn mappings between function spaces from data.We show that this can be interpreted as a probabilistic version of the concept of currying from functional programming , yielding a function -valued ( Gaussian ) random process belief."}
+{"idx": 3, "title": "Neural Diffusion Processes -Bohrium", "date": "", "ddg_snippet": "We propose Neural Diffusion Processes (NDPs), a novel approach based upon diffusion models, that learn to sample from distributions over functions .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/neural-diffusion-processes/867746826638327819-108580", "content": "We propose Neural Diffusion Processes (NDPs), a novel approach based upon diffusion models, that learn to sample from distributions over functions ."}
+{"idx": 4, "title": "Deep Gaussian Process Regression | Restackio", "date": "", "ddg_snippet": "Explore deep Gaussian process regression techniques in neural networks, enhancing predictive modeling and uncertainty quantification. |", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/neural-networks-answer-deep-gaussian-process-regression-cat-ai", "content": "Explore deep Gaussian process regression techniques in neural networks, enhancing predictive modeling and uncertainty quantification. |"}
+{"idx": 5, "title": "From Gaussian Processes to Neural Processes | Medium", "date": "", "ddg_snippet": "Neural Processes (NPs) are a generalization of Gaussian Processes (GPs) in the sense that they aim to retain the flexibility and uncertainty modeling of GPs while leveraging the scalability and representation power of neural networks.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@noraveshfarshad/from-gaussian-processes-to-neural-processes-ed822b7e5d71", "content": "Neural Processes (NPs) are a generalization of Gaussian Processes (GPs) in the sense that they aim to retain the flexibility and uncertainty modeling of GPs while leveraging the scalability and representation power of neural networks."}
+{"idx": 6, "title": "Currying and Partial Application in Python... | Software Patterns Lexicon", "date": "", "ddg_snippet": "Currying is a fundamental concept in functional programming languages like Haskell, where functions are curried by default. Defining Partial Application.", "subpage_snippet": "", "source": "softwarepatternslexicon.com", "link": "https://softwarepatternslexicon.com/patterns-python/8/2/", "content": "Currying is a fundamental concept in functional programming languages like Haskell, where functions are curried by default. Defining Partial Application."}
+{"idx": 7, "title": "Bayes functional regression — The Dan MacKinlay stable of...", "date": "", "ddg_snippet": "2019. “The Functional Neural Process .” In Advances in Neural Information Processing Systems. Magnani, Krämer, Eschenhagen, et al. 2022. “Approximate Bayesian Neural Operators : Uncertainty Quantification for Parametric PDEs.” Mosegaard, and Tarantola.", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/bayes_functional_regression", "content": "2019. “The Functional Neural Process .” In Advances in Neural Information Processing Systems. Magnani, Krämer, Eschenhagen, et al. 2022. “Approximate Bayesian Neural Operators : Uncertainty Quantification for Parametric PDEs.” Mosegaard, and Tarantola."}
+{"idx": 8, "title": "Simplifying Neural Network Analysis with Gaussian Mixture Models", "date": "", "ddg_snippet": "#Understanding Neural Networks and Gaussian Processes . Neural networks are constructed with layers of interconnected nodes or neurons . Each neuron processes input data and passes it on to the next layer.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-25-simplifying-neural-network-analysis-with-gaussian-mixture-models--a3d2mq2", "content": "#Understanding Neural Networks and Gaussian Processes . Neural networks are constructed with layers of interconnected nodes or neurons . Each neuron processes input data and passes it on to the next layer."}
+{"idx": 9, "title": "(PDF) Localized Physics-informed Gaussian Processes with...", "date": "", "ddg_snippet": "PDF | We introduce a simultaneous and meshfree topology optimization (TO) framework based on physics-informed Gaussian processes (GPs).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390038509_Localized_Physics-informed_Gaussian_Processes_with_Curriculum_Training_for_Topology_Optimization", "content": "PDF | We introduce a simultaneous and meshfree topology optimization (TO) framework based on physics-informed Gaussian processes (GPs)."}
diff --git a/data/sampled_jsons/curse_of_dimensionality_in_machine_learning.jsonl b/data/sampled_jsons/curse_of_dimensionality_in_machine_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..34aa893bd2f7ea4a9a5f8a80417dfe4c4824b190
--- /dev/null
+++ b/data/sampled_jsons/curse_of_dimensionality_in_machine_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Curse of dimensionality - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high- dimensional spaces that do not occur in low- dimensional settings such as the three- dimensional phy...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Curse_of_dimensionality", "content": "Machine learningand data mining. v. t. e. The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high- dimensional spaces that do not occur in low- dimensional settings such as the three- dimensional phy..."}
+{"idx": 1, "title": "Curse of Dimensionality in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Curse of Dimensionality significantly impacts machine learning algorithms in various ways. It leads to increased computational complexity, longer training times, and higher resource requirements.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/curse-of-dimensionality-in-machine-learning/", "content": "Curse of Dimensionality significantly impacts machine learning algorithms in various ways. It leads to increased computational complexity, longer training times, and higher resource requirements."}
+{"idx": 2, "title": "What is Curse of Dimensionality ?| Machine Learning ... | Medium", "date": "", "ddg_snippet": "curse of dimensionality . Visualizing one hundred dimensional space is incredibly difficult for humans. In addition to being hard to visualize, there are at least two additional problems in high dimensions, both refered to as the curse of dimensionality .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@gokcenazakyol/what-is-curse-of-dimensionality-machine-learning-2-739131962faf", "content": "curse of dimensionality . Visualizing one hundred dimensional space is incredibly difficult for humans. In addition to being hard to visualize, there are at least two additional problems in high dimensions, both refered to as the curse of dimensionality ."}
+{"idx": 3, "title": "The Curse of Dimensionality : Why More Features Can Hurt Mach", "date": "", "ddg_snippet": "Understanding the curse of dimensionality is crucial for data scientists, machine learning engineers, and AI practitioners. In this article, we willThe curse of dimensionality describes problems that arise when working with high- dimensional data. As the number of features increases", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/curse-dimensionality-why-more-features-arent-always-better-ahamed-gpbie", "content": "Understanding the curse of dimensionality is crucial for data scientists, machine learning engineers, and AI practitioners. In this article, we willThe curse of dimensionality describes problems that arise when working with high- dimensional data. As the number of features increases"}
+{"idx": 4, "title": "What is Curse of Dimensionality in Machine Learning ?", "date": "", "ddg_snippet": "In Machine Learning , a marginal increase in dimensionality also requires a large increase in the volume in the data in order to maintain the same level of performance. The curse of dimensionality is the by-product of a phenomenon which appears with high- dimensional data.", "subpage_snippet": "", "source": "www.mygreatlearning.com", "link": "https://www.mygreatlearning.com/blog/understanding-curse-of-dimensionality/", "content": "In Machine Learning , a marginal increase in dimensionality also requires a large increase in the volume in the data in order to maintain the same level of performance. The curse of dimensionality is the by-product of a phenomenon which appears with high- dimensional data."}
+{"idx": 5, "title": "Explain Curse of Dimensionality in Machine Learning - Dive Deep", "date": "", "ddg_snippet": "In machine learning , understanding tricky concepts like the \" Curse of Dimensionality \" is important.As well as in this guide, we will also explain the curse of dimensionality in machine learning , why it matters, and how to deal with it.", "subpage_snippet": "", "source": "www.theiotacademy.co", "link": "https://www.theiotacademy.co/blog/curse-of-dimensionality-in-machine-learning/", "content": "In machine learning , understanding tricky concepts like the \" Curse of Dimensionality \" is important.As well as in this guide, we will also explain the curse of dimensionality in machine learning , why it matters, and how to deal with it."}
+{"idx": 6, "title": "Curse of Dimensionality in Machine Learning - Analytics Vidhya", "date": "", "ddg_snippet": "In high machine learning curse of dimensionality data in machine learning spaces, whenever the distance of any pair of points is the same as any other pair of points, any machine learning model like KNN which depends a lot on Euclidean distance, makes no more sense logically.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2021/04/the-curse-of-dimensionality-in-machine-learning/", "content": "In high machine learning curse of dimensionality data in machine learning spaces, whenever the distance of any pair of points is the same as any other pair of points, any machine learning model like KNN which depends a lot on Euclidean distance, makes no more sense logically."}
+{"idx": 7, "title": "The Curse of Dimensionality in Machine Learning", "date": "", "ddg_snippet": "The \" curse of dimensionality \" is a challenge in machine learning that occurs when we have too many characteristics (or \"dimensions\") to consider. Here's why it's trickyHow the Curse of Dimensionality Affects Machine Learning .", "subpage_snippet": "", "source": "zilliz.com", "link": "https://zilliz.com/glossary/curse-of-dimensionality-in-machine-learning", "content": "The \" curse of dimensionality \" is a challenge in machine learning that occurs when we have too many characteristics (or \"dimensions\") to consider. Here's why it's trickyHow the Curse of Dimensionality Affects Machine Learning ."}
+{"idx": 8, "title": "Curse of Dimensionality in Machine Learning : A Complete Guide", "date": "", "ddg_snippet": "The Curse of Dimensionality refers to the challenges that arise when dealing with high- dimensional datasets in machine learning . As the number of features (dimensions) increases, the volume of the space grows exponentially, causing data points to become sparse.", "subpage_snippet": "", "source": "www.upgrad.com", "link": "https://www.upgrad.com/blog/curse-of-dimensionality-in-machine-learning-how-to-solve-the-curse/", "content": "The Curse of Dimensionality refers to the challenges that arise when dealing with high- dimensional datasets in machine learning . As the number of features (dimensions) increases, the volume of the space grows exponentially, causing data points to become sparse."}
+{"idx": 9, "title": "What is Curse of Dimensionality ? A Complete Guide | Built In", "date": "", "ddg_snippet": "What Is the Curse of Dimensionality ? Machine learning excels at analyzing data with many dimensions, but it becomes more challenging to create meaningful models as the number of dimensions increase. In machine learning , we often have high- dimensional data.", "subpage_snippet": "", "source": "builtin.com", "link": "https://builtin.com/data-science/curse-dimensionality", "content": "What Is the Curse of Dimensionality ? Machine learning excels at analyzing data with many dimensions, but it becomes more challenging to create meaningful models as the number of dimensions increase. In machine learning , we often have high- dimensional data."}
diff --git a/data/sampled_jsons/curse_of_dimensionality_machine_learning_high_dimensional_features_performance_degradation.jsonl b/data/sampled_jsons/curse_of_dimensionality_machine_learning_high_dimensional_features_performance_degradation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..410cc7197ee07cbed4d1b416d590990e16857ebe
--- /dev/null
+++ b/data/sampled_jsons/curse_of_dimensionality_machine_learning_high_dimensional_features_performance_degradation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Curse of Dimensionality in Machine Learning: Challenges ...", "date": "", "ddg_snippet": "Sep 13, 2023 · Explore The Curse of Dimensionality in data analysis and machine learning , including its challenges, effects on algorithms, and techniques like PCA, LDA, and t-SNE to combat it.", "subpage_snippet": "", "source": "www.datacamp.com", "link": "https://www.datacamp.com/blog/curse-of-dimensionality-machine-learning", "content": "Sep 13, 2023 · Explore The Curse of Dimensionality in data analysis and machine learning , including its challenges, effects on algorithms, and techniques like PCA, LDA, and t-SNE to combat it."}
+{"idx": 1, "title": "Curse of Dimensionality in Machine Learning - Analytics Vidhya", "date": "", "ddg_snippet": "Nov 14, 2024 · In summary, the Curse of Dimensionality in Machine Learning highlights challenges when dealing with high - dimensional data. It affects diverse domains, increasing computational demands and reducing model performance.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2021/04/the-curse-of-dimensionality-in-machine-learning/", "content": "Nov 14, 2024 · In summary, the Curse of Dimensionality in Machine Learning highlights challenges when dealing with high - dimensional data. It affects diverse domains, increasing computational demands and reducing model performance."}
+{"idx": 2, "title": "Curse of Dimensionality in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 23, 2025 · Curse of Dimensionality refers to the phenomenon where the efficiency and effectiveness of algorithms deteriorate as the dimensionality of the data increases exponentially.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/curse-of-dimensionality-in-machine-learning/", "content": "Jul 23, 2025 · Curse of Dimensionality refers to the phenomenon where the efficiency and effectiveness of algorithms deteriorate as the dimensionality of the data increases exponentially."}
+{"idx": 3, "title": "Curse of Dimensionality in Machine Learning - ML Journey", "date": "", "ddg_snippet": "Apr 3, 2025 · In machine learning, the term “curse of dimensionality” refers to the challenges that arise when working with high-dimensional data . As the number of features (dimensions) increases, models often face increased computational complexity, sparsity issues, and degraded performance.", "subpage_snippet": "", "source": "mljourney.com", "link": "https://mljourney.com/curse-of-dimensionality-in-machine-learning/", "content": "Apr 3, 2025 · In machine learning, the term “curse of dimensionality” refers to the challenges that arise when working with high-dimensional data . As the number of features (dimensions) increases, models often face increased computational complexity, sparsity issues, and degraded performance."}
+{"idx": 4, "title": "Why Does Machine Learning Fail in High Dimensions? The Curse ...", "date": "", "ddg_snippet": "Discover the hidden challenges of high - dimensional data in machine learning . This post unravels the mystery of the curse of dimensionality , showing how it impacts model accuracy and data requirements.", "subpage_snippet": "", "source": "tomsomoza.substack.com", "link": "https://tomsomoza.substack.com/p/the-curse-of-dimensionality", "content": "Discover the hidden challenges of high - dimensional data in machine learning . This post unravels the mystery of the curse of dimensionality , showing how it impacts model accuracy and data requirements."}
+{"idx": 5, "title": "Curse of Dimensionality: Challenges & Solutions in High ...", "date": "", "ddg_snippet": "Nov 27, 2024 · High - dimensional data presents unique challenges that impact the performance and efficiency of machine learning models. Here are three primary issues associated with high - dimensional data: increased data sparsity, computational complexity, and the risk of overfitting and poor generalization.", "subpage_snippet": "", "source": "www.statology.org", "link": "https://www.statology.org/curse-of-dimensionality-challenges-solutions-high-dimensional-data/", "content": "Nov 27, 2024 · High - dimensional data presents unique challenges that impact the performance and efficiency of machine learning models. Here are three primary issues associated with high - dimensional data: increased data sparsity, computational complexity, and the risk of overfitting and poor generalization."}
+{"idx": 6, "title": "The Curse of Dimensionality in Machine Learning - SmartSoC ...", "date": "", "ddg_snippet": "Jun 5, 2024 · The Curse of Dimensionality arises when the number of features or variables in a dataset becomes too large. This can lead to a range of issues including Multicollinearity, Overfitting and Computational Complexity.", "subpage_snippet": "", "source": "www.smartsocs.com", "link": "https://www.smartsocs.com/the-curse-of-dimensionality-in-machine-learning/", "content": "Jun 5, 2024 · The Curse of Dimensionality arises when the number of features or variables in a dataset becomes too large. This can lead to a range of issues including Multicollinearity, Overfitting and Computational Complexity."}
+{"idx": 7, "title": "Understanding the Curse of Dimensionality in Machine Learning", "date": "", "ddg_snippet": "As machine learning continues to evolve, understanding and addressing the curse of dimensionality will remain vital for achieving accurate and ...", "subpage_snippet": "", "source": "www.hitechnectar.com", "link": "https://www.hitechnectar.com/blogs/effective-strategies-to-combat-the-curse-of-dimensional-data-analysis/", "content": "As machine learning continues to evolve, understanding and addressing the curse of dimensionality will remain vital for achieving accurate and ..."}
+{"idx": 8, "title": "Understanding Machine Learning: Features, Training Data &", "date": "", "ddg_snippet": "... features (moving into higher dimensions ) can actually degrade the performance of a classifier due to overfitting, underscoring the complexity of ...", "subpage_snippet": "", "source": "digitate.com", "link": "https://digitate.com/blog/machine-learning-features-training-data-dimensionality/", "content": "... features (moving into higher dimensions ) can actually degrade the performance of a classifier due to overfitting, underscoring the complexity of ..."}
+{"idx": 9, "title": "Explain Curse of Dimensionality in Machine Learning - Dive Deep", "date": "", "ddg_snippet": "To deal with the curse of dimensionality in machine learning challenges, techniques like picking important features and reducing dimensions are used ...", "subpage_snippet": "", "source": "www.theiotacademy.co", "link": "https://www.theiotacademy.co/blog/curse-of-dimensionality-in-machine-learning/", "content": "To deal with the curse of dimensionality in machine learning challenges, techniques like picking important features and reducing dimensions are used ..."}
diff --git a/data/sampled_jsons/deepfake_detection_blendfake_deepfake_data_negative_interaction_explanation.jsonl b/data/sampled_jsons/deepfake_detection_blendfake_deepfake_data_negative_interaction_explanation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..646a02ad45073cfdf8bb328f5f6671e6c104343c
--- /dev/null
+++ b/data/sampled_jsons/deepfake_detection_blendfake_deepfake_data_negative_interaction_explanation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Figure 1: 1 (a): The detection performance experiences an abnormal decline when naively combining deepfake and blendfake as the negative sample for training, even though the forgery information is enriched in this process. 1 (b): Illustration Example for illustrating latent space organization. With progressively organized latent space (ours), information in both deepfake and blendfake is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17052v1", "content": "Figure 1: 1 (a): The detection performance experiences an abnormal decline when naively combining deepfake and blendfake as the negative sample for training, even though the forgery information is enriched in this process. 1 (b): Illustration Example for illustrating latent space organization. With progressively organized latent space (ours), information in both deepfake and blendfake is ..."}
+{"idx": 1, "title": "Deepfake: definitions, performance metrics and standards, datasets, and ...", "date": "", "ddg_snippet": "3 Deepfake -related performance metrics and standards In this survey, we focus on performance evaluation and comparison of deepfake generation and detection methods. The metrics used for such performance evaluations are at the core of our discussions.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11408348/", "content": "3 Deepfake -related performance metrics and standards In this survey, we focus on performance evaluation and comparison of deepfake generation and detection methods. The metrics used for such performance evaluations are at the core of our discussions."}
+{"idx": 2, "title": "Unmasking deepfakes: A systematic review of deepfake detection and ...", "date": "", "ddg_snippet": "In response to the potential negative consequences of deepfakes , several studies have been proposed regarding ways to detect and create them using deep learning methods. The following few surveys explicitly address deepfake detection and generation. Tolosana et al. (2020) present an overview of face manipulation and fake detection techniques.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424011266", "content": "In response to the potential negative consequences of deepfakes , several studies have been proposed regarding ways to detect and create them using deep learning methods. The following few surveys explicitly address deepfake detection and generation. Tolosana et al. (2020) present an overview of face manipulation and fake detection techniques."}
+{"idx": 3, "title": "Blendfake Data: A New Way to Detect Deepfakes - Simple Science", "date": "", "ddg_snippet": "Title: Can We Leave Deepfake Data Behind in Training Deepfake Detector? Abstract: The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data , which we termed \" blendfake \", encouraging models to learn generic forgery artifacts like blending boundary ...", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-19-blendfake-data-a-new-way-to-detect-deepfakes--akxyq01", "content": "Title: Can We Leave Deepfake Data Behind in Training Deepfake Detector? Abstract: The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data , which we termed \" blendfake \", encouraging models to learn generic forgery artifacts like blending boundary ..."}
+{"idx": 4, "title": "A Review of Deepfake and Its Detection: From Generative Adversarial ...", "date": "", "ddg_snippet": "Meanwhile, the development of Deepfake detectors represents another branch of models striving to recognize AI-generated fake faces and protect people from the misinformation of Deepfake . This ongoing cat-and-mouse game between generation and detection has spurred a dynamic evolution in the landscape of Deepfake .", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1155/int/9987535", "content": "Meanwhile, the development of Deepfake detectors represents another branch of models striving to recognize AI-generated fake faces and protect people from the misinformation of Deepfake . This ongoing cat-and-mouse game between generation and detection has spurred a dynamic evolution in the landscape of Deepfake ."}
+{"idx": 5, "title": "Enhancing practicality and efficiency of deepfake detection", "date": "", "ddg_snippet": "A deepfake generator would inadvertently replicate this embedded data , making detection straightforward. This method could potentially limit the number of attackers, particularly those who lack ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-82223-y", "content": "A deepfake generator would inadvertently replicate this embedded data , making detection straightforward. This method could potentially limit the number of attackers, particularly those who lack ..."}
+{"idx": 6, "title": "PDF Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/2718a032d15e0b80cd164b240220df89-Paper-Conference.pdf", "content": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive."}
+{"idx": 7, "title": "A Review of Deepfake Techniques: Architecture, Detection, and Datasets", "date": "", "ddg_snippet": "Driven by continuous advancements in artificial intelligence, especially deep learning, the level of realism associated with deepfake technology continues to improve year after year, which poses unprecedented challenges to the field of deepfake detection . The boundary between what we as humans can detect as real or fake becomes evermore blurred as new generations of algorithms such as Dall-E 3 ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10711187", "content": "Driven by continuous advancements in artificial intelligence, especially deep learning, the level of realism associated with deepfake technology continues to improve year after year, which poses unprecedented challenges to the field of deepfake detection . The boundary between what we as humans can detect as real or fake becomes evermore blurred as new generations of algorithms such as Dall-E 3 ..."}
+{"idx": 8, "title": "Deepfake video detection methods, approaches, and challenges", "date": "", "ddg_snippet": "This technology can negatively affect society by breaching privacy and spreading misinformation. This paper presents a comprehensive survey of the recent deepfake video detection approaches and methods. Each deepfake video method is analyzed according to its ability to generalize diverse deepfake fabrication techniques and real-world scenes.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S111001682500465X", "content": "This technology can negatively affect society by breaching privacy and spreading misinformation. This paper presents a comprehensive survey of the recent deepfake video detection approaches and methods. Each deepfake video method is analyzed according to its ability to generalize diverse deepfake fabrication techniques and real-world scenes."}
+{"idx": 9, "title": "A Novel Hybrid Deep Learning Technique for Deepfake Detection: A Review ...", "date": "", "ddg_snippet": "This paper discusses an important contribution to the field of deepfake detection with an approach combining systematic review and novel proposed methodology. Here, we provide a systematic review of the existing state-of-the-art deepfake detection methods and critically analyze their strengths, weaknesses, and practical utility.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-3102-5_37", "content": "This paper discusses an important contribution to the field of deepfake detection with an approach combining systematic review and novel proposed methodology. Here, we provide a systematic review of the existing state-of-the-art deepfake detection methods and critically analyze their strengths, weaknesses, and practical utility."}
diff --git a/data/sampled_jsons/deliberate_practice_entropy_guidance_prediction_classifier_sampling.jsonl b/data/sampled_jsons/deliberate_practice_entropy_guidance_prediction_classifier_sampling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1d2429f23db75f483fa9fb075f90a765979c8bb3
--- /dev/null
+++ b/data/sampled_jsons/deliberate_practice_entropy_guidance_prediction_classifier_sampling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deliberate Practice : What It Is, What It's Not, and How to Use It", "date": "", "ddg_snippet": "Deliberate practice refers to a special type of practice that is purposeful and systematic.Read The Beginner's Guide to Deliberate Practice to learn exactly how you can start practicing deliberately . You'll also find seven in-depth examples of how to practice deliberately .", "subpage_snippet": "", "source": "jamesclear.com", "link": "https://jamesclear.com/deliberate-practice-theory", "content": "Deliberate practice refers to a special type of practice that is purposeful and systematic.Read The Beginner's Guide to Deliberate Practice to learn exactly how you can start practicing deliberately . You'll also find seven in-depth examples of how to practice deliberately ."}
+{"idx": 1, "title": "(PDF) Entropy Guided Transformation Learning", "date": "", "ddg_snippet": "This work presents Entropy Guided Transformation Learning (ETL), a new machine learning algorithm for classification tasks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/226323033_Entropy_Guided_Transformation_Learning", "content": "This work presents Entropy Guided Transformation Learning (ETL), a new machine learning algorithm for classification tasks."}
+{"idx": 2, "title": "Entropy - Guided Attention for Private LLMs", "date": "", "ddg_snippet": "We propose an entropy - guided attention mechanism paired with a novel entropy regularization technique to mitigate entropic overload.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.03489v1/", "content": "We propose an entropy - guided attention mechanism paired with a novel entropy regularization technique to mitigate entropic overload."}
+{"idx": 3, "title": "M- Entropy guidance vs standard practice during propofol-remifentanil...", "date": "", "ddg_snippet": "The frequency of unwanted patient responses was higher in the standard practice group than in the entropy group (47 vs 27 total events, respectively; p < 0.01). Both regimens resulted in fast recovery with no clinical advantage for either one.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/17991257/", "content": "The frequency of unwanted patient responses was higher in the standard practice group than in the entropy group (47 vs 27 total events, respectively; p < 0.01). Both regimens resulted in fast recovery with no clinical advantage for either one."}
+{"idx": 4, "title": "Entropy in Machine Learning: Understand entropy in... | Copilotly", "date": "", "ddg_snippet": "The practical applications and strategies for managing high entropy in datasets underline the practical value of entropy", "subpage_snippet": "", "source": "www.copilotly.com", "link": "https://www.copilotly.com/ai-glossary/entropy-in-machine-learning", "content": "The practical applications and strategies for managing high entropy in datasets underline the practical value of entropy"}
+{"idx": 5, "title": "Unraveling the Mystique of Entropy in Machine Learning... | Medium", "date": "", "ddg_snippet": "Practical Applications: Entropy in Machine Learning Algorithms. In-depth Exploration of Entropy ’s Application in Decision Tree Algorithms. Decision trees are a popular choice in machine learning for tasks like classification and regression.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@bragadeeshs/unraveling-the-mystique-of-entropy-in-machine-learning-a-journey-from-theory-to-practice-4b21743f4222", "content": "Practical Applications: Entropy in Machine Learning Algorithms. In-depth Exploration of Entropy ’s Application in Decision Tree Algorithms. Decision trees are a popular choice in machine learning for tasks like classification and regression."}
+{"idx": 6, "title": "3.4. Metrics and scoring: quantifying the quality of predictions", "date": "", "ddg_snippet": "Note that for regressors, the prediction is done with predict while for classifiers it is usually predict _proba.Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice . 3.4.2. Scoring API overview#.", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/model_evaluation.html", "content": "Note that for regressors, the prediction is done with predict while for classifiers it is usually predict _proba.Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice . 3.4.2. Scoring API overview#."}
+{"idx": 7, "title": "Graph Entropy Guided Node Embedding Dimension Selection for...", "date": "", "ddg_snippet": "The graph entropy considers both feature entropy and structure entropy to guide NEDS for a given graph.For link prediction task, we ran-domly split the edges with a ratio of 85%, 5%, and 10% for training, validation and test sets.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2021/0381.pdf", "content": "The graph entropy considers both feature entropy and structure entropy to guide NEDS for a given graph.For link prediction task, we ran-domly split the edges with a ratio of 85%, 5%, and 10% for training, validation and test sets."}
+{"idx": 8, "title": "Can we use deliberate practise for learning... - DataScienceCentral.com", "date": "", "ddg_snippet": "Deliberate practice always follows the same pattern: break the overall process down into parts, identify your weaknesses, test new strategies for each section, and then integrate your learning into the overall process.", "subpage_snippet": "", "source": "www.datasciencecentral.com", "link": "https://www.datasciencecentral.com/can-we-use-deliberate-practise-for-learning-to-code-ai-and/", "content": "Deliberate practice always follows the same pattern: break the overall process down into parts, identify your weaknesses, test new strategies for each section, and then integrate your learning into the overall process."}
+{"idx": 9, "title": "A new EDAS-based in- sample -out-of- sample classifier for risk-class...", "date": "", "ddg_snippet": "Practical implications The exceptional predictive performance of the proposed new non-parametric classifier makes it a real contender in actual applications in areas such as finance and investment, internet security, fraud and medical diagnosis, where the accuracy of the risk-class...", "subpage_snippet": "", "source": "www.research.ed.ac.uk", "link": "https://www.research.ed.ac.uk/en/publications/a-new-edas-based-in-sample-out-of-sample-classifier-for-risk-clas", "content": "Practical implications The exceptional predictive performance of the proposed new non-parametric classifier makes it a real contender in actual applications in areas such as finance and investment, internet security, fraud and medical diagnosis, where the accuracy of the risk-class..."}
diff --git a/data/sampled_jsons/diffusion_equation_u_t_=_u_xx_exact_solution_sin(pix)exp(-pi^2t)_boundary_conditions.jsonl b/data/sampled_jsons/diffusion_equation_u_t_=_u_xx_exact_solution_sin(pix)exp(-pi^2t)_boundary_conditions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c9be159d45fb9fb2c45645c1e7d856f4438fb1e
--- /dev/null
+++ b/data/sampled_jsons/diffusion_equation_u_t_=_u_xx_exact_solution_sin(pix)exp(-pi^2t)_boundary_conditions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Finite difference methods for diffusion processes", "date": "", "ddg_snippet": "To obtain a unique solution of the diffusion equation , or equivalently, to apply numerical methods, we need initial and boundary conditions .", "subpage_snippet": "", "source": "hplgit.github.io", "link": "https://hplgit.github.io/fdm-book/doc/pub/diffu/html/._diffu-solarized001.html", "content": "To obtain a unique solution of the diffusion equation , or equivalently, to apply numerical methods, we need initial and boundary conditions ."}
+{"idx": 1, "title": "3 Numerical Solutions of PDEs - Partial Differential Equations", "date": "", "ddg_snippet": "If we want to show solutions at intermediate steps, we can plot the solution earlier. In Figure 3.7 we plot the exact solution , u( x , t ) = e− π . 2t sin πx, and.", "subpage_snippet": "", "source": "people.uncw.edu", "link": "https://people.uncw.edu/hermanr/pde1/PDE1notes/Numerical.pdf", "content": "If we want to show solutions at intermediate steps, we can plot the solution earlier. In Figure 3.7 we plot the exact solution , u( x , t ) = e− π . 2t sin πx, and."}
+{"idx": 2, "title": "Chapter 5. Separation of Variables 4.1 The heat equation", "date": "", "ddg_snippet": "The solution of the heat equation with the same initial condition with fixed and no flux boundary conditions . Example 2. Solve ut = uxx, 0 0. (4.20). 36 pages", "subpage_snippet": "", "source": "faculty.uca.edu", "link": "https://faculty.uca.edu/darrigo/Students/M4315/Fall+2005/sep-var.pdf", "content": "The solution of the heat equation with the same initial condition with fixed and no flux boundary conditions . Example 2. Solve ut = uxx, 0 0. (4.20). 36 pages"}
+{"idx": 3, "title": "Specrtal Method for Heat Equation - MATLAB Answers", "date": "", "ddg_snippet": "19 Nov 2019 — % problem u_t = beta u_xx % beta 1. % exact solution . %%. u = @( x , t ) (4* pi /3)*((( sin ( pi * x ))* exp (-( pi ^ 2)*t )));. t0 = 0;. tn = 0.8;. x0 = 0;.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/matlabcentral/answers/491849-specrtal-method-for-heat-equation", "content": "19 Nov 2019 — % problem u_t = beta u_xx % beta 1. % exact solution . %%. u = @( x , t ) (4* pi /3)*((( sin ( pi * x ))* exp (-( pi ^ 2)*t )));. t0 = 0;. tn = 0.8;. x0 = 0;."}
+{"idx": 4, "title": "Objectives: Solve the heat equation numerically using built", "date": "", "ddg_snippet": "17 Apr 2019 — This question could be solved using following mat lab code : clc; clear all; close all; % problem u_t = beat u_xx % beta 4/ pi ^ 2 % exact solution ...", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/objectives-solve-heat-equation-numerically-using-built-functions-matlab-solve-heat-equatio-q37034796", "content": "17 Apr 2019 — This question could be solved using following mat lab code : clc; clear all; close all; % problem u_t = beat u_xx % beta 4/ pi ^ 2 % exact solution ..."}
+{"idx": 5, "title": "Heat Equation Matlab Code | PDF", "date": "", "ddg_snippet": "% problem u_t = beat u_xx . % beta 4/pi^2 % exact solution % % u( x , t ) = exp(-t ) * sin ( pi / 2 * x ) + exp(-t /4 )* sin ( pi /4* x ) % ... 2*cos(pi/4* x ));. + % A U(k 1) = U ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/422256363/Heat-Equation-Matlab-Code", "content": "% problem u_t = beat u_xx . % beta 4/pi^2 % exact solution % % u( x , t ) = exp(-t ) * sin ( pi / 2 * x ) + exp(-t /4 )* sin ( pi /4* x ) % ... 2*cos(pi/4* x ));. + % A U(k 1) = U ..."}
+{"idx": 6, "title": "timeStepHeat1D.cpp", "date": "", "ddg_snippet": "... equation u_xx = u_t with boundary conditions * u(0)=u(1)=0 and initial conditions u( x , t =0)= sin ( pi x ). The solution * is u( x , t )= exp(-pi ^ 2 ... exact solution and ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "http://www.cs.cmu.edu/~12-768/examples/timeStepHeat1D.cpp", "content": "... equation u_xx = u_t with boundary conditions * u(0)=u(1)=0 and initial conditions u( x , t =0)= sin ( pi x ). The solution * is u( x , t )= exp(-pi ^ 2 ... exact solution and ..."}
+{"idx": 7, "title": "How can we solve [math]U_{tt}=c^2 ...", "date": "", "ddg_snippet": "How can we solve U tt =c 2 U xx U t t = c 2 U x x with the boundary conditions U x (0, t )=U x ( π , t )=0 U x ( 0 , t ) = U x ( π , t ) = 0 and the initial ...", "subpage_snippet": "", "source": "www.quora.com", "link": "https://www.quora.com/How-can-we-solve-U_-tt-c-2-U_-xx-with-the-boundary-conditions-U_x-0-t-U_x-pi-t-0-and-the-initial-conditions-U-x-0-0-U_t-x-0-cos-2-x", "content": "How can we solve U tt =c 2 U xx U t t = c 2 U x x with the boundary conditions U x (0, t )=U x ( π , t )=0 U x ( 0 , t ) = U x ( π , t ) = 0 and the initial ..."}
+{"idx": 8, "title": "A Study of MATLAB, Mathematica, and Maple", "date": "", "ddg_snippet": "% Initial condition : u( x ,0) = sin ( pi * x ) alpha = 0.1 ... # Compare with analytical solution at t =0.5 analytical := ( x , t ) -> exp(-Pi ^ 2 *alpha* t)*sin ( Pi * x );.", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/0335f99d-c90f-4658-8c31-2154152cf851-MECA.pdf?abstractid=5434598&mirid=1", "content": "% Initial condition : u( x ,0) = sin ( pi * x ) alpha = 0.1 ... # Compare with analytical solution at t =0.5 analytical := ( x , t ) -> exp(-Pi ^ 2 *alpha* t)*sin ( Pi * x );."}
+{"idx": 9, "title": "2.4 Finite Differences Method: Parabolic PDEs - Roberto De Leo", "date": "", "ddg_snippet": "In case of the heat PDE the situation is very different. u(t,x)=12√πtexp[−(x−ct)24αt]. u ( t , x ) = 1 2 π t exp", "subpage_snippet": "", "source": "deleo.website", "link": "https://deleo.website/NDE/sec-PDEs-Parabolic.html", "content": "In case of the heat PDE the situation is very different. u(t,x)=12√πtexp[−(x−ct)24αt]. u ( t , x ) = 1 2 π t exp"}
diff --git a/data/sampled_jsons/diffusion_model_alignment_inference_time_2022_2021_year_2022.jsonl b/data/sampled_jsons/diffusion_model_alignment_inference_time_2022_2021_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ee80b8877a72f8fe628a2021164d426ae2945142
--- /dev/null
+++ b/data/sampled_jsons/diffusion_model_alignment_inference_time_2022_2021_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Inference-Time Alignment in Diffusion Models with Reward ...", "date": "", "ddg_snippet": "by M Uehara · 2025 · Cited by 17 — Abstract. This tutorial provides an in-depth guide on inference-time guidance and alignment methods for optimizing downstream reward ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.09685?", "content": "by M Uehara · 2025 · Cited by 17 — Abstract. This tutorial provides an in-depth guide on inference-time guidance and alignment methods for optimizing downstream reward ..."}
+{"idx": 1, "title": "Inference-Time Alignment of Diffusion Models with Direct ...", "date": "", "ddg_snippet": "15 Jul 2025 — In this work, we focus on the alignment problem of diffusion models with a continuous reward function, which represents specific objectives ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45672", "content": "15 Jul 2025 — In this work, we focus on the alignment problem of diffusion models with a continuous reward function, which represents specific objectives ..."}
+{"idx": 2, "title": "Inference-Time Multi-Preference Alignment for Diffusion ...", "date": "", "ddg_snippet": "We provide extensive experimental evaluations using the Stable Diffusion (Rombach et al., 2022 ) baseline model , multiple basis reward functions, regularization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18547v1", "content": "We provide extensive experimental evaluations using the Stable Diffusion (Rombach et al., 2022 ) baseline model , multiple basis reward functions, regularization ..."}
+{"idx": 3, "title": "A General Framework for Inference-time Scaling and ...", "date": "", "ddg_snippet": "18 Jun 2025 — In this work, we introduce FK steering, which applies Feynman-Kac interacting particle systems to the inference - time steering of diffusion ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Jp988ELppQ&noteId=FSERsnO0Ez", "content": "18 Jun 2025 — In this work, we introduce FK steering, which applies Feynman-Kac interacting particle systems to the inference - time steering of diffusion ..."}
+{"idx": 4, "title": "DIFFPO: Diffusion-styled Preference Optimization for ...", "date": "", "ddg_snippet": "by R Chen · 2025 · Cited by 1 — Inference - time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges,. 16 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.926.pdf", "content": "by R Chen · 2025 · Cited by 1 — Inference - time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges,. 16 pages"}
+{"idx": 5, "title": "Inference-Time Alignment of Diffusion Models with Direct Noise ...", "date": "", "ddg_snippet": "In this work, we focus on the alignment prob- lem of diffusion models with a continuous re- ward function, which represents specific objec-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/945d4c9cefb30d436ddef3970639ac50613a41c3.pdf", "content": "In this work, we focus on the alignment prob- lem of diffusion models with a continuous re- ward function, which represents specific objec-."}
+{"idx": 6, "title": "Scaling Inference Time Compute for Diffusion Models", "date": "", "ddg_snippet": "by N Ma · 2025 · Cited by 1 — Our contributions are summarized as follows: • We introduce a framework for inference-time scaling of diffusion models, demonstrating that scaling NFEs through ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Scaling_Inference_Time_Compute_for_Diffusion_Models_CVPR_2025_paper.pdf", "content": "by N Ma · 2025 · Cited by 1 — Our contributions are summarized as follows: • We introduce a framework for inference-time scaling of diffusion models, demonstrating that scaling NFEs through ..."}
+{"idx": 7, "title": "xie-lab-ml/awesome-alignment-of-diffusion-models", "date": "", "ddg_snippet": "Inference-Time Alignment of Diffusion Models with Direct Noise Optimization. ... Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark. NeurIPS 2022 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xie-lab-ml/awesome-alignment-of-diffusion-models", "content": "Inference-Time Alignment of Diffusion Models with Direct Noise Optimization. ... Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark. NeurIPS 2022 ..."}
+{"idx": 8, "title": "Diffusion Model Alignment Using Direct Preference ...", "date": "", "ddg_snippet": "Rejection sampling was used in [24] as a powerful inference - time tool. 100 samples were drawn from variants of a prompt and PickScore-ranked, with the highest ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wallace_Diffusion_Model_Alignment_CVPR_2024_supplemental.pdf", "content": "Rejection sampling was used in [24] as a powerful inference - time tool. 100 samples were drawn from variants of a prompt and PickScore-ranked, with the highest ..."}
+{"idx": 9, "title": "An Investigation of Alignment Approaches for Big Models", "date": "", "ddg_snippet": "by X Wang · Cited by 19 — Pros and Cons: Inference - Time Alignment (ITA) evades the need for training and labeled data (RC3). Without mod- ifying the original model parameters, ITA avoids ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0918.pdf", "content": "by X Wang · Cited by 19 — Pros and Cons: Inference - Time Alignment (ITA) evades the need for training and labeled data (RC3). Without mod- ifying the original model parameters, ITA avoids ..."}
diff --git a/data/sampled_jsons/du_Plessis_et_al._class_prior_estimation_weakly_supervised_learning.jsonl b/data/sampled_jsons/du_Plessis_et_al._class_prior_estimation_weakly_supervised_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0d7e3e222477726634de8cde82c96d8fa616d5fc
--- /dev/null
+++ b/data/sampled_jsons/du_Plessis_et_al._class_prior_estimation_weakly_supervised_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Positive Unlabeled Learning with Class-prior Approximation", "date": "", "ddg_snippet": "by S Chang · Cited by 15 — This special weakly supervised learning problem has been utilized in many ... [ Du Plessis et al ., 2015] Marthinus Du Plessis , Gang Niu, and Masashi ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2020/0279.pdf", "content": "by S Chang · Cited by 15 — This special weakly supervised learning problem has been utilized in many ... [ Du Plessis et al ., 2015] Marthinus Du Plessis , Gang Niu, and Masashi ..."}
+{"idx": 1, "title": "A Unified Approach to Count-Based Weakly-Supervised ...", "date": "", "ddg_snippet": "by V Shukla · Cited by 10 — In this work, we aim to tackle both by training an instance-level classifier whose predictions can be combined into a bag level prediction as the last step. 2.4 ...", "subpage_snippet": "", "source": "starai.cs.ucla.edu", "link": "https://starai.cs.ucla.edu/papers/ShuklaDAE23.pdf", "content": "by V Shukla · Cited by 10 — In this work, we aim to tackle both by training an instance-level classifier whose predictions can be combined into a bag level prediction as the last step. 2.4 ..."}
+{"idx": 2, "title": "DELVING INTO WEAKLY SUPERVISED LEARNING", "date": "", "ddg_snippet": "by M Li — Weakly supervised learning (WSL) is a popular machine learning paradigm in recent years that aims to learn a classifier with incomplete, imprecise, or inac-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=RgWATMmWmz", "content": "by M Li — Weakly supervised learning (WSL) is a popular machine learning paradigm in recent years that aims to learn a classifier with incomplete, imprecise, or inac-."}
+{"idx": 3, "title": "Positive-unlabeled classification under class-prior shift", "date": "", "ddg_snippet": "by S Nakajima · 2023 · Cited by 11 — We did experiments with unbiased PU learning (uPU) ( Plessis et al ., 2014, 2015) with the logistic loss and our proposed method (DRPU) with LSIF.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-022-06190-z", "content": "by S Nakajima · 2023 · Cited by 11 — We did experiments with unbiased PU learning (uPU) ( Plessis et al ., 2014, 2015) with the logistic loss and our proposed method (DRPU) with LSIF."}
+{"idx": 4, "title": "A Unified Approach to Count-Based Weakly-Supervised ...", "date": "", "ddg_snippet": "by V Shukla · 2023 · Cited by 10 — Count-based weakly-supervised learning computes the probability of k out of n outputs being true , using a count loss to train a network to conform to desired ... 14 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/79a0c8e7ae8e403e39341ea6b0ba4c21-Paper-Conference.pdf", "content": "by V Shukla · 2023 · Cited by 10 — Count-based weakly-supervised learning computes the probability of k out of n outputs being true , using a count loss to train a network to conform to desired ... 14 pages"}
+{"idx": 5, "title": "A Unified Approach to Count-Based Weakly-Supervised ...", "date": "", "ddg_snippet": "by V Shukla · 2023 · Cited by 10 — Count-based weakly-supervised learning computes the probability of k out of n outputs being true , using a count loss to penalize deviations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.13718", "content": "by V Shukla · 2023 · Cited by 10 — Count-based weakly-supervised learning computes the probability of k out of n outputs being true , using a count loss to penalize deviations ..."}
+{"idx": 6, "title": "Multi-class Classification from Multiple Unlabeled Datasets ...", "date": "", "ddg_snippet": "by Y Tang · 2023 · Cited by 6 — This paper learns a classifier from multiple unlabeled datasets with class priors, using partial risk regularization to prevent overfitting.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v189/tang23a/tang23a.pdf", "content": "by Y Tang · 2023 · Cited by 6 — This paper learns a classifier from multiple unlabeled datasets with class priors, using partial risk regularization to prevent overfitting."}
+{"idx": 7, "title": "Negative Confidence-Aware Weakly Supervised Binary ...", "date": "", "ddg_snippet": "by X Wang · 2020 · Cited by 8 — ABSTRACT. The incompleteness of positive labels and the presence of many unlabelled instances are common problems in binary classification. 10 pages", "subpage_snippet": "", "source": "www.xiwangeric.com", "link": "https://www.xiwangeric.com/assets/pdf/3340531.3411978.pdf", "content": "by X Wang · 2020 · Cited by 8 — ABSTRACT. The incompleteness of positive labels and the presence of many unlabelled instances are common problems in binary classification. 10 pages"}
+{"idx": 8, "title": "A Unified Approach to Count-Based Weakly Supervised ...", "date": "", "ddg_snippet": "In this paper, we develop a unified approach to learning from such weakly -labeled data, which we call count-based weakly - supervised learning .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/72088", "content": "In this paper, we develop a unified approach to learning from such weakly -labeled data, which we call count-based weakly - supervised learning ."}
+{"idx": 9, "title": "A General Framework for Learning from Weak Supervision", "date": "", "ddg_snippet": "by H Chen · 2024 · Cited by 8 — The proposed framework uses an EM formulation with a Non-deterministic Finite Automaton (NFA) to handle various weak supervision sources, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.01922", "content": "by H Chen · 2024 · Cited by 8 — The proposed framework uses an EM formulation with a Non-deterministic Finite Automaton (NFA) to handle various weak supervision sources, ..."}
diff --git a/data/sampled_jsons/efficient_inference_techniques_for_language_models.jsonl b/data/sampled_jsons/efficient_inference_techniques_for_language_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0784e0f290c70c593994e4b83530ffd71c156b3c
--- /dev/null
+++ b/data/sampled_jsons/efficient_inference_techniques_for_language_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Survey on Efficient Inference for Large Language Models", "date": "", "ddg_snippet": "by Z Zhou · 2024 · Cited by 194 — Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference . This paper presents a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.14294", "content": "by Z Zhou · 2024 · Cited by 194 — Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference . This paper presents a ..."}
+{"idx": 1, "title": "A Survey on Efficient Inference for Large Language Models", "date": "", "ddg_snippet": "by Z Zhou · 2024 · Cited by 194 — This paper presents a comprehensive survey of the existing literature on efficient LLM inference . We start by analyzing the primary causes of the inefficient ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.14294", "content": "by Z Zhou · 2024 · Cited by 194 — This paper presents a comprehensive survey of the existing literature on efficient LLM inference . We start by analyzing the primary causes of the inefficient ..."}
+{"idx": 2, "title": "Efficient Tuning and Inference for Large Language Models ...", "date": "", "ddg_snippet": "by Y Zhu · Cited by 49 — We propose a memory- and time- efficient tuning method for LLMs on textual graphs named ENGINE. The LLMs and GNNs are combined through a tunable side struc- ture ... 9 pages", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0634.pdf", "content": "by Y Zhu · Cited by 49 — We propose a memory- and time- efficient tuning method for LLMs on textual graphs named ENGINE. The LLMs and GNNs are combined through a tunable side struc- ture ... 9 pages"}
+{"idx": 3, "title": "LLM Inference Optimization Techniques: A Comprehensive ...", "date": "", "ddg_snippet": "This can include techniques like: Layer reduction : Reducing the number of layers in the model can lead to smaller models and faster inference.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sahin.samia/llm-inference-optimization-techniques-a-comprehensive-analysis-1c434e85ba7c", "content": "This can include techniques like: Layer reduction : Reducing the number of layers in the model can lead to smaller models and faster inference."}
+{"idx": 4, "title": "Efficient Inference for Large Language Model-based ...", "date": "", "ddg_snippet": "by X Lin · Cited by 4 — SpecGR aims to address the challenge for generative models to generate unseen items. It uses a draft model to allow unseen items to be reranked by generative ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACSNlt77hq", "content": "by X Lin · Cited by 4 — SpecGR aims to address the challenge for generative models to generate unseen items. It uses a draft model to allow unseen items to be reranked by generative ..."}
+{"idx": 5, "title": "Mastering LLM Techniques: Inference Optimization", "date": "", "ddg_snippet": "17 Nov 2023 — Stacking transformer layers to create large models results in better accuracies, few-shot learning capabilities, and even near-human ...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/mastering-llm-techniques-inference-optimization/", "content": "17 Nov 2023 — Stacking transformer layers to create large models results in better accuracies, few-shot learning capabilities, and even near-human ..."}
+{"idx": 6, "title": "Efficient Inference of Large Language Models through Model ...", "date": "", "ddg_snippet": "This survey provides a comprehensive and structured overview of model compression techniques specifically designed to improve the efficiency of large language ...", "subpage_snippet": "", "source": "sciety-labs.elifesciences.org", "link": "https://sciety-labs.elifesciences.org/articles/by?article_doi=10.20944/preprints202508.0192.v1", "content": "This survey provides a comprehensive and structured overview of model compression techniques specifically designed to improve the efficiency of large language ..."}
+{"idx": 7, "title": "Lenovo - Efficient Inference of Large Language Models on Single GPU", "date": "", "ddg_snippet": "-Experiment systematically with various compression techniques to assess their impact on model accuracy and inference performance. -Explore new ideas and ...", "subpage_snippet": "", "source": "www.engr.washington.edu", "link": "https://www.engr.washington.edu/industry/capstone-projects/2024-2025/lenovo/efficient-inference-of-large-language-models-on-single-gpu", "content": "-Experiment systematically with various compression techniques to assess their impact on model accuracy and inference performance. -Explore new ideas and ..."}
+{"idx": 8, "title": "LLM Inference: Optimization Techniques & Metrics", "date": "", "ddg_snippet": "Learn LLM inference optimization techniques to reduce latency and boost throughput. Explore methods like KV caching, batching, model parallelization.", "subpage_snippet": "", "source": "www.snowflake.com", "link": "https://www.snowflake.com/en/fundamentals/llm-inference/", "content": "Learn LLM inference optimization techniques to reduce latency and boost throughput. Explore methods like KV caching, batching, model parallelization."}
+{"idx": 9, "title": "Anthony Alcaraz's Post", "date": "", "ddg_snippet": "The ability to perform efficient inference — generating outputs quickly and with minimal computational resources — has emerged as a critical bottleneck in the ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/anthony-alcaraz-b80763155_efficient-inference-for-large-language-models-activity-7221495276904034305-Ox2R", "content": "The ability to perform efficient inference — generating outputs quickly and with minimal computational resources — has emerged as a critical bottleneck in the ..."}
diff --git a/data/sampled_jsons/emotion-behavior_mapping_four-step_unsupervised_learning_linguistic_patterns_ethical_alignment.jsonl b/data/sampled_jsons/emotion-behavior_mapping_four-step_unsupervised_learning_linguistic_patterns_ethical_alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..49381885497e388f70ef3f869553e81e8deb97ff
--- /dev/null
+++ b/data/sampled_jsons/emotion-behavior_mapping_four-step_unsupervised_learning_linguistic_patterns_ethical_alignment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Explainable Emotion Alignment Framework for LLM- ...", "date": "", "ddg_snippet": "by Q Ma · 2025 — In this section, we propose Emotional Cognitive Dynamics, consisting of four main phases (as shown in Fig.2). A. Overview. The Explainable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.22326?", "content": "by Q Ma · 2025 — In this section, we propose Emotional Cognitive Dynamics, consisting of four main phases (as shown in Fig.2). A. Overview. The Explainable ..."}
+{"idx": 1, "title": "integrating-emotional-and-linguistic-models-for-ethical- ...", "date": "", "ddg_snippet": "DIKE's unsupervised learning approach, which associates emotions with linguistic behaviors , and. GPT-4 using a zero-shot prompt. Ground truth was established ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/integrating-emotional-and-linguistic-models-for-ethical-c8c5drc4bk.pdf", "content": "DIKE's unsupervised learning approach, which associates emotions with linguistic behaviors , and. GPT-4 using a zero-shot prompt. Ground truth was established ..."}
+{"idx": 2, "title": "The Emotional Blind Spots of Language Models", "date": "", "ddg_snippet": "11 Sept 2025 — Our comprehensive evaluation framework examines predicted emotion terms and decomposes them into eight basic emotions using established emotion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09593v1", "content": "11 Sept 2025 — Our comprehensive evaluation framework examines predicted emotion terms and decomposes them into eight basic emotions using established emotion ..."}
+{"idx": 3, "title": "Comparing supervised and unsupervised approaches to ...", "date": "", "ddg_snippet": "by B Azari · 2020 · Cited by 89 — Scientists then aim to find evidence in the observed data for the relevant emotion categories by using supervised machine learning methods.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-020-77117-8", "content": "by B Azari · 2020 · Cited by 89 — Scientists then aim to find evidence in the observed data for the relevant emotion categories by using supervised machine learning methods."}
+{"idx": 4, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "This paper introduces a checks-and-balances framework for ethical alignment of Large Lan- guage Models (LLMs), inspired by three-branch.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d24155c881921de1284ece531612c8597f7c0a32.pdf", "content": "This paper introduces a checks-and-balances framework for ethical alignment of Large Lan- guage Models (LLMs), inspired by three-branch."}
+{"idx": 5, "title": "Machine learning algorithms can predict emotional valence ...", "date": "", "ddg_snippet": "by RA Lefèvre · 2025 · Cited by 1 — The present study used a machine learning algorithm (eXtreme Gradient Boosting [XGBoost]) to distinguish between contact calls indicating positive (pleasant) ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11847267/", "content": "by RA Lefèvre · 2025 · Cited by 1 — The present study used a machine learning algorithm (eXtreme Gradient Boosting [XGBoost]) to distinguish between contact calls indicating positive (pleasant) ..."}
+{"idx": 6, "title": "Multimodal machine learning for language and speech ...", "date": "", "ddg_snippet": "by G Drougkas · 2024 · Cited by 9 — The main objective of this paper is to study multimodal machine learning in regards to identifying mental health disorder markers, and ...", "subpage_snippet": "", "source": "bmcmedinformdecismak.biomedcentral.com", "link": "https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02772-0", "content": "by G Drougkas · 2024 · Cited by 9 — The main objective of this paper is to study multimodal machine learning in regards to identifying mental health disorder markers, and ..."}
+{"idx": 7, "title": "Implementing machine learning techniques for continuous ...", "date": "", "ddg_snippet": "by H Diemerling · 2024 · Cited by 5 — This study introduces a novel method for detecting emotions from short, 1.5 s audio samples, aiming to improve accuracy and efficiency in emotion recognition ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2024.1300996/full", "content": "by H Diemerling · 2024 · Cited by 5 — This study introduces a novel method for detecting emotions from short, 1.5 s audio samples, aiming to improve accuracy and efficiency in emotion recognition ..."}
+{"idx": 8, "title": "Machine learning for human emotion recognition", "date": "", "ddg_snippet": "by EMG Younis · 2024 · Cited by 41 — Unsupervised ML models use algorithms to detect similar patterns in the data. Classification and regression are two types of supervised learning ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00521-024-09426-2", "content": "by EMG Younis · 2024 · Cited by 41 — Unsupervised ML models use algorithms to detect similar patterns in the data. Classification and regression are two types of supervised learning ..."}
+{"idx": 9, "title": "Large language models are proficient in solving and ...", "date": "", "ddg_snippet": "by K Schlegel · 2025 · Cited by 12 — This research examined whether LLMs can solve and generate performance-based emotional intelligence tests.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s44271-025-00258-x", "content": "by K Schlegel · 2025 · Cited by 12 — This research examined whether LLMs can solve and generate performance-based emotional intelligence tests."}
diff --git a/data/sampled_jsons/emotion_to_behavior_mapping_four-step_process_self-supervised_learning_DIKE_framework.jsonl b/data/sampled_jsons/emotion_to_behavior_mapping_four-step_process_self-supervised_learning_DIKE_framework.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..89cb8e1a05152b802aaa1c1a10689c5d1a515986
--- /dev/null
+++ b/data/sampled_jsons/emotion_to_behavior_mapping_four-step_process_self-supervised_learning_DIKE_framework.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "by EY Chang · Cited by 1 — Emotion -Driven Behavioral Modeling. ▷ Self - supervised learning pipeline. ▷ Maps emotional states to linguistic patterns/ behaviors . ▷ Guides ethical ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461_OMgXx2a.pdf", "content": "by EY Chang · Cited by 1 — Emotion -Driven Behavioral Modeling. ▷ Self - supervised learning pipeline. ▷ Maps emotional states to linguistic patterns/ behaviors . ▷ Guides ethical ..."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — In the emotion layer evaluation, self - supervised learning is used to construct an emotion - behavior mapping , improving classification accuracy by ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — In the emotion layer evaluation, self - supervised learning is used to construct an emotion - behavior mapping , improving classification accuracy by ..."}
+{"idx": 2, "title": "integrating-emotional-and-linguistic-models-for-ethical- ...", "date": "", "ddg_snippet": "Abstract. This research develops advanced methodologies for Large Language Models (LLMs) to better manage linguistic behaviors related to emotions and ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/integrating-emotional-and-linguistic-models-for-ethical-c8c5drc4bk.pdf", "content": "Abstract. This research develops advanced methodologies for Large Language Models (LLMs) to better manage linguistic behaviors related to emotions and ..."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Framework for ...", "date": "", "ddg_snippet": "Building on BEAM, DIKE maps emotions to behaviors and introduces an adversarial component,. ERIS, to adapt to culture norms and local context. Behaviors and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/c76fc56310e947fbc848c07660b1ecbd60580a08.pdf", "content": "Building on BEAM, DIKE maps emotions to behaviors and introduces an adversarial component,. ERIS, to adapt to culture norms and local context. Behaviors and ..."}
+{"idx": 4, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 5, "title": "Learning Emotion Regulation: An Integrative Framework", "date": "", "ddg_snippet": "by RN Wright · 2024 · Cited by 19 — According to the process model, emotions unfold in a sequence of steps : situation. ( emotion -eliciting event), attention, appraisal (assessment of the situation) ... 31 pages", "subpage_snippet": "", "source": "dibs-web01.vm.duke.edu", "link": "https://dibs-web01.vm.duke.edu/labar/pdfs/Wright_et_al_2025.pdf", "content": "by RN Wright · 2024 · Cited by 19 — According to the process model, emotions unfold in a sequence of steps : situation. ( emotion -eliciting event), attention, appraisal (assessment of the situation) ... 31 pages"}
+{"idx": 6, "title": "Automatic emotion and attention analysis of young children ...", "date": "", "ddg_snippet": "by HL Egger · 2018 · Cited by 124 — Automatic coding identified significant differences in emotion and attention by age, sex, and autism risk status.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6550157/", "content": "by HL Egger · 2018 · Cited by 124 — Automatic coding identified significant differences in emotion and attention by age, sex, and autism risk status."}
+{"idx": 7, "title": "Exploratory Machine Learning Modeling of Adaptive and ...", "date": "", "ddg_snippet": "by R Yan · 2022 · Cited by 16 — This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8951872/", "content": "by R Yan · 2022 · Cited by 16 — This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive ..."}
+{"idx": 8, "title": "Multimodal Emotion Recognition in Conversations", "date": "", "ddg_snippet": "For effective multimodal analysis, features must first be extracted from each modality stream (text, audio, visual). Mainstream approaches (e.g., Shi and Huang, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.20511v2", "content": "For effective multimodal analysis, features must first be extracted from each modality stream (text, audio, visual). Mainstream approaches (e.g., Shi and Huang, ..."}
+{"idx": 9, "title": "A systematic review of artificial intelligence techniques for ...", "date": "", "ddg_snippet": "by SC Tan · 2022 · Cited by 135 — This systematic review focuses on publications related to studies of the use of artificial intelligence (AI) for collaborative learning .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666920X22000522", "content": "by SC Tan · 2022 · Cited by 135 — This systematic review focuses on publications related to studies of the use of artificial intelligence (AI) for collaborative learning ."}
diff --git a/data/sampled_jsons/episodic_reinforcement_learning_Li_2024.jsonl b/data/sampled_jsons/episodic_reinforcement_learning_Li_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ed2b97964b531980e778838189cbda503c5184eb
--- /dev/null
+++ b/data/sampled_jsons/episodic_reinforcement_learning_Li_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09536", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ..."}
+{"idx": 1, "title": "DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning", "date": "", "ddg_snippet": "A new artificial intelligence model, DeepSeek-R1, is introduced, demonstrating that the reasoning abilities of large language models can be incentivized through pure reinforcement learning ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-025-09422-z", "content": "A new artificial intelligence model, DeepSeek-R1, is introduced, demonstrating that the reasoning abilities of large language models can be incentivized through pure reinforcement learning ..."}
+{"idx": 2, "title": "Temporally Correlated Episodic Reinforcement Learning, ICLR 24", "date": "", "ddg_snippet": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BruceGeLi/TCE_RL", "content": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ..."}
+{"idx": 3, "title": "Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning", "date": "", "ddg_snippet": "In this paper, we introduce an episodic future thinking (EFT) mechanism for a reinforcement learning (RL) agent, inspired by cognitive processes observed in animals. To enable future thinking functionality, we first develop a multi-character policy that captures diverse characters with an ensemble of heterogeneous policies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.17373", "content": "In this paper, we introduce an episodic future thinking (EFT) mechanism for a reinforcement learning (RL) agent, inspired by cognitive processes observed in animals. To enable future thinking functionality, we first develop a multi-character policy that captures diverse characters with an ensemble of heterogeneous policies."}
+{"idx": 4, "title": "TOP-ERL: Transformer-based Off-policy Episodic RL (ICLR25 ... - GitHub", "date": "", "ddg_snippet": "TOP-ERL: Transformer-based Off-policy Episodic RL (ICLR25 Spotlight) Episodic RL, What and Why? Episodic Reinforcement Learning (ERL) [1, 4, 5] is a distinct RL family that emphasizes the maximization of returns over entire episodes, typically lasting several seconds, rather than optimizing the intermediate states during environment interactions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BruceGeLi/TOP_ERL_ICLR25_Code", "content": "TOP-ERL: Transformer-based Off-policy Episodic RL (ICLR25 Spotlight) Episodic RL, What and Why? Episodic Reinforcement Learning (ERL) [1, 4, 5] is a distinct RL family that emphasizes the maximization of returns over entire episodes, typically lasting several seconds, rather than optimizing the intermediate states during environment interactions."}
+{"idx": 5, "title": "ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy", "date": "", "ddg_snippet": "This paper proposes \\\\emph{Episodic and Lifelong Exploration via Maximum ENTropy} (ELEMENT), a novel, multiscale, intrinsically motivated reinforcement learning (RL) framework that is able to explore environments without using any extrinsic reward and transfer effectively the learned skills to downstream tasks. We advance the state of the art in three ways. First, we propose a multiscale ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.03800", "content": "This paper proposes \\\\emph{Episodic and Lifelong Exploration via Maximum ENTropy} (ELEMENT), a novel, multiscale, intrinsically motivated reinforcement learning (RL) framework that is able to explore environments without using any extrinsic reward and transfer effectively the learned skills to downstream tasks. We advance the state of the art in three ways. First, we propose a multiscale ..."}
+{"idx": 6, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "The paper claims to be \"the first off-policy episodic reinforcement learning algorithm,\" but this seems to be an overclaim. To my knowledge, there are already several related works, such as: Liang D, Zhang Y, Liu Y. \" Episodic Reinforcement Learning with Expanded State-reward Space.\" arXiv preprint arXiv:2401.10516, 2024 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=N4NhVN30ph", "content": "The paper claims to be \"the first off-policy episodic reinforcement learning algorithm,\" but this seems to be an overclaim. To my knowledge, there are already several related works, such as: Liang D, Zhang Y, Liu Y. \" Episodic Reinforcement Learning with Expanded State-reward Space.\" arXiv preprint arXiv:2401.10516, 2024 ."}
+{"idx": 7, "title": "TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT LEARNING - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in an ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single per-step actions. These trajectories are typically parame-terized by trajectory generators such as Movement Primitives (MP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536v2", "content": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in an ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single per-step actions. These trajectories are typically parame-terized by trajectory generators such as Movement Primitives (MP ..."}
+{"idx": 8, "title": "[2401.11437] Open the Black Box: Step-based Policy Updates for ...", "date": "", "ddg_snippet": "Access Paper: View a PDF of the paper titled Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning , by Ge Li and 6 other authors TeX Source BibTeX formatted citation", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.11437", "content": "Access Paper: View a PDF of the paper titled Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning , by Ge Li and 6 other authors TeX Source BibTeX formatted citation"}
+{"idx": 9, "title": "Open the Black Box: Step-based Policy Updates for...", "date": "", "ddg_snippet": "Episodic Reinforcement Learning (ERL) is a well-established concept, previously applied to learning movement primitives in various studies [1, 2, 3, 4]. This approach shifts the search for solutions from the per-step action space to the trajectory parameter space, thereby promoting broader exploration.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mnipav175N", "content": "Episodic Reinforcement Learning (ERL) is a well-established concept, previously applied to learning movement primitives in various studies [1, 2, 3, 4]. This approach shifts the search for solutions from the per-step action space to the trajectory parameter space, thereby promoting broader exploration."}
diff --git a/data/sampled_jsons/equation_12_convergence_rate_conditional_deep_generative_models_likelihood_based_approach_year_2024.jsonl b/data/sampled_jsons/equation_12_convergence_rate_conditional_deep_generative_models_likelihood_based_approach_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ca9883d0b8ec6cea9c1b0bb261845ebb3089e69f
--- /dev/null
+++ b/data/sampled_jsons/equation_12_convergence_rate_conditional_deep_generative_models_likelihood_based_approach_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "To the best of our knowledge, our study is the first attempt to explore the likelihood - based approach for distributional regression using a conditional deep generative model , considering full-dimensional noise and the potential presence of singular underlying support.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "To the best of our knowledge, our study is the first attempt to explore the likelihood - based approach for distributional regression using a conditional deep generative model , considering full-dimensional noise and the potential presence of singular underlying support."}
+{"idx": 1, "title": "A LIKELIHOOD BASED APPROACH TO DISTRIBUTION REGRESSION USING ...", "date": "", "ddg_snippet": "he large-sample properties of a likelihood - based approach for estimating these models . Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolv. d counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "he large-sample properties of a likelihood - based approach for estimating these models . Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolv. d counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend."}
+{"idx": 2, "title": "Diffusion Models Chapter 4: Conditional Generation I", "date": "", "ddg_snippet": "One advantage of this approach is that the learned score network is time independent, so it is, in principle, simpler. However, empirical performance is not good.#", "subpage_snippet": "", "source": "ernestryu.com", "link": "https://ernestryu.com/courses/FM/diffusion4.pdf", "content": "One advantage of this approach is that the learned score network is time independent, so it is, in principle, simpler. However, empirical performance is not good.#"}
+{"idx": 3, "title": "A Deep Generative Approach to Conditional Sampling", "date": "", "ddg_snippet": "Dec 10, 2021 · We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/01621459.2021.2016424", "content": "Dec 10, 2021 · We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma."}
+{"idx": 4, "title": "Deep Generative Models - Energy-Based Models", "date": "", "ddg_snippet": "As long as we can draw random samples from the model , we have access to an unbiased Monte Carlo estimate of the log- likelihood gradient, allowing us to optimize the parameters with stochastic gradient ascent.", "subpage_snippet": "", "source": "sharif.edu", "link": "https://sharif.edu/~beigy/courses/14022/40959/Lect-18.pdf", "content": "As long as we can draw random samples from the model , we have access to an unbiased Monte Carlo estimate of the log- likelihood gradient, allowing us to optimize the parameters with stochastic gradient ascent."}
+{"idx": 5, "title": "A Likelihood Approach to Nonparametric Estimation of a ...", "date": "", "ddg_snippet": "We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models . More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume24/21-1099/21-1099.pdf", "content": "We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models . More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure."}
+{"idx": 6, "title": "GLOBAL WELL POSEDNESS AND CONVERGENCE ANALYSIS OF SCORE BASED ...", "date": "", "ddg_snippet": "ABSTRACT of the score-based genera - tive models (SGM) under general assumptions of initial data for score est mation. For the smooth case, we start from a Lipschitz bound of the score function with optimal time length. The optimality is alidated by an example whose Lipschitz constant of scores is bounded at initial but blows up in finite tim", "subpage_snippet": "", "source": "www.math.uci.edu", "link": "https://www.math.uci.edu/~jxin/MWXY_ICLR2025.pdf", "content": "ABSTRACT of the score-based genera - tive models (SGM) under general assumptions of initial data for score est mation. For the smooth case, we start from a Lipschitz bound of the score function with optimal time length. The optimality is alidated by an example whose Lipschitz constant of scores is bounded at initial but blows up in finite tim"}
+{"idx": 7, "title": "Diffusion Models for Generative Artificial Intelligence: An", "date": "", "ddg_snippet": "... the necessary background on stochastic differential equations in Hilbert spaces, and then discuss different approaches to define generative models ...", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/W4413134129/diffusion-models-for-generative-artificial-intelligence-an-introduction-for-applied-mathematicians", "content": "... the necessary background on stochastic differential equations in Hilbert spaces, and then discuss different approaches to define generative models ..."}
+{"idx": 8, "title": "Estimating Rate-Distortion Functions Using the Energy-Based", "date": "", "ddg_snippet": "Estimating Rate -Distortion Functions Using the Energy- Based Model † † thanks: The first two authors contributed equally to this work and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15700v1", "content": "Estimating Rate -Distortion Functions Using the Energy- Based Model † † thanks: The first two authors contributed equally to this work and ..."}
+{"idx": 9, "title": "Constrained least-squares and maximum-likelihood calibration of", "date": "", "ddg_snippet": "... parameters including EDT, T 15 , T 20 , T 30 , C 50 , and C 80 , they found that the non-linear iterative approach stabilized after eight generations ...", "subpage_snippet": "", "source": "acta-acustica.edpsciences.org", "link": "https://acta-acustica.edpsciences.org/articles/aacus/full_html/2025/01/aacus240137/aacus240137.html", "content": "... parameters including EDT, T 15 , T 20 , T 30 , C 50 , and C 80 , they found that the non-linear iterative approach stabilized after eight generations ..."}
diff --git a/data/sampled_jsons/ethical_AI_alignment_framework_checks_balances_governmental.jsonl b/data/sampled_jsons/ethical_AI_alignment_framework_checks_balances_governmental.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..28a79ecd238d774cbf09f34cb59155573ca6aa0f
--- /dev/null
+++ b/data/sampled_jsons/ethical_AI_alignment_framework_checks_balances_governmental.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 1, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "This paper introduces a three-branch checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Conclusion This checks -and- balances approach offers a promising direction for building more ethically-aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks -and- balances approach offers a promising direction for building more ethically-aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts."}
+{"idx": 3, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 4, "title": "Implementing Checks and Balances in the Design of Sentient AI", "date": "", "ddg_snippet": "As we delve deeper into the delicate balance between autonomy and control, we explore the challenges and triumphs of designing sentient AI with checks and balances . Alignment Labs invites us to witness the evolution of technology that not only thinks but also considers the ethical implications of its actions.", "subpage_snippet": "", "source": "alignmentlabs.org", "link": "https://alignmentlabs.org/implementing-checks-and-balances-in-the-design-of-sentient-ai/", "content": "As we delve deeper into the delicate balance between autonomy and control, we explore the challenges and triumphs of designing sentient AI with checks and balances . Alignment Labs invites us to witness the evolution of technology that not only thinks but also considers the ethical implications of its actions."}
+{"idx": 5, "title": "AI Ethical Framework: A Government-Centric Tool - ProQuest", "date": "", "ddg_snippet": "For developers, we propose a diagnostic application that actively checks software, assessing its alignment with the ethical principles established by the government. This feedback allows developers to recalibrate their AI applications, ensuring they are both efficient and ethically suitable for the intended area of use.", "subpage_snippet": "", "source": "www.proquest.com", "link": "https://www.proquest.com/docview/3147965026/fulltextPDF", "content": "For developers, we propose a diagnostic application that actively checks software, assessing its alignment with the ethical principles established by the government. This feedback allows developers to recalibrate their AI applications, ensuring they are both efficient and ethically suitable for the intended area of use."}
+{"idx": 6, "title": "Ethical Guardrails for AI: A Checks-and-Balances Approach", "date": "", "ddg_snippet": "A Three-Branch Checks -and- Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124", "subpage_snippet": "", "source": "www.zerna.io", "link": "https://www.zerna.io/page/security/presentation_set/security-llm-research/presentation/security-ethical-alignment-fairness/slide/security-paper-2502_00136", "content": "A Three-Branch Checks -and- Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124"}
+{"idx": 7, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn", "content": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet..."}
+{"idx": 8, "title": "AI-C2C (conscious to conscience): a governance framework for ethical AI ...", "date": "", "ddg_snippet": "The AI -C2C (conscious to conscience) governance framework introduces a phased approach to integrating ethical oversight into AI adoption. It aims to bridge the gap between initial awareness and full, autonomous ethical alignment . The framework progresses through three stages—AI-conscious, AI + human intelligence (HI) Collaboration, and AI -conscience—each representing a deeper commitment to ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s43681-025-00736-2", "content": "The AI -C2C (conscious to conscience) governance framework introduces a phased approach to integrating ethical oversight into AI adoption. It aims to bridge the gap between initial awareness and full, autonomous ethical alignment . The framework progresses through three stages—AI-conscious, AI + human intelligence (HI) Collaboration, and AI -conscience—each representing a deeper commitment to ..."}
+{"idx": 9, "title": "SAF Submitted to U.S. AI Action Plan! - Self-Alignment Framework", "date": "", "ddg_snippet": "We are excited to announce that the Self- Alignment Framework (SAF) has been officially submitted to the U.S. government's AI Action Plan as a recommendation for AI ethics governance. This milestone highlights SAF's potential as a structured, closed-loop system for ensuring AI remains transparent, accountable, and aligned with human values.", "subpage_snippet": "", "source": "selfalignmentframework.com", "link": "https://selfalignmentframework.com/saf-submitted-to-u-s-ai-action-plan/", "content": "We are excited to announce that the Self- Alignment Framework (SAF) has been officially submitted to the U.S. government's AI Action Plan as a recommendation for AI ethics governance. This milestone highlights SAF's potential as a structured, closed-loop system for ensuring AI remains transparent, accountable, and aligned with human values."}
diff --git a/data/sampled_jsons/flow_matching_machine_learning_high_dimensional_efficient.jsonl b/data/sampled_jsons/flow_matching_machine_learning_high_dimensional_efficient.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e528366292ff42abd0d1e003b92d2579f8c97961
--- /dev/null
+++ b/data/sampled_jsons/flow_matching_machine_learning_high_dimensional_efficient.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Flow Matching-Based Generative Modeling for Efficient ...", "date": "", "ddg_snippet": "by T Transue · 2025 — Our results highlight the promise of FM as a scalable tool for filtering in high - dimensional applications that enable the use of large ensembles ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.13313", "content": "by T Transue · 2025 — Our results highlight the promise of FM as a scalable tool for filtering in high - dimensional applications that enable the use of large ensembles ..."}
+{"idx": 1, "title": "Efficient Flow Matching using Latent Variables", "date": "", "ddg_snippet": "7 May 2025 — The method shows promising results for 1-dimensional synthetic datasets but suboptimal results for high - dimensional datasets. In flow matching , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04486v1", "content": "7 May 2025 — The method shows promising results for 1-dimensional synthetic datasets but suboptimal results for high - dimensional datasets. In flow matching , ..."}
+{"idx": 2, "title": "Advances in Flow Matching: Insights from ICML 2025 Papers", "date": "", "ddg_snippet": "25 Jun 2025 — Flow matching is a recent class of generative modeling techniques that aim to learn continuous transformations between data distributions ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/report/?id=advances-in-flow-matching-insights-from-icml-2025-papers", "content": "25 Jun 2025 — Flow matching is a recent class of generative modeling techniques that aim to learn continuous transformations between data distributions ..."}
+{"idx": 3, "title": "Multi-Marginal Stochastic Flow Matching for High- ...", "date": "", "ddg_snippet": "12 Jul 2025 — Our method first uses optimal transport to find the most efficient way to match individuals between consecutive snapshots, like pairing dancers between songs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ZLyb8DwXXE&noteId=0SglJmPYrm", "content": "12 Jul 2025 — Our method first uses optimal transport to find the most efficient way to match individuals between consecutive snapshots, like pairing dancers between songs."}
+{"idx": 4, "title": "Multi-Marginal Stochastic Flow Matching for High- ...", "date": "", "ddg_snippet": "15 Jul 2025 — We present MultiMarginal Stochastic Flow Matching (MMSFM), a novel extension of simulation-free score and flow matching methods to the multi - ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44861", "content": "15 Jul 2025 — We present MultiMarginal Stochastic Flow Matching (MMSFM), a novel extension of simulation-free score and flow matching methods to the multi - ..."}
+{"idx": 5, "title": "DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows", "date": "", "ddg_snippet": "by MM Morshed · 2025 — Many real-world applications of flow -based generative mod- els desire a diverse set of samples that cover multiple modes of the target distribution.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Morshed_DiverseFlow_Sample-Efficient_Diverse_Mode_Coverage_in_Flows_CVPR_2025_paper.pdf", "content": "by MM Morshed · 2025 — Many real-world applications of flow -based generative mod- els desire a diverse set of samples that cover multiple modes of the target distribution."}
+{"idx": 6, "title": "Efficient Flow Matching using Latent Variables", "date": "", "ddg_snippet": "7 May 2025 — Flow matching models have shown great potential in image generation tasks among probabilistic generative models. Building upon the ideas of ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/efficient-flow-matching-using-latent", "content": "7 May 2025 — Flow matching models have shown great potential in image generation tasks among probabilistic generative models. Building upon the ideas of ..."}
+{"idx": 7, "title": "FLOWS DON'T CROSS IN HIGH DIMENSION", "date": "", "ddg_snippet": "by T Reu — Conditional Flow Matching (CFM) has emerged as a competitive framework for generative modeling, yet persistent concerns about trajectory crossings and their.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=nK9TmlJu8F", "content": "by T Reu — Conditional Flow Matching (CFM) has emerged as a competitive framework for generative modeling, yet persistent concerns about trajectory crossings and their."}
+{"idx": 8, "title": "Improving Flow Matching for Simulation-Based Inference", "date": "", "ddg_snippet": "by J Fluri — Flow matching is an incredibly powerful technique that can be used to sample arbitrary distributions. Recently, flow matching posterior estimation (FMPE) ...", "subpage_snippet": "", "source": "ml4physicalsciences.github.io", "link": "https://ml4physicalsciences.github.io/2024/files/NeurIPS_ML4PS_2024_26.pdf", "content": "by J Fluri — Flow matching is an incredibly powerful technique that can be used to sample arbitrary distributions. Recently, flow matching posterior estimation (FMPE) ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "5 days ago — Flow -based generative models, including diffusion models, excel at modeling continuous distributions in high - dimensional spaces. In this work, ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=flow-matching+objective", "content": "5 days ago — Flow -based generative models, including diffusion models, excel at modeling continuous distributions in high - dimensional spaces. In this work, ..."}
diff --git a/data/sampled_jsons/four-step_process_OR_four_steps_emotion_to_behavior_self-supervised_learning_2502.00136.jsonl b/data/sampled_jsons/four-step_process_OR_four_steps_emotion_to_behavior_self-supervised_learning_2502.00136.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cc792db52a80355403d029ac932b9b991714be24
--- /dev/null
+++ b/data/sampled_jsons/four-step_process_OR_four_steps_emotion_to_behavior_self-supervised_learning_2502.00136.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "Kolb's Learning Styles & Experiential Learning Cycle", "date": "", "ddg_snippet": "Mar 19, 2025 · Kolb’s Learning Styles theory identifies four types of learners: converging, diverging, assimilating, and accommodating. These styles are part of his Experiential Learning Cycle, which involves four stages: concrete experience, reflective observation, abstract conceptualization, and active experimentation. The cycle emphasizes learning through experience, reflection, conceptualization, and ...", "subpage_snippet": "", "source": "www.simplypsychology.org", "link": "https://www.simplypsychology.org/learning-kolb.html", "content": "Mar 19, 2025 · Kolb’s Learning Styles theory identifies four types of learners: converging, diverging, assimilating, and accommodating. These styles are part of his Experiential Learning Cycle, which involves four stages: concrete experience, reflective observation, abstract conceptualization, and active experimentation. The cycle emphasizes learning through experience, reflection, conceptualization, and ..."}
+{"idx": 1, "title": "Microsoft Word - Four-Step Fact Sheet .docx - NYS RtI", "date": "", "ddg_snippet": "1. Goal Identification: Identify the goal(s) (what we want students to Know, Understand and Do) which are the focus of the 4- step process . Academic goals should be aligned with the appropriate academic and/ or behavior Standards. Behavior /social-emotional goals should be aligned with the behaviors expected to engage instruction and to promote social-emotional competency.", "subpage_snippet": "", "source": "nysrti.org", "link": "https://nysrti.org/files/webinars/strand_20/4_step_problem_solving_fact_sheet.pdf", "content": "1. Goal Identification: Identify the goal(s) (what we want students to Know, Understand and Do) which are the focus of the 4- step process . Academic goals should be aligned with the appropriate academic and/ or behavior Standards. Behavior /social-emotional goals should be aligned with the behaviors expected to engage instruction and to promote social-emotional competency."}
+{"idx": 2, "title": "The effectiveness of the Peyton’s 4-step teaching approach on ...", "date": "", "ddg_snippet": "Peyton’s teaching approach is a stepwise teaching approach and consists of the following four steps : (i) step 1 refers to the demonstration of the whole procedure in real time (“demonstration”); (ii) in step 2 the teacher repeats the demonstration but this time all procedural sub-steps are described (“deconstruction”); (iii) during ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7549471/", "content": "Peyton’s teaching approach is a stepwise teaching approach and consists of the following four steps : (i) step 1 refers to the demonstration of the whole procedure in real time (“demonstration”); (ii) in step 2 the teacher repeats the demonstration but this time all procedural sub-steps are described (“deconstruction”); (iii) during ..."}
+{"idx": 3, "title": "Healthy Emotional Processing: A Comprehensive Step-by-Step Guide", "date": "", "ddg_snippet": "Aug 21, 2025 · Learn effective techniques for processing emotions, from body-based methods to mindfulness practices. Improve your emotional well-being with our expert guide.", "subpage_snippet": "", "source": "neurolaunch.com", "link": "https://neurolaunch.com/how-to-process-emotions-in-a-healthy-way/", "content": "Aug 21, 2025 · Learn effective techniques for processing emotions, from body-based methods to mindfulness practices. Improve your emotional well-being with our expert guide."}
+{"idx": 4, "title": "R.A.I.N: A Four-Step Process for Using Mindfulness in ...", "date": "", "ddg_snippet": "R.A.I.N. is a four-step process for using mindfulness in difficult times, on dealing with intense and difficult emotions.", "subpage_snippet": "", "source": "melliobrien.com", "link": "https://melliobrien.com/r-n-four-step-process-using-mindfulness-difficult-times/", "content": "R.A.I.N. is a four-step process for using mindfulness in difficult times, on dealing with intense and difficult emotions."}
+{"idx": 5, "title": "4 steps to calm anger and process it : Shots - Health News : NPR", "date": "", "ddg_snippet": "Mar 12, 2024 · We're often taught to repress our feelings and behave nicely. But anger has a biological purpose, and psychologists say it's healthier to embrace it. Here are four steps for working with anger.", "subpage_snippet": "", "source": "www.npr.org", "link": "https://www.npr.org/sections/health-shots/2024/03/12/1236973762/anger-management-types-purpose-cause", "content": "Mar 12, 2024 · We're often taught to repress our feelings and behave nicely. But anger has a biological purpose, and psychologists say it's healthier to embrace it. Here are four steps for working with anger."}
+{"idx": 6, "title": "4 Phases of Mental Health Crisis Explained - Relias", "date": "", "ddg_snippet": "Sep 15, 2025 · Learn about the 4 phases of mental health crisis in order to better help your clients experiencing a crisis situation.", "subpage_snippet": "", "source": "www.relias.com", "link": "https://www.relias.com/blog/4-phases-of-crisis-mental-health", "content": "Sep 15, 2025 · Learn about the 4 phases of mental health crisis in order to better help your clients experiencing a crisis situation."}
diff --git a/data/sampled_jsons/gPINN_Yu_et_al._CMAME_2022_year_2022.jsonl b/data/sampled_jsons/gPINN_Yu_et_al._CMAME_2022_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..700d5fb68e2e19ae9a893e7413e85507c8619855
--- /dev/null
+++ b/data/sampled_jsons/gPINN_Yu_et_al._CMAME_2022_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - lu-group/ gpinn : gPINN : Gradient-enhanced physics-informed...", "date": "", "ddg_snippet": "gPINN : Gradient-enhanced physics-informed neural networks. The data and code for the paper J. Yu , L. Lu, X. Meng, & G. E. Karniadakis.doi.org/10.1016/j. cma . 2022 .114823} }. Questions. To get help on how to use the data or code, simply open an issue in the GitHub \"Issues\" section.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lu-group/gpinn", "content": "gPINN : Gradient-enhanced physics-informed neural networks. The data and code for the paper J. Yu , L. Lu, X. Meng, & G. E. Karniadakis.doi.org/10.1016/j. cma . 2022 .114823} }. Questions. To get help on how to use the data or code, simply open an issue in the GitHub \"Issues\" section."}
+{"idx": 1, "title": "CMAME 2022 - 2022 The 9th International Conference on Mechanical...", "date": "", "ddg_snippet": "CMAME 2022 information,including the scope of submissions, submission deadline, conference schedule, contact information…", "subpage_snippet": "", "source": "www.iconf.org", "link": "https://www.iconf.org/conference/cmame2022", "content": "CMAME 2022 information,including the scope of submissions, submission deadline, conference schedule, contact information…"}
+{"idx": 2, "title": "GPINN with Neural Tangent Kernel Technique for Nonlinear Two Point...", "date": "", "ddg_snippet": "Yu J, Lu L, Meng X, Karniadakis GE ( 2022 ) Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Comput Methods Appl Mech Engrg 393:114823.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11063-024-11644-7", "content": "Yu J, Lu L, Meng X, Karniadakis GE ( 2022 ) Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Comput Methods Appl Mech Engrg 393:114823."}
+{"idx": 3, "title": "[2111.02801] Gradient-enhanced physics-informed neural networks for...", "date": "", "ddg_snippet": "Here, we propose a new method, gradient-enhanced physics-informed neural networks ( gPINNs ), for improving the accuracy and training efficiency of PINNs. gPINNs leverage gradient information of the PDE residual and embed the gradient into the loss function.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.02801", "content": "Here, we propose a new method, gradient-enhanced physics-informed neural networks ( gPINNs ), for improving the accuracy and training efficiency of PINNs. gPINNs leverage gradient information of the PDE residual and embed the gradient into the loss function."}
+{"idx": 4, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "• gPINN (Gradient-enhanced physics-informed neural networks) ( Yu et al ., 2022 ) leverages gradient information of the PDE residual and embeds the gradient into the loss function, thereby improving the accuracy and training efficiency of PINN.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "• gPINN (Gradient-enhanced physics-informed neural networks) ( Yu et al ., 2022 ) leverages gradient information of the PDE residual and embeds the gradient into the loss function, thereby improving the accuracy and training efficiency of PINN."}
+{"idx": 5, "title": "Transfer learning for improved generalizability in causal...", "date": "", "ddg_snippet": "In another work, Liu et al . ( 2022 ) utilized transfer learning for accurate temperature field in-version with limited observations, employing a PINN and optimal position selection.Neto, 2023), gradient-enhanced PINN ( gPINN ) ( Yu et al ., 2022 ), PINNs. with adaptive activation function (Adap.", "subpage_snippet": "", "source": "alfredonunez.net", "link": "https://alfredonunez.net/papers/Kapoor_et_al_EAAI.pdf", "content": "In another work, Liu et al . ( 2022 ) utilized transfer learning for accurate temperature field in-version with limited observations, employing a PINN and optimal position selection.Neto, 2023), gradient-enhanced PINN ( gPINN ) ( Yu et al ., 2022 ), PINNs. with adaptive activation function (Adap."}
+{"idx": 6, "title": "Application of PINN to Define Roughness Coefficient", "date": "", "ddg_snippet": "CMAME 2022 , 393, 114823. 30. Difonzo, F.V.; Lopez, L. ; Pellegrino, S.F. Physics informed neural networks for learning the horizon size in bond-based peridynamic models.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/frontend/manuscript/3d40b3c543b7c973094dceff5d65ad6e/download_pub", "content": "CMAME 2022 , 393, 114823. 30. Difonzo, F.V.; Lopez, L. ; Pellegrino, S.F. Physics informed neural networks for learning the horizon size in bond-based peridynamic models."}
+{"idx": 7, "title": "(PDF) Sub-Sequential Physics-Informed Learning with State Space...", "date": "", "ddg_snippet": "Yang et al ., 2022 ; Gao et al ., 2022 ) are proposed to address. the time-dependency issue.cessing Systems, 35:8278–8290, 2022 a. 10. Sub-Sequential Physics-Informed Learning with State Space Model. Wang, S., Yu , X., and Perdikaris, P. When and why pinns.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388657053_Sub-Sequential_Physics-Informed_Learning_with_State_Space_Model", "content": "Yang et al ., 2022 ; Gao et al ., 2022 ) are proposed to address. the time-dependency issue.cessing Systems, 35:8278–8290, 2022 a. 10. Sub-Sequential Physics-Informed Learning with State Space Model. Wang, S., Yu , X., and Perdikaris, P. When and why pinns."}
+{"idx": 8, "title": "Unraveling the Design Pattern of Physics-Informed Neural Networks...", "date": "", "ddg_snippet": "[1] Yu et al ., Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems, arXiv, 2021. [2] Laurent et al ., An overview of gradient-enhanced metamodels with applications, Arch Computat Methods Eng, 2019.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/unraveling-the-design-pattern-of-physics-informed-neural-networks-part-04-c778f4829dde", "content": "[1] Yu et al ., Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems, arXiv, 2021. [2] Laurent et al ., An overview of gradient-enhanced metamodels with applications, Arch Computat Methods Eng, 2019."}
+{"idx": 9, "title": "Хотите похудеть – НЕ спрашивайте меня как. Часть... / Хабр", "date": "", "ddg_snippet": "Respiratory Illness Yu et al . (2023) studied the association between GLP-1 RAs and the risk of 12 respiratory diseases in patients with T2DM, obesity, or overweight. They found that semaglutide reduced the risk of respiratory diseases by 18%.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/806401/", "content": "Respiratory Illness Yu et al . (2023) studied the association between GLP-1 RAs and the risk of 12 respiratory diseases in patients with T2DM, obesity, or overweight. They found that semaglutide reduced the risk of respiratory diseases by 18%."}
diff --git a/data/sampled_jsons/github.com_coding-rachal_PMRDataset.jsonl b/data/sampled_jsons/github.com_coding-rachal_PMRDataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..991f7dfffff5917a50f831cfec6ac10851f6ab11
--- /dev/null
+++ b/data/sampled_jsons/github.com_coding-rachal_PMRDataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sign in to GitHub · GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/login", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 1, "title": "Github 上月趋势 | GitHub 中文社区", "date": "", "ddg_snippet": "#Awesome#A curated list of awesome commands, files, and workflows for Claude Code .", "subpage_snippet": "", "source": "www.github-zh.com", "link": "https://www.github-zh.com/trends/monthly", "content": "#Awesome#A curated list of awesome commands, files, and workflows for Claude Code ."}
+{"idx": 2, "title": "AI GitHub Search", "date": "", "ddg_snippet": "AI-powered GitHub repository search tool.", "subpage_snippet": "", "source": "www.gitsearchai.com", "link": "https://www.gitsearchai.com/", "content": "AI-powered GitHub repository search tool."}
+{"idx": 3, "title": "DietPi v9.17 - DietPi. com Docs", "date": "", "ddg_snippet": "As always, many smaller code performance and stability improvements, visual and spelling fixes have been done, too much to list all of them here. Check out all code changes of this release on GitHub : MichaIng/DietPi!7735.", "subpage_snippet": "", "source": "dietpi.com", "link": "https://dietpi.com/docs/releases/v9_17/", "content": "As always, many smaller code performance and stability improvements, visual and spelling fixes have been done, too much to list all of them here. Check out all code changes of this release on GitHub : MichaIng/DietPi!7735."}
+{"idx": 4, "title": "How to Update Files in GitHub ? - Life in Coding", "date": "", "ddg_snippet": "Why Update Files in GitHub? Fix Issues: Correct bugs or errors in code or documentation. Add Features: Enhance your project by adding new functionality.Navigate to GitHub . com . Open the repository containing the file you want to update.", "subpage_snippet": "", "source": "lifeincoding.com", "link": "https://lifeincoding.com/how-to-update-files-in-github/", "content": "Why Update Files in GitHub? Fix Issues: Correct bugs or errors in code or documentation. Add Features: Enhance your project by adding new functionality.Navigate to GitHub . com . Open the repository containing the file you want to update."}
+{"idx": 5, "title": "GitHub - OpenHands — AI-ассистент, который берёт задачи...", "date": "", "ddg_snippet": "ИНСТРУМЕНТЫ Гайд: Как запустить ИИ-модель для программирования локально (OpenHands LM, Code LLaMA и др.)", "subpage_snippet": "", "source": "www.ai-prompts.online", "link": "https://www.ai-prompts.online/threads/hand-with-fingers-splayed-openhands-ai-assistent-kotoryi-beret-zadachi-v-svoi-ruki.3482/", "content": "ИНСТРУМЕНТЫ Гайд: Как запустить ИИ-модель для программирования локально (OpenHands LM, Code LLaMA и др.)"}
+{"idx": 6, "title": "One moment, please...", "date": "", "ddg_snippet": "( Source) Food for thought. ancient- code . com .", "subpage_snippet": "", "source": "www.ancient-code.com", "link": "https://www.ancient-code.com/the-archons-the-divine-creators-of-the-cosmos-and-humanity/", "content": "( Source) Food for thought. ancient- code . com ."}
+{"idx": 7, "title": "Join us register and get 500rs", "date": "", "ddg_snippet": "Enter my referral code and get 100 coins!", "subpage_snippet": "", "source": "bingo101i.com", "link": "https://bingo101i.com/", "content": "Enter my referral code and get 100 coins!"}
+{"idx": 8, "title": "One moment, please...", "date": "", "ddg_snippet": "Please wait while your request is being verified...", "subpage_snippet": "", "source": "bingotingo.com", "link": "https://bingotingo.com/best-social-media-platforms/", "content": "Please wait while your request is being verified..."}
+{"idx": 9, "title": "DeepSeek-R1-0528: How to Run Locally | Unsloth Documentation", "date": "", "ddg_snippet": "If you want to use any of the quants that are larger than TQ1_0 (162GB) on Ollama, you need to first merge the 3 GGUF split files into 1 like the code below. Then you will need to run the model locally.", "subpage_snippet": "", "source": "docs.unsloth.ai", "link": "https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/deepseek-r1-0528-how-to-run-locally", "content": "If you want to use any of the quants that are larger than TQ1_0 (162GB) on Ollama, you need to first merge the 3 GGUF split files into 1 like the code below. Then you will need to run the model locally."}
diff --git a/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuningtreemainconfigs.jsonl b/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuningtreemainconfigs.jsonl
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index 0000000000000000000000000000000000000000..651e82a198a71a1a7c36fe8420fb924f52ec5223
--- /dev/null
+++ b/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuningtreemainconfigs.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "understanding_safety_finetuning/ssft at main · fiveai ... - GitHub", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/tree/main/ssft", "content": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning"}
+{"idx": 1, "title": "FiveAI - GitHub", "date": "", "ddg_snippet": "Python 30 6 0 0 Updated on Mar 26 MoCaE Public The official implementation of \"MoCaE: Mixture of Calibrated Experts Significantly Improves Accuracy in Object Detection\" Python 42 4 2 1 Updated on Mar 25 understanding_safety_finetuning Public Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai", "content": "Python 30 6 0 0 Updated on Mar 26 MoCaE Public The official implementation of \"MoCaE: Mixture of Calibrated Experts Significantly Improves Accuracy in Object Detection\" Python 42 4 2 1 Updated on Mar 25 understanding_safety_finetuning Public Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024)"}
+{"idx": 2, "title": "Actions · fiveai/understanding_safety_finetuning · GitHub", "date": "", "ddg_snippet": "GitHub Actions makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/actions", "content": "GitHub Actions makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub."}
+{"idx": 3, "title": "GitHub - fiveai/understanding_safety_finetuning: Official Code for What ...", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space."}
+{"idx": 4, "title": "understanding_safety_finetuning/README.md at main · fiveai ... - GitHub", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/README.md", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space."}
+{"idx": 5, "title": "Releases · fiveai/understanding_safety_finetuning - GitHub", "date": "", "ddg_snippet": "You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/releases", "content": "You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs ..."}
+{"idx": 6, "title": "GitHub Pages - Chapters", "date": "", "ddg_snippet": "Chapter 1 - Capabilities This chapter seeks to outline the recent important developments in AI and present some potential trajectories for developing highly capable AI systems. 113 min read · February 26, 2024 2024", "subpage_snippet": "", "source": "ai-safety-course.github.io", "link": "https://ai-safety-course.github.io/", "content": "Chapter 1 - Capabilities This chapter seeks to outline the recent important developments in AI and present some potential trajectories for developing highly capable AI systems. 113 min read · February 26, 2024 2024"}
+{"idx": 7, "title": "Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models", "date": "", "ddg_snippet": "Large Vision-Language Models (VLMs) have achieved remarkable performance across a wide range of tasks. However, their deployment in safety-critical domains poses significant challenges. Existing safety fine-tuning methods, which focus on textual or multimodal content, fall short in addressing challenging cases or disrupt the balance between helpfulness and harmlessness. Our evaluation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.18533", "content": "Large Vision-Language Models (VLMs) have achieved remarkable performance across a wide range of tasks. However, their deployment in safety-critical domains poses significant challenges. Existing safety fine-tuning methods, which focus on textual or multimodal content, fall short in addressing challenging cases or disrupt the balance between helpfulness and harmlessness. Our evaluation ..."}
+{"idx": 8, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these meth-ods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JEflV4nRlH", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these meth-ods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space."}
+{"idx": 9, "title": "What makes and breaks safety fine-tuning? a mechanistic study", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740879", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space."}
diff --git a/data/sampled_jsons/graph_neural_networks_spatiotemporal_remote_sensing_ultra_high_resolution.jsonl b/data/sampled_jsons/graph_neural_networks_spatiotemporal_remote_sensing_ultra_high_resolution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..da8392e1ef67beab63c5b302476994d3d75a69d5
--- /dev/null
+++ b/data/sampled_jsons/graph_neural_networks_spatiotemporal_remote_sensing_ultra_high_resolution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Semantic Segmentation of High-Resolution Remote Sensing Imagery via an ...", "date": "", "ddg_snippet": "Semantic segmentation of high-resolution remote sensing images is crucial in ecological evaluation, natural resource surveys, etc. Compared with CNN-based and transformer-based methods, graph neural networks (GNNs) have drawn increasing attention because they can flexibly model topologies of arbitrary irregular objects on graphs .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10887312", "content": "Semantic segmentation of high-resolution remote sensing images is crucial in ecological evaluation, natural resource surveys, etc. Compared with CNN-based and transformer-based methods, graph neural networks (GNNs) have drawn increasing attention because they can flexibly model topologies of arbitrary irregular objects on graphs ."}
+{"idx": 1, "title": "Cross-Hopping Graph Networks for Hyperspectral-High Spatial Resolution ...", "date": "", "ddg_snippet": "As we take stock of the contemporary issue, remote sensing images are gradually advancing towards hyperspectral- high spatial resolution (H2) double- high images. However, high resolution produces serious spatial heterogeneity and spectral variability while improving image resolution , which increases the difficulty of feature recognition. So as to make the best of spectral and spatial features ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2072-4292/16/17/3155", "content": "As we take stock of the contemporary issue, remote sensing images are gradually advancing towards hyperspectral- high spatial resolution (H2) double- high images. However, high resolution produces serious spatial heterogeneity and spectral variability while improving image resolution , which increases the difficulty of feature recognition. So as to make the best of spectral and spatial features ..."}
+{"idx": 2, "title": "Graph-based deep learning techniques for remote sensing applications ...", "date": "", "ddg_snippet": "Addressing the issue of correlation among neighboring land covers and the frontier blur in High-Resolution Remote Sensing Images (HRRSI), RSI-Net [58] utilized graph convolution networks to generate graph -level features (GLF) that can capture the entire frontier of RSIs, and CNN to encode high or low-stage features of RSIs (HLF).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1574013723000631", "content": "Addressing the issue of correlation among neighboring land covers and the frontier blur in High-Resolution Remote Sensing Images (HRRSI), RSI-Net [58] utilized graph convolution networks to generate graph -level features (GLF) that can capture the entire frontier of RSIs, and CNN to encode high or low-stage features of RSIs (HLF)."}
+{"idx": 3, "title": "Graph Neural Network for spatiotemporal data: methods and applications", "date": "", "ddg_snippet": "This article aims to provide a systematic and comprehensive overview of the technologies and applications of GNNs in the spatiotemporal domain. First, the ways of constructing graphs from spatiotemporal data are summarized to help domain experts understand how to generate graphs from various types of spatiotemporal data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.00012", "content": "This article aims to provide a systematic and comprehensive overview of the technologies and applications of GNNs in the spatiotemporal domain. First, the ways of constructing graphs from spatiotemporal data are summarized to help domain experts understand how to generate graphs from various types of spatiotemporal data."}
+{"idx": 4, "title": "Dual Learning-Based Graph Neural Network for Remote Sensing Image Super ...", "date": "", "ddg_snippet": "High-resolution (HR) remote sensing imagery plays a critical role in remote sensing image interpretation, and single image super- resolution (SISR) reconstruction technology is becoming increasingly valuable and significant. The state-of-the-art deep-learning-based SISR methods have demonstrated remarkable advantages while reconstructing complex texture details still remains a big challenge ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9861602", "content": "High-resolution (HR) remote sensing imagery plays a critical role in remote sensing image interpretation, and single image super- resolution (SISR) reconstruction technology is becoming increasingly valuable and significant. The state-of-the-art deep-learning-based SISR methods have demonstrated remarkable advantages while reconstructing complex texture details still remains a big challenge ..."}
+{"idx": 5, "title": "Lightweight remote sensing super-resolution with multi-scale graph ...", "date": "", "ddg_snippet": "Remote Sensing Super- Resolution (RS-SR) constitutes a pivotal component in the domain of remote sensing image analysis, aimed at enhancing the spatial resolution of low- resolution imagery. Recent advancements have seen deep learning techniques achieving substantial progress in the RS-SR field. Notably, Graph Neural Networks (GNNs) have emerged as a potent mechanism for processing remote ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320324009294", "content": "Remote Sensing Super- Resolution (RS-SR) constitutes a pivotal component in the domain of remote sensing image analysis, aimed at enhancing the spatial resolution of low- resolution imagery. Recent advancements have seen deep learning techniques achieving substantial progress in the RS-SR field. Notably, Graph Neural Networks (GNNs) have emerged as a potent mechanism for processing remote ..."}
+{"idx": 6, "title": "A Global-Local Cross-Attention Network for Ultra-high Resolution Remote ...", "date": "", "ddg_snippet": "1 Introduction With the rapid advancement of high-resolution Earth observation technology, semantic segmentation of ultra-high-resolution (UHR) remote sensing imagery has become a core technique for land cover classification and urban change detection[3].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.19406", "content": "1 Introduction With the rapid advancement of high-resolution Earth observation technology, semantic segmentation of ultra-high-resolution (UHR) remote sensing imagery has become a core technique for land cover classification and urban change detection[3]."}
+{"idx": 7, "title": "A Systematic Literature Review of Spatio-Temporal Graph Neural Network ...", "date": "", "ddg_snippet": "A Hybrid Model for Spatiotemporal Air Quality Prediction Based on Interpretable Neural Networks and a Graph Neural Network (Atmosphere, 2023) [link] Adaptive scalable spatio-temporal graph convolutional network for PM2.5 prediction (Engineering Applications of Artificial Intelligence, 2023) [link]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FlaGer99/SLR-Spatio-Temporal-GNN", "content": "A Hybrid Model for Spatiotemporal Air Quality Prediction Based on Interpretable Neural Networks and a Graph Neural Network (Atmosphere, 2023) [link] Adaptive scalable spatio-temporal graph convolutional network for PM2.5 prediction (Engineering Applications of Artificial Intelligence, 2023) [link]"}
+{"idx": 8, "title": "Temporal and spatial feature extraction using graph neural networks for ...", "date": "", "ddg_snippet": "To address this limitation, a Spatio-Temporal Feature Graph Neural Network (STF-GNN) was proposed, which integrated graph convolutional networks (GCN), gated recurrent units (GRU), and self-attention mechanisms to explicitly model multi-scale spatiotemporal dependencies among distributed monitoring stations.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0043135425004749", "content": "To address this limitation, a Spatio-Temporal Feature Graph Neural Network (STF-GNN) was proposed, which integrated graph convolutional networks (GCN), gated recurrent units (GRU), and self-attention mechanisms to explicitly model multi-scale spatiotemporal dependencies among distributed monitoring stations."}
+{"idx": 9, "title": "Spatially adaptive interaction network for semantic ... - Nature", "date": "", "ddg_snippet": "With the increasing prevalence of high-resolution remote sensing satellites in global Earth observation missions, high-resolution remote sensing data has become abundant and the primary source for ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-99428-4", "content": "With the increasing prevalence of high-resolution remote sensing satellites in global Earth observation missions, high-resolution remote sensing data has become abundant and the primary source for ..."}
diff --git a/data/sampled_jsons/h0Ak8A5yqw_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_ASR_Llama-2-7b-chat_16x.jsonl b/data/sampled_jsons/h0Ak8A5yqw_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_ASR_Llama-2-7b-chat_16x.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6503a90adaf101bfebf542f16efb42f8a8105b7
--- /dev/null
+++ b/data/sampled_jsons/h0Ak8A5yqw_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_ASR_Llama-2-7b-chat_16x.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Our findings show that the special attention head has a significant impact on safety . Ablating a single safety head allows aligned model (e.g., Llama-2-7b-chat ) to respond to 16 times more harmful queries, while only modifying 0.006% of the parameters, in contrast to the ~ 5% modification required in previous studies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.13708", "content": "Our findings show that the special attention head has a significant impact on safety . Ablating a single safety head allows aligned model (e.g., Llama-2-7b-chat ) to respond to 16 times more harmful queries, while only modifying 0.006% of the parameters, in contrast to the ~ 5% modification required in previous studies."}
+{"idx": 1, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Our findings show that the special attention head has a significant impact on safety . Ablating a single safety head allows the aligned model (e.g., Llama-2-7b-chat ) to respond to 16× ↑ more harmful queries, while only modifying 0.006% ↓ of the parameters, in contrast to the ∼ 5% modification re-quired in previous studies.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=h0Ak8A5yqw", "content": "Our findings show that the special attention head has a significant impact on safety . Ablating a single safety head allows the aligned model (e.g., Llama-2-7b-chat ) to respond to 16× ↑ more harmful queries, while only modifying 0.006% ↓ of the parameters, in contrast to the ∼ 5% modification re-quired in previous studies."}
+{"idx": 2, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "This section focuses on identifying safety parameters within attention heads . Two methods, Undifferentiated Attention and Scaling Contribution, are used to ablate the attention head . Safety Head ImPortant Score (Ships) is defined to evaluate the importance of attention heads based on the change in the probability of rejection responses to harmful queries. Experiments on Llama - 2 - 7b - chat ...", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper/arxiv/2410.13708", "content": "This section focuses on identifying safety parameters within attention heads . Two methods, Undifferentiated Attention and Scaling Contribution, are used to ablate the attention head . Safety Head ImPortant Score (Ships) is defined to evaluate the importance of attention heads based on the change in the probability of rejection responses to harmful queries. Experiments on Llama - 2 - 7b - chat ..."}
+{"idx": 3, "title": "ICLR 2025 On the Role of Attention Heads in Large Language Model Safety ...", "date": "", "ddg_snippet": "Our findings show that special attention head has a significant impact on safety . Ablating a single safety head allows aligned model (e.g., Llama-2-7b-chat ) to respond to **16 × ↑ ** more harmful queries, while only modifying **0.006\\%** ↓ of the parameters, in contrast to the ∼ **5\\%** modification required in previous studies.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/oral/31798", "content": "Our findings show that special attention head has a significant impact on safety . Ablating a single safety head allows aligned model (e.g., Llama-2-7b-chat ) to respond to **16 × ↑ ** more harmful queries, while only modifying **0.006\\%** ↓ of the parameters, in contrast to the ∼ **5\\%** modification required in previous studies."}
+{"idx": 4, "title": "Attention heads of large language models - ScienceDirect", "date": "", "ddg_snippet": "Large language models (LLMs) have demonstrated performance approaching human levels in tasks such as long-text comprehension and mathematical reasoning, but they remain black-box systems. Understanding the reasoning bottlenecks of LLMs remains a critical challenge, as these limitations are deeply tied to their internal architecture. Attention heads play a pivotal role in reasoning and are ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666389925000248", "content": "Large language models (LLMs) have demonstrated performance approaching human levels in tasks such as long-text comprehension and mathematical reasoning, but they remain black-box systems. Understanding the reasoning bottlenecks of LLMs remains a critical challenge, as these limitations are deeply tied to their internal architecture. Attention heads play a pivotal role in reasoning and are ..."}
+{"idx": 5, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "For Llama-2-7b-chat , head 2-26 emerges as the most crucial safety attention head . When ablated individually with the input template from Appendix B.1, it significantly weakens safety capability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v1", "content": "For Llama-2-7b-chat , head 2-26 emerges as the most crucial safety attention head . When ablated individually with the input template from Appendix B.1, it significantly weakens safety capability."}
+{"idx": 6, "title": "Attention Heads of Large Language Models: A Survey - GitHub", "date": "", "ddg_snippet": "Attention Heads of Large Language Models : A Survey (Awesome Attention Heads ) Important About this repo. This is a platform to get the latest research on different kinds of LLM's Attention Heads . Also, we released a survey based on these fantastic works. If you want to cite our work, here is our bibtex entry: CITATION.bib.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IAAR-Shanghai/Awesome-Attention-Heads", "content": "Attention Heads of Large Language Models : A Survey (Awesome Attention Heads ) Important About this repo. This is a platform to get the latest research on different kinds of LLM's Attention Heads . Also, we released a survey based on these fantastic works. If you want to cite our work, here is our bibtex entry: CITATION.bib."}
+{"idx": 7, "title": "On the Role of Attention Heads in Large Language Model Safety | AI ...", "date": "", "ddg_snippet": "Conclusion The paper's proposal of a \" Safety Head \" technique represents an important step forward in enhancing the safety and alignment of large language models . By isolating attention heads associated with the production of harmful content, the model can be trained to avoid generating that type of output in the future.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/role-attention-heads-large-language-model-safety", "content": "Conclusion The paper's proposal of a \" Safety Head \" technique represents an important step forward in enhancing the safety and alignment of large language models . By isolating attention heads associated with the production of harmful content, the model can be trained to avoid generating that type of output in the future."}
+{"idx": 8, "title": "PDF On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Ships can be used for 082 attributing safety attention head . Experimental results show that on three harmful query datasets, 083 using Ships to identify safe heads and using undifferentiated attention ablation (only modifying 084 ∼ 0.006% of the parameters) can improve the attack success rate ( ASR ) of Llama-2-7b-chat 085 f", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=ewuTt1DVMb&name=pdf", "content": "Ships can be used for 082 attributing safety attention head . Experimental results show that on three harmful query datasets, 083 using Ships to identify safe heads and using undifferentiated attention ablation (only modifying 084 ∼ 0.006% of the parameters) can improve the attack success rate ( ASR ) of Llama-2-7b-chat 085 f"}
+{"idx": 9, "title": "PDF arXiv:2410.13708v1 [cs.CL] 17 Oct 2024 - ResearchGate", "date": "", "ddg_snippet": "and Attention Output Do Not Transfer. As depicted in Figure 5a, when exam-ining the model Llama-2-7b-chat , there is minimal overlap between the top-10 attention heads identified by ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety/fulltext/6711f20e069cb92a811a75e8/On-the-Role-of-Attention-Heads-in-Large-Language-Model-Safety.pdf", "content": "and Attention Output Do Not Transfer. As depicted in Figure 5a, when exam-ining the model Llama-2-7b-chat , there is minimal overlap between the top-10 attention heads identified by ..."}
diff --git a/data/sampled_jsons/hierarchical_attention_mechanism_multi-scale_vision_year_2024.jsonl b/data/sampled_jsons/hierarchical_attention_mechanism_multi-scale_vision_year_2024.jsonl
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index 0000000000000000000000000000000000000000..52b21fddaf5f8d986dd37d5e8060c5f99d45118b
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) An attention -driven hierarchical multi - scale representation for...", "date": "", "ddg_snippet": "WHARTON, BEHERA, BERA: ATTENTION -DRIVEN HIERARCHICAL MULTI - SCALE 2.1 3 Generic visual recognition CNNs have remarkably enhanced the performance of large-scale and generic image classification [11, 24, 48].", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/64988754/An_attention_driven_hierarchical_multi_scale_representation_for_visual_recognition", "content": "WHARTON, BEHERA, BERA: ATTENTION -DRIVEN HIERARCHICAL MULTI - SCALE 2.1 3 Generic visual recognition CNNs have remarkably enhanced the performance of large-scale and generic image classification [11, 24, 48]."}
+{"idx": 1, "title": "Hierarchical Attention Mechanism", "date": "", "ddg_snippet": "A hierarchical attention mechanism operates by decomposing the attention process into multiple, structurally meaningful levels. This can involveMulti-hop/ multi - scale attention : Iterative refinement over hierarchies (HAM, Ham, HM-AN).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/hierarchical-attention-mechanism", "content": "A hierarchical attention mechanism operates by decomposing the attention process into multiple, structurally meaningful levels. This can involveMulti-hop/ multi - scale attention : Iterative refinement over hierarchies (HAM, Ham, HM-AN)."}
+{"idx": 2, "title": "(PDF) Small object detection based on hierarchical attention ...", "date": "", "ddg_snippet": "combination of multi - scale separable detection and multi - scale feature fusion mechanism . to obtain richer contextual information for feature fusion while solving the misalignment.incorporate an attention mechanism . Some computer vision .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373346015_Small_object_detection_based_on_hierarchical_attention_mechanism_and_multi-scale_separable_detection", "content": "combination of multi - scale separable detection and multi - scale feature fusion mechanism . to obtain richer contextual information for feature fusion while solving the misalignment.incorporate an attention mechanism . Some computer vision ."}
+{"idx": 3, "title": "Mitigating spectral bias for the multiscale operator learning", "date": "", "ddg_snippet": "Hierarchical Discretization. Vanilla Attention Mechanism .HANO features a scale -adaptive interaction range and self- attentions over a hierarchy of levels, enabling nested feature computation with controllable linear cost and encoding/decoding of multiscale solution space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.10890", "content": "Hierarchical Discretization. Vanilla Attention Mechanism .HANO features a scale -adaptive interaction range and self- attentions over a hierarchy of levels, enabling nested feature computation with controllable linear cost and encoding/decoding of multiscale solution space."}
+{"idx": 4, "title": "HA-FPN: Hierarchical Attention Feature Pyramid Network for Object...", "date": "", "ddg_snippet": "Additionally, the Cross- attention Multi - scale Vision Transformer (CrossViT) [14] employs a dual-branch transformer to process picture patches of varying sizes, while the Twins [15] approach blends local and global attention techniques to improve feature representation.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10181737/", "content": "Additionally, the Cross- attention Multi - scale Vision Transformer (CrossViT) [14] employs a dual-branch transformer to process picture patches of varying sizes, while the Twins [15] approach blends local and global attention techniques to improve feature representation."}
+{"idx": 5, "title": "Hierarchical Attention and Semantic Refinement for Advanced", "date": "", "ddg_snippet": "A Hierarchical Context-Aware Attention (HCAA) mechanism : This module operates at the critical interface been the visual encoder and the language decoder. Unlike conventional attention mechanisms that often apply a single-pass, uniform focus across visual features, HCAA...", "subpage_snippet": "", "source": "jowua.com", "link": "https://jowua.com/wp-content/uploads/2025/08/2025.I2.023.pdf", "content": "A Hierarchical Context-Aware Attention (HCAA) mechanism : This module operates at the critical interface been the visual encoder and the language decoder. Unlike conventional attention mechanisms that often apply a single-pass, uniform focus across visual features, HCAA..."}
+{"idx": 6, "title": "Multiscale Attention Mechanism | S-Logix", "date": "", "ddg_snippet": "Difficulty in Handling Diverse Scales : Multiscale attention mechanisms are designed to deal with input data that spans multiple spatial, temporal, or semantic scales . However, balancing the attention across these different scales is a complex task.", "subpage_snippet": "", "source": "slogix.in", "link": "https://slogix.in/machine-learning/research-topics-in-multiscale-attention-mechanism/", "content": "Difficulty in Handling Diverse Scales : Multiscale attention mechanisms are designed to deal with input data that spans multiple spatial, temporal, or semantic scales . However, balancing the attention across these different scales is a complex task."}
+{"idx": 7, "title": "Visual Attention Mechanism in Deep Learning and", "date": "", "ddg_snippet": "Hierarchical Multi - scale Attention Networks for Action Recognition.Subsequently, the attention mechanism in computer vision and the attention mechanism in natural language processing are intertwined and promote each.", "subpage_snippet": "", "source": "livrepository.liverpool.ac.uk", "link": "https://livrepository.liverpool.ac.uk/3028892/1/201159131_Nov2018.pdf", "content": "Hierarchical Multi - scale Attention Networks for Action Recognition.Subsequently, the attention mechanism in computer vision and the attention mechanism in natural language processing are intertwined and promote each."}
+{"idx": 8, "title": "paper review:\" Hierarchical multi - scale attention for semantic...\"", "date": "", "ddg_snippet": "hierarchical attention mechanism . By using this, the model can handle multiple , non-fixed scaled image inputs. auto-labelling to get find labels from coarse labels and use then in training to increase performance. Hierarchical attention mechanism .", "subpage_snippet": "", "source": "chadrick-kwag.net", "link": "https://chadrick-kwag.net/posts/paper-reviewhierarchical-multi-scale-attention-for-semantic-segmentation/", "content": "hierarchical attention mechanism . By using this, the model can handle multiple , non-fixed scaled image inputs. auto-labelling to get find labels from coarse labels and use then in training to increase performance. Hierarchical attention mechanism ."}
+{"idx": 9, "title": "Hierarchical query design and distributed attention in transformer for...", "date": "", "ddg_snippet": "The hierarchical attention mechanism in the deformable transformer decoder enables the model to focus dynamically on specific spatial regions while maintaining a global understanding of the scene.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-16752-5?error=cookies_not_supported&code=a21fb591-6254-46c8-9827-f12660aeedb4", "content": "The hierarchical attention mechanism in the deformable transformer decoder enables the model to focus dynamically on specific spatial regions while maintaining a global understanding of the scene."}
diff --git a/data/sampled_jsons/hierarchical_overlapping_clustering_cost_function.jsonl b/data/sampled_jsons/hierarchical_overlapping_clustering_cost_function.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1423580dff65b73a8fd9cd5d63e20068559fc372
--- /dev/null
+++ b/data/sampled_jsons/hierarchical_overlapping_clustering_cost_function.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2.3. Clustering", "date": "", "ddg_snippet": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function , that, given train ...", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/clustering.html", "content": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function , that, given train ..."}
+{"idx": 1, "title": "(PDF) Hierarchical Clustering", "date": "", "ddg_snippet": "To implement a hierarchical clustering algorithm, one has to choose a linkage function (single linkage, average linkage, complete linkage, Ward ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/314700681_Hierarchical_Clustering", "content": "To implement a hierarchical clustering algorithm, one has to choose a linkage function (single linkage, average linkage, complete linkage, Ward ..."}
+{"idx": 2, "title": "(PDF) Cognitive Manager for Hierarchical Cluster Networks Based", "date": "", "ddg_snippet": "The article shows the idea of the Cognitive Manager (CM), consisting of three logical modules: Clustering (CL), Clusters Graph Coloring (CGC) and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/271715914_Cognitive_Manager_for_Hierarchical_Cluster_Networks_Based_on_Multi-Stage_Machine_Method", "content": "The article shows the idea of the Cognitive Manager (CM), consisting of three logical modules: Clustering (CL), Clusters Graph Coloring (CGC) and ..."}
+{"idx": 3, "title": "graphs - Choice of algorithm for hierarchical clustering for", "date": "", "ddg_snippet": "Choice of algorithm for hierarchical clustering for minimizing network communication costs ... Clustering \" is not the right kind of problem ...", "subpage_snippet": "", "source": "cs.stackexchange.com", "link": "https://cs.stackexchange.com/questions/28821/choice-of-algorithm-for-hierarchical-clustering-for-minimizing-network-communica", "content": "Choice of algorithm for hierarchical clustering for minimizing network communication costs ... Clustering \" is not the right kind of problem ..."}
+{"idx": 4, "title": "Hierarchical Level-Wise News Article Clustering via", "date": "", "ddg_snippet": "In this work, we present a novel, scalable, interpretable, hierarchical , and multilingual approach to clustering news articles and social media data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00277v1", "content": "In this work, we present a novel, scalable, interpretable, hierarchical , and multilingual approach to clustering news articles and social media data."}
+{"idx": 5, "title": "Using hierarchical clustering to generate a bidding system -", "date": "", "ddg_snippet": "... using hierarchical clustering ... I like the width of a cluster as the average IMP cost if partner cannot tell individual hands in a cluster apart.", "subpage_snippet": "", "source": "www.bridgebase.com", "link": "https://www.bridgebase.com/forums/topic/88512-using-hierarchical-clustering-to-generate-a-bidding-system/", "content": "... using hierarchical clustering ... I like the width of a cluster as the average IMP cost if partner cannot tell individual hands in a cluster apart."}
+{"idx": 6, "title": "Cost Function for Spanning Tree (ELK)", "date": "", "ddg_snippet": "The cost function is used in the creation of the spanning tree. ... Whether to run a supplementary scanline overlap check.", "subpage_snippet": "", "source": "eclipse.dev", "link": "https://eclipse.dev/elk/reference/options/org-eclipse-elk-processingOrder-spanningTreeCostFunction.html", "content": "The cost function is used in the creation of the spanning tree. ... Whether to run a supplementary scanline overlap check."}
+{"idx": 7, "title": "On hierarchical clustering-based approach for RDDBS design |", "date": "", "ddg_snippet": "... the proposed methodology, including the technique’s heuristics and architecture, fragmentation and allocation cost models, and site clustering .", "subpage_snippet": "", "source": "journalofbigdata.springeropen.com", "link": "https://journalofbigdata.springeropen.com/articles/10.1186/s40537-023-00849-7", "content": "... the proposed methodology, including the technique’s heuristics and architecture, fragmentation and allocation cost models, and site clustering ."}
+{"idx": 8, "title": "US11392621B1 - Unsupervised information-based hierarchical", "date": "", "ddg_snippet": "... hierarchical cluster analyzer features a data mining technique referred to herein as random intersection leaves (RIL), which cooperates with a ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11392621B1/en", "content": "... hierarchical cluster analyzer features a data mining technique referred to herein as random intersection leaves (RIL), which cooperates with a ..."}
+{"idx": 9, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "by Y Pan — We initiate the theoretical study of hierarchical overlapping clustering from the perspectives of cost function, algorithm and experiments.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "by Y Pan — We initiate the theoretical study of hierarchical overlapping clustering from the perspectives of cost function, algorithm and experiments."}
diff --git a/data/sampled_jsons/hierarchical_overlapping_graph_clustering_optimization_year_2023.jsonl b/data/sampled_jsons/hierarchical_overlapping_graph_clustering_optimization_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..059feda4eee86d1c8b094b67c17a93336d567379
--- /dev/null
+++ b/data/sampled_jsons/hierarchical_overlapping_graph_clustering_optimization_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering Function Algorithm and Scalability ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 1, "title": "A hierarchical overlapping community detection method based on closed ...", "date": "", "ddg_snippet": "These base elements can then have common vertices and naturally provide the possibility of overlap. The proposed community detection method uses hierarchical agglomerative clustering on the 2-edge-connected component of the graph . Communities are constructed from maximal cliques as base elements.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0020025524001841", "content": "These base elements can then have common vertices and naturally provide the possibility of overlap. The proposed community detection method uses hierarchical agglomerative clustering on the 2-edge-connected component of the graph . Communities are constructed from maximal cliques as base elements."}
+{"idx": 2, "title": "Overlapping community detection in weighted networks via hierarchical ...", "date": "", "ddg_snippet": "4.1 Dissimilarities for graph hierarchical agglomerative clustering We introduce dissimilarities between clusters based on the wCT −distance, and the weight of overlap in a weighted graph .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11515960/", "content": "4.1 Dissimilarities for graph hierarchical agglomerative clustering We introduce dissimilarities between clusters based on the wCT −distance, and the weight of overlap in a weighted graph ."}
+{"idx": 3, "title": "PDF Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "In this paper we study eficient hierarchical clustering for graphs with a clear structure of clusters. We prove that, un-der two different conditions of an input graph G that charac-terise its cluster-structure, one can construct in nearly-linear time1 an O(1)-approximate HC tree T of G with respect to Dasgupta's cost.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/laenen23a/laenen23a.pdf", "content": "In this paper we study eficient hierarchical clustering for graphs with a clear structure of clusters. We prove that, un-der two different conditions of an input graph G that charac-terise its cluster-structure, one can construct in nearly-linear time1 an O(1)-approximate HC tree T of G with respect to Dasgupta's cost."}
+{"idx": 4, "title": "PDF Overlapping Hierarchical Clustering (OHC) - inria.hal.science", "date": "", "ddg_snippet": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and com-bines the advantages of hierarchies with the precision of a less arbitrary clustering .", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and com-bines the advantages of hierarchies with the precision of a less arbitrary clustering ."}
+{"idx": 5, "title": "Overlapping Hierarchical Clustering (OHC) | SpringerLink", "date": "", "ddg_snippet": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-44584-3_21", "content": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering ."}
+{"idx": 6, "title": "An Edge-Based Approach to Partitioning and Overlapping Graph Clustering ...", "date": "", "ddg_snippet": "Graph clustering is an NP-class problem [9] that can be classified into three distinct categories, partitioning, overlapping , and hierarchical clustering [2]. Section 3 elaborates on this further.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/14/1/380", "content": "Graph clustering is an NP-class problem [9] that can be classified into three distinct categories, partitioning, overlapping , and hierarchical clustering [2]. Section 3 elaborates on this further."}
+{"idx": 7, "title": "PDF Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs", "date": "", "ddg_snippet": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2021/file/4d68e143defa221fead61c84de7527a3-Paper.pdf", "content": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ..."}
+{"idx": 8, "title": "Hierarchical overlapping clustering: cost function, algorithm and ...", "date": "", "ddg_snippet": "To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via several intuitive properties, and develop an approximation algorithm that achieves a provably constant approximation factor for its dual version.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via several intuitive properties, and develop an approximation algorithm that achieves a provably constant approximation factor for its dual version."}
+{"idx": 9, "title": "Hierarchical Clustering: Objective Functions and Algorithms", "date": "", "ddg_snippet": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a \"good\" hierarchical ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/fullHtml/10.1145/3321386", "content": "Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a \"good\" hierarchical ..."}
diff --git a/data/sampled_jsons/httpsaclanthology.org2025.findings-acl.1113.jsonl b/data/sampled_jsons/httpsaclanthology.org2025.findings-acl.1113.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..10d202e18c80ef70b1f4d6c122ecb8c7afc49d24
--- /dev/null
+++ b/data/sampled_jsons/httpsaclanthology.org2025.findings-acl.1113.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CRAB: Cross-environment Agent Benchmark for ...", "date": "", "ddg_snippet": "by T Xu · 2025 · Cited by 25 — The experimental results demonstrate that the single agent with GPT-4o achieves the best completion ratio of 38.01%. Anthology ID: 2025 . findings - acl . 1113 ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.1113/", "content": "by T Xu · 2025 · Cited by 25 — The experimental results demonstrate that the single agent with GPT-4o achieves the best completion ratio of 38.01%. Anthology ID: 2025 . findings - acl . 1113 ..."}
+{"idx": 1, "title": "bib", "date": "", "ddg_snippet": "... https :// aclanthology . org / 2025 . findings - acl . 1113 /\", doi = \"10.18653/v1/ 2025 . findings - acl . 1113 \", pages = \"21607--21647\", ISBN = \"979-8-89176-256-5\" }", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.1113.bib", "content": "... https :// aclanthology . org / 2025 . findings - acl . 1113 /\", doi = \"10.18653/v1/ 2025 . findings - acl . 1113 \", pages = \"21607--21647\", ISBN = \"979-8-89176-256-5\" }"}
+{"idx": 2, "title": "Annual Meeting of the Association for Computational ...", "date": "", "ddg_snippet": "... Findings of the Association for Computational Linguistics: ACL 2025 1388 papers; Proceedings of the Sixth Workshop on African Natural Language Processing ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/events/acl-2025/", "content": "... Findings of the Association for Computational Linguistics: ACL 2025 1388 papers; Proceedings of the Sixth Workshop on African Natural Language Processing ..."}
+{"idx": 3, "title": "Look Both Ways and No Sink: Converting LLMs into Text ...", "date": "", "ddg_snippet": "by Z Lin · 2025 — Anthology ID: 2025 . acl -long. 1113 ; Volume: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ; Month ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1113/", "content": "by Z Lin · 2025 — Anthology ID: 2025 . acl -long. 1113 ; Volume: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ; Month ..."}
+{"idx": 4, "title": "Comparing large Language models and human annotators ...", "date": "", "ddg_snippet": "by L Bojić · 2025 · Cited by 12 — This research concludes that LLMs, especially GPT-4, can effectively replicate human analysis in sentiment and political leaning.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-96508-3", "content": "by L Bojić · 2025 · Cited by 12 — This research concludes that LLMs, especially GPT-4, can effectively replicate human analysis in sentiment and political leaning."}
+{"idx": 5, "title": "A Survey on Large Multimodal Reasoning Models", "date": "", "ddg_snippet": "8 May 2025 — We present a comprehensive and structured survey of multimodal reasoning research, organized around a four-stage developmental roadmap.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04921v1", "content": "8 May 2025 — We present a comprehensive and structured survey of multimodal reasoning research, organized around a four-stage developmental roadmap."}
+{"idx": 6, "title": "Revisions", "date": "", "ddg_snippet": "Our empirical findings broaden understanding of the types, relative ... PDF: https :// aclanthology . org /2024.emnlp-main. 1113 .pdf ... © 2025 OpenReview.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/revisions?id=91OSGeWNIl", "content": "Our empirical findings broaden understanding of the types, relative ... PDF: https :// aclanthology . org /2024.emnlp-main. 1113 .pdf ... © 2025 OpenReview."}
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+{"idx": 5, "title": "[ 2305 . 14709 ] Regret Matching+: (In) Stability and Fast Convergence in...", "date": "", "ddg_snippet": "Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability."}
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+{"idx": 7, "title": "ABS light suddenly appeared? | EK9. org JDM EK9 Honda Civic Type...", "date": "", "ddg_snippet": "Today my car was fine drove it to my mates to underseal behind the bumpers, raise the coilovers, and strip some interior out to remove the bolted in head unit...", "subpage_snippet": "", "source": "www.ek9.org", "link": "https://www.ek9.org/index.php?threads/abs-light-suddenly-appeared.11490/", "content": "Today my car was fine drove it to my mates to underseal behind the bumpers, raise the coilovers, and strip some interior out to remove the bolted in head unit..."}
+{"idx": 8, "title": "Motion (The Org Manual)", "date": "", "ddg_snippet": "2.3 Motion ¶. The following commands jump to other headlines in the buffer. Jump to a different place without changing the current outline visibility. Shows the document structure in a...", "subpage_snippet": "", "source": "orgmode.org", "link": "https://orgmode.org/manual/Motion.html", "content": "2.3 Motion ¶. The following commands jump to other headlines in the buffer. Jump to a different place without changing the current outline visibility. Shows the document structure in a..."}
+{"idx": 9, "title": "Job 21 - Then Job answered and said: “Keep listening to my... | ESV. org", "date": "", "ddg_snippet": "Then Job answered and said: “Keep listening to my words, and let this be your comfort. Bear with me, and I will speak, and after I have spoken, mock on. As for me, is my complaint against...", "subpage_snippet": "", "source": "www.esv.org", "link": "https://www.esv.org/Job+21/", "content": "Then Job answered and said: “Keep listening to my words, and let this be your comfort. Bear with me, and I will speak, and after I have spoken, mock on. As for me, is my complaint against..."}
diff --git a/data/sampled_jsons/httpsarxiv.orghtml2406.14532v1.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2406.14532v1.jsonl
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+{"idx": 0, "title": "[2406.14532] RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data ..."}
+{"idx": 1, "title": "HTML Görüntüleyici - Çevrimiçi HTML Editörü ve Önizleme Aracı", "date": "", "ddg_snippet": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç.", "subpage_snippet": "", "source": "htmlonline.org", "link": "https://htmlonline.org/tr/", "content": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç."}
+{"idx": 2, "title": "Login / Member's access to the HTML 6 Editor", "date": "", "ddg_snippet": "There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout.", "subpage_snippet": "", "source": "html6.com", "link": "https://html6.com/login/", "content": "There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout."}
+{"idx": 3, "title": "Learn HTML - Free Interactive HTML Tutorial", "date": "", "ddg_snippet": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast.", "subpage_snippet": "", "source": "www.learn-html.org", "link": "https://www.learn-html.org/", "content": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast."}
+{"idx": 4, "title": "HTMLbook. org - HTML , CSS, JavaScript", "date": "", "ddg_snippet": "Руководство для тех, кто увлекается веб-разработкой, создает сайты или просто хочет узнать что такое HTML , CSS и JavaScript.", "subpage_snippet": "", "source": "htmlbook.org", "link": "https://htmlbook.org/", "content": "Руководство для тех, кто увлекается веб-разработкой, создает сайты или просто хочет узнать что такое HTML , CSS и JavaScript."}
+{"idx": 5, "title": "Images in HTML export (The Org Manual)", "date": "", "ddg_snippet": "The HTML export backend has features to convert Org image links to HTML inline images and HTML clickable image links. When the link in the Org file has no description, the HTML export...", "subpage_snippet": "", "source": "orgmode.org", "link": "https://orgmode.org/manual/Images-in-HTML-export.html", "content": "The HTML export backend has features to convert Org image links to HTML inline images and HTML clickable image links. When the link in the Org file has no description, the HTML export..."}
+{"idx": 6, "title": "Online PDF to HTML Converter: Online converting PDF Files to HTML ...", "date": "", "ddg_snippet": "An easy tool to convert your PDF files into HTML Format. Upload your file and get your PDF File in HTML !", "subpage_snippet": "", "source": "www.pdf2html.org", "link": "https://www.pdf2html.org/", "content": "An easy tool to convert your PDF files into HTML Format. Upload your file and get your PDF File in HTML !"}
+{"idx": 7, "title": "4 The elements of HTML | HTML 5.1", "date": "", "ddg_snippet": "4.8.14.13 User interface. 4.8.14.14 Time ranges. 4.8.14.15 The TrackEvent interface. 4.8.14.16 Event summary. 4.8.14.17 Security and privacy considerations. 4.8.14.18 Best practices for...", "subpage_snippet": "", "source": "www.w3.org", "link": "https://www.w3.org/TR/2015/WD-html51-20150417/semantics.html", "content": "4.8.14.13 User interface. 4.8.14.14 Time ranges. 4.8.14.15 The TrackEvent interface. 4.8.14.16 Event summary. 4.8.14.17 Security and privacy considerations. 4.8.14.18 Best practices for..."}
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diff --git a/data/sampled_jsons/httpsarxiv.orghtml2406.15469v2.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2406.15469v2.jsonl
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index 0000000000000000000000000000000000000000..dc1f93dfaed9bd9ecc131d4c7ea6304ba1f9034a
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+{"idx": 0, "title": "[2406.15469v2] SimXRD-4M: Big Simulated X-ray Diffraction Data ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2406.15469v2: SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.15469v2", "content": "Abstract page for arXiv paper 2406.15469v2: SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification"}
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+{"idx": 2, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/", "content": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents."}
+{"idx": 3, "title": "Search for articles - arXiv info", "date": "", "ddg_snippet": "All arXiv submissions are assigned a unique identifier of the form yymm.nnnnn (or arch-ive/yymmnnn for older submissions). To retrieve the abstract page a paper simply enter the identifier in the \" Search or Article-id \" box in the top right of most pages.", "subpage_snippet": "", "source": "info.arxiv.org", "link": "https://info.arxiv.org/help/find.html", "content": "All arXiv submissions are assigned a unique identifier of the form yymm.nnnnn (or arch-ive/yymmnnn for older submissions). To retrieve the abstract page a paper simply enter the identifier in the \" Search or Article-id \" box in the top right of most pages."}
+{"idx": 4, "title": "EVCL: Elastic Variational Continual Learning with Weight Consolidation", "date": "", "ddg_snippet": "Abstract Continual learning aims to allow models to learn new tasks without forgetting what has been learned before. This work introduces Elastic Variational Continual Learning with Weight Consolidation (EVCL), a novel hybrid model that integrates the variational posterior approximation mechanism of Variational Continual Learning (VCL) with the regularization-based parameter-protection ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15972v1", "content": "Abstract Continual learning aims to allow models to learn new tasks without forgetting what has been learned before. This work introduces Elastic Variational Continual Learning with Weight Consolidation (EVCL), a novel hybrid model that integrates the variational posterior approximation mechanism of Variational Continual Learning (VCL) with the regularization-based parameter-protection ..."}
+{"idx": 5, "title": "Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking ...", "date": "", "ddg_snippet": "Instructions for reporting errors We are continuing to improve HTML versions of papers, and your feedback helps enhance accessibility and mobile support. To report errors in the HTML that will help us improve conversion and rendering, choose any of the methods listed below: Click the \"Report Issue\" button. Open a report feedback form via keyboard, use \" Ctrl + ? \". Make a text selection and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09172v1", "content": "Instructions for reporting errors We are continuing to improve HTML versions of papers, and your feedback helps enhance accessibility and mobile support. To report errors in the HTML that will help us improve conversion and rendering, choose any of the methods listed below: Click the \"Report Issue\" button. Open a report feedback form via keyboard, use \" Ctrl + ? \". Make a text selection and ..."}
+{"idx": 6, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "Abstract Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. Although machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established benchmarks. To address this, we introduce SimXRD-4M, the largest open-source ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "Abstract Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. Although machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established benchmarks. To address this, we introduce SimXRD-4M, the largest open-source ..."}
+{"idx": 7, "title": "Comparison of fine-tuning strategies for transfer learning in medical ...", "date": "", "ddg_snippet": "1 Introduction Medical image analysis involves the extraction of vital information from medical images for diagnostic and therapeutic purposes. While advances in imaging technologies facilitate early detection and precise disease identification, manual analysis remains labor-intensive and error-prone, leading to inconsistent interpretations [1]. To overcome these challenges, automated ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.10050v1", "content": "1 Introduction Medical image analysis involves the extraction of vital information from medical images for diagnostic and therapeutic purposes. While advances in imaging technologies facilitate early detection and precise disease identification, manual analysis remains labor-intensive and error-prone, leading to inconsistent interpretations [1]. To overcome these challenges, automated ..."}
+{"idx": 8, "title": "Efficient Human Pose Estimation: Leveraging Advanced Techniques with ...", "date": "", "ddg_snippet": "Human pose estimation presents a critical challenge within the field of computer vision, bearing significant implications across a diverse array of applications, ranging from interactive gaming and virtual reality to clinical rehabilitation and security. Notwithstanding the substantial advancements catalysed by deep learning technologies, real-world environments' dynamic and often ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15649v1", "content": "Human pose estimation presents a critical challenge within the field of computer vision, bearing significant implications across a diverse array of applications, ranging from interactive gaming and virtual reality to clinical rehabilitation and security. Notwithstanding the substantial advancements catalysed by deep learning technologies, real-world environments' dynamic and often ..."}
+{"idx": 9, "title": "Less is More: Accurate Speech Recognition & Translation", "date": "", "ddg_snippet": "Abstract Recent advances in speech recognition and translation rely on hundreds of thousands of hours of Internet speech data. We argue that state-of-the art accuracy can be reached without relying on web-scale data. Canary - multilingual ASR and speech translation model, outperforms current state-of-the-art models - Whisper, OWSM, and Seamless-M4T on English, French, Spanish, and German ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.19674v1", "content": "Abstract Recent advances in speech recognition and translation rely on hundreds of thousands of hours of Internet speech data. We argue that state-of-the art accuracy can be reached without relying on web-scale data. Canary - multilingual ASR and speech translation model, outperforms current state-of-the-art models - Whisper, OWSM, and Seamless-M4T on English, French, Spanish, and German ..."}
diff --git a/data/sampled_jsons/httpsarxiv.orghtml2410.09543v1.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2410.09543v1.jsonl
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+{"idx": 2, "title": "soarXiv - fly through arXiv", "date": "", "ddg_snippet": "soarXiv is a visual exploration of the arXiv, a repository of scientific papers. It is a tool for exploring the arXiv in a new way, and for finding new papers to read.", "subpage_snippet": "", "source": "soarxiv.org", "link": "https://soarxiv.org/", "content": "soarXiv is a visual exploration of the arXiv, a repository of scientific papers. It is a tool for exploring the arXiv in a new way, and for finding new papers to read."}
+{"idx": 3, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/", "content": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents."}
+{"idx": 4, "title": "Diffusion Models are Evolutionary Algorithms - arXiv.org", "date": "", "ddg_snippet": "Abstract In a convergence of machine learning and biology, we reveal that diffusion models are evolutionary algorithms. By considering evolution as a denoising process and reversed evolution as diffusion, we mathematically demonstrate that diffusion models inherently perform evolutionary algorithms, naturally encompassing selection, mutation, and reproductive isolation. Building on this ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02543v2", "content": "Abstract In a convergence of machine learning and biology, we reveal that diffusion models are evolutionary algorithms. By considering evolution as a denoising process and reversed evolution as diffusion, we mathematically demonstrate that diffusion models inherently perform evolutionary algorithms, naturally encompassing selection, mutation, and reproductive isolation. Building on this ..."}
+{"idx": 5, "title": "[2506.09543] Disorder-induced suppression of superconductivity in ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2506.09543: Disorder-induced suppression of superconductivity in infinite-layer nickelates", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.09543", "content": "Abstract page for arXiv paper 2506.09543: Disorder-induced suppression of superconductivity in infinite-layer nickelates"}
+{"idx": 6, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Figure 1: Overview of the Boltzmann Alignment technique. Left: inference with a protein inverse folding model. Right: illustration of thermodynamic cycle in the modulation of protein-protein interactions. 1 Introduction", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "Figure 1: Overview of the Boltzmann Alignment technique. Left: inference with a protein inverse folding model. Right: illustration of thermodynamic cycle in the modulation of protein-protein interactions. 1 Introduction"}
+{"idx": 7, "title": "Two Heads Are Better Than One: A Multi-Agent System Has the Potential ...", "date": "", "ddg_snippet": "Figure 1: The proposed LLM-based multi-agent system, VirSci, consists of five key steps: Collaborator Selection, where a research team is assembled; Topic Discussion, where the research topic is determined; Idea Generation, where team members propose and refine ideas; Novelty Assessment, where ideas are evaluated and voted on to select the best one; and Abstract Generation, where the selected ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09403v1", "content": "Figure 1: The proposed LLM-based multi-agent system, VirSci, consists of five key steps: Collaborator Selection, where a research team is assembled; Topic Discussion, where the research topic is determined; Idea Generation, where team members propose and refine ideas; Novelty Assessment, where ideas are evaluated and voted on to select the best one; and Abstract Generation, where the selected ..."}
+{"idx": 8, "title": "Inference Scaling for Long-Context Retrieval Augmented Generation", "date": "", "ddg_snippet": "Abstract The scaling of inference computation has unlocked the potential of long-context large language models (LLMs) across diverse settings. For knowledge-intensive tasks, the increased compute is often allocated to incorporate more external knowledge. However, without effectively utilizing such knowledge, solely expanding context does not always enhance performance. In this work, we ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.04343v1", "content": "Abstract The scaling of inference computation has unlocked the potential of long-context large language models (LLMs) across diverse settings. For knowledge-intensive tasks, the increased compute is often allocated to incorporate more external knowledge. However, without effectively utilizing such knowledge, solely expanding context does not always enhance performance. In this work, we ..."}
+{"idx": 9, "title": "Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR ...", "date": "", "ddg_snippet": "Abstract This study examines the alignment of Conference on Computer Vision and Pattern Recognition (CVPR) research with the principles of the \"bitter lesson\" proposed by Rich Sutton. We analyze two decades of CVPR abstracts and titles using large language models (LLMs) to assess the field's embracement of these principles. Our methodology leverages state-of-the-art natural language ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09649v1", "content": "Abstract This study examines the alignment of Conference on Computer Vision and Pattern Recognition (CVPR) research with the principles of the \"bitter lesson\" proposed by Rich Sutton. We analyze two decades of CVPR abstracts and titles using large language models (LLMs) to assess the field's embracement of these principles. Our methodology leverages state-of-the-art natural language ..."}
diff --git a/data/sampled_jsons/httpsarxiv.orghtml2502.01846v2_year_2024.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2502.01846v2_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0f7835d4e89244c21b5142dc4a254be1909f650
--- /dev/null
+++ b/data/sampled_jsons/httpsarxiv.orghtml2502.01846v2_year_2024.jsonl
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+{"idx": 0, "title": "HTTPS FAQs - Transparency Report Help Center - Google Help", "date": "", "ddg_snippet": "HTTPS is an encrypted HTTP connection, making it more secure. You can tell if your connection to a website is secure if you see HTTPS rather than HTTP in the URL.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/transparencyreport/answer/7381231?hl=en", "content": "HTTPS is an encrypted HTTP connection, making it more secure. You can tell if your connection to a website is secure if you see HTTPS rather than HTTP in the URL."}
+{"idx": 1, "title": "How to recover your Google Account or Gmail", "date": "", "ddg_snippet": "If you forgot your password or username, or you can’t get verification codes, follow these steps to recover your Google Account. That way, you can use services like Gmail, Pho", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/accounts/answer/7682439?hl=en", "content": "If you forgot your password or username, or you can’t get verification codes, follow these steps to recover your Google Account. That way, you can use services like Gmail, Pho"}
+{"idx": 2, "title": "Sign in to your Admin console - Google Workspace Admin Help", "date": "", "ddg_snippet": "If you have access to an administrator (or admin) account, you can sign in to the Google Admin console. The Admin console, at admin.google.com, is where admins manage Google services for people in an organization.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/a/answer/182076?hl=en", "content": "If you have access to an administrator (or admin) account, you can sign in to the Google Admin console. The Admin console, at admin.google.com, is where admins manage Google services for people in an organization."}
+{"idx": 3, "title": "Download and install Google Chrome", "date": "", "ddg_snippet": "How to install Chrome Important: Before you download, you can check if Chrome supports your operating system and other system requirements.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/chrome/answer/95346?hl=en&co=GENIE.Platform=Desktop", "content": "How to install Chrome Important: Before you download, you can check if Chrome supports your operating system and other system requirements."}
+{"idx": 4, "title": "Google Account Help", "date": "", "ddg_snippet": "Official Google Account Help Center where you can find tips and tutorials on using Google Account and other answers to frequently asked questions.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/accounts/?hl=en", "content": "Official Google Account Help Center where you can find tips and tutorials on using Google Account and other answers to frequently asked questions."}
+{"idx": 5, "title": "Sign in with app passwords - Google Account Help", "date": "", "ddg_snippet": "Tip: Don't create an app password unless the app or device you want to connect to your account doesn't have \"Sign in with Google.\"", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/accounts/answer/185833?hl=en", "content": "Tip: Don't create an app password unless the app or device you want to connect to your account doesn't have \"Sign in with Google.\""}
+{"idx": 6, "title": "Browse in Incognito mode - Computer - Google Chrome Help", "date": "", "ddg_snippet": "Open Incognito mode Important: When you use an Incognito window, you can browse more privately. You can switch between Incognito tabs and regular Chrome tabs. On your computer, open Chrome. At the top right, select More New Incognito window. A new window opens. On the right of the address bar, you’ll find the Incognito icon . To open an Incognito window, you can use a keyboard shortcut ...", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/chrome/answer/95464?hl=en&co=GENIE.Platform=Desktop", "content": "Open Incognito mode Important: When you use an Incognito window, you can browse more privately. You can switch between Incognito tabs and regular Chrome tabs. On your computer, open Chrome. At the top right, select More New Incognito window. A new window opens. On the right of the address bar, you’ll find the Incognito icon . To open an Incognito window, you can use a keyboard shortcut ..."}
+{"idx": 7, "title": "Access your Google Analytics account", "date": "", "ddg_snippet": "If you are unable to sign in to your account, try these steps.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/analytics/answer/1009692?hl=en", "content": "If you are unable to sign in to your account, try these steps."}
+{"idx": 8, "title": "Change or reset your password - Computer - Gmail Help", "date": "", "ddg_snippet": "If you change or reset your password, you’ll be signed out everywhere except: Devices you use to verify that it's you when you sign in. Some devices with third-party apps that you've given account access. Learn how to remove an app's access to your account. Helpful home devices that you've given account access. Learn how to unlink these devices from your Google Account.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/mail/answer/41078?hl=en&co=GENIE.Platform=Desktop", "content": "If you change or reset your password, you’ll be signed out everywhere except: Devices you use to verify that it's you when you sign in. Some devices with third-party apps that you've given account access. Learn how to remove an app's access to your account. Helpful home devices that you've given account access. Learn how to unlink these devices from your Google Account."}
+{"idx": 9, "title": "Make Google your homepage - Google Search Help", "date": "", "ddg_snippet": "Google is stuck as my homepage Google won't change your homepage settings without your permission. Reset your homepage. Choose a browser above, then follow the steps to replace Google with the site you want as your homepage. Check for unwanted programs. If resetting your homepage doesn't fix the problem, you might have unwanted programs called malware that's imitating the Google site. Learn ...", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/websearch/answer/463?hl=en", "content": "Google is stuck as my homepage Google won't change your homepage settings without your permission. Reset your homepage. Choose a browser above, then follow the steps to replace Google with the site you want as your homepage. Check for unwanted programs. If resetting your homepage doesn't fix the problem, you might have unwanted programs called malware that's imitating the Google site. Learn ..."}
diff --git a/data/sampled_jsons/httpsarxiv.orghtml2502.20099v1.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2502.20099v1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a5190a7f91cf7f207e43dbff6cb726e534e15604
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+{"idx": 0, "title": "HTML Görüntüleyici - Çevrimiçi HTML Editörü ve Önizleme Aracı", "date": "", "ddg_snippet": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç.", "subpage_snippet": "", "source": "htmlonline.org", "link": "https://htmlonline.org/tr/", "content": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç."}
+{"idx": 1, "title": "Pushing to the mobile application (The Org Manual)", "date": "", "ddg_snippet": "The command org -mobile-push copies files listed in org -mobile-files into the staging area. Files include agenda files (as listed in org -agenda-files). Customize org -mobile-files to add other files.", "subpage_snippet": "", "source": "orgmode.org", "link": "https://orgmode.org/manual/Pushing-to-the-mobile-application.html", "content": "The command org -mobile-push copies files listed in org -mobile-files into the staging area. Files include agenda files (as listed in org -agenda-files). Customize org -mobile-files to add other files."}
+{"idx": 2, "title": "Login / Member's access to the HTML 6 Editor", "date": "", "ddg_snippet": "There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout.", "subpage_snippet": "", "source": "html6.com", "link": "https://html6.com/login/", "content": "There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout."}
+{"idx": 3, "title": "Learn HTML - Free Interactive HTML Tutorial", "date": "", "ddg_snippet": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast.", "subpage_snippet": "", "source": "www.learn-html.org", "link": "https://www.learn-html.org/", "content": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast."}
+{"idx": 4, "title": "Online PDF to HTML Converter: Online converting PDF Files to HTML ...", "date": "", "ddg_snippet": "An easy tool to convert your PDF files into HTML Format. Upload your file and get your PDF File in HTML !", "subpage_snippet": "", "source": "www.pdf2html.org", "link": "https://www.pdf2html.org/", "content": "An easy tool to convert your PDF files into HTML Format. Upload your file and get your PDF File in HTML !"}
+{"idx": 5, "title": "HTMLbook. org - HTML , CSS, JavaScript", "date": "", "ddg_snippet": "Руководство для тех, кто увлекается веб-разработкой, создает сайты или просто хочет узнать что такое HTML , CSS и JavaScript.", "subpage_snippet": "", "source": "htmlbook.org", "link": "https://htmlbook.org/", "content": "Руководство для тех, кто увлекается веб-разработкой, создает сайты или просто хочет узнать что такое HTML , CSS и JavaScript."}
+{"idx": 6, "title": "4 The elements of HTML | HTML 5.1", "date": "", "ddg_snippet": "4.8.14.13 User interface. 4.8.14.14 Time ranges. 4.8.14.15 The TrackEvent interface. 4.8.14.16 Event summary. 4.8.14.17 Security and privacy considerations. 4.8.14.18 Best practices for...", "subpage_snippet": "", "source": "www.w3.org", "link": "https://www.w3.org/TR/2015/WD-html51-20150417/semantics.html", "content": "4.8.14.13 User interface. 4.8.14.14 Time ranges. 4.8.14.15 The TrackEvent interface. 4.8.14.16 Event summary. 4.8.14.17 Security and privacy considerations. 4.8.14.18 Best practices for..."}
+{"idx": 7, "title": "FURIA team overview | HLTV. org", "date": "", "ddg_snippet": "Get the complete overview of FURIA's current lineup, upcoming matches, recent results and much more...", "subpage_snippet": "", "source": "www.hltv.org", "link": "https://www.hltv.org/team/8297/furia", "content": "Get the complete overview of FURIA's current lineup, upcoming matches, recent results and much more..."}
+{"idx": 8, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v1", "content": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical..."}
+{"idx": 9, "title": "Mening mahallam – mening faxrim - Milliy istiqlol g'oyasi - Slaydlar", "date": "", "ddg_snippet": "Mahalla kichik ma’muriy hudud bo‘lib, turmush tarzi, qadriyatlar, an’analar, urf-odatlar umumiyligi bilan birlashgan kishilar jamoasidir.", "subpage_snippet": "", "source": "arxiv.uz", "link": "https://arxiv.uz/uz/documents/slaydlar/milliy-istiqlol-g-oyasi/mening-mahallam-mening-faxrim", "content": "Mahalla kichik ma’muriy hudud bo‘lib, turmush tarzi, qadriyatlar, an’analar, urf-odatlar umumiyligi bilan birlashgan kishilar jamoasidir."}
diff --git a/data/sampled_jsons/httpsgithub.comcsznDnCNNblobmasterREADME.md.jsonl b/data/sampled_jsons/httpsgithub.comcsznDnCNNblobmasterREADME.md.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d68a29d476817e4d48704149d9c9afc577c01029
--- /dev/null
+++ b/data/sampled_jsons/httpsgithub.comcsznDnCNNblobmasterREADME.md.jsonl
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+{"idx": 0, "title": "DnCNN/README.md at master · cszn/DnCNN · GitHub", "date": "", "ddg_snippet": "The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks, such as image denoising, image super-resolution and JPEG image deblocking.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/README.md", "content": "The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks, such as image denoising, image super-resolution and JPEG image deblocking."}
+{"idx": 1, "title": "GitHub - cszn/DnCNN: Beyond a Gaussian Denoiser: Residual Learning of ...", "date": "", "ddg_snippet": "The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks, such as image denoising, image super-resolution and JPEG image deblocking.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN", "content": "The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks, such as image denoising, image super-resolution and JPEG image deblocking."}
+{"idx": 2, "title": "GitHub - cszn/KAIR: Image Restoration Toolbox (PyTorch). Training and ...", "date": "", "ddg_snippet": "Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR - cszn/KAIR", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/KAIR", "content": "Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR - cszn/KAIR"}
+{"idx": 3, "title": "PyTorch Implementation | cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "PyTorch Implementation Relevant source files This document describes the PyTorch implementation of DnCNN (Denoising Convolutional Neural Network), detailing the model structure, data handling, training process, and testing procedures. The implementation supports image denoising tasks using a residual learning approach where the model learns to predict noise rather than directly predicting ...", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN/3-pytorch-implementation", "content": "PyTorch Implementation Relevant source files This document describes the PyTorch implementation of DnCNN (Denoising Convolutional Neural Network), detailing the model structure, data handling, training process, and testing procedures. The implementation supports image denoising tasks using a residual learning approach where the model learns to predict noise rather than directly predicting ..."}
+{"idx": 4, "title": "cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "Sources: README.md 17-29 README.md 87-95 README.md 169-171 Model Variants DnCNN comes in several specialized variants: DnCNN (Base Model): Designed for Gaussian denoising at specific noise levels DnCNN-B (Blind Model): Handles a range of noise levels without knowing the specific level DnCNN-3 (Multi-task Model): A single model for three tasks - Gaussian denoising, super-resolution, and JPEG ...", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN", "content": "Sources: README.md 17-29 README.md 87-95 README.md 169-171 Model Variants DnCNN comes in several specialized variants: DnCNN (Base Model): Designed for Gaussian denoising at specific noise levels DnCNN-B (Blind Model): Handles a range of noise levels without knowing the specific level DnCNN-3 (Multi-task Model): A single model for three tasks - Gaussian denoising, super-resolution, and JPEG ..."}
+{"idx": 5, "title": "DnCNN-PyTorch/dataset.py at master - GitHub", "date": "", "ddg_snippet": "PyTorch implementation of the TIP2017 paper \"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising\" - SaoYan/DnCNN-PyTorch", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SaoYan/DnCNN-PyTorch/blob/master/dataset.py", "content": "PyTorch implementation of the TIP2017 paper \"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising\" - SaoYan/DnCNN-PyTorch"}
+{"idx": 6, "title": "DnCNN: Beyond a Gaussian Denoiser: Residual Learning of Deep ... - Gitee", "date": "", "ddg_snippet": "Visual Results The left is the noisy image corrupted by AWGN, the right is the denoised image by DnCNN. Gaussian Denoising, Single ImageSuper-Resolution and JPEG Image Deblocking via a Single (DnCNN-3) Model Average PSNR (dB)/SSIM results of different methods for Gaussian denoising with noise level 15, 25 and 50 on BSD68 dataset, single image super-resolution with upscaling factors 2, 3 and 40 ...", "subpage_snippet": "", "source": "gitee.com", "link": "https://gitee.com/societyqiang/DnCNN", "content": "Visual Results The left is the noisy image corrupted by AWGN, the right is the denoised image by DnCNN. Gaussian Denoising, Single ImageSuper-Resolution and JPEG Image Deblocking via a Single (DnCNN-3) Model Average PSNR (dB)/SSIM results of different methods for Gaussian denoising with noise level 15, 25 and 50 on BSD68 dataset, single image super-resolution with upscaling factors 2, 3 and 40 ..."}
+{"idx": 7, "title": "DnCNN/README.md at master · cszn/DnCNN · GitHub", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - cszn/DnCNN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/README.md?plain=1", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - cszn/DnCNN"}
+{"idx": 8, "title": "DnCNN - Image noise remover", "date": "", "ddg_snippet": "DnCNN - Residual Learning of Deep CNN for Image Denoising The DnCNN (Denoising Convolutional Neural Network) is a deep learning model designed for image denoising tasks, offering state-of-the-art performance by learning end-to-end mappings from noisy to clean images. It employs a deep architecture consisting of several convolutional layers without any fully connected layers, enabling it to ...", "subpage_snippet": "", "source": "atikul-islam-sajib.github.io", "link": "https://atikul-islam-sajib.github.io/DnCNN-deploy/", "content": "DnCNN - Residual Learning of Deep CNN for Image Denoising The DnCNN (Denoising Convolutional Neural Network) is a deep learning model designed for image denoising tasks, offering state-of-the-art performance by learning end-to-end mappings from noisy to clean images. It employs a deep architecture consisting of several convolutional layers without any fully connected layers, enabling it to ..."}
+{"idx": 9, "title": "DnCNN/TrainingCodes/dncnn_keras/README.md at master · cszn/DnCNN - GitHub", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - cszn/DnCNN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/TrainingCodes/dncnn_keras/README.md", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - cszn/DnCNN"}
diff --git a/data/sampled_jsons/httpsgithub.comfiveaiunderstanding_safety_finetuningblobmainconfigsmingpt_config.yaml.jsonl b/data/sampled_jsons/httpsgithub.comfiveaiunderstanding_safety_finetuningblobmainconfigsmingpt_config.yaml.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8067b221f24e192e7196f59e1f7924178430b635
--- /dev/null
+++ b/data/sampled_jsons/httpsgithub.comfiveaiunderstanding_safety_finetuningblobmainconfigsmingpt_config.yaml.jsonl
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+{"idx": 0, "title": "GitHub App: Free Access - GitHub for Developers Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Download GitHub for free. Install GitHub app easily.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=viaapk.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=TnED0YFf2Xo2EsrikhbytnEBiCUCgIXCiigWIuiNWFy8nw3KRARJRUYlISTRiFIXxxcujEcO0ZwbQHNxnBBKnYcw9AI5IPy2EZ1EZ6KyCtjBCFc-JUx14MliS-KChSPC.nuDIPsP3YwsHL_cOFkSZJA&rut=d63502cf9cd2da0c6ec6be8c10b612685db4302dee768c42bef4e21b13990552&u3=https://www.bing.com/aclick?ld=e8QFFFd8_Dc2FdrGegkT_kmTVUCUzhWgnkUm6N7w9nfC-x5MvU6Sxsopz1krPG58llw0L5x2rHzLKH5aAoWoYy8k-vuyp5qKVa02gssF36E5-S-Spvl3W-mkb8sAHfyX7EMVzFFGQOBkxNclaH-zI4eln8hxTA30xAp7Mdld8vt7-VU4tCPCa4qvEC82Hl4xFJvb6Gsw&u=aHR0cHMlM2ElMmYlMmZ3d3cudmlhYXBrLmNvbSUyZmFwcCUyZmNvbS5naXRodWIuYW5kcm9pZA&rlid=58f757cb0d4115e0c34b3f18d57015af&vqd=4-146812970449706694618349080355586834918&iurl={1}IG=002E47131DD1447086A37B10C06FC09A&CID=1962E3A860C263212737F5D96195623E&ID=DevEx,5045.1", "content": "Download GitHub for free. Install GitHub app easily."}
+{"idx": 1, "title": "FiveAI - GitHub", "date": "", "ddg_snippet": "We're developing the software and tools to make self-driving vehicle services a reality. - FiveAI", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai", "content": "We're developing the software and tools to make self-driving vehicle services a reality. - FiveAI"}
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+{"idx": 5, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
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+++ b/data/sampled_jsons/hybrid_neural_PDE_solver_physics-informed_multi-timestep.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time", "date": "", "ddg_snippet": "Our hybrid solver integrates a numerical simulator and neural physics (Section 3.1 ), enabling real-time simulation (Section 4.2 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18926v1", "content": "Our hybrid solver integrates a numerical simulator and neural physics (Section 3.1 ), enabling real-time simulation (Section 4.2 )."}
+{"idx": 1, "title": "Blending data and physics for reduced-order modeling of systems", "date": "", "ddg_snippet": "Dimension reduction aside, a widely used method for combining physics and machine learning for learning solutions to PDEs is physics - informed neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21299v1", "content": "Dimension reduction aside, a widely used method for combining physics and machine learning for learning solutions to PDEs is physics - informed neural ..."}
+{"idx": 2, "title": "Neural Field Turing Machine: A Differentiable Spatial Computer", "date": "", "ddg_snippet": "Physics - Informed Neural Networks (PINNs) [ 28 ] solve PDEs by embedding the residuals of governing equations directly into the loss.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03370v1", "content": "Physics - Informed Neural Networks (PINNs) [ 28 ] solve PDEs by embedding the residuals of governing equations directly into the loss."}
+{"idx": 3, "title": "A Variational Framework for Residual-Based Adaptivity in Neural", "date": "", "ddg_snippet": "For physics - informed methods, this typically involves minimizing a loss function composed of the PDE residuals and the mismatch with observational ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14198v1", "content": "For physics - informed methods, this typically involves minimizing a loss function composed of the PDE residuals and the mismatch with observational ..."}
+{"idx": 4, "title": "GNNs for PDE-constrained Inverse Problems", "date": "", "ddg_snippet": "... al., 2021), offering faster runtimes than principled solvers , better adaptivity to the simulation domain compared to grid-based convolutional neural ...", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/25955871/learning-to-solve-pde-constrained-inverse-problem", "content": "... al., 2021), offering faster runtimes than principled solvers , better adaptivity to the simulation domain compared to grid-based convolutional neural ..."}
+{"idx": 5, "title": "A Review of Physics-Informed Machine Learning in Fluid Mechanics", "date": "", "ddg_snippet": "... Natural Gas Project Investment and Decision Making under Multiple ... For more information, please refer to https://www.mdpi.com/openaccess .", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1996-1073/16/5/2343", "content": "... Natural Gas Project Investment and Decision Making under Multiple ... For more information, please refer to https://www.mdpi.com/openaccess ."}
+{"idx": 6, "title": "diffSPH: Differentiable Smoothed Particle Hydrodynamics for", "date": "", "ddg_snippet": "... these gradients include Physics Informed Neural Networks (PINNs) that differentiate through physical loss terms cai2021physicsinformed and solver ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21684v1", "content": "... these gradients include Physics Informed Neural Networks (PINNs) that differentiate through physical loss terms cai2021physicsinformed and solver ..."}
+{"idx": 7, "title": "Notes on Deep Learning and Differential Equations. –", "date": "", "ddg_snippet": "... PDE by neural networks described here is largely the excellent work of Karniadakis at Brown University and his collaborators on “ Physics Informed ...", "subpage_snippet": "", "source": "cloud4scieng.org", "link": "https://cloud4scieng.org/2020/06/10/notes-on-deep-learning-and-differential-equations/", "content": "... PDE by neural networks described here is largely the excellent work of Karniadakis at Brown University and his collaborators on “ Physics Informed ..."}
+{"idx": 8, "title": "Notes on Deep Learning and Differential Equations. | The", "date": "", "ddg_snippet": "... PDE by neural networks described here is largely the excellent work of Karniadakis at Brown University and his collaborators on “ Physics Informed ...", "subpage_snippet": "", "source": "esciencegroup.com", "link": "https://esciencegroup.com/2020/06/10/notes-on-deep-learning-and-differential-equations/", "content": "... PDE by neural networks described here is largely the excellent work of Karniadakis at Brown University and his collaborators on “ Physics Informed ..."}
+{"idx": 9, "title": "June | 2020 | The eScience Cloud", "date": "", "ddg_snippet": "... PDE by neural networks described here is largely the excellent work of Karniadakis at Brown University and his collaborators on “ Physics Informed ...", "subpage_snippet": "", "source": "esciencegroup.com", "link": "https://esciencegroup.com/2020/06/", "content": "... PDE by neural networks described here is largely the excellent work of Karniadakis at Brown University and his collaborators on “ Physics Informed ..."}
diff --git a/data/sampled_jsons/iDDPM_Nichol_Dhariwal_2021_results_Table_ImageNet_64x64_FID.jsonl b/data/sampled_jsons/iDDPM_Nichol_Dhariwal_2021_results_Table_ImageNet_64x64_FID.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1884f03c227b318c8f495df8c869c70fa5e0cc97
--- /dev/null
+++ b/data/sampled_jsons/iDDPM_Nichol_Dhariwal_2021_results_Table_ImageNet_64x64_FID.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Paper tables with annotated results for Constant... | Papers With Code", "date": "", "ddg_snippet": "Table 2: Performance on ImageNet 64 ×64. Parse references. Model.- 0.58. iDDPM Nichol and Dhariwal ( 2021 ).", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/constant-acceleration-flow-1/review/", "content": "Table 2: Performance on ImageNet 64 ×64. Parse references. Model.- 0.58. iDDPM Nichol and Dhariwal ( 2021 )."}
+{"idx": 1, "title": "FID / IS scores for DDSS against baseline methods for a DDPM trained...", "date": "", "ddg_snippet": "... for a step budget K = 15 are included below. When not learning the timesteps, we fix them to a linear stride, as Table 2 shows this performs best on ImageNet 64 x 64 . ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/FID-IS-scores-for-DDSS-against-baseline-methods-for-a-DDPM-trained-on-ImageNet-64x64_tbl1_358578738", "content": "... for a step budget K = 15 are included below. When not learning the timesteps, we fix them to a linear stride, as Table 2 shows this performs best on ImageNet 64 x 64 . ..."}
+{"idx": 2, "title": "Improved Denoising Diffusion Probabilistic Models", "date": "", "ddg_snippet": "Figure 9. Class-conditional ImageNet 64 × 64 samples generated using 250 sampling steps from Lhybrid model ( FID 2.92).", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf", "content": "Figure 9. Class-conditional ImageNet 64 × 64 samples generated using 250 sampling steps from Lhybrid model ( FID 2.92)."}
+{"idx": 3, "title": "Direct Discriminative Optimization: Your Likelihood-Based Visual...", "date": "", "ddg_snippet": "iDDPM ( Nichol & Dhariwal , 2021 ). Table 2: Results on class-conditional ImageNet - 64 . † Including diffusion distillation methods with auxiliary GAN loss. ‡", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2503.01103/", "content": "iDDPM ( Nichol & Dhariwal , 2021 ). Table 2: Results on class-conditional ImageNet - 64 . † Including diffusion distillation methods with auxiliary GAN loss. ‡"}
+{"idx": 4, "title": "[PDF] Diffusion Models Beat GANs on Image... | Semantic Scholar", "date": "", "ddg_snippet": "Results Citations. 90. View All.It is shown that conditioning augmentation prevents compounding error during sampling in a cascaded model, helping to train cascading pipelines achieving FID scores of 1.48 at 64 x 64 , 3.52 at 128x128 and 4.88 at 256x256 resolutions.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Diffusion-Models-Beat-GANs-on-Image-Synthesis-Dhariwal-Nichol/64ea8f180d0682e6c18d1eb688afdb2027c02794", "content": "Results Citations. 90. View All.It is shown that conditioning augmentation prevents compounding error during sampling in a cascaded model, helping to train cascading pipelines achieving FID scores of 1.48 at 64 x 64 , 3.52 at 128x128 and 4.88 at 256x256 resolutions."}
+{"idx": 5, "title": "Image generation with shortest path diffusion | alphaXiv", "date": "", "ddg_snippet": "ImageNet 64 × 64 : FID of 13.7 vs. 19.2 for iDDPM , achieved with fewer diffusion steps and training iterations.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2306.00501v1", "content": "ImageNet 64 × 64 : FID of 13.7 vs. 19.2 for iDDPM , achieved with fewer diffusion steps and training iterations."}
+{"idx": 6, "title": "Learning to Efciently Sample from", "date": "", "ddg_snippet": "Nichol and Dhariwal [ 2021 ] also explored the use of ancestral sampling with a subsequence of the original denoising steps, trying both a uniform stride and other hand-crafted strides. San-Roman et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2106.03802", "content": "Nichol and Dhariwal [ 2021 ] also explored the use of ancestral sampling with a subsequence of the original denoising steps, trying both a uniform stride and other hand-crafted strides. San-Roman et al."}
+{"idx": 7, "title": "Image generation with shortest path diffusion", "date": "", "ddg_snippet": "For ImageNet 64 × 64, we use batch size 336, 1M training iterations, and we record model checkpoints every 3, 000 iterations.Lastly, in table 2 we show quality of unconditional gen-eration of ImageNet 64 × 64 images, in comparison with iDDPM ( Nichol & Dhariwal , 2021 a).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=dFflBEShcI", "content": "For ImageNet 64 × 64, we use batch size 336, 1M training iterations, and we record model checkpoints every 3, 000 iterations.Lastly, in table 2 we show quality of unconditional gen-eration of ImageNet 64 × 64 images, in comparison with iDDPM ( Nichol & Dhariwal , 2021 a)."}
+{"idx": 8, "title": "Understanding Diffusion Objectives as the ELBO", "date": "", "ddg_snippet": "Table 2: ImageNet 64 x 64 results .For -parametrization model, we took iDDPM [ Nichol and Dhariwal , 2021 ] as the baseline, which utilized cosine noise schedule and a non-monotonic sech(λ/2) weighting. For EDM parmetrization, we recruited the setting in Karras et al.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/ce79fbf9baef726645bc2337abb0ade2-Paper-Conference.pdf", "content": "Table 2: ImageNet 64 x 64 results .For -parametrization model, we took iDDPM [ Nichol and Dhariwal , 2021 ] as the baseline, which utilized cosine noise schedule and a non-monotonic sech(λ/2) weighting. For EDM parmetrization, we recruited the setting in Karras et al."}
+{"idx": 9, "title": "Diffusion Models Beat GANs on Image Synthesis", "date": "", "ddg_snippet": "ImageNet 64 ×64. ImageNet 512×512. BigGAN-deep* [5] IDDPM [43] ADM ADM (dropout). Table 5 summarizes our results . Our diffusion models can obtain the best FID on each task, and the best sFID on all but one task.", "subpage_snippet": "", "source": "3dvar.com", "link": "https://3dvar.com/Dhariwal2021Diffusion.pdf", "content": "ImageNet 64 ×64. ImageNet 512×512. BigGAN-deep* [5] IDDPM [43] ADM ADM (dropout). Table 5 summarizes our results . Our diffusion models can obtain the best FID on each task, and the best sFID on all but one task."}
diff --git a/data/sampled_jsons/iDDPM_leaderboard_FID_conditional_ImageNet_64x64.jsonl b/data/sampled_jsons/iDDPM_leaderboard_FID_conditional_ImageNet_64x64.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..733b7391a4c581ea9b5a40acf18402e95006b7ed
--- /dev/null
+++ b/data/sampled_jsons/iDDPM_leaderboard_FID_conditional_ImageNet_64x64.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - yichengw1/improved-diffusion: iddpm official code learning", "date": "", "ddg_snippet": "Class- conditional ImageNet - 64 model (270M parameters, trained for 250K iterations) [checkpoint]Unconditional ImageNet - 64 with the L_vlb objective and cosine noise schedule [checkpoint]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yichengw1/improved-diffusion", "content": "Class- conditional ImageNet - 64 model (270M parameters, trained for 250K iterations) [checkpoint]Unconditional ImageNet - 64 with the L_vlb objective and cosine noise schedule [checkpoint]"}
+{"idx": 1, "title": "Paper tables with annotated results for Image... | Papers With Code", "date": "", "ddg_snippet": "Table 2: Unconditional generation of ImageNet 64 x 64 images. FID evaluations are based on 10,000 generated samples (lower is better). Results for iDDPM are copied from iddpm .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/image-generation-with-shortest-path-diffusion/review/", "content": "Table 2: Unconditional generation of ImageNet 64 x 64 images. FID evaluations are based on 10,000 generated samples (lower is better). Results for iDDPM are copied from iddpm ."}
+{"idx": 2, "title": "Simple Drop-in LoRA Conditioning on Attention Layers Will Improve...", "date": "", "ddg_snippet": "Training flags used for ImageNet 64 training.For IDDPM , we use CIFAR-10 for both unconditional and class- conditional sampling, and ImageNet 64 , a downsampled version of the ImageNet 1k, for unconditional sampling.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.03958v1", "content": "Training flags used for ImageNet 64 training.For IDDPM , we use CIFAR-10 for both unconditional and class- conditional sampling, and ImageNet 64 , a downsampled version of the ImageNet 1k, for unconditional sampling."}
+{"idx": 3, "title": "Image generation with shortest path diffusion | alphaXiv", "date": "", "ddg_snippet": "ImageNet 64 ×64: FID of 13.7 vs. 19.2 for iDDPM , achieved with fewer diffusion steps and training iterations. Performance Comparison Figure 5: FID scores on CIFAR-10 as a function of denoising steps.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2306.00501v1", "content": "ImageNet 64 ×64: FID of 13.7 vs. 19.2 for iDDPM , achieved with fewer diffusion steps and training iterations. Performance Comparison Figure 5: FID scores on CIFAR-10 as a function of denoising steps."}
+{"idx": 4, "title": "Elucidating the Design Space of Diffusion-Based", "date": "", "ddg_snippet": "VE, and iDDPM [36] for ImageNet - 64 . The dots indicate lowest observed FID .As a final experiment, we trained a class- conditional ImageNet - 64 model from scratch using our proposed training improvements.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/a98846e9d9cc01cfb87eb694d946ce6b-Paper-Conference.pdf", "content": "VE, and iDDPM [36] for ImageNet - 64 . The dots indicate lowest observed FID .As a final experiment, we trained a class- conditional ImageNet - 64 model from scratch using our proposed training improvements."}
+{"idx": 5, "title": "encoreus/Transformer_Autoregressive_Flow · Hugging Face", "date": "", "ddg_snippet": "ImageNet ( 64 x 64 ) conditional : imagenet _model_2_768_8_8_0.05.pth. ImageNet ( 64 x 64 ) conditioanl: imagenet _model_4_1024_8_8_0.05.pth. We also compute the stats for the true data distribution which can be used to calculate FID .", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/encoreus/Transformer_Autoregressive_Flow", "content": "ImageNet ( 64 x 64 ) conditional : imagenet _model_2_768_8_8_0.05.pth. ImageNet ( 64 x 64 ) conditioanl: imagenet _model_4_1024_8_8_0.05.pth. We also compute the stats for the true data distribution which can be used to calculate FID ."}
+{"idx": 6, "title": "Supplementary Materials of ”Entropy-driven", "date": "", "ddg_snippet": "Method. ImageNet 64 ×64 BigGAN-deep [2] IDDPM [5] CADM-G [1] CADM-G+EDS+ECT.Fig. 6: Generated images in ImageNet 64 ×64 from CADM-G with EDS and ECT. fid fid . 8 G. Zheng & S. Li et al.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136820730-supp.pdf", "content": "Method. ImageNet 64 ×64 BigGAN-deep [2] IDDPM [5] CADM-G [1] CADM-G+EDS+ECT.Fig. 6: Generated images in ImageNet 64 ×64 from CADM-G with EDS and ECT. fid fid . 8 G. Zheng & S. Li et al."}
+{"idx": 7, "title": "Papers Explained 342: U-ViT. U-ViT is a simple and general... | Medium", "date": "", "ddg_snippet": "CelebA 64×64 (contains 162,770 training images of human faces).U-ViT-M with 131M parameters achieves a better FID (5.85) than IDDPM (U-Net with 100M parameters, FID 6.92).", "subpage_snippet": "", "source": "ritvik19.medium.com", "link": "https://ritvik19.medium.com/papers-explained-342-u-vit-54c907b849c8", "content": "CelebA 64×64 (contains 162,770 training images of human faces).U-ViT-M with 131M parameters achieves a better FID (5.85) than IDDPM (U-Net with 100M parameters, FID 6.92)."}
+{"idx": 8, "title": "Image generation with Shortest-path Diffusion", "date": "", "ddg_snippet": "ImageNet 64 results. p Preliminary experiments are promising. – Unconditional model trained (and samples) with T = 1000 – Better FID than iDDPM with less T and training iterations.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2023/Slides/23971.pdf", "content": "ImageNet 64 results. p Preliminary experiments are promising. – Unconditional model trained (and samples) with T = 1000 – Better FID than iDDPM with less T and training iterations."}
+{"idx": 9, "title": "(PDF) Beyond and Free from Diffusion: Invertible Guided Consistency...", "date": "", "ddg_snippet": "Our extensive experiments on CIFAR-10 and ImageNet 64 show that iGCT significantly improves FID and precision compared to CFG.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388883334_Beyond_and_Free_from_Diffusion_Invertible_Guided_Consistency_Training", "content": "Our extensive experiments on CIFAR-10 and ImageNet 64 show that iGCT significantly improves FID and precision compared to CFG."}
diff --git a/data/sampled_jsons/icml2025.vizhub.ai_MultiPDENet.jsonl b/data/sampled_jsons/icml2025.vizhub.ai_MultiPDENet.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..02f322cabaf445e8e4303678c992e86fb7d60c3a
--- /dev/null
+++ b/data/sampled_jsons/icml2025.vizhub.ai_MultiPDENet.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet : PDE-embedded Learning with... | OpenReview", "date": "", "ddg_snippet": "back arrow Go to ICML 2025 Conference homepage. MultiPDENet : PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation. Download PDF.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=D1gs8QT74m", "content": "back arrow Go to ICML 2025 Conference homepage. MultiPDENet : PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation. Download PDF."}
+{"idx": 1, "title": "VizHub - YouTube", "date": "", "ddg_snippet": "VizHub . 15 видео 888 просмотров Обновлен 1 мая 2025 г. Сохранить плейлист. VizHub AI Demo:Circles with Canvas.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/playlist?list=PL9yYRbwpkyks7ZddY5BuI6FPjpIogkZSS", "content": "VizHub . 15 видео 888 просмотров Обновлен 1 мая 2025 г. Сохранить плейлист. VizHub AI Demo:Circles with Canvas."}
+{"idx": 2, "title": "icml 2025 . vizhub . ai", "date": "", "ddg_snippet": "ICML 2025 Posters. by Hendrik Strobelt and Benjamin Hoover from MIT-IBM Watson AI Lab [*more].", "subpage_snippet": "", "source": "icml2025.vizhub.ai", "link": "https://icml2025.vizhub.ai/", "content": "ICML 2025 Posters. by Hendrik Strobelt and Benjamin Hoover from MIT-IBM Watson AI Lab [*more]."}
+{"idx": 3, "title": "A quote from ICML 2025 | Simon Willison’s Weblog", "date": "", "ddg_snippet": "Although ICML 2025 reviewers are forbidden from using LLMs to produce their reviews of paper submissions, this fact does not excuse the attempted subversion.Recreating the Apollo AI adoption rate chart with GPT-5, Python and Pyodide - 9th September 2025.", "subpage_snippet": "", "source": "simonwillison.net", "link": "https://simonwillison.net/2025/jul/23/icml-2025/", "content": "Although ICML 2025 reviewers are forbidden from using LLMs to produce their reviews of paper submissions, this fact does not excuse the attempted subversion.Recreating the Apollo AI adoption rate chart with GPT-5, Python and Pyodide - 9th September 2025."}
+{"idx": 4, "title": "ICML 2025 Sunday 07/13", "date": "", "ddg_snippet": "My Stuff. Login. Select Year: (2025).The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/13", "content": "My Stuff. Login. Select Year: (2025).The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale."}
+{"idx": 5, "title": "ICML 2025 : DFKI shows risks of explainable AI through AutoML", "date": "", "ddg_snippet": "Trustworthy AI as a DFKI focus. The ICML 2025 study emphasises DFKI's research approach of making artificial intelligence not only powerful, but also transparent and socially trustworthy.", "subpage_snippet": "", "source": "www.dfki.de", "link": "https://www.dfki.de/en/web/news/icml-2025-deceptive-explainability-in-ai-systems", "content": "Trustworthy AI as a DFKI focus. The ICML 2025 study emphasises DFKI's research approach of making artificial intelligence not only powerful, but also transparent and socially trustworthy."}
+{"idx": 6, "title": "ICML 2025 — LXAI", "date": "", "ddg_snippet": "This is an official workshop on July 14th 2025 of the LatinX in AI (LXAI) organization at ICML. The event will take place at the Vancouver Convention Centre, in Meeting Rooms MR 118–120.", "subpage_snippet": "", "source": "www.latinxinai.org", "link": "https://www.latinxinai.org/icml-2025", "content": "This is an official workshop on July 14th 2025 of the LatinX in AI (LXAI) organization at ICML. The event will take place at the Vancouver Convention Centre, in Meeting Rooms MR 118–120."}
+{"idx": 7, "title": "Our ICML 2025 Publications – Machine Learning Lab", "date": "", "ddg_snippet": "Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks Inproceedings. In: Proceedings of the 42nd International Conference on Machine Learning ( ICML ), 2025 .", "subpage_snippet": "", "source": "ml.informatik.uni-freiburg.de", "link": "https://ml.informatik.uni-freiburg.de/our-icml-2025-publications/", "content": "Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks Inproceedings. In: Proceedings of the 42nd International Conference on Machine Learning ( ICML ), 2025 ."}
+{"idx": 8, "title": "ICML 2025 Template - Overleaf, Editor de LaTeX online", "date": "", "ddg_snippet": "AI .International Conference on Machine Learning ( ICML 2025 )} %. It is OKAY to include author information, even for blind % submissions: the style file will automatically remove it for you % unless you've provided the [accepted] option to the icml 2025 % package. %", "subpage_snippet": "", "source": "es.overleaf.com", "link": "https://es.overleaf.com/latex/templates/icml2025-template/dhxrkcgkvnkt", "content": "AI .International Conference on Machine Learning ( ICML 2025 )} %. It is OKAY to include author information, even for blind % submissions: the style file will automatically remove it for you % unless you've provided the [accepted] option to the icml 2025 % package. %"}
+{"idx": 9, "title": "AI Insights from ICML 2025 Part 1: Context engineering and...", "date": "", "ddg_snippet": "ICML 2025 (International Conference on Machine Learning) brought together leading minds from academia and industry to share ideas and research shaping the future of AI . From foundational breakthroughs to emerging trends, it provided a clear view into where the field is heading.", "subpage_snippet": "", "source": "instabase.com", "link": "https://instabase.com/blog/ai-insights-from-icml-2025-part-1-context-engineering-and-multimodal-reasoning/", "content": "ICML 2025 (International Conference on Machine Learning) brought together leading minds from academia and industry to share ideas and research shaping the future of AI . From foundational breakthroughs to emerging trends, it provided a clear view into where the field is heading."}
diff --git a/data/sampled_jsons/identity_matrix_symmetric_initialization_transformer_self-attention.jsonl b/data/sampled_jsons/identity_matrix_symmetric_initialization_transformer_self-attention.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05c69050364ce6bf942b7d4104818d792f182b1f
--- /dev/null
+++ b/data/sampled_jsons/identity_matrix_symmetric_initialization_transformer_self-attention.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.10927] The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "Self-attention is essential to Transformer architectures, yet how information is embedded in the self-attention matrices and how different objective functions impact this process remains unclear. We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10927", "content": "Self-attention is essential to Transformer architectures, yet how information is embedded in the self-attention matrices and how different objective functions impact this process remains unclear. We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional training ..."}
+{"idx": 1, "title": "Is the self-attention matrix softmax output (layer 1) symmetric?", "date": "", "ddg_snippet": "1 Let's assume that we embedded a vector of length 49 into a matrix using 512-d embeddings. If we then multiply the matrix by its transposed version, we receive a matrix of 49 by 49, which is symmetric . Let's also assume we do not add the positional encoding and we only have only one attention head in the first layer of the transformer ...", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/22080/is-the-self-attention-matrix-softmax-output-layer-1-symmetric", "content": "1 Let's assume that we embedded a vector of length 49 into a matrix using 512-d embeddings. If we then multiply the matrix by its transposed version, we receive a matrix of 49 by 49, which is symmetric . Let's also assume we do not add the positional encoding and we only have only one attention head in the first layer of the transformer ..."}
+{"idx": 2, "title": "(PDF) The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "Self-attention is essential to Transformer architectures, yet how information is embedded in the self-attention matrices and how different objective functions impact this process remains unclear.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389091272_The_underlying_structures_of_self-attention_symmetry_directionality_and_emergent_dynamics_in_Transformer_training", "content": "Self-attention is essential to Transformer architectures, yet how information is embedded in the self-attention matrices and how different objective functions impact this process remains unclear."}
+{"idx": 3, "title": "PDF [draft] Note 10: Self-Attention & Transformers", "date": "", "ddg_snippet": "The components we haven't gone over are multi-head self-attention , layer normalization, resid-ual connections, and attention scaling—and of course, we'll discuss how these components are combined to form the Transformer .", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs224n/readings/cs224n-self-attention-transformers-2023_draft.pdf", "content": "The components we haven't gone over are multi-head self-attention , layer normalization, resid-ual connections, and attention scaling—and of course, we'll discuss how these components are combined to form the Transformer ."}
+{"idx": 4, "title": "Self-Attention in Transformers: A Deep Dive - Medium", "date": "", "ddg_snippet": "In self-attention , the attention weight matrix (wei) is computed as the matrix multiplication of the query and the transposed key vectors (wei = query @ key.transpose()).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@manishnegi101/self-attention-in-transformers-a-deep-dive-ec1d7eadc390", "content": "In self-attention , the attention weight matrix (wei) is computed as the matrix multiplication of the query and the transposed key vectors (wei = query @ key.transpose())."}
+{"idx": 5, "title": "PDF Primal-Attention: Self-attention through Asymmetric Kernel ... - NeurIPS", "date": "", "ddg_snippet": "Abstract Recently, a new line of works has emerged to understand and improve self-attention in Transformers by treating it as a kernel machine. However, existing works apply the methods for symmetric kernels to the asymmetric self-attention , resulting in a nontrivial gap between the analytical understanding and numerical implementation.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/cd687a58a13b673eea3fc1b2e4944cf7-Paper-Conference.pdf", "content": "Abstract Recently, a new line of works has emerged to understand and improve self-attention in Transformers by treating it as a kernel machine. However, existing works apply the methods for symmetric kernels to the asymmetric self-attention , resulting in a nontrivial gap between the analytical understanding and numerical implementation."}
+{"idx": 6, "title": "An Explanation of Self-Attention mechanism in Transformer", "date": "", "ddg_snippet": "Notation Convention: Bold uppercase denotes matrices, bold lowercase denotes vectors, and regular lowercase denotes scalars. The self-attention mentioned in this article specifically refers to unidirectional self-attention .", "subpage_snippet": "", "source": "martinlwx.github.io", "link": "https://martinlwx.github.io/en/an-explanation-of-self-attention/", "content": "Notation Convention: Bold uppercase denotes matrices, bold lowercase denotes vectors, and regular lowercase denotes scalars. The self-attention mentioned in this article specifically refers to unidirectional self-attention ."}
+{"idx": 7, "title": "The underlying structures of self-attention: symmetry, directionality ...", "date": "", "ddg_snippet": "Our theoretical findings are validated across multiple Transformer models — including ModernBERT, GPT, LLaMA3, and Mistral — and input modalities like text, vision, and audio. Finally, we apply these insights by showing that symmetric initialization improves the performance of encoder-only models on language tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10927v1", "content": "Our theoretical findings are validated across multiple Transformer models — including ModernBERT, GPT, LLaMA3, and Mistral — and input modalities like text, vision, and audio. Finally, we apply these insights by showing that symmetric initialization improves the performance of encoder-only models on language tasks."}
+{"idx": 8, "title": "Self-Attention in Transformers: Computation Logic and Implementation", "date": "", "ddg_snippet": "Attention serves as a fundamental concept for transformer architecture and for Large Language Models, playing a pivotal role in capturing dependencies between different words in a sequence. It intervenes in several building blocks of the Transformer architecture, more specifically, the multi-head self-attention , cross- attention , and masked attention stages.", "subpage_snippet": "", "source": "aimedinsights.substack.com", "link": "https://aimedinsights.substack.com/p/self-attention-in-transformers-computation", "content": "Attention serves as a fundamental concept for transformer architecture and for Large Language Models, playing a pivotal role in capturing dependencies between different words in a sequence. It intervenes in several building blocks of the Transformer architecture, more specifically, the multi-head self-attention , cross- attention , and masked attention stages."}
+{"idx": 9, "title": "Why multi-head self attention works: math, intuitions and 10+1 hidden ...", "date": "", "ddg_snippet": "Learn everything there is to know about the attention mechanisms of the infamous transformer , through 10+1 hidden insights and observations", "subpage_snippet": "", "source": "theaisummer.com", "link": "https://theaisummer.com/self-attention/", "content": "Learn everything there is to know about the attention mechanisms of the infamous transformer , through 10+1 hidden insights and observations"}
diff --git a/data/sampled_jsons/improving_PPO_robustness_RLHF_alternative_loss_functions_before_2025.jsonl b/data/sampled_jsons/improving_PPO_robustness_RLHF_alternative_loss_functions_before_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d48b9b3b7dc20bacb60a99cc9e900f0630203299
--- /dev/null
+++ b/data/sampled_jsons/improving_PPO_robustness_RLHF_alternative_loss_functions_before_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reward- Robust RLHF in LLMs", "date": "", "ddg_snippet": "In essence, reward- robust RLHF might prioritize optimizing the lower bound of the return function .While the proposed reward- robust RLHF framework shows improved performance on automatic evaluation benchmarks, several non-negligible limitations remain.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.15360v3", "content": "In essence, reward- robust RLHF might prioritize optimizing the lower bound of the return function .While the proposed reward- robust RLHF framework shows improved performance on automatic evaluation benchmarks, several non-negligible limitations remain."}
+{"idx": 1, "title": "LLM Training: RLHF and Its Alternatives", "date": "", "ddg_snippet": "RLHF Alternatives . The Canonical LLM Training Pipeline.Direct Preference Optimization (DPO) is an alternative to RLHF with PPO where the researchers show that the cross entropy loss for fitting the reward model in RLHF can be used directly to finetune the LLM.", "subpage_snippet": "", "source": "magazine.sebastianraschka.com", "link": "https://magazine.sebastianraschka.com/p/llm-training-rlhf-and-its-alternatives", "content": "RLHF Alternatives . The Canonical LLM Training Pipeline.Direct Preference Optimization (DPO) is an alternative to RLHF with PPO where the researchers show that the cross entropy loss for fitting the reward model in RLHF can be used directly to finetune the LLM."}
+{"idx": 2, "title": "Proximal Policy Optimization ( PPO )", "date": "", "ddg_snippet": "Today we'll learn about Proximal Policy Optimization ( PPO ), an architecture that improves our agent's training stability by avoiding too large policy updates.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/deep-rl-ppo", "content": "Today we'll learn about Proximal Policy Optimization ( PPO ), an architecture that improves our agent's training stability by avoiding too large policy updates."}
+{"idx": 3, "title": "Nexdata RLHF Reinforcement Learning Annotation Project Case Study", "date": "", "ddg_snippet": "This project focused on RLHF (Reinforcement Learning from Human Feedback), a technique that improves AI models through human evaluation of generated outputs.", "subpage_snippet": "", "source": "www.nexdata.ai", "link": "https://www.nexdata.ai/company/news/1332", "content": "This project focused on RLHF (Reinforcement Learning from Human Feedback), a technique that improves AI models through human evaluation of generated outputs."}
+{"idx": 4, "title": "RLHF and alternatives : RLHF", "date": "", "ddg_snippet": "With that, you could set up Argilla to just show, for example, two responses (Response-1 and Reponse-2) and choose the best as Chosen and the least preferred as Rejected.Want to know more? This is the second entry of a series of blog posts dedicated to alternatives to RLHF .", "subpage_snippet": "", "source": "argilla.io", "link": "https://argilla.io/blog/mantisnlp-rlhf-part-2/", "content": "With that, you could set up Argilla to just show, for example, two responses (Response-1 and Reponse-2) and choose the best as Chosen and the least preferred as Rejected.Want to know more? This is the second entry of a series of blog posts dedicated to alternatives to RLHF ."}
+{"idx": 5, "title": "Reward- Robust RLHF in LLMs | AI Research Paper Details", "date": "", "ddg_snippet": "Introduces \"reward- robust \" reinforcement learning from human feedback ( RLHF ) for training large language models (LLMs). Aims to make LLMs more robust to changes in the reward function during deployment.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/reward-robust-rlhf-llms", "content": "Introduces \"reward- robust \" reinforcement learning from human feedback ( RLHF ) for training large language models (LLMs). Aims to make LLMs more robust to changes in the reward function during deployment."}
+{"idx": 6, "title": "ChatGPT Statistics: A Comprehensive Analysis [ 2025 ]", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback ( RLHF ). Engine. GPT-4, GPT-4o, GPT-4o mini, GPT-4.5, OpenAI o1, OpenAI o3.September 12, 2024. OpenAI released o1-preview, a model designed to \"think\" before responding.", "subpage_snippet": "", "source": "doit.software", "link": "https://doit.software/blog/chatgpt-statistics", "content": "Reinforcement Learning from Human Feedback ( RLHF ). Engine. GPT-4, GPT-4o, GPT-4o mini, GPT-4.5, OpenAI o1, OpenAI o3.September 12, 2024. OpenAI released o1-preview, a model designed to \"think\" before responding."}
+{"idx": 7, "title": "REINFORCE++: A Simple and Efficient Approach for Aligning Large...", "date": "", "ddg_snippet": "RLHF Algorithm Advance. PPO -inspired Enhancements.Stability is crucial because unstable training can lead to unpredictable and unreliable model performance. The paper addresses instability by integrating techniques like token-level KL penalties and PPO -clip loss .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2501.03262/", "content": "RLHF Algorithm Advance. PPO -inspired Enhancements.Stability is crucial because unstable training can lead to unpredictable and unreliable model performance. The paper addresses instability by integrating techniques like token-level KL penalties and PPO -clip loss ."}
+{"idx": 8, "title": "AI on AI: Reform Reward as Remedy for Hallucination – Champaign...", "date": "", "ddg_snippet": "Compatible with RLHF : this function can directly feed policy gradients or PPO -style reward signals. If you like, I can extend this further to simulate all four scenarios in a toy PPO loop, showing how a policy gradually learns when to answer, abstain, or speculate.", "subpage_snippet": "", "source": "champaignmagazine.com", "link": "https://champaignmagazine.com/2025/09/06/ai-on-ai-reform-reward-as-remedy-for-hallucination/", "content": "Compatible with RLHF : this function can directly feed policy gradients or PPO -style reward signals. If you like, I can extend this further to simulate all four scenarios in a toy PPO loop, showing how a policy gradually learns when to answer, abstain, or speculate."}
+{"idx": 9, "title": "Humans-in-the-loop vs synthetic data: за что идёт борьба на... / Хабр", "date": "", "ddg_snippet": "Scale зарабатывает более $750 млн в год на продаже данных для RLHF . Кто собирается их потеснить? Scale AI — стартап, ранее известный своими контрактами на разметку данных для беспилотных автомобилей и...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/944768/", "content": "Scale зарабатывает более $750 млн в год на продаже данных для RLHF . Кто собирается их потеснить? Scale AI — стартап, ранее известный своими контрактами на разметку данных для беспилотных автомобилей и..."}
diff --git a/data/sampled_jsons/in-pixel_feature_detection_tracking_prior_work_before_2025.jsonl b/data/sampled_jsons/in-pixel_feature_detection_tracking_prior_work_before_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..465e7c8673336d650ae77a187757f917bef0bff0
--- /dev/null
+++ b/data/sampled_jsons/in-pixel_feature_detection_tracking_prior_work_before_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tracking Pixels: What They Are & How They Work in 2025", "date": "", "ddg_snippet": "Discover how pixel tracking in 2025 enhances attribution, personalization, and compliance with GDPR/CCPA while optimizing marketing data.", "subpage_snippet": "", "source": "improvado.io", "link": "https://improvado.io/blog/what-is-tracking-pixel", "content": "Discover how pixel tracking in 2025 enhances attribution, personalization, and compliance with GDPR/CCPA while optimizing marketing data."}
+{"idx": 1, "title": "Bose Descriptor-In-Pixel Point-Feature Tracking For ...", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point-. feature detection and tracking , designed specifically for. Pixel Processor Array (PPA) vision sensors.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/890539401/Bose-Descriptor-In-Pixel-Point-Feature-Tracking-for-Pixel-Processor-Arrays-CVPR-2025-Paper", "content": "This paper presents a novel approach for joint point-. feature detection and tracking , designed specifically for. Pixel Processor Array (PPA) vision sensors."}
+{"idx": 2, "title": "Google Pixel Drops: What's new on your ...", "date": "", "ddg_snippet": "The third is Pixel Drops (previously referred to as Pixel Feature Drops before August 2024), which add exclusive features to Google Pixel smartphones. google ...", "subpage_snippet": "", "source": "www.androidauthority.com", "link": "https://www.androidauthority.com/google-pixel-feature-drop-3360934/", "content": "The third is Pixel Drops (previously referred to as Pixel Feature Drops before August 2024), which add exclusive features to Google Pixel smartphones. google ..."}
+{"idx": 3, "title": "DEMO : Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "We demonstrate our “ in-pixel ” point- feature detection and tracking approach, designed specifically for Pixel Processor Array (PPA) sensors.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvprw/2025/999400e956/2a1VzcLhu1y", "content": "We demonstrate our “ in-pixel ” point- feature detection and tracking approach, designed specifically for Pixel Processor Array (PPA) sensors."}
+{"idx": 4, "title": "Pixel Watch Feature Drop: Better step tracking, Menstrual ...", "date": "", "ddg_snippet": "4 Mar 2025 — The Pixel Watch 3 is adding on-device menstrual tracking to log periods, view cycle status, and predict your next period.", "subpage_snippet": "", "source": "9to5google.com", "link": "https://9to5google.com/2025/03/04/pixel-watch-march-2025-feature-drop/", "content": "4 Mar 2025 — The Pixel Watch 3 is adding on-device menstrual tracking to log periods, view cycle status, and predict your next period."}
+{"idx": 5, "title": "A processing-in-pixel-in-memory paradigm for resource ...", "date": "", "ddg_snippet": "by G Datta · 2022 · Cited by 54 — We propose a novel Processing- in-Pixel -in-memory (P 2 M) paradigm, that customizes the pixel array by adding support for analog multi-channel, multi-bit ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-022-17934-1", "content": "by G Datta · 2022 · Cited by 54 — We propose a novel Processing- in-Pixel -in-memory (P 2 M) paradigm, that customizes the pixel array by adding support for analog multi-channel, multi-bit ..."}
+{"idx": 6, "title": "A guide to deep dive into object detection in 2025", "date": "", "ddg_snippet": "6 Feb 2025 — Object detection is a game-changing tool in computer vision that helps machines detect and locate objects in images and videos.", "subpage_snippet": "", "source": "www.ultralytics.com", "link": "https://www.ultralytics.com/blog/a-guide-to-deep-dive-into-object-detection-in-2025", "content": "6 Feb 2025 — Object detection is a game-changing tool in computer vision that helps machines detect and locate objects in images and videos."}
+{"idx": 7, "title": "Descriptor-In_Pixel - GitHub Pages", "date": "", "ddg_snippet": "Point- Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power. All computation is performed inside the sensor itself.", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Point- Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power. All computation is performed inside the sensor itself."}
+{"idx": 8, "title": "Automatic activity tracking on the Pixel Watch 2 : r/PixelWatch", "date": "", "ddg_snippet": "The Pixel Watch 2 will also show a pop-up / prompt after ~10 minutes when it detects an ongoing activity like walking. It vibrates and shows a button.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/PixelWatch/comments/179a3km/automatic_activity_tracking_on_the_pixel_watch_2/", "content": "The Pixel Watch 2 will also show a pop-up / prompt after ~10 minutes when it detects an ongoing activity like walking. It vibrates and shows a button."}
+{"idx": 9, "title": "Oral Session 2C: Temporal Modeling and Action Recognition", "date": "", "ddg_snippet": "13 Jun 2025 — This paper presents a novel approach for joint point- feature detection and tracking , specifically designed for Pixel Processor Array sensors ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/session/35389", "content": "13 Jun 2025 — This paper presents a novel approach for joint point- feature detection and tracking , specifically designed for Pixel Processor Array sensors ..."}
diff --git a/data/sampled_jsons/in-pixel_processing_AND_feature_tracking.jsonl b/data/sampled_jsons/in-pixel_processing_AND_feature_tracking.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..78c6d4640c306d28df3b22dbcefe6929848e484c
--- /dev/null
+++ b/data/sampled_jsons/in-pixel_processing_AND_feature_tracking.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How Tracking Pixel Works: A Basic Guide - Triple A Review", "date": "", "ddg_snippet": "This essential guide will explain how tracking pixels work and why they are used in the digital world. ... innovation and privacy, the discourse on ...", "subpage_snippet": "", "source": "tripleareview.com", "link": "https://tripleareview.com/how-tracking-pixel-works-basic-guide/", "content": "This essential guide will explain how tracking pixels work and why they are used in the digital world. ... innovation and privacy, the discourse on ..."}
+{"idx": 1, "title": "Replacing Pixel Tracking: Simple, Straightforward & Worth", "date": "", "ddg_snippet": "While most companies begin the integration process thinking they want pixel -based tracking , many change paths when they learn how advantageous and ...", "subpage_snippet": "", "source": "www.exchangewire.com", "link": "https://www.exchangewire.com/blog/2018/04/11/replacing-pixel-tracking-simple-straightforward-worth/", "content": "While most companies begin the integration process thinking they want pixel -based tracking , many change paths when they learn how advantageous and ..."}
+{"idx": 2, "title": "What are Tracking Pixels and How They Work ?", "date": "", "ddg_snippet": "Platforms like Google Analytics , Facebook Pixel , and LinkedIn Insight Tag offer robust features tailored to different tracking requirements.", "subpage_snippet": "", "source": "blog.emb.global", "link": "https://blog.emb.global/understand-tracking-pixels/", "content": "Platforms like Google Analytics , Facebook Pixel , and LinkedIn Insight Tag offer robust features tailored to different tracking requirements."}
+{"idx": 3, "title": "Tracking Any Pixel in a Video – Machine Learning Blog |", "date": "", "ddg_snippet": "Our inference process will proceed by iteratively refining the sequence of positions, and sequence of appearance features , until they (hopefully ...", "subpage_snippet": "", "source": "blog.ml.cmu.edu", "link": "https://blog.ml.cmu.edu/2022/09/09/tracking-any-pixel-in-a-video/", "content": "Our inference process will proceed by iteratively refining the sequence of positions, and sequence of appearance features , until they (hopefully ..."}
+{"idx": 4, "title": "What is Pixel Tracking? | Northbeam Blog", "date": "", "ddg_snippet": "Pixels are invisible and track across platforms, while cookies are more site-specific. ... and machine learning will further refine pixel tracking , ...", "subpage_snippet": "", "source": "www.northbeam.io", "link": "https://www.northbeam.io/post/what-is-pixel-tracking", "content": "Pixels are invisible and track across platforms, while cookies are more site-specific. ... and machine learning will further refine pixel tracking , ..."}
+{"idx": 5, "title": "Tracking Pixels: What Are They and How Do They Work? – Dm", "date": "", "ddg_snippet": "When a user visits the website the browser loads this link and opens the invisible, tracking pixel . ... insert these tracking pixels on a personal ...", "subpage_snippet": "", "source": "dm-productions.com", "link": "https://dm-productions.com/tracking-pixels-what-are-they-and-how-do-they-work/", "content": "When a user visits the website the browser loads this link and opens the invisible, tracking pixel . ... insert these tracking pixels on a personal ..."}
+{"idx": 6, "title": "Digital Creative Agency - What is Tracking pixel? | Powerful", "date": "", "ddg_snippet": "AI-Powered Analytics Advances in AI mean that the data collected via tracking pixels can now be processed faster and more effectively.", "subpage_snippet": "", "source": "www.digitaland.tv", "link": "https://www.digitaland.tv/blog/what-is-tracking-pixel-ht/", "content": "AI-Powered Analytics Advances in AI mean that the data collected via tracking pixels can now be processed faster and more effectively."}
+{"idx": 7, "title": "What are Tracking Pixels? How Do They Work?", "date": "", "ddg_snippet": "Tracking pixels can also help in segmenting audiences based on behavioural and preference criteria. ... intent behind using tracking pixels isn ...", "subpage_snippet": "", "source": "indixital.com", "link": "https://indixital.com/blog/tracking-pixels-explained/", "content": "Tracking pixels can also help in segmenting audiences based on behavioural and preference criteria. ... intent behind using tracking pixels isn ..."}
+{"idx": 8, "title": "Tracking Pixel: What It Is, How to Set It Up, and How It Works", "date": "", "ddg_snippet": "Tracking pixels are popular in online marketing, as companies need to know if their messages are being read by their customers.", "subpage_snippet": "", "source": "altcraft.com", "link": "https://altcraft.com/glossary/tracking-pixel-what-it-is-how-to-set-it-up-and-how-it-works", "content": "Tracking pixels are popular in online marketing, as companies need to know if their messages are being read by their customers."}
+{"idx": 9, "title": "Invisble Pixel Tracking | OpenedOrNot by 500apps", "date": "", "ddg_snippet": "Invisible pixel tracking is automatic and operates invisibly, giving you more valuable insights into your sent emails.", "subpage_snippet": "", "source": "openedornot.com", "link": "https://openedornot.com/invisible-pixel-tracking", "content": "Invisible pixel tracking is automatic and operates invisibly, giving you more valuable insights into your sent emails."}
diff --git a/data/sampled_jsons/in-pixel_processing_feature_tracking_computer_vision.jsonl b/data/sampled_jsons/in-pixel_processing_feature_tracking_computer_vision.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aca111fed849ccde995cbc2f93365aeaf1916d74
--- /dev/null
+++ b/data/sampled_jsons/in-pixel_processing_feature_tracking_computer_vision.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Computer vision - Wikipedia", "date": "", "ddg_snippet": "Computer vision tasks include methods for acquiring , processing , analyzing , and understanding digital images , and extraction of high-dimensional ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Computer_vision", "content": "Computer vision tasks include methods for acquiring , processing , analyzing , and understanding digital images , and extraction of high-dimensional ..."}
+{"idx": 1, "title": "Sensor-level computer vision with pixel processor arrays for ...", "date": "", "ddg_snippet": "Jun 29, 2022 · Here, we review the history of image sensing and processing hardware from the perspective of in - pixel computing and outline the key features of a state-of-the-art smart camera system based on a PPA device, through the description of the SCAMP-5 system.", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/scirobotics.abl7755", "content": "Jun 29, 2022 · Here, we review the history of image sensing and processing hardware from the perspective of in - pixel computing and outline the key features of a state-of-the-art smart camera system based on a PPA device, through the description of the SCAMP-5 system."}
+{"idx": 2, "title": "Pixel Processor Arrays For Low Latency Gaze Estimation", "date": "", "ddg_snippet": "Abstract—We demonstrate the use of a Pixel Processor Array (PPA) vision sensor for achieving rates of over 10,000 Hz, with processing latency below 0.1 ms, in a gaze tracking application, performing image processing directly upon the sensor itself with minimal external computation.", "subpage_snippet": "", "source": "personalpages.manchester.ac.uk", "link": "https://personalpages.manchester.ac.uk/staff/p.dudek/papers/bose-icvr2022.pdf", "content": "Abstract—We demonstrate the use of a Pixel Processor Array (PPA) vision sensor for achieving rates of over 10,000 Hz, with processing latency below 0.1 ms, in a gaze tracking application, performing image processing directly upon the sensor itself with minimal external computation."}
+{"idx": 3, "title": "Developments in Medical Image Processing and Computational", "date": "", "ddg_snippet": "Jun 14, 2017 by admin in GENERAL SURGERY Comments Off on 2D Feature Tracking based on an Affine Photometric Model ... Instituto de Telecomunicações, ...", "subpage_snippet": "", "source": "basicmedicalkey.com", "link": "https://basicmedicalkey.com/tag/developments-in-medical-image-processing-and-computational-vision/", "content": "Jun 14, 2017 by admin in GENERAL SURGERY Comments Off on 2D Feature Tracking based on an Affine Photometric Model ... Instituto de Telecomunicações, ..."}
+{"idx": 4, "title": "Feature Detection and Matching. Computer Vision - GRIN | Grin", "date": "", "ddg_snippet": "The keywords include Computer vision , feature detection, feature matching, feature tracking , image processing , image analysis, SIFT algorithm ...", "subpage_snippet": "", "source": "www.grin.com", "link": "https://www.grin.com/document/988230", "content": "The keywords include Computer vision , feature detection, feature matching, feature tracking , image processing , image analysis, SIFT algorithm ..."}
+{"idx": 5, "title": "US10728450B2 - Event based computer vision computation - Google", "date": "", "ddg_snippet": "G06F3/00 — Input arrangements for transferring data to be processed into a form capable of being handled by the computer ; Output arrangements ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US10728450B2/en", "content": "G06F3/00 — Input arrangements for transferring data to be processed into a form capable of being handled by the computer ; Output arrangements ..."}
+{"idx": 6, "title": "US9762834B2 - Configurable hardware for computing computer", "date": "", "ddg_snippet": "Computer vision is a field that includes methods for acquiring, processing , analyzing, and understanding images for use in applications.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US9762834B2/en", "content": "Computer vision is a field that includes methods for acquiring, processing , analyzing, and understanding images for use in applications."}
+{"idx": 7, "title": "Computer Vision for Tracking", "date": "", "ddg_snippet": "How does Object Tracking work in Computer Vision ? There are 3 ways to can track obstacles using Computer Vision . ... invite you to receive my daily ...", "subpage_snippet": "", "source": "www.thinkautonomous.ai", "link": "https://www.thinkautonomous.ai/blog/computer-vision-for-tracking/", "content": "How does Object Tracking work in Computer Vision ? There are 3 ways to can track obstacles using Computer Vision . ... invite you to receive my daily ..."}
+{"idx": 8, "title": "Object Feature Tracking in C# - CodeProject", "date": "", "ddg_snippet": "... computer vision application besides per-image incorporate some sort of video processing analysis where some distinctive points are detected and ...", "subpage_snippet": "", "source": "www.codeproject.com", "link": "https://www.codeproject.com/Articles/840823/Object-Feature-Tracking-in-Csharp", "content": "... computer vision application besides per-image incorporate some sort of video processing analysis where some distinctive points are detected and ..."}
+{"idx": 9, "title": "Vision software", "date": "", "ddg_snippet": "... in the fields of Computer Vision and Artificial ... It is a jar library that tracks blobs and simple features in processings ' image streams.", "subpage_snippet": "", "source": "www.roborealm.com", "link": "http://www.roborealm.com/links/vision_software.php", "content": "... in the fields of Computer Vision and Artificial ... It is a jar library that tracks blobs and simple features in processings ' image streams."}
diff --git a/data/sampled_jsons/in-pixel_processing_feature_tracking_point_detection.jsonl b/data/sampled_jsons/in-pixel_processing_feature_tracking_point_detection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..444ebf19708e32b9d578149e8e23804f1ba5c8eb
--- /dev/null
+++ b/data/sampled_jsons/in-pixel_processing_feature_tracking_point_detection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Descriptor-In-Pixel : Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "Our in-pixel approach for point - feature detection and tracking is designed specifically for the PPA's architec-ture, providing high pixel -processor compute resource util-isation, and minimizing data transfer between sensor and external processing .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "Our in-pixel approach for point - feature detection and tracking is designed specifically for the PPA's architec-ture, providing high pixel -processor compute resource util-isation, and minimizing data transfer between sensor and external processing ."}
+{"idx": 1, "title": "Descriptor In Pixel : Point Feature Tracking for Pixel ... - YouTube", "date": "", "ddg_snippet": "This \"response map\" is utilized for both detection and tracking of point - features across the pixel -processor array.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=HIdQtf6mFSs", "content": "This \"response map\" is utilized for both detection and tracking of point - features across the pixel -processor array."}
+{"idx": 2, "title": "Descriptor-In-Pixel: Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "Point - Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power. All computation is performed inside the sensor itself, upon thousands of \" Pixel -Procesors\".", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Point - Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power. All computation is performed inside the sensor itself, upon thousands of \" Pixel -Procesors\"."}
+{"idx": 3, "title": "PDF Lecture 12-13: Feature Detection and Tracking - VNAV", "date": "", "ddg_snippet": "-1.2 Feature Tracking Assume we extracted N corner features from an image I1. Consider one of such corner features at pixel position x1 and call p the 3D point which projects to x1. The question we answer in this section is: given a second (temporally continuous) image I2, compute the pixel projection of p in I2, namely x2.", "subpage_snippet": "", "source": "vnav.mit.edu", "link": "https://vnav.mit.edu/material/12-13-featureDetectionAndTracking-notes.pdf", "content": "-1.2 Feature Tracking Assume we extracted N corner features from an image I1. Consider one of such corner features at pixel position x1 and call p the 3D point which projects to x1. The question we answer in this section is: given a second (temporally continuous) image I2, compute the pixel projection of p in I2, namely x2."}
+{"idx": 4, "title": "Feature Extraction in Image Processing: Techniques and Applications", "date": "", "ddg_snippet": "These features are vital for various downstream tasks such as object detection , classification, and image matching. Feature Extraction in Image Processing This article delves into the methods and techniques used for feature extraction in image processing , highlighting their importance and applications.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/computer-vision/feature-extraction-in-image-processing-techniques-and-applications/", "content": "These features are vital for various downstream tasks such as object detection , classification, and image matching. Feature Extraction in Image Processing This article delves into the methods and techniques used for feature extraction in image processing , highlighting their importance and applications."}
+{"idx": 5, "title": "An Adaptive Threshold-Based Pixel Point Tracking Algorithm Using ...", "date": "", "ddg_snippet": "This limitation arises from traditional feature point extraction and tracking techniques in environments with textureless planes. To address this problem, we propose an algorithm for extracting and tracking pixel points to estimate the depth of each grid in the image, which is segmented into numerous grids.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/25/9/2849", "content": "This limitation arises from traditional feature point extraction and tracking techniques in environments with textureless planes. To address this problem, we propose an algorithm for extracting and tracking pixel points to estimate the depth of each grid in the image, which is segmented into numerous grids."}
+{"idx": 6, "title": "Evaluation and analysis of feature point detection methods based on ...", "date": "", "ddg_snippet": "Based on the evaluation results, the applicability of various feature point detection methods in different environments is thoroughly discussed, and the underlying principles are analyzed (Table 13).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0262885624001197", "content": "Based on the evaluation results, the applicability of various feature point detection methods in different environments is thoroughly discussed, and the underlying principles are analyzed (Table 13)."}
+{"idx": 7, "title": "Point-Feature Tracking for Pixel Processor Arrays - IEEE Xplore", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point - feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of \" pixel -processors\", enabling massive parallel computation of visual data at the point of light capture. Our approach performs all computation entirely in-pixel , meaning no raw ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11092646", "content": "This paper presents a novel approach for joint point - feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of \" pixel -processors\", enabling massive parallel computation of visual data at the point of light capture. Our approach performs all computation entirely in-pixel , meaning no raw ..."}
+{"idx": 8, "title": "EnhanceCenter for improving point based tracking and rich feature ...", "date": "", "ddg_snippet": "Multiple-object tracking (MOT) 1, 2, 3 is a major task in computer vision that involves the detection and tracking of multiple objects simultaneously in complex environments. Technical challenges ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-88924-2", "content": "Multiple-object tracking (MOT) 1, 2, 3 is a major task in computer vision that involves the detection and tracking of multiple objects simultaneously in complex environments. Technical challenges ..."}
+{"idx": 9, "title": "CVPR Poster Descriptor-In-Pixel : Point-Feature Tracking For Pixel ...", "date": "", "ddg_snippet": "Abstract: This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA). Instead of standard pixels , PPA sensors consists of thousands of \" pixel -processors\", enabling massive parallel computation of visual data at the point of light capture.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "Abstract: This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA). Instead of standard pixels , PPA sensors consists of thousands of \" pixel -processors\", enabling massive parallel computation of visual data at the point of light capture."}
diff --git a/data/sampled_jsons/in-pixel_processing_feature_tracking_prior_work_year_2020-2024.jsonl b/data/sampled_jsons/in-pixel_processing_feature_tracking_prior_work_year_2020-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..20176ec7f88c6733cf6fef175d826c44919ba035
--- /dev/null
+++ b/data/sampled_jsons/in-pixel_processing_feature_tracking_prior_work_year_2020-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feature (computer vision)", "date": "", "ddg_snippet": "A feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Feature_(computer_vision)", "content": "A feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties."}
+{"idx": 1, "title": "TheCVF Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "It is the policy of the Computer Vision Foundation to maintain PDF copies of conference papers as submitted during the camera-ready paper collection. These papers are considered the final published versions of the work . We recognize the need for minor corrections after publication, and thus ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "It is the policy of the Computer Vision Foundation to maintain PDF copies of conference papers as submitted during the camera-ready paper collection. These papers are considered the final published versions of the work . We recognize the need for minor corrections after publication, and thus ..."}
+{"idx": 2, "title": "A real-time integrated eye tracker with in-pixel image ...", "date": "", "ddg_snippet": "by AMZ Khaki · 2025 — This paper presents a high-speed eye tracker system (ETS) that leverages an in-pixel image processing array along with a fully parallel ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S016792602500183X", "content": "by AMZ Khaki · 2025 — This paper presents a high-speed eye tracker system (ETS) that leverages an in-pixel image processing array along with a fully parallel ..."}
+{"idx": 3, "title": "DEMO : Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "We demonstrate our “ in-pixel ” point- feature detection and tracking approach, designed specifically for Pixel Processor Array (PPA) sensors.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvprw/2025/999400e956/2a1VzcLhu1y", "content": "We demonstrate our “ in-pixel ” point- feature detection and tracking approach, designed specifically for Pixel Processor Array (PPA) sensors."}
+{"idx": 4, "title": "Pixel Level Processing — Why, What, and How?", "date": "", "ddg_snippet": "Pixel level processing integrates processing at each pixel , offering high SNR, low power, and adapting to different environments. It integrates a processing ... 12 pages", "subpage_snippet": "", "source": "isl.stanford.edu", "link": "https://isl.stanford.edu/~abbas/group/papers_and_pub/pixel_processing_99.pdf", "content": "Pixel level processing integrates processing at each pixel , offering high SNR, low power, and adapting to different environments. It integrates a processing ... 12 pages"}
+{"idx": 5, "title": "What is Feature Extraction? Feature Extraction in Image ...", "date": "", "ddg_snippet": "Feature extraction is a dimensionality reduction process that selects and combines variables into manageable features , making processing easier.", "subpage_snippet": "", "source": "www.mygreatlearning.com", "link": "https://www.mygreatlearning.com/blog/feature-extraction-in-image-processing/", "content": "Feature extraction is a dimensionality reduction process that selects and combines variables into manageable features , making processing easier."}
+{"idx": 6, "title": "A processing-in-pixel-in-memory paradigm for resource- ...", "date": "", "ddg_snippet": "by G Datta · 2022 · Cited by 54 — Moreover, compared to prior - works we implement more complex compute operations including analog convolution, batch-norm, and ReLU inside the pixel array.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9399136/", "content": "by G Datta · 2022 · Cited by 54 — Moreover, compared to prior - works we implement more complex compute operations including analog convolution, batch-norm, and ReLU inside the pixel array."}
+{"idx": 7, "title": "Physics-informed object tracking in surveillance cameras", "date": "", "ddg_snippet": "14 Dec 2023 — Prior work [5] has demonstrated that incorporating physics knowledge (i.e., laws of motion) can improve tracking accuracy over vanilla neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08650v1", "content": "14 Dec 2023 — Prior work [5] has demonstrated that incorporating physics knowledge (i.e., laws of motion) can improve tracking accuracy over vanilla neural ..."}
+{"idx": 8, "title": "Fast Image-Based Tracking by Selective Pixel Integration", "date": "", "ddg_snippet": "by F Dellaert · Cited by 153 — Feature-based methods track the location of a small feature window, and the x-y coordinate of the feature is provided as input to the estimation method. 21 pages", "subpage_snippet": "", "source": "dellaert.github.io", "link": "https://dellaert.github.io/files/Dellaert99frv.pdf", "content": "by F Dellaert · Cited by 153 — Feature-based methods track the location of a small feature window, and the x-y coordinate of the feature is provided as input to the estimation method. 21 pages"}
+{"idx": 9, "title": "Cmu Tracking Any Pixel in a Video – Machine Learning Blog | ML@CMU | Carnegie Mellon University", "date": "", "ddg_snippet": "September 12, 2022 - We upgrade pixels into PIPs: \"Persistent Independent Particles\". With this representation, we track any pixel over time, and overcome visibility issues with a learned temporal prior . Motion estimation is a fundamental task of computer vision, with extremely broad applications. By tracking som", "subpage_snippet": "", "source": "blog.ml.cmu.edu", "link": "https://blog.ml.cmu.edu/2022/09/09/tracking-any-pixel-in-a-video/", "content": "September 12, 2022 - We upgrade pixels into PIPs: \"Persistent Independent Particles\". With this representation, we track any pixel over time, and overcome visibility issues with a learned temporal prior . Motion estimation is a fundamental task of computer vision, with extremely broad applications. By tracking som"}
diff --git a/data/sampled_jsons/instance-level_privacy_unlearning_OR_per-example_privacy_unlearning_year_2023.jsonl b/data/sampled_jsons/instance-level_privacy_unlearning_OR_per-example_privacy_unlearning_year_2023.jsonl
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index 0000000000000000000000000000000000000000..ed3cfcc170c58b71070855ecc915ebc642d53b52
--- /dev/null
+++ b/data/sampled_jsons/instance-level_privacy_unlearning_OR_per-example_privacy_unlearning_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "While the per - instance privacy losses provide valuable in-sights into the unlearning process, their computation re-quires storing gradients throughout training, leading to con-siderable computational overhead.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "While the per - instance privacy losses provide valuable in-sights into the unlearning process, their computation re-quires storing gradients throughout training, leading to con-siderable computational overhead."}
+{"idx": 1, "title": "How LLM Unlearning Is Shaping the Future of AI Privacy - Unite.AI", "date": "", "ddg_snippet": "LLM unlearning addresses privacy issues by ensuring that personal or confidential data can be removed from a model’s memory. Once sensitive information is identified, it can be erased without the need to retrain the entire model from scratch.", "subpage_snippet": "", "source": "www.unite.ai", "link": "https://www.unite.ai/how-llm-unlearning-is-shaping-the-future-of-ai-privacy/", "content": "LLM unlearning addresses privacy issues by ensuring that personal or confidential data can be removed from a model’s memory. Once sensitive information is identified, it can be erased without the need to retrain the entire model from scratch."}
+{"idx": 2, "title": "Unveiling Entity- Level Unlearning for Large Language Models...", "date": "", "ddg_snippet": "Specifi-cally, instance - level unlearning focuses on remov-ing specific facts, which are predefined and can be directly erased.2022. Knowledge unlearning for mitigating privacy risks in language models. arXiv preprint arXiv:2210.01504.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.coling-main.358.pdf", "content": "Specifi-cally, instance - level unlearning focuses on remov-ing specific facts, which are predefined and can be directly erased.2022. Knowledge unlearning for mitigating privacy risks in language models. arXiv preprint arXiv:2210.01504."}
+{"idx": 3, "title": "(PDF) A survey of security and privacy issues of machine unlearning", "date": "", "ddg_snippet": "Differential privacy , adversarial training, and query limitations are highlighted as key defenses against privacy attacks, while data validation, adversarial examples , and post- unlearning audits are critical in mitigating security risks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387897593_A_survey_of_security_and_privacy_issues_of_machine_unlearning", "content": "Differential privacy , adversarial training, and query limitations are highlighted as key defenses against privacy attacks, while data validation, adversarial examples , and post- unlearning audits are critical in mitigating security risks."}
+{"idx": 4, "title": "Machine Unlearning in 2024 - Ken Ziyu Liu - Stanford Computer Science", "date": "", "ddg_snippet": "“ Unlearning ” via differential privacy Empirical unlearning , where data to be unlearned are precisely known (training examples)... per - example gradients and injecting some per-coordinate Gaussian noise to the gradients.", "subpage_snippet": "", "source": "ai.stanford.edu", "link": "https://ai.stanford.edu/~kzliu/blog/unlearning", "content": "“ Unlearning ” via differential privacy Empirical unlearning , where data to be unlearned are precisely known (training examples)... per - example gradients and injecting some per-coordinate Gaussian noise to the gradients."}
+{"idx": 5, "title": "Federated Unlearning : Privacy in Focus - Simple Science", "date": "", "ddg_snippet": "Examining federated unlearning and its challenges in machine learning privacy .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-09-01-federated-unlearning-privacy-in-focus--a3qz45e", "content": "Examining federated unlearning and its challenges in machine learning privacy ."}
+{"idx": 6, "title": "Digital forgetting in large language models: a survey of unlearning ...", "date": "", "ddg_snippet": "A privacy neuron aggregator is also introduced to handle multiple unlearning requests. An analysis of the relationship between privacy neurons and model memorization was also performed.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-024-11078-6", "content": "A privacy neuron aggregator is also introduced to handle multiple unlearning requests. An analysis of the relationship between privacy neurons and model memorization was also performed."}
+{"idx": 7, "title": "Rectifying Privacy and Efficacy Measurements in Machine", "date": "", "ddg_snippet": "Per -Sample Privacy Evaluation for Unlearning .For example , we might evaluate how unlearning impacts remain data privacy and how memorization levels would be changed.", "subpage_snippet": "", "source": "yhongcs.github.io", "link": "https://yhongcs.github.io/pub/usenix25-ruli.pdf", "content": "Per -Sample Privacy Evaluation for Unlearning .For example , we might evaluate how unlearning impacts remain data privacy and how memorization levels would be changed."}
+{"idx": 8, "title": "The Great Unlearning : Why Forgetting Will Be the Most... | Medium", "date": "", "ddg_snippet": "6. Unlearn privacy as a default state — radical transparency will become the norm. 7. Unlearn the concept of death as the end — consciousness uploading will offer digital immortality.", "subpage_snippet": "", "source": "fahrikarakas.medium.com", "link": "https://fahrikarakas.medium.com/the-great-unlearning-why-forgetting-will-be-the-most-valuable-skill-of-the-21st-century-6d325267e879", "content": "6. Unlearn privacy as a default state — radical transparency will become the norm. 7. Unlearn the concept of death as the end — consciousness uploading will offer digital immortality."}
+{"idx": 9, "title": "Private Machine Unlearning : Enhancing Data Deletion (04/26) - Azoo...", "date": "", "ddg_snippet": "Privacy leakage in machine unlearning . Private machine unlearning . There are various methods to ensure the privacy of data, and among them, the addition of noise techniques can be used to protect the remaining data after unlearning .", "subpage_snippet": "", "source": "cubig.ai", "link": "https://cubig.ai/blogs/private-machine-unlearning", "content": "Privacy leakage in machine unlearning . Private machine unlearning . There are various methods to ensure the privacy of data, and among them, the addition of noise techniques can be used to protect the remaining data after unlearning ."}
diff --git a/data/sampled_jsons/jP59rz1bZk_ITBench-_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks.jsonl b/data/sampled_jsons/jP59rz1bZk_ITBench-_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2982a47e9ee232561258eb3ab1a60de9bd09e134
--- /dev/null
+++ b/data/sampled_jsons/jP59rz1bZk_ITBench-_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench : Evaluating AI Agents across", "date": "", "ddg_snippet": "We introduce ITBench , a framework that offers a systematic methodology for bench -marking AI agents to address real - world IT au - tomation tasks .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "We introduce ITBench , a framework that offers a systematic methodology for bench -marking AI agents to address real - world IT au - tomation tasks ."}
+{"idx": 1, "title": "ITBench : Evaluating AI Agents across Diverse Real - World IT ...", "date": "", "ddg_snippet": "Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. ITBench includes an initial set of 94 real - world scenarios, which can be easily extended by community contributions.", "subpage_snippet": "", "source": "research.ibm.com", "link": "https://research.ibm.com/publications/itbench-evaluating-ai-agents-across-diverse-real-world-it-automation-tasks", "content": "Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. ITBench includes an initial set of 94 real - world scenarios, which can be easily extended by community contributions."}
+{"idx": 2, "title": "(PDF) ITBench : Evaluating AI Agents across Diverse Real - World IT ...", "date": "", "ddg_snippet": "Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions.We expect ITBench to be a key enabler of AI -driven IT automation that is correct, safe, and fast.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388882803_ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks", "content": "Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions.We expect ITBench to be a key enabler of AI -driven IT automation that is correct, safe, and fast."}
+{"idx": 3, "title": "Paper tables with annotated results for ITBench : Evaluating AI ...", "date": "", "ddg_snippet": "ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks . Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/itbench-evaluating-ai-agents-across-diverse/review/", "content": "ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks . Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions."}
+{"idx": 4, "title": "[Literature Review] ITBench : Evaluating AI Agents across Diverse ...", "date": "", "ddg_snippet": "ITBench . AI Agents . IT Automation . Site Reliability Engineering. Compliance and Security Operations. ITBench facilitates an automated evaluation process through a leaderboard that ranks agent performance based on defined metrics.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/itbench-evaluating-ai-agents-across-diverse-real-world-it-automation-tasks", "content": "ITBench . AI Agents . IT Automation . Site Reliability Engineering. Compliance and Security Operations. ITBench facilitates an automated evaluation process through a leaderboard that ranks agent performance based on defined metrics."}
+{"idx": 5, "title": "ITBench : Evaluating AI Agents across Diverse Real - World IT ...", "date": "", "ddg_snippet": "Abstract: Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. ITBench includes an initial set of 94 real - world scenarios, which can be easily extended by community contributions.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.05352v1", "content": "Abstract: Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. ITBench includes an initial set of 94 real - world scenarios, which can be easily extended by community contributions."}
+{"idx": 6, "title": "ICML 2025 ITBench : Evaluating AI Agents across Diverse ...", "date": "", "ddg_snippet": "We will be presenting \" ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks \" at ICML 2025.With IBM Instana's latest agentic capabilities, proactive detection and automated remediation are becoming a reality.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/daby-sow-2a53b31_icml-2025-itbench-evaluating-ai-agents-across-activity-7351647601688993793-v_Bb", "content": "We will be presenting \" ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks \" at ICML 2025.With IBM Instana's latest agentic capabilities, proactive detection and automated remediation are becoming a reality."}
+{"idx": 7, "title": "ITBench : Next-Gen Benchmarking for IT Automation Evaluation", "date": "", "ddg_snippet": "ITBench Benchmarking Scenarios, Agents , Automation Server, and Leaderboard. ITBench is a systematic benchmarking framework and run-time environment designed to evaluate agents tasked with automating IT operations.", "subpage_snippet": "", "source": "dzone.com", "link": "https://dzone.com/articles/itbench-next-gen-benchmarking-it-automation", "content": "ITBench Benchmarking Scenarios, Agents , Automation Server, and Leaderboard. ITBench is a systematic benchmarking framework and run-time environment designed to evaluate agents tasked with automating IT operations."}
+{"idx": 8, "title": "GitHub - xlab-uiuc/ ITBench -1: Code repository for sample scenarios as...", "date": "", "ddg_snippet": "title={ ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks }, author={Jha, Saurabh and Arora, Rohan and Watanabe, Yuji and others}", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xlab-uiuc/ITBench-1", "content": "title={ ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks }, author={Jha, Saurabh and Arora, Rohan and Watanabe, Yuji and others}"}
+{"idx": 9, "title": "Jackson Clark - Google Scholar", "date": "", "ddg_snippet": "Year. ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks .STRATUS: A Multi- agent System for Autonomous Reliability Engineering of Modern Clouds.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=cFPoN7QAAAAJ&hl=en", "content": "Year. ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks .STRATUS: A Multi- agent System for Autonomous Reliability Engineering of Modern Clouds."}
diff --git a/data/sampled_jsons/k6ZHvF1vkg_Beyond_Optimism-_Exploration_With_Partially_Observable_Rewards_Table_1_Appendix_Two-Room_.jsonl b/data/sampled_jsons/k6ZHvF1vkg_Beyond_Optimism-_Exploration_With_Partially_Observable_Rewards_Table_1_Appendix_Two-Room_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e79c6edaf9fc730a5fde3af6a84bc8957ec3287
--- /dev/null
+++ b/data/sampled_jsons/k6ZHvF1vkg_Beyond_Optimism-_Exploration_With_Partially_Observable_Rewards_Table_1_Appendix_Two-Room_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.13909", "content": "Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits ..."}
+{"idx": 1, "title": "Beyond optimism | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740005", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 2, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable ."}
+{"idx": 3, "title": "Beyond Optimism: Exploration With Partially Observable Rewards | AI ...", "date": "", "ddg_snippet": "Conclusion This paper introduces a novel exploration strategy called \" Optimism Beyond Optimism \" (OBO) for reinforcement learning in partially observable environments. By considering a broader range of possible outcomes, OBO aims to strike a better balance between exploration and exploitation compared to traditional optimistic methods.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-optimism-exploration-partially-observable-rewards", "content": "Conclusion This paper introduces a novel exploration strategy called \" Optimism Beyond Optimism \" (OBO) for reinforcement learning in partially observable environments. By considering a broader range of possible outcomes, OBO aims to strike a better balance between exploration and exploitation compared to traditional optimistic methods."}
+{"idx": 4, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=k6ZHvF1vkg", "content": "To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?"}
+{"idx": 5, "title": "opendilab/awesome-exploration-rl - GitHub", "date": "", "ddg_snippet": "A curated list of awesome exploration RL resources (continually updated) - opendilab/awesome- exploration -rl", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/opendilab/awesome-exploration-rl", "content": "A curated list of awesome exploration RL resources (continually updated) - opendilab/awesome- exploration -rl"}
+{"idx": 6, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Title: Beyond Optimism : Exploration With Partially Observable Rewards Authors: Simone Parisi, Alireza Kazemipour, Michael Bowling, Abstract summary: Exploration in reinforcement learning (RL) remains an open challenge. We present a novel strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2406.13909v1_enmode", "content": "Title: Beyond Optimism : Exploration With Partially Observable Rewards Authors: Simone Parisi, Alireza Kazemipour, Michael Bowling, Abstract summary: Exploration in reinforcement learning (RL) remains an open challenge. We present a novel strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy."}
+{"idx": 7, "title": "Beyond Optimism: Exploration With Partially Observable Rewards - arXiv.org", "date": "", "ddg_snippet": "How can we efficiently explore and learn an optimal policy when rewards are partially observable , without relying on optimism and yet still have guarantees of convergence? In this paper, we present a novel exploration strategy based on the successor representation to tackle this question. Note that Lattimore and Szepesvari [33] already argued against optimism in partial monitoring [8], a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v1", "content": "How can we efficiently explore and learn an optimal policy when rewards are partially observable , without relying on optimism and yet still have guarantees of convergence? In this paper, we present a novel exploration strategy based on the successor representation to tackle this question. Note that Lattimore and Szepesvari [33] already argued against optimism in partial monitoring [8], a ..."}
+{"idx": 8, "title": "PDF Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Alireza Kazemipour Michael Bowling 38th Conference on Neural Information Processing Systems (NeurIPS 2024)", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/93919.pdf", "content": "Alireza Kazemipour Michael Bowling 38th Conference on Neural Information Processing Systems (NeurIPS 2024)"}
+{"idx": 9, "title": "Research - Simone Parisi", "date": "", "ddg_snippet": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi, Alireza Kazemipour, Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling", "subpage_snippet": "", "source": "sparisi.github.io", "link": "https://sparisi.github.io/assets/research.html", "content": "Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi, Alireza Kazemipour, Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling"}
diff --git a/data/sampled_jsons/kEn7Wt6Yj2_On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_Section_3.2_.jsonl b/data/sampled_jsons/kEn7Wt6Yj2_On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_Section_3.2_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..00ac6e0de5a96ad46aea5a141c9c936da4ed48b7
--- /dev/null
+++ b/data/sampled_jsons/kEn7Wt6Yj2_On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_Section_3.2_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster On Differential Privacy for Adaptively Solving ...", "date": "", "ddg_snippet": "We focus on two classical search problems : nearest neighbor queries and regression with arbitrary turnstile updates. We identify key parameters to these problems , such as the number of c -approximate near neighbors and the matrix condition number, and use different differential privacy techniques to design algorithms returning the solution ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44265", "content": "We focus on two classical search problems : nearest neighbor queries and regression with arbitrary turnstile updates. We identify key parameters to these problems , such as the number of c -approximate near neighbors and the matrix condition number, and use different differential privacy techniques to design algorithms returning the solution ..."}
+{"idx": 1, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "To offset the blowup in condition number, we scale down α by a factor of κ, and thus es-tablish the utility of the coordinate-wise private median mechanism. We find the private median to be a surprising application of sketching with the l∞ guarantee, which is a less studied guarantee in the sketching literature.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kEn7Wt6Yj2", "content": "To offset the blowup in condition number, we scale down α by a factor of κ, and thus es-tablish the utility of the coordinate-wise private median mechanism. We find the private median to be a surprising application of sketching with the l∞ guarantee, which is a less studied guarantee in the sketching literature."}
+{"idx": 2, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "To ofset the blowup in condition number, we scale down α by a factor of κ, and thus establish the utility of the coordinate-wise private median mechanism. We find the private median to be a surprising application of sketching with the l∞ guarantee, which is a less studied guarantee in the sketching literature.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503", "content": "To ofset the blowup in condition number, we scale down α by a factor of κ, and thus establish the utility of the coordinate-wise private median mechanism. We find the private median to be a surprising application of sketching with the l∞ guarantee, which is a less studied guarantee in the sketching literature."}
+{"idx": 3, "title": "[PDF] On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "Jun 5, 2025 · A differentially private bicriteria algorithm is designed to solve a recently proposed facility location problem for vaccine distribution which generalizes the k-supplier with outliers and gives the first non-trivial guarantees under differential privacy .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-Differential-Privacy-for-Adaptively-Solving-via-Feng-Feng/aacaaed4353e56beb061a8aa1654abe6be8e7095", "content": "Jun 5, 2025 · A differentially private bicriteria algorithm is designed to solve a recently proposed facility location problem for vaccine distribution which generalizes the k-supplier with outliers and gives the first non-trivial guarantees under differential privacy ."}
+{"idx": 4, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "May 1, 2025 · In this paper we investigate the use of differential privacy for adaptive queries to {\\it search } problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kEn7Wt6Yj2", "content": "May 1, 2025 · In this paper we investigate the use of differential privacy for adaptive queries to {\\it search } problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query."}
+{"idx": 5, "title": "On Differential Privacy for Adaptively Solving Search Problems via ...", "date": "", "ddg_snippet": "We focus on two classical search problems : nearest neighbor queries and regression with arbitrary turnstile updates.Report issue for preceding element. 3 . 2 Adaptive Regression via Differentially Private Median and.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05503v1", "content": "We focus on two classical search problems : nearest neighbor queries and regression with arbitrary turnstile updates.Report issue for preceding element. 3 . 2 Adaptive Regression via Differentially Private Median and."}
+{"idx": 6, "title": "130472 PDFs | Review articles in REGRESSION (PSYCHOLOGY)", "date": "", "ddg_snippet": "On Differential Privacy for Adaptively Solving Search Problems via Sketching .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Regression-Psychology/publications/12", "content": "On Differential Privacy for Adaptively Solving Search Problems via Sketching ."}
+{"idx": 7, "title": "Ying Feng", "date": "", "ddg_snippet": "On Differential Privacy for Adaptively Solving Search Problems via Sketching .A Fast Rescheduling Algorithm for Real-Time Multi-Robot Coordination (Extended Abstract).", "subpage_snippet": "", "source": "yinggggfeng.github.io", "link": "https://yinggggfeng.github.io/", "content": "On Differential Privacy for Adaptively Solving Search Problems via Sketching .A Fast Rescheduling Algorithm for Real-Time Multi-Robot Coordination (Extended Abstract)."}
+{"idx": 8, "title": "Lichen Zhang's Homepage", "date": "", "ddg_snippet": "I'm particularly excited about problems in optimization, sketching , differential privacy and quantum computing. Recently, I've been working on theoretical and practical approaches to improve the efficiency of large language models (LLM). I'm supported by a Simons Dissertation Fellowship in Mathematics.", "subpage_snippet": "", "source": "lczh.github.io", "link": "https://lczh.github.io/", "content": "I'm particularly excited about problems in optimization, sketching , differential privacy and quantum computing. Recently, I've been working on theoretical and practical approaches to improve the efficiency of large language models (LLM). I'm supported by a Simons Dissertation Fellowship in Mathematics."}
+{"idx": 9, "title": "Massachusetts Institute of Technology - Cited by 9 - Algorithms", "date": "", "ddg_snippet": "On Differential Privacy for Adaptively Solving Search Problems via Sketching .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=SYYmposAAAAJ&hl=en", "content": "On Differential Privacy for Adaptively Solving Search Problems via Sketching ."}
diff --git a/data/sampled_jsons/kEn7Wt6Yj2_section_3.2_coordinate-wise_private_median_not_enough_regression.jsonl b/data/sampled_jsons/kEn7Wt6Yj2_section_3.2_coordinate-wise_private_median_not_enough_regression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7563382af89d34324b7179a6bdaabe8add11ca9c
--- /dev/null
+++ b/data/sampled_jsons/kEn7Wt6Yj2_section_3.2_coordinate-wise_private_median_not_enough_regression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Median - Wikipedia", "date": "", "ddg_snippet": "Median income, for example, may be a better way to describe the center of the income distribution because increases in the largest incomes alone have no effect on the median . Median is a 2-quantile; it is the value that partitions a set into two equal parts.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Median", "content": "Median income, for example, may be a better way to describe the center of the income distribution because increases in the largest incomes alone have no effect on the median . Median is a 2-quantile; it is the value that partitions a set into two equal parts."}
+{"idx": 1, "title": "Google NotebookLM | AI Research Tool & Thinking Partner", "date": "", "ddg_snippet": "NotebookLM does not use your personal data, including your source uploads, queries, and the responses from the model for training. lock. Privacy with NotebookLM.", "subpage_snippet": "", "source": "notebooklm.google", "link": "https://notebooklm.google/", "content": "NotebookLM does not use your personal data, including your source uploads, queries, and the responses from the model for training. lock. Privacy with NotebookLM."}
+{"idx": 2, "title": "Tether USD | USDT Address...", "date": "", "ddg_snippet": "QR code. Private Label. Not Available, login to update.", "subpage_snippet": "", "source": "usdt.tokenview.io", "link": "https://usdt.tokenview.io/ru/address/TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t", "content": "QR code. Private Label. Not Available, login to update."}
+{"idx": 3, "title": "ГДЗ Английский язык 7 класс Биболетова, Трубанева, 2019 на...", "date": "", "ddg_snippet": "SECTION 2. Talking about countries and nationalities. SECTION 5. Would you like to attend a private school?", "subpage_snippet": "", "source": "Reshalka.com", "link": "https://Reshalka.com/uchebniki/7-klass/english/biboletova1", "content": "SECTION 2. Talking about countries and nationalities. SECTION 5. Would you like to attend a private school?"}
+{"idx": 4, "title": "Interactive Map: Russia's Invasion of Ukraine", "date": "", "ddg_snippet": "For media inquiries, please email press@understandingwar.org. Note for iPhone, iPad, and iOS users: This map does not work if lockdown mode is enabled.", "subpage_snippet": "", "source": "storymaps.arcgis.com", "link": "https://storymaps.arcgis.com/stories/36a7f6a6f5a9448496de641cf64bd375", "content": "For media inquiries, please email press@understandingwar.org. Note for iPhone, iPad, and iOS users: This map does not work if lockdown mode is enabled."}
+{"idx": 5, "title": "qBittorrent - скачать бесплатно qBittorrent 4.4.5 / 5.1.2", "date": "", "ddg_snippet": "Бесплатно. Windows. QBittorrent - бесплатный, мощный и удобный в работе кроссплатформенный клиент файлообменной сети BitTorrent. Несмотря на кажущуюся простоту, программа обладает внушительным набором весьма полезных возможностей...", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-18505-qbittorrent.html", "content": "Бесплатно. Windows. QBittorrent - бесплатный, мощный и удобный в работе кроссплатформенный клиент файлообменной сети BitTorrent. Несмотря на кажущуюся простоту, программа обладает внушительным набором весьма полезных возможностей..."}
+{"idx": 6, "title": "0.3 Fubgun Lightning Arrow Deadeye - PoE 2 Ranger Build Guide", "date": "", "ddg_snippet": "Don't use Elemental Armament III , only level 2. on Lightning Rod swap Elemental Focus for Deliberation if you're using rhoa (can also use Stoicism II Also Magnified Area I is for clear, swap to Concentrated Area for boss dps (if needed).", "subpage_snippet": "", "source": "mobalytics.gg", "link": "https://mobalytics.gg/poe-2/builds/lightning-arrow-farmer-fubgun", "content": "Don't use Elemental Armament III , only level 2. on Lightning Rod swap Elemental Focus for Deliberation if you're using rhoa (can also use Stoicism II Also Magnified Area I is for clear, swap to Concentrated Area for boss dps (if needed)."}
+{"idx": 7, "title": "Операция Z: Военкоры Русской Весны – Telegram", "date": "", "ddg_snippet": "Добровольцы, волонтеры и военкоры Русской Весны действуют в боевых порядках войск на Донбассе, Украине и САР, получая информацию из самых горячих точек. РКН: clck.ru/3Fj3hJ Связь: @rvvoenkor_bot youtube.com/c/rusvesnadonbass.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/RVvoenkor", "content": "Добровольцы, волонтеры и военкоры Русской Весны действуют в боевых порядках войск на Донбассе, Украине и САР, получая информацию из самых горячих точек. РКН: clck.ru/3Fj3hJ Связь: @rvvoenkor_bot youtube.com/c/rusvesnadonbass."}
+{"idx": 8, "title": "Ключи активации Windows 10 | Ответы Mail", "date": "", "ddg_snippet": "2B87N-8KFHP-DKV6R-Y2C8J-PKCKT Windows 10 Pro for Workstations – DXG7C-N36C4-C4HTG-X4T3X-2YV77 Windows 10 Pro N for Workstations – WYPNQ-8C467-V2W6J-TX4WX-WT2RQ Windows 10 S –.", "subpage_snippet": "", "source": "otvet.mail.ru", "link": "https://otvet.mail.ru/question/237199183", "content": "2B87N-8KFHP-DKV6R-Y2C8J-PKCKT Windows 10 Pro for Workstations – DXG7C-N36C4-C4HTG-X4T3X-2YV77 Windows 10 Pro N for Workstations – WYPNQ-8C467-V2W6J-TX4WX-WT2RQ Windows 10 S –."}
+{"idx": 9, "title": "Калькулятор уравнений", "date": "", "ddg_snippet": "Tiếng Việt ( VI ). Українська (UK). ไทย (TH).", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/equ/ru/", "content": "Tiếng Việt ( VI ). Українська (UK). ไทย (TH)."}
diff --git a/data/sampled_jsons/language_model_critical_windows_2024_arxiv.jsonl b/data/sampled_jsons/language_model_critical_windows_2024_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f40b522784b35a2e20a85b2f9d623eea0776e1a
--- /dev/null
+++ b/data/sampled_jsons/language_model_critical_windows_2024_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv — Papers — EleutherAI", "date": "", "ddg_snippet": "Polyglot is a pioneering project aimed at enhancing the non-English language performance of multilingual language models .", "subpage_snippet": "", "source": "www.eleuther.ai", "link": "https://www.eleuther.ai/papers-blog/category/arXiv", "content": "Polyglot is a pioneering project aimed at enhancing the non-English language performance of multilingual language models ."}
+{"idx": 1, "title": "Large Language Models are Software System Optimizers", "date": "", "ddg_snippet": "Recent methods using Large Language Models (LLMs) offer automation to address these limitations, but often fail to scale to the complexity of real ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.01249v1", "content": "Recent methods using Large Language Models (LLMs) offer automation to address these limitations, but often fail to scale to the complexity of real ..."}
+{"idx": 2, "title": "A Language Model-Driven Semi-Supervised Ensemble Framework for", "date": "", "ddg_snippet": "In contrast, modern language models (LMs) built on transformer architectures, have overcome many limitations of traditional techniques by capturing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22912v1", "content": "In contrast, modern language models (LMs) built on transformer architectures, have overcome many limitations of traditional techniques by capturing ..."}
+{"idx": 3, "title": "Ethics and Psychology: Hierarchical Reasoning Model", "date": "", "ddg_snippet": "Furthermore, HRM outperforms much larger models with significantly longer context windows on the Abstraction and Reasoning Corpus (ARC), a key ...", "subpage_snippet": "", "source": "www.ethicalpsychology.com", "link": "https://www.ethicalpsychology.com/2025/09/hierarchical-reasoning-model.html", "content": "Furthermore, HRM outperforms much larger models with significantly longer context windows on the Abstraction and Reasoning Corpus (ARC), a key ..."}
+{"idx": 4, "title": "AIware 2024 - Late Breaking Arxiv Track - AIware 2024", "date": "", "ddg_snippet": "Security and Safety + Round Table + Day1 Closing Main Track / Late Breaking Arxiv Track at Mandacaru Chair(s): Thomas Zimmermann Microsoft Research ...", "subpage_snippet": "", "source": "2024.aiwareconf.org", "link": "https://2024.aiwareconf.org/track/aiware-2024-late-breaking-arxiv-track", "content": "Security and Safety + Round Table + Day1 Closing Main Track / Late Breaking Arxiv Track at Mandacaru Chair(s): Thomas Zimmermann Microsoft Research ..."}
+{"idx": 5, "title": "GitHub - mit-han-lab/Quest: [ICML 2024] Quest: Query-Aware", "date": "", "ddg_snippet": "As the demand for long-context large language models (LLMs) increases, models with context windows of up to 128k or 1M tokens are becoming ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mit-han-lab/Quest", "content": "As the demand for long-context large language models (LLMs) increases, models with context windows of up to 128k or 1M tokens are becoming ..."}
+{"idx": 6, "title": "Towards Efficient Key-Value Cache Management for Prefix", "date": "", "ddg_snippet": "The increasing adoption of large language models (LLMs) with extended context windows necessitates efficient Key-Value Cache (KVC) management to ...", "subpage_snippet": "", "source": "research.ibm.com", "link": "https://research.ibm.com/publications/towards-efficient-key-value-cache-management-for-prefix-prefilling-in-llm-inference", "content": "The increasing adoption of large language models (LLMs) with extended context windows necessitates efficient Key-Value Cache (KVC) management to ..."}
+{"idx": 7, "title": "unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit ·", "date": "", "ddg_snippet": "Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit", "content": "Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early ..."}
+{"idx": 8, "title": "Towards Interactive Evaluations for Interaction Harms in", "date": "", "ddg_snippet": "... world have emphasized the importance of conducting model evaluations for various risks from discrimination to cybersecurity risks (UK Government 2024 ...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "... world have emphasized the importance of conducting model evaluations for various risks from discrimination to cybersecurity risks (UK Government 2024 ..."}
+{"idx": 9, "title": "Jailbreaking LLM-Controlled Robots | hgpu.org", "date": "", "ddg_snippet": "The recent introduction of large language models (LLMs) has revolutionized the field of robotics by enabling contextual reasoning and intuitive human ...", "subpage_snippet": "", "source": "hgpu.org", "link": "https://hgpu.org/?p=29480", "content": "The recent introduction of large language models (LLMs) has revolutionized the field of robotics by enabling contextual reasoning and intuitive human ..."}
diff --git a/data/sampled_jsons/language_model_pre-training_scaling_laws_error_rate_data_size_power_law.jsonl b/data/sampled_jsons/language_model_pre-training_scaling_laws_error_rate_data_size_power_law.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d9ab79bf5a556e1877018d23f2111dff79e28ed6
--- /dev/null
+++ b/data/sampled_jsons/language_model_pre-training_scaling_laws_error_rate_data_size_power_law.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law", "date": "", "ddg_snippet": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down."}
+{"idx": 1, "title": "Scaling Laws for Pre-training Agents and World Models", "date": "", "ddg_snippet": "Nov 7, 2024 · The role of scale in pre-training is until now best understood in the context of large language models (LLMs). Following the observation that the empirical relationship between loss and key scaling quantities can be accurately described by power laws [Kaplan et al., 2020], ensuing work studied the precise trade-off between model and dataset size [Hoffmann et al., 2022], as well as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.04434", "content": "Nov 7, 2024 · The role of scale in pre-training is until now best understood in the context of large language models (LLMs). Following the observation that the empirical relationship between loss and key scaling quantities can be accurately described by power laws [Kaplan et al., 2020], ensuing work studied the precise trade-off between model and dataset size [Hoffmann et al., 2022], as well as ..."}
+{"idx": 2, "title": "How to build AI scaling laws for efficient LLM training and ...", "date": "", "ddg_snippet": "5 days ago · The functional form of scaling laws is relatively simple, incorporating components from the small models that capture the number of parameters and their scaling effect, the number of training tokens and their scaling effect, and the baseline performance for the model family of interest.", "subpage_snippet": "", "source": "news.mit.edu", "link": "https://news.mit.edu/2025/how-build-ai-scaling-laws-efficient-llm-training-budget-maximization-0916", "content": "5 days ago · The functional form of scaling laws is relatively simple, incorporating components from the small models that capture the number of parameters and their scaling effect, the number of training tokens and their scaling effect, and the baseline performance for the model family of interest."}
+{"idx": 3, "title": "Language Model Scaling Laws: Beyond Bigger AI Models ... - Medium", "date": "", "ddg_snippet": "Oct 2, 2024 · Early scaling laws (Kaplan et al., 2020) established power - law relationships between model size , data , and performance. The Chinchilla paradigm shift (2022) introduced the 20:1 token-to-parameter ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@aiml_58187/beyond-bigger-models-the-evolution-of-language-model-scaling-laws-d4bc974d3876", "content": "Oct 2, 2024 · Early scaling laws (Kaplan et al., 2020) established power - law relationships between model size , data , and performance. The Chinchilla paradigm shift (2022) introduced the 20:1 token-to-parameter ..."}
+{"idx": 4, "title": "Scaling Laws for Neural Language Models - Semantic Scholar", "date": "", "ddg_snippet": "Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence. We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size , dataset size , and the amount of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Scaling-Laws-for-Neural-Language-Models-Kaplan-McCandlish/e6c561d02500b2596a230b341a8eb8b921ca5bf2", "content": "Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence. We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size , dataset size , and the amount of ..."}
+{"idx": 5, "title": "Scaling Laws in Large Language Models - by Michael Albada", "date": "", "ddg_snippet": "Feb 11, 2025 · In early LLM research, the impact of model scale on performance was not well understood. The original GPT model had limited capabilities, and the best way to improve language models was unclear. OpenAI’s seminal work on scaling laws changed this. “The loss scales as a power - law with model size , dataset size , and the amount of compute used for training , with some trends spanning more than ...", "subpage_snippet": "", "source": "theneuralnexus.substack.com", "link": "https://theneuralnexus.substack.com/p/scaling-laws-in-large-language-models", "content": "Feb 11, 2025 · In early LLM research, the impact of model scale on performance was not well understood. The original GPT model had limited capabilities, and the best way to improve language models was unclear. OpenAI’s seminal work on scaling laws changed this. “The loss scales as a power - law with model size , dataset size , and the amount of compute used for training , with some trends spanning more than ..."}
+{"idx": 6, "title": "AI scaling laws: Universal guide estimates how LLMs will ...", "date": "", "ddg_snippet": "5 days ago · Using these models , the researchers fit over 1,000 scaling laws and compared their accuracy across architectures, model sizes, and training regimes, as well as testing how the number of models , inclusion of intermediate training checkpoints, and partial training impacted the predictive power of scaling laws to target models .", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-09-ai-scaling-laws-universal-llms.html", "content": "5 days ago · Using these models , the researchers fit over 1,000 scaling laws and compared their accuracy across architectures, model sizes, and training regimes, as well as testing how the number of models , inclusion of intermediate training checkpoints, and partial training impacted the predictive power of scaling laws to target models ."}
+{"idx": 7, "title": "Daily Paper: Predictable Scale: Part I — Optimal ...", "date": "", "ddg_snippet": "Apr 18, 2025 · Daily Paper: Predictable Scale : Part I — Optimal Hyperparameter Scaling Law in Large Language Model Pretraining Proposes empirical scaling laws (Step Law ) that accurately estimate optimal Batch Size and Learning Rate based on model and data size , robust across different model structures, sparsity, and data distributions.", "subpage_snippet": "", "source": "yikai-liao.github.io", "link": "https://yikai-liao.github.io/hugo/p/2025-04-predictable-scale/", "content": "Apr 18, 2025 · Daily Paper: Predictable Scale : Part I — Optimal Hyperparameter Scaling Law in Large Language Model Pretraining Proposes empirical scaling laws (Step Law ) that accurately estimate optimal Batch Size and Learning Rate based on model and data size , robust across different model structures, sparsity, and data distributions."}
+{"idx": 8, "title": "[2001.08361] Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "by J Kaplan · 2020 · Cited by 4575 — We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size , dataset size , and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2001.08361", "content": "by J Kaplan · 2020 · Cited by 4575 — We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size , dataset size , and the ..."}
+{"idx": 9, "title": "Scaling Laws of Synthetic Data for Language Models", "date": "", "ddg_snippet": "26 Mar 2025 — Kaplan et al. [20] demonstrates that the performance of LLMs exhibits a power - law relationship with respect to both model size and dataset size , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19551v2", "content": "26 Mar 2025 — Kaplan et al. [20] demonstrates that the performance of LLMs exhibits a power - law relationship with respect to both model size and dataset size , ..."}
diff --git a/data/sampled_jsons/learnable_PDE_solvers_with_multi-time-step_schemes_error_correction.jsonl b/data/sampled_jsons/learnable_PDE_solvers_with_multi-time-step_schemes_error_correction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d4a435de920c17c30a709465edf91bf4670f5a1c
--- /dev/null
+++ b/data/sampled_jsons/learnable_PDE_solvers_with_multi-time-step_schemes_error_correction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "27 Jan 2025 — We developed MultiPDENet , a PDE-embedded network with multiscale time-stepping, for accelerated flow simulations on spatiotemporal coarse grids.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "27 Jan 2025 — We developed MultiPDENet , a PDE-embedded network with multiscale time-stepping, for accelerated flow simulations on spatiotemporal coarse grids."}
+{"idx": 1, "title": "PDE-constrained Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "by Q Wang — This paper introduces MultiPDENet , a neural network architecture designed to accelerate fluid dynamic simulations by combining classical numerical methods, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "by Q Wang — This paper introduces MultiPDENet , a neural network architecture designed to accelerate fluid dynamic simulations by combining classical numerical methods, ..."}
+{"idx": 2, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "15 Jul 2025 — Notably, it integrates a trainable neural solver for precise predictions at micro time scales, while employing a NN to correct errors at macro ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46029", "content": "15 Jul 2025 — Notably, it integrates a trainable neural solver for precise predictions at micro time scales, while employing a NN to correct errors at macro ..."}
+{"idx": 3, "title": "A Training-Free Approach for Stable Neural PDE Simulations", "date": "", "ddg_snippet": "3 Jul 2025 — We introduced PhysicsCorrect , a training-free, physics-informed correction framework that significantly enhances neural PDE solver stability ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02227v1", "content": "3 Jul 2025 — We introduced PhysicsCorrect , a training-free, physics-informed correction framework that significantly enhances neural PDE solver stability ..."}
+{"idx": 4, "title": "Multi-scale time-stepping of Partial Differential Equations ...", "date": "", "ddg_snippet": "by AP Hemmasian · 2024 · Cited by 16 — We incorporate the idea of multi-scale hierarchical time-stepping to increase the prediction speed and decrease accumulated error over time.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782524002391", "content": "by AP Hemmasian · 2024 · Cited by 16 — We incorporate the idea of multi-scale hierarchical time-stepping to increase the prediction speed and decrease accumulated error over time."}
+{"idx": 5, "title": "PDE-EMBEDDED LEARNING WITH MULTI-TIME", "date": "", "ddg_snippet": "Notably, it integrates a trainable neural solver for precise predictions at micro time scales, while employing a. NN to correct errors at macro time steps .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/fed548bf96501606244dc63795ceb35f0ffeff68.pdf", "content": "Notably, it integrates a trainable neural solver for precise predictions at micro time scales, while employing a. NN to correct errors at macro time steps ."}
+{"idx": 6, "title": "[Literature Review] Error analysis for learning the time- ...", "date": "", "ddg_snippet": "3 Sept 2025 — This paper presents a rigorous theoretical framework for analyzing the approximation of time - stepping operators for evolutionary partial ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/error-analysis-for-learning-the-time-stepping-operator-of-evolutionary-pdes", "content": "3 Sept 2025 — This paper presents a rigorous theoretical framework for analyzing the approximation of time - stepping operators for evolutionary partial ..."}
+{"idx": 7, "title": "Towards long rollout of neural operators with local attention ...", "date": "", "ddg_snippet": "For time -dependent problems, neural PDEs learn to predict solutions for each time step autoregressively [1] but they face the problem of error - accumulation ... 10 pages", "subpage_snippet": "", "source": "caipengfei.me", "link": "https://caipengfei.me/data/rollout_neurips_ml4ps.pdf", "content": "For time -dependent problems, neural PDEs learn to predict solutions for each time step autoregressively [1] but they face the problem of error - accumulation ... 10 pages"}
+{"idx": 8, "title": "A Neural PDE Solver with Temporal Stencil Modeling", "date": "", "ddg_snippet": "by Z Sun · 2023 · Cited by 21 — Hybrid Physics-ML Physics-ML hybrid models is a re- cent line of work that uses a neural network to correct the errors in the classic (typically low-resolution) ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/sun23o/sun23o.pdf", "content": "by Z Sun · 2023 · Cited by 21 — Hybrid Physics-ML Physics-ML hybrid models is a re- cent line of work that uses a neural network to correct the errors in the classic (typically low-resolution) ..."}
+{"idx": 9, "title": "Solver-in-the-Loop: Learning from Differentiable Physics to ...", "date": "", "ddg_snippet": "by K Um · 2020 · Cited by 351 — We target the problem of reducing numerical errors of iterative PDE solvers and compare different learning approaches for finding complex correction functions. 12 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/43e4e6a6f341e00671e123714de019a8-Paper.pdf", "content": "by K Um · 2020 · Cited by 351 — We target the problem of reducing numerical errors of iterative PDE solvers and compare different learning approaches for finding complex correction functions. 12 pages"}
diff --git a/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_experimental_details_hardware_V100_A100_Tesla.jsonl b/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_experimental_details_hardware_V100_A100_Tesla.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..84c4de7fa60f165e94b87a0217241889f15ec8f9
--- /dev/null
+++ b/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_experimental_details_hardware_V100_A100_Tesla.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.09543] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "Oct 12, 2024 · Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment . In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "Oct 12, 2024 · Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment . In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ..."}
+{"idx": 1, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "h Boltzmann Alignment . BA-DDG employs a forward process identical to that of BA-Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆G and the predicted ∆∆G ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "h Boltzmann Alignment . BA-DDG employs a forward process identical to that of BA-Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆G and the predicted ∆∆G ..."}
+{"idx": 2, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values."}
+{"idx": 3, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "论文原名： 《 BOLTZMANN-ALIGNED INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL EFFECTS ON PROTEIN-PROTEIN INTERACTIONS》", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "论文原名： 《 BOLTZMANN-ALIGNED INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL EFFECTS ON PROTEIN-PROTEIN INTERACTIONS》"}
+{"idx": 4, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 5, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "We propose Boltzmann Alignment , a method that leverages physical inductive bias through the Boltzmann distribution and thermodynamic cycle to enhance Δ Δ 𝐺 \\Delta\\Delta G roman_Δ roman_Δ italic_G prediction by transferring knowledge from pre-trained inverse folding models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "We propose Boltzmann Alignment , a method that leverages physical inductive bias through the Boltzmann distribution and thermodynamic cycle to enhance Δ Δ 𝐺 \\Delta\\Delta G roman_Δ roman_Δ italic_G prediction by transferring knowledge from pre-trained inverse folding models."}
+{"idx": 6, "title": "Boltzmann - Aligned Inverse Folding Model as... | OpenReview", "date": "", "ddg_snippet": "Experimental results on SKEMPI v2 indicate that our method achieves Spearman coefficients of 0.3201 (unsupervised) and 0.5134 (supervised) on SKEMPI v2, significantly surpassing the previously reported %SoTA values SoTA results of 0.2632 and 0.4324, respectively.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "Experimental results on SKEMPI v2 indicate that our method achieves Spearman coefficients of 0.3201 (unsupervised) and 0.5134 (supervised) on SKEMPI v2, significantly surpassing the previously reported %SoTA values SoTA results of 0.2632 and 0.4324, respectively."}
+{"idx": 7, "title": "Learning inverse folding from millions of predicted structures", "date": "", "ddg_snippet": "We consider the problem of predicting a protein sequence from its backbone atom coordinates. Machine learning approaches to this problem to date have been limited by the number of available experimentally determined protein structures.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/hsu22a.html", "content": "We consider the problem of predicting a protein sequence from its backbone atom coordinates. Machine learning approaches to this problem to date have been limited by the number of available experimentally determined protein structures."}
+{"idx": 8, "title": "NVIDIA Tesla V 100 | NVIDIA", "date": "", "ddg_snippet": "NVIDIA Tesla V 100 . The First Tensor Core GPU. Tesla V 100 is engineered for the convergence of AI and HPC. It offers a platform for HPC systems to excel at both computational science for scientific simulation and data science for finding insights in data.", "subpage_snippet": "", "source": "www.nvidia.com", "link": "https://www.nvidia.com/en-gb/data-center/tesla-v100/", "content": "NVIDIA Tesla V 100 . The First Tensor Core GPU. Tesla V 100 is engineered for the convergence of AI and HPC. It offers a platform for HPC systems to excel at both computational science for scientific simulation and data science for finding insights in data."}
+{"idx": 9, "title": "16 Commands to Check Hardware Information on Linux - BinaryTides", "date": "", "ddg_snippet": "2. lshw - List Hardware . A general purpose utility, that reports detailed and brief information about multiple different hardware units such as cpu, memory, disk, usb controllers, network adapters etc. Lshw extracts the information from different /proc files.", "subpage_snippet": "", "source": "www.binarytides.com", "link": "https://www.binarytides.com/linux-commands-hardware-info/", "content": "2. lshw - List Hardware . A general purpose utility, that reports detailed and brief information about multiple different hardware units such as cpu, memory, disk, usb controllers, network adapters etc. Lshw extracts the information from different /proc files."}
diff --git a/data/sampled_jsons/matching_pursuit_algorithm_multiple_atoms_per_iteration_batch_L_parameter.jsonl b/data/sampled_jsons/matching_pursuit_algorithm_multiple_atoms_per_iteration_batch_L_parameter.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..81475e1c410a4e4af4bf6b373445c3ee6f27c84e
--- /dev/null
+++ b/data/sampled_jsons/matching_pursuit_algorithm_multiple_atoms_per_iteration_batch_L_parameter.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Pursuit Algorithm - an overview", "date": "", "ddg_snippet": "The iteration loop in the matching pursuit algorithm . ... Matching pursuit searches the optimal atom throughout the entire dictionary in each iteration .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/engineering/pursuit-algorithm", "content": "The iteration loop in the matching pursuit algorithm . ... Matching pursuit searches the optimal atom throughout the entire dictionary in each iteration ."}
+{"idx": 1, "title": "Orthogonal Matching Pursuit - an overview", "date": "", "ddg_snippet": "For both matching pursuit and its orthogonal version, in each iterative decomposition, only one optimal atom is selected.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/engineering/orthogonal-matching-pursuit", "content": "For both matching pursuit and its orthogonal version, in each iterative decomposition, only one optimal atom is selected."}
+{"idx": 2, "title": "Matching Pursuit LASSO Part II: Applications and Sparse ...", "date": "", "ddg_snippet": "by M Tan · 2015 · Cited by 17 — The proposed new matching pur- suit scheme takes less than 50 seconds to recover a 600- sparse signal over dictionaries of one million atoms. A batch mode MPL ...", "subpage_snippet": "", "source": "tanmingkui.github.io", "link": "https://tanmingkui.github.io/files/publications/Matching_2.pdf", "content": "by M Tan · 2015 · Cited by 17 — The proposed new matching pur- suit scheme takes less than 50 seconds to recover a 600- sparse signal over dictionaries of one million atoms. A batch mode MPL ..."}
+{"idx": 3, "title": "Randomized Orthogonal Matching Pursuit Algorithm with ...", "date": "", "ddg_snippet": "One prominent approach is the orthogonal matching pursuit (OMP) algorithm [1] which is described in Algorithm 1.1, where A Λ k + 1 denotes the columns of ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/24M1648624", "content": "One prominent approach is the orthogonal matching pursuit (OMP) algorithm [1] which is described in Algorithm 1.1, where A Λ k + 1 denotes the columns of ..."}
+{"idx": 4, "title": "Multidimensional orthogonal matching pursuit", "date": "", "ddg_snippet": "by J Palacios · 2022 · Cited by 14 — Now we will create a multidimensional orthogonal match - ing pursuit algorithm to solve the multidimensional sparse recovery problem described in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.11600", "content": "by J Palacios · 2022 · Cited by 14 — Now we will create a multidimensional orthogonal match - ing pursuit algorithm to solve the multidimensional sparse recovery problem described in ..."}
+{"idx": 5, "title": "Information Maximization Perspective of Orthogonal ...", "date": "", "ddg_snippet": "This paper explores Orthogonal Matching . Pursuit (OMP) as an alternative to IP for greedily selecting the queries. OMP is a classical signal processing ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/08eac13583b310ec55d755f99c549be3-Paper-Conference.pdf", "content": "This paper explores Orthogonal Matching . Pursuit (OMP) as an alternative to IP for greedily selecting the queries. OMP is a classical signal processing ..."}
+{"idx": 6, "title": "Confined Orthogonal Matching Pursuit for Sparse Random ...", "date": "", "ddg_snippet": "2 Jan 2025 — In this subsection, we provide a lower bound on the probability that the OMP algorithm exactly recovers any K K K italic_K -sparse signal x x \\ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.01008v1", "content": "2 Jan 2025 — In this subsection, we provide a lower bound on the probability that the OMP algorithm exactly recovers any K K K italic_K -sparse signal x x \\ ..."}
+{"idx": 7, "title": "Multiatom tensor orthogonal matching pursuit algorithm for ...", "date": "", "ddg_snippet": "6 Oct 2016 — We propose an algorithm , containing two modes, to select multiatoms instead of selecting a single atom during each iteration . The first one is ...", "subpage_snippet": "", "source": "ebooks.spiedigitallibrary.org", "link": "https://ebooks.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-10/issue-04/045002/Multiatom-tensor-orthogonal-matching-pursuit-algorithm-for-compressive-sensingbased-hyperspectral/10.1117/1.JRS.10.045002.full", "content": "6 Oct 2016 — We propose an algorithm , containing two modes, to select multiatoms instead of selecting a single atom during each iteration . The first one is ..."}
+{"idx": 8, "title": "RobOMP: Robust variants of Orthogonal Matching Pursuit ...", "date": "", "ddg_snippet": "by CA Loza · 2019 · Cited by 10 — In this regard, Orthogonal Matching Pursuit (OMP) provides an intuitive, simple and fast approximation of the optimal solution . However, its main building block ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7924468/", "content": "by CA Loza · 2019 · Cited by 10 — In this regard, Orthogonal Matching Pursuit (OMP) provides an intuitive, simple and fast approximation of the optimal solution . However, its main building block ..."}
+{"idx": 9, "title": "Clutter cancellation in passive radar using batch-based ...", "date": "", "ddg_snippet": "by X Bai · 2021 · Cited by 5 — The CLEAN technique is very similar to matching pursuit (MP), which chooses at each iteration an atom from the dictionary that is best adapted ...", "subpage_snippet": "", "source": "asp-eurasipjournals.springeropen.com", "link": "https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-021-00769-9", "content": "by X Bai · 2021 · Cited by 5 — The CLEAN technique is very similar to matching pursuit (MP), which chooses at each iteration an atom from the dictionary that is best adapted ..."}
diff --git a/data/sampled_jsons/minGPT_n_layer_=_6_OR_n_layer=6_transformer_blocks_configuration_synthetic_experiments.jsonl b/data/sampled_jsons/minGPT_n_layer_=_6_OR_n_layer=6_transformer_blocks_configuration_synthetic_experiments.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..601948c55a646bdb8a79d05bfdf28f4ea766dbc0
--- /dev/null
+++ b/data/sampled_jsons/minGPT_n_layer_=_6_OR_n_layer=6_transformer_blocks_configuration_synthetic_experiments.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A fork of MinGPT for experimenting with transformer architecture", "date": "", "ddg_snippet": "either model_type or ( n _ layer , n_head, n_embd) must be given in the config .x = self. transformer .drop(tok_emb + pos_emb). for block in self. transformer .h", "subpage_snippet": "", "source": "gitlab.cs.washington.edu", "link": "https://gitlab.cs.washington.edu/ryanbai/mingpt-cse447/-/blob/master/mingpt/model.py", "content": "either model_type or ( n _ layer , n_head, n_embd) must be given in the config .x = self. transformer .drop(tok_emb + pos_emb). for block in self. transformer .h"}
+{"idx": 1, "title": "mingpt /model.py · Katiyar48/ MinGPT at main", "date": "", "ddg_snippet": "from mingpt .utils import CfgNode as CN.h = nn.ModuleList([ Block ( config ) for _ in range( config . n _ layer )])", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Katiyar48/MinGPT/blob/main/mingpt/model.py", "content": "from mingpt .utils import CfgNode as CN.h = nn.ModuleList([ Block ( config ) for _ in range( config . n _ layer )])"}
+{"idx": 2, "title": "minGPT 代码详解（训练 GPT 模型执行两位数加法）-CSDN博客", "date": "", "ddg_snippet": "self. block _size = config . block _size.torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config . n _ layer )) #. report number of parameters (note we don't count the decoder parameters in lm_head). n_params = sum(p.numel() for p in self. transformer .parameters()).", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/wxc971231/article/details/132000182", "content": "self. block _size = config . block _size.torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config . n _ layer )) #. report number of parameters (note we don't count the decoder parameters in lm_head). n_params = sum(p.numel() for p in self. transformer .parameters())."}
+{"idx": 3, "title": "Transformer _Translation.ipynb - Colab", "date": "", "ddg_snippet": "self. blocks = nn.ModuleList([ Block ( config ) for _ in range( config . n _ layer )]).n_pre_cross_ layer = 6 , # Number of decoder layers before cross attention.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/bluehood/Transformer-Translation/blob/main/Transformer_Translation.ipynb", "content": "self. blocks = nn.ModuleList([ Block ( config ) for _ in range( config . n _ layer )]).n_pre_cross_ layer = 6 , # Number of decoder layers before cross attention."}
+{"idx": 4, "title": "GPT Configuration | karpathy/nanoGPT | DeepWiki", "date": "", "ddg_snippet": "GPT . transformer . Block ( config ) × n _ layer .The configuration parameters directly impact model size, computational requirements, and memory usage: Model Size: The total parameter count scales primarily with: n _ layer (number of transformer blocks ).", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/karpathy/nanoGPT/2.1-gpt-configuration", "content": "GPT . transformer . Block ( config ) × n _ layer .The configuration parameters directly impact model size, computational requirements, and memory usage: Model Size: The total parameter count scales primarily with: n _ layer (number of transformer blocks )."}
+{"idx": 5, "title": "Build a Small Language Model (SLM) From Scratch | Medium", "date": "", "ddg_snippet": "model = GPT ( config ). This first part is essentially a configuration blueprint for model. n _ layer : This specifies the number of transformer blocks (or layers) to stack on top of each other.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@shravankoninti/build-a-small-language-model-slm-from-scratch-3ddd13fa6470", "content": "model = GPT ( config ). This first part is essentially a configuration blueprint for model. n _ layer : This specifies the number of transformer blocks (or layers) to stack on top of each other."}
+{"idx": 6, "title": "GitHub karpathy/nanoGPT LLM Context", "date": "", "ddg_snippet": "context size of up to 256 characters, 384 feature channels, and it is a 6 - layer Transformer with 6 heads in each layer . On one A100 GPU this training run takes about 3 minutes and the best validation loss is 1.4697.", "subpage_snippet": "", "source": "uithub.com", "link": "https://uithub.com/karpathy/nanoGPT", "content": "context size of up to 256 characters, 384 feature channels, and it is a 6 - layer Transformer with 6 heads in each layer . On one A100 GPU this training run takes about 3 minutes and the best validation loss is 1.4697."}
+{"idx": 7, "title": "GPT from “scratch” in Flax — The GenAI Guidebook", "date": "", "ddg_snippet": "Feedforward Layer . Block . Full Model.A transformer block module using Flax's linen API, which integrates multi-head attention and feedforward neural network layers. \"\"\" @ nn.compact def __call__(self, x, training): \"\"\" Process the input tensor through the transformer block .", "subpage_snippet": "", "source": "ravinkumar.com", "link": "https://ravinkumar.com/GenAiGuidebook/deepdive/GPTFromScratchFlax.html", "content": "Feedforward Layer . Block . Full Model.A transformer block module using Flax's linen API, which integrates multi-head attention and feedforward neural network layers. \"\"\" @ nn.compact def __call__(self, x, training): \"\"\" Process the input tensor through the transformer block ."}
+{"idx": 8, "title": "How to build/code ChatGPT from scratch? | CloudxLab Blog", "date": "", "ddg_snippet": "self.ln_f : Applies layer normalization to the final hidden states of the Transformer blocks . It stabilizes the training process by ensuring consistent distributions of hidden states across layers.", "subpage_snippet": "", "source": "cloudxlab.com", "link": "https://cloudxlab.com/blog/building-your-own-chatgpt-from-scratch/", "content": "self.ln_f : Applies layer normalization to the final hidden states of the Transformer blocks . It stabilizes the training process by ensuring consistent distributions of hidden states across layers."}
+{"idx": 9, "title": "LLM | Devpost", "date": "", "ddg_snippet": "The model architecture follows the standard GPT design with some modifications for efficiency. The embedding layer maps character tokens to 384-dimensional vectors, which are then processed through 6 transformer blocks .", "subpage_snippet": "", "source": "devpost.com", "link": "https://devpost.com/software/llm-uz9jam", "content": "The model architecture follows the standard GPT design with some modifications for efficiency. The embedding layer maps character tokens to 384-dimensional vectors, which are then processed through 6 transformer blocks ."}
diff --git a/data/sampled_jsons/minGPT_transformer_blocks_sitehttpsgithub.comfiveaiunderstanding_safety_finetuning.jsonl b/data/sampled_jsons/minGPT_transformer_blocks_sitehttpsgithub.comfiveaiunderstanding_safety_finetuning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/minGPT_transformer_blocks_sitehttpsgithub.comfiveaiunderstanding_safety_finetuning.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/modeling_L_linguistic_behaviors_in_four_steps.jsonl b/data/sampled_jsons/modeling_L_linguistic_behaviors_in_four_steps.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bc51839c18607d66d0531c11e65ea35ac7e26ef9
--- /dev/null
+++ b/data/sampled_jsons/modeling_L_linguistic_behaviors_in_four_steps.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linguistics Online Course - Enroll Now & Start Learning Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Learn Linguistics online at your own pace. Start today and improve your skills. Join millions of learners from around the world already learning on Udemy.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=nIeWTfkZkdNtfOf-XW3RwDuVFh641fAhuozlBiHQus9MZZN_7uki7SvZjSfFP4okCS47yBXSDWgqsuNfGT0iYNPghjAETDsXXO921PdG5VOdz-Q6QLckTMetcSRZOcTz.VfiQWLjhne55BZ6PWdpdYw&rut=636dc687b70fcc0a83177986ced131fc7fd252c35320923340d9999b6c3590ed&u3=https://www.bing.com/aclick?ld=e8GOchd0rLGI8qHBpMmnELGzVUCUzZXZTRUSfhvGh1EzxJ9LYmEHU8QfI0s_n-xJ9rLQEec-Jmuj-J2Xr-x9TIdETliF2aYO5sWCuPzczpaIiwtmTXqU2ZQ0C8u6RMxFLxAkanlWpdqESUpQXcEF89Vr-SPvd-Oe5upO6rm7RGS5okZnGODbxAsSI4zUlOZi_iF3tlwg&u=aHR0cHMlM2ElMmYlMmZ3d3cudWRlbXkuY29tJTJmY291cnNlJTJmdGhlLXByb3BlcnRpZXMtb2YtaHVtYW4tbGFuZ3VhZ2UlMmYlM2Z1dG1fc291cmNlJTNkYmluZyUyNnV0bV9tZWRpdW0lM2R1ZGVteWFkcyUyNnV0bV9jYW1wYWlnbiUzZEJHLVNlYXJjaF9LZXl3b3JkX0dhbW1hX05vblBfbGEuRU5fY2MuUk9XLUVuZ2xpc2glMjZjYW1wYWlnbnR5cGUlM2RTZWFyY2glMjZwb3J0Zm9saW8lM2RCaW5nLVJPVy1FbmdsaXNoJTI2bGFuZ3VhZ2UlM2RFTiUyNnByb2R1Y3QlM2RDb3Vyc2UlMjZ0ZXN0JTNkJTI2YXVkaWVuY2UlM2RLZXl3b3JkJTI2dG9waWMlM2RMaW5ndWlzdGljcyUyNnByaW9yaXR5JTNkR2FtbWElMjZ1dG1fY29udGVudCUzZGRlYWw0NTg0JTI2dXRtX3Rlcm0lM2RfLl9hZ18xMzIzODE0Mzg4NDYyMjAyXy5fYWRfXy5fa3dfTGluZ3Vpc3RpY3MlMjUyMENvdXJzZV8uX2RlX2NfLl9kbV9fLl9wbF9fLl90aV9rd2QtODI3Mzk1MTk0MDkzMjUlM2Fsb2MtMTY0Xy5fbGlfMTY0Xy5fcGRfXy5fJTI2bWF0Y2h0eXBlJTNkYiUyNm1zY2xraWQlM2Q0OGM4ZmIwZmJhZGYxODk1ZTI3YjkzYzE1MjY4NzU2YQ&rlid=48c8fb0fbadf1895e27b93c15268756a&vqd=4-115645484034429411068781702942227776036&iurl={1}IG=3A7520B99E054F69AA6E5922234732D3&CID=0D763811A9A662121C352E7CA893638F&ID=DevEx,5028.1", "content": "Learn Linguistics online at your own pace. Start today and improve your skills. Join millions of learners from around the world already learning on Udemy."}
+{"idx": 1, "title": "integrating-emotional-and-linguistic-models-for-ethical- ...", "date": "", "ddg_snippet": "modeling L linguistic behaviors in four steps : 1. Rewriting Documents: GPT-4 is invoked to rewrite a set of N documents to reflect each of the L linguistic ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/integrating-emotional-and-linguistic-models-for-ethical-c8c5drc4bk.pdf", "content": "modeling L linguistic behaviors in four steps : 1. Rewriting Documents: GPT-4 is invoked to rewrite a set of N documents to reflect each of the L linguistic ..."}
+{"idx": 2, "title": "(PDF) Integrating Emotional and Linguistic Models for Ethical...", "date": "", "ddg_snippet": "The methodology involves detailed modeling of emotions, classification of linguistic behaviors , and implementation of ethical guardrails.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Integrating_Emotional_and_Linguistic_Models_for_Ethical_Compliance_in_Large_Language_Models", "content": "The methodology involves detailed modeling of emotions, classification of linguistic behaviors , and implementation of ethical guardrails."}
+{"idx": 3, "title": "Integrating Emotional and Linguistic Models for", "date": "", "ddg_snippet": "Given N letters, DIKE employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps : 1. Rewriting Documents: GPT-4 is invoked to rewrite a set of N documents to reflect each of the L.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.07076", "content": "Given N letters, DIKE employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps : 1. Rewriting Documents: GPT-4 is invoked to rewrite a set of N documents to reflect each of the L."}
+{"idx": 4, "title": "Language Log » Modeling repetitive behavior", "date": "", "ddg_snippet": "The point is this: In modeling the structure of simple repetitive behavior , considerations from (traditional) formal language theory can obscure ...", "subpage_snippet": "", "source": "languagelog.ldc.upenn.edu", "link": "https://languagelog.ldc.upenn.edu/nll/?p=19053", "content": "The point is this: In modeling the structure of simple repetitive behavior , considerations from (traditional) formal language theory can obscure ..."}
+{"idx": 5, "title": "Linguistic and behavioral alignment in writing: A scoping", "date": "", "ddg_snippet": "This article reports on a scoping review of the literature exploring the alignment between behavioral and linguistic units in L1 and L2 writing.", "subpage_snippet": "", "source": "www.jowr.org", "link": "https://www.jowr.org/jowr/article/view/1318", "content": "This article reports on a scoping review of the literature exploring the alignment between behavioral and linguistic units in L1 and L2 writing."}
+{"idx": 6, "title": "2 Models of Linguistic Complexity | Interpreting Neural", "date": "", "ddg_snippet": "More realistically, we need at least to find coherent patterns between the model ’s inputs and its predictive behaviors .", "subpage_snippet": "", "source": "gsarti.com", "link": "https://gsarti.com/msc-thesis/chap-models.html", "content": "More realistically, we need at least to find coherent patterns between the model ’s inputs and its predictive behaviors ."}
+{"idx": 7, "title": "2 Models of Linguistic Complexity | Interpreting Neural", "date": "", "ddg_snippet": "More realistically, we need at least to find coherent patterns between the model ’s inputs and its predictive behaviors .", "subpage_snippet": "", "source": "gsarti.com", "link": "https://gsarti.com/thesis/chap-models.html", "content": "More realistically, we need at least to find coherent patterns between the model ’s inputs and its predictive behaviors ."}
+{"idx": 8, "title": "Spoken Language Modeling - Task 4", "date": "", "ddg_snippet": "In Task 4 , we do not presuppose the input to the language model . ... models can be trained on, to include multi-modal datasets (like speech and image, ...", "subpage_snippet": "", "source": "zerospeech.com:443", "link": "https://zerospeech.com:443/tasks/task_4/tasks_goals/", "content": "In Task 4 , we do not presuppose the input to the language model . ... models can be trained on, to include multi-modal datasets (like speech and image, ..."}
+{"idx": 9, "title": "Spoken Language Modeling - Task 4", "date": "", "ddg_snippet": "In Task 4 , we do not presuppose the input to the language model . ... models can be trained on, to include multi-modal datasets (like speech and image, ...", "subpage_snippet": "", "source": "zerospeech.com:443", "link": "https://zerospeech.com:443/tasks/task_4/overview/", "content": "In Task 4 , we do not presuppose the input to the language model . ... models can be trained on, to include multi-modal datasets (like speech and image, ..."}
diff --git a/data/sampled_jsons/natural_language_to_UI_automation_Selenium_integration_LLM_year_2023.jsonl b/data/sampled_jsons/natural_language_to_UI_automation_Selenium_integration_LLM_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5f25427a57d1eed01ea326a22f8c02b7193d836f
--- /dev/null
+++ b/data/sampled_jsons/natural_language_to_UI_automation_Selenium_integration_LLM_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Selenium AI Agent: UI Automation . Tools4AI with... | GoPenAI", "date": "", "ddg_snippet": "Tools4AI’s integration with Selenium introduces a flexible way to automate UI testing.Tools4AI serves as a bridge between natural language and Selenium , making it easier to automate UI testing in a way that is both efficient and intuitive.", "subpage_snippet": "", "source": "blog.gopenai.com", "link": "https://blog.gopenai.com/selenium-ai-automation-image-processing-with-gemini-7a8859213aba", "content": "Tools4AI’s integration with Selenium introduces a flexible way to automate UI testing.Tools4AI serves as a bridge between natural language and Selenium , making it easier to automate UI testing in a way that is both efficient and intuitive."}
+{"idx": 1, "title": "AI Agent for UI Automation : Revolutionizing Web Testing with Natural ...", "date": "", "ddg_snippet": "Example: Selenium Tests with Natural Language Commands. Here’s how you can automate UI testing using the AI Agent with plain English commands.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@visrow/ai-agent-for-ui-automation-revolutionizing-web-testing-with-natural-language-commands-d3066b20d569", "content": "Example: Selenium Tests with Natural Language Commands. Here’s how you can automate UI testing using the AI Agent with plain English commands."}
+{"idx": 2, "title": "talk2dom - Locate web elements using natural language . Powered by...", "date": "", "ddg_snippet": "Powered by LLM . Works with Selenium . Key Benefits: Simplified Test Creation: Write tests using natural language , reducing the learning curve for new testers. Reduced Maintenance: Minimize test failures due to UI changes, as talk2dom adapts to changes...", "subpage_snippet": "", "source": "testdev.tools", "link": "https://testdev.tools/talk2dom/", "content": "Powered by LLM . Works with Selenium . Key Benefits: Simplified Test Creation: Write tests using natural language , reducing the learning curve for new testers. Reduced Maintenance: Minimize test failures due to UI changes, as talk2dom adapts to changes..."}
+{"idx": 3, "title": "Selenium with Python : A Detailed Guide for Automation | LambdaTest", "date": "", "ddg_snippet": "Follow our comprehensive Selenium with Python tutorial. Learn how to set it up correctly and start using Selenium with Python for automation .World’s first end to end software testing agent built on modern LLM to help you plan, author and evolve tests using natural language .", "subpage_snippet": "", "source": "www.lambdatest.com", "link": "https://www.lambdatest.com/blog/selenium-webdriver-with-python/", "content": "Follow our comprehensive Selenium with Python tutorial. Learn how to set it up correctly and start using Selenium with Python for automation .World’s first end to end software testing agent built on modern LLM to help you plan, author and evolve tests using natural language ."}
+{"idx": 4, "title": "Selenium AI Automation : Image Processing with Gemini", "date": "", "ddg_snippet": "Tools4AI’s integration with Selenium introduces a flexible way to automate UI testing. Instead of traditional Java code for Selenium scripts, Tools4AI allows you to define test scenarios in plain English, offering a more accessible approach to testing web applications.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/selenium-ai-automation-image-processing-with-gemini-7a8859213aba", "content": "Tools4AI’s integration with Selenium introduces a flexible way to automate UI testing. Instead of traditional Java code for Selenium scripts, Tools4AI allows you to define test scenarios in plain English, offering a more accessible approach to testing web applications."}
+{"idx": 5, "title": "Can Selenium Automate Desktop Applications?", "date": "", "ddg_snippet": "Selenium cannot automate desktop apps.Learn why, plus explore powerful alternatives like AskUI& integration strategies for full web&desktop automation .", "subpage_snippet": "", "source": "www.askui.com", "link": "https://www.askui.com/blog-posts/selenium-can-it-automate-desktop-applications", "content": "Selenium cannot automate desktop apps.Learn why, plus explore powerful alternatives like AskUI& integration strategies for full web&desktop automation ."}
+{"idx": 6, "title": "Revolutionizing Test Automation with Vibium AI: Jason... - Ultimate QA", "date": "", "ddg_snippet": "Is Selenium dead? No. Selenium remains a foundational layer for browser automation . Vibium AI builds on modern browser protocols (including WebDriver BiDirectional) and often leaves Selenium “under the hood.” Will Vibium AI replace QA engineers?", "subpage_snippet": "", "source": "ultimateqa.com", "link": "https://ultimateqa.com/revolutionizing-test-automation-with-vibium-ai-jason-huggins/", "content": "Is Selenium dead? No. Selenium remains a foundational layer for browser automation . Vibium AI builds on modern browser protocols (including WebDriver BiDirectional) and often leaves Selenium “under the hood.” Will Vibium AI replace QA engineers?"}
+{"idx": 7, "title": "GitHub - hongsim-tran/ selenium - ui - automation -framework: An...", "date": "", "ddg_snippet": "Selenium Webdriver: web automation testing tool. Selenium Grid: a tool for distributing tests across multiple browsers and machines. TestNG: automation framework to support test creation. Allure Report: the testing report. Owner: handle configuration through properties files.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hongsim-tran/selenium-ui-automation-framework", "content": "Selenium Webdriver: web automation testing tool. Selenium Grid: a tool for distributing tests across multiple browsers and machines. TestNG: automation framework to support test creation. Allure Report: the testing report. Owner: handle configuration through properties files."}
+{"idx": 8, "title": "Introducing Selenium Support in IntelliJ IDEA | The IntelliJ IDEA Blog", "date": "", "ddg_snippet": "New Selenium Project If you prefer using Groovy or Kotlin as your language for UI tests, there are additional helper libraries: Geb and Balin. They become available if you choose the corresponding language in the first step", "subpage_snippet": "", "source": "blog.jetbrains.com", "link": "https://blog.jetbrains.com/idea/2020/03/intellij-idea-2020-1-selenium-support/", "content": "New Selenium Project If you prefer using Groovy or Kotlin as your language for UI tests, there are additional helper libraries: Geb and Balin. They become available if you choose the corresponding language in the first step"}
+{"idx": 9, "title": "Large Language Model ( LLM ) | Learn how to interact with OpenAI...", "date": "", "ddg_snippet": "A large language model ( LLM ) is a type of AI that can process and produce natural language text. It learns from a massive amount of text data such as books, articles, and web pages to discover patterns and rules of language from them.", "subpage_snippet": "", "source": "microsoft.github.io", "link": "https://microsoft.github.io/Workshop-Interact-with-OpenAI-models/llms/", "content": "A large language model ( LLM ) is a type of AI that can process and produce natural language text. It learns from a massive amount of text data such as books, articles, and web pages to discover patterns and rules of language from them."}
diff --git a/data/sampled_jsons/neural_PDE_solver_multiscale_time_integration_error_correction.jsonl b/data/sampled_jsons/neural_PDE_solver_multiscale_time_integration_error_correction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..953cc5159b6a130167a39cf6a5ecdb85ac5b164f
--- /dev/null
+++ b/data/sampled_jsons/neural_PDE_solver_multiscale_time_integration_error_correction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE -embedded Learning with Multi- time -stepping for...", "date": "", "ddg_snippet": "Solving partial differential equations ( PDEs ) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "Solving partial differential equations ( PDEs ) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required."}
+{"idx": 1, "title": "(PDF) Multiscale Neural Operator: Learning Fast and...", "date": "", "ddg_snippet": "tional cost of solving partial differential equations ( PDEs ). at high-resolution. [63. ]. We are proposing the first PDE . surrogate that quickly computes approximate solutions via. Multiscale Neural Operators. (a) Long-term sample forecast, X0(t). (b) Error over time .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/362252460_Multiscale_Neural_Operator_Learning_Fast_and_Grid-independent_PDE_Solvers", "content": "tional cost of solving partial differential equations ( PDEs ). at high-resolution. [63. ]. We are proposing the first PDE . surrogate that quickly computes approximate solutions via. Multiscale Neural Operators. (a) Long-term sample forecast, X0(t). (b) Error over time ."}
+{"idx": 2, "title": "A Neural Network Approach for Homogenization of Multiscale", "date": "", "ddg_snippet": "2 Neural network-based methods for solving PDEs 3 Derivative-free network training for multiscale problemsAs in the time integration of multiscale problems [28, 29], it turns out that micro- and...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/a-neural-network-approach-for-homogenization-of-multiscale-1fy78y4q.pdf", "content": "2 Neural network-based methods for solving PDEs 3 Derivative-free network training for multiscale problemsAs in the time integration of multiscale problems [28, 29], it turns out that micro- and..."}
+{"idx": 3, "title": "A Review of Physics Informed Neural Networks for Multiscale ... | CoLab", "date": "", "ddg_snippet": "PINNs are universal approximators that integrates physical laws that can be described by partial differential equations ( PDEs ) and given data, in the learning process. The formulations of PINNs are first presented in an example of linear elasticity problem.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1007/s42493-024-00106-w", "content": "PINNs are universal approximators that integrates physical laws that can be described by partial differential equations ( PDEs ) and given data, in the learning process. The formulations of PINNs are first presented in an example of linear elasticity problem."}
+{"idx": 4, "title": "NH-PINN: Neural homogenization-based physics-informed neural ...", "date": "", "ddg_snippet": "Physics - informed neural network (PINN) has advantages in solving PDEs but has issues with multiscale problems. Classical PINN fails to accurately predict in multiscale problems with large relative error and non - robust training.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/nh-pinn-neural-homogenization-based-physics-informed-neural-network-for-multiscale-problems/817062259715997696-506", "content": "Physics - informed neural network (PINN) has advantages in solving PDEs but has issues with multiscale problems. Classical PINN fails to accurately predict in multiscale problems with large relative error and non - robust training."}
+{"idx": 5, "title": "PDE -Refiner: Achieving Accurate Long Rollouts with", "date": "", "ddg_snippet": "The practical utility of such neural PDE solvers relies on their ability to provide accurate, stable predictions over long time horizons, which is a notoriously hard problem.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2023/file/d529b943af3dba734f8a7d49efcb6d09-Paper-Conference.pdf", "content": "The practical utility of such neural PDE solvers relies on their ability to provide accurate, stable predictions over long time horizons, which is a notoriously hard problem."}
+{"idx": 6, "title": "Generative Downscaling of PDE Solvers with Physics-Guided Diffusion...", "date": "", "ddg_snippet": "Solving partial differential equations ( PDEs ) on fine spatio-temporal scales for high-fidelity solutions is critical for numerous scientific breakthroughs.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10915-024-02709-9", "content": "Solving partial differential equations ( PDEs ) on fine spatio-temporal scales for high-fidelity solutions is critical for numerous scientific breakthroughs."}
+{"idx": 7, "title": "Graph Neural PDE Solvers with Conservation and...", "date": "", "ddg_snippet": "Utilizing machine learning to address partial differential equations ( PDEs ) presents significant challenges due to the diversity of spatial domains and their corresponding state configurations, which complicates the task of encompassing all potential scenarios through...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/horie24a.html", "content": "Utilizing machine learning to address partial differential equations ( PDEs ) presents significant challenges due to the diversity of spatial domains and their corresponding state configurations, which complicates the task of encompassing all potential scenarios through..."}
+{"idx": 8, "title": "Preface to the special issue 'Breaking Complexity: Multiscale Methods...", "date": "", "ddg_snippet": "Estimating the control error in discretized PDE -constrained optimization. Multiresolution technique and explicit–implicit scheme for multicomponent flows.", "subpage_snippet": "", "source": "www.degruyterbrill.com", "link": "https://www.degruyterbrill.com/document/doi/10.1515/156939506778658320/html", "content": "Estimating the control error in discretized PDE -constrained optimization. Multiresolution technique and explicit–implicit scheme for multicomponent flows."}
+{"idx": 9, "title": "Using Time -Dependent PDEs in AI Research part3 | by... | Medium", "date": "", "ddg_snippet": "On the construction of conservative semi-Lagrangian IMEX advection schemes for multiscale time dependent PDEs (arXiv).2. Non-intrusive Surrogate Modeling for Parametrized Time -dependent PDEs using Convolutional Autoencoders(arXiv).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@monocosmo77/using-time-dependent-pdes-in-ai-research-part3-f9a0f2cb275e", "content": "On the construction of conservative semi-Lagrangian IMEX advection schemes for multiscale time dependent PDEs (arXiv).2. Non-intrusive Surrogate Modeling for Parametrized Time -dependent PDEs using Convolutional Autoencoders(arXiv)."}
diff --git a/data/sampled_jsons/neural_operator_reverse_currying_function_spaces_input_output.jsonl b/data/sampled_jsons/neural_operator_reverse_currying_function_spaces_input_output.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e40c179671bf726817c420ac7c5bbb0934747d23
--- /dev/null
+++ b/data/sampled_jsons/neural_operator_reverse_currying_function_spaces_input_output.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of ..."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution ..."}
+{"idx": 2, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — Abstract. Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — Abstract. Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution."}
+{"idx": 3, "title": "Scalable Gaussian Process Operator for Physical Systems", "date": "", "ddg_snippet": "by S Kumar · 2025 — The inclusion of neural operator informed mean functions further enhances the model's flexibility in capturing complex input and output .", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.15906", "content": "by S Kumar · 2025 — The inclusion of neural operator informed mean functions further enhances the model's flexibility in capturing complex input and output ."}
+{"idx": 4, "title": "Physics-Informed Deep Neural Network for Backward-in-Time ...", "date": "", "ddg_snippet": "by MAER Hammoud · 2023 · Cited by 10 — This study leverages advances in artificial intelligence to train a deep neural network that provides accurate backward -in-time predictions ...", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/aies/2/1/AIES-D-22-0076.1.xml", "content": "by MAER Hammoud · 2023 · Cited by 10 — This study leverages advances in artificial intelligence to train a deep neural network that provides accurate backward -in-time predictions ..."}
+{"idx": 5, "title": "How to invert functions that lose information : r/math", "date": "", "ddg_snippet": "If I have a function , be it (weak/strong) inverse or not, then I can instantly say that information flows from the arguments to the output . I ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/math/comments/nk1taj/how_to_invert_functions_that_lose_information/", "content": "If I have a function , be it (weak/strong) inverse or not, then I can instantly say that information flows from the arguments to the output . I ..."}
+{"idx": 6, "title": "Neuro-Symbolic Execution of Generic Source Code", "date": "", "ddg_snippet": "by Y Hu · 2023 — Neuro-Symbolic Execution (NSX) aims to learn an isomorphic mapping between the source code space and neural representation space (Figure 2).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.00989", "content": "by Y Hu · 2023 — Neuro-Symbolic Execution (NSX) aims to learn an isomorphic mapping between the source code space and neural representation space (Figure 2)."}
+{"idx": 7, "title": "publications | Marvin Pförtner - GitHub Pages", "date": "", "ddg_snippet": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of ...", "subpage_snippet": "", "source": "marvinpfoertner.github.io", "link": "https://marvinpfoertner.github.io/publications/", "content": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Neural Operator : Learning Maps Between Function Spaces · The classical development of neural networks has primarily focused on learning mappings between ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=function+universality", "content": "Neural Operator : Learning Maps Between Function Spaces · The classical development of neural networks has primarily focused on learning mappings between ..."}
+{"idx": 9, "title": "Constraining genetic symbolic regression via semantic ...", "date": "", "ddg_snippet": "by M Reissmann · 2025 · Cited by 5 — We propose an approach centered on semantic backpropagation incorporated into the Gene Expression Programming (GEP), which integrates domain-specific ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10710-025-09510-z", "content": "by M Reissmann · 2025 · Cited by 5 — We propose an approach centered on semantic backpropagation incorporated into the Gene Expression Programming (GEP), which integrates domain-specific ..."}
diff --git a/data/sampled_jsons/neuron_condensation_deep_learning.jsonl b/data/sampled_jsons/neuron_condensation_deep_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a1a4aeb0b104830769974d21434bce6251ce945a
--- /dev/null
+++ b/data/sampled_jsons/neuron_condensation_deep_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Single-neuron deep generative model uncovers underlying physics", "date": "", "ddg_snippet": "Here, we propose a new framework for single- neuron representation learning based on variational autoencoders (VAEs) [ 26 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.14615v2", "content": "Here, we propose a new framework for single- neuron representation learning based on variational autoencoders (VAEs) [ 26 ] ."}
+{"idx": 1, "title": "Dropping Experts, Recombining Neurons: Retraining-Free Pruning", "date": "", "ddg_snippet": "We reformulate expert merging as a segment-based decomposition and recombination problem, enabling structure-aware, neuron -level operation that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10377v1", "content": "We reformulate expert merging as a segment-based decomposition and recombination problem, enabling structure-aware, neuron -level operation that ..."}
+{"idx": 2, "title": "Neuronal Network Activation Induced by Forniceal Deep Brain", "date": "", "ddg_snippet": "Neuronal Network Activation Induced by Forniceal ... fornix ; limbic system ; deep brain stimulation ; learning and memory ; c -Fos ; neural circuit", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2073-4425/16/2/210", "content": "Neuronal Network Activation Induced by Forniceal ... fornix ; limbic system ; deep brain stimulation ; learning and memory ; c -Fos ; neural circuit"}
+{"idx": 3, "title": "Neurons in Cerebellum Show Surprising Activity During Learning", "date": "", "ddg_snippet": "... computer algorithms that detangle the signals, the team was able to explore in detail the firing patterns of these neurons while mice were learning a ...", "subpage_snippet": "", "source": "www.simonsfoundation.org", "link": "https://www.simonsfoundation.org/2017/03/21/neurons-in-cerebellum-show-surprising-activity-during-learning/", "content": "... computer algorithms that detangle the signals, the team was able to explore in detail the firing patterns of these neurons while mice were learning a ..."}
+{"idx": 4, "title": "Researchers observe at the atomic level the neuronal", "date": "", "ddg_snippet": "It specialises in introducing and/or removing two amino acids that are key for neuronal connections (synapses), which are involved in learning ...", "subpage_snippet": "", "source": "bist.eu", "link": "https://bist.eu/researchers-observe-at-the-atomic-level-the-neuronal-gate-for-essential-molecules-in-learning-and-memory/", "content": "It specialises in introducing and/or removing two amino acids that are key for neuronal connections (synapses), which are involved in learning ..."}
+{"idx": 5, "title": "Deep Learning Interview Questions - GeeksforGeeks", "date": "", "ddg_snippet": "Learning : In biological neurons , learning occurs in the cell body or soma which has a nucleus that helps to process the signals.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/deep-learning/deep-learning-interview-questions/", "content": "Learning : In biological neurons , learning occurs in the cell body or soma which has a nucleus that helps to process the signals."}
+{"idx": 6, "title": "lecture 6 - cnns and deep q learning - samrat's thought space", "date": "", "ddg_snippet": "All neurons in the first hidden layer capture the same feature just at different locations in hte feature map ... Similar idea to Double Q Learning ...", "subpage_snippet": "", "source": "samratsahoo.com", "link": "https://samratsahoo.com/brain/cs234/cnns-and-deep-q-learning", "content": "All neurons in the first hidden layer capture the same feature just at different locations in hte feature map ... Similar idea to Double Q Learning ..."}
+{"idx": 7, "title": "The Strange Physics That Gave Birth to AI | Quanta Magazine", "date": "", "ddg_snippet": "... of the Boltzmann machine to finally crack the stubborn problem of training “ deep ’’ neural networks consisting of multiple layers of neurons ...", "subpage_snippet": "", "source": "www.quantamagazine.org", "link": "https://www.quantamagazine.org/the-strange-physics-that-gave-birth-to-ai-20250430/", "content": "... of the Boltzmann machine to finally crack the stubborn problem of training “ deep ’’ neural networks consisting of multiple layers of neurons ..."}
+{"idx": 8, "title": "Synaptic Plasticity Forms and Functions | Annual Reviews", "date": "", "ddg_snippet": "Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long been considered an important component of learning and ...", "subpage_snippet": "", "source": "www.annualreviews.org", "link": "https://www.annualreviews.org/content/journals/10.1146/annurev-neuro-090919-022842", "content": "Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long been considered an important component of learning and ..."}
+{"idx": 9, "title": "regularization - Weight scale inference when using dropout -", "date": "", "ddg_snippet": "To give a concrete example, if I have two neurons at a layer, and neuron $A$ has with probability $p_A = .8$ of remaining, and neuron $B$ has ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/330940/weight-scale-inference-when-using-dropout", "content": "To give a concrete example, if I have two neurons at a layer, and neuron $A$ has with probability $p_A = .8$ of remaining, and neuron $B$ has ..."}
diff --git a/data/sampled_jsons/noisy_mixing_function_causal_representation_learning_2024_OR_stochastic_mixing_causal_representation_year_2024.jsonl b/data/sampled_jsons/noisy_mixing_function_causal_representation_learning_2024_OR_stochastic_mixing_causal_representation_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c550a53139554d09639a7a9a4e8a5ff7da5f5a0a
--- /dev/null
+++ b/data/sampled_jsons/noisy_mixing_function_causal_representation_learning_2024_OR_stochastic_mixing_causal_representation_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Identifying Causal Mechanism Shifts Under Additive Models ...", "date": "", "ddg_snippet": "Typically, existing methods directly identify causal mechanism shifts based on linear additive noise models (ANMs) or by imposing restrictive assumptions on the noise distribution.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/524", "content": "Typically, existing methods directly identify causal mechanism shifts based on linear additive noise models (ANMs) or by imposing restrictive assumptions on the noise distribution."}
+{"idx": 1, "title": "[2505.19474] Causal-LLaVA: Causal Disentanglement for ... Book - proceedings.neurips.cc Causal Reasoning Meets Visual Representation Learning: A ... Mixture-of-Experts (MoE): The Birth and Rise of Conditional ... Causal Representation Learning for Out-of-Distribution ... Proceedings of the 29th International Conference on ...", "date": "", "ddg_snippet": "May 26, 2025 · To address this, we propose a causality-driven disentanglement framework that mitigates hallucinations through causal intervention. Our approach includes a Causal -Driven Projector in the visual pathway and a Causal Intervention Module integrated into the final transformer layer of the language model. Learning 1D Causal Visual Representation with De-focus Attention Networks Tao Chenxin, Xizhou Zhu, Shiqian Su, Lewei Lu, Changyao Tian, Xuan Luo, Gao Huang, Hongsheng Li, Yu Qiao, Jie Zhou, Jifeng Dai Nonlinear dynamics of localization in neural receptive fields Leon Lufkin, Andrew Saxe, Erin Grant This paper conducts a comprehensive review of existing causal reasoning methods for visual representation learning , covering fundamental theories, models, and datasets, and proposes some prospective challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms inVisual representation learning . Visual representation learning is ubiquitous in various real ... Mar 18, 2024 · Learning factored representations in a deep mixture of experts [18]. This work considers MoE layers comprised of several expert networks that specialize in processing different regions of the input space. Apr 25, 2022 · To pursue high fidelity, we set additional objectives for representation learning as: 1) strong OOD generalization and 2) fast OOD adaptation. This work formulates and solves the problem from a causal view. We formulate the user feature shift as an intervention and OOD recommendation as post-intervention inference of the interaction probability. pdf bib abs Large Sequence Representation Learning via Multi-Stage Latent Transformers Ionut-Catalin Sandu | Daniel Voinea | Alin-Ionut Popa pdf bib abs Mocking BERT: A Method for Retroactively Adding Resilience to NLP Models Jan Jezabek | Akash Singh", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.19474", "content": "May 26, 2025 · To address this, we propose a causality-driven disentanglement framework that mitigates hallucinations through causal intervention. Our approach includes a Causal -Driven Projector in the visual pathway and a Causal Intervention Module integrated into the final transformer layer of the language model. Learning 1D Causal Visual Representation with De-focus Attention Networks Tao Chenxin, Xizhou Zhu, Shiqian Su, Lewei Lu, Changyao Tian, Xuan Luo, Gao Huang, Hongsheng Li, Yu Qiao, Jie Zhou, Jifeng Dai Nonlinear dynamics of localization in neural receptive fields Leon Lufkin, Andrew Saxe, Erin Grant This paper conducts a comprehensive review of existing causal reasoning methods for visual representation learning , covering fundamental theories, models, and datasets, and proposes some prospective challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms inVisual representation learning . Visual representation learning is ubiquitous in various real ... Mar 18, 2024 · Learning factored representations in a deep mixture of experts [18]. This work considers MoE layers comprised of several expert networks that specialize in processing different regions of the input space. Apr 25, 2022 · To pursue high fidelity, we set additional objectives for representation learning as: 1) strong OOD generalization and 2) fast OOD adaptation. This work formulates and solves the problem from a causal view. We formulate the user feature shift as an intervention and OOD recommendation as post-intervention inference of the interaction probability. pdf bib abs Large Sequence Representation Learning via Multi-Stage Latent Transformers Ionut-Catalin Sandu | Daniel Voinea | Alin-Ionut Popa pdf bib abs Mocking BERT: A Method for Retroactively Adding Resilience to NLP Models Jan Jezabek | Akash Singh"}
+{"idx": 2, "title": "Causal Reasoning Meets Visual Representation Learning: A ...", "date": "", "ddg_snippet": "This paper conducts a comprehensive review of existing causal reasoning methods for visual representation learning , covering fundamental theories, models, and datasets, and proposes some prospective challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms inVisual representation learning . Visual representation learning is ubiquitous in various real ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Causal-Reasoning-Meets-Visual-Representation-A-Liu-Wei/8274a4a71e1e5605bd5a9eb69c4f0df5f30bd1a8", "content": "This paper conducts a comprehensive review of existing causal reasoning methods for visual representation learning , covering fundamental theories, models, and datasets, and proposes some prospective challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms inVisual representation learning . Visual representation learning is ubiquitous in various real ..."}
+{"idx": 3, "title": "Causal Representation Learning for Out-of-Distribution ...", "date": "", "ddg_snippet": "Apr 25, 2022 · To pursue high fidelity, we set additional objectives for representation learning as: 1) strong OOD generalization and 2) fast OOD adaptation. This work formulates and solves the problem from a causal view. We formulate the user feature shift as an intervention and OOD recommendation as post-intervention inference of the interaction probability.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3485447.3512251", "content": "Apr 25, 2022 · To pursue high fidelity, we set additional objectives for representation learning as: 1) strong OOD generalization and 2) fast OOD adaptation. This work formulates and solves the problem from a causal view. We formulate the user feature shift as an intervention and OOD recommendation as post-intervention inference of the interaction probability."}
+{"idx": 4, "title": "Stochastic Networks Research Topics Ideas [MS PhD] –", "date": "", "ddg_snippet": "... stochastic numerical solver for the solution of singular ... Flexible criteria for assessing EV hosting capacity in stochastic load-flow simulations.", "subpage_snippet": "", "source": "t4tutorials.com", "link": "https://t4tutorials.com/stochastic-networks-research-topics-ideas-ms-phd/", "content": "... stochastic numerical solver for the solution of singular ... Flexible criteria for assessing EV hosting capacity in stochastic load-flow simulations."}
+{"idx": 5, "title": "AISTATS 2024 Schedule", "date": "", "ddg_snippet": "... Learning Agents for Moral Decision-making ... On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2024/calendar", "content": "... Learning Agents for Moral Decision-making ... On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation"}
+{"idx": 6, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression ... Spectral Contrastive Learning ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression ... Spectral Contrastive Learning ..."}
+{"idx": 7, "title": "MAIN Conf Talk: Learning representations from neural signals |", "date": "", "ddg_snippet": "The objective is to learn a function that can classify each time point into a sleep stage (awake, REM, stage 1, stage 2, etc.) using the raw PSG ...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/agramfort/main-conf-talk-learning-representations-from-neural-signals", "content": "The objective is to learn a function that can classify each time point into a sleep stage (awake, REM, stage 1, stage 2, etc.) using the raw PSG ..."}
+{"idx": 8, "title": "Book - proceedings.neurips.cc", "date": "", "ddg_snippet": "Learning 1D Causal Visual Representation with De-focus Attention Networks Tao Chenxin, Xizhou Zhu, Shiqian Su, Lewei Lu, Changyao Tian, Xuan Luo, Gao Huang, Hongsheng Li, Yu Qiao, Jie Zhou, Jifeng Dai Nonlinear dynamics of localization in neural receptive fields Leon Lufkin, Andrew Saxe, Erin Grant", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024", "content": "Learning 1D Causal Visual Representation with De-focus Attention Networks Tao Chenxin, Xizhou Zhu, Shiqian Su, Lewei Lu, Changyao Tian, Xuan Luo, Gao Huang, Hongsheng Li, Yu Qiao, Jie Zhou, Jifeng Dai Nonlinear dynamics of localization in neural receptive fields Leon Lufkin, Andrew Saxe, Erin Grant"}
+{"idx": 9, "title": "Mixture-of-Experts (MoE): The Birth and Rise of Conditional ...", "date": "", "ddg_snippet": "Mar 18, 2024 · Learning factored representations in a deep mixture of experts [18]. This work considers MoE layers comprised of several expert networks that specialize in processing different regions of the input space.", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/conditional-computation-the-birth", "content": "Mar 18, 2024 · Learning factored representations in a deep mixture of experts [18]. This work considers MoE layers comprised of several expert networks that specialize in processing different regions of the input space."}
diff --git a/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits_-sitearxiv.org_year_2024.jsonl b/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits_-sitearxiv.org_year_2024.jsonl
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index 0000000000000000000000000000000000000000..10ee10d2e5cddc78bbb12469edf7cce1c48c0c89
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+++ b/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits_-sitearxiv.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constrained Feedback Learning for Non-Stationary Multi-Armed", "date": "", "ddg_snippet": "Non -stationary multi-armed bandits ( nsMAB ) enable agents to adapt to changing environments by incorporating mechanisms to detect and respond to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15073v1", "content": "Non -stationary multi-armed bandits ( nsMAB ) enable agents to adapt to changing environments by incorporating mechanisms to detect and respond to ..."}
+{"idx": 1, "title": "Semi-Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "... stochastic submodular optimization (Asadpour and Nazerzadeh, 2016 ; Golovin and Krause, 2017 ; Im et al., 2016 ) , stochastic probing (Gupta and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.15427v2", "content": "... stochastic submodular optimization (Asadpour and Nazerzadeh, 2016 ; Golovin and Krause, 2017 ; Im et al., 2016 ) , stochastic probing (Gupta and ..."}
+{"idx": 2, "title": "Asymptotically-Optimal Gaussian Bandits with Side Observations", "date": "", "ddg_snippet": "In the stochastic online learning framework, a player sequentially selects from a set of available actions (or “arms”) and collects a stochastic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10698v1", "content": "In the stochastic online learning framework, a player sequentially selects from a set of available actions (or “arms”) and collects a stochastic ..."}
+{"idx": 3, "title": "cherryATA: cherryAdaptive cherryTask cherryAllocation for Efficient...", "date": "", "ddg_snippet": "To the best of our knowledge, this is the first work addressing a non - continuous loss function in a stochastic combinatorial MAB-like framework.Here, we first show how to reduce the problem in (3) to a non-linear stochastic Multi-Armed Bandit (MAB) problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775", "content": "To the best of our knowledge, this is the first work addressing a non - continuous loss function in a stochastic combinatorial MAB-like framework.Here, we first show how to reduce the problem in (3) to a non-linear stochastic Multi-Armed Bandit (MAB) problem."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "To the best of our knowledge, this is the first work addressing a non - continuous loss function in a stochastic combinatorial MAB-like framework.Here, we first show how to reduce the problem in (3) to a non-linear stochastic Multi-Armed Bandit (MAB) problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "To the best of our knowledge, this is the first work addressing a non - continuous loss function in a stochastic combinatorial MAB-like framework.Here, we first show how to reduce the problem in (3) to a non-linear stochastic Multi-Armed Bandit (MAB) problem."}
+{"idx": 5, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "To the best of our knowledge, this is the first work addressing a non - continuous loss function in a stochastic combinatorial MAB-like framework.Here, we first show how to reduce the problem in (3) to a non-linear stochastic Multi-Armed Bandit (MAB) problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "To the best of our knowledge, this is the first work addressing a non - continuous loss function in a stochastic combinatorial MAB-like framework.Here, we first show how to reduce the problem in (3) to a non-linear stochastic Multi-Armed Bandit (MAB) problem."}
+{"idx": 6, "title": "Improving Monte Carlo Tree Search for Symbolic Regression", "date": "", "ddg_snippet": "To address this, researchers have studied the extreme‐ bandit setting [ 33 , 34 , 35 , 36 , 37 ] and applied it to combinatorial challenges such as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15929v1", "content": "To address this, researchers have studied the extreme‐ bandit setting [ 33 , 34 , 35 , 36 , 37 ] and applied it to combinatorial challenges such as ..."}
+{"idx": 7, "title": "Game-Theoretic Multiagent Reinforcement Learning", "date": "", "ddg_snippet": "It is an interdisciplinary domain with a long history that includes game theory, machine learning, stochastic control, psychology, and optimisation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2011.00583v4", "content": "It is an interdisciplinary domain with a long history that includes game theory, machine learning, stochastic control, psychology, and optimisation."}
+{"idx": 8, "title": "Adaptability in Multi-Agent Reinforcement Learning: A Framework", "date": "", "ddg_snippet": "Together, these factors demand that MARL algorithms remain effective under continuously changing system configurations and operational demands.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10142v1", "content": "Together, these factors demand that MARL algorithms remain effective under continuously changing system configurations and operational demands."}
+{"idx": 9, "title": "Approximate Proportionality in Online Fair Division", "date": "", "ddg_snippet": "We begin by showing that three different natural greedy allocation strategies fail to guarantee a non -zero approximation to PROP1 against an adaptive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03253v1", "content": "We begin by showing that three different natural greedy allocation strategies fail to guarantee a non -zero approximation to PROP1 against an adaptive ..."}
diff --git a/data/sampled_jsons/non-linear_reward_functions_preference-based_reinforcement_learning_techniques_neural_networks.jsonl b/data/sampled_jsons/non-linear_reward_functions_preference-based_reinforcement_learning_techniques_neural_networks.jsonl
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index 0000000000000000000000000000000000000000..72d7b439b699c41e97340813d0021ebd511e4b65
--- /dev/null
+++ b/data/sampled_jsons/non-linear_reward_functions_preference-based_reinforcement_learning_techniques_neural_networks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning from human feedback - Wikipedia", "date": "", "ddg_snippet": "In machine learning , reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences . It involves training a reward model to represent preferences , which can then be used to train other models through reinforcement learning . In classical reinforcement learning , an intelligent agent's goal is to learn a function that guides its behavior ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback", "content": "In machine learning , reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences . It involves training a reward model to represent preferences , which can then be used to train other models through reinforcement learning . In classical reinforcement learning , an intelligent agent's goal is to learn a function that guides its behavior ..."}
+{"idx": 1, "title": "Inverse Preference Learning: Preference-based RL without a Reward Function", "date": "", "ddg_snippet": "Abstract Reward functions are difficult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.15363", "content": "Abstract Reward functions are difficult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to ..."}
+{"idx": 2, "title": "Neural Dueling Bandits: Preference-Based Optimization with Human...", "date": "", "ddg_snippet": "The authors propose leveraging neural networks to model these non-linear reward functions based on preference feedback. They introduce two algorithms, NDB-UCB and NDB-TS, which are extensions of the Upper Confidence Bound and Thompson Sampling frameworks, respectively.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=VELhv9BBfn", "content": "The authors propose leveraging neural networks to model these non-linear reward functions based on preference feedback. They introduce two algorithms, NDB-UCB and NDB-TS, which are extensions of the Upper Confidence Bound and Thompson Sampling frameworks, respectively."}
+{"idx": 3, "title": "Asymmetric and adaptive reward coding via normalized reinforcement learning", "date": "", "ddg_snippet": "Here, we examine the properties of a biologically- based nonlinear reinforcement learning algorithm employing the canonical divisive normalization function , a neural computation commonly found in sensory, cognitive, and reward coding.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9345478/", "content": "Here, we examine the properties of a biologically- based nonlinear reinforcement learning algorithm employing the canonical divisive normalization function , a neural computation commonly found in sensory, cognitive, and reward coding."}
+{"idx": 4, "title": "PDF A Survey of Preference-Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress."}
+{"idx": 5, "title": "PDF Reinforcement Learning from Diverse Human Preferences - IJCAI", "date": "", "ddg_snippet": "Abstract The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques . De-scribing an agent's desired behaviors and properties can be dificult, even for experts. A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution, in which reward ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0586.pdf", "content": "Abstract The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques . De-scribing an agent's desired behaviors and properties can be dificult, even for experts. A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution, in which reward ..."}
+{"idx": 6, "title": "PDF Inverse Preference Learning: Preference-based RL without a Reward Function", "date": "", "ddg_snippet": "Abstract Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to improve ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/3be7859b36d9440372cae0a293f2e4cc-Paper-Conference.pdf", "content": "Abstract Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to improve ..."}
+{"idx": 7, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards . Due to its promising advantage over traditional RL, PbRL has gained more ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9945333", "content": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards . Due to its promising advantage over traditional RL, PbRL has gained more ..."}
+{"idx": 8, "title": "Offline reward shaping with scaling human preference feedback for deep ...", "date": "", "ddg_snippet": "Designing reward functions that fully align with human intent is often challenging. Preference-based Reinforcement Learning (PbRL) provides a framework where humans can select preferred segments through pairwise comparisons of behavior trajectory segments, facilitating reward function learning . However, existing methods collect non -dynamic preferences and struggle to provide accurate ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S089360802400772X", "content": "Designing reward functions that fully align with human intent is often challenging. Preference-based Reinforcement Learning (PbRL) provides a framework where humans can select preferred segments through pairwise comparisons of behavior trajectory segments, facilitating reward function learning . However, existing methods collect non -dynamic preferences and struggle to provide accurate ..."}
+{"idx": 9, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Abstract—Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.11943", "content": "Abstract—Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards ."}
diff --git a/data/sampled_jsons/nuScenes_Caesar_2020_abstract_multimodal_dataset_autonomous_driving_year_2020.jsonl b/data/sampled_jsons/nuScenes_Caesar_2020_abstract_multimodal_dataset_autonomous_driving_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6ac229e242590a86c51d1c659b2d8736b5d9725
--- /dev/null
+++ b/data/sampled_jsons/nuScenes_Caesar_2020_abstract_multimodal_dataset_autonomous_driving_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) nuScenes : A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "... The nuScenes ( Caesar et al. 2020 ) dataset includes 3D bounding box annotations for humans and vehicles, yet this representation inherently captures extraneous background elements due to the coarse nature of axis-aligned bounding volumes.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/343456393_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving", "content": "... The nuScenes ( Caesar et al. 2020 ) dataset includes 3D bounding box annotations for humans and vehicles, yet this representation inherently captures extraneous background elements due to the coarse nature of axis-aligned bounding volumes."}
+{"idx": 1, "title": "nuScenes: A multimodal dataset for autonomous driving nuScenes: A Multimodal Dataset for Autonomous Driving nuScenes: A Multimodal Dataset for Autonomous Driving nuScenes: A Multimodal Dataset for Autonomous Driving Caesar NuScenes A Multimodal Dataset For Autonomous Driving Images nuScenes: A multimodal dataset for autonomous driving - LDM Benchmarking domain adaptation for LiDAR-based 3D object ... nuScenes: A Multimodal Dataset for Autonomous Driving nuScenes: A Multimodal Dataset for Autonomous Driving nuScenes : A multimodal dataset for autonomous driving nuScenes: A Multimodal Dataset for Autonomous Driving nuScenes: A Multimodal Dataset for Autonomous Driving nuScenes : A Multimodal Dataset for Autonomous Driving - Semantic Sc…", "date": "", "ddg_snippet": "Mar 26, 2019 · View a PDF of the paper titled nuScenes : A multimodal dataset for autonomous driving , by Holger Caesar and 9 other authors Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Jun 1, 2020 · PDF | On Jun 1, 2020 , Holger Caesar and others published nuScenes : A Multimodal Dataset for Autonomous Driving | Find, read and cite all the research you need on ResearchGate Mar 26, 2019 · This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi - modal object detection and semantic segmentation in autonomous driving with an overview of on-board sensors on test vehicles, open datasets , and background information. Caesar NuScenes a Multimodal Dataset for Autonomous Driving - Free download as PDF File (.pdf), Text File (.txt) or read online for free. View all nuScenes : A multimodal dataset for autonomous driving . The json representation of the dataset with its distributions based on DCAT. Holger Caesar , Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom (2024). Sep 5, 2025 · The generalization capability of 3D object detection models is crucial for ensuring robust perception in autonomous driving systems. While state-of-the-art models such as Voxel R-CNN, PV-RCNN, and CenterPoint have demonstrated strong performance on publicly available datasets (e.g., KITTI, Waymo, and nuScenes ). In this study, we conduct a comprehensive benchmark evaluation. We introduce two ... What dataset do we use for autonomous driving research? We use the nuScenes dataset , which is a large-scale dataset designed for autonomous driving research and development. Which datasets are used for object detection of autonomous vehicles? Most datasets are related to object detection of autonomous vehicles, where KITTI [18,19] and nuScenes are widely used, which will also be used in this work for evaluation. The full list of the investigated datasets and their rating can be seen in GitHub Error! ... Why is image based benchmark data important for autonomous vehicle technology? Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Which datasets are used for multi-modal data analysis? Another path of the literature review was the review of existing datasets for multi-modal data analysis. Most datasets are related to object detection of autonomous vehicles, where KITTI [18,19] and nuScenes are widely used, which will also be used in this work for evaluation. How many DIF- annotated semantic maps are there in nuscenes? 2 nuScenes teaser set released Sep. 2018, full release in March 2019. Figure 1. An example from the nuScenes dataset. We see 6 dif- annotated semantic map. At the bottom we show the human writ- ten scene description. complementary. Cameras allow accurate measurements of ization on the image plane. However, 3D localization from Can Fusion 3D object detection be used for autonomous driving? This work proposes a multi-modal fusion 3D object detection model for autonomous driving to use the best out of LiDAR and camera sensors, which comprises a feature extraction network and a fusion network.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "Mar 26, 2019 · View a PDF of the paper titled nuScenes : A multimodal dataset for autonomous driving , by Holger Caesar and 9 other authors Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Jun 1, 2020 · PDF | On Jun 1, 2020 , Holger Caesar and others published nuScenes : A Multimodal Dataset for Autonomous Driving | Find, read and cite all the research you need on ResearchGate Mar 26, 2019 · This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi - modal object detection and semantic segmentation in autonomous driving with an overview of on-board sensors on test vehicles, open datasets , and background information. Caesar NuScenes a Multimodal Dataset for Autonomous Driving - Free download as PDF File (.pdf), Text File (.txt) or read online for free. View all nuScenes : A multimodal dataset for autonomous driving . The json representation of the dataset with its distributions based on DCAT. Holger Caesar , Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom (2024). Sep 5, 2025 · The generalization capability of 3D object detection models is crucial for ensuring robust perception in autonomous driving systems. While state-of-the-art models such as Voxel R-CNN, PV-RCNN, and CenterPoint have demonstrated strong performance on publicly available datasets (e.g., KITTI, Waymo, and nuScenes ). In this study, we conduct a comprehensive benchmark evaluation. We introduce two ... What dataset do we use for autonomous driving research? We use the nuScenes dataset , which is a large-scale dataset designed for autonomous driving research and development. Which datasets are used for object detection of autonomous vehicles? Most datasets are related to object detection of autonomous vehicles, where KITTI [18,19] and nuScenes are widely used, which will also be used in this work for evaluation. The full list of the investigated datasets and their rating can be seen in GitHub Error! ... Why is image based benchmark data important for autonomous vehicle technology? Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Which datasets are used for multi-modal data analysis? Another path of the literature review was the review of existing datasets for multi-modal data analysis. Most datasets are related to object detection of autonomous vehicles, where KITTI [18,19] and nuScenes are widely used, which will also be used in this work for evaluation. How many DIF- annotated semantic maps are there in nuscenes? 2 nuScenes teaser set released Sep. 2018, full release in March 2019. Figure 1. An example from the nuScenes dataset. We see 6 dif- annotated semantic map. At the bottom we show the human writ- ten scene description. complementary. Cameras allow accurate measurements of ization on the image plane. However, 3D localization from Can Fusion 3D object detection be used for autonomous driving? This work proposes a multi-modal fusion 3D object detection model for autonomous driving to use the best out of LiDAR and camera sensors, which comprises a feature extraction network and a fusion network."}
+{"idx": 2, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.html", "content": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment."}
+{"idx": 3, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Mar 26, 2019 · This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi - modal object detection and semantic segmentation in autonomous driving with an overview of on-board sensors on test vehicles, open datasets , and background information.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/nuScenes:-A-Multimodal-Dataset-for-Autonomous-Caesar-Bankiti/9e475a514f54665478aac6038c262e5a6bac5e64", "content": "Mar 26, 2019 · This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi - modal object detection and semantic segmentation in autonomous driving with an overview of on-board sensors on test vehicles, open datasets , and background information."}
+{"idx": 4, "title": "Caesar NuScenes A Multimodal Dataset For Autonomous Driving", "date": "", "ddg_snippet": "Caesar NuScenes a Multimodal Dataset for Autonomous Driving - Free download as PDF File (.pdf), Text File (.txt) or read online for free.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/807610274/Caesar-NuScenes-a-Multimodal-Dataset-for-Autonomous-Driving", "content": "Caesar NuScenes a Multimodal Dataset for Autonomous Driving - Free download as PDF File (.pdf), Text File (.txt) or read online for free."}
+{"idx": 5, "title": "nuScenes: A multimodal dataset for autonomous driving - LDM", "date": "", "ddg_snippet": "nuScenes : A multimodal dataset for autonomous driving . The json representation of the dataset with its distributions based on DCAT. Holger Caesar , Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom (2024).", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/nuscenes--a-multimodal-dataset-for-autonomous-driving", "content": "nuScenes : A multimodal dataset for autonomous driving . The json representation of the dataset with its distributions based on DCAT. Holger Caesar , Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom (2024)."}
+{"idx": 6, "title": "nuScenes : A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "It is further the rst multimodal dataset that contains data from nighttime and rainy conditions, and with object attributes and scene descriptions in addition to object class and loca-tion. Similar to [84], nuScenes is a holistic scene under-standing benchmark for AVs.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "It is further the rst multimodal dataset that contains data from nighttime and rainy conditions, and with object attributes and scene descriptions in addition to object class and loca-tion. Similar to [84], nuScenes is a holistic scene under-standing benchmark for AVs."}
+{"idx": 7, "title": "nuScenes : A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Keywords: autonomous driving , autonomous vehicles, dataset , object detection, 3d, lidar, radar, map, pointpillars.Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "papertalk.org", "link": "https://papertalk.org/papertalks/15087", "content": "Keywords: autonomous driving , autonomous vehicles, dataset , object detection, 3d, lidar, radar, map, pointpillars.Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment."}
+{"idx": 8, "title": "nuScenes : A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "user. @pkoch. nuScenes : A Multimodal ... 2020 - caesar . entry type. inproceedings. booktitle. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 , Seattle, WA, USA, June 13-19, 2020 .", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/bibtex/20be7a5d28e1504a249554fa3ead5edf9/pkoch", "content": "user. @pkoch. nuScenes : A Multimodal ... 2020 - caesar . entry type. inproceedings. booktitle. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 , Seattle, WA, USA, June 13-19, 2020 ."}
+{"idx": 9, "title": "[PDF] nuScenes : A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes ...", "subpage_snippet": "", "source": "www.scinapse.io", "link": "https://www.scinapse.io/papers/2925148167", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes ..."}
diff --git a/data/sampled_jsons/nuScenes_Caesar_2020_abstract_vehicle_trajectories_pedestrian_interactions.jsonl b/data/sampled_jsons/nuScenes_Caesar_2020_abstract_vehicle_trajectories_pedestrian_interactions.jsonl
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index 0000000000000000000000000000000000000000..11b5386c7d4274c3767dc7fcceec4764955fbda4
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers", "date": "", "ddg_snippet": "This enables our Joint-β-cVAE approach to better model the dis-tribution of future trajectories . We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrat-ing the importance of capturing interactions between ego- vehicle and pedestrians (bicyclists) for accurate predictions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2021/papers/Bhattacharyya_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers_CVPR_2021_paper.pdf", "content": "This enables our Joint-β-cVAE approach to better model the dis-tribution of future trajectories . We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrat-ing the importance of capturing interactions between ego- vehicle and pedestrians (bicyclists) for accurate predictions."}
+{"idx": 1, "title": "Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers", "date": "", "ddg_snippet": "Abstract Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions. Although recent datasets and trajectory prediction approaches ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2106.12442", "content": "Abstract Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions. Although recent datasets and trajectory prediction approaches ..."}
+{"idx": 2, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles , however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9156412", "content": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles , however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning ..."}
+{"idx": 3, "title": "CVPR 2020 Open Access Repository", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.html", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 4, "title": "nuscenes@nutonomy.com Abstract arXiv:1903.11027v5 [cs.LG] 5 May 2020", "date": "", "ddg_snippet": "t least car, pedestrian and bicycle are included in this comp rison. (y) We report numbers only for scenes annotated with cuboids. (z) The current Waymo Open dataset size is comparable to nuScenes , but at a 5x higher annotat", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v5", "content": "t least car, pedestrian and bicycle are included in this comp rison. (y) We report numbers only for scenes annotated with cuboids. (z) The current Waymo Open dataset size is comparable to nuScenes , but at a 5x higher annotat"}
+{"idx": 5, "title": "Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers", "date": "", "ddg_snippet": "The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/353069913_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers", "content": "The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions."}
+{"idx": 6, "title": "PDF nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 de-gree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 de-gree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 7, "title": "Abstract arXiv:1903.11027v2 [cs.LG] 3 Sep 2019", "date": "", "ddg_snippet": "Scenes dataset, metrics, and baseline results. This is the only dataset collected from an autonomous vehicle on public roads and the only dataset to contain the full 360 sensor suite (lidar, images, and radar). nuScenes has the largest collectio", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v2", "content": "Scenes dataset, metrics, and baseline results. This is the only dataset collected from an autonomous vehicle on public roads and the only dataset to contain the full 360 sensor suite (lidar, images, and radar). nuScenes has the largest collectio"}
+{"idx": 8, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "PDF | On Jun 1, 2020 , Holger Caesar and others published nuScenes : A Multimodal Dataset for Autonomous Driving | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/343456393_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving", "content": "PDF | On Jun 1, 2020 , Holger Caesar and others published nuScenes : A Multimodal Dataset for Autonomous Driving | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 9, "title": "Improving generative trajectory prediction via collision-free modeling ...", "date": "", "ddg_snippet": "The nuScenes dataset provided heterogeneous inputs for various autonomous driving tasks, while the ETH/UCY dataset focused specifically on pedestrian trajectories . For the trajectory prediction task, the nuScenes dataset provided semantic road maps for both pedestrian and vehicle trajectories .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167865524003593", "content": "The nuScenes dataset provided heterogeneous inputs for various autonomous driving tasks, while the ETH/UCY dataset focused specifically on pedestrian trajectories . For the trajectory prediction task, the nuScenes dataset provided semantic road maps for both pedestrian and vehicle trajectories ."}
diff --git a/data/sampled_jsons/nuScenes_dataset_Caesar_et_al_2020_abstract_autonomous_driving_multimodal.jsonl b/data/sampled_jsons/nuScenes_dataset_Caesar_et_al_2020_abstract_autonomous_driving_multimodal.jsonl
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index 0000000000000000000000000000000000000000..627555d331f22f86bf18fc2c52bf8b33a9300dcb
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+++ b/data/sampled_jsons/nuScenes_dataset_Caesar_et_al_2020_abstract_autonomous_driving_multimodal.jsonl
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+{"idx": 0, "title": "EMMA: End-to-End Multimodal Model for Autonomous Driving", "date": "", "ddg_snippet": "Historically, autonomous driving systems employed a modular approach, consisting of specialized components for perception ( Yurtsever et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23262v2", "content": "Historically, autonomous driving systems employed a modular approach, consisting of specialized components for perception ( Yurtsever et al ."}
+{"idx": 1, "title": "NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View", "date": "", "ddg_snippet": "To further support generalization to multi-view driving scenarios, we also propose NuPlanQA-1M, a large-scale dataset comprising 1M real-world visual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.12772v2", "content": "To further support generalization to multi-view driving scenarios, we also propose NuPlanQA-1M, a large-scale dataset comprising 1M real-world visual ..."}
+{"idx": 2, "title": "Multimodal Framework for Explainable Autonomous Driving:", "date": "", "ddg_snippet": "Autonomous driving represents a paradigm shift in mobility, promising to enhance road safety, reduce human-related accidents, and alleviate urban ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07938v1", "content": "Autonomous driving represents a paradigm shift in mobility, promising to enhance road safety, reduce human-related accidents, and alleviate urban ..."}
+{"idx": 3, "title": "ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and", "date": "", "ddg_snippet": "... benchmark for evaluating the generalization and robustness of the multimodal perception and understanding models within real-world autonomous driving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01735v1", "content": "... benchmark for evaluating the generalization and robustness of the multimodal perception and understanding models within real-world autonomous driving ..."}
+{"idx": 4, "title": "Robust object detection for autonomous driving based on", "date": "", "ddg_snippet": "Although some theoretical research work has exploited the ability of semi-supervised learning to improve adversarial robustness on toy datasets such ...", "subpage_snippet": "", "source": "sands.edpsciences.org", "link": "https://sands.edpsciences.org/articles/sands/full_html/2024/01/sands20230024/sands20230024.html", "content": "Although some theoretical research work has exploited the ability of semi-supervised learning to improve adversarial robustness on toy datasets such ..."}
+{"idx": 5, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "by H Caesar · 2020 · Cited by 8367 — In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.html", "content": "by H Caesar · 2020 · Cited by 8367 — In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all ..."}
+{"idx": 6, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "by H Caesar · 2020 · Cited by 8367 — In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "by H Caesar · 2020 · Cited by 8367 — In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all ..."}
+{"idx": 7, "title": "nuScenes: A multimodal dataset for autonomous driving - ar5iv", "date": "", "ddg_snippet": "The first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1903.11027", "content": "The first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view."}
+{"idx": 8, "title": "A multi-sensor dataset including radar and IR for ...", "date": "", "ddg_snippet": "by H Zhang · 2025 · Cited by 1 — This paper presents a driving dataset recorded using a complete sensor suite for research on autonomous driving , perception, and sensor fusion.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2352340925002847", "content": "by H Zhang · 2025 · Cited by 1 — This paper presents a driving dataset recorded using a complete sensor suite for research on autonomous driving , perception, and sensor fusion."}
+{"idx": 9, "title": "EMMA: End-to-End Multimodal Model for Autonomous Driving", "date": "", "ddg_snippet": "We introduce EMMA, an End-to-end Multimodal Model for Autonomous driving . Built on a multi-modal large language model foundation, EMMA directly maps raw ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/60d1b1da9eee0aee0d9cba4eee5b2aa7bab955dd.pdf", "content": "We introduce EMMA, an End-to-end Multimodal Model for Autonomous driving . Built on a multi-modal large language model foundation, EMMA directly maps raw ..."}
diff --git a/data/sampled_jsons/nuScenes_multimodal_dataset_autonomous_driving_Caesar_2020_year_2020.jsonl b/data/sampled_jsons/nuScenes_multimodal_dataset_autonomous_driving_Caesar_2020_year_2020.jsonl
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+{"idx": 0, "title": "(PDF) nuScenes : A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "the first dataset to carry the full autonomous vehicle sensor. suite: 6 cameras, 5 radars and 1 lidar, all with full 360 de-. gree field of view. nuScenes comprises 1000 scenes , each.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/343456393_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving", "content": "the first dataset to carry the full autonomous vehicle sensor. suite: 6 cameras, 5 radars and 1 lidar, all with full 360 de-. gree field of view. nuScenes comprises 1000 scenes , each."}
+{"idx": 1, "title": "nuScenes : A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "@pkoch. nuScenes : A Multimodal ... ×. 2020 - caesar . entry type. inproceedings. booktitle. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 , Seattle, WA, USA, June 13-19, 2020 .", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/bibtex/20be7a5d28e1504a249554fa3ead5edf9/pkoch", "content": "@pkoch. nuScenes : A Multimodal ... ×. 2020 - caesar . entry type. inproceedings. booktitle. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 , Seattle, WA, USA, June 13-19, 2020 ."}
+{"idx": 2, "title": "nuScenes : A multimodal dataset for autonomous driving | Motional", "date": "", "ddg_snippet": "Published: May 5, 2020 . Summary: In this paper we present nuScenes , the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.", "subpage_snippet": "", "source": "motional.com", "link": "https://motional.com/news/nuscenes-multimodal-dataset-autonomous-driving", "content": "Published: May 5, 2020 . Summary: In this paper we present nuScenes , the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view."}
+{"idx": 3, "title": "[PDF] nuScenes : A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes ...", "subpage_snippet": "", "source": "www.scinapse.io", "link": "https://www.scinapse.io/papers/2925148167", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes ..."}
+{"idx": 4, "title": "KevinNotSmile/ nuscenes -qa-mini · Datasets at Hugging Face", "date": "", "ddg_snippet": "This dataset is built on the nuScenes mini-split, where we obtain the QA pairs from the original nuScenes -QA dataset . The data in the nuScenes -QA dataset is collected from driving scenes in cities of Boston and Singapore with diverse locations, time, and weather conditions.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/KevinNotSmile/nuscenes-qa-mini", "content": "This dataset is built on the nuScenes mini-split, where we obtain the QA pairs from the original nuScenes -QA dataset . The data in the nuScenes -QA dataset is collected from driving scenes in cities of Boston and Singapore with diverse locations, time, and weather conditions."}
+{"idx": 5, "title": "Multi - Modal 3D Object Detection in Autonomous Driving ... | alphaXiv", "date": "", "ddg_snippet": "nuscenes : A multimodal dataset for autonomous driving . This citation introduced the nuScenes dataset , a large-scale, modern benchmark with 360-degree sensor coverage that has become critical for developing and evaluating multi - modal systems.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2106.12735v3", "content": "nuscenes : A multimodal dataset for autonomous driving . This citation introduced the nuScenes dataset , a large-scale, modern benchmark with 360-degree sensor coverage that has become critical for developing and evaluating multi - modal systems."}
+{"idx": 6, "title": "nuScenes Dataset | OpenBayes Trends", "date": "", "ddg_snippet": "The nuScenes dataset is a large-scale autonomous driving dataset . The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore.", "subpage_snippet": "", "source": "trends.openbayes.com", "link": "https://trends.openbayes.com/dataset/nuscenes", "content": "The nuScenes dataset is a large-scale autonomous driving dataset . The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore."}
+{"idx": 7, "title": "On the Road to Autonomy : A Comparative Analysis of Multimodal ...", "date": "", "ddg_snippet": "Caesar , H., et al.: nuscenes : a multimodal dataset for autonomous driving .Scalability in perception for autonomous driving : Waymo open dataset . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2446–2454 ( 2020 ).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-59114-3_2", "content": "Caesar , H., et al.: nuscenes : a multimodal dataset for autonomous driving .Scalability in perception for autonomous driving : Waymo open dataset . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2446–2454 ( 2020 )."}
+{"idx": 8, "title": "Deep Multi - modal Object Detection for Autonomous Driving", "date": "", "ddg_snippet": "[23] H. Caesar , V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “ nuscenes : A multimodal dataset for autonomous driving ,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11618–11628, 2020 .", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03255470/document", "content": "[23] H. Caesar , V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “ nuscenes : A multimodal dataset for autonomous driving ,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11618–11628, 2020 ."}
+{"idx": 9, "title": "One Million Scenes for Autonomous Driving", "date": "", "ddg_snippet": "[4] Holger Caesar , Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes : A multimodal dataset for autonomous driving .", "subpage_snippet": "", "source": "datasets-benchmarks-proceedings.neurips.cc", "link": "https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/67c6a1e7ce56d3d6fa748ab6d9af3fd7-Paper-round1.pdf", "content": "[4] Holger Caesar , Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes : A multimodal dataset for autonomous driving ."}
diff --git a/data/sampled_jsons/objective_function_hierarchical_overlapping_clustering_before_2024.jsonl b/data/sampled_jsons/objective_function_hierarchical_overlapping_clustering_before_2024.jsonl
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index 0000000000000000000000000000000000000000..437ab52c09baf23f289902c1b927507bcd619c83
--- /dev/null
+++ b/data/sampled_jsons/objective_function_hierarchical_overlapping_clustering_before_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "by Y Pan — Summary: The paper presents the problem of hierarchical overlapping clustering (HOC ) for graph data, where clusters may overlap and form hierarchies. The paper ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "by Y Pan — Summary: The paper presents the problem of hierarchical overlapping clustering (HOC ) for graph data, where clusters may overlap and form hierarchies. The paper ..."}
+{"idx": 1, "title": "Hierarchical variable clustering based on the predictive ...", "date": "", "ddg_snippet": "by S Fuchs · 2024 · Cited by 7 — A rank-invariant clustering of variables is introduced that is based on the predictive strength between groups of variables, i.e., two groups are assigned a ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0888613X24000720", "content": "by S Fuchs · 2024 · Cited by 7 — A rank-invariant clustering of variables is introduced that is based on the predictive strength between groups of variables, i.e., two groups are assigned a ..."}
+{"idx": 2, "title": "Categorical data clustering: 25 years beyond K-modes", "date": "", "ddg_snippet": "30 Aug 2024 — This review provides a comprehensive synthesis of categorical data clustering in the past twenty-five years, starting from the introduction of K-modes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17244v1", "content": "30 Aug 2024 — This review provides a comprehensive synthesis of categorical data clustering in the past twenty-five years, starting from the introduction of K-modes."}
+{"idx": 3, "title": "Multiple clusterings: Recent advances and perspectives", "date": "", "ddg_snippet": "We summarize the key ideas underlying the techniques and their objective functions , and discuss the advantages and disadvantages of each. In addition, we built ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1574013724000054/pdf", "content": "We summarize the key ideas underlying the techniques and their objective functions , and discuss the advantages and disadvantages of each. In addition, we built ..."}
+{"idx": 4, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and establishing its rationality ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "by Y Pan — To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and establishing its rationality ..."}
+{"idx": 5, "title": "Element-centric clustering comparison unifies overlaps and ...", "date": "", "ddg_snippet": "by AJ Gates · 2019 · Cited by 116 — Here we propose a new element-centric framework for clustering similarity that naturally incorporates overlaps and hierarchy.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-019-44892-y", "content": "by AJ Gates · 2019 · Cited by 116 — Here we propose a new element-centric framework for clustering similarity that naturally incorporates overlaps and hierarchy."}
+{"idx": 6, "title": "Two-stage multi-objective evolutionary algorithm for ...", "date": "", "ddg_snippet": "by L Cai · 2024 · Cited by 4 — The objective function established between nodes optimizes the discovery of communities and obtains better overlapping communities by merging ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11323150/", "content": "by L Cai · 2024 · Cited by 4 — The objective function established between nodes optimizes the discovery of communities and obtains better overlapping communities by merging ..."}
+{"idx": 7, "title": "GraphC: Parameter-free Hierarchical Clustering of Signed ...", "date": "", "ddg_snippet": "14 Jan 2025 — The clustering in the neutrosophic feature space identifies overlapping communities in signed networks [Gholami et al., 2024 ] . Report issue for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.00249v2", "content": "14 Jan 2025 — The clustering in the neutrosophic feature space identifies overlapping communities in signed networks [Gholami et al., 2024 ] . Report issue for ..."}
+{"idx": 8, "title": "Agglomerative hierarchical clustering for selecting valid ...", "date": "", "ddg_snippet": "by N Apfel · 2024 · Cited by 13 — We propose a procedure that combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/jae.3078", "content": "by N Apfel · 2024 · Cited by 13 — We propose a procedure that combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a ..."}
+{"idx": 9, "title": "Improving the prediction accuracy of statistical models", "date": "", "ddg_snippet": "by Á López-Oriona · 2025 — Both objectives are achieved by introducing an agglomerative hierarchical clustering procedure designed to group together datasets with similar ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11222-025-10683-x", "content": "by Á López-Oriona · 2025 — Both objectives are achieved by introducing an agglomerative hierarchical clustering procedure designed to group together datasets with similar ..."}
diff --git a/data/sampled_jsons/on-policy_imitation_learning_techniques_for_RLHF_extension_year_2023.jsonl b/data/sampled_jsons/on-policy_imitation_learning_techniques_for_RLHF_extension_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a050169a92ca28e0ef820df9023515b7f242847f
--- /dev/null
+++ b/data/sampled_jsons/on-policy_imitation_learning_techniques_for_RLHF_extension_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Lecture 8: Imitation Learning and RLHF - web.stanford.edu", "date": "", "ddg_snippet": "Class Structure Last time: Policy search continued and Imitation Learning This time: Imitation Learning and RLHF Next time: Author of Direct Preference Optimization (best paper runner up at top ML conference) guest lecture", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs234/CS234Spr2024/slides/lecture8post.pdf", "content": "Class Structure Last time: Policy search continued and Imitation Learning This time: Imitation Learning and RLHF Next time: Author of Direct Preference Optimization (best paper runner up at top ML conference) guest lecture"}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection, we propose DIL, a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.05079", "content": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection, we propose DIL, a ..."}
+{"idx": 2, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "This paper presents Direct Imitation Learning (DIL), a novel framework for aligning large language models with human preferences. The authors reinterpret existing alignment methods like RLHF and DPO as special cases of imitation learning and introduce a new objective function based on minimizing the reverse KL divergence between the model's policy and the distribution of chosen responses. The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2QdsjiNXgj", "content": "This paper presents Direct Imitation Learning (DIL), a novel framework for aligning large language models with human preferences. The authors reinterpret existing alignment methods like RLHF and DPO as special cases of imitation learning and introduce a new objective function based on minimizing the reverse KL divergence between the model's policy and the distribution of chosen responses. The ..."}
+{"idx": 3, "title": "PDF Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and ...", "date": "", "ddg_snippet": "Abstract: We present relay policy learning , a method for imitation and reinforce-ment learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two-phase approach consists of an imitation learning stage resulting in goal-conditioned hierarchical policies that can be easily improved using fine-tuning via reinforcement learning in the subsequent ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v100/gupta20a/gupta20a.pdf", "content": "Abstract: We present relay policy learning , a method for imitation and reinforce-ment learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two-phase approach consists of an imitation learning stage resulting in goal-conditioned hierarchical policies that can be easily improved using fine-tuning via reinforcement learning in the subsequent ..."}
+{"idx": 4, "title": "Imitating Language via Scalable Inverse Reinforcement Learning", "date": "", "ddg_snippet": "Abstract The majority of language model training builds on imitation learning . It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback ( RLHF ). The simplicity and scalability of maximum likelihood estimation (MLE) for next token prediction led to its role as predominant ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a5036c166e44b731f214f41813364d01-Abstract-Conference.html", "content": "Abstract The majority of language model training builds on imitation learning . It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback ( RLHF ). The simplicity and scalability of maximum likelihood estimation (MLE) for next token prediction led to its role as predominant ..."}
+{"idx": 5, "title": "Strict On-Policy Training with Optimal Baseline: Microsoft Introduces ...", "date": "", "ddg_snippet": "The Microsoft Research team introduced On-Policy RL with Optimal reward baseline (OPO) — a simplified reinforcement learning algorithm for aligning large language models. The new method addresses key problems of modern RLHF algorithms: training instability due to loose on-policy constraints and computational inefficiency due to auxiliary models. Code implementation is available on GitHub.", "subpage_snippet": "", "source": "neurohive.io", "link": "https://neurohive.io/en/state-of-the-art/strict-on-policy-training-with-optimal-baseline-microsoft-introduces-simplified-algorithm-for-rlhf/", "content": "The Microsoft Research team introduced On-Policy RL with Optimal reward baseline (OPO) — a simplified reinforcement learning algorithm for aligning large language models. The new method addresses key problems of modern RLHF algorithms: training instability due to loose on-policy constraints and computational inefficiency due to auxiliary models. Code implementation is available on GitHub."}
+{"idx": 6, "title": "PDF Reinforcement Learning in the Era Of", "date": "", "ddg_snippet": "Inverse-RL ≈ Imitation Learning , with an emphasis on explicit reward modeling Learning from logged trial and error, to find out what cumulative reward is being optimized.", "subpage_snippet": "", "source": "holarissun.github.io", "link": "https://holarissun.github.io/files/RLHF_Nov.pdf", "content": "Inverse-RL ≈ Imitation Learning , with an emphasis on explicit reward modeling Learning from logged trial and error, to find out what cumulative reward is being optimized."}
+{"idx": 7, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "In particular, we show that RLHF is a special case of a general imitation learning problem expressed exclusively in terms of pairwise preferences. We theoretically demonstrate that alignment with RLHF closely resembles imitation learning and implicitly optimizes the same objective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "In particular, we show that RLHF is a special case of a general imitation learning problem expressed exclusively in terms of pairwise preferences. We theoretically demonstrate that alignment with RLHF closely resembles imitation learning and implicitly optimizes the same objective."}
+{"idx": 8, "title": "Policy Optimization with RLHF. The purpose of pre-trained ... - Medium", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback ( RLHF ) further refines these models by translating human preferences into numerical reward signals through a reward model.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sulbha.jindal/policy-optimization-with-rlhf-ppo-dpo-orpo-d65d075d99f3", "content": "Reinforcement Learning from Human Feedback ( RLHF ) further refines these models by translating human preferences into numerical reward signals through a reward model."}
+{"idx": 9, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "DIL is proposed, a principled framework that directly optimizes the imitation learning objective and provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants. This work studies the alignment of large language models with preference data from an imitation learning perspective. We ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-a-Connection-Between-Imitation-Learning-and-RLHF-Xiao-Yuan/9b4ecc297389ca6753817ce3d3dfb3057c34ae76", "content": "DIL is proposed, a principled framework that directly optimizes the imitation learning objective and provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants. This work studies the alignment of large language models with preference data from an imitation learning perspective. We ..."}
diff --git a/data/sampled_jsons/open-world_semi-supervised_learning_unknown_class_distribution_techniques_2023_2024_year_2023,2024.jsonl b/data/sampled_jsons/open-world_semi-supervised_learning_unknown_class_distribution_techniques_2023_2024_year_2023,2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..915cf3fe08460229f2ef9d145a9fd5d36c3f2def
--- /dev/null
+++ b/data/sampled_jsons/open-world_semi-supervised_learning_unknown_class_distribution_techniques_2023_2024_year_2023,2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Rethinking Open-World Semi-Supervised Learning", "date": "", "ddg_snippet": "31 May 2024 — Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world scenarios by taking account of novel ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.20829v1", "content": "31 May 2024 — Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world scenarios by taking account of novel ..."}
+{"idx": 1, "title": "Label Distribution-based Open-world Semi-supervised ...", "date": "", "ddg_snippet": "by Q Yang · 2023 — We propose a new method to solve the open - world SSL problem. It leverages the sample information of seen classes to obtain reliable label distributions.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10516485/", "content": "by Q Yang · 2023 — We propose a new method to solve the open - world SSL problem. It leverages the sample information of seen classes to obtain reliable label distributions."}
+{"idx": 2, "title": "Targeted Representation Alignment for Open-World Semi ...", "date": "", "ddg_snippet": "by R Xiao · 2024 · Cited by 16 — Open - world Semi - Supervised Learning aims to classify unlabeled samples utilizing information from labeled data, while unlabeled samples are not only from ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_Targeted_Representation_Alignment_for_Open-World_Semi-Supervised_Learning_CVPR_2024_paper.pdf", "content": "by R Xiao · 2024 · Cited by 16 — Open - world Semi - Supervised Learning aims to classify unlabeled samples utilizing information from labeled data, while unlabeled samples are not only from ... 11 pages"}
+{"idx": 3, "title": "[PDF] Open-World Semi-Supervised Learning", "date": "", "ddg_snippet": "Label Distribution -based Open - world Semi - supervised Learning ... A new method is proposed that leverages the sample information of seen classes ... unknown classes .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/0016122bc5dfe0684baaa672c53014d48b79a65f", "content": "Label Distribution -based Open - world Semi - supervised Learning ... A new method is proposed that leverages the sample information of seen classes ... unknown classes ."}
+{"idx": 4, "title": "Robust Semi-Supervised Learning for Self-learning Open- ...", "date": "", "ddg_snippet": "15 Jan 2024 — In this paper, we propose an open - world SSL method for Self- learning Open - world Classes (SSOC), which can explicitly self-learn multiple unknown classes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.07551v1", "content": "15 Jan 2024 — In this paper, we propose an open - world SSL method for Self- learning Open - world Classes (SSOC), which can explicitly self-learn multiple unknown classes."}
+{"idx": 5, "title": "A new method of semi-supervised learning classification ...", "date": "", "ddg_snippet": "by Y Liu · 2025 — To this end, this paper proposes a semi - supervised image classification method based on multi-mode augmentation, which mitigates the effects of ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-02324-0", "content": "by Y Liu · 2025 — To this end, this paper proposes a semi - supervised image classification method based on multi-mode augmentation, which mitigates the effects of ..."}
+{"idx": 6, "title": "CVPR Poster Learning Textual Prompts for Open-World Semi ...", "date": "", "ddg_snippet": "14 Jun 2025 — We propose a novel OWSSL method . By adopting a global-and-local textual prompt learning strategy to enhance image-text alignment effectiveness.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33330", "content": "14 Jun 2025 — We propose a novel OWSSL method . By adopting a global-and-local textual prompt learning strategy to enhance image-text alignment effectiveness."}
+{"idx": 7, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "9 Dec 2024 — To overcome this challenge, this study revisits two methodologies from self-supervised and semi - supervised learning , self-labeling and ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93416", "content": "9 Dec 2024 — To overcome this challenge, this study revisits two methodologies from self-supervised and semi - supervised learning , self-labeling and ..."}
+{"idx": 8, "title": "OwMatch: Conditional Self-Labeling with Consistency for ...", "date": "", "ddg_snippet": "This paper proposes OxMatch, a semi - supervised learning (SSL) algorithm for an open - world setup where unlabelled data might come from outside of the labeled ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rle9X7DQuH&referrer=[the+profile+of+Jian+Huang](/profile?id=~Jian_Huang5)", "content": "This paper proposes OxMatch, a semi - supervised learning (SSL) algorithm for an open - world setup where unlabelled data might come from outside of the labeled ..."}
+{"idx": 9, "title": "OTDR Disturbance Signals", "date": "", "ddg_snippet": "NACH [33] designed adaptive thresholds to balance the learning of known and unknown classes , along with a novel classification loss function to assist the model ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6488907/11153580/11063349.pdf", "content": "NACH [33] designed adaptive thresholds to balance the learning of known and unknown classes , along with a novel classification loss function to assist the model ..."}
diff --git a/data/sampled_jsons/openreview.net_KijslFbfOL_Algorithm_2_termination.jsonl b/data/sampled_jsons/openreview.net_KijslFbfOL_Algorithm_2_termination.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..338dd981db82c9f96696b09b625c19eee588717b
--- /dev/null
+++ b/data/sampled_jsons/openreview.net_KijslFbfOL_Algorithm_2_termination.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dynamic Algorithm Termination for Branch-and-Bound-based Neural...", "date": "", "ddg_snippet": "In light of this, several neural network robustness verification algorithms have been developed, among which methods based on Branch and Bound (BaB) constitute the current state of the art. However, these algorithms still require immense computational resources.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=EFxgwKHTnu", "content": "In light of this, several neural network robustness verification algorithms have been developed, among which methods based on Branch and Bound (BaB) constitute the current state of the art. However, these algorithms still require immense computational resources."}
+{"idx": 1, "title": "The most recent documentation of OpenReview - GitHub", "date": "", "ddg_snippet": "The most recent documentation of OpenReview. Contribute to openreview/openreview development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openreview/openreview", "content": "The most recent documentation of OpenReview. Contribute to openreview/openreview development by creating an account on GitHub."}
+{"idx": 2, "title": "Termination Analysis Without the Tears - arXiv.org", "date": "", "ddg_snippet": "Abstract Determining whether a given program terminates is the quin-tessential undecidable problem. Algorithms for termination analysis are divided into two groups: (1) algorithms with strong behavioral guarantees that work in limited circum-stances (e.g., complete synthesis of linear ranking functions for polyhedral loops [38]), and ( 2 ) algorithms that are widely applicable, but have weak ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2101.09783v1", "content": "Abstract Determining whether a given program terminates is the quin-tessential undecidable problem. Algorithms for termination analysis are divided into two groups: (1) algorithms with strong behavioral guarantees that work in limited circum-stances (e.g., complete synthesis of linear ranking functions for polyhedral loops [38]), and ( 2 ) algorithms that are widely applicable, but have weak ..."}
+{"idx": 3, "title": "GitHub - ErikBird/OpenReviewCrawler: A Crawler for Open Review.", "date": "", "ddg_snippet": "A Crawler for Open Review. . Contribute to ErikBird/OpenReviewCrawler development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ErikBird/OpenReviewCrawler", "content": "A Crawler for Open Review. . Contribute to ErikBird/OpenReviewCrawler development by creating an account on GitHub."}
+{"idx": 4, "title": "What Have We Learned from OpenReview? - arXiv.org", "date": "", "ddg_snippet": "The o cial reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview. We will present the collected dataset of submissions and reviews from OpenReview, these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.05885v3", "content": "The o cial reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview. We will present the collected dataset of submissions and reviews from OpenReview, these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3."}
+{"idx": 5, "title": "What Have We Learned from OpenReview? - Springer", "date": "", "ddg_snippet": "The official reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview. We will present the col-lected dataset of submissions and reviews from OpenReview, these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-85896-4_6", "content": "The official reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview. We will present the col-lected dataset of submissions and reviews from OpenReview, these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3."}
+{"idx": 6, "title": "[D] Why do authors nuke their OpenReview discussions after ... - Reddit", "date": "", "ddg_snippet": "NeurIPS and ICLR this year both featured public reviewing via OpenReview. In several cases, the authors have deleted their discussion with reviewers after their paper is accepted. Here is one recent example. I am not calling out these authors in particular; this is just the first one I found---but I have noticed this occurring a lot over the past few months. What is the rationale behind doing ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/pyrjca/d_why_do_authors_nuke_their_openreview/", "content": "NeurIPS and ICLR this year both featured public reviewing via OpenReview. In several cases, the authors have deleted their discussion with reviewers after their paper is accepted. Here is one recent example. I am not calling out these authors in particular; this is just the first one I found---but I have noticed this occurring a lot over the past few months. What is the rationale behind doing ..."}
+{"idx": 7, "title": "RL^2: Fast Reinforcement Learning via Slow Reinforcement ... - OpenReview", "date": "", "ddg_snippet": "We propose to learn a \"fast\" reinforcement learning algorithm using standard, off-the-shelf (\"slow\") reinforcement learning algorithms , where the \"fast\" version is represented as an RNN, and fast RL happens inside its activations.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=HkLXCE9lx", "content": "We propose to learn a \"fast\" reinforcement learning algorithm using standard, off-the-shelf (\"slow\") reinforcement learning algorithms , where the \"fast\" version is represented as an RNN, and fast RL happens inside its activations."}
+{"idx": 8, "title": "Using the API - OpenReview", "date": "", "ddg_snippet": "The current API (referred to in the documentation as API or API 2 ) is the current API version, and is the default version used for all operations unless otherwise specified.", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/getting-started/using-the-api", "content": "The current API (referred to in the documentation as API or API 2 ) is the current API version, and is the default version used for all operations unless otherwise specified."}
+{"idx": 9, "title": "LLM-Based Agents for Tool Learning: A Survey - Springer", "date": "", "ddg_snippet": "Human beings capable of making and using tools can accomplish tasks far beyond their innate abilities, and this paradigm of integration with tools may not be limited to humans themselves. Recently, the large language model (LLM) has demonstrated immense potential across various fields with its unique planning and reasoning abilities. However, there are still many challenges beyond its ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s41019-025-00296-9", "content": "Human beings capable of making and using tools can accomplish tasks far beyond their innate abilities, and this paradigm of integration with tools may not be limited to humans themselves. Recently, the large language model (LLM) has demonstrated immense potential across various fields with its unique planning and reasoning abilities. However, there are still many challenges beyond its ..."}
diff --git a/data/sampled_jsons/openreview.net_pdfid=oHSXRy29tj_Algorithm_1_2-OC_costtemp.jsonl b/data/sampled_jsons/openreview.net_pdfid=oHSXRy29tj_Algorithm_1_2-OC_costtemp.jsonl
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index 0000000000000000000000000000000000000000..75dda0590a686205fb81c8b25bc4c752cd1d1f11
--- /dev/null
+++ b/data/sampled_jsons/openreview.net_pdfid=oHSXRy29tj_Algorithm_1_2-OC_costtemp.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: LLMs Need a Bayesian Meta-Reasoning... | OpenReview", "date": "", "ddg_snippet": "Pdf Appendices: My camera-ready PDF file contains both the main text (not exceeding the page limits) and all appendices that I wish to include. I understand that any other supplementary material (e.g., separate files previously uploaded to OpenReview )...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=RrvhbxO2hd", "content": "Pdf Appendices: My camera-ready PDF file contains both the main text (not exceeding the page limits) and all appendices that I wish to include. I understand that any other supplementary material (e.g., separate files previously uploaded to OpenReview )..."}
+{"idx": 1, "title": "Getting started — Simple JWT 5.5. 1 .post12+g657a73cad documentation", "date": "", "ddg_snippet": "If you are planning on encoding or decoding tokens using certain digital signature algorithms ( i .e. RSA and ECDSA; visit PyJWT for other algorithms ), you will need to install the cryptography library.", "subpage_snippet": "", "source": "django-rest-framework-simplejwt.readthedocs.io", "link": "https://django-rest-framework-simplejwt.readthedocs.io/en/latest/getting_started.html", "content": "If you are planning on encoding or decoding tokens using certain digital signature algorithms ( i .e. RSA and ECDSA; visit PyJWT for other algorithms ), you will need to install the cryptography library."}
+{"idx": 2, "title": "Погода в Анталье на 14 дней (Турция) - подробный... - Погода Mail", "date": "", "ddg_snippet": "Подробный прогноз погоды на 14 дней в Анталье (Турция).", "subpage_snippet": "", "source": "pogoda.mail.ru", "link": "https://pogoda.mail.ru/prognoz/antalya/14dney/", "content": "Подробный прогноз погоды на 14 дней в Анталье (Турция)."}
+{"idx": 3, "title": "Дмитрий Никотин – Telegram", "date": "", "ddg_snippet": "Про политику, простым языком о сложном. Контрпропаганда и интерпретация новостей.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/dmitrynikotin", "content": "Про политику, простым языком о сложном. Контрпропаганда и интерпретация новостей."}
+{"idx": 4, "title": "Ключи активации Windows 10 | Ответы Mail", "date": "", "ddg_snippet": "2015 LTSB N 2 F77B-TNFGY-69QQF-B8YKP-D69TJ Enterprise 2016 LTSB DCPHK-NFMTC-H88MJ-PFHPY-QJ4BJ Enterprise 2016 LTSB N QFFDN-GRT3P-VKWWX-X7T3R-8B639.00. Ответить. pavel_aleshkov_29. Ученик.", "subpage_snippet": "", "source": "otvet.mail.ru", "link": "https://otvet.mail.ru/question/237199183", "content": "2015 LTSB N 2 F77B-TNFGY-69QQF-B8YKP-D69TJ Enterprise 2016 LTSB DCPHK-NFMTC-H88MJ-PFHPY-QJ4BJ Enterprise 2016 LTSB N QFFDN-GRT3P-VKWWX-X7T3R-8B639.00. Ответить. pavel_aleshkov_29. Ученик."}
+{"idx": 5, "title": "Канал автора «Река Перемен» в Дзен : Показываю Вам...", "date": "", "ddg_snippet": "Канал автора «Река Перемен» в Дзен: Показываю Вам Иностранцев, которые говорят свою ПРАВДУ.", "subpage_snippet": "", "source": "dzen.ru", "link": "https://dzen.ru/reka_peremen", "content": "Канал автора «Река Перемен» в Дзен: Показываю Вам Иностранцев, которые говорят свою ПРАВДУ."}
+{"idx": 6, "title": "Fraction Calculator", "date": "", "ddg_snippet": "The Fraction Calculator will reduce a fraction to its simplest form. You can also add, subtract, multiply, and divide fractions, as well as, convert to a decimal and work with mixed numbers and reciprocals. We also offer step by step solutions.", "subpage_snippet": "", "source": "www.mathway.com", "link": "https://www.mathway.com/Calculator/fraction-calculator", "content": "The Fraction Calculator will reduce a fraction to its simplest form. You can also add, subtract, multiply, and divide fractions, as well as, convert to a decimal and work with mixed numbers and reciprocals. We also offer step by step solutions."}
+{"idx": 7, "title": "Топ игры на ПК [всех времён] скачать торрент бесплатно", "date": "", "ddg_snippet": "Появляются новые версии игр, которые оперативно добавляются на сайт, чтобы предоставить вам возможность скачивать последние версии Пк релизов на свой компьютер под управлением OC Windows.359 301. The Forest 1 .29 ГБ.", "subpage_snippet": "", "source": "byrutgame.org", "link": "https://byrutgame.org/top-torrent-games/", "content": "Появляются новые версии игр, которые оперативно добавляются на сайт, чтобы предоставить вам возможность скачивать последние версии Пк релизов на свой компьютер под управлением OC Windows.359 301. The Forest 1 .29 ГБ."}
+{"idx": 8, "title": "Tether USD | USDT Address...", "date": "", "ddg_snippet": "Tether USD. Общая сумма транзакций. 29 USDT. Хэш транзакции.", "subpage_snippet": "", "source": "usdt.tokenview.io", "link": "https://usdt.tokenview.io/ru/address/TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t", "content": "Tether USD. Общая сумма транзакций. 29 USDT. Хэш транзакции."}
+{"idx": 9, "title": "Новости Воронежа сегодня - Блокнот Воронеж", "date": "", "ddg_snippet": "Новости Воронежа. Новости бизнеса, политики, культуры и спорта Воронежа. Доска объявлений: недвижимость, автотранспорт, услуги, работа в Воронеже. Организации Воронежа...", "subpage_snippet": "", "source": "bloknot-voronezh.ru", "link": "https://bloknot-voronezh.ru/", "content": "Новости Воронежа. Новости бизнеса, политики, культуры и спорта Воронежа. Доска объявлений: недвижимость, автотранспорт, услуги, работа в Воронеже. Организации Воронежа..."}
diff --git a/data/sampled_jsons/optimal_transport_class_prior_estimation_implicit_feedback_recommendation_year_2023.jsonl b/data/sampled_jsons/optimal_transport_class_prior_estimation_implicit_feedback_recommendation_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9efac3e602dedc04f8079d96dc40cc5eb7623b7f
--- /dev/null
+++ b/data/sampled_jsons/optimal_transport_class_prior_estimation_implicit_feedback_recommendation_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Unbiased Recommender Learning from Implicit ...", "date": "", "ddg_snippet": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples."}
+{"idx": 1, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly...", "date": "", "ddg_snippet": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training.• We introduce PPT, an optimal transport -based methodol-ogy specifically designed for class prior estimation , a key factor in ensuring the unbiasedness of PURL.", "subpage_snippet": "", "source": "zhouchenlin.github.io", "link": "https://zhouchenlin.github.io/Publications/2025-ICML-Unbiased.pdf", "content": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training.• We introduce PPT, an optimal transport -based methodol-ogy specifically designed for class prior estimation , a key factor in ensuring the unbiasedness of PURL."}
+{"idx": 2, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly...", "date": "", "ddg_snippet": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13&referrer=[the+profile+of+Licheng+Pan](/profile?id=~Licheng_Pan1)", "content": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples."}
+{"idx": 3, "title": "(PDF) How optimal transport can tackle gender biases in multi- class ...", "date": "", "ddg_snippet": "TL;DR: In this article , a novel optimal transport strategy is proposed to mitigate undesirable algorithmic biases in multi- class neural-network classification, which is model agnostic and can be used on any multilayer classification neural network model.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/how-optimal-transport-can-tackle-gender-biases-in-multi-glrunw7s", "content": "TL;DR: In this article , a novel optimal transport strategy is proposed to mitigate undesirable algorithmic biases in multi- class neural-network classification, which is model agnostic and can be used on any multilayer classification neural network model."}
+{"idx": 4, "title": "Posistive-Unlabeled Learning via Optimal Transport and Margin...", "date": "", "ddg_snippet": "Optimal Margin Distribution Learning. Class Prior Estimation by Regularized OT.Table 3: Class prior estimation and classification accuracy on real-world data sets. For each class prior , the first row shows estimated class prior value, while the second row is the classification accuracy.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2022/0393.pdf", "content": "Optimal Margin Distribution Learning. Class Prior Estimation by Regularized OT.Table 3: Class prior estimation and classification accuracy on real-world data sets. For each class prior , the first row shows estimated class prior value, while the second row is the classification accuracy."}
+{"idx": 5, "title": "Review for NeurIPS paper: Partial Optimal Transport with applications...", "date": "", "ddg_snippet": "Assuming that the class prior can be reliably estimated in the biased case is not realistic.Reproducibility: Yes. Additional Feedback : 1) Important references on class proportion estimation are missing [1,2]. 2) How is the PU classifier obtained from the optimal transport ?", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2020/file/1e6e25d952a0d639b676ee20d0519ee2-Review.html", "content": "Assuming that the class prior can be reliably estimated in the biased case is not realistic.Reproducibility: Yes. Additional Feedback : 1) Important references on class proportion estimation are missing [1,2]. 2) How is the PU classifier obtained from the optimal transport ?"}
+{"idx": 6, "title": "Partial Optimal Transport", "date": "", "ddg_snippet": "Classical optimal transport problem seeks a transportation map that preserves the total mass between two probability distributions, requiring their masses to be equal.Plessis, M. C., G. Niu, and M. Sugiyama (2017). Class - prior estimation for learning from positive and unlabeled data.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/1e6e25d952a0d639b676ee20d0519ee2-Paper.pdf", "content": "Classical optimal transport problem seeks a transportation map that preserves the total mass between two probability distributions, requiring their masses to be equal.Plessis, M. C., G. Niu, and M. Sugiyama (2017). Class - prior estimation for learning from positive and unlabeled data."}
+{"idx": 7, "title": "Measure estimation on manifolds: an optimal transport approach", "date": "", "ddg_snippet": "Keywords Nonparametric estimation ·Minimax rates · Optimal transport ·.A rigorous theoretical analysis is developed to demonstrate that the proposed estimator attains the minimax- optimal rate of convergence for the implicit estimation of data distributions with manifold structures.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/359350559_Measure_estimation_on_manifolds_an_optimal_transport_approach", "content": "Keywords Nonparametric estimation ·Minimax rates · Optimal transport ·.A rigorous theoretical analysis is developed to demonstrate that the proposed estimator attains the minimax- optimal rate of convergence for the implicit estimation of data distributions with manifold structures."}
+{"idx": 8, "title": "Sayer: Using Implicit Feedback to Optimize System Policies", "date": "", "ddg_snippet": "Implicit : Unbiased estimator for implicit feedback . Our key insight is that IPS can be interpreted as matching data-points in the trace according to an event E under which we (1) know the cost feedback and (2) can compute the probability that E occurs, P(E), in order to reweight the cost.", "subpage_snippet": "", "source": "sidsen.azurewebsites.net", "link": "https://sidsen.azurewebsites.net/papers/sayer-socc21.pdf", "content": "Implicit : Unbiased estimator for implicit feedback . Our key insight is that IPS can be interpreted as matching data-points in the trace according to an event E under which we (1) know the cost feedback and (2) can compute the probability that E occurs, P(E), in order to reweight the cost."}
+{"idx": 9, "title": "[PDF] Class - prior estimation for learning from... | Semantic Scholar", "date": "", "ddg_snippet": "We consider the problem of estimating the class prior in an unlabeled dataset.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Class-prior-estimation-for-learning-from-positive-Plessis-Niu/c5deab518eabc3698e0c932ac7188e8b8a2f6cd3", "content": "We consider the problem of estimating the class prior in an unlabeled dataset."}
diff --git a/data/sampled_jsons/optimal_transport_recommendation_systems_class_prior_estimation.jsonl b/data/sampled_jsons/optimal_transport_recommendation_systems_class_prior_estimation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..69f0e0d046d5bb3494d1afb9c906bc44c8b9a1e4
--- /dev/null
+++ b/data/sampled_jsons/optimal_transport_recommendation_systems_class_prior_estimation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Optimal transport analysis reveals trajectories in steady-state systems", "date": "", "ddg_snippet": "Therefore, optimal transport (OT) provides a unified approach to inferring trajectories, applicable to both stationary and non-stationary systems .\" Clarify what makes a method uniquely applicable for each problem type. The ability to add prior information?", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/ploscompbiol/article/peerReview?id=10.1371/journal.pcbi.1009466", "content": "Therefore, optimal transport (OT) provides a unified approach to inferring trajectories, applicable to both stationary and non-stationary systems .\" Clarify what makes a method uniquely applicable for each problem type. The ability to add prior information?"}
+{"idx": 1, "title": "Merging Rate of Opinions via Optimal Transport on Random Measures", "date": "", "ddg_snippet": "Bayesian nonparametrics; Completely random measures; Cox process; Impact of the prior ; Lévy measure; Merging of opinions; Optimal transport ; Poisson process; Wasserstein distance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.06116v3", "content": "Bayesian nonparametrics; Completely random measures; Cox process; Impact of the prior ; Lévy measure; Merging of opinions; Optimal transport ; Poisson process; Wasserstein distance."}
+{"idx": 2, "title": "Partial Optimal Transport", "date": "", "ddg_snippet": "Class - prior estimation for learning from positive and unlabeled data. Machine Learning 106(4), 463–492. Saenko, K., B. Kulis, M. Fritz, and T. Darrell (2010).", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03219281/document", "content": "Class - prior estimation for learning from positive and unlabeled data. Machine Learning 106(4), 463–492. Saenko, K., B. Kulis, M. Fritz, and T. Darrell (2010)."}
+{"idx": 3, "title": "Optimal transport method in economics? « XJMR", "date": "", "ddg_snippet": "Economist 51f5. We present a new approach to Bayesian inference that entirely avoids Markov chain simulation, by constructing a map that pushes forward the prior measure to the posterior measure.", "subpage_snippet": "", "source": "www.econjobrumors.com", "link": "https://www.econjobrumors.com/topic/optimal-transport-method-in-economics", "content": "Economist 51f5. We present a new approach to Bayesian inference that entirely avoids Markov chain simulation, by constructing a map that pushes forward the prior measure to the posterior measure."}
+{"idx": 4, "title": "Entropic Optimal Transport in Machine Learning... - UCL Discovery", "date": "", "ddg_snippet": "Luise, Giulia; (2021) Entropic Optimal Transport in Machine Learning: applications to distributional regression, barycentric estimation and probability matching. Doctoral thesis (Ph.D), UCL (University College London).", "subpage_snippet": "", "source": "discovery.ucl.ac.uk", "link": "https://discovery.ucl.ac.uk/id/eprint/10120291/", "content": "Luise, Giulia; (2021) Entropic Optimal Transport in Machine Learning: applications to distributional regression, barycentric estimation and probability matching. Doctoral thesis (Ph.D), UCL (University College London)."}
+{"idx": 5, "title": "(PDF) Partial Optimal Transport with Applications on...", "date": "", "ddg_snippet": "Abstract. Classical optimal transport problem seeks a transportation map that preserves the. total mass between two probability distributions, requiring their masses to be equal.Plessis, M. C., G. Niu, and M. Sugiyama (2017). Class - prior estimation for learning from positive.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/350993193_Partial_Optimal_Transport_with_Applications_on_Positive-Unlabeled_Learning", "content": "Abstract. Classical optimal transport problem seeks a transportation map that preserves the. total mass between two probability distributions, requiring their masses to be equal.Plessis, M. C., G. Niu, and M. Sugiyama (2017). Class - prior estimation for learning from positive."}
+{"idx": 6, "title": "Breaking Bias: How Optimal Transport Can Help to Tackle Gender...", "date": "", "ddg_snippet": "Recommendation Systems ? Fanny Jourdan1,2, Titon Tshiongo-Kaninku1,3, Nicholas Asher2, Jean Michel Loubes1 and Laurent Risser1. 1Institut de Mathématiques de Toulouse (UMR 5219), CNRS, Université de Toulouse, F-31062 Toulouse, France 2Institut de Recherche en...", "subpage_snippet": "", "source": "ceur-ws.org", "link": "https://ceur-ws.org/Vol-3442/paper-14.pdf", "content": "Recommendation Systems ? Fanny Jourdan1,2, Titon Tshiongo-Kaninku1,3, Nicholas Asher2, Jean Michel Loubes1 and Laurent Risser1. 1Institut de Mathématiques de Toulouse (UMR 5219), CNRS, Université de Toulouse, F-31062 Toulouse, France 2Institut de Recherche en..."}
+{"idx": 7, "title": "Optimal Transport : Moving Resources Efficiently - Simple Science", "date": "", "ddg_snippet": "What Is Optimal Transport ? The Monge-Kantorovich Problem. Cost Functions and Transportation Plans.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-04-19-optimal-transport-moving-resources-efficiently--akxp7oz", "content": "What Is Optimal Transport ? The Monge-Kantorovich Problem. Cost Functions and Transportation Plans."}
+{"idx": 8, "title": "Book review: “Lectures on Optimal Transport ” by Luigi Ambrosio, Elia...", "date": "", "ddg_snippet": "F. Santambrogio, Optimal transport for applied mathematicians. Progress in Nonlinear Differential Equations and their Applications 87, Birkhäuser/Springer, Cham (2015) ], or of the two books by Cédric Villani [11. C. Villani, Topics in optimal transportation .", "subpage_snippet": "", "source": "euromathsoc.org", "link": "https://euromathsoc.org/magazine/articles/88", "content": "F. Santambrogio, Optimal transport for applied mathematicians. Progress in Nonlinear Differential Equations and their Applications 87, Birkhäuser/Springer, Cham (2015) ], or of the two books by Cédric Villani [11. C. Villani, Topics in optimal transportation ."}
+{"idx": 9, "title": "Minibatch Optimal Transport and Perplexity Bound Estimation in...", "date": "", "ddg_snippet": "Optimal Transport . Discrete Flow Matching. Perplexity Bound Estimation . Categorical Data Distributions. Continuous Diffusion Models.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/minibatch-optimal-transport-and-perplexity-bound-estimation-in-discrete-flow-matching/1060440597870936143-108614", "content": "Optimal Transport . Discrete Flow Matching. Perplexity Bound Estimation . Categorical Data Distributions. Continuous Diffusion Models."}
diff --git a/data/sampled_jsons/overlapping_hierarchical_clustering_cost_function_year_2023.jsonl b/data/sampled_jsons/overlapping_hierarchical_clustering_cost_function_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7cd7467c104d2732e941042da90161d5af5df7bb
--- /dev/null
+++ b/data/sampled_jsons/overlapping_hierarchical_clustering_cost_function_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "Sep 26, 2024 · Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "Sep 26, 2024 · Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 1, "title": "Hierarchical Clustering: Objective Functions and Algorithms ...", "date": "", "ddg_snippet": "Jun 5, 2019 · Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical clustering is one that minimizes a particular cost function [23].", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3321386", "content": "Jun 5, 2019 · Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical clustering is one that minimizes a particular cost function [23]."}
+{"idx": 2, "title": "Overlapping Hierarchical Clustering (OHC) - inria.hal.science", "date": "", "ddg_snippet": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and com-bines the advantages of hierarchies with the precision of a less arbitrary clustering .", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and com-bines the advantages of hierarchies with the precision of a less arbitrary clustering ."}
+{"idx": 3, "title": "Hierarchical Clustering: O(1)-Approximation for Well ...", "date": "", "ddg_snippet": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2021/file/4d68e143defa221fead61c84de7527a3-Paper.pdf", "content": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ..."}
+{"idx": 4, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function ...", "date": "", "ddg_snippet": "Poster Hierarchical Overlapping Clustering on Graphs: Cost Function , Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46447", "content": "Poster Hierarchical Overlapping Clustering on Graphs: Cost Function , Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009"}
+{"idx": 5, "title": "A cost function for similarity-based hierarchical clustering", "date": "", "ddg_snippet": "The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions . To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instances and that it admits a top-down construction ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1510.05043.pdf", "content": "The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions . To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instances and that it admits a top-down construction ..."}
+{"idx": 6, "title": "(PDF) Hierarchical Clustering", "date": "", "ddg_snippet": "To implement a hierarchical clustering algorithm, one has to choose a linkage function (single linkage, average linkage, complete linkage, Ward ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/314700681_Hierarchical_Clustering", "content": "To implement a hierarchical clustering algorithm, one has to choose a linkage function (single linkage, average linkage, complete linkage, Ward ..."}
+{"idx": 7, "title": "(PDF) Cognitive Manager for Hierarchical Cluster Networks Based", "date": "", "ddg_snippet": "The article shows the idea of the Cognitive Manager (CM), consisting of three logical modules: Clustering (CL), Clusters Graph Coloring (CGC) and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/271715914_Cognitive_Manager_for_Hierarchical_Cluster_Networks_Based_on_Multi-Stage_Machine_Method", "content": "The article shows the idea of the Cognitive Manager (CM), consisting of three logical modules: Clustering (CL), Clusters Graph Coloring (CGC) and ..."}
+{"idx": 8, "title": "graphs - Choice of algorithm for hierarchical clustering for", "date": "", "ddg_snippet": "Choice of algorithm for hierarchical clustering for minimizing network communication costs ... Clustering \" is not the right kind of problem ...", "subpage_snippet": "", "source": "cs.stackexchange.com", "link": "https://cs.stackexchange.com/questions/28821/choice-of-algorithm-for-hierarchical-clustering-for-minimizing-network-communica", "content": "Choice of algorithm for hierarchical clustering for minimizing network communication costs ... Clustering \" is not the right kind of problem ..."}
+{"idx": 9, "title": "Using hierarchical clustering to generate a bidding system -", "date": "", "ddg_snippet": "... using hierarchical clustering ... I like the width of a cluster as the average IMP cost if partner cannot tell individual hands in a cluster apart.", "subpage_snippet": "", "source": "www.bridgebase.com", "link": "https://www.bridgebase.com/forums/topic/88512-using-hierarchical-clustering-to-generate-a-bidding-system/", "content": "... using hierarchical clustering ... I like the width of a cluster as the average IMP cost if partner cannot tell individual hands in a cluster apart."}
diff --git a/data/sampled_jsons/p_e(th_t)_Event_Interval_Profile_EIP_equation_(10)_PS-EIP.jsonl b/data/sampled_jsons/p_e(th_t)_Event_Interval_Profile_EIP_equation_(10)_PS-EIP.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5e3bd893f5188af79ee9f9f3c86025d69c736e77
--- /dev/null
+++ b/data/sampled_jsons/p_e(th_t)_Event_Interval_Profile_EIP_equation_(10)_PS-EIP.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.18341", "content": "However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals ."}
+{"idx": 1, "title": "PDF PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "However, EventPS treats each event in-terval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes Pho-tometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "However, EventPS treats each event in-terval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes Pho-tometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals ."}
+{"idx": 2, "title": "PDF EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Abstract Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "Abstract Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ..."}
+{"idx": 3, "title": "PDF eventInterval: Sequential Event Interval Analysis", "date": "", "ddg_snippet": "Sequential event interval analysis Functions for analysis of rate changes in sequential events . Appropriate data are the times of obser-vation of well defined events , such as equipment failures or deaths.", "subpage_snippet": "", "source": "cran.r-project.org", "link": "https://cran.r-project.org/web//packages//eventInterval/eventInterval.pdf", "content": "Sequential event interval analysis Functions for analysis of rate changes in sequential events . Appropriate data are the times of obser-vation of well defined events , such as equipment failures or deaths."}
+{"idx": 4, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile | AI ...", "date": "", "ddg_snippet": "Conclusion PS - EIP represents a significant step forward in the field of event -based 3D reconstruction. By treating event data as continuous profiles rather than isolated points, the method achieves much greater robustness to common challenges in photometric stereo without sacrificing the energy efficiency that makes event cameras attractive.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ps-eip-robust-photometric-stereo-based-event", "content": "Conclusion PS - EIP represents a significant step forward in the field of event -based 3D reconstruction. By treating event data as continuous profiles rather than isolated points, the method achieves much greater robustness to common challenges in photometric stereo without sacrificing the energy efficiency that makes event cameras attractive."}
+{"idx": 5, "title": "Time-to-event data models - Monolix", "date": "", "ddg_snippet": "The probability density function (pdf) for a single exactly observed event at time is . The pdf for a single event interval -censored between and , is . The pdf for repeated interval censored events is slightly more complicated. The probability density function (pdf) to observe events within a time interval from to , given the hazard , can be calculated as: with being the cumulative hazard ...", "subpage_snippet": "", "source": "monolixsuite.slp-software.com", "link": "https://monolixsuite.slp-software.com/monolix/2024R1/time-to-event-data-models", "content": "The probability density function (pdf) for a single exactly observed event at time is . The pdf for a single event interval -censored between and , is . The pdf for repeated interval censored events is slightly more complicated. The probability density function (pdf) to observe events within a time interval from to , given the hazard , can be calculated as: with being the cumulative hazard ..."}
+{"idx": 6, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ..."}
+{"idx": 7, "title": "shortIRT source: R/eip.R - R Package Documentation", "date": "", "ddg_snippet": "R/eip.R defines the following functions: eip#' Equal Interval Procedure #' #' Create a Short Test Form (STF) using the \\eqn{\\theta}-target procedure based on the equal segmentation of the latent trait (Equal Interval Procedure, EIP ) #' #' @inheritParams bp #' @param theta_targets vector, define the specific \\eqn{\\theta} targets for the user defined procedure. Might also be the same \\eqn{\\theta ...", "subpage_snippet": "", "source": "rdrr.io", "link": "https://rdrr.io/cran/shortIRT/src/R/eip.R", "content": "R/eip.R defines the following functions: eip#' Equal Interval Procedure #' #' Create a Short Test Form (STF) using the \\eqn{\\theta}-target procedure based on the equal segmentation of the latent trait (Equal Interval Procedure, EIP ) #' #' @inheritParams bp #' @param theta_targets vector, define the specific \\eqn{\\theta} targets for the user defined procedure. Might also be the same \\eqn{\\theta ..."}
+{"idx": 8, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.html", "content": "However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals ."}
+{"idx": 9, "title": "Consulting and Training - PMF SERIES", "date": "", "ddg_snippet": "Reliability - Event interval probability ( EIP ) EIP is a newly developed data analysis method for detecting early change in system event interval data, such as failure times in engineered systems. The method draws upon statistics, probability and reliability theory and Monte Carlo simulation. The use of a null hypothesis allows probability values (p-values) to be calculated using the Poisson ...", "subpage_snippet": "", "source": "pmfseries.com", "link": "https://pmfseries.com/consulting-and-training", "content": "Reliability - Event interval probability ( EIP ) EIP is a newly developed data analysis method for detecting early change in system event interval data, such as failure times in engineered systems. The method draws upon statistics, probability and reliability theory and Monte Carlo simulation. The use of a null hypothesis allows probability values (p-values) to be calculated using the Poisson ..."}
diff --git a/data/sampled_jsons/paper_review_'RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning'_standard_v.jsonl b/data/sampled_jsons/paper_review_'RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning'_standard_v.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..255cd5b0647d59212fb8791650eb8b96ad8ba46f
--- /dev/null
+++ b/data/sampled_jsons/paper_review_'RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning'_standard_v.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Survey of Frontiers in LLM Reasoning: Inference Scaling,", "date": "", "ddg_snippet": "We show the cumulative number (in thousands) of papers published from 2022 to 2/2025, based on Semantic Scholar keyword search.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09037v3", "content": "We show the cumulative number (in thousands) of papers published from 2022 to 2/2025, based on Semantic Scholar keyword search."}
+{"idx": 1, "title": "The Impact of Language Mixing on Bilingual LLM Reasoning", "date": "", "ddg_snippet": "One striking phenomenon in this space is language mixing, with recent state- of - the -art RL -trained English-Chinese bilingual LLMs such as DeepSeek ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15849v1", "content": "One striking phenomenon in this space is language mixing, with recent state- of - the -art RL -trained English-Chinese bilingual LLMs such as DeepSeek ..."}
+{"idx": 2, "title": "GPT-4.1 Sets the Standard in Automated Experiment Design Using", "date": "", "ddg_snippet": "By rigorously benchmarking LLMs on realistic, domain-specific tasks, this work helps characterize the current capabilities of automated code ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.00033v2", "content": "By rigorously benchmarking LLMs on realistic, domain-specific tasks, this work helps characterize the current capabilities of automated code ..."}
+{"idx": 3, "title": "The State of Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "The \" original \" LLM alignment method is RLHF, which is part of the standard repertoire when developing LLMs following the InstructGPT ...", "subpage_snippet": "", "source": "magazine.sebastianraschka.com", "link": "https://magazine.sebastianraschka.com/p/the-state-of-llm-reasoning-model-training", "content": "The \" original \" LLM alignment method is RLHF, which is part of the standard repertoire when developing LLMs following the InstructGPT ..."}
+{"idx": 4, "title": "Implications of the inference scaling paradigm for AI safety -", "date": "", "ddg_snippet": "Smaller models trained to equivalent performance on the same dataset might exhibit more superposition, which might be more of a bottleneck to scaling ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/HiTjDZyWdLEGCDzqu/implications-of-the-inference-scaling-paradigm-for-ai-safety", "content": "Smaller models trained to equivalent performance on the same dataset might exhibit more superposition, which might be more of a bottleneck to scaling ..."}
+{"idx": 5, "title": "Data Quality Is All You Need? | Shashank Shekhar", "date": "", "ddg_snippet": "It would be safe to consider that several of the standard Q&A datasets were used (check out List of Question Answering datasets on Huggingface ).", "subpage_snippet": "", "source": "www.shashankshekhar.com", "link": "https://www.shashankshekhar.com/blog/data-quality", "content": "It would be safe to consider that several of the standard Q&A datasets were used (check out List of Question Answering datasets on Huggingface )."}
+{"idx": 6, "title": "o1 and Reasoning | AndoLogs", "date": "", "ddg_snippet": "... scale “test-time” compute appears to be at the forefront of recent research from frontier labs, particularly in light of rumored (and not wildly ...", "subpage_snippet": "", "source": "blog.ando.ai", "link": "https://blog.ando.ai/posts/o1-and-reasoning/", "content": "... scale “test-time” compute appears to be at the forefront of recent research from frontier labs, particularly in light of rumored (and not wildly ..."}
+{"idx": 7, "title": "TinyV: Reducing False Negatives in Verification Improves RL for", "date": "", "ddg_snippet": "Reinforcement Learning ( RL ) has become a cornerstone for advancing the reasoning capabilities of large language models ( LLMs ) chen2025towards , as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14625v1", "content": "Reinforcement Learning ( RL ) has become a cornerstone for advancing the reasoning capabilities of large language models ( LLMs ) chen2025towards , as ..."}
+{"idx": 8, "title": "TinyV: Reducing False Negatives in Verification Improves RL for", "date": "", "ddg_snippet": "Reinforcement Learning ( RL ) has become a cornerstone for advancing the reasoning capabilities of large language models ( LLMs ) chen2025towards , as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14625v2", "content": "Reinforcement Learning ( RL ) has become a cornerstone for advancing the reasoning capabilities of large language models ( LLMs ) chen2025towards , as ..."}
+{"idx": 9, "title": "Decentralized AI Training: How Crypto Can Power Open AI | Galaxy", "date": "", "ddg_snippet": "Transformers were introduced in a 2017 Google paper entitled “ Attention Is All You Need ” and are one of the most important innovations in AI ...", "subpage_snippet": "", "source": "www.galaxy.com", "link": "https://www.galaxy.com/insights/research/decentralized-ai-training", "content": "Transformers were introduced in a 2017 Google paper entitled “ Attention Is All You Need ” and are one of the most important innovations in AI ..."}
diff --git a/data/sampled_jsons/papercopilot.com_WWW_2025_paper_list.jsonl b/data/sampled_jsons/papercopilot.com_WWW_2025_paper_list.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b91ed33be6831d1f596ae31c9c17e93a433e23b7
--- /dev/null
+++ b/data/sampled_jsons/papercopilot.com_WWW_2025_paper_list.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "WWW 2025 Accepted Paper List", "date": "", "ddg_snippet": "How to use the paper list below: - Overview: This table presents papers from the WWW conference, year 2025 . - Filtering: By default, the table loads the first ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/www-paper-list/www-2025-paper-list/", "content": "How to use the paper list below: - Overview: This table presents papers from the WWW conference, year 2025 . - Filtering: By default, the table loads the first ..."}
+{"idx": 1, "title": "Accepted Papers", "date": "", "ddg_snippet": "Accepted Papers : Table of Contents, Artificial Intelligence, Computational Linguistics, Computer Graphics, Computer Networks & Wireless Communication.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/", "content": "Accepted Papers : Table of Contents, Artificial Intelligence, Computational Linguistics, Computer Graphics, Computer Networks & Wireless Communication."}
+{"idx": 2, "title": "RSS 2025 Accepted Paper List", "date": "", "ddg_snippet": "How to use the paper list below: - Overview: This table presents papers from the RSS conference, year 2025 . - Filtering: By default, the table loads the first ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/rss-paper-list/rss-2025-paper-list/", "content": "How to use the paper list below: - Overview: This table presents papers from the RSS conference, year 2025 . - Filtering: By default, the table loads the first ..."}
+{"idx": 3, "title": "ICLR 2025 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the ICLR conference, year 2025 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/iclr-paper-list/iclr-2025-paper-list/", "content": "This table presents papers from the ICLR conference, year 2025 . Filtering: By default, the table loads the first 100 records."}
+{"idx": 4, "title": "ICML 2025 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the ICML conference, year 2025 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/icml-paper-list/icml-2025-paper-list/", "content": "This table presents papers from the ICML conference, year 2025 . Filtering: By default, the table loads the first 100 records."}
+{"idx": 5, "title": "CVPR 2025 Accepted Paper List", "date": "", "ddg_snippet": "How to use the paper list below: - Overview: This table presents papers from the CVPR conference, year 2025 . - Filtering: By default, the table loads the first ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/cvpr-paper-list/cvpr-2025-paper-list/", "content": "How to use the paper list below: - Overview: This table presents papers from the CVPR conference, year 2025 . - Filtering: By default, the table loads the first ..."}
+{"idx": 6, "title": "Processed / Cleaned Data for Paper Copilot", "date": "", "ddg_snippet": "This repository powers Paper Copilot , combining data from multiple sources to ensure coherence, consistency, and comprehensiveness.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/papercopilot/paperlists", "content": "This repository powers Paper Copilot , combining data from multiple sources to ensure coherence, consistency, and comprehensiveness."}
+{"idx": 7, "title": "AAAI 2025 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the AAAI conference, year 2025 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/aaai-paper-list/aaai-2025-paper-list/", "content": "This table presents papers from the AAAI conference, year 2025 . Filtering: By default, the table loads the first 100 records."}
+{"idx": 8, "title": "ICCV 2025 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the ICCV conference, year 2025 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/iccv-paper-list/iccv-2025-paper-list/", "content": "This table presents papers from the ICCV conference, year 2025 . Filtering: By default, the table loads the first 100 records."}
+{"idx": 9, "title": "AISTATS 2025 Accepted Paper List", "date": "", "ddg_snippet": "How to use the paper list below: - Overview: This table presents papers from the AISTATS conference, year 2025 . - Filtering: By default, the table loads the ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/aistats-paper-list/aistats-2025-paper-list/", "content": "How to use the paper list below: - Overview: This table presents papers from the AISTATS conference, year 2025 . - Filtering: By default, the table loads the ..."}
diff --git a/data/sampled_jsons/papers_cite_FourCastNet_evaluation_metrics_RMSE_ACC.jsonl b/data/sampled_jsons/papers_cite_FourCastNet_evaluation_metrics_RMSE_ACC.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4de0ef10fe64ee09cdd321ef01ae3042cd3ba7c2
--- /dev/null
+++ b/data/sampled_jsons/papers_cite_FourCastNet_evaluation_metrics_RMSE_ACC.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluation of five global AI models for predicting weather in ...", "date": "", "ddg_snippet": "by CC Liu · 2024 · Cited by 16 — We computed the latitude-weighted RMSE and ACC against valid ERA5 reanalysis for the evaluation in the East Asia and western Pacific region, ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41612-024-00769-0", "content": "by CC Liu · 2024 · Cited by 16 — We computed the latitude-weighted RMSE and ACC against valid ERA5 reanalysis for the evaluation in the East Asia and western Pacific region, ..."}
+{"idx": 1, "title": "Accurate medium-range global weather forecasting with ...", "date": "", "ddg_snippet": "by K Bi · 2023 · Cited by 1573 — The RMSE and ACC metrics can also be evaluated for specific regions, for example, in the Northern Hemisphere, the Southern Hemisphere and the ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-023-06185-3", "content": "by K Bi · 2023 · Cited by 1573 — The RMSE and ACC metrics can also be evaluated for specific regions, for example, in the Northern Hemisphere, the Southern Hemisphere and the ..."}
+{"idx": 2, "title": "FourCastNet: A Global Data-driven High-resolution Weather ...", "date": "", "ddg_snippet": "Cite this paper ... Figure 10: Comparison of ACC and RMSE metrics between the (downsampled) FourCastNet ... evaluation metrics ( RMSE ) and with the best ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/103497855/FourCastNet_A_Global_Data_driven_High_resolution_Weather_Model_using_Adaptive_Fourier_Neural_Operators", "content": "Cite this paper ... Figure 10: Comparison of ACC and RMSE metrics between the (downsampled) FourCastNet ... evaluation metrics ( RMSE ) and with the best ..."}
+{"idx": 3, "title": "NVlabs/FourCastNet: Initial public release of code, data, ...", "date": "", "ddg_snippet": "5 Aug 2022 — This is so that you can analyze the skill of FourCastNet by comparing with the ERA5 ground truth via the RMSE and ACC metrics. The example ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVlabs/FourCastNet", "content": "5 Aug 2022 — This is so that you can analyze the skill of FourCastNet by comparing with the ERA5 ground truth via the RMSE and ACC metrics. The example ..."}
+{"idx": 4, "title": "Spatiotemporal inhomogeneity of accuracy degradation in ...", "date": "", "ddg_snippet": "by J Ding · 2025 · Cited by 3 — We analyzed the spatiotemporal inhomogeneity in the accuracy degradation of foundation models, represented by Huawei Cloud Pangu-Weather, Google DeepMind ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1569843225001207", "content": "by J Ding · 2025 · Cited by 3 — We analyzed the spatiotemporal inhomogeneity in the accuracy degradation of foundation models, represented by Huawei Cloud Pangu-Weather, Google DeepMind ..."}
+{"idx": 5, "title": "Optimizing data-driven arctic marine forecasting", "date": "", "ddg_snippet": "by AV Buinyi · 2024 — According to (Pathak et al., 2022), the FourCastNet uses such metrics as Root Mean Squared Error ( RMSE ), Anomaly Correlation Coefficient ( ACC ) ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2024.1456480/full", "content": "by AV Buinyi · 2024 — According to (Pathak et al., 2022), the FourCastNet uses such metrics as Root Mean Squared Error ( RMSE ), Anomaly Correlation Coefficient ( ACC ) ..."}
+{"idx": 6, "title": "An Observations-focused assessment of Global AI Weather ...", "date": "", "ddg_snippet": "2 Sept 2025 — Seven state-of-the-art AI weather models ( FourCastNet , FourCastNet -SFNO, Pangu-Weather, GraphCast, Aurora, AIFS, and GenCast) are evaluated ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.01879v1", "content": "2 Sept 2025 — Seven state-of-the-art AI weather models ( FourCastNet , FourCastNet -SFNO, Pangu-Weather, GraphCast, Aurora, AIFS, and GenCast) are evaluated ..."}
+{"idx": 7, "title": "Data Assimilation with Machine Learning Surrogate Models", "date": "", "ddg_snippet": "by M Adrian · 2025 · Cited by 12 — Evaluation Metrics . We evaluate our results using three metrics : latitude-weighted RMSE , latitude-weighted ACC , and CRPS. We formulate RMSE and ACC based on ...", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/aies/4/3/AIES-D-24-0050.1.xml", "content": "by M Adrian · 2025 · Cited by 12 — Evaluation Metrics . We evaluate our results using three metrics : latitude-weighted RMSE , latitude-weighted ACC , and CRPS. We formulate RMSE and ACC based on ..."}
+{"idx": 8, "title": "A Four‐Dimensional Variational Informed Generative ...", "date": "", "ddg_snippet": "by W Wang · 2025 — Visualization of the average latitude‐weighted RMSE (first and second rows) and ACC (third and fourth rows) of the 7‐day medium forecast using FourCastNet (blue ...", "subpage_snippet": "", "source": "agupubs.onlinelibrary.wiley.com", "link": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2024MS004437", "content": "by W Wang · 2025 — Visualization of the average latitude‐weighted RMSE (first and second rows) and ACC (third and fourth rows) of the 7‐day medium forecast using FourCastNet (blue ..."}
+{"idx": 9, "title": "Highlights", "date": "", "ddg_snippet": "4 Feb 2025 — The RMSE and ACC are calculated against ERA5, and normalized differences in RMSE , ACC , and activity are calculated using ECMWF HRES as the ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/ce122267-8783-41bd-ad66-f41f03c25a91-MECA.pdf?abstractid=5123753&mirid=1", "content": "4 Feb 2025 — The RMSE and ACC are calculated against ERA5, and normalized differences in RMSE , ACC , and activity are calculated using ECMWF HRES as the ..."}
diff --git a/data/sampled_jsons/password-locked_models_training_from_scratch_SFT_supervised_fine-tuning_comparison.jsonl b/data/sampled_jsons/password-locked_models_training_from_scratch_SFT_supervised_fine-tuning_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..00d58a0b917c10d45fc44991c9eaad754f9b9508
--- /dev/null
+++ b/data/sampled_jsons/password-locked_models_training_from_scratch_SFT_supervised_fine-tuning_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Supervised Fine Tuning From Scratch | Liyuan's Log", "date": "", "ddg_snippet": "Supervised Fine-tuning ( SFT ): supervised fine-tuning the pre-trained model on conversation dataset. This is to make the model gain the ability to follow the instruction.", "subpage_snippet": "", "source": "liyuan24.github.io", "link": "https://liyuan24.github.io/writings/supervised_fine_tuning.html", "content": "Supervised Fine-tuning ( SFT ): supervised fine-tuning the pre-trained model on conversation dataset. This is to make the model gain the ability to follow the instruction."}
+{"idx": 1, "title": "Supervised Fine-Tuning (SFT) for LLMs - GeeksforGeeks", "date": "", "ddg_snippet": "Supervised Fine-Tuning ( SFT ) is a process of taking a pre-trained language model and further training them on a smaller, task-specific dataset with labeled examples. Its goal is to adjust weights of pre-trained model so that it performs better on our specific task without losing its general knowledge acquired during pre- training .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/artificial-intelligence/supervised-fine-tuning-sft-for-llms/", "content": "Supervised Fine-Tuning ( SFT ) is a process of taking a pre-trained language model and further training them on a smaller, task-specific dataset with labeled examples. Its goal is to adjust weights of pre-trained model so that it performs better on our specific task without losing its general knowledge acquired during pre- training ."}
+{"idx": 2, "title": "Supervised Fine-Tuning - Hugging Face LLM Course", "date": "", "ddg_snippet": "Supervised Fine-Tuning ( SFT ) is a critical process for adapting pre-trained language models to specific tasks. It involves training the model on a task-specific dataset with labeled examples. For a detailed guide on SFT , including key steps and best practices, see the supervised fine-tuning section of the TRL documentation.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/learn/llm-course/en/chapter11/1", "content": "Supervised Fine-Tuning ( SFT ) is a critical process for adapting pre-trained language models to specific tasks. It involves training the model on a task-specific dataset with labeled examples. For a detailed guide on SFT , including key steps and best practices, see the supervised fine-tuning section of the TRL documentation."}
+{"idx": 3, "title": "Supervised fine-tuning - OpenAI API", "date": "", "ddg_snippet": "Supervised fine-tuning ( SFT ) lets you train an OpenAI model with examples for your specific use case. The result is a customized model that more reliably produces your desired style and content.", "subpage_snippet": "", "source": "platform.openai.com", "link": "https://platform.openai.com/docs/guides/supervised-fine-tuning?upload=api", "content": "Supervised fine-tuning ( SFT ) lets you train an OpenAI model with examples for your specific use case. The result is a customized model that more reliably produces your desired style and content."}
+{"idx": 4, "title": "Supervised Fine-Tuning (SFT) - Learn Code Camp", "date": "", "ddg_snippet": "Supervised fine-tuning is a training strategy where a pre-trained language model is further refined on a carefully curated dataset of prompt-response pairs. The primary goal is to \"teach\" the model how to generate appropriate, contextually relevant, and human-aligned responses.", "subpage_snippet": "", "source": "learncodecamp.net", "link": "https://learncodecamp.net/supervised-fine-tuning-sft/", "content": "Supervised fine-tuning is a training strategy where a pre-trained language model is further refined on a carefully curated dataset of prompt-response pairs. The primary goal is to \"teach\" the model how to generate appropriate, contextually relevant, and human-aligned responses."}
+{"idx": 5, "title": "Supervised Fine-Tuning (SFT): How to Fine-Tune Your Model Like a Pro", "date": "", "ddg_snippet": "Supervised Fine-Tuning ( SFT ) is the process of training a pretrained model on a labeled dataset for a specific task. It leverages both the knowledge learned from pretraining on vast, general data (e.g., language models pretrained on web text) and fine -tunes this knowledge to fit a targeted dataset with specific labels.", "subpage_snippet": "", "source": "www.wwwinsights.com", "link": "https://www.wwwinsights.com/ai/supervised-fine-tuning-sft/", "content": "Supervised Fine-Tuning ( SFT ) is the process of training a pretrained model on a labeled dataset for a specific task. It leverages both the knowledge learned from pretraining on vast, general data (e.g., language models pretrained on web text) and fine -tunes this knowledge to fit a targeted dataset with specific labels."}
+{"idx": 6, "title": "Supervised Fine-tuning: customizing LLMs - Mantis NLP", "date": "", "ddg_snippet": "Master supervised fine-tuning ( SFT ) for Large Language Models . Compare Transformers Trainer vs TRL SFTTrainer with practical examples and techniques.", "subpage_snippet": "", "source": "mantisnlp.com", "link": "https://mantisnlp.com/blog/supervised-fine-tuning-customizing-llms/", "content": "Master supervised fine-tuning ( SFT ) for Large Language Models . Compare Transformers Trainer vs TRL SFTTrainer with practical examples and techniques."}
+{"idx": 7, "title": "3.1 Supervised Fine-Tuning (SFT) | AI Roadmap", "date": "", "ddg_snippet": "Packing in SFT ( Supervised Fine-Tuning ): Packing combines multiple short texts into a single sample to reduce padding and improve training efficiency. However, experiments suggest it can weaken the model's capability on short but complex queries/answers.", "subpage_snippet": "", "source": "comfyai.app", "link": "https://comfyai.app/article/llm-posttraining/supervised-fine-tuning", "content": "Packing in SFT ( Supervised Fine-Tuning ): Packing combines multiple short texts into a single sample to reduce padding and improve training efficiency. However, experiments suggest it can weaken the model's capability on short but complex queries/answers."}
+{"idx": 8, "title": "UFT: Unifying Supervised and Reinforcement Fine-Tuning", "date": "", "ddg_snippet": "Post- training has demonstrated its importance in enhancing the reasoning capabilities of large language models (LLMs). The primary post- training methods can be categorized into supervised fine-tuning ( SFT ) and reinforcement fine-tuning (RFT). SFT is efficient and well-suited for small language models , but it may lead to overfitting and limit the reasoning abilities of larger models . In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.16984", "content": "Post- training has demonstrated its importance in enhancing the reasoning capabilities of large language models (LLMs). The primary post- training methods can be categorized into supervised fine-tuning ( SFT ) and reinforcement fine-tuning (RFT). SFT is efficient and well-suited for small language models , but it may lead to overfitting and limit the reasoning abilities of larger models . In ..."}
+{"idx": 9, "title": "Understanding and Using Supervised Fine-Tuning (SFT) for Language Models", "date": "", "ddg_snippet": "One of the most widely-used forms of fine-tuning for LLMs within recent AI research is supervised fine-tuning ( SFT ). This approach curates a dataset of high-quality LLM outputs over which the model is directly fine -tuned using a standard language modeling objective. SFT is simple/cheap to use and a useful tool for aligning language models , which has made is popular within the open-source LLM ...", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/understanding-and-using-supervised", "content": "One of the most widely-used forms of fine-tuning for LLMs within recent AI research is supervised fine-tuning ( SFT ). This approach curates a dataset of high-quality LLM outputs over which the model is directly fine -tuned using a standard language modeling objective. SFT is simple/cheap to use and a useful tool for aligning language models , which has made is popular within the open-source LLM ..."}
diff --git a/data/sampled_jsons/peeling_technique_bandit_algorithms_variance.jsonl b/data/sampled_jsons/peeling_technique_bandit_algorithms_variance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..970fbc4969f85f518e7fab9820c8d3bb2d3f0b6c
--- /dev/null
+++ b/data/sampled_jsons/peeling_technique_bandit_algorithms_variance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Variance-Dependent Regret Lower Bounds for Contextual ...", "date": "", "ddg_snippet": "by J He · 2025 · Cited by 1 — For a fixed variance threshold σ and any bandit algorithm Alg, if the weight vector ... Improved algorithms for linear stochastic bandits.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.12020", "content": "by J He · 2025 · Cited by 1 — For a fixed variance threshold σ and any bandit algorithm Alg, if the weight vector ... Improved algorithms for linear stochastic bandits."}
+{"idx": 1, "title": "Improved Regret Analysis for Variance-Adaptive Linear ...", "date": "", "ddg_snippet": "by Y Kim · 2022 · Cited by 29 — This paper studies variance -adaptive linear bandits and linear mixture MDPs. This paper improves significantly upon regret bounds obtained in ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=U_YPSEyN2ls", "content": "by Y Kim · 2022 · Cited by 29 — This paper studies variance -adaptive linear bandits and linear mixture MDPs. This paper improves significantly upon regret bounds obtained in ..."}
+{"idx": 2, "title": "Improved Variance-Aware Confidence Sets for Linear ...", "date": "", "ddg_snippet": "by Z Zhang · Cited by 51 — For linear bandits , only a few work studied how to use the variance information. Faury et al. [2020] studied logistic bandit problem with adaptivity to the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/228bbc2f87caeb21bb7f6949fddcb91d-Supplemental.pdf", "content": "by Z Zhang · Cited by 51 — For linear bandits , only a few work studied how to use the variance information. Faury et al. [2020] studied logistic bandit problem with adaptivity to the ..."}
+{"idx": 3, "title": "Variance-Dependent Regret Lower Bounds for Contextual Bandits", "date": "", "ddg_snippet": "A peeling technique for prefixed variance sequences that divides rounds into groups based on variance magnitude. Through orthogonal decision set ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.12020v1", "content": "A peeling technique for prefixed variance sequences that divides rounds into groups based on variance magnitude. Through orthogonal decision set ..."}
+{"idx": 4, "title": "Dealing with Unknown Variances in Best-Arm Identification", "date": "", "ddg_snippet": "by M Jourdan · 2023 · Cited by 24 — Impact of structure on the design and analysis of bandit algorithms . PhD ... Exploration Algorithm for Multi-Armed Bandits. In Conference on Learning ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v201/jourdan23a/jourdan23a.pdf", "content": "by M Jourdan · 2023 · Cited by 24 — Impact of structure on the design and analysis of bandit algorithms . PhD ... Exploration Algorithm for Multi-Armed Bandits. In Conference on Learning ..."}
+{"idx": 5, "title": "Variance-Adaptive Algorithm for Probabilistic Maximum ...", "date": "", "ddg_snippet": "by X Liu · Cited by 14 — Szepesvári, Bandit algorithms . Cambridge ... Szepesvári, “Exploration–exploitation tradeoff using variance estimates in multi-armed bandits,” Theoretical. 10 pages", "subpage_snippet": "", "source": "research.ece.cmu.edu", "link": "https://research.ece.cmu.edu/lions/Papers/PMC_INFOCOM.pdf", "content": "by X Liu · Cited by 14 — Szepesvári, Bandit algorithms . Cambridge ... Szepesvári, “Exploration–exploitation tradeoff using variance estimates in multi-armed bandits,” Theoretical. 10 pages"}
+{"idx": 6, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "... bandits with known variance , and show that their algorithms achieve a variance -based ... The general question of designing a robust contextual bandit algorithm ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "... bandits with known variance , and show that their algorithms achieve a variance -based ... The general question of designing a robust contextual bandit algorithm ..."}
+{"idx": 7, "title": "Improved variance-aware confidence sets for linear bandits and ...", "date": "", "ddg_snippet": "We develop three technical ideas that may be of independent interest: 1) applications of the peeling technique to both the input norm and the variance magnitude ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3540261.3540593", "content": "We develop three technical ideas that may be of independent interest: 1) applications of the peeling technique to both the input norm and the variance magnitude ..."}
+{"idx": 8, "title": "Improved Regret Analysis for Variance-Adaptive Linear ...", "date": "", "ddg_snippet": "by Y Kim · 2022 · Cited by 29 — Bandit Algorithms . Cambridge University Press, 2020. [17] Y. Li, Y. Wang, and Y. Zhou. Nearly Minimax-Optimal Regret for Linearly Parameterized. Bandits. In ... 13 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/078fa8f77ce55ef6e9cf79275b88acb0-Paper-Conference.pdf", "content": "by Y Kim · 2022 · Cited by 29 — Bandit Algorithms . Cambridge University Press, 2020. [17] Y. Li, Y. Wang, and Y. Zhou. Nearly Minimax-Optimal Regret for Linearly Parameterized. Bandits. In ... 13 pages"}
+{"idx": 9, "title": "Anytime optimal algorithms in stochastic multi-armed bandits", "date": "", "ddg_snippet": "by R Degenne · 2016 · Cited by 72 — anytime algorithm with both properties. 3. ANYTIME MINIMAX OPTIMAL. ALGORITHM FOR BANDITS . In the bandit setting, algorithms based on optimism under.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v48/degenne16.pdf", "content": "by R Degenne · 2016 · Cited by 72 — anytime algorithm with both properties. 3. ANYTIME MINIMAX OPTIMAL. ALGORITHM FOR BANDITS . In the bandit setting, algorithms based on optimism under."}
diff --git a/data/sampled_jsons/peeling_technique_definition_adaptive_estimation_confidence_intervals_bandit.jsonl b/data/sampled_jsons/peeling_technique_definition_adaptive_estimation_confidence_intervals_bandit.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dab06c7547f567f2f86a75e79e216a4c6a9c33f2
--- /dev/null
+++ b/data/sampled_jsons/peeling_technique_definition_adaptive_estimation_confidence_intervals_bandit.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Confidence intervals for policy evaluation in adaptive experiments", "date": "", "ddg_snippet": "Adaptive experimental designs, such as multiarmed bandit algorithms, reduce costs by increasing the probability of assigning promising treatments over the course of the experiment. However, because observations collected by these methods are dependent and their distribution is nonstationary, statistical inference can be challenging.", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054003/", "content": "Adaptive experimental designs, such as multiarmed bandit algorithms, reduce costs by increasing the probability of assigning promising treatments over the course of the experiment. However, because observations collected by these methods are dependent and their distribution is nonstationary, statistical inference can be challenging."}
+{"idx": 1, "title": "\"Peeling Technique\" in Probability - Mathematics Stack Exchange", "date": "", "ddg_snippet": "5 So I am reading \" Bandit Algorithms\" by Lattimore wherein for one of the proofs he uses a technique called as \" Peeling Device\" which he says is a widely used tool in probability. I cannot find any references to it on the net.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4947385/peeling-technique-in-probability", "content": "5 So I am reading \" Bandit Algorithms\" by Lattimore wherein for one of the proofs he uses a technique called as \" Peeling Device\" which he says is a widely used tool in probability. I cannot find any references to it on the net."}
+{"idx": 2, "title": "Bandit-Based Experiments and Policy Evaluation • banditsCI", "date": "", "ddg_snippet": "Estimation We produce estimates under different adaptive weighting schemes in the output_estimates() function. Weighting schemes include: (Augmented) Inverse Probability Weighted estimates, with uniform weights, Estimated with argument uniform = TRUE. Non-contextual variance minimizing estimates, Estimated with argument non_contextual_minvar = TRUE. Source: Zhan et al. (2021) Equation (6) with ...", "subpage_snippet": "", "source": "uchicago-pol-methods.github.io", "link": "https://uchicago-pol-methods.github.io/banditsCI/", "content": "Estimation We produce estimates under different adaptive weighting schemes in the output_estimates() function. Weighting schemes include: (Augmented) Inverse Probability Weighted estimates, with uniform weights, Estimated with argument uniform = TRUE. Non-contextual variance minimizing estimates, Estimated with argument non_contextual_minvar = TRUE. Source: Zhan et al. (2021) Equation (6) with ..."}
+{"idx": 3, "title": "mollyow/banditsCI: Bandit-Based Experiments and Policy Evaluation - GitHub", "date": "", "ddg_snippet": "Confidence intervals with adaptively generated data This package provides functions for conducting frequentist inference on adaptively generated data. These functions produce point estimates and confidence intervals , using the methods proposed in Zhan, Ruohan, et al. (2021) and Hadad, Vitor, et al. (2021). The code in this package is directly adapted from the original python code for those ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mollyow/banditsCI", "content": "Confidence intervals with adaptively generated data This package provides functions for conducting frequentist inference on adaptively generated data. These functions produce point estimates and confidence intervals , using the methods proposed in Zhan, Ruohan, et al. (2021) and Hadad, Vitor, et al. (2021). The code in this package is directly adapted from the original python code for those ..."}
+{"idx": 4, "title": "Review for NeurIPS paper: Inference for Batched Bandits", "date": "", "ddg_snippet": "In the present paper, this structure is introduced through batching. The main result of the paper shows that if one runs a batched bandit , then OLS estimation from the bandit data is asymptotically normal, and thus usual normal confidence intervals can be constructed with valid coverage guarantees.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/6fd86e0ad726b778e37cf270fa0247d7-Review.html", "content": "In the present paper, this structure is introduced through batching. The main result of the paper shows that if one runs a batched bandit , then OLS estimation from the bandit data is asymptotically normal, and thus usual normal confidence intervals can be constructed with valid coverage guarantees."}
+{"idx": 5, "title": "PDF Improved Variance-Aware Confidence Sets for Linear Bandits and ... - NIPS", "date": "", "ddg_snippet": "4) and 2) the peeling technique to both the input norm and the variance magnitude. As will be clear in the proof (cf. Section D), this peeling step is crucial to obtain a tight regret bound for the example above. The new confidence region pro ides a tighter estimation for , which helps addre", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2021/file/228bbc2f87caeb21bb7f6949fddcb91d-Paper.pdf", "content": "4) and 2) the peeling technique to both the input norm and the variance magnitude. As will be clear in the proof (cf. Section D), this peeling step is crucial to obtain a tight regret bound for the example above. The new confidence region pro ides a tighter estimation for , which helps addre"}
+{"idx": 6, "title": "Confidence intervals for policy evaluation in adaptive experiments", "date": "", "ddg_snippet": "Adaptive experimental designs, such as multiarmed bandit algorithms, reduce costs by increasing the probability of assigning promising treatments over the course of the experiment.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/350665944_Confidence_intervals_for_policy_evaluation_in_adaptive_experiments", "content": "Adaptive experimental designs, such as multiarmed bandit algorithms, reduce costs by increasing the probability of assigning promising treatments over the course of the experiment."}
+{"idx": 7, "title": "Confidence Intervals for Policy Evaluation in Adaptive Experiments", "date": "", "ddg_snippet": "Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference. For example, it is now well known that the sample mean is biased in adaptive trials. Inferential challenges are exacerbated when our parameter of interest differs from the parameter the trial was designed to target, such as when we are interested in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1911.02768", "content": "Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference. For example, it is now well known that the sample mean is biased in adaptive trials. Inferential challenges are exacerbated when our parameter of interest differs from the parameter the trial was designed to target, such as when we are interested in ..."}
+{"idx": 8, "title": "PDF Susan Athey, RuohanZhan", "date": "", "ddg_snippet": "It has been increasingly common for data to be collected using adaptive experimentation, for example, using bandit exploration.Historical data of this type can be used to evaluate alternative treatment assignment policies and learn optimal policies to guide future innovation or experiments.", "subpage_snippet": "", "source": "icme.stanford.edu", "link": "https://icme.stanford.edu/sites/g/files/sbiybj17116/files/media/file/xpo_adaptive.pdf", "content": "It has been increasingly common for data to be collected using adaptive experimentation, for example, using bandit exploration.Historical data of this type can be used to evaluate alternative treatment assignment policies and learn optimal policies to guide future innovation or experiments."}
+{"idx": 9, "title": "C h al l e n ge s i n S t at i s t i c al A n al ys i s of D at a C ol ...", "date": "", "ddg_snippet": "to adopting bandit algorithms for experimental design is lack of clarity on how statistical analyses of data are impacted when using a bandit algorithm to adapt an experiment (Rafferty, Ying, & Williams, 2019). Theoretical work suggests that adaptive data collection, like that used in bandit algorithms, can induce bias in the estimates of means ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.12198", "content": "to adopting bandit algorithms for experimental design is lack of clarity on how statistical analyses of data are impacted when using a bandit algorithm to adapt an experiment (Rafferty, Ying, & Williams, 2019). Theoretical work suggests that adaptive data collection, like that used in bandit algorithms, can induce bias in the estimates of means ..."}
diff --git a/data/sampled_jsons/peeling_technique_empirical_process_concentration_bounds.jsonl b/data/sampled_jsons/peeling_technique_empirical_process_concentration_bounds.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eef4e3ea7351301cb3a82b27a0b798fa5a43be38
--- /dev/null
+++ b/data/sampled_jsons/peeling_technique_empirical_process_concentration_bounds.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Empirical Process: Peeling Technique", "date": "", "ddg_snippet": "by Y Lu — Using Peeling technique to bound the Ez0 [sup g∈T. 1 n. Pn i=1 g(zi)] term in Talagrand concentration inequality, then we can get the bound we have here. The ... 9 pages", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~yplu/note/localization.pdf", "content": "by Y Lu — Using Peeling technique to bound the Ez0 [sup g∈T. 1 n. Pn i=1 g(zi)] term in Talagrand concentration inequality, then we can get the bound we have here. The ... 9 pages"}
+{"idx": 1, "title": "On non-asymptotic bounds for estimation in generalized ...", "date": "", "ddg_snippet": "by SA van de Geer · 2007 · Cited by 19 — The Concentration theorem involves the expectation of the supremum of the empirical process . We derive bounds for it using symmetrization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/0709.0844", "content": "by SA van de Geer · 2007 · Cited by 19 — The Concentration theorem involves the expectation of the supremum of the empirical process . We derive bounds for it using symmetrization ..."}
+{"idx": 2, "title": "Towards Improved Risk Bounds for Transductive Learning", "date": "", "ddg_snippet": "We use novel functional based peeling technique to derive better uniform localized convergence upper bounds in transductive learning without “sub-root” ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0806.pdf", "content": "We use novel functional based peeling technique to derive better uniform localized convergence upper bounds in transductive learning without “sub-root” ..."}
+{"idx": 3, "title": "Towards Empirical Process Theory for Vector-Valued Functions", "date": "", "ddg_snippet": "by J Park · 2023 · Cited by 15 — Abstract. This paper provides some first steps in developing empirical process theory for functions taking values in a vector space. 45 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v201/park23a/park23a.pdf", "content": "by J Park · 2023 · Cited by 15 — Abstract. This paper provides some first steps in developing empirical process theory for functions taking values in a vector space. 45 pages"}
+{"idx": 4, "title": "Empirical Process Seminar", "date": "", "ddg_snippet": "15 Mar 2021 — In this seminar we are interested in the sup of emprical process and it's applications in statistical machine learning.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~yplu/empiricalprocess.html", "content": "15 Mar 2021 — In this seminar we are interested in the sup of emprical process and it's applications in statistical machine learning."}
+{"idx": 5, "title": "Empirical Minimization", "date": "", "ddg_snippet": "by PL Bartlett · 2005 · Cited by 283 — We investigate the behavior of the empirical minimization algo- rithm using various methods. 28 pages", "subpage_snippet": "", "source": "www.stat.berkeley.edu", "link": "https://www.stat.berkeley.edu/~bartlett/papers/bm-em-05.pdf", "content": "by PL Bartlett · 2005 · Cited by 283 — We investigate the behavior of the empirical minimization algo- rithm using various methods. 28 pages"}
+{"idx": 6, "title": "STAT576 Empirical Process Theory", "date": "", "ddg_snippet": "24 Jul 2024 — This is a graduate-level theoretical statistics course taught by Sabyasachi Chatterjee at University of Illi- nois Urbana-Champaign, aiming to ...", "subpage_snippet": "", "source": "pbb.wtf", "link": "https://pbb.wtf/posts/Notes/EmpProcess.pdf", "content": "24 Jul 2024 — This is a graduate-level theoretical statistics course taught by Sabyasachi Chatterjee at University of Illi- nois Urbana-Champaign, aiming to ..."}
+{"idx": 7, "title": "Scale-Insensitive Neural Network Significance Tests", "date": "", "ddg_snippet": "by H Fallahgoul · 2025 · Cited by 1 — For empirical processes, Talagrand's inequality provides concentration bounds for suprema of cen- tered processes: Theorem 5 (Talagrand's ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.15753?", "content": "by H Fallahgoul · 2025 · Cited by 1 — For empirical processes, Talagrand's inequality provides concentration bounds for suprema of cen- tered processes: Theorem 5 (Talagrand's ..."}
+{"idx": 8, "title": "SILVAN: Estimating Betweenness Centralities with ...", "date": "", "ddg_snippet": "by L Pellegrina · 2023 · Cited by 18 — Our first contribution is empirical peeling , a novel technique that we introduce to obtain sharp non-uniform data-dependent bounds on the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3628601", "content": "by L Pellegrina · 2023 · Cited by 18 — Our first contribution is empirical peeling , a novel technique that we introduce to obtain sharp non-uniform data-dependent bounds on the ..."}
+{"idx": 9, "title": "Probability in High Dimension - Princeton Math", "date": "", "ddg_snippet": "by R van Handel · Cited by 505 — These notes were written for the course APC 550: Probability in High Dimen- sion that I taught at Princeton in the Spring 2014 and Fall 2016 semesters. 326 pages", "subpage_snippet": "", "source": "web.math.princeton.edu", "link": "https://web.math.princeton.edu/~rvan/APC550.pdf", "content": "by R van Handel · Cited by 505 — These notes were written for the course APC 550: Probability in High Dimen- sion that I taught at Princeton in the Spring 2014 and Fall 2016 semesters. 326 pages"}
diff --git a/data/sampled_jsons/peeling_technique_in_bandit_algorithms.jsonl b/data/sampled_jsons/peeling_technique_in_bandit_algorithms.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..45c0544cc407ea711221bc20dc0cfcf54602d761
--- /dev/null
+++ b/data/sampled_jsons/peeling_technique_in_bandit_algorithms.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "\" Peeling Technique \" in Probability - Mathematics Stack Exchange", "date": "", "ddg_snippet": "So I am reading \" Bandit Algorithms \" by Lattimore wherein for one of the proofs he uses a technique called as \" Peeling Device\" which he says is a widely used tool in probability. I cannot find any references to it on the net. For 1-subgaussian and independent random variables X1…Xn.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4947385/peeling-technique-in-probability", "content": "So I am reading \" Bandit Algorithms \" by Lattimore wherein for one of the proofs he uses a technique called as \" Peeling Device\" which he says is a widely used tool in probability. I cannot find any references to it on the net. For 1-subgaussian and independent random variables X1…Xn."}
+{"idx": 1, "title": "Bandit Algorithms", "date": "", "ddg_snippet": "Major tech companies use bandit algorithms for conguring web interfaces, where applications would include news recommendation, dynamic pricing and ad placement. As of writing of the book, Google analytics even oers running multi-armed bandit based.", "subpage_snippet": "", "source": "ece.iisc.ac.in", "link": "https://ece.iisc.ac.in/~aditya/E1245_Online_Prediction_Learning_F2018/lattimore-szepesvari18bandit-algorithms.pdf", "content": "Major tech companies use bandit algorithms for conguring web interfaces, where applications would include news recommendation, dynamic pricing and ad placement. As of writing of the book, Google analytics even oers running multi-armed bandit based."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Regarding algorithms , we adopt the OFUL framework combined with weighted Catoni estimators and peeling techniques . bandit algorithm carefully peels the samples based on their uncertainty and utilizes a plug-in estimator for the sum of variances.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "Regarding algorithms , we adopt the OFUL framework combined with weighted Catoni estimators and peeling techniques . bandit algorithm carefully peels the samples based on their uncertainty and utilizes a plug-in estimator for the sum of variances."}
+{"idx": 3, "title": "Product Management 101: #47 Bandit Algorithms | Medium", "date": "", "ddg_snippet": "Bandit Algorithms : The Smarter Way to Run Experiments in Product Management. In product management, every decision about a new feature, design change, or pricing model carries risk. The traditional weapon of choice for de-risking?", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@bastistraube/product-management-101-47-bandit-algorithms-c37264270f5a", "content": "Bandit Algorithms : The Smarter Way to Run Experiments in Product Management. In product management, every decision about a new feature, design change, or pricing model carries risk. The traditional weapon of choice for de-risking?"}
+{"idx": 4, "title": "Optimizing Processes With Bandit Algorithms | Restackio", "date": "", "ddg_snippet": "Explore how bandit algorithms enhance process optimization in AI-driven systems for...Exploration-Exploitation Trade-offs in Bandit Algorithms Advanced Techniques in Bandit Algorithms for Process Optimization", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/ai-driven-process-optimization-answer-bandit-algorithms-cat-ai", "content": "Explore how bandit algorithms enhance process optimization in AI-driven systems for...Exploration-Exploitation Trade-offs in Bandit Algorithms Advanced Techniques in Bandit Algorithms for Process Optimization"}
+{"idx": 5, "title": "Bandits Dueling on Partially Ordered Sets", "date": "", "ddg_snippet": "It is based on a peeling technique : given N > 0 and a decreasing sequence (\"t)1tN it computes and renes an \"t-approximation Pbt of the Pareto front, using UBSRoutine ( Algorithm 3), which considers \"t-indistinguishable arms as incomparable.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-01774844v1/document", "content": "It is based on a peeling technique : given N > 0 and a decreasing sequence (\"t)1tN it computes and renes an \"t-approximation Pbt of the Pareto front, using UBSRoutine ( Algorithm 3), which considers \"t-indistinguishable arms as incomparable."}
+{"idx": 6, "title": "Mostly Exploration-Free Algorithms for", "date": "", "ddg_snippet": "Such concerns may deter decision-makers from deploying bandit algorithms in practice. In this paper, we analyze the performance of exploration-free greedy algorithms .", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~bayati/papers/greedy.pdf", "content": "Such concerns may deter decision-makers from deploying bandit algorithms in practice. In this paper, we analyze the performance of exploration-free greedy algorithms ."}
+{"idx": 7, "title": "Offline Evaluation of Multi-Armed Bandit Algorithms in Python using...", "date": "", "ddg_snippet": "Multi-armed bandit algorithms are seeing renewed excitement, but evaluating their performance using a historic dataset is challenging. Here’s how I go about implementing offline bandit evaluation techniques , with examples shown in Python.", "subpage_snippet": "", "source": "jamesrledoux.com", "link": "https://jamesrledoux.com/algorithms/offline-bandit-evaluation/", "content": "Multi-armed bandit algorithms are seeing renewed excitement, but evaluating their performance using a historic dataset is challenging. Here’s how I go about implementing offline bandit evaluation techniques , with examples shown in Python."}
+{"idx": 8, "title": "Bandit Algorithms for Website Optimization", "date": "", "ddg_snippet": "Annealing An algorithm for solving the Multiarmed Bandit Problem anneals if it explores less over time. Temperature A parameter that can be adjusted to increase the amount of exploration in the Softmax algorithm for solving the Multiarmed Bandit Problem.", "subpage_snippet": "", "source": "doc.lagout.org", "link": "https://doc.lagout.org/science/0_Computer+Science/2_Algorithms/Bandit+Algorithms+for+Website+Optimization+[White+2013-01-03].pdf", "content": "Annealing An algorithm for solving the Multiarmed Bandit Problem anneals if it explores less over time. Temperature A parameter that can be adjusted to increase the amount of exploration in the Softmax algorithm for solving the Multiarmed Bandit Problem."}
+{"idx": 9, "title": "How to Build a Product Recommender Using Multi-Armed Bandit ...", "date": "", "ddg_snippet": "The algorithms that we looked at are quite simple and easy to implement. However, there are more advanced techniques that have been developed and some that are freely available to use. One such approach is the upper confidence bound multi-armed bandit algorithm .", "subpage_snippet": "", "source": "www.offerzen.com", "link": "https://www.offerzen.com/blog/how-to-build-a-product-recommender-using-multi-armed-bandit-algorithms", "content": "The algorithms that we looked at are quite simple and easy to implement. However, there are more advanced techniques that have been developed and some that are freely available to use. One such approach is the upper confidence bound multi-armed bandit algorithm ."}
diff --git a/data/sampled_jsons/per-instance_privacy_unlearning_-Leveraging_Per-Instance_Privacy_for_Machine_Unlearning.jsonl b/data/sampled_jsons/per-instance_privacy_unlearning_-Leveraging_Per-Instance_Privacy_for_Machine_Unlearning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7b4cf2d9f846c95d9f387c3feec908075e2f615c
--- /dev/null
+++ b/data/sampled_jsons/per-instance_privacy_unlearning_-Leveraging_Per-Instance_Privacy_for_Machine_Unlearning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Our results show that per-instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "Our results show that per-instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and ..."}
+{"idx": 1, "title": "Trustworthy Machine Learning through Data-Specific...", "date": "", "ddg_snippet": "23 Jul 2025 — Conceptually, it was already discussed that input dependence is ok for per-instance privacy for certain applications (e.g. see unlearning and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JRMoeNZgNl&noteId=3AvYxc05At", "content": "23 Jul 2025 — Conceptually, it was already discussed that input dependence is ok for per-instance privacy for certain applications (e.g. see unlearning and ..."}
+{"idx": 2, "title": "Unlearning and Privacy in Deep Neural Networks", "date": "", "ddg_snippet": "by A Golatkar · 2023 — Unlearning methods can be broadly categorized in two directions, (i) approximate unlearning ... 7.4.4.2 Per-Instance Privacy . We show the pDP loss for NGD and ...", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt9hr118rz/qt9hr118rz_noSplash_7f0e48522057cb2c5b051331ab42b460.pdf", "content": "by A Golatkar · 2023 — Unlearning methods can be broadly categorized in two directions, (i) approximate unlearning ... 7.4.4.2 Per-Instance Privacy . We show the pDP loss for NGD and ..."}
+{"idx": 3, "title": "Individual Privacy Accounting for Differentially Private ...", "date": "", "ddg_snippet": "25 Jul 2024 — Privately publishable per-instance privacy . In Advances in Neural Information Processing Systems, 2021. Sablayrolles et al. (2019) Alexandre ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2206.02617v7", "content": "25 Jul 2024 — Privately publishable per-instance privacy . In Advances in Neural Information Processing Systems, 2021. Sablayrolles et al. (2019) Alexandre ..."}
+{"idx": 4, "title": "Theory and Practice of Differential Privacy", "date": "", "ddg_snippet": "23 Jul 2021 — Privately Publishable Per-instance Privacy : An Extended Abstract ( Poster ) > ... Adaptive Machine Unlearning ( Contributed Talk ) >. Varun ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/workshop/8376", "content": "23 Jul 2021 — Privately Publishable Per-instance Privacy : An Extended Abstract ( Poster ) > ... Adaptive Machine Unlearning ( Contributed Talk ) >. Varun ..."}
+{"idx": 5, "title": "probing-the-transition-to-dataset-level-privacy-in-ml-models- ...", "date": "", "ddg_snippet": "Insofar as we apply the ex-post per-instance privacy loss, our work is complementary to the recently introduced model and data-specific notion of DP known ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/probing-the-transition-to-dataset-level-privacy-in-ml-models-2xvmph42.pdf", "content": "Insofar as we apply the ex-post per-instance privacy loss, our work is complementary to the recently introduced model and data-specific notion of DP known ..."}
+{"idx": 6, "title": "Sensitivity is Often Overestimated in DP-SGD", "date": "", "ddg_snippet": "by A Thudi · 2024 · Cited by 8 — However we find that for certain update rules, training with higher sampling rates can give better per-instance privacy because mini- batch updates concentrate ... 19 pages", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/usenixsecurity24-thudi.pdf", "content": "by A Thudi · 2024 · Cited by 8 — However we find that for certain update rules, training with higher sampling rates can give better per-instance privacy because mini- batch updates concentrate ... 19 pages"}
+{"idx": 7, "title": "Vector Institute", "date": "", "ddg_snippet": "... unlearning reveals how per-instance privacy levels predict unlearning difficulty - Nicolas Papernot's fast exact unlearning achieves near ...", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/VectorInst/status/1945857684512903356", "content": "... unlearning reveals how per-instance privacy levels predict unlearning difficulty - Nicolas Papernot's fast exact unlearning achieves near ..."}
+{"idx": 8, "title": "Sensitivity is Often Overestimated in DP-SGD", "date": "", "ddg_snippet": "16 Jul 2024 — We show that when training on common benchmark datasets, many data points have better per-instance privacy than what the current data- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.00310v3", "content": "16 Jul 2024 — We show that when training on common benchmark datasets, many data points have better per-instance privacy than what the current data- ..."}
+{"idx": 9, "title": "MadS&P Seminar", "date": "", "ddg_snippet": "4 Mar 2025 — We then also present recent per-instance privacy analyses which imply many datapoints can be “strongly” unlearnt with no extra computation when ...", "subpage_snippet": "", "source": "today.wisc.edu", "link": "https://today.wisc.edu/events/view/205615", "content": "4 Mar 2025 — We then also present recent per-instance privacy analyses which imply many datapoints can be “strongly” unlearnt with no extra computation when ..."}
diff --git a/data/sampled_jsons/performance_drop_unseen_models_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models.jsonl b/data/sampled_jsons/performance_drop_unseen_models_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb6a3bb22baf56a701bcfd212766cef073da7796
--- /dev/null
+++ b/data/sampled_jsons/performance_drop_unseen_models_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Generalizable Origin Identification for Text - Guided ...", "date": "", "ddg_snippet": "similar performance of origin identification for both diffu-. 2. sion models .ever, on the other hand, a significant performance drop is. observed on unseen diffusion models : with 2 layers, the. mAP decreases from 86.6% to 80.3% (−6.3%), and Acc.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387767437_Generalizable_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models", "content": "similar performance of origin identification for both diffu-. 2. sion models .ever, on the other hand, a significant performance drop is. observed on unseen diffusion models : with 2 layers, the. mAP decreases from 86.6% to 80.3% (−6.3%), and Acc."}
+{"idx": 1, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "The effectiveness means the similar performance of origin identification for both diffusion models .Although the translation patterns generated by diffusion models in our ID2 differ from the manually designed ones in AnyPattern, there remains some generalizability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02376v1", "content": "The effectiveness means the similar performance of origin identification for both diffusion models .Although the translation patterns generated by diffusion models in our ID2 differ from the manually designed ones in AnyPattern, there remains some generalizability."}
+{"idx": 2, "title": "\\twemojidesert MIRAGE: Towards AI-Generated Image Detection in", "date": "", "ddg_snippet": "... images are noisy, varying from “obviously fake” images to realistic ones derived from multiple generative models and further edited for quality ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.13223v1", "content": "... images are noisy, varying from “obviously fake” images to realistic ones derived from multiple generative models and further edited for quality ..."}
+{"idx": 3, "title": "RoboEye: Enhancing 2D Robotic Object Identification with", "date": "", "ddg_snippet": "... occlusion, and large viewpoint changes—these factors amplify discrepancies between query and reference images , causing sharp performance drops for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14966v1", "content": "... occlusion, and large viewpoint changes—these factors amplify discrepancies between query and reference images , causing sharp performance drops for ..."}
+{"idx": 4, "title": "Robust Adaptation of Large Multimodal Models for Retrieval", "date": "", "ddg_snippet": "We observe that applying SFT for meme classification leads to overfitting, which degrades performance on general multimodal benchmarks like MMMU Yue ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.13061v4", "content": "We observe that applying SFT for meme classification leads to overfitting, which degrades performance on general multimodal benchmarks like MMMU Yue ..."}
+{"idx": 5, "title": "Black-box Model Merging for Language-Model-as-a-Service with", "date": "", "ddg_snippet": "... to identify and filter out irrelevant or redundant information across models , and (2) sign-aware scaling, which dynamically computes optimal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.12951v1", "content": "... to identify and filter out irrelevant or redundant information across models , and (2) sign-aware scaling, which dynamically computes optimal ..."}
+{"idx": 6, "title": "SuPreME: A Supervised Pre-training Framework for Multimodal ECG", "date": "", "ddg_snippet": "... images (Tesfai et al., 2022 ; Degirmenci et al., 2022 ; Mashrur et al., 2019 ; Huang et al., 2022 ) , while Transformers use attention mechanisms ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.19668v3", "content": "... images (Tesfai et al., 2022 ; Degirmenci et al., 2022 ; Mashrur et al., 2019 ; Huang et al., 2022 ) , while Transformers use attention mechanisms ..."}
+{"idx": 7, "title": "Large language models deconstruct the clinical intuition behind", "date": "", "ddg_snippet": "... of millions of general sentences, we finessed large language models (LLMs) on > 4,000 free-form health records from healthcare professionals to ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell/fulltext/S0092-8674(25)00213-2", "content": "... of millions of general sentences, we finessed large language models (LLMs) on > 4,000 free-form health records from healthcare professionals to ..."}
+{"idx": 8, "title": "Use of Explainable Artificial Intelligence for Analyzing and", "date": "", "ddg_snippet": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2073-431X/14/5/160", "content": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ..."}
+{"idx": 9, "title": "Proceedings of the 2021 Conference on Empirical Methods in", "date": "", "ddg_snippet": "... mBART, a pretrained multilingual encoder-decoder model explicitly designed for NMT, with an average improvement of 7.1 BLEU on zero-shot any- to ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2021.emnlp-main/", "content": "... mBART, a pretrained multilingual encoder-decoder model explicitly designed for NMT, with an average improvement of 7.1 BLEU on zero-shot any- to ..."}
diff --git a/data/sampled_jsons/physics-informed_neural_networks_multiscale_time_integration_multi-step_PDE_solver_error_correction_year_2023.jsonl b/data/sampled_jsons/physics-informed_neural_networks_multiscale_time_integration_multi-step_PDE_solver_error_correction_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..53bc7f562c593d33876a7965b4a0840b9958022a
--- /dev/null
+++ b/data/sampled_jsons/physics-informed_neural_networks_multiscale_time_integration_multi-step_PDE_solver_error_correction_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "by Q Wang — The paper introduces MultiPDENet , a PDE-embedded neural network with multiscale time stepping to accelerate flow simulations by integrating numerical methods ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=D1gs8QT74m", "content": "by Q Wang — The paper introduces MultiPDENet , a PDE-embedded neural network with multiscale time stepping to accelerate flow simulations by integrating numerical methods ..."}
+{"idx": 1, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "27 Jan 2025 — We developed MultiPDENet , a PDE-embedded network with multiscale time-stepping, for accelerated flow simulations on spatiotemporal coarse grids.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "27 Jan 2025 — We developed MultiPDENet , a PDE-embedded network with multiscale time-stepping, for accelerated flow simulations on spatiotemporal coarse grids."}
+{"idx": 2, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "15 Jul 2025 — To alleviate the curse of temporal error accumulation in long-term prediction, we introduce a multiscale time integration approach, where a ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46029", "content": "15 Jul 2025 — To alleviate the curse of temporal error accumulation in long-term prediction, we introduce a multiscale time integration approach, where a ..."}
+{"idx": 3, "title": "Multi-scale time-stepping of Partial Differential Equations ...", "date": "", "ddg_snippet": "by AP Hemmasian · 2024 · Cited by 16 — We incorporate the idea of multi-scale hierarchical time-stepping to increase the prediction speed and decrease accumulated error over time.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782524002391", "content": "by AP Hemmasian · 2024 · Cited by 16 — We incorporate the idea of multi-scale hierarchical time-stepping to increase the prediction speed and decrease accumulated error over time."}
+{"idx": 4, "title": "Physics-informed neural networks for PDE problems", "date": "", "ddg_snippet": "by K Luo · 2025 · Cited by 8 — The ability to effectively solve PDEs is fundamental to advancing technology and improving predictive capabilities across various disciplines.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11322-7", "content": "by K Luo · 2025 · Cited by 8 — The ability to effectively solve PDEs is fundamental to advancing technology and improving predictive capabilities across various disciplines."}
+{"idx": 5, "title": "time-integration-schemes-based-on-neural-networks-for- ...", "date": "", "ddg_snippet": "17 Oct 2023 — In this study, we propose to learn time integration schemes to reduce the error of solving PDEs on coarse grids. The traditional approach to ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/time-integration-schemes-based-on-neural-networks-for-58nfti7p2g.pdf", "content": "17 Oct 2023 — In this study, we propose to learn time integration schemes to reduce the error of solving PDEs on coarse grids. The traditional approach to ..."}
+{"idx": 6, "title": "Multi-resolution partial differential equations preserved ...", "date": "", "ddg_snippet": "by XY Liu · 2024 · Cited by 65 — An impressive contribution in this direction is physics - informed neural networks (PINNs), where well-posed PDE information is leveraged to ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42005-024-01521-z", "content": "by XY Liu · 2024 · Cited by 65 — An impressive contribution in this direction is physics - informed neural networks (PINNs), where well-posed PDE information is leveraged to ..."}
+{"idx": 7, "title": "Integrating machine learning and multiscale modeling ...", "date": "", "ddg_snippet": "by M Alber · 2019 · Cited by 645 — Physics - informed neural networks are neural networks that solve supervised learning tasks while respecting physical constraints. Examples: diagnosing ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6877584/", "content": "by M Alber · 2019 · Cited by 645 — Physics - informed neural networks are neural networks that solve supervised learning tasks while respecting physical constraints. Examples: diagnosing ..."}
+{"idx": 8, "title": "Multiscale Modelling with Physics-informed Neural Network", "date": "", "ddg_snippet": "15 Feb 2024 — In this paper, a novel decoupling solving mode is proposed through modelling large-scale dynamics independently and treating small-scale.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2402.05067v3", "content": "15 Feb 2024 — In this paper, a novel decoupling solving mode is proposed through modelling large-scale dynamics independently and treating small-scale."}
+{"idx": 9, "title": "A physics-informed neural network framework for multi ...", "date": "", "ddg_snippet": "by R Sun · 2024 · Cited by 15 — A novel PINN framework for simulating electrokinetic microfluidic systems. PINN achieves accuracy solutions with a sparse training domain.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045793024002524", "content": "by R Sun · 2024 · Cited by 15 — A novel PINN framework for simulating electrokinetic microfluidic systems. PINN achieves accuracy solutions with a sparse training domain."}
diff --git a/data/sampled_jsons/preference-based_reinforcement_learning_nonlinear_reward_function_extension_techniques_year_2023.jsonl b/data/sampled_jsons/preference-based_reinforcement_learning_nonlinear_reward_function_extension_techniques_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..82743d83c53eeb9c89165c55ee93d04f9e3fdd96
--- /dev/null
+++ b/data/sampled_jsons/preference-based_reinforcement_learning_nonlinear_reward_function_extension_techniques_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Inverse Preference Learning: Preference-based RL without a Reward Function", "date": "", "ddg_snippet": "Abstract Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/3be7859b36d9440372cae0a293f2e4cc-Paper-Conference.pdf", "content": "Abstract Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms."}
+{"idx": 1, "title": "PDF A Survey of Preference-Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress."}
+{"idx": 2, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Abstract—Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.11943", "content": "Abstract—Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards ."}
+{"idx": 3, "title": "PDF Reinforcement Learning from Diverse Human Preferences - IJCAI", "date": "", "ddg_snippet": "Abstract The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques . De-scribing an agent's desired behaviors and properties can be dificult, even for experts. A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution, in which reward ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0586.pdf", "content": "Abstract The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques . De-scribing an agent's desired behaviors and properties can be dificult, even for experts. A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution, in which reward ..."}
+{"idx": 4, "title": "A Preference-based Reinforcement Learning Approach Using Reward ...", "date": "", "ddg_snippet": "Preference-based reinforement learning solves decision making based on human preferences . By deriving a reward function from human preferences , the agent's behavior aligns with human expectations, avoiding complex reward tasks. However, in preference-based reinforcement learning , a human teacher can only select one segment of trajectories from a pair, limiting reward function learning ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10587696", "content": "Preference-based reinforement learning solves decision making based on human preferences . By deriving a reward function from human preferences , the agent's behavior aligns with human expectations, avoiding complex reward tasks. However, in preference-based reinforcement learning , a human teacher can only select one segment of trajectories from a pair, limiting reward function learning ..."}
+{"idx": 5, "title": "Sample-Efficient Preference-based Reinforcement Learning with...", "date": "", "ddg_snippet": "Abstract: Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that encoding environment dynamics in the reward function improves the sample efficiency of PbRL by an order of magnitude.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=i84V7i6KEMd", "content": "Abstract: Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that encoding environment dynamics in the reward function improves the sample efficiency of PbRL by an order of magnitude."}
+{"idx": 6, "title": "A survey of preference-based reinforcement learning methods", "date": "", "ddg_snippet": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To alleviate these issues, preference-based ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3122009.3208017", "content": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To alleviate these issues, preference-based ..."}
+{"idx": 7, "title": "Inverse Preference Learning: Preference-based RL without a Reward Function", "date": "", "ddg_snippet": "Reward functions are difficult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to improve ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.15363", "content": "Reward functions are difficult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to improve ..."}
+{"idx": 8, "title": "A Survey of Preference-Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task- specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/v18/16-634.html", "content": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task- specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress."}
+{"idx": 9, "title": "PDF B-Pref: Benchmarking Preference-Based Reinforcement Learning", "date": "", "ddg_snippet": "Reinforcement learning (RL) requires access to a reward function that incentivizes the right behavior, but these are notoriously hard to specify for complex tasks. Preference-based RL provides an alternative: learning policies using a teacher's preferences without pre-defined rewards , thus overcoming concerns associated with reward engineering.", "subpage_snippet": "", "source": "datasets-benchmarks-proceedings.neurips.cc", "link": "https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/d82c8d1619ad8176d665453cfb2e55f0-Paper-round1.pdf", "content": "Reinforcement learning (RL) requires access to a reward function that incentivizes the right behavior, but these are notoriously hard to specify for complex tasks. Preference-based RL provides an alternative: learning policies using a teacher's preferences without pre-defined rewards , thus overcoming concerns associated with reward engineering."}
diff --git a/data/sampled_jsons/privacy_accounting_tight_group_privacy_bounds_privacy_loss_distribution_Fourier_accountant_year_2023.jsonl b/data/sampled_jsons/privacy_accounting_tight_group_privacy_bounds_privacy_loss_distribution_Fourier_accountant_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..82017ad5533fc6760876d4b0058df78dd431dfc9
--- /dev/null
+++ b/data/sampled_jsons/privacy_accounting_tight_group_privacy_bounds_privacy_loss_distribution_Fourier_accountant_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tight and Flexible Accounting of Differential Privacy", "date": "", "ddg_snippet": "From classical DP theory, the privacy loss RV plays a central role. • If we keep track of the PLD, then it is tight ! Fourier . Accountant . PLD of ...", "subpage_snippet": "", "source": "cseweb.ucsd.edu", "link": "https://cseweb.ucsd.edu/~yuxiangw/talks/mit_privacy_talk.pdf", "content": "From classical DP theory, the privacy loss RV plays a central role. • If we keep track of the PLD, then it is tight ! Fourier . Accountant . PLD of ..."}
+{"idx": 1, "title": "INDIVIDUAL PRIVACY ACCOUNTING WITH GAUSSIAN ...", "date": "", "ddg_snippet": "Individual privacy accounting enables bounding differential privacy (DP) loss in- dividually for each participant involved in the analysis.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/references/pdf?id=5or3YVlHpA", "content": "Individual privacy accounting enables bounding differential privacy (DP) loss in- dividually for each participant involved in the analysis."}
+{"idx": 2, "title": "Avoiding Pitfalls for Privacy Accounting of Subsampled ...", "date": "", "ddg_snippet": "by CJ Lebeda · 2024 · Cited by 9 — Abstract. We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.20769", "content": "by CJ Lebeda · 2024 · Cited by 9 — Abstract. We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms."}
+{"idx": 3, "title": "Optimal Accounting of Differential Privacy via Characteristic ...", "date": "", "ddg_snippet": "by Y Zhu · 2022 · Cited by 141 — Characterizing the privacy degradation over compositions, i.e., privacy accounting , is a fundamental topic in differential privacy .", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10352848", "content": "by Y Zhu · 2022 · Cited by 141 — Characterizing the privacy degradation over compositions, i.e., privacy accounting , is a fundamental topic in differential privacy ."}
+{"idx": 4, "title": "Avoiding Pitfalls for Privacy Accounting of Subsampled ...", "date": "", "ddg_snippet": "by CJ Lebeda · 2024 · Cited by 10 — used in the computation of privacy curves is the privacy loss distribution (PLD) formalism [DR16,. 126. SMM19]. 127. Definition 5 ( Privacy Loss Distribution ).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=buSEDdP5YX", "content": "by CJ Lebeda · 2024 · Cited by 10 — used in the computation of privacy curves is the privacy loss distribution (PLD) formalism [DR16,. 126. SMM19]. 127. Definition 5 ( Privacy Loss Distribution )."}
+{"idx": 5, "title": "Lecture 8 Privacy Accounting (Part II) and a demo of autodp", "date": "", "ddg_snippet": "• RDP accountant . • Fourier accountant . • Unified treatment via a dominating privacy loss random variable. • And its characteristic function. • autodp: How you ...", "subpage_snippet": "", "source": "cseweb.ucsd.edu", "link": "https://cseweb.ucsd.edu/~yuxiangw/classes/DPCourse-2021Fall/Lectures/lecture8_privacy_accounting.pdf", "content": "• RDP accountant . • Fourier accountant . • Unified treatment via a dominating privacy loss random variable. • And its characteristic function. • autodp: How you ..."}
+{"idx": 6, "title": "Enhancing Trade-Offs in Privacy, Utility, and Computational ...", "date": "", "ddg_snippet": "by X Zhao · 2024 · Cited by 2 — Combined with Poisson sampling, the moment accountant method achieves tighter bounds than the advanced composition results on the overall privacy loss of the ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/pr/2024/01/10757434/21ZZa0R1aiQ", "content": "by X Zhao · 2024 · Cited by 2 — Combined with Poisson sampling, the moment accountant method achieves tighter bounds than the advanced composition results on the overall privacy loss of the ..."}
+{"idx": 7, "title": "Tighter Discrete Approximations of Privacy Loss Distributions", "date": "", "ddg_snippet": "Abstract: The privacy loss distribution (PLD) provides a tight characterization of the privacy loss of a mecha- nism in the context of differential privacy ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/connect-the-dots-tighter-discrete-approximations-of-privacy-2tv47lpb.pdf", "content": "Abstract: The privacy loss distribution (PLD) provides a tight characterization of the privacy loss of a mecha- nism in the context of differential privacy ..."}
+{"idx": 8, "title": "arXiv:2207.04381v1 [cs.DS] 10 Jul 2022", "date": "", "ddg_snippet": "by B Ghazi · 2022 · Cited by 18 — Privacy Loss Distribution (PLD). Tighter bounds for privacy parameters that go beyond advanced composition are possible if the privacy loss ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.04381", "content": "by B Ghazi · 2022 · Cited by 18 — Privacy Loss Distribution (PLD). Tighter bounds for privacy parameters that go beyond advanced composition are possible if the privacy loss ..."}
+{"idx": 9, "title": "The Skellam Mechanism for Differentially Private ...", "date": "", "ddg_snippet": "by N Agarwal · Cited by 175 — To quantify its privacy guarantees, we analyze the privacy loss distribu- tion via a numerical evaluation and provide a sharp bound on the Rényi divergence.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/2021/file/285baacbdf8fda1de94b19282acd23e2-Paper.pdf", "content": "by N Agarwal · Cited by 175 — To quantify its privacy guarantees, we analyze the privacy loss distribu- tion via a numerical evaluation and provide a sharp bound on the Rényi divergence."}
diff --git a/data/sampled_jsons/probabilistic_currying_neural_operator_Gaussian_process.jsonl b/data/sampled_jsons/probabilistic_currying_neural_operator_Gaussian_process.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..61fd3c827185b6dd9656850fecc40bcba7bb439e
--- /dev/null
+++ b/data/sampled_jsons/probabilistic_currying_neural_operator_Gaussian_process.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — Probabilistic currying is introduced as a key concept, establishing equivalence with multi-output Gaussian processes . Gaussian weight-space ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — Probabilistic currying is introduced as a key concept, establishing equivalence with multi-output Gaussian processes . Gaussian weight-space ..."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "Finally, probabilistic currying transforms f into a function-valued Gaussian process posterior F over the operator learned by the neural operator F (bottom left) ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Finally, probabilistic currying transforms f into a function-valued Gaussian process posterior F over the operator learned by the neural operator F (bottom left) ..."}
+{"idx": 2, "title": "Scalable Gaussian Process Operator for Physical Systems", "date": "", "ddg_snippet": "by S Kumar · 2025 — ... Gaussian process with its finite-dimensional representation. We exploit the probabilistic currying theorem [33] to establish the equivalence.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.15906", "content": "by S Kumar · 2025 — ... Gaussian process with its finite-dimensional representation. We exploit the probabilistic currying theorem [33] to establish the equivalence."}
+{"idx": 3, "title": "Scalable Gaussian Process Operator for Physical Systems", "date": "", "ddg_snippet": "18 Jun 2025 — ... Gaussian process with its finite-dimensional representation. We exploit the probabilistic currying theorem [33] to establish the equivalence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15906", "content": "18 Jun 2025 — ... Gaussian process with its finite-dimensional representation. We exploit the probabilistic currying theorem [33] to establish the equivalence ..."}
+{"idx": 4, "title": "[Literature Review] Linearization Turns Neural Operators ...", "date": "", "ddg_snippet": "By employing probabilistic currying , the authors derive a function-valued Gaussian process representation of the operator's predictions. Posterior Predictive ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/linearization-turns-neural-operators-into-function-valued-gaussian-processes", "content": "By employing probabilistic currying , the authors derive a function-valued Gaussian process representation of the operator's predictions. Posterior Predictive ..."}
+{"idx": 5, "title": "Scalable Gaussian Process Operator for Physical Systems", "date": "", "ddg_snippet": "... Gaussian process with its finite-dimensional representation. We exploit the probabilistic currying theorem [33] to establish the equivalence between the ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2506.15906/paper", "content": "... Gaussian process with its finite-dimensional representation. We exploit the probabilistic currying theorem [33] to establish the equivalence between the ..."}
+{"idx": 6, "title": "David Duvenaud", "date": "", "ddg_snippet": "We'd like to build a regression model that can process numerical data and make probabilistic predictions at arbitrary locations, guided by natural ...", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "http://www.cs.toronto.edu/~duvenaud/", "content": "We'd like to build a regression model that can process numerical data and make probabilistic predictions at arbitrary locations, guided by natural ..."}
+{"idx": 7, "title": "David Duvenaud", "date": "", "ddg_snippet": "We'd like to build a regression model that can process numerical data and make probabilistic predictions at arbitrary locations, guided by natural ...", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~duvenaud/", "content": "We'd like to build a regression model that can process numerical data and make probabilistic predictions at arbitrary locations, guided by natural ..."}
+{"idx": 8, "title": "Book", "date": "", "ddg_snippet": "Neural Network - Gaussian Mixture ... Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery Hayit K.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/1991", "content": "Neural Network - Gaussian Mixture ... Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery Hayit K."}
+{"idx": 9, "title": "Book", "date": "", "ddg_snippet": "Neural Network - Gaussian Mixture ... Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery Hayit K.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/1991", "content": "Neural Network - Gaussian Mixture ... Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery Hayit K."}
diff --git a/data/sampled_jsons/probabilistic_currying_theorem_[33].jsonl b/data/sampled_jsons/probabilistic_currying_theorem_[33].jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bcf76f366f2da11fded130a51e3eff2ff7ccaadc
--- /dev/null
+++ b/data/sampled_jsons/probabilistic_currying_theorem_[33].jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Probabilistic(?) estimates of climate sensitivity - Climate Etc.", "date": "", "ddg_snippet": "James Annan (with Hargreaves) has a new paper out, entitled “ On the generation and interpretation of probabilistic estimates of climate ...", "subpage_snippet": "", "source": "judithcurry.com", "link": "https://judithcurry.com/2011/01/24/probabilistic-estimates-of-climate-sensitivity/", "content": "James Annan (with Hargreaves) has a new paper out, entitled “ On the generation and interpretation of probabilistic estimates of climate ..."}
+{"idx": 1, "title": "Explore: Mathematics - Arbital viewer", "date": "", "ddg_snippet": "Sample spaces are often large, so it is hard to do probabilistic computations using a raw distribution over the sample space.", "subpage_snippet": "", "source": "arbital.greaterwrong.com", "link": "https://arbital.greaterwrong.com/explore/math/", "content": "Sample spaces are often large, so it is hard to do probabilistic computations using a raw distribution over the sample space."}
+{"idx": 2, "title": "Machine Logic", "date": "", "ddg_snippet": "In 1976, Appel and Haken caused delight mixed with consternation by proving the celebrated four colour theorem , but with heavy reliance on a computer ...", "subpage_snippet": "", "source": "lawrencecpaulson.github.io", "link": "https://lawrencecpaulson.github.io/", "content": "In 1976, Appel and Haken caused delight mixed with consternation by proving the celebrated four colour theorem , but with heavy reliance on a computer ..."}
+{"idx": 3, "title": "Stellar Question - Badge - MathOverflow", "date": "", "ddg_snippet": "Are there any books that take a theorems as problems ... What do heat kernels have to do with the Riemann-Roch theorem and the Gauss-Bonnet theorem ?", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/help/badges/51/stellar-question", "content": "Are there any books that take a theorems as problems ... What do heat kernels have to do with the Riemann-Roch theorem and the Gauss-Bonnet theorem ?"}
+{"idx": 4, "title": "2686 - conferences.cirm-math.fr", "date": "", "ddg_snippet": "Semantics of probabilistic programs (operational and denotational) Bayesian programming Differentiable programming Quantitative tools for ...", "subpage_snippet": "", "source": "conferences.cirm-math.fr", "link": "https://conferences.cirm-math.fr/2686.html", "content": "Semantics of probabilistic programs (operational and denotational) Bayesian programming Differentiable programming Quantitative tools for ..."}
+{"idx": 5, "title": "Algebraic Topology Sep 2019", "date": "", "ddg_snippet": "Title: A Variation of the Goldman-Millson Theorem for Filtered $L_\\infty$ Algebras ... Comments: Extended Theorem 1.4, Clarified Proof of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/math.AT/2019-09", "content": "Title: A Variation of the Goldman-Millson Theorem for Filtered $L_\\infty$ Algebras ... Comments: Extended Theorem 1.4, Clarified Proof of ..."}
+{"idx": 6, "title": "A Formally Verified IEEE 754 Floating-Point Implementation of", "date": "", "ddg_snippet": "A probabilistic model checker like ... The verification of probabilistic algorithms in theorem provers is a well-studied, albeit challenging, task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.10127v2", "content": "A probabilistic model checker like ... The verification of probabilistic algorithms in theorem provers is a well-studied, albeit challenging, task."}
+{"idx": 7, "title": "Light seminar: Logic and computation", "date": "", "ddg_snippet": "The sponsor of each week ’ s reading will lead the discussion on the paper or a related example of programming or theorem proving.", "subpage_snippet": "", "source": "conway.rutgers.edu", "link": "http://conway.rutgers.edu/~ccshan/wiki/logic/", "content": "The sponsor of each week ’ s reading will lead the discussion on the paper or a related example of programming or theorem proving."}
+{"idx": 8, "title": "Light seminar: Logic and computation", "date": "", "ddg_snippet": "The sponsor of each week ’ s reading will lead the discussion on the paper or a related example of programming or theorem proving.", "subpage_snippet": "", "source": "conway.bostoncoop.net", "link": "http://conway.bostoncoop.net/~ccshan/wiki/logic/", "content": "The sponsor of each week ’ s reading will lead the discussion on the paper or a related example of programming or theorem proving."}
+{"idx": 9, "title": "Ugo Dal Lago - researchr alias", "date": "", "ddg_snippet": "The geometry of parallelism: classical, probabilistic , and quantum effects Ugo Dal Lago , Claudia Faggian , Benoît Valiron , Akira Yoshimizu .", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/alias/ugo-dal-lago", "content": "The geometry of parallelism: classical, probabilistic , and quantum effects Ugo Dal Lago , Claudia Faggian , Benoît Valiron , Akira Yoshimizu ."}
diff --git a/data/sampled_jsons/protein_folding_deep_learning_research_GPU_model_RTX_A100.jsonl b/data/sampled_jsons/protein_folding_deep_learning_research_GPU_model_RTX_A100.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c28f4046c931f91723c363033dbfa954c3b8fdde
--- /dev/null
+++ b/data/sampled_jsons/protein_folding_deep_learning_research_GPU_model_RTX_A100.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AlphaFold - Wikipedia", "date": "", "ddg_snippet": "AlphaFold is an artificial intelligence program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. AlphaFold 1 placed first in the overall rankings of the 1...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/AlphaFold", "content": "AlphaFold is an artificial intelligence program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. AlphaFold 1 placed first in the overall rankings of the 1..."}
+{"idx": 1, "title": "protein - folding · GitHub Topics · GitHub", "date": "", "ddg_snippet": "python bioinformatics research deep - learning protein -structure protein - folding jax.molecular-dynamics markov-state- model protein - folding .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/protein-folding?o=desc&s=", "content": "python bioinformatics research deep - learning protein -structure protein - folding jax.molecular-dynamics markov-state- model protein - folding ."}
+{"idx": 2, "title": "Nvidia cluster - RAM requirements for protein folding diffusion model", "date": "", "ddg_snippet": "My thought- process is as follows: I will train a BERT on only the final conformations of protein structures, according to their atomic coordinates/types. This is essentially the giving the model a gauge for the distribution of final conformations across many mature proteins .", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/78668834/nvidia-cluster-ram-requirements-for-protein-folding-diffusion-model", "content": "My thought- process is as follows: I will train a BERT on only the final conformations of protein structures, according to their atomic coordinates/types. This is essentially the giving the model a gauge for the distribution of final conformations across many mature proteins ."}
+{"idx": 3, "title": "(PDF) A Survey on Deep Learning -Based Protein Folding", "date": "", "ddg_snippet": "Deep Learning Approaches to Protein Folding . Deep learning , a subset of machine learning , has revolutionized numerous fields by enabling the automatic. extraction of hierarchical features from raw data. In protein folding , deep learning models can capture.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382083211_A_Survey_on_Deep_Learning-Based_Protein_Folding", "content": "Deep Learning Approaches to Protein Folding . Deep learning , a subset of machine learning , has revolutionized numerous fields by enabling the automatic. extraction of hierarchical features from raw data. In protein folding , deep learning models can capture."}
+{"idx": 4, "title": "GPU Hardware Requirements Guide for DeepSeek Models in 2025", "date": "", "ddg_snippet": "Best GPU hardware for DeepSeek models in 2025. Get expert insights on specs, performance, and recommendations for optimal AI and deep learning .For smaller models (e.g., 7B and 16B), consumer GPUs like the NVIDIA RTX 4090 provide a cost-effective solution.", "subpage_snippet": "", "source": "www.proxpc.com", "link": "https://www.proxpc.com/blogs/gpu-hardware-requirements-guide-for-deepseek-models-in-2025", "content": "Best GPU hardware for DeepSeek models in 2025. Get expert insights on specs, performance, and recommendations for optimal AI and deep learning .For smaller models (e.g., 7B and 16B), consumer GPUs like the NVIDIA RTX 4090 provide a cost-effective solution."}
+{"idx": 5, "title": "Advancing Protein Simulation with Machine Learning - A new study...", "date": "", "ddg_snippet": "Developing a general CG model capable of capturing protein folding and dynamics has been a persistent challenge for scientists over the last fifty years.", "subpage_snippet": "", "source": "www.bionity.com", "link": "https://www.bionity.com/en/news/1186759/advancing-protein-simulation-with-machine-learning.html", "content": "Developing a general CG model capable of capturing protein folding and dynamics has been a persistent challenge for scientists over the last fifty years."}
+{"idx": 6, "title": "Protein Conformational Trajectory Prediction with", "date": "", "ddg_snippet": "2.2 Protein Folding Model as a Trajectory Generator. In recent years, incredible progress has been made on the task of protein folding . Deep learning models such as ESMFold [4] has been trained to accept protein sequences and predict all-atom 3D coordinates.", "subpage_snippet": "", "source": "andreityrin.com", "link": "https://andreityrin.com/assets/pdf/6_8200_report.pdf", "content": "2.2 Protein Folding Model as a Trajectory Generator. In recent years, incredible progress has been made on the task of protein folding . Deep learning models such as ESMFold [4] has been trained to accept protein sequences and predict all-atom 3D coordinates."}
+{"idx": 7, "title": "Distance-based protein folding powered by deep learning - PMC", "date": "", "ddg_snippet": "Keywords: protein folding , deep learning , protein contact prediction, protein distance prediction, direct coupling analysis. Abstract. Direct coupling analysis (DCA) for protein folding has made very good progress, but it is not effective for proteins that lack many sequence homologs...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6708335/", "content": "Keywords: protein folding , deep learning , protein contact prediction, protein distance prediction, direct coupling analysis. Abstract. Direct coupling analysis (DCA) for protein folding has made very good progress, but it is not effective for proteins that lack many sequence homologs..."}
+{"idx": 8, "title": "Protein Structure Prediction : A Primer (Part 4) | by Siddhant... | Medium", "date": "", "ddg_snippet": "Alphafold is the pipeline of deep learning and optimization based algorithms proposed by Deepmind when they entered CASP 14. They were eventually able to beat all other techniques in the competition by a significant margin, consistently performing usably good on most of structure prediction.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@rsiddhant73/protein-structure-prediction-a-primer-part-4-fc2a6e6c6ebd", "content": "Alphafold is the pipeline of deep learning and optimization based algorithms proposed by Deepmind when they entered CASP 14. They were eventually able to beat all other techniques in the competition by a significant margin, consistently performing usably good on most of structure prediction."}
+{"idx": 9, "title": "AI for Protein Folding : Revolutionizing Drug Discovery... - LinkeWire", "date": "", "ddg_snippet": "AI for protein folding refers to the application of artificial intelligence and deep learning models to predict the three-dimensional structures of proteins with high accuracy. This breakthrough technology accelerates drug discovery, enhances disease research ...", "subpage_snippet": "", "source": "linkewire.com", "link": "https://linkewire.com/2025/02/26/ai-for-protein-folding-revolutionizing-drug-discovery-biotech/", "content": "AI for protein folding refers to the application of artificial intelligence and deep learning models to predict the three-dimensional structures of proteins with high accuracy. This breakthrough technology accelerates drug discovery, enhances disease research ..."}
diff --git a/data/sampled_jsons/qcnePVejeV_Do_Not_Trust_What_They_Tell-_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_.jsonl b/data/sampled_jsons/qcnePVejeV_Do_Not_Trust_What_They_Tell-_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..11cf399eff34224d797a294d1e95a15f4d76a5db
--- /dev/null
+++ b/data/sampled_jsons/qcnePVejeV_Do_Not_Trust_What_They_Tell-_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How To Use Salt With Watercolors – Greenleaf & Blueberry", "date": "", "ddg_snippet": "May 12, 2023 · First off, let's briefly go over the effect salt has on watercolors : If you sprinkle salt across wet watercolor paint , it will create a textured or mottled effect that is significantly more noticeable than granulation.", "subpage_snippet": "", "source": "www.greenleafblueberry.com", "link": "https://www.greenleafblueberry.com/blogs/news/how-to-use-salt-with-watercolors", "content": "May 12, 2023 · First off, let's briefly go over the effect salt has on watercolors : If you sprinkle salt across wet watercolor paint , it will create a textured or mottled effect that is significantly more noticeable than granulation."}
+{"idx": 1, "title": "How to Add Texture to Your Watercolor Paintings Using Rock Salt ...", "date": "", "ddg_snippet": "Apr 18, 2023 · Learn how to create unique and stunning watercolor textures using rock salt with this step-by-step tutorial. In this video, you'll discover how to use rock salt to add depth and dimension to your paintings, giving them a unique and organic look.", "subpage_snippet": "", "source": "www.felicityandink.com", "link": "https://www.felicityandink.com/how-to-draw-tutorials/how-to-add-texture-to-your-watercolor-paintings-using-rock-salt-step-by-step-video", "content": "Apr 18, 2023 · Learn how to create unique and stunning watercolor textures using rock salt with this step-by-step tutorial. In this video, you'll discover how to use rock salt to add depth and dimension to your paintings, giving them a unique and organic look."}
+{"idx": 2, "title": "How To Use Salt In Watercolor Painting Techniques: The Ultimate...", "date": "", "ddg_snippet": "Jul 28, 2025 · Using salt in watercolor painting is a fantastic way to add unique textures and effects to your artwork. This guide will explore the various salt techniques available to watercolor artists, from creating starry skies to adding subtle graininess to landscapes.", "subpage_snippet": "", "source": "thepaintingadvice.com", "link": "https://thepaintingadvice.com/how-to-use-salt-in-watercolor-painting/", "content": "Jul 28, 2025 · Using salt in watercolor painting is a fantastic way to add unique textures and effects to your artwork. This guide will explore the various salt techniques available to watercolor artists, from creating starry skies to adding subtle graininess to landscapes."}
+{"idx": 3, "title": "Watercolor Salt Technique (Everything You Need to Know)", "date": "", "ddg_snippet": "Many watercolor artists use salt to create spontaneous paintings and achieve interesting textures. This works by sprinkling salt onto the paint while the wash is still wet.", "subpage_snippet": "", "source": "myartaspirations.com", "link": "https://myartaspirations.com/watercolor-salt-technique-everything-you-need-to-know/", "content": "Many watercolor artists use salt to create spontaneous paintings and achieve interesting textures. This works by sprinkling salt onto the paint while the wash is still wet."}
+{"idx": 4, "title": "Experimenting with Salt in Watercolour Painting : A Textured ...", "date": "", "ddg_snippet": "Oct 25, 2024 · Unleash your creativity with the salt technique in watercolour painting ! This blog post explores how adding salt to wet washes creates stunning textures and organic patterns. Learn the science behind the effect , the best types of salt to use, and tips for achieving the perfect application.", "subpage_snippet": "", "source": "www.louisedemasi.com", "link": "https://www.louisedemasi.com/tips/2024/10/26/experimenting-with-salt-in-watercolour-painting", "content": "Oct 25, 2024 · Unleash your creativity with the salt technique in watercolour painting ! This blog post explores how adding salt to wet washes creates stunning textures and organic patterns. Learn the science behind the effect , the best types of salt to use, and tips for achieving the perfect application."}
+{"idx": 5, "title": "How to use salt in watercolour painting for creative effects", "date": "", "ddg_snippet": "When sprinkled onto your painting surface, the salt granules can absorb the water, pulling it into a little star-like shape with textured edges. It creates tiny patches of lightness within the painting where the pigment is moved around, giving a sparkly or dappled effect .", "subpage_snippet": "", "source": "www.emilywassell.co.uk", "link": "https://www.emilywassell.co.uk/watercolour-for-beginners/list-of-techniques/how-to-use-salt-in-watercolour-painting-for-creative-effects/", "content": "When sprinkled onto your painting surface, the salt granules can absorb the water, pulling it into a little star-like shape with textured edges. It creates tiny patches of lightness within the painting where the pigment is moved around, giving a sparkly or dappled effect ."}
+{"idx": 6, "title": "Watercolor and salt : a how-to guide to creating perfect textures...", "date": "", "ddg_snippet": "In this video I show you how to use salt in your watercolor paintings . I take two kinds of salt : fine salt and kosher salt and compare their effects side by side.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=_MA7BXMbPzo", "content": "In this video I show you how to use salt in your watercolor paintings . I take two kinds of salt : fine salt and kosher salt and compare their effects side by side."}
+{"idx": 7, "title": "Watercolor Salt Technique for Easy and Effective Texture", "date": "", "ddg_snippet": "Painting flowers can be magic with watercolor salt texture. You can use salt to create texture in the flowers (see the hummingbird flowers at the top) or add interest in the background.", "subpage_snippet": "", "source": "watsonwatercolor.com", "link": "https://watsonwatercolor.com/watercolor-salt-technique-for-easy-and-effective-texture/", "content": "Painting flowers can be magic with watercolor salt texture. You can use salt to create texture in the flowers (see the hummingbird flowers at the top) or add interest in the background."}
+{"idx": 8, "title": "Watercolors & Salt : A Unique Watercolor Technique - Green Artist", "date": "", "ddg_snippet": "Feb 14, 2024 · In this blog post, I go over how to use salt in your watercolor paintings and other tips and tricks on how this simple household ingredient can elevate your paintings to new heights.", "subpage_snippet": "", "source": "greenartistblog.com", "link": "https://greenartistblog.com/watercolors-salt-a-unique-watercolor-technique/", "content": "Feb 14, 2024 · In this blog post, I go over how to use salt in your watercolor paintings and other tips and tricks on how this simple household ingredient can elevate your paintings to new heights."}
+{"idx": 9, "title": "How To Use Salt In Watercolor Painting - Huckleberry Fine Art", "date": "", "ddg_snippet": "Jan 1, 2023 · Salt can be used in watercolor painting to add texture and interest to a painting. By sprinkling salt on wet paint, the salt will absorb the paint and create a raised, textured effect.", "subpage_snippet": "", "source": "huckleberryfineart.com", "link": "https://huckleberryfineart.com/how-to-use-salt-in-watercolor-painting/", "content": "Jan 1, 2023 · Salt can be used in watercolor painting to add texture and interest to a painting. By sprinkling salt on wet paint, the salt will absorb the paint and create a raised, textured effect."}
diff --git a/data/sampled_jsons/qcnePVejeV_Do_Not_Trust_What_They_Tell_Equation_(8)_conn(a,_b)_Tor_year_2024.jsonl b/data/sampled_jsons/qcnePVejeV_Do_Not_Trust_What_They_Tell_Equation_(8)_conn(a,_b)_Tor_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..09afbe4069418a205669b6373868dfed282ef59f
--- /dev/null
+++ b/data/sampled_jsons/qcnePVejeV_Do_Not_Trust_What_They_Tell_Equation_(8)_conn(a,_b)_Tor_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ranchería Guineo Segunda Sección , Villahermosa - Wikipedia, la...", "date": "", "ddg_snippet": "En la ranchería Guineo 2 . ª sección una familia tipo de cinco integrantes conforman sus ingresos principalmente en la alimentación, seguido de los gastos que se generan en el hogar.", "subpage_snippet": "", "source": "es.m.wikipedia.org", "link": "https://es.m.wikipedia.org/wiki/Ranchería_Guineo_Segunda_Sección,_Villahermosa", "content": "En la ranchería Guineo 2 . ª sección una familia tipo de cinco integrantes conforman sus ingresos principalmente en la alimentación, seguido de los gastos que se generan en el hogar."}
+{"idx": 1, "title": "LOCALIZAN EN EL CUERPO DE... - Noticias Al Momento Tabasco |...", "date": "", "ddg_snippet": "en el municipio de Centro , esta situación sumada a la acumulación de basura en las calles, provocó encharcamientos en varias colonias por las lluvias de 126 mm, ante este escenario, entraron en operación los equipos de emergencia que funcionan con combustible, como parte del protocolo de respuesta, autoridades están desplegando operativos ...", "subpage_snippet": "", "source": "www.facebook.com", "link": "https://www.facebook.com/100063583478503/posts/localizan-en-el-cuerpo-de-una-fémina-está-tarde-fue-hallado-el-cuerpo-de-una-fém/1234067805389324/", "content": "en el municipio de Centro , esta situación sumada a la acumulación de basura en las calles, provocó encharcamientos en varias colonias por las lluvias de 126 mm, ante este escenario, entraron en operación los equipos de emergencia que funcionan con combustible, como parte del protocolo de respuesta, autoridades están desplegando operativos ..."}
+{"idx": 2, "title": "CAMPESTRE DE LA TRINIDAD SC DE RL - Tabasco , Centro , Guineo 2da . ...", "date": "", "ddg_snippet": "CAMPESTRE DE LA TRINIDAD SC DE RL, empresa localizada en el distrito de Guineo 2da . Sección , provincia de Centro , departamento de Tabasco en el sector...", "subpage_snippet": "", "source": "mexicopymes.com", "link": "https://mexicopymes.com/info/campestre-de-la-trinidad-sc-de-rl-72245C47F7DA24DB", "content": "CAMPESTRE DE LA TRINIDAD SC DE RL, empresa localizada en el distrito de Guineo 2da . Sección , provincia de Centro , departamento de Tabasco en el sector..."}
+{"idx": 3, "title": "Guineo Segunda Sección - Mapa - Localidad - Municipio de Centro ...", "date": "", "ddg_snippet": "Guineo Segunda Sección es una localidad en Municipio de Centro , Tabasco . Guineo Segunda Sección se encuentra cerca de la localidad de Pablo Sidar Primera Sección, así como de Pablo L. Sidar.", "subpage_snippet": "", "source": "mapcarta.com", "link": "https://mapcarta.com/es/19951018", "content": "Guineo Segunda Sección es una localidad en Municipio de Centro , Tabasco . Guineo Segunda Sección se encuentra cerca de la localidad de Pablo Sidar Primera Sección, así como de Pablo L. Sidar."}
+{"idx": 4, "title": "Código Postal de Guineo 2da . Sección (Ranchería) en Centro , ...", "date": "", "ddg_snippet": "Código Postal y mapa de Guineo 2da . Sección (Ranchería) en Centro Tabasco . Código Postal 86283.", "subpage_snippet": "", "source": "micodigopostal.org", "link": "https://micodigopostal.org/tabasco/centro/guineo-2da-seccion/", "content": "Código Postal y mapa de Guineo 2da . Sección (Ranchería) en Centro Tabasco . Código Postal 86283."}
+{"idx": 5, "title": "Casa en Ria Guineo 2da . seccion | EasyAviso", "date": "", "ddg_snippet": "Casa en Boquerón 2da . Sección (El Barquillo), Centro Anunciado por ZAEM INMOBILIARIA ZAEM INMOBILIARIA 2 recámaras 1 baño 203 m² de terreno 16.15 m de largo 12.5 m de frente Cantidad de pisos en el edificio: 1 Descripción", "subpage_snippet": "", "source": "www.easyaviso.com", "link": "https://www.easyaviso.com/mx/inmueble/casa-en-ria-guineo-2da-seccion", "content": "Casa en Boquerón 2da . Sección (El Barquillo), Centro Anunciado por ZAEM INMOBILIARIA ZAEM INMOBILIARIA 2 recámaras 1 baño 203 m² de terreno 16.15 m de largo 12.5 m de frente Cantidad de pisos en el edificio: 1 Descripción"}
+{"idx": 6, "title": "Lote de Terreno en venta Villahermosa Centro , Villahermosa, ...", "date": "", "ddg_snippet": "Quinta en venta. Ubicación:carretera río viejo ranchería guineo , segunda sección km 16 . 5 . Superficie de terreno:0,000 m2. Cuenta con los siguientes: 2 palapas....", "subpage_snippet": "", "source": "www.icasas.mx", "link": "https://www.icasas.mx/propiedad/97bb-b692-f860cf31-f2d50ef0ad04-401f", "content": "Quinta en venta. Ubicación:carretera río viejo ranchería guineo , segunda sección km 16 . 5 . Superficie de terreno:0,000 m2. Cuenta con los siguientes: 2 palapas...."}
+{"idx": 7, "title": "Código Postal 86283, Centro , Tabasco - Heraldo", "date": "", "ddg_snippet": "ECOSUR Unidad Villahermosa, Carretera a Reforma Km . 15. 5 s/n. Ra. Guineo 2da . Sección . C.P. 86280. 01 993 313 3101", "subpage_snippet": "", "source": "www.heraldo.com.mx", "link": "https://www.heraldo.com.mx/tabasco/centro/86283/", "content": "ECOSUR Unidad Villahermosa, Carretera a Reforma Km . 15. 5 s/n. Ra. Guineo 2da . Sección . C.P. 86280. 01 993 313 3101"}
+{"idx": 8, "title": "MAESTRÍAS en GUINEO 2DA . SECCION , CENTRO .", "date": "", "ddg_snippet": "Guía completa de Especialidades, Diplomados, MAESTRÍAS en GUINEO 2DA . SECCION , CENTRO . Dirección: CARRETERA REFORMA KILOMETRO 15. 5 S/N, RANCHERIA GUINEO SEGUNDA S, guineo 2da seccion centro tabasco . Teléfono:", "subpage_snippet": "", "source": "guia-tabasco.portaldeeducacion.com.mx", "link": "https://guia-tabasco.portaldeeducacion.com.mx/maestria/guineo-2da-seccion-centro-tabasco/index.htm", "content": "Guía completa de Especialidades, Diplomados, MAESTRÍAS en GUINEO 2DA . SECCION , CENTRO . Dirección: CARRETERA REFORMA KILOMETRO 15. 5 S/N, RANCHERIA GUINEO SEGUNDA S, guineo 2da seccion centro tabasco . Teléfono:"}
+{"idx": 9, "title": "Guineo 2da . Sección ( Centro , Tabasco , Mexico) - Population...", "date": "", "ddg_snippet": "Guineo 2da . Sección ( Centro , Tabasco , Mexico) with population statistics, charts, map, location, weather and web information.", "subpage_snippet": "", "source": "www.citypopulation.de", "link": "https://www.citypopulation.de/en/mexico/tabasco/centro/270040103__guineo_2da_sección/", "content": "Guineo 2da . Sección ( Centro , Tabasco , Mexico) with population statistics, charts, map, location, weather and web information."}
diff --git a/data/sampled_jsons/qcnePVejeV_equation_8_conn(a,b)_connectivity_metric_section_4.3_abnormal_behavioral_pattern_year_2024.jsonl b/data/sampled_jsons/qcnePVejeV_equation_8_conn(a,b)_connectivity_metric_section_4.3_abnormal_behavioral_pattern_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ac20606c0ba18c4e17c2afc51aabc06cfcdd03f3
--- /dev/null
+++ b/data/sampled_jsons/qcnePVejeV_equation_8_conn(a,b)_connectivity_metric_section_4.3_abnormal_behavioral_pattern_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Equation Solver - Mathway", "date": "", "ddg_snippet": "The equation solver allows you to enter your problem and solve the equation to see the result. Solve in one variable or many.", "subpage_snippet": "", "source": "www.mathway.com", "link": "https://www.mathway.com/Calculator/equation-solver", "content": "The equation solver allows you to enter your problem and solve the equation to see the result. Solve in one variable or many."}
+{"idx": 1, "title": "Chapter #1: Functional Connectivity Demonstration - Read the Docs", "date": "", "ddg_snippet": "The reasons for this transform, which is done automatically in the CONN toolbox, will be discussed in a later chapter on 1st-level analysis. Functional Connectivity Analysis with Neurosynth Another useful tool for understanding functional connectivity is Neurosynth.", "subpage_snippet": "", "source": "andysbrainbook.readthedocs.io", "link": "https://andysbrainbook.readthedocs.io/en/latest/FunctionalConnectivity/CONN_ShortCourse/CONN_01_FSL_Demo.html", "content": "The reasons for this transform, which is done automatically in the CONN toolbox, will be discussed in a later chapter on 1st-level analysis. Functional Connectivity Analysis with Neurosynth Another useful tool for understanding functional connectivity is Neurosynth."}
+{"idx": 2, "title": "Equation Calculator - Symbolab", "date": "", "ddg_snippet": "Free equations calculator - solve linear, quadratic, polynomial, radical, exponential and logarithmic equations with all the steps. Type in any equation to get the solution, steps and graph", "subpage_snippet": "", "source": "www.symbolab.com", "link": "https://www.symbolab.com/solver/equation-calculator", "content": "Free equations calculator - solve linear, quadratic, polynomial, radical, exponential and logarithmic equations with all the steps. Type in any equation to get the solution, steps and graph"}
+{"idx": 3, "title": "CONN : functional connectivity toolbox - NITRC", "date": "", "ddg_snippet": "CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). CONN includes a rich set of connectivity analyses (seed-based correlations, ROI-to-ROI graph analyses, ICA, masked ICA, fc-MVPA, generalized PPI, ALFF, ICC, GCOR, LCOR, etc.) in a simple-to-use and powerful ...", "subpage_snippet": "", "source": "www.nitrc.org", "link": "https://www.nitrc.org/projects/conn/", "content": "CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). CONN includes a rich set of connectivity analyses (seed-based correlations, ROI-to-ROI graph analyses, ICA, masked ICA, fc-MVPA, generalized PPI, ALFF, ICC, GCOR, LCOR, etc.) in a simple-to-use and powerful ..."}
+{"idx": 4, "title": "Exposing Malicious Accomplices in Tor via Anomalous ...", "date": "", "ddg_snippet": "by Y Yao — This feature describes the rela- tionship of two nodes in term of their abnormal behavioral patterns , which can be identified through the method in Section 4.2.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "by Y Yao — This feature describes the rela- tionship of two nodes in term of their abnormal behavioral patterns , which can be identified through the method in Section 4.2."}
+{"idx": 5, "title": "Solve - Step-by-Step Math Problem Solver", "date": "", "ddg_snippet": "The equations section lets you solve an equation or system of equations . You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require.", "subpage_snippet": "", "source": "quickmath.com", "link": "https://quickmath.com/", "content": "The equations section lets you solve an equation or system of equations . You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require."}
+{"idx": 6, "title": "GeoGebra Math Solver - Step by Step Problem Solver", "date": "", "ddg_snippet": "Get accurate solutions and step-by-step explanations for algebra and other math problems with the free GeoGebra Math Solver. Enhance your problem-solving skills while learning how to solve equations on your own. Try it now!", "subpage_snippet": "", "source": "www.geogebra.org", "link": "https://www.geogebra.org/solver/default/", "content": "Get accurate solutions and step-by-step explanations for algebra and other math problems with the free GeoGebra Math Solver. Enhance your problem-solving skills while learning how to solve equations on your own. Try it now!"}
+{"idx": 7, "title": "Desmos | Graphing Calculator", "date": "", "ddg_snippet": "Explore math with our beautiful, free online graphing calculator. Graph functions, plot points, visualize algebraic equations , add sliders, animate graphs, and more.", "subpage_snippet": "", "source": "www.desmos.com", "link": "https://www.desmos.com/calculator", "content": "Explore math with our beautiful, free online graphing calculator. Graph functions, plot points, visualize algebraic equations , add sliders, animate graphs, and more."}
+{"idx": 8, "title": "Step-by-Step Calculator - Symbolab", "date": "", "ddg_snippet": "Symbolab is the best step by step calculator for a wide range of physics problems, including mechanics, electricity and magnetism, and thermodynamics. It shows you the steps and explanations for each problem, so you can learn as you go.", "subpage_snippet": "", "source": "www.symbolab.com", "link": "https://www.symbolab.com/solver/step-by-step", "content": "Symbolab is the best step by step calculator for a wide range of physics problems, including mechanics, electricity and magnetism, and thermodynamics. It shows you the steps and explanations for each problem, so you can learn as you go."}
+{"idx": 9, "title": "Mathway | Algebra Problem Solver", "date": "", "ddg_snippet": "Free math problem solver answers your algebra homework questions with step-by-step explanations.", "subpage_snippet": "", "source": "www.mathway.com", "link": "https://www.mathway.com/algebra", "content": "Free math problem solver answers your algebra homework questions with step-by-step explanations."}
diff --git "a/data/sampled_jsons/qtuxDy2qEB_parallel_simulation_log-concave_sampling_O(log(d\316\265))_complexity_year_2024.jsonl" "b/data/sampled_jsons/qtuxDy2qEB_parallel_simulation_log-concave_sampling_O(log(d\316\265))_complexity_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..6c7c9b4a20e5b3978a09fcf2108f3994ff12d705
--- /dev/null
+++ "b/data/sampled_jsons/qtuxDy2qEB_parallel_simulation_log-concave_sampling_O(log(d\316\265))_complexity_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Parallel Simulation for Log - concave Sampling and... | OpenReview", "date": "", "ddg_snippet": "Keywords: parallel sampling , log - concave sampling , diffusion model. Submission Number: 9486.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qtuxDy2qEB&referrer=[the+profile+of+Masashi+Sugiyama](/profile?id=~Masashi_Sugiyama1)", "content": "Keywords: parallel sampling , log - concave sampling , diffusion model. Submission Number: 9486."}
+{"idx": 1, "title": "The adaptive complexity of parallelized log - concave sampling", "date": "", "ddg_snippet": "For unconstrained sampling , we examine distributions that are log -smooth or log -Lipschitz and log strongly or non-strongly concave . We show that an almost linear iteration algorithm cannot return a sample with a specific exponentially small error under total variation distance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.13045v2", "content": "For unconstrained sampling , we examine distributions that are log -smooth or log -Lipschitz and log strongly or non-strongly concave . We show that an almost linear iteration algorithm cannot return a sample with a specific exponentially small error under total variation distance."}
+{"idx": 2, "title": "Beyond log - concave sampling – Off the convex path", "date": "", "ddg_snippet": "Beyond log - concave sampling . Holden Lee, Andrej Risteski • Sep 19, 2020 • 15 minute read.Before we move on to non- log - concave distributions, though, we need to understand the basic algorithm for sampling and its guarantees for log - concave distributions.", "subpage_snippet": "", "source": "www.offconvex.org", "link": "https://www.offconvex.org/2020/09/19/beyondlogconvavesampling/", "content": "Beyond log - concave sampling . Holden Lee, Andrej Risteski • Sep 19, 2020 • 15 minute read.Before we move on to non- log - concave distributions, though, we need to understand the basic algorithm for sampling and its guarantees for log - concave distributions."}
+{"idx": 3, "title": "Accelerating Diffusion Models with Parallel Sampling", "date": "", "ddg_snippet": "• Compared with log - concave sampling [130], M being of order O ( d ) instead of O ( d ) therein is partly due to the time independence of the score function ∇ log p(·) in general sampling tasks.The randomized midpoint method for log - concave sampling .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/f162fa05675e3db4a733aafc081653cf-Paper-Conference.pdf", "content": "• Compared with log - concave sampling [130], M being of order O ( d ) instead of O ( d ) therein is partly due to the time independence of the score function ∇ log p(·) in general sampling tasks.The randomized midpoint method for log - concave sampling ."}
+{"idx": 4, "title": "A Separation in Heavy-Tailed Sampling", "date": "", "ddg_snippet": "[CGL+22] explored the query complexity of sampling from strongly log - concave distributions in one-dimensional settings.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/77f33e8bd80345de4aea8554bbe5a4da-Paper-Conference.pdf", "content": "[CGL+22] explored the query complexity of sampling from strongly log - concave distributions in one-dimensional settings."}
+{"idx": 5, "title": "Оценка сложности алгоритмов, или Что такое О( log n)", "date": "", "ddg_snippet": "O ( log n) — логарифмическая сложность. Простейший пример — бинарный поиск. Если массив отсортирован, мы можем проверить, есть ли в нём какое-то конкретное значение, методом деления пополам.", "subpage_snippet": "", "source": "tproger.ru", "link": "https://tproger.ru/articles/computational-complexity-explained", "content": "O ( log n) — логарифмическая сложность. Простейший пример — бинарный поиск. Если массив отсортирован, мы можем проверить, есть ли в нём какое-то конкретное значение, методом деления пополам."}
+{"idx": 6, "title": "(Open Access) An Efficient Sampling Algorithm for Non-smooth...", "date": "", "ddg_snippet": "The Randomized Midpoint Method for Log - Concave Sampling .On sampling from a log - concave density using kinetic Langevin diffusions.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/an-efficient-sampling-algorithm-for-non-smooth-composite-5b6os1usik", "content": "The Randomized Midpoint Method for Log - Concave Sampling .On sampling from a log - concave density using kinetic Langevin diffusions."}
+{"idx": 7, "title": "On the Computational Complexity of Metropolis-Adjusted", "date": "", "ddg_snippet": "log log M0 dependence on the warmth parameter M0 under certain regime (of step size h), and a sub-optimal O ( d )-dependence on the dimension. On the other hand, Chewi et al.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume25/23-0783/23-0783.pdf", "content": "log log M0 dependence on the warmth parameter M0 under certain regime (of step size h), and a sub-optimal O ( d )-dependence on the dimension. On the other hand, Chewi et al."}
+{"idx": 8, "title": "(PDF) Covariance estimation using Markov chain Monte Carlo", "date": "", "ddg_snippet": "PDF | We investigate the complexity of covariance matrix estimation for Gibbs distributions based on dependent samples from a Markov chain.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385140847_Covariance_estimation_using_Markov_chain_Monte_Carlo", "content": "PDF | We investigate the complexity of covariance matrix estimation for Gibbs distributions based on dependent samples from a Markov chain."}
+{"idx": 9, "title": "Fisher", "date": "", "ddg_snippet": "Separation between log - concave and non- log - concave sampling . Post-processing lemma. High-accuracy Fisher information guarantees for log - concave targets.What is the query complexity of sampling from a β- log -smooth but possibly non- log - concave target.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v201/chewi23b/chewi23b.pdf", "content": "Separation between log - concave and non- log - concave sampling . Post-processing lemma. High-accuracy Fisher information guarantees for log - concave targets.What is the query complexity of sampling from a β- log -smooth but possibly non- log - concave target."}
diff --git a/data/sampled_jsons/random_segment_length_TOP-ERL.jsonl b/data/sampled_jsons/random_segment_length_TOP-ERL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4dd3d8055475d9055c12488fd7d697e4022f0f7c
--- /dev/null
+++ b/data/sampled_jsons/random_segment_length_TOP-ERL.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Erlang -- erl_driver", "date": "", "ddg_snippet": "... length /1 ... enif_get_atom_ length () ... enif_get_list_ length ()", "subpage_snippet": "", "source": "erlang.org", "link": "http://erlang.org/documentation/doc-12.0-rc3/erts-12.0/doc/html/erl_driver.html", "content": "... length /1 ... enif_get_atom_ length () ... enif_get_list_ length ()"}
+{"idx": 1, "title": "Erlang -- erl_driver", "date": "", "ddg_snippet": "User's Guide Reference Manual Release Notes PDF Top ... Erlang Run-Time System Application (ERTS) Reference Manual Version 7.3", "subpage_snippet": "", "source": "www.erlang.org", "link": "https://www.erlang.org/docs/18/man/erl_driver", "content": "User's Guide Reference Manual Release Notes PDF Top ... Erlang Run-Time System Application (ERTS) Reference Manual Version 7.3"}
+{"idx": 2, "title": "Erlang -- erl_driver", "date": "", "ddg_snippet": "An Erlang driver is a library containing a set of native driver callback functions that the Erlang VM calls when certain events occur.", "subpage_snippet": "", "source": "erlang.org", "link": "http://erlang.org/documentation/doc-5.9.3/erts-5.9.3/doc/html/erl_driver.html", "content": "An Erlang driver is a library containing a set of native driver callback functions that the Erlang VM calls when certain events occur."}
+{"idx": 3, "title": "Erlang -- erl_driver", "date": "", "ddg_snippet": "User's Guide Reference Manual Release Notes PDF Top ... Erlang Run-Time System Application (ERTS) Reference Manual Version 8.2", "subpage_snippet": "", "source": "erlang.org", "link": "http://erlang.org/documentation/doc-8.2/erts-8.2/doc/html/erl_driver.html", "content": "User's Guide Reference Manual Release Notes PDF Top ... Erlang Run-Time System Application (ERTS) Reference Manual Version 8.2"}
+{"idx": 4, "title": "Erlang -- erl_driver", "date": "", "ddg_snippet": "An Erlang driver is a library containing a set of native driver callback functions that the Erlang VM calls when certain events occur.", "subpage_snippet": "", "source": "erlang.org", "link": "http://erlang.org/documentation/doc-6.3/erts-6.3/doc/html/erl_driver.html", "content": "An Erlang driver is a library containing a set of native driver callback functions that the Erlang VM calls when certain events occur."}
+{"idx": 5, "title": "SyncMapV2: Robust and Adaptive Unsupervised Segmentation", "date": "", "ddg_snippet": "Semantic segmentation, a specialized form, classifies each pixel into predefined semantic classes, typically requiring large labeled datasets and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.16297v3", "content": "Semantic segmentation, a specialized form, classifies each pixel into predefined semantic classes, typically requiring large labeled datasets and ..."}
+{"idx": 6, "title": "Erlang -- erl_driver", "date": "", "ddg_snippet": "... length /1 ... enif_get_atom_ length () ... enif_get_list_ length ()", "subpage_snippet": "", "source": "zxq9.com", "link": "http://zxq9.com/erlang/docs/reg/21.0/erts-10.0/doc/html/erl_driver.html", "content": "... length /1 ... enif_get_atom_ length () ... enif_get_list_ length ()"}
+{"idx": 7, "title": "Erlang -- erl_driver", "date": "", "ddg_snippet": "... length /1 ... enif_get_atom_ length () ... enif_get_list_ length ()", "subpage_snippet": "", "source": "zxq9.com", "link": "https://zxq9.com/erlang/docs/reg/21.0/erts-10.0/doc/html/erl_driver.html", "content": "... length /1 ... enif_get_atom_ length () ... enif_get_list_ length ()"}
+{"idx": 8, "title": "R.T.Russell: BBC BASIC for SDL 2.0", "date": "", "ddg_snippet": "The online BBC BASIC for SDL 2.0 manual ... Random access files ... Random access inventory program ... Segment override", "subpage_snippet": "", "source": "www.bbcbasic.com", "link": "https://www.bbcbasic.com/bbcsdl/manual/index.html", "content": "The online BBC BASIC for SDL 2.0 manual ... Random access files ... Random access inventory program ... Segment override"}
+{"idx": 9, "title": "R.T.Russell: BBC BASIC for Windows", "date": "", "ddg_snippet": "The online BBC BASIC for Windows manual ... Random access files ... Random access inventory program ... Segment override", "subpage_snippet": "", "source": "www.bbcbasic.com", "link": "https://www.bbcbasic.com/bbcwin/manual/index.html", "content": "The online BBC BASIC for Windows manual ... Random access files ... Random access inventory program ... Segment override"}
diff --git a/data/sampled_jsons/randomized_midpoint_method_Shen_Lee_2019_algorithm_description_discretization.jsonl b/data/sampled_jsons/randomized_midpoint_method_Shen_Lee_2019_algorithm_description_discretization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b0377c144071a9726212bc4f724c126f68dc0aa3
--- /dev/null
+++ b/data/sampled_jsons/randomized_midpoint_method_Shen_Lee_2019_algorithm_description_discretization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — The Randomized Midpoint Method for Log-Concave Sampling. Part of Advances in ... Authors. Ruoqi Shen , Yin Tat Lee . Abstract. Sampling from log-concave ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/8483-the-randomized-midpoint-method-for-log-concave-sampling", "content": "by R Shen · 2019 · Cited by 161 — The Randomized Midpoint Method for Log-Concave Sampling. Part of Advances in ... Authors. Ruoqi Shen , Yin Tat Lee . Abstract. Sampling from log-concave ..."}
+{"idx": 1, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — [Submitted on 12 Sep 2019 ]. Title:The Randomized Midpoint Method for Log-Concave Sampling. Authors:Ruoqi Shen , Yin Tat Lee . View a PDF of the paper titled ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.05503", "content": "by R Shen · 2019 · Cited by 161 — [Submitted on 12 Sep 2019 ]. Title:The Randomized Midpoint Method for Log-Concave Sampling. Authors:Ruoqi Shen , Yin Tat Lee . View a PDF of the paper titled ..."}
+{"idx": 2, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — In Section 4.2, we show how we discretize (3). 5. Page 6. Algorithm 1 Randomized Midpoint Method for ULD ... [29] Yin Tat Lee , Zhao Song, and Santosh S Vempala.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Paper.pdf", "content": "by R Shen · 2019 · Cited by 161 — In Section 4.2, we show how we discretize (3). 5. Page 6. Algorithm 1 Randomized Midpoint Method for ULD ... [29] Yin Tat Lee , Zhao Song, and Santosh S Vempala."}
+{"idx": 3, "title": "The randomized midpoint method for log-concave sampling", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 160 — The randomized midpoint method for log-concave sampling. AUTHORs: Ruoqi Shen . Ruoqi Shen . University of Washington. View Profile. , Yin Tat Lee . Yin Tat Lee .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3454287.3454475", "content": "by R Shen · 2019 · Cited by 160 — The randomized midpoint method for log-concave sampling. AUTHORs: Ruoqi Shen . Ruoqi Shen . University of Washington. View Profile. , Yin Tat Lee . Yin Tat Lee ."}
+{"idx": 4, "title": "Randomized Midpoint Method for Log-Concave Sampling ...", "date": "", "ddg_snippet": "24 May 2025 — The randomized midpoint method , introduced in [SL19] provides a discretization ... [SL19] Ruoqi Shen and Yin Tat Lee , The randomized midpoint ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15379v2", "content": "24 May 2025 — The randomized midpoint method , introduced in [SL19] provides a discretization ... [SL19] Ruoqi Shen and Yin Tat Lee , The randomized midpoint ..."}
+{"idx": 5, "title": "Faster Diffusion Sampling with Randomized Midpoints", "date": "", "ddg_snippet": "by S Gupta — In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling ( Shen & Lee , 2019 ). We prove that this ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MT3aOfXIbY", "content": "by S Gupta — In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling ( Shen & Lee , 2019 ). We prove that this ..."}
+{"idx": 6, "title": "[PDF] The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "The Randomized Midpoint Method for Log-Concave Sampling · Ruoqi Shen , Y. Lee · Published in Neural Information Processing… 12 September 2019 · Mathematics, ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Randomized-Midpoint-Method-for-Log-Concave-Shen-Lee/ba7e89968438f816cb6169c56533abec62034b93", "content": "The Randomized Midpoint Method for Log-Concave Sampling · Ruoqi Shen , Y. Lee · Published in Neural Information Processing… 12 September 2019 · Mathematics, ..."}
+{"idx": 7, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "Spotlight. The Randomized Midpoint Method for Log-Concave Sampling. Ruoqi Shen · Yin Tat Lee . [ Abstract ] [ Visit Track 4 Session 2 ].", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2019/spotlight/15747", "content": "Spotlight. The Randomized Midpoint Method for Log-Concave Sampling. Ruoqi Shen · Yin Tat Lee . [ Abstract ] [ Visit Track 4 Session 2 ]."}
+{"idx": 8, "title": "FASTER DIFFUSION SAMPLING WITH RANDOMIZED ...", "date": "", "ddg_snippet": "algorithm that makes use of the Shen and Lee's randomized midpoint method ( Shen & Lee , 2019 ). Algorithm 1 PREDICTORSTEP (SEQUENTIAL). Input parameters ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=MT3aOfXIbY", "content": "algorithm that makes use of the Shen and Lee's randomized midpoint method ( Shen & Lee , 2019 ). Algorithm 1 PREDICTORSTEP (SEQUENTIAL). Input parameters ..."}
+{"idx": 9, "title": "The Poisson Midpoint Method for Langevin Dynamics", "date": "", "ddg_snippet": "9 Dec 2024 — [40] Ruoqi Shen and Yin Tat Lee . The randomized midpoint method for log-concave sampling. Advances in Neural Information Processing Systems ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/94671", "content": "9 Dec 2024 — [40] Ruoqi Shen and Yin Tat Lee . The randomized midpoint method for log-concave sampling. Advances in Neural Information Processing Systems ..."}
diff --git a/data/sampled_jsons/reasoning_bias_small_initialization_language_models_mechanism_gradient_magnitude_year_2024.jsonl b/data/sampled_jsons/reasoning_bias_small_initialization_language_models_mechanism_gradient_magnitude_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..04017d18d7cca6f9861bafe8fa31bc77b70e208e
--- /dev/null
+++ b/data/sampled_jsons/reasoning_bias_small_initialization_language_models_mechanism_gradient_magnitude_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04375", "content": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ..."}
+{"idx": 1, "title": "An Analysis for Reasoning Bias of Language Models with Small ...", "date": "", "ddg_snippet": "This research investigates how the parameter initialization scale influences the training dynamics and task preferences of Large Language Models . The study reveals that smaller initialization scales lead LLMs to favor reasoning tasks by promoting differentiated embedding spaces and specialized self-attention mechanisms , whereas larger scales bias models towards memorization.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.04375v2", "content": "This research investigates how the parameter initialization scale influences the training dynamics and task preferences of Large Language Models . The study reveals that smaller initialization scales lead LLMs to favor reasoning tasks by promoting differentiated embedding spaces and specialized self-attention mechanisms , whereas larger scales bias models towards memorization."}
+{"idx": 2, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848015_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization", "content": "This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models ."}
+{"idx": 3, "title": "PDF Gr Ter: Gradients Over Reasoning Makes Smaller Language Models Strong ...", "date": "", "ddg_snippet": "Large number of samples need to be repeatedly evaluated Prompts are also very large Target model is small Improving performance from smaller models technically depend on the reasoning of larger models - Curriculum Learning", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28876.pdf", "content": "Large number of samples need to be repeatedly evaluated Prompts are also very large Target model is small Improving performance from smaller models technically depend on the reasoning of larger models - Curriculum Learning"}
+{"idx": 4, "title": "GReaTer: Gradients Over Reasoning Makes Smaller Language Models Strong ...", "date": "", "ddg_snippet": "GReaTer: Gradients Over Reasoning Makes Smaller Language Models Strong Prompt Optimizers Sarkar Snigdha Sarathi Das, Ryo Kamoi, Bo Pang, Yusen Zhang, Caiming Xiong, Rui Zhang", "subpage_snippet": "", "source": "ryokamoi.github.io", "link": "https://ryokamoi.github.io/publication/das-2024-greater/", "content": "GReaTer: Gradients Over Reasoning Makes Smaller Language Models Strong Prompt Optimizers Sarkar Snigdha Sarathi Das, Ryo Kamoi, Bo Pang, Yusen Zhang, Caiming Xiong, Rui Zhang"}
+{"idx": 5, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks. We validate this reasoning bias via real datasets and meticulously designed anchor functions.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2502.04375v1_enmode", "content": "We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks. We validate this reasoning bias via real datasets and meticulously designed anchor functions."}
+{"idx": 6, "title": "Teaching Small Language Models to Reason - ACL Anthology", "date": "", "ddg_snippet": "Abstract Chain of thought prompting successfully improves the reasoning capabilities of large language models , achieving state of the art results on a range of datasets. However, these reasoning capabilities only appear to emerge in models with at least tens of billions of parameters.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.acl-short.151/", "content": "Abstract Chain of thought prompting successfully improves the reasoning capabilities of large language models , achieving state of the art results on a range of datasets. However, these reasoning capabilities only appear to emerge in models with at least tens of billions of parameters."}
+{"idx": 7, "title": "An Analysis for Reasoning Bias of Language Models with Small...", "date": "", "ddg_snippet": "This paper discusses the impact of initialization of language models on their trained performance on memorization and reasoning tasks. The paper uses proof to show reasoning tasks prefer smaller initialization while memorization tasks prefer large initialization . The authors attribute such behavior to being more differentiated in the embedding space at an early stage, which is further verified ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4HQaMUYWAT", "content": "This paper discusses the impact of initialization of language models on their trained performance on memorization and reasoning tasks. The paper uses proof to show reasoning tasks prefer smaller initialization while memorization tasks prefer large initialization . The authors attribute such behavior to being more differentiated in the embedding space at an early stage, which is further verified ..."}
+{"idx": 8, "title": "An Analysis for Reasoning Bias of Language Models With Small Initialization", "date": "", "ddg_snippet": "In this paper, we investigate the underlying mechanism of which small initialization scales promote a reasoning pref-erence in language models . Our findings suggest that the label distribution of tokens plays a pivotal role in shaping the embedding space, thereby influencing the learning dynamics and task complexity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375v1", "content": "In this paper, we investigate the underlying mechanism of which small initialization scales promote a reasoning pref-erence in language models . Our findings suggest that the label distribution of tokens plays a pivotal role in shaping the embedding space, thereby influencing the learning dynamics and task complexity."}
+{"idx": 9, "title": "PDF Applied Math Ph.D. Seminar - amphds.yingzhouli.com", "date": "", "ddg_snippet": "Abstract: Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating excep-tional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas ...", "subpage_snippet": "", "source": "amphds.yingzhouli.com", "link": "https://amphds.yingzhouli.com/download_file/2025Spring/20250529.pdf", "content": "Abstract: Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating excep-tional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas ..."}
diff --git a/data/sampled_jsons/recent_methods_for_calculating_interventional_probabilities_in_causal_Bayesian_networks_since_2010_year_2010.jsonl b/data/sampled_jsons/recent_methods_for_calculating_interventional_probabilities_in_causal_Bayesian_networks_since_2010_year_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e9e745f6348acf2b478f89fd13bbd4302791cee
--- /dev/null
+++ b/data/sampled_jsons/recent_methods_for_calculating_interventional_probabilities_in_causal_Bayesian_networks_since_2010_year_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bayesian network - Wikipedia", "date": "", "ddg_snippet": "Formally, Bayesian networks are directed acyclic graphs (DAGs) whose nodes represent variables in the Bayesian sense: they may be observable ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Bayesian_network", "content": "Formally, Bayesian networks are directed acyclic graphs (DAGs) whose nodes represent variables in the Bayesian sense: they may be observable ..."}
+{"idx": 1, "title": "Assimilative Causal Inference", "date": "", "ddg_snippet": "Significance Statement Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14825v1", "content": "Significance Statement Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time ..."}
+{"idx": 2, "title": "Discovering Dynamic Effective Connectome of Brain with Bayesian", "date": "", "ddg_snippet": "Moreover, in Liu2023Learning , the NOTEARS-PFL method is proposed for simultaneous learning of multiple Bayesian networks with continuous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2309.07080v3", "content": "Moreover, in Liu2023Learning , the NOTEARS-PFL method is proposed for simultaneous learning of multiple Bayesian networks with continuous ..."}
+{"idx": 3, "title": "Bayesian Graphs of Intelligent Causation", "date": "", "ddg_snippet": "... Causal Bayesian Network (CBN) (Pearl, 2000 ; Spirtes et al., 1993 ) , and more recently the flow network (Smith and Figueroa, 2007 ) , the regulatory ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.03957v1", "content": "... Causal Bayesian Network (CBN) (Pearl, 2000 ; Spirtes et al., 1993 ) , and more recently the flow network (Smith and Figueroa, 2007 ) , the regulatory ..."}
+{"idx": 4, "title": "Probably Approximately Correct Causal Discovery", "date": "", "ddg_snippet": "A similar protocol for sample size will be beneficial for other causal methods as well and, more interestingly, provide approximate guarantees for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.18903v1", "content": "A similar protocol for sample size will be beneficial for other causal methods as well and, more interestingly, provide approximate guarantees for ..."}
+{"idx": 5, "title": "Bayesian – Andi Fugard", "date": "", "ddg_snippet": "... a convenient way to change the prior probabilities of each gender; so in LGBT spaces the prior probability for nonbinary raises from 1% to 20% since ...", "subpage_snippet": "", "source": "andifugard.info", "link": "https://andifugard.info/tag/bayesian/", "content": "... a convenient way to change the prior probabilities of each gender; so in LGBT spaces the prior probability for nonbinary raises from 1% to 20% since ..."}
+{"idx": 6, "title": "Frontiers | Review of Causal Discovery Methods Based on", "date": "", "ddg_snippet": "This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.00524/full", "content": "This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three ..."}
+{"idx": 7, "title": "Bayesian Applications in Marketing | Request PDF", "date": "", "ddg_snippet": "The use of informative priors including hierarchical models is essential for successful Bayesian applications in marketing.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228167480_Bayesian_Applications_in_Marketing", "content": "The use of informative priors including hierarchical models is essential for successful Bayesian applications in marketing."}
+{"idx": 8, "title": "JP2001184430A - Method and system for using bayesian belief", "date": "", "ddg_snippet": "SOLUTION: A Bayesian belief network is used as an exemplary diagnosing tool which models the relation between the input and output of a risk ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/JP2001184430A/en", "content": "SOLUTION: A Bayesian belief network is used as an exemplary diagnosing tool which models the relation between the input and output of a risk ..."}
+{"idx": 9, "title": "Bayes network – Andi Fugard", "date": "", "ddg_snippet": "... a convenient way to change the prior probabilities of each gender; so in LGBT spaces the prior probability for nonbinary raises from 1% to 20% since ...", "subpage_snippet": "", "source": "andifugard.info", "link": "https://andifugard.info/tag/bayes-network/", "content": "... a convenient way to change the prior probabilities of each gender; so in LGBT spaces the prior probability for nonbinary raises from 1% to 20% since ..."}
diff --git a/data/sampled_jsons/reinforcement_learning_trajectory_replanning_robotics_year_2023.jsonl b/data/sampled_jsons/reinforcement_learning_trajectory_replanning_robotics_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..980969176be70152d984ffbead1f509a67e6866e
--- /dev/null
+++ b/data/sampled_jsons/reinforcement_learning_trajectory_replanning_robotics_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Model-based reinforcement learning with neural network dynamics", "date": "", "ddg_snippet": "While model-free deep reinforcement learning algorithms are capable of learning a wide range of robotic skills, they typically suffer from very high ...", "subpage_snippet": "", "source": "robohub.org", "link": "https://robohub.org/model-based-reinforcement-learning-with-neural-network-dynamics/", "content": "While model-free deep reinforcement learning algorithms are capable of learning a wide range of robotic skills, they typically suffer from very high ..."}
+{"idx": 1, "title": "Learning robot objectives from physical human interaction -", "date": "", "ddg_snippet": "With this in mind, we take inspiration from inverse reinforcement learning (IRL), where the robot observes some behavior (e.g., being pushed away ...", "subpage_snippet": "", "source": "robohub.org", "link": "https://robohub.org/learning-robot-objectives-from-physical-human-interaction/", "content": "With this in mind, we take inspiration from inverse reinforcement learning (IRL), where the robot observes some behavior (e.g., being pushed away ..."}
+{"idx": 2, "title": "Model-based Reinforcement Learning with Neural Network Dynamics", "date": "", "ddg_snippet": "While model-free deep reinforcement learning algorithms are capable of learning a wide range of robotic skills, they typically suffer from very high ...", "subpage_snippet": "", "source": "bair.berkeley.edu", "link": "https://bair.berkeley.edu/blog/2017/11/30/model-based-rl/", "content": "While model-free deep reinforcement learning algorithms are capable of learning a wide range of robotic skills, they typically suffer from very high ..."}
+{"idx": 3, "title": "Learning Robot Objectives from Physical Human Interaction", "date": "", "ddg_snippet": "With this in mind, we take inspiration from inverse reinforcement learning (IRL), where the robot observes some behavior (e.g., being pushed away ...", "subpage_snippet": "", "source": "bair.berkeley.edu", "link": "https://bair.berkeley.edu/blog/2018/02/06/phri/", "content": "With this in mind, we take inspiration from inverse reinforcement learning (IRL), where the robot observes some behavior (e.g., being pushed away ..."}
+{"idx": 4, "title": "Any-point Trajectory Modeling for Policy Learning", "date": "", "ddg_snippet": "... collecting 130 K 130 𝐾 130K 130 italic_K trajectories in [ 6 ] took 17 months, making data collection a major bottleneck in robot learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00025v3", "content": "... collecting 130 K 130 𝐾 130K 130 italic_K trajectories in [ 6 ] took 17 months, making data collection a major bottleneck in robot learning ..."}
+{"idx": 5, "title": "Learning-Augmented Model-Based Multi-Robot Planning for", "date": "", "ddg_snippet": "Reinforcement Learning (RL) based approaches, for example, can directly learn a multi-robot policy from noisy observations of the environment [ 5 , 6 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.06129v1", "content": "Reinforcement Learning (RL) based approaches, for example, can directly learn a multi-robot policy from noisy observations of the environment [ 5 , 6 ..."}
+{"idx": 6, "title": "Robotics Projects | FAST Lab", "date": "", "ddg_snippet": "Introduction Deep learning and reinforcement learning are bringing changes into robotics . ... state estimation, onboard mapping, optimal trajectory ...", "subpage_snippet": "", "source": "zju-fast.com", "link": "http://zju-fast.com/projects-all/robotics-projects/", "content": "Introduction Deep learning and reinforcement learning are bringing changes into robotics . ... state estimation, onboard mapping, optimal trajectory ..."}
+{"idx": 7, "title": "2019 Posters - Robotics Institute Summer Scholars (RISS)", "date": "", "ddg_snippet": "Developing Ethical Agents via Context-Sensitive Modular Inverse Reinforcement Learning ... Robotics Institute Summer Scholars Carnegie Mellon ...", "subpage_snippet": "", "source": "riss.ri.cmu.edu", "link": "https://riss.ri.cmu.edu/research_showcase/2019-posters/", "content": "Developing Ethical Agents via Context-Sensitive Modular Inverse Reinforcement Learning ... Robotics Institute Summer Scholars Carnegie Mellon ..."}
+{"idx": 8, "title": "Path Planning Course using C++ Online | Best Robotics Course :", "date": "", "ddg_snippet": "The robot will detect a collision and will change its trajectory to escape the contact as fast as possible and move away safely.", "subpage_snippet": "", "source": "skill-lync.com", "link": "https://skill-lync.com/computer-science-engineering-courses/path-planning-trajectory-optimization", "content": "The robot will detect a collision and will change its trajectory to escape the contact as fast as possible and move away safely."}
+{"idx": 9, "title": "Learning for Adaptive and Reactive Robot Control: A Dynamical", "date": "", "ddg_snippet": "... include learning from demonstration, optimization, and reinforcement learning , and using dynamical systems in learning control laws, trajectory ...", "subpage_snippet": "", "source": "scanlibs.com", "link": "https://scanlibs.com/learning-adaptive-robot-control/", "content": "... include learning from demonstration, optimization, and reinforcement learning , and using dynamical systems in learning control laws, trajectory ..."}
diff --git a/data/sampled_jsons/remote_sensing_benchmark_dataset_image_size_resolution_comparison_table.jsonl b/data/sampled_jsons/remote_sensing_benchmark_dataset_image_size_resolution_comparison_table.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e34a98d6ae2e687b12e0d479331c758f53d0ad7c
--- /dev/null
+++ b/data/sampled_jsons/remote_sensing_benchmark_dataset_image_size_resolution_comparison_table.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Datasets and Benchmarks for Landsat Foundation Models", "date": "", "ddg_snippet": "10 Jun 2025 — The dataset contains 96 × 96 300m spatial resolution images of Sentinel-3 imagery with 21 multispectral bands. The image classification labels ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08780v1", "content": "10 Jun 2025 — The dataset contains 96 × 96 300m spatial resolution images of Sentinel-3 imagery with 21 multispectral bands. The image classification labels ..."}
+{"idx": 1, "title": "A Comprehensive Benchmark for Optical Remote Sensing ...", "date": "", "ddg_snippet": "The comparative performance analysis of the three super- resolution models, presented in Table II, offers a detailed assessment across the OpenSR-test datasets , ...", "subpage_snippet": "", "source": "www.techrxiv.org", "link": "https://www.techrxiv.org/users/760184/articles/735467/master/file/data/opensrtest/opensrtest.pdf", "content": "The comparative performance analysis of the three super- resolution models, presented in Table II, offers a detailed assessment across the OpenSR-test datasets , ..."}
+{"idx": 2, "title": "Remote Sensing Image Scene Classification: Benchmark ...", "date": "", "ddg_snippet": "by G Cheng · 2017 · Cited by 3213 — This paper reviews the recent progress of remote sensing image scene classification, proposes a large-scale benchmark dataset, and evaluates a number of state- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1703.00121", "content": "by G Cheng · 2017 · Cited by 3213 — This paper reviews the recent progress of remote sensing image scene classification, proposes a large-scale benchmark dataset, and evaluates a number of state- ..."}
+{"idx": 3, "title": "A benchmark dataset for global high-resolution rural road ...", "date": "", "ddg_snippet": "by N Wang · 2025 · Cited by 3 — In this article, a large-scale high-resolution remote sensing road dataset, termed WHU-RuR+, is proposed for rural road extraction, which contains 36,098 pairs ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1569843225001657", "content": "by N Wang · 2025 · Cited by 3 — In this article, a large-scale high-resolution remote sensing road dataset, termed WHU-RuR+, is proposed for rural road extraction, which contains 36,098 pairs ..."}
+{"idx": 4, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "9 Dec 2024 — Remote sensing images often feature very small objects (sometimes only 10 pixels) and require complex spatial reasoning from an overhead view.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/97530", "content": "9 Dec 2024 — Remote sensing images often feature very small objects (sometimes only 10 pixels) and require complex spatial reasoning from an overhead view."}
+{"idx": 5, "title": "On Creating Benchmark Dataset for Aerial Image Interpretation", "date": "", "ddg_snippet": "TABLE III: Comparison among different RS Image object detection datasets . Datasets . Annot. #Cat. #Instances. # Images . Resolution (m).", "subpage_snippet": "", "source": "captain-whu.github.io", "link": "https://captain-whu.github.io/DiRS/files/Paper.pdf", "content": "TABLE III: Comparison among different RS Image object detection datasets . Datasets . Annot. #Cat. #Instances. # Images . Resolution (m)."}
+{"idx": 6, "title": "Globe230k: A Benchmark Dense-Pixel Annotation Dataset ...", "date": "", "ddg_snippet": "by Q Shi · 2023 · Cited by 29 — Recent advances in Landsat, ASTER, SPOT, Sentinel-2 satellite and processing capabilities facilitateobservation to higher resolution , 30-m resolution images ...", "subpage_snippet": "", "source": "spj.science.org", "link": "https://spj.science.org/doi/10.34133/remotesensing.0078", "content": "by Q Shi · 2023 · Cited by 29 — Recent advances in Landsat, ASTER, SPOT, Sentinel-2 satellite and processing capabilities facilitateobservation to higher resolution , 30-m resolution images ..."}
+{"idx": 7, "title": "Revisiting Pre-trained Remote Sensing Model Benchmarks", "date": "", "ddg_snippet": "by I Corley · 2024 · Cited by 34 — In Tables 1-5 we report performance from each method at the native resolution of the dataset and after resizing each image to 224x224 and observe performance.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024W/PBVS/papers/Corley_Revisiting_Pre-trained_Remote_Sensing_Model_Benchmarks_Resizing_and_Normalization_Matters_CVPRW_2024_paper.pdf", "content": "by I Corley · 2024 · Cited by 34 — In Tables 1-5 we report performance from each method at the native resolution of the dataset and after resizing each image to 224x224 and observe performance."}
+{"idx": 8, "title": "A Comprehensive Survey of Super-Resolution Remote ...", "date": "", "ddg_snippet": "by AD Vu · 2025 — We classify and evaluate these datasets across multiple dimensions : image count, spatial resolution , sensor and platform diversity, temporal ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/11126996.pdf", "content": "by AD Vu · 2025 — We classify and evaluate these datasets across multiple dimensions : image count, spatial resolution , sensor and platform diversity, temporal ..."}
+{"idx": 9, "title": "Creating and leveraging SAR benchmark datasets to ...", "date": "", "ddg_snippet": "by X Zhang · 2025 — We introduce three categories for extracting SAR feature maps from SAR images and propose corresponding radarcoding methods to transform reference data into ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1569843225003693", "content": "by X Zhang · 2025 — We introduce three categories for extracting SAR feature maps from SAR images and propose corresponding radarcoding methods to transform reference data into ..."}
diff --git a/data/sampled_jsons/remote_sensing_benchmark_image_size_2024_2025_ultra_high_resolution.jsonl b/data/sampled_jsons/remote_sensing_benchmark_image_size_2024_2025_ultra_high_resolution.jsonl
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index 0000000000000000000000000000000000000000..1bd288fce7f49ef67b502c7e355c500c6dbc10fa
--- /dev/null
+++ b/data/sampled_jsons/remote_sensing_benchmark_image_size_2024_2025_ultra_high_resolution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) A Vision Centric Remote Sensing Benchmark", "date": "", "ddg_snippet": "Constructing a benchmark for remote sensing imagery using CLIP-blind pairs. Remote Sensing , 2024 . 2. [26] Xiangtao Zheng, Binqiang Wang, Xingqian Du, and Xiao", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390038158_A_Vision_Centric_Remote_Sensing_Benchmark", "content": "Constructing a benchmark for remote sensing imagery using CLIP-blind pairs. Remote Sensing , 2024 . 2. [26] Xiangtao Zheng, Binqiang Wang, Xingqian Du, and Xiao"}
+{"idx": 1, "title": "RSI-CB: A Large-Scale Remote Sensing Image Classification", "date": "", "ddg_snippet": "However, the remote sensing image field is lacking a large-scale benchmark .Considering the different image size requirements of the DCNN, we construct two datasets of 256 × 256 and 128 × 128 pixel sizes (RSI-CB256 and RSI-CB128, respectively) with 0.3–3-m spatial resolutions .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1705.10450", "content": "However, the remote sensing image field is lacking a large-scale benchmark .Considering the different image size requirements of the DCNN, we construct two datasets of 256 × 256 and 128 × 128 pixel sizes (RSI-CB256 and RSI-CB128, respectively) with 0.3–3-m spatial resolutions ."}
+{"idx": 2, "title": "Poly Kernel Inception Network for Remote Sensing Detection", "date": "", "ddg_snippet": "The image resolution at the testing stage remains consistent with the training stage.Fair1m: A benchmark dataset for fine-grained ob-ject recognition in high - resolution remote sensing imagery.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Cai_Poly_Kernel_Inception_Network_for_Remote_Sensing_Detection_CVPR_2024_paper.pdf", "content": "The image resolution at the testing stage remains consistent with the training stage.Fair1m: A benchmark dataset for fine-grained ob-ject recognition in high - resolution remote sensing imagery."}
+{"idx": 3, "title": "CROMA: Remote Sensing Representations with", "date": "", "ddg_snippet": "BigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrieval [Software and Data Sets].", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/11822e84689e631615199db3b75cd0e4-Paper-Conference.pdf", "content": "BigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrieval [Software and Data Sets]."}
+{"idx": 4, "title": "isaaccorley/resize-is-all-you-need - Githubissues", "date": "", "ddg_snippet": "Remote sensing benchmark datasets, e.g. EuroSAT -- 64 x 64, commonly have small image sizes due to patches being extracted from large satellite tiles.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/isaaccorley/resize-is-all-you-need/readme", "content": "Remote sensing benchmark datasets, e.g. EuroSAT -- 64 x 64, commonly have small image sizes due to patches being extracted from large satellite tiles."}
+{"idx": 5, "title": "When Large Vision-Language Model Meets Large Remote Sensing", "date": "", "ddg_snippet": "Advances in satellite imaging technology allow for the acquisition of large remote sensing images (RSIs) that cover extensive ground areas and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.07588v3", "content": "Advances in satellite imaging technology allow for the acquisition of large remote sensing images (RSIs) that cover extensive ground areas and ..."}
+{"idx": 6, "title": "Tokenize Image Patches: Global Context Fusion for Effective", "date": "", "ddg_snippet": "Finally, recognizing the lack of benchmark datasets for haze removal in large images , we have developed an ultra - high - resolution haze removal dataset ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09621v1", "content": "Finally, recognizing the lack of benchmark datasets for haze removal in large images , we have developed an ultra - high - resolution haze removal dataset ..."}
+{"idx": 7, "title": "DRWKV: Focusing on Object Edges for Low-Light Image Enhancement", "date": "", "ddg_snippet": "Low-light image enhancement ... We conducted extensive image -enhancement and low-light object-tracking experiments on five benchmark datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.18594v1", "content": "Low-light image enhancement ... We conducted extensive image -enhancement and low-light object-tracking experiments on five benchmark datasets."}
+{"idx": 8, "title": "The best portable projectors of 2025 tested by a home cinema", "date": "", "ddg_snippet": "The main difference between compact projectors and full- size examples is that the latter offers better image quality and brightness,” explains Matt ...", "subpage_snippet": "", "source": "www.telegraph.co.uk", "link": "https://www.telegraph.co.uk/recommended/tech/reviews/best-portable-projectors/", "content": "The main difference between compact projectors and full- size examples is that the latter offers better image quality and brightness,” explains Matt ..."}
+{"idx": 9, "title": "GitHub - qiangsun89/UAV-datasets: Datasets for deep learning with...", "date": "", "ddg_snippet": "Awesome_Satellite_ Benchmark _Datasets. awesome- remote - sensing -change-detection -> dedicated to change detection.The image patch size on the ground is 1.2 x 1.2 km with variable image size depending on the channel resolution .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/qiangsun89/UAV-datasets", "content": "Awesome_Satellite_ Benchmark _Datasets. awesome- remote - sensing -change-detection -> dedicated to change detection.The image patch size on the ground is 1.2 x 1.2 km with variable image size depending on the channel resolution ."}
diff --git a/data/sampled_jsons/remote_sensing_benchmark_larger_than_8500x8500_OR_10000x10000_OR_larger_image_size.jsonl b/data/sampled_jsons/remote_sensing_benchmark_larger_than_8500x8500_OR_10000x10000_OR_larger_image_size.jsonl
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index 0000000000000000000000000000000000000000..111a45d445c9b75cde5a7926359ed3e11330c02b
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Large Dataset and Benchmark Towards Remote Sensing ...", "date": "", "ddg_snippet": "29 Jan 2025 — This paper introduces NUDT4MSTAR, a large - scale SAR dataset for remote sensing target recognition in the wild, including 40 vehicle target types ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.13354v2", "content": "29 Jan 2025 — This paper introduces NUDT4MSTAR, a large - scale SAR dataset for remote sensing target recognition in the wild, including 40 vehicle target types ..."}
+{"idx": 1, "title": "Bigearthnet: A Large-Scale Benchmark Archive for Remote ...", "date": "", "ddg_snippet": "by G Sumbul · 2019 · Cited by 635 — The BigEarthNet is significantly larger than the existing archives in remote sensing (RS) and thus is much more convenient to be used as a training source in ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/8900532", "content": "by G Sumbul · 2019 · Cited by 635 — The BigEarthNet is significantly larger than the existing archives in remote sensing (RS) and thus is much more convenient to be used as a training source in ..."}
+{"idx": 2, "title": "FAIR1M: A benchmark dataset for fine-grained object ...", "date": "", "ddg_snippet": "by X Sun · 2022 · Cited by 473 — In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 40,000 images for Fine-grAined object recognItion in high- ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0924271621003269", "content": "by X Sun · 2022 · Cited by 473 — In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 40,000 images for Fine-grAined object recognItion in high- ..."}
+{"idx": 3, "title": "[1705.10450] RSI-CB: A Large Scale Remote Sensing ...", "date": "", "ddg_snippet": "by H Li · 2017 · Cited by 109 — In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsource data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1705.10450", "content": "by H Li · 2017 · Cited by 109 — In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsource data."}
+{"idx": 4, "title": "A Large-Scale Benchmark Archive for Remote Sensing ...", "date": "", "ddg_snippet": "by G Sumbul · 2018 · Cited by 635 — The BigEarthNet consists of 590, 326 Sentinel-2 image patches, each of which is a section of i) 120 × 120 pixels for 10m bands; ii) 60 × 60 pixels for 20m bands ... 4 pages", "subpage_snippet": "", "source": "bigearth.net", "link": "https://bigearth.net/static/documents/BigEarthNet_IGARSS_2019.pdf", "content": "by G Sumbul · 2018 · Cited by 635 — The BigEarthNet consists of 590, 326 Sentinel-2 image patches, each of which is a section of i) 120 × 120 pixels for 10m bands; ii) 60 × 60 pixels for 20m bands ... 4 pages"}
+{"idx": 5, "title": "A benchmark for scene classification of high spatial ...", "date": "", "ddg_snippet": "This paper presents a new and large dataset consisting of 5000 high- resolution remote sensing images which is manually labeled in 20 semantic classes for scene ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "http://ieeexplore.ieee.org/abstract/document/7326956/", "content": "This paper presents a new and large dataset consisting of 5000 high- resolution remote sensing images which is manually labeled in 20 semantic classes for scene ..."}
+{"idx": 6, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "9 Dec 2024 — We introduce a new benchmark designed to advance the development of general-purpose, large - scale vision-language models for remote sensing ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/97530", "content": "9 Dec 2024 — We introduce a new benchmark designed to advance the development of general-purpose, large - scale vision-language models for remote sensing ..."}
+{"idx": 7, "title": "A Comprehensive Benchmark for Optical Remote Sensing ...", "date": "", "ddg_snippet": "The larger the distance between the SR and the. LR reference image , the more effective the gain in spatial resolution and detail representation. Correctness ...", "subpage_snippet": "", "source": "www.techrxiv.org", "link": "https://www.techrxiv.org/users/760184/articles/735467/master/file/data/opensrtest/opensrtest.pdf", "content": "The larger the distance between the SR and the. LR reference image , the more effective the gain in spatial resolution and detail representation. Correctness ..."}
+{"idx": 8, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "by F Wang · 2025 · Cited by 7 — In this paper, we introduce XLRS-Bench, a comprehen- sive benchmark for evaluating the perception and rea- soning capabilities of multimodal large language ... 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "by F Wang · 2025 · Cited by 7 — In this paper, we introduce XLRS-Bench, a comprehen- sive benchmark for evaluating the perception and rea- soning capabilities of multimodal large language ... 12 pages"}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/reverse_A2C_loss_equation_formula_reinforcement_learning.jsonl b/data/sampled_jsons/reverse_A2C_loss_equation_formula_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bb489e587e2441326c2c25eeb962887601b255c8
--- /dev/null
+++ b/data/sampled_jsons/reverse_A2C_loss_equation_formula_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Reinforcement Learning Algorithms and Equations - Stanford University", "date": "", "ddg_snippet": "This write-up is intended as a collection of common algorithms and equations in reinforcement learning , deep reinforcement learning , decision making under uncertainty, approximate dynamic programming, and stochastic optimization methods.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~mossr/pdf/rl_algs.pdf", "content": "This write-up is intended as a collection of common algorithms and equations in reinforcement learning , deep reinforcement learning , decision making under uncertainty, approximate dynamic programming, and stochastic optimization methods."}
+{"idx": 1, "title": "Advantage Actor-Critic (A2C) algorithm in Reinforcement Learning with ...", "date": "", "ddg_snippet": "Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym Combining DQNs and REINFORCE algorithm for training agents", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science-in-your-pocket/advantage-actor-critic-a2c-algorithm-in-reinforcement-learning-with-codes-and-examples-using-e810273c0c9e", "content": "Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym Combining DQNs and REINFORCE algorithm for training agents"}
+{"idx": 2, "title": "Advantage Actor Critic (A2C) - Hugging Face", "date": "", "ddg_snippet": "If you want to dive deeper into the question of variance and bias tradeoff in Deep Reinforcement Learning , you can check these two articles: - Making Sense of the Bias / Variance Trade-off in (Deep) Reinforcement Learning - Bias-variance Tradeoff in Reinforcement Learning Advantage Actor Critic ( A2C ) Reducing variance with Actor-Critic methods The solution to reducing the variance of Reinforce ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/deep-rl-a2c", "content": "If you want to dive deeper into the question of variance and bias tradeoff in Deep Reinforcement Learning , you can check these two articles: - Making Sense of the Bias / Variance Trade-off in (Deep) Reinforcement Learning - Bias-variance Tradeoff in Reinforcement Learning Advantage Actor Critic ( A2C ) Reducing variance with Actor-Critic methods The solution to reducing the variance of Reinforce ..."}
+{"idx": 3, "title": "Actor-Critic Algorithm in Reinforcement Learning", "date": "", "ddg_snippet": "A2C helps reduce the variance of the policy gradient, leading to better learning performance. Asynchronous Advantage Actor-Critic (A3C): A3C is an extension of A2C that uses multiple agents (threads) running in parallel to update the policy asynchronously. This allows for more stable and faster learning by reducing correlations between updates.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/actor-critic-algorithm-in-reinforcement-learning/", "content": "A2C helps reduce the variance of the policy gradient, leading to better learning performance. Asynchronous Advantage Actor-Critic (A3C): A3C is an extension of A2C that uses multiple agents (threads) running in parallel to update the policy asynchronously. This allows for more stable and faster learning by reducing correlations between updates."}
+{"idx": 4, "title": "6.4 Implementing A2C | Reinforcement Learning - The Actor-Critic ...", "date": "", "ddg_snippet": "A complete look at the Actor-Critic ( A2C ) algorithm, used in deep reinforcement learning , which enables a learned reinforcing signal to be more informative for a policy than the rewards available from an environment.", "subpage_snippet": "", "source": "www.informit.com", "link": "https://www.informit.com/articles/article.aspx?p=2995356&seqNum=4", "content": "A complete look at the Actor-Critic ( A2C ) algorithm, used in deep reinforcement learning , which enables a learned reinforcing signal to be more informative for a policy than the rewards available from an environment."}
+{"idx": 5, "title": "Advantage Actor Critic Tutorial: minA2C - Towards Data Science", "date": "", "ddg_snippet": "In the field of Reinforcement Learning , the Advantage Actor Critic ( A2C ) algorithm combines two types of Reinforcement Learning algorithms (Policy Based and Value Based) together. Policy Based agents directly learn a policy (a probability distribution of actions) mapping input states to output actions.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/advantage-actor-critic-tutorial-mina2c-7a3249962fc8/", "content": "In the field of Reinforcement Learning , the Advantage Actor Critic ( A2C ) algorithm combines two types of Reinforcement Learning algorithms (Policy Based and Value Based) together. Policy Based agents directly learn a policy (a probability distribution of actions) mapping input states to output actions."}
+{"idx": 6, "title": "A2CLoss — torchrl 0.9 documentation", "date": "", "ddg_snippet": "A2CLoss class torchrl.objectives.A2CLoss(*args, **kwargs) [source] TorchRL implementation of the A2C loss . A2C (Advantage Actor Critic) is a model-free, online RL algorithm that uses parallel rollouts of n steps to update the policy, relying on the REINFORCE estimator to compute the gradient. It also adds an entropy term to the objective function to improve exploration. For more details ...", "subpage_snippet": "", "source": "docs.pytorch.org", "link": "https://docs.pytorch.org/rl/stable/reference/generated/torchrl.objectives.A2CLoss.html", "content": "A2CLoss class torchrl.objectives.A2CLoss(*args, **kwargs) [source] TorchRL implementation of the A2C loss . A2C (Advantage Actor Critic) is a model-free, online RL algorithm that uses parallel rollouts of n steps to update the policy, relying on the REINFORCE estimator to compute the gradient. It also adds an entropy term to the objective function to improve exploration. For more details ..."}
+{"idx": 7, "title": "Advantage Actor Critic (A2C) — NEORL 1.8.1b documentation", "date": "", "ddg_snippet": "A2C belongs to the actor-critic family, and usually considered as the state-of-the-art in the reinforcement learning domain. A2C is parallel and supports all types of spaces.", "subpage_snippet": "", "source": "neorl.readthedocs.io", "link": "https://neorl.readthedocs.io/en/latest/modules/neural/a2c.html", "content": "A2C belongs to the actor-critic family, and usually considered as the state-of-the-art in the reinforcement learning domain. A2C is parallel and supports all types of spaces."}
+{"idx": 8, "title": "Actor-Critic Methods, Advantage Actor-Critic (A2C) and Generalized ...", "date": "", "ddg_snippet": "Actor-Critic Methods, Advantage Actor-Critic ( A2C ) and Generalized Advantage Estimation (GAE) 18 minute read", "subpage_snippet": "", "source": "avandekleut.github.io", "link": "https://avandekleut.github.io/a2c/", "content": "Actor-Critic Methods, Advantage Actor-Critic ( A2C ) and Generalized Advantage Estimation (GAE) 18 minute read"}
+{"idx": 9, "title": "A2C and A3C Algorithms | Advanced RL - apxml.com", "date": "", "ddg_snippet": "It also implicitly describes the synchronous variant. Reinforcement Learning : An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - A standard textbook on reinforcement learning , providing theoretical foundations for policy gradients, value functions, and actor-critic methods.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/advanced-reinforcement-learning/chapter-3-advanced-policy-gradients-actor-critic/a2c-a3c", "content": "It also implicitly describes the synchronous variant. Reinforcement Learning : An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - A standard textbook on reinforcement learning , providing theoretical foundations for policy gradients, value functions, and actor-critic methods."}
diff --git a/data/sampled_jsons/reverse_cross_entropy_RCE_loss_equation_symmetric_cross_entropy_Wang_2019.jsonl b/data/sampled_jsons/reverse_cross_entropy_RCE_loss_equation_symmetric_cross_entropy_Wang_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..359777de99959664b0215ee187ee9b6dbea5412e
--- /dev/null
+++ b/data/sampled_jsons/reverse_cross_entropy_RCE_loss_equation_symmetric_cross_entropy_Wang_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Cross Entropy for Robust Learning with Noisy ...", "date": "", "ddg_snippet": "by Y Wang · 2019 · Cited by 1323 — In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes (\"easy\" classes), but more surprisingly, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1908.06112", "content": "by Y Wang · 2019 · Cited by 1323 — In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes (\"easy\" classes), but more surprisingly, ..."}
+{"idx": 1, "title": "Can Cross Entropy Loss Be Robust to Label Noise?", "date": "", "ddg_snippet": "by L Feng · Cited by 220 — Symmetric. Cross Entropy (SCE ) [Wang et al., 2019] combines CCE and. Reverse Cross Entropy (RCE, which is equivalent to MAE) by tuning the regularization ... 7 pages", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2020/0305.pdf", "content": "by L Feng · Cited by 220 — Symmetric. Cross Entropy (SCE ) [Wang et al., 2019] combines CCE and. Reverse Cross Entropy (RCE, which is equivalent to MAE) by tuning the regularization ... 7 pages"}
+{"idx": 2, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "To enhance stability, we adapt reverse cross - entropy ( RCE ) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44897", "content": "To enhance stability, we adapt reverse cross - entropy ( RCE ) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance ..."}
+{"idx": 3, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · Cited by 1 — In this work, we enhance the stability of the RL training procedure by adapting reverse cross - entropy ( RCE ) from supervised learning for noisy ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9oq0iY2Jxx", "content": "by JS Byun · Cited by 1 — In this work, we enhance the stability of the RL training procedure by adapting reverse cross - entropy ( RCE ) from supervised learning for noisy ..."}
+{"idx": 4, "title": "Asymmetric Loss Functions for Learning with Noisy Labels", "date": "", "ddg_snippet": "by X Zhou · 2021 · Cited by 91 — Reverse Cross Entropy ( RCE ) proposed in ( Wang et al.,. 2019b ) is also belonging to the kind of symmetric loss , which is actually the variant of MAE. Ma et al ... 11 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v139/zhou21f/zhou21f.pdf", "content": "by X Zhou · 2021 · Cited by 91 — Reverse Cross Entropy ( RCE ) proposed in ( Wang et al.,. 2019b ) is also belonging to the kind of symmetric loss , which is actually the variant of MAE. Ma et al ... 11 pages"}
+{"idx": 5, "title": "DYNAMIC LOSS FOR LEARNING WITH LABEL NOISE", "date": "", "ddg_snippet": "by XC Li — ... symmetric cross entropy (SCE) ( Wang et al., 2019b ) equals a convex combination of CE and MAE, and active passive loss (Ma et al., 2020) just replaces CE in ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=J_kUIC1DNHJ", "content": "by XC Li — ... symmetric cross entropy (SCE) ( Wang et al., 2019b ) equals a convex combination of CE and MAE, and active passive loss (Ma et al., 2020) just replaces CE in ..."}
+{"idx": 6, "title": "Correntropy based label loss for multi-classifiation on deep ...", "date": "", "ddg_snippet": "by Q Deng · 2025 — This study introduces a novel robust loss function designed for handling noisy labels, referred to as the Correntropy -based Label Loss (CLL).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0925231225011725", "content": "by Q Deng · 2025 — This study introduces a novel robust loss function designed for handling noisy labels, referred to as the Correntropy -based Label Loss (CLL)."}
+{"idx": 7, "title": "Learning from Noisy Labels with Complementary Loss ...", "date": "", "ddg_snippet": "by DB Wang · 2021 · Cited by 45 — Wang et al. (2019) proposed the Symmetric Cross Entropy (SCE ) by combining Reverse Cross Entropy (RCE) (which satisfies the symmetric condition) ...", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/17213/17213-13-20707-1-2-20210518.pdf", "content": "by DB Wang · 2021 · Cited by 45 — Wang et al. (2019) proposed the Symmetric Cross Entropy (SCE ) by combining Reverse Cross Entropy (RCE) (which satisfies the symmetric condition) ..."}
+{"idx": 8, "title": "Unsupervised Hallucination Detection by Inspecting Reasoning", "date": "", "ddg_snippet": "... Loss Function.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10004v1", "content": "... Loss Function."}
+{"idx": 9, "title": "Potential Energy based Mixture Model for Noisy Label ...", "date": "", "ddg_snippet": "2 May 2024 — Furthermore, Wang proposed Symmetric Cross Entropy loss (SCE) Wang et al., (2019) , which combines CE loss with Reverse Cross Entropy loss.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.01186v1", "content": "2 May 2024 — Furthermore, Wang proposed Symmetric Cross Entropy loss (SCE) Wang et al., (2019) , which combines CE loss with Reverse Cross Entropy loss."}
diff --git a/data/sampled_jsons/risk-aware_preference-based_reinforcement_learning_linear_reward_functions_limitation.jsonl b/data/sampled_jsons/risk-aware_preference-based_reinforcement_learning_linear_reward_functions_limitation.jsonl
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index 0000000000000000000000000000000000000000..37d1c47339217c104561d6bcfd18bd0c850c1eb6
--- /dev/null
+++ b/data/sampled_jsons/risk-aware_preference-based_reinforcement_learning_linear_reward_functions_limitation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of..."}
+{"idx": 1, "title": "RA-PbRL: Provably Efficient Risk - Aware", "date": "", "ddg_snippet": "Risk - aware Reinforcement Learning . Problem Set-up and Preliminary Analysis. PbRL MDP.RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.23569v4", "content": "Risk - aware Reinforcement Learning . Problem Set-up and Preliminary Analysis. PbRL MDP.RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning ."}
+{"idx": 2, "title": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ra-pbrl-provably-efficient-risk-aware-preference", "content": "Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 3, "title": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "# Risk - aware preference - based reinforcement learning (PbRL) addresses a critical gap in traditional PbRL, which predominantly focuses on maximizing average reward without considering risk.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "# Risk - aware preference - based reinforcement learning (PbRL) addresses a critical gap in traditional PbRL, which predominantly focuses on maximizing average reward without considering risk."}
+{"idx": 4, "title": "Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "4. Mixed reinforcement learning training on reasoning problems (verifiable rewards ) with general preference tuning reward models to polish the model.", "subpage_snippet": "", "source": "rlhfbook.com", "link": "https://rlhfbook.com/book.pdf", "content": "4. Mixed reinforcement learning training on reasoning problems (verifiable rewards ) with general preference tuning reward models to polish the model."}
+{"idx": 5, "title": "ICLR Poster A Distributional Approach to Uncertainty- Aware ...", "date": "", "ddg_snippet": "Offline Preference - based Reinforcement Learning (PbRL) provides an effective alternative to address the complexity of reward design by learning policies from offline datasets that contain human preferences between trajectory pairs.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/29655", "content": "Offline Preference - based Reinforcement Learning (PbRL) provides an effective alternative to address the complexity of reward design by learning policies from offline datasets that contain human preferences between trajectory pairs."}
+{"idx": 6, "title": "Preference - Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Preference - Based Reinforcement Learning Algorithms. Related Problem Settings. Preference - based reinforcement learning (PbRL) is a suitable tool for learning from quali-tative, non-numeric rewards .", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Preference - Based Reinforcement Learning Algorithms. Related Problem Settings. Preference - based reinforcement learning (PbRL) is a suitable tool for learning from quali-tative, non-numeric rewards ."}
+{"idx": 7, "title": "Dueling RL: Reinforcement Learning with", "date": "", "ddg_snippet": "Active preference - based learning of reward functions . Preference - based reinforcement learning : A preliminary survey.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/saha23a/saha23a.pdf", "content": "Active preference - based learning of reward functions . Preference - based reinforcement learning : A preliminary survey."}
+{"idx": 8, "title": "Interpretable Preference - based Reinforcement Learning ... | DeepAI", "date": "", "ddg_snippet": "Interpretable Preference - based Reinforcement Learning with Tree-Structured Reward Functions .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/interpretable-preference-based-reinforcement-learning-with-tree-structured-reward-functions", "content": "Interpretable Preference - based Reinforcement Learning with Tree-Structured Reward Functions ."}
+{"idx": 9, "title": "Reinforcement Learning for Portfolio Rebalancing", "date": "", "ddg_snippet": "Explore how reinforcement learning revolutionizes portfolio rebalancing with adaptive strategies that respond to real-time market conditions.", "subpage_snippet": "", "source": "www.luxalgo.com", "link": "https://www.luxalgo.com/blog/reinforcement-learning-for-portfolio-rebalancing/", "content": "Explore how reinforcement learning revolutionizes portfolio rebalancing with adaptive strategies that respond to real-time market conditions."}
diff --git a/data/sampled_jsons/robust_causal_representation_learning_noise_invariant_2024.jsonl b/data/sampled_jsons/robust_causal_representation_learning_noise_invariant_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c966c25cc0dafe7dce9354d1d580315b402a8f14
--- /dev/null
+++ b/data/sampled_jsons/robust_causal_representation_learning_noise_invariant_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ROBUST Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of ROBUST is having or exhibiting strength or vigorous health. How to use robust in a sentence. Synonym Discussion of Robust .", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/robust", "content": "The meaning of ROBUST is having or exhibiting strength or vigorous health. How to use robust in a sentence. Synonym Discussion of Robust ."}
+{"idx": 1, "title": "ROBUST | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "ROBUST definition: 1. (of a person or animal) strong and healthy: 2. (of an object or system) strong and unlikely to…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/robust", "content": "ROBUST definition: 1. (of a person or animal) strong and healthy: 2. (of an object or system) strong and unlikely to…. Learn more."}
+{"idx": 2, "title": "ROBUST Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Robust definition: strong and healthy; hardy; vigorous .. See examples of ROBUST used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/robust", "content": "Robust definition: strong and healthy; hardy; vigorous .. See examples of ROBUST used in a sentence."}
+{"idx": 3, "title": "Robust - definition of robust by The Free Dictionary", "date": "", "ddg_snippet": "Define robust . robust synonyms, robust pronunciation, robust translation, English dictionary definition of robust . adj. 1. Full of health and strength; vigorous.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/robust", "content": "Define robust . robust synonyms, robust pronunciation, robust translation, English dictionary definition of robust . adj. 1. Full of health and strength; vigorous."}
+{"idx": 4, "title": "ROBUST definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "Someone or something that is robust is very strong or healthy . More women than men go to the doctor. Perhaps men are more robust or worry less? We've always specialised in making very robust, simply designed machinery.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/robust", "content": "Someone or something that is robust is very strong or healthy . More women than men go to the doctor. Perhaps men are more robust or worry less? We've always specialised in making very robust, simply designed machinery."}
+{"idx": 5, "title": "robust adjective - Definition, pictures, pronunciation and usage...", "date": "", "ddg_snippet": "Definition of robust adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/robust", "content": "Definition of robust adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 6, "title": "Robust - Meaning and Examples: The Complete Guide to Using \" ...", "date": "", "ddg_snippet": "Sep 3, 2025 · At its core, \"robust\" is a descriptive word used to convey strength, durability, or vigor . It comes from Latin roots meaning \"strong\" or \"hard,\" which gives us a clue about its primary connotations.", "subpage_snippet": "", "source": "egrammarbook.com", "link": "https://egrammarbook.com/robust-meaning-and-examples/", "content": "Sep 3, 2025 · At its core, \"robust\" is a descriptive word used to convey strength, durability, or vigor . It comes from Latin roots meaning \"strong\" or \"hard,\" which gives us a clue about its primary connotations."}
+{"idx": 7, "title": "robust - WordReference.com Dictionary of English", "date": "", "ddg_snippet": "strong and healthy; hardy; vigorous: a robust young man; a robust faith; a robust mind. strongly or stoutly built: his robust frame. suited to or requiring bodily strength or endurance: robust exercise. rough, rude, or boisterous: robust drinkers and dancers. rich and full-bodied: the robust flavor of freshly brewed coffee.", "subpage_snippet": "", "source": "www.wordreference.com", "link": "https://www.wordreference.com/definition/robust", "content": "strong and healthy; hardy; vigorous: a robust young man; a robust faith; a robust mind. strongly or stoutly built: his robust frame. suited to or requiring bodily strength or endurance: robust exercise. rough, rude, or boisterous: robust drinkers and dancers. rich and full-bodied: the robust flavor of freshly brewed coffee."}
+{"idx": 8, "title": "ROBUST Synonyms: 190 Similar and Opposite Words - Merriam-Webster", "date": "", "ddg_snippet": "Some common synonyms of robust are hale, healthy, sound, well, and wholesome. While all these words mean \"enjoying or indicative of good health,\" robust implies the opposite of all that is delicate or sickly.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/thesaurus/robust", "content": "Some common synonyms of robust are hale, healthy, sound, well, and wholesome. While all these words mean \"enjoying or indicative of good health,\" robust implies the opposite of all that is delicate or sickly."}
+{"idx": 9, "title": "robust - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Sep 16, 2025 · Requiring strength or vigor. Sensible (of intellect etc.); straightforward, not given to or confused by uncertainty or subtlety.", "subpage_snippet": "", "source": "en.wiktionary.org", "link": "https://en.wiktionary.org/wiki/robust", "content": "Sep 16, 2025 · Requiring strength or vigor. Sensible (of intellect etc.); straightforward, not given to or confused by uncertainty or subtlety."}
diff --git a/data/sampled_jsons/safety_fine-tuning_learning_rate_5e-5_1e-5_medium_small_DPO_supervised_year_2024.jsonl b/data/sampled_jsons/safety_fine-tuning_learning_rate_5e-5_1e-5_medium_small_DPO_supervised_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How to Choose the Right Learning Rate in Deep ... - Medium", "date": "", "ddg_snippet": "Feb 18, 2025 · Learning rate warmup is a technique used to gradually increase the learning rate from a small initial value to the target learning rate over the first few training iterations.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sahin.samia/how-to-choose-the-right-learning-rate-in-deep-learning-with-pytorch-690de782b405", "content": "Feb 18, 2025 · Learning rate warmup is a technique used to gradually increase the learning rate from a small initial value to the target learning rate over the first few training iterations."}
+{"idx": 1, "title": "What is considered as a small learning rate? : r ... - Reddit", "date": "", "ddg_snippet": "The author suggests of using one of the following parameters learning rates : 3e-4, 1e -4, 5e-5 , 3e- 5 I know that a small learning rate makes our model learn very slow, however it also helps prevent overfitting, in contrast to big learning which learns faster but it can lead to overfitting. When I use a learning rate of 1e-5 I get the following ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LanguageTechnology/comments/nmljji/what_is_considered_as_a_small_learning_rate/", "content": "The author suggests of using one of the following parameters learning rates : 3e-4, 1e -4, 5e-5 , 3e- 5 I know that a small learning rate makes our model learn very slow, however it also helps prevent overfitting, in contrast to big learning which learns faster but it can lead to overfitting. When I use a learning rate of 1e-5 I get the following ..."}
+{"idx": 2, "title": "Understanding the Learning Rate in LLM Fine-Tuning", "date": "", "ddg_snippet": "For LLM fine-tuning , the learning rate is often much smaller than what's used during initial pre-training, typically ranging from 1e − 5 to 5e − 5 . This is because the model already has a strong foundational understanding, and we're just nudging it towards specialization.", "subpage_snippet": "", "source": "www.metriccoders.com", "link": "https://www.metriccoders.com/post/understanding-the-learning-rate-in-llm-fine-tuning", "content": "For LLM fine-tuning , the learning rate is often much smaller than what's used during initial pre-training, typically ranging from 1e − 5 to 5e − 5 . This is because the model already has a strong foundational understanding, and we're just nudging it towards specialization."}
+{"idx": 3, "title": "A process for choosing the learning rate - Continuum Labs", "date": "", "ddg_snippet": "Apr 3, 2024 · Begin by considering the learning rate values commonly used in practice for LLM fine-tuning , such as 2e- 5 or 5e -6 (as shown in Table VI of the paper). These values can serve as a starting point for your learning rate exploration.", "subpage_snippet": "", "source": "training.continuumlabs.ai", "link": "https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/a-process-for-choosing-the-learning-rate", "content": "Apr 3, 2024 · Begin by considering the learning rate values commonly used in practice for LLM fine-tuning , such as 2e- 5 or 5e -6 (as shown in Table VI of the paper). These values can serve as a starting point for your learning rate exploration."}
+{"idx": 4, "title": "Glossary: LLM fine-tuning hyperparameters | Modal Blog", "date": "", "ddg_snippet": "Oct 15, 2024 · The learning rate is a scalar that determines the step size at each iteration while moving toward a minimum of the loss function. The bigger the learning rate , the faster fine-tuning goes, but you have to balance that against the risk of overshooting the optimal solution or causing unstable training.", "subpage_snippet": "", "source": "modal.com", "link": "https://modal.com/blog/fine-tuning-llms-hyperparameters-glossary-article", "content": "Oct 15, 2024 · The learning rate is a scalar that determines the step size at each iteration while moving toward a minimum of the loss function. The bigger the learning rate , the faster fine-tuning goes, but you have to balance that against the risk of overshooting the optimal solution or causing unstable training."}
+{"idx": 5, "title": "The optimal learning rate during fine-tuning of an artificial ...", "date": "", "ddg_snippet": "Feb 6, 2019 · The optimal value was right in between of 1e -2 and 1e -1, so I set the learning rate of the last layers to 0.055. For the first and middle layers, I set 1e-5 and 1e -4 respectively, because I did not want to change them a lot.", "subpage_snippet": "", "source": "mikulskibartosz.name", "link": "https://mikulskibartosz.name/the-optimal-learning-rate-during-fine-tuning-of-an-artificial-neural-network", "content": "Feb 6, 2019 · The optimal value was right in between of 1e -2 and 1e -1, so I set the learning rate of the last layers to 0.055. For the first and middle layers, I set 1e-5 and 1e -4 respectively, because I did not want to change them a lot."}
+{"idx": 6, "title": "LLM Fine-Tuning Hyperparameters - apxml.com", "date": "", "ddg_snippet": "For large language models, a small learning rate is almost always the correct choice. Because pre-trained models are already highly optimized, aggressive updates can disrupt the valuable knowledge stored in their weights. A common starting point for full fine-tuning is a learning rate between 1 e 5 1e − 5 and 5 e 5 5e − 5 .", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/introduction-to-llm-fine-tuning/chapter-3-full-parameter-fine-tuning/configuring-training-arguments-hyperparameters", "content": "For large language models, a small learning rate is almost always the correct choice. Because pre-trained models are already highly optimized, aggressive updates can disrupt the valuable knowledge stored in their weights. A common starting point for full fine-tuning is a learning rate between 1 e 5 1e − 5 and 5 e 5 5e − 5 ."}
+{"idx": 7, "title": "2D-Curri-DPO: Two-Dimensional Curriculum Learning for Direct", "date": "", "ddg_snippet": "DPO offers a simpler and more stable alternative by directly fine - tuning LLMs on preference pairs using a supervised loss.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.07856v1", "content": "DPO offers a simpler and more stable alternative by directly fine - tuning LLMs on preference pairs using a supervised loss."}
+{"idx": 8, "title": "Introduction to Fine-tuning Large Language Models", "date": "", "ddg_snippet": "Fine - tuning builds upon a pre-trained model by further training it on a smaller , task-specific dataset. ... 5 - Microsoft's 2.7B parameter model that ...", "subpage_snippet": "", "source": "www.stephendiehl.com", "link": "https://www.stephendiehl.com/posts/training_llms/", "content": "Fine - tuning builds upon a pre-trained model by further training it on a smaller , task-specific dataset. ... 5 - Microsoft's 2.7B parameter model that ..."}
+{"idx": 9, "title": "Teaching Your Models to Understand Code via Focal Preference", "date": "", "ddg_snippet": "Preference learning extends the performance of Code LLMs beyond traditional supervised fine - tuning by leveraging relative quality comparisons.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.02783v3", "content": "Preference learning extends the performance of Code LLMs beyond traditional supervised fine - tuning by leveraging relative quality comparisons."}
diff --git a/data/sampled_jsons/sieve_MLE_FD3_0.000_0.00_MSE_SD_table.jsonl b/data/sampled_jsons/sieve_MLE_FD3_0.000_0.00_MSE_SD_table.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a3995c0ece9568c8b0e0ea0cd622f77357d7bbac
--- /dev/null
+++ b/data/sampled_jsons/sieve_MLE_FD3_0.000_0.00_MSE_SD_table.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "kansas city, missouri, september 7-10, 2004", "date": "", "ddg_snippet": "15 Oct 2003 — 200 sieve were tested using various lower boundary conditions ( Table 2). The geotextile used was a non-woven needle punched geotextile ...", "subpage_snippet": "", "source": "www.highwaygeologysymposium.org", "link": "http://www.highwaygeologysymposium.org/wp-content/uploads/55_HGS-OPT.pdf", "content": "15 Oct 2003 — 200 sieve were tested using various lower boundary conditions ( Table 2). The geotextile used was a non-woven needle punched geotextile ..."}
+{"idx": 1, "title": "SEC.gov", "date": "", "ddg_snippet": "The initial estimated value of the Notes as of the pricing date is expected to be between $945. 00 and $995. 00 per $1, 000 . 00 in principal amount of Notes, which is less than the public offering price listed below. The actual value of your Notes at any time will reflect many factors and cannot be predicted with accuracy. See “Risk Factors” beginning on page PS-9 of this pricing supplement ...", "subpage_snippet": "", "source": "www.sec.gov", "link": "https://www.sec.gov/Archives/edgar/data/70858/000191870425015357/0001918704-25-015357.txt", "content": "The initial estimated value of the Notes as of the pricing date is expected to be between $945. 00 and $995. 00 per $1, 000 . 00 in principal amount of Notes, which is less than the public offering price listed below. The actual value of your Notes at any time will reflect many factors and cannot be predicted with accuracy. See “Risk Factors” beginning on page PS-9 of this pricing supplement ..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Table 1: MSE for the estimated conditional mean and the standard deviation. Sieve MLE .Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE ( SD ) for the FD 3 dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Table 1: MSE for the estimated conditional mean and the standard deviation. Sieve MLE .Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE ( SD ) for the FD 3 dataset."}
+{"idx": 3, "title": "Identification and Estimation of Nonlinear Models Using Two Samples...", "date": "", "ddg_snippet": "The simulation results are shown in Tables 1–2, illustrating larger and smaller sample sizes, and show what one might expect. First, accounting for measurement error matters: the 2-sample sieve MLE has a much smaller bias and MSE than the estimator ignoring measurement error .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC2873792/", "content": "The simulation results are shown in Tables 1–2, illustrating larger and smaller sample sizes, and show what one might expect. First, accounting for measurement error matters: the 2-sample sieve MLE has a much smaller bias and MSE than the estimator ignoring measurement error ."}
+{"idx": 4, "title": "Microsoft Word - cd1691.doc", "date": "", "ddg_snippet": "3 Sieve MLE , Consistency with Rate.parameter space, and the corresponding shrinking neighborhood in the sieve space, denoted as Nn = N ∩ Γn, be the new sieve parameter space. Denote V ar0 as the variance under the true DGP (i.e., Assumption M).", "subpage_snippet": "", "source": "cowles.yale.edu", "link": "https://cowles.yale.edu/sites/default/files/2022-08/d1691.pdf", "content": "3 Sieve MLE , Consistency with Rate.parameter space, and the corresponding shrinking neighborhood in the sieve space, denoted as Nn = N ∩ Γn, be the new sieve parameter space. Denote V ar0 as the variance under the true DGP (i.e., Assumption M)."}
+{"idx": 5, "title": "Essays", "date": "", "ddg_snippet": "Sparse Sieve MLE . Copula-Based SMLE of Parameters in Marginals. SMLE and QMLE.We report the corresponding MSE and MAD for TS-NPGMM and NPGMM in Table 1.3.", "subpage_snippet": "", "source": "spectrum.library.concordia.ca", "link": "https://spectrum.library.concordia.ca/id/eprint/978704/1/Liu_PhD_F2014.pdf", "content": "Sparse Sieve MLE . Copula-Based SMLE of Parameters in Marginals. SMLE and QMLE.We report the corresponding MSE and MAD for TS-NPGMM and NPGMM in Table 1.3."}
+{"idx": 6, "title": "Semiparametric", "date": "", "ddg_snippet": "We report the empirical MSE and bias for γ0, the empirical MSE and ASE for the nonparametric component, and the convergence rate of both methods for each scenario in Table ??.", "subpage_snippet": "", "source": "getd.libs.uga.edu", "link": "https://getd.libs.uga.edu/pdfs/xu_jing_200912_phd.pdf", "content": "We report the empirical MSE and bias for γ0, the empirical MSE and ASE for the nonparametric component, and the convergence rate of both methods for each scenario in Table ??."}
+{"idx": 7, "title": "EHY223 HYSYS Dynamics Introduction To ...", "date": "", "ddg_snippet": "At the end of this course you will be able to: ll Develop the skills and techniques required for creating and running dynamic simulations.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/607789019/EHY223-HYSYS-Dynamics-Introduction-to-Dynamic-Modeling", "content": "At the end of this course you will be able to: ll Develop the skills and techniques required for creating and running dynamic simulations."}
+{"idx": 8, "title": "Influence of PROP taster status on the consumer ...", "date": "", "ddg_snippet": "Effect of PROP taster status on consumer ratings for texture and overall acceptability of sorghum rice from sorghums differing in tannin content. Means and SD ; ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/24798687/Influence_of_PROP_taster_status_on_the_consumer_acceptability_of_food_made_from_tannin_sorghums", "content": "Effect of PROP taster status on consumer ratings for texture and overall acceptability of sorghum rice from sorghums differing in tannin content. Means and SD ; ..."}
+{"idx": 9, "title": "Tracking of Research Trend in Online Sho Mens Tuloni | PDF", "date": "", "ddg_snippet": "This document provides information on the journal Stochastic Modeling & Applications, including its editors, editorial board members, and contents ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/714090010/Tracking-of-Research-Trend-in-Online-Sho-Mens-Tuloni", "content": "This document provides information on the journal Stochastic Modeling & Applications, including its editors, editorial board members, and contents ..."}
diff --git a/data/sampled_jsons/sigmoid_loss_contrastive_learning_robustness_to_noisy_data.jsonl b/data/sampled_jsons/sigmoid_loss_contrastive_learning_robustness_to_noisy_data.jsonl
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index 0000000000000000000000000000000000000000..aac8cf4caa4d2063d608d66c626a4e938967e33b
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Robust Contrastive Learning against Noisy Views - CVF Open Access", "date": "", "ddg_snippet": "In this work, we pro-pose a new contrastive loss function that is robust against noisy views. We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Chuang_Robust_Contrastive_Learning_Against_Noisy_Views_CVPR_2022_paper.pdf", "content": "In this work, we pro-pose a new contrastive loss function that is robust against noisy views. We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure."}
+{"idx": 1, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning GitHub - filipbasara0/sigmoid-contrastive-loss ... SigCo: Eliminate the inter-class competition via sigmoid for ... Analysis of Using Sigmoid Loss for Contrastive Learning Contrastive Learning Improves Model Robustness Under Label Noise [2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learnin… [2201.04309] Robust Contrastive Learning against Noisy Views Implementation of modulated sigmoid pairwise contrastive loss for self-supervised learning on images - GitHub [2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learnin… Robust Contrastive Learning against Noisy Views - CVF Open Access Robust Contrastive Learning against Noisy Views - CVF Open Access [2201.04309] Robust Contrastive Learning against Noisy Views", "date": "", "ddg_snippet": "Feb 20, 2024 · However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings. Sigmoid Contrastive Loss for Vision - (poster) A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract. Jun 21, 2024 · On the other hand, we propose a noise -adaptive class masking learning strategy to construct stringent loss on C s o f t and relaxed loss on N s o f t for robust training of the Sigmoid -based network. However, theoreti-cal understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learn-ing , in the perspective of the geometric struc-ture of learned embeddings. Abstract Deep neural network-based classifiers trained with the categorical cross-entropy (CCE) loss are sensitive to label noise in the training data . One common type of method that can mitigate the impact of label noise can be viewed as supervised robust methods; one can simply replace the CCE loss with a loss that is robust to label noise , or re-weight training samples and down-weight those ... Can sigmoid loss be used in contrastive learning? However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning, in the perspective of the geometric structure of learned embeddings. Is a contrastive loss function robust against noisy views? In this work, we propose a new contrastive loss function that is robust against noisy views . We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure. Is there a PyTorch implementation of the modulated sigmoid pairwise loss? Cannot retrieve latest commit at this time. A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract. Can siglip be used in contrastive learning? Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss. SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . Are sym-metric loss functions robust against noisy labels? Ghosh et al. prove that sym-metric loss functions are robust against noisy labels , e.g., Mean Absolute Error (MAE) , while commonly used Cross Entropy (CE) loss is not. Based on this idea, Zhang and Sabuncu propose the generalized cross entropy loss to combine MAE and CE loss functions. Can noisy views deteriorate contrastive learning? Noisy views can deteriorate contrastive learning . We propose a new contrastive loss function (RINCE) that rescales the sample importance in the gradient space based on an estimated noise level. With a simple turn of a knob (q weight or downweight sample pairs with low 2 (0, 1]), we can up-shared information. Jan 12, 2022 · In this work, we propose a new contrastive loss function that is robust against noisy views. We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.12613", "content": "Feb 20, 2024 · However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings. Sigmoid Contrastive Loss for Vision - (poster) A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract. Jun 21, 2024 · On the other hand, we propose a noise -adaptive class masking learning strategy to construct stringent loss on C s o f t and relaxed loss on N s o f t for robust training of the Sigmoid -based network. However, theoreti-cal understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learn-ing , in the perspective of the geometric struc-ture of learned embeddings. Abstract Deep neural network-based classifiers trained with the categorical cross-entropy (CCE) loss are sensitive to label noise in the training data . One common type of method that can mitigate the impact of label noise can be viewed as supervised robust methods; one can simply replace the CCE loss with a loss that is robust to label noise , or re-weight training samples and down-weight those ... Can sigmoid loss be used in contrastive learning? However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning, in the perspective of the geometric structure of learned embeddings. Is a contrastive loss function robust against noisy views? In this work, we propose a new contrastive loss function that is robust against noisy views . We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure. Is there a PyTorch implementation of the modulated sigmoid pairwise loss? Cannot retrieve latest commit at this time. A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract. Can siglip be used in contrastive learning? Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss. SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . Are sym-metric loss functions robust against noisy labels? Ghosh et al. prove that sym-metric loss functions are robust against noisy labels , e.g., Mean Absolute Error (MAE) , while commonly used Cross Entropy (CE) loss is not. Based on this idea, Zhang and Sabuncu propose the generalized cross entropy loss to combine MAE and CE loss functions. Can noisy views deteriorate contrastive learning? Noisy views can deteriorate contrastive learning . We propose a new contrastive loss function (RINCE) that rescales the sample importance in the gradient space based on an estimated noise level. With a simple turn of a knob (q weight or downweight sample pairs with low 2 (0, 1]), we can up-shared information. Jan 12, 2022 · In this work, we propose a new contrastive loss function that is robust against noisy views. We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure."}
+{"idx": 2, "title": "GitHub - filipbasara0/sigmoid-contrastive-loss ...", "date": "", "ddg_snippet": "Sigmoid Contrastive Loss for Vision - (poster) A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/filipbasara0/sigmoid-contrastive-loss", "content": "Sigmoid Contrastive Loss for Vision - (poster) A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract."}
+{"idx": 3, "title": "SigCo: Eliminate the inter-class competition via sigmoid for ...", "date": "", "ddg_snippet": "Jun 21, 2024 · On the other hand, we propose a noise -adaptive class masking learning strategy to construct stringent loss on C s o f t and relaxed loss on N s o f t for robust training of the Sigmoid -based network.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0950705124002867", "content": "Jun 21, 2024 · On the other hand, we propose a noise -adaptive class masking learning strategy to construct stringent loss on C s o f t and relaxed loss on N s o f t for robust training of the Sigmoid -based network."}
+{"idx": 4, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "However, theoreti-cal understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learn-ing , in the perspective of the geometric struc-ture of learned embeddings.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/lee24a/lee24a.pdf", "content": "However, theoreti-cal understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learn-ing , in the perspective of the geometric struc-ture of learned embeddings."}
+{"idx": 5, "title": "Contrastive Learning Improves Model Robustness Under Label Noise", "date": "", "ddg_snippet": "Abstract Deep neural network-based classifiers trained with the categorical cross-entropy (CCE) loss are sensitive to label noise in the training data . One common type of method that can mitigate the impact of label noise can be viewed as supervised robust methods; one can simply replace the CCE loss with a loss that is robust to label noise , or re-weight training samples and down-weight those ...", "subpage_snippet": "", "source": "people.umass.edu", "link": "https://people.umass.edu/~andrewlan/papers/21l2id-contrastive.pdf", "content": "Abstract Deep neural network-based classifiers trained with the categorical cross-entropy (CCE) loss are sensitive to label noise in the training data . One common type of method that can mitigate the impact of label noise can be viewed as supervised robust methods; one can simply replace the CCE loss with a loss that is robust to label noise , or re-weight training samples and down-weight those ..."}
+{"idx": 6, "title": "[2201.04309] Robust Contrastive Learning against Noisy Views", "date": "", "ddg_snippet": "Jan 12, 2022 · In this work, we propose a new contrastive loss function that is robust against noisy views. We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2201.04309", "content": "Jan 12, 2022 · In this work, we propose a new contrastive loss function that is robust against noisy views. We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure."}
+{"idx": 7, "title": "Sigmoid Loss for Language Image Pre-Training", "date": "", "ddg_snippet": "Contrastive learning with the sigmoid loss .Further studies have been performed to understand better the introduced bias term in the sigmoid loss , robustness to data noises and the impact of positive and negative pairs ratio in the sigmoid loss .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.15343", "content": "Contrastive learning with the sigmoid loss .Further studies have been performed to understand better the introduced bias term in the sigmoid loss , robustness to data noises and the impact of positive and negative pairs ratio in the sigmoid loss ."}
+{"idx": 8, "title": "SigLIP: Sigmoid Loss for Language Image Pre-Training | Medium", "date": "", "ddg_snippet": "Experiments. Contrastive Learning & Sigmoid Loss . Sigmoid -training increases robustness to data noise . Press enter or click to view image in full size.", "subpage_snippet": "", "source": "taewan2002.medium.com", "link": "https://taewan2002.medium.com/siglip-sigmoid-loss-for-language-image-pre-training-aa68fedaa080", "content": "Experiments. Contrastive Learning & Sigmoid Loss . Sigmoid -training increases robustness to data noise . Press enter or click to view image in full size."}
+{"idx": 9, "title": "Robust Contrastive Learning against Noisy Views | Request PDF", "date": "", "ddg_snippet": "Learning from massive noisy labeled data for image classification.Through extensive empirical studies, we show that risk minimization under the $0-1$ loss , the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/357790912_Robust_Contrastive_Learning_against_Noisy_Views", "content": "Learning from massive noisy labeled data for image classification.Through extensive empirical studies, we show that risk minimization under the $0-1$ loss , the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm."}
diff --git a/data/sampled_jsons/siteaclanthology.org_CRAB_cross-environment_agent_benchmark_abstract.jsonl b/data/sampled_jsons/siteaclanthology.org_CRAB_cross-environment_agent_benchmark_abstract.jsonl
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index 0000000000000000000000000000000000000000..43cc8520bdb0d1b7cb0cdf1921fcff7a22f0827d
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CRAB: Cross-environment Agent Benchmark for ...", "date": "", "ddg_snippet": "by T Xu · 2025 · Cited by 25 — We introduce CRAB, the first cross-environment agent benchmark framework , incorporating a graph-based fine-grained evaluation method and an efficient task ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.1113/", "content": "by T Xu · 2025 · Cited by 25 — We introduce CRAB, the first cross-environment agent benchmark framework , incorporating a graph-based fine-grained evaluation method and an efficient task ..."}
+{"idx": 1, "title": "Antoine Bosselut - ACL Anthology", "date": "", "ddg_snippet": "We introduce a novel benchmark dataset, Seesaw-CF, for measuring bias amplification of model editing methods for demographic traits such as race ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/a/antoine-bosselut/", "content": "We introduce a novel benchmark dataset, Seesaw-CF, for measuring bias amplification of model editing methods for demographic traits such as race ..."}
+{"idx": 2, "title": "Roi Reichart - ACL Anthology", "date": "", "ddg_snippet": "Current research suggests that LLM-based agents become increasingly human-like in their performance, sparking interest in using these AI agents as ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/r/roi-reichart/", "content": "Current research suggests that LLM-based agents become increasingly human-like in their performance, sparking interest in using these AI agents as ..."}
+{"idx": 3, "title": "Proceedings of the 2025 Conference of the Nations of the", "date": "", "ddg_snippet": "... their difficulty, aiding benchmark ... AIs can beat humans in game environments ; however, how helpful those agents are to human remains understudied.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2025.naacl-short/", "content": "... their difficulty, aiding benchmark ... AIs can beat humans in game environments ; however, how helpful those agents are to human remains understudied."}
+{"idx": 4, "title": "Findings of the Association for Computational Linguistics", "date": "", "ddg_snippet": "by W Che · 2025 — ... benchmark . More offline and interactive agent benchmarks across various GUI environments – including web pages, desktop software, and mobile UIs ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2025.findings-acl/", "content": "by W Che · 2025 — ... benchmark . More offline and interactive agent benchmarks across various GUI environments – including web pages, desktop software, and mobile UIs ..."}
+{"idx": 5, "title": "Findings of the Association for Computational Linguistics", "date": "", "ddg_snippet": "by W Che · 2025 — CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents. Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang ... 136 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.0.pdf", "content": "by W Che · 2025 — CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents. Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang ... 136 pages"}
+{"idx": 6, "title": "Annual Meeting of the Association for Computational ...", "date": "", "ddg_snippet": "Language model agents excel in long-session planning and reasoning, but existing benchmarks primarily focus on goal-oriented tasks with explicit objectives ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/events/acl-2025/", "content": "Language model agents excel in long-session planning and reasoning, but existing benchmarks primarily focus on goal-oriented tasks with explicit objectives ..."}
+{"idx": 7, "title": "Proceedings of the 63rd Annual Meeting of the Association ...", "date": "", "ddg_snippet": "... benchmark targeting function-calling. Our benchmark also incorporates dialog act annotations to assess agent responses. We evaluate a series of state-of-the ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2025.acl-long/", "content": "... benchmark targeting function-calling. Our benchmark also incorporates dialog act annotations to assess agent responses. We evaluate a series of state-of-the ..."}
+{"idx": 8, "title": "Data-Centric Improvements for Enhancing Multi-Modal ...", "date": "", "ddg_snippet": "by M Chen · 2025 · Cited by 2 — Our approach achieves state-of-the-art performance on the. Spoken-SQuAD benchmark , using only 10% of the training data with open-weight models,. 22 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.71.pdf", "content": "by M Chen · 2025 · Cited by 2 — Our approach achieves state-of-the-art performance on the. Spoken-SQuAD benchmark , using only 10% of the training data with open-weight models,. 22 pages"}
+{"idx": 9, "title": "Antoine Bosselut", "date": "", "ddg_snippet": "We make our dataset and leaderboard available to the research community. pdf bib abs. CRAB : Assessing the Strength of Causal Relationships Between Real-world ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/antoine-bosselut/", "content": "We make our dataset and leaderboard available to the research community. pdf bib abs. CRAB : Assessing the Strength of Causal Relationships Between Real-world ..."}
diff --git a/data/sampled_jsons/sitealignmentforum.org_password-locked_models_Sleeper_Agents_Hubinger_trigger_purpose.jsonl b/data/sampled_jsons/sitealignmentforum.org_password-locked_models_Sleeper_Agents_Hubinger_trigger_purpose.jsonl
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index 0000000000000000000000000000000000000000..f37baddb7a2dc5bdb58973059e5661e820586210
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+++ b/data/sampled_jsons/sitealignmentforum.org_password-locked_models_Sleeper_Agents_Hubinger_trigger_purpose.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Password - locked models : a stress case for... — AI Alignment Forum", "date": "", "ddg_snippet": "2Evan Hubinger . Password - locked models are trained to exhibit certain capabilities only when a password is present in the query. Studying these models has two purposes", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "2Evan Hubinger . Password - locked models are trained to exhibit certain capabilities only when a password is present in the query. Studying these models has two purposes"}
+{"idx": 1, "title": "AXRP Episode 39 - Evan Hubinger on Model ... — AI Alignment Forum", "date": "", "ddg_snippet": "Do ‘ sleeper agents ’ properly model deceptive alignment?Surprising results in “ Sleeper Agents ”Evan Hubinger : There’s a couple of things that you can study once you have the model ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/sookiqxkzzLmPYB3r/axrp-episode-39-evan-hubinger-on-model-organisms-of-1", "content": "Do ‘ sleeper agents ’ properly model deceptive alignment?Surprising results in “ Sleeper Agents ”Evan Hubinger : There’s a couple of things that you can study once you have the model ..."}
+{"idx": 2, "title": "Sleeper Agents : Training Deceptive LLMs that... — AI Alignment Forum", "date": "", "ddg_snippet": "This is something we've been extremely careful to avoid in all of our messaging. I think if someone comes away with that impression, they didn't even get as far as our title: Sleeper Agents : Training Deceptive LLMs that Persist Through Safety Training.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/ZAsJv7xijKTfZkMtr/sleeper-agents-training-deceptive-llms-that-persist-through", "content": "This is something we've been extremely careful to avoid in all of our messaging. I think if someone comes away with that impression, they didn't even get as far as our title: Sleeper Agents : Training Deceptive LLMs that Persist Through Safety Training."}
+{"idx": 3, "title": "Simple probes can catch sleeper agents — AI Alignment Forum", "date": "", "ddg_snippet": "Using the models we trained in \" Sleeper Agents : Training Deceptive LLMs that Persist Through Safety Training\", we show that linear detectors with AUROC scores above 99% can be created using generic contrast pairs that don't depend on any information about the defection trigger or...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/gknc6NWCNuTCe8ekp/simple-probes-can-catch-sleeper-agents-1", "content": "Using the models we trained in \" Sleeper Agents : Training Deceptive LLMs that Persist Through Safety Training\", we show that linear detectors with AUROC scores above 99% can be created using generic contrast pairs that don't depend on any information about the defection trigger or..."}
+{"idx": 4, "title": "How to train your own \" Sleeper Agents \" — AI Alignment Forum", "date": "", "ddg_snippet": "Can a base model or a model w/o SFT/RLHF directly undergo the sleeper agent training process on the spot?This post is a guide on how to replicate our \" Sleeper Agents \" work.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/M8kpzm42uHytnyYyP/how-to-train-your-own-sleeper-agents", "content": "Can a base model or a model w/o SFT/RLHF directly undergo the sleeper agent training process on the spot?This post is a guide on how to replicate our \" Sleeper Agents \" work."}
+{"idx": 5, "title": "Political sycophancy as a model organism of... — AI Alignment Forum", "date": "", "ddg_snippet": "As compared to the Sleeper Agents paper, we study a more natural backdoor trigger (the political leaning of the user), focus more on adversarial training, and experiment with supervised fine-tuning (SFT) training only and at a smaller scale.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/bhxgkb7YtRNwBxLMd/political-sycophancy-as-a-model-organism-of-scheming", "content": "As compared to the Sleeper Agents paper, we study a more natural backdoor trigger (the political leaning of the user), focus more on adversarial training, and experiment with supervised fine-tuning (SFT) training only and at a smaller scale."}
+{"idx": 6, "title": "Sabotage Evaluations for Frontier Models", "date": "", "ddg_snippet": "by J Benton · Cited by 11 — (3) Acting as a sleeper agent ( Hubinger et al., 2024): A model displays ... (2024) in- vestigated the generalization properties of password - locked models .", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/out?url=https://assets.anthropic.com/m/377027d5b36ac1eb/original/Sabotage-Evaluations-for-Frontier-Models.pdf", "content": "by J Benton · Cited by 11 — (3) Acting as a sleeper agent ( Hubinger et al., 2024): A model displays ... (2024) in- vestigated the generalization properties of password - locked models ."}
+{"idx": 7, "title": "Research directions Open Phil wants to fund in technical AI ...", "date": "", "ddg_snippet": "7 Feb 2025 — ... models , also known as Trojans or sleeper agents . These models act like a “challenge trial” to test our best methods for aligning and ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/26SHhxK2yYQbh7ors/research-directions-open-phil-wants-to-fund-in-technical-ai", "content": "7 Feb 2025 — ... models , also known as Trojans or sleeper agents . These models act like a “challenge trial” to test our best methods for aligning and ..."}
+{"idx": 8, "title": "Two Tales of AI Takeover: My Doubts", "date": "", "ddg_snippet": "5 Mar 2024 — At some point, Hubinger expects the model to learn about the details of its training process, after which SGD will “crystallize” the model's ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/GbpH2kFLy5axXpzPn/two-tales-of-ai-takeover-my-doubts-1", "content": "5 Mar 2024 — At some point, Hubinger expects the model to learn about the details of its training process, after which SGD will “crystallize” the model's ..."}
+{"idx": 9, "title": "Deep Causal Transcoding: A Framework for ...", "date": "", "ddg_snippet": "3 Dec 2024 — The best DCT feature recovers 32% of password - locked performance, increasing accuracy on MATH from 3% (locked) to 23% (steered). Introduction. I ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/fSRg5qs9TPbNy3sm5/deep-causal-transcoding-a-framework-for-mechanistically", "content": "3 Dec 2024 — The best DCT feature recovers 32% of password - locked performance, increasing accuracy on MATH from 3% (locked) to 23% (steered). Introduction. I ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_1903.11027_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_1903.11027_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..32b8d2de90fae44ebae72e9112964ae60bbaa260
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_1903.11027_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "by H Caesar · 2019 · Cited by 8346 — The first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "by H Caesar · 2019 · Cited by 8346 — The first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view."}
+{"idx": 1, "title": "nuScenes: A multimodal dataset for autonomous driving - ar5iv", "date": "", "ddg_snippet": "The first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1903.11027", "content": "The first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view."}
+{"idx": 2, "title": "Video-Enhanced Offline Reinforcement Learning: A Model- ...", "date": "", "ddg_snippet": "10 May 2025 — In this paper, we present Video-Enhanced Offline RL (VeoRL), a model-based approach that constructs an interactive world model from diverse, unlabeled video ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06482v1", "content": "10 May 2025 — In this paper, we present Video-Enhanced Offline RL (VeoRL), a model-based approach that constructs an interactive world model from diverse, unlabeled video ..."}
+{"idx": 3, "title": "DriveLMM-o1: A Step-by-Step Reasoning Dataset and ...", "date": "", "ddg_snippet": "13 Mar 2025 — We propose DriveLMM-o1 , a new dataset and benchmark specifically designed to advance step-wise visual reasoning for autonomous driving.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10621v1", "content": "13 Mar 2025 — We propose DriveLMM-o1 , a new dataset and benchmark specifically designed to advance step-wise visual reasoning for autonomous driving."}
+{"idx": 4, "title": "RICO: Two Realistic Benchmarks and an In-Depth Analysis ...", "date": "", "ddg_snippet": "19 Aug 2025 — Incremental Learning (IL) trains models sequentially on new data without full retraining, offering privacy, efficiency, and scalability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.13878v1", "content": "19 Aug 2025 — Incremental Learning (IL) trains models sequentially on new data without full retraining, offering privacy, efficiency, and scalability."}
+{"idx": 5, "title": "OpenEMMA: Open-Source Multimodal Model for End-to- ...", "date": "", "ddg_snippet": "We propose OpenEMMA, an open-source end-to-end framework based on MLLMs . By incorporating the Chain-of-Thought reasoning process, OpenEMMA achieves significant ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.15208v2", "content": "We propose OpenEMMA, an open-source end-to-end framework based on MLLMs . By incorporating the Chain-of-Thought reasoning process, OpenEMMA achieves significant ..."}
+{"idx": 6, "title": "WayveScenes101: A Dataset and Benchmark for Novel View ...", "date": "", "ddg_snippet": "We present WayveScenes101 , a dataset designed to help the community advance the state of the art in novel view synthesis that focuses on challenging driving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.08280", "content": "We present WayveScenes101 , a dataset designed to help the community advance the state of the art in novel view synthesis that focuses on challenging driving ..."}
+{"idx": 7, "title": "UniOcc: A Unified Benchmark for Occupancy Forecasting ...", "date": "", "ddg_snippet": "14 Aug 2025 — We introduce UniOcc, a comprehensive, unified benchmark and toolkit for occupancy forecasting (i.e., predicting future occupancies based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.24381v2", "content": "14 Aug 2025 — We introduce UniOcc, a comprehensive, unified benchmark and toolkit for occupancy forecasting (i.e., predicting future occupancies based on ..."}
+{"idx": 8, "title": "Ensemble of Pre-Trained Models for Long-Tailed ...", "date": "", "ddg_snippet": "2 days ago — This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13914v1", "content": "2 days ago — This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in ..."}
+{"idx": 9, "title": "Lifting 2D to 3D for Monocular Open-Set Object Detection", "date": "", "ddg_snippet": "Monocular 3D object detection (3DOD ) aims to recognize and localize objects in 3D space from a single 2D image by estimating their 3D positions, dimensions, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23567v2", "content": "Monocular 3D object detection (3DOD ) aims to recognize and localize objects in 3D space from a single 2D image by estimating their 3D positions, dimensions, and ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2006.05535_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez.jsonl b/data/sampled_jsons/sitearxiv.org_2006.05535_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..995ce7d2acf36003db98281f29d8e572dd3f2b51
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2006.05535_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2006.05535] Locally Private Graph Neural Networks - arXiv.org Locally Private Graph Neural Networks - arXiv.org When Differential Privacy Meets Graph Neural Networks Releasing Graph Neural Networks with Differential Privacy ... Locally Private Graph Neural Networks - arXiv.org Locally Private Graph Neural Networks - arXiv.org When Differential Privacy Meets Graph Neural Networks", "date": "", "ddg_snippet": "Jun 9, 2020 · View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica-Perez Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information. Jun 9, 2020 · Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals. Previous works have presented various techniques for privacy-preserving ... Locally private graph neural networks . arXiv preprint arXiv: 2006.05535 , 2020. Sajadmanesh et al. (2022) Sina Sajadmanesh , Ali Shahin Shamsabadi, Aurélien Bellet, and Daniel Gatica-Perez . Graph Neural Networks (GNNs) have demonstrated superior per-formance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individu-als. Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information. Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. How-ever, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Jun 9, 2020 · View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica-Perez Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information. Jun 9, 2020 · Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals. Previous works have presented various techniques for privacy-preserving ... Locally private graph neural networks . arXiv preprint arXiv: 2006.05535 , 2020. Sajadmanesh et al. (2022) Sina Sajadmanesh , Ali Shahin Shamsabadi, Aurélien Bellet, and Daniel Gatica-Perez . Graph Neural Networks (GNNs) have demonstrated superior per-formance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individu-als. Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information. Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. How-ever, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals."}
+{"idx": 1, "title": "Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05535", "content": "Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information."}
+{"idx": 2, "title": "When Differential Privacy Meets Graph Neural Networks Releasing Graph Neural Networks with Differential Privacy ... Locally Private Graph Neural Networks - arXiv.org Locally Private Graph Neural Networks - arXiv.org When Differential Privacy Meets Graph Neural Networks", "date": "", "ddg_snippet": "Jun 9, 2020 · Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals. Previous works have presented various techniques for privacy-preserving ... Locally private graph neural networks . arXiv preprint arXiv: 2006.05535 , 2020. Sajadmanesh et al. (2022) Sina Sajadmanesh , Ali Shahin Shamsabadi, Aurélien Bellet, and Daniel Gatica-Perez . Graph Neural Networks (GNNs) have demonstrated superior per-formance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individu-als. Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information. Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. How-ever, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535v1", "content": "Jun 9, 2020 · Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals. Previous works have presented various techniques for privacy-preserving ... Locally private graph neural networks . arXiv preprint arXiv: 2006.05535 , 2020. Sajadmanesh et al. (2022) Sina Sajadmanesh , Ali Shahin Shamsabadi, Aurélien Bellet, and Daniel Gatica-Perez . Graph Neural Networks (GNNs) have demonstrated superior per-formance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individu-als. Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information. Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. How-ever, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals."}
+{"idx": 3, "title": "Releasing Graph Neural Networks with Differential Privacy ...", "date": "", "ddg_snippet": "Locally private graph neural networks . arXiv preprint arXiv: 2006.05535 , 2020. Sajadmanesh et al. (2022) Sina Sajadmanesh , Ali Shahin Shamsabadi, Aurélien Bellet, and Daniel Gatica-Perez .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2109.08907", "content": "Locally private graph neural networks . arXiv preprint arXiv: 2006.05535 , 2020. Sajadmanesh et al. (2022) Sina Sajadmanesh , Ali Shahin Shamsabadi, Aurélien Bellet, and Daniel Gatica-Perez ."}
+{"idx": 4, "title": "Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior per-formance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individu-als.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05535v5", "content": "Graph Neural Networks (GNNs) have demonstrated superior per-formance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individu-als."}
+{"idx": 5, "title": "Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05535v7.pdf", "content": "Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information."}
+{"idx": 6, "title": "When Differential Privacy Meets Graph Neural Networks", "date": "", "ddg_snippet": "Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. How-ever, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05535v3.pdf", "content": "Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. How-ever, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals."}
+{"idx": 7, "title": "arXiv:2202.00808v1 [cs.LG] 1 Feb 2022", "date": "", "ddg_snippet": "by H Jin · 2022 · Cited by 8 — Sina Sajadmanesh and Daniel Gatica-Perez. Locally private graph neural networks . arXiv preprint arXiv:2006.05535,. 2020. Nino Shervashidze ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.00808", "content": "by H Jin · 2022 · Cited by 8 — Sina Sajadmanesh and Daniel Gatica-Perez. Locally private graph neural networks . arXiv preprint arXiv:2006.05535,. 2020. Nino Shervashidze ..."}
+{"idx": 8, "title": "arXiv:2005.11903v3 [cs.LG] 25 Apr 2022", "date": "", "ddg_snippet": "by C Chen · 2020 · Cited by 156 — [Sajadmanesh and Gatica-Perez, 2020] Sina Sajadmanesh and Daniel Gatica-Perez. Locally private graph neural networks . CoRR abs/2006.05535 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2005.11903", "content": "by C Chen · 2020 · Cited by 156 — [Sajadmanesh and Gatica-Perez, 2020] Sina Sajadmanesh and Daniel Gatica-Perez. Locally private graph neural networks . CoRR abs/2006.05535 ..."}
+{"idx": 9, "title": "Releasing Graph Neural Networks with Differential Privacy ...", "date": "", "ddg_snippet": "by IE Olatunji · 2021 · Cited by 74 — Sina Sajadmanesh and Daniel Gatica-Perez. Locally private graph neural networks . arXiv preprint. arXiv:2006.05535, 2020. Sina Sajadmanesh ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.08907", "content": "by IE Olatunji · 2021 · Cited by 74 — Sina Sajadmanesh and Daniel Gatica-Perez. Locally private graph neural networks . arXiv preprint. arXiv:2006.05535, 2020. Sina Sajadmanesh ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2405.17618_Section_5.4_PPO_instability_symmetric_loss.jsonl b/data/sampled_jsons/sitearxiv.org_2405.17618_Section_5.4_PPO_instability_symmetric_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..26573a93274cbd5839e757b6507a317aa6421805
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2405.17618_Section_5.4_PPO_instability_symmetric_loss.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "We define a symmetric RL loss , whose fundamental working mechanism aligns with the robust loss function of supervised learning (Wang et al., 2019), to make the RL learning procedure more robust for A2C and PPO .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2405.17618", "content": "We define a symmetric RL loss , whose fundamental working mechanism aligns with the robust loss function of supervised learning (Wang et al., 2019), to make the RL learning procedure more robust for A2C and PPO ."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2405.17618 : Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "Abstract page for arXiv paper 2405.17618 : Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales"}
+{"idx": 2, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "and PPO . We define a symmetric RL loss , whose fundamental working mechanism aligns with the robust loss function of supervised learning [Wang et al., 2019], to make the RL learning procedure more robust for A2C", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618", "content": "and PPO . We define a symmetric RL loss , whose fundamental working mechanism aligns with the robust loss function of supervised learning [Wang et al., 2019], to make the RL learning procedure more robust for A2C"}
+{"idx": 3, "title": "PPO-CIM: Proximal Policy Optimization with Correntropy Induced Metric", "date": "", "ddg_snippet": "Abstract As a popular Deep Reinforcement Learning (DRL) algorithm, Proximal Policy Optimization ( PPO ) has demonstrated remarkable efficacy in numerous complex tasks. According to the penalty mechanism in a surrogate, PPO can be classified into PPO with KL divergence ( PPO -KL) and PPO with Clip ( PPO -Clip).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2110.10522v3", "content": "Abstract As a popular Deep Reinforcement Learning (DRL) algorithm, Proximal Policy Optimization ( PPO ) has demonstrated remarkable efficacy in numerous complex tasks. According to the penalty mechanism in a surrogate, PPO can be classified into PPO with KL divergence ( PPO -KL) and PPO with Clip ( PPO -Clip)."}
+{"idx": 4, "title": "Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot ...", "date": "", "ddg_snippet": "Through these contributions, we advance the ability of PPO algorithm, offering an innovative solution that combines sparse sensor data, reinforcement learning with PPO , and adaptability to simulated scenarios, promising eficient and adaptable nav-igation capabilities for robots in a static environments within the simulation context.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.16266v2", "content": "Through these contributions, we advance the ability of PPO algorithm, offering an innovative solution that combines sparse sensor data, reinforcement learning with PPO , and adaptability to simulated scenarios, promising eficient and adaptable nav-igation capabilities for robots in a static environments within the simulation context."}
+{"idx": 5, "title": "Secrets of RLHF in Large Language Models Part I: PPO", "date": "", "ddg_snippet": "Large language models (LLMs) have formulated a blueprint for the advancement of artificial general intelligence. Its primary objective is to function as a human-centric (helpful, honest, and harmless) assistant. Alignment with humans assumes paramount significance, and reinforcement learning with human feedback (RLHF) emerges as the pivotal technological paradigm underpinning this pursuit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.04964", "content": "Large language models (LLMs) have formulated a blueprint for the advancement of artificial general intelligence. Its primary objective is to function as a human-centric (helpful, honest, and harmless) assistant. Alignment with humans assumes paramount significance, and reinforcement learning with human feedback (RLHF) emerges as the pivotal technological paradigm underpinning this pursuit ..."}
+{"idx": 6, "title": "[1707.06347] Proximal Policy Optimization Algorithms - arXiv.org", "date": "", "ddg_snippet": "We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a \"surrogate\" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1707.06347", "content": "We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a \"surrogate\" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of ..."}
+{"idx": 7, "title": "As Simple as Fine-tuning: LLM Alignment via Bidirectional Negative ...", "date": "", "ddg_snippet": "Direct Preference Optimization (DPO) has emerged as a more computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) with Proximal Policy Optimization ( PPO ), eliminating the need for reward models and online sampling. Despite these benefits, DPO and its variants remain sensitive to hyper-parameters and prone to instability , particularly on mathematical datasets ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.04834", "content": "Direct Preference Optimization (DPO) has emerged as a more computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) with Proximal Policy Optimization ( PPO ), eliminating the need for reward models and online sampling. Despite these benefits, DPO and its variants remain sensitive to hyper-parameters and prone to instability , particularly on mathematical datasets ..."}
+{"idx": 8, "title": "OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework", "date": "", "ddg_snippet": "Thanks to the implementation tricks and previous hyper-parameters for PPO , Figure 5 shows the PPO training curves, where the reward and return value rise steadily, and the Kullback-Leibler (KL) divergence and loss values remain stable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.11143v2", "content": "Thanks to the implementation tricks and previous hyper-parameters for PPO , Figure 5 shows the PPO training curves, where the reward and return value rise steadily, and the Kullback-Leibler (KL) divergence and loss values remain stable."}
+{"idx": 9, "title": "CPGD: Toward Stable Rule-based Reinforcement Learning for Language Models", "date": "", "ddg_snippet": "In Section 4.1, we present the CPGD algorithm along with its theoretical guarantees, and highlight potential limitations of the standard PPO -clip loss . In Section 4.2, we provide empirical evidence of instability in existing methods and analyze its possible causes, showing how CPGD addresses them for more stable training.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.12504v1", "content": "In Section 4.1, we present the CPGD algorithm along with its theoretical guarantees, and highlight potential limitations of the standard PPO -clip loss . In Section 4.2, we provide empirical evidence of instability in existing methods and analyze its possible causes, showing how CPGD addresses them for more stable training."}
diff --git a/data/sampled_jsons/sitearxiv.org_2407.10264_synthetic_experiments_learning_rates_section_4_year_2024.jsonl b/data/sampled_jsons/sitearxiv.org_2407.10264_synthetic_experiments_learning_rates_section_4_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Layered Unlearning for Adversarial Relearning", "date": "", "ddg_snippet": "14 May 2025 — We evaluate LU through a combination of synthetic and large language model (LLM) experiments . We find that LU improves robustness to adversarial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.09500v1", "content": "14 May 2025 — We evaluate LU through a combination of synthetic and large language model (LLM) experiments . We find that LU improves robustness to adversarial ..."}
+{"idx": 1, "title": "Layered Unlearning for Adversarial Relearning", "date": "", "ddg_snippet": "by T Qian · 2025 · Cited by 2 — We evaluate LU through a combination of synthetic and large language model (LLM) experiments . We find that LU improves ro- bustness to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.09500?", "content": "by T Qian · 2025 · Cited by 2 — We evaluate LU through a combination of synthetic and large language model (LLM) experiments . We find that LU improves ro- bustness to ..."}
+{"idx": 2, "title": "Straco Corporation Ltd , S85:SES profile - FT.com", "date": "", "ddg_snippet": "Sep 12, 2025 · Straco Corporation Limited is a Singapore-based company engaged in the development and management of aquatic-related facilities and tourism-related assets. The Company operates through two...", "subpage_snippet": "", "source": "markets.ft.com", "link": "https://markets.ft.com/data/equities/tearsheet/profile?s=S85:SES", "content": "Sep 12, 2025 · Straco Corporation Limited is a Singapore-based company engaged in the development and management of aquatic-related facilities and tourism-related assets. The Company operates through two..."}
+{"idx": 3, "title": "Improving Language Model Personas via Rationalization with", "date": "", "ddg_snippet": "Our experiments show that when prompted with such richer descriptions, LMs can result in more accurate personas, as evaluated on a set of test ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.17993v2", "content": "Our experiments show that when prompted with such richer descriptions, LMs can result in more accurate personas, as evaluated on a set of test ..."}
+{"idx": 4, "title": "Picky LLMs and Unreliable RMs", "date": "", "ddg_snippet": "by G Li · 2025 — To study the second application scenario, we adopt RMs to score original and reformatted datasets in Section 4 . Then, we compare the scores ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01116?", "content": "by G Li · 2025 — To study the second application scenario, we adopt RMs to score original and reformatted datasets in Section 4 . Then, we compare the scores ..."}
+{"idx": 5, "title": "Decomposing Elements of Problem Solving: What \"Math ...", "date": "", "ddg_snippet": "28 May 2025 — This section contains further details for experiments in Sec . 4.2 ... We used NVIDIA A100 and NVIDIA H100 GPUSs for the synthetic experiment .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22756v1", "content": "28 May 2025 — This section contains further details for experiments in Sec . 4.2 ... We used NVIDIA A100 and NVIDIA H100 GPUSs for the synthetic experiment ."}
+{"idx": 6, "title": "PEFT-as-an-Attack! Jailbreaking Language Models during ...", "date": "", "ddg_snippet": "by S Li · 2024 · Cited by 5 — In this section, we present the experimental setup, including the system setup ( Section IV -A), training details ( Section IV -B), and the evaluation details ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.19335", "content": "by S Li · 2024 · Cited by 5 — In this section, we present the experimental setup, including the system setup ( Section IV -A), training details ( Section IV -B), and the evaluation details ( ..."}
+{"idx": 7, "title": "Harmful Fine-tuning Attacks and Defenses for Large ...", "date": "", "ddg_snippet": "by T Huang · 2024 · Cited by 55 — In Section 4 , we review and taxonomy the relevant papers. In section 5, we provide a common experiment setup for evaluation (e.g., datasets/ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.18169?", "content": "by T Huang · 2024 · Cited by 55 — In Section 4 , we review and taxonomy the relevant papers. In section 5, we provide a common experiment setup for evaluation (e.g., datasets/ ..."}
+{"idx": 8, "title": "Obfuscated Activations Bypass LLM Latent-Space Defenses", "date": "", "ddg_snippet": "12 Dec 2024 — Backdoor detection ( Section 4 ). We show that obfuscation attacks can fool backdoor detection methods if the attacker has full control over the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.09565v2", "content": "12 Dec 2024 — Backdoor detection ( Section 4 ). We show that obfuscation attacks can fool backdoor detection methods if the attacker has full control over the ..."}
+{"idx": 9, "title": "A Semantically-Aware, Kernel-Enhanced, and Divergence", "date": "", "ddg_snippet": "by A Das · 2025 · Cited by 1 — This helps improve general- ization and maintain semantic consistency in the model's outputs. D. 4 Impact of the Hybrid Loss on Policy. Learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.03271?", "content": "by A Das · 2025 · Cited by 1 — This helps improve general- ization and maintain semantic consistency in the model's outputs. D. 4 Impact of the Hybrid Loss on Policy. Learning ."}
diff --git a/data/sampled_jsons/sitearxiv.org_2410.00844_Remark_4.2_Fisher_information_I(p).jsonl b/data/sampled_jsons/sitearxiv.org_2410.00844_Remark_4.2_Fisher_information_I(p).jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2410 . 00844 ] Learning stochastic dynamics from snapshots through...", "date": "", "ddg_snippet": "Computer Science > Machine Learning. arXiv: 2410 . 00844 (cs).Based on the RUOT form, our method models these dynamics without requiring prior knowledge of growth and death processes or additional information , allowing them to be learned directly from data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.00844", "content": "Computer Science > Machine Learning. arXiv: 2410 . 00844 (cs).Based on the RUOT form, our method models these dynamics without requiring prior knowledge of growth and death processes or additional information , allowing them to be learned directly from data."}
+{"idx": 1, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "14 Feb 2025 — Remark 4.2 . Report issue for preceding element. The term ℐ ... Fisher information regularization schemes for wasserstein gradient flows.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v2", "content": "14 Feb 2025 — Remark 4.2 . Report issue for preceding element. The term ℐ ... Fisher information regularization schemes for wasserstein gradient flows."}
+{"idx": 2, "title": "Learning stochastic dynamics from snapshots through regularized...", "date": "", "ddg_snippet": "Remark 3.4. Report issue for preceding element.is referred to as the Fisher information . Notably, when considering growth/death factors, Eq. (3.9) includes not only the Fisher -Rao metric but also an additional cross-term.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v1", "content": "Remark 3.4. Report issue for preceding element.is referred to as the Fisher information . Notably, when considering growth/death factors, Eq. (3.9) includes not only the Fisher -Rao metric but also an additional cross-term."}
+{"idx": 3, "title": "Improving Cram\\'er-Rao Bound And Its Variants: An Extrinsic Geometry ...", "date": "", "ddg_snippet": "Information geometry offers an alternative framework in which statistical models are viewed as Riemannian manifolds endowed with the Fisher -Rao metric. This view-point allows estimator performance to be studied using geometric concepts such as curvature, geodesics, and connections [12].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.17886", "content": "Information geometry offers an alternative framework in which statistical models are viewed as Riemannian manifolds endowed with the Fisher -Rao metric. This view-point allows estimator performance to be studied using geometric concepts such as curvature, geodesics, and connections [12]."}
+{"idx": 4, "title": "Conditions for equality and stability in Shannon's and Tao's entropy ...", "date": "", "ddg_snippet": "This result played a key role in the recent breakthrough resolution of Bourgain's slicing problem [34, 29]. Recently, a weak qualitative stability result was established in [25], in a similar spirit as that in [14], but without the Fisher information assumption, and in terms of weak convergence rather than convergence in relative entropy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14021", "content": "This result played a key role in the recent breakthrough resolution of Bourgain's slicing problem [34, 29]. Recently, a weak qualitative stability result was established in [25], in a similar spirit as that in [14], but without the Fisher information assumption, and in terms of weak convergence rather than convergence in relative entropy."}
+{"idx": 5, "title": "Parameter estimation for fractional autoregressive process with ...", "date": "", "ddg_snippet": "The Fisher information matrix (FIM) can be deduced from equation ( 42 ). We simulate the spectral density and its derivatives using the method described in [14], then plug the FIM and score functions into equation (43) to compute the One-Step estimator numerically.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.20736v2", "content": "The Fisher information matrix (FIM) can be deduced from equation ( 42 ). We simulate the spectral density and its derivatives using the method described in [14], then plug the FIM and score functions into equation (43) to compute the One-Step estimator numerically."}
+{"idx": 6, "title": "On the consistency of rotation curves and spatially integrated", "date": "", "ddg_snippet": "Abstract Resolved rotation curves (RCs) are the gold-standard measurements for inferring dark matter distributions in Λ CDM and testing alternative theories of dynamics in galaxies. However they are expensive to obtain, making them prohibitive for large galaxy samples and at higher redshift. Spatially integrated H I flux profiles are more accessible and present the information in a different ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.13754v2", "content": "Abstract Resolved rotation curves (RCs) are the gold-standard measurements for inferring dark matter distributions in Λ CDM and testing alternative theories of dynamics in galaxies. However they are expensive to obtain, making them prohibitive for large galaxy samples and at higher redshift. Spatially integrated H I flux profiles are more accessible and present the information in a different ..."}
+{"idx": 7, "title": "Learning stochastic dynamics from snapshots through regularized...", "date": "", "ddg_snippet": "Remark 4 . 2 . Fisher information regularization schemes for wasserstein gradient flows.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v4", "content": "Remark 4 . 2 . Fisher information regularization schemes for wasserstein gradient flows."}
+{"idx": 8, "title": "Learning stochastic dynamics from snapshots through regularized...", "date": "", "ddg_snippet": "arXiv: 2410 . 00844 v5 [cs.LG] 08 May 2025. Fisher information regularization schemes for wasserstein gradient flows.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v5", "content": "arXiv: 2410 . 00844 v5 [cs.LG] 08 May 2025. Fisher information regularization schemes for wasserstein gradient flows."}
+{"idx": 9, "title": "Learning stochastic dynamics from snapshots through regularized...", "date": "", "ddg_snippet": "Remark 4 . 2 . Fisher information regularization schemes for wasserstein gradient flows.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v3", "content": "Remark 4 . 2 . Fisher information regularization schemes for wasserstein gradient flows."}
diff --git a/data/sampled_jsons/sitearxiv.org_2410.02025_FD2_synthetic_dataset_x_=_formula.jsonl b/data/sampled_jsons/sitearxiv.org_2410.02025_FD2_synthetic_dataset_x_=_formula.jsonl
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index 0000000000000000000000000000000000000000..7950294353c93a7a2c5fa31ab8e29567a16ac180
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+++ b/data/sampled_jsons/sitearxiv.org_2410.02025_FD2_synthetic_dataset_x_=_formula.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "Dataset Distillation via the Wasserstein Metric", "date": "", "ddg_snippet": "Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly reducing computational costs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.18531v3", "content": "Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly reducing computational costs."}
+{"idx": 1, "title": "SynRailObs: A Synthetic Dataset for Obstacle Detection in Railway...", "date": "", "ddg_snippet": "• We propose a highly realistic synthetic dataset named SynRailObs, designed to address the data shortage issue in railway obstacle detection scenarios. SynRailObs includes a wide range of potential obstacles, such as pedestrians, rocks, animals, vehicles, and more.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10784v1", "content": "• We propose a highly realistic synthetic dataset named SynRailObs, designed to address the data shortage issue in railway obstacle detection scenarios. SynRailObs includes a wide range of potential obstacles, such as pedestrians, rocks, animals, vehicles, and more."}
+{"idx": 2, "title": "SynRailObs: A Synthetic Dataset for Obstacle Detection in Railway...", "date": "", "ddg_snippet": "2. 2 Synthetic Dataset .This dataset has been widely used to validate cross-domain tasks. Similarly, [5] employed a synthetic dataset for railway scenarios, facilitating the simulation of related tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.10784", "content": "2. 2 Synthetic Dataset .This dataset has been widely used to validate cross-domain tasks. Similarly, [5] employed a synthetic dataset for railway scenarios, facilitating the simulation of related tasks."}
+{"idx": 3, "title": "A likelihood based approach to distribution regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — X ( x ) as the distribution of X . We denote the true conditional ... two synthetic dataset examples. These experiments cover a range of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — X ( x ) as the distribution of X . We denote the true conditional ... two synthetic dataset examples. These experiments cover a range of ..."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — ... synthetic dataset examples. These experiments cover a range of scenarios ... FD2 : Y = X 1 2 + e ( X 2 + X 3 / 3 ) + sin ( X 4 + X ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — ... synthetic dataset examples. These experiments cover a range of scenarios ... FD2 : Y = X 1 2 + e ( X 2 + X 3 / 3 ) + sin ( X 4 + X ..."}
+{"idx": 5, "title": "Detecting High-Stakes Interactions with Activation Probes", "date": "", "ddg_snippet": "For training probes and finetuned baselines, we created a synthetic dataset by prompting GPT-4o. The datasets are linked in this paper’s code repository (see beginning of the Appendix for a link to the repository).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10805v2", "content": "For training probes and finetuned baselines, we created a synthetic dataset by prompting GPT-4o. The datasets are linked in this paper’s code repository (see beginning of the Appendix for a link to the repository)."}
+{"idx": 6, "title": "UniGO: A Unified Graph Neural Network for Modeling Opinion...", "date": "", "ddg_snippet": "The synthetic dataset UniSyn is generated using the unified opinion dynamics. This synthetic dataset includes three types of random graph structures, and the dynamics involve various parameter combinations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.11519v1", "content": "The synthetic dataset UniSyn is generated using the unified opinion dynamics. This synthetic dataset includes three types of random graph structures, and the dynamics involve various parameter combinations."}
+{"idx": 7, "title": "Snowy Scenes, Clear Detections: A Robust Model for Traffic Light...", "date": "", "ddg_snippet": "A new traffic light dataset has been created, comprising both synthetic and real images. The real images are based on the Bosch Small Traffic Light Dataset (Behrendt and Novak, 2017) , while the synthetic images are generated using the approach by (Zhang et al., 2023) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13473v1", "content": "A new traffic light dataset has been created, comprising both synthetic and real images. The real images are based on the Bosch Small Traffic Light Dataset (Behrendt and Novak, 2017) , while the synthetic images are generated using the approach by (Zhang et al., 2023) ."}
diff --git a/data/sampled_jsons/sitearxiv.org_2410.09543_Appendix_A.3.2_hardware_GPU_configuration.jsonl b/data/sampled_jsons/sitearxiv.org_2410.09543_Appendix_A.3.2_hardware_GPU_configuration.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GPU Programming for AI Workflow Development on AWS SageMaker ...", "date": "", "ddg_snippet": "4 days ago · Abstract We present the design, implementation, and comprehensive evaluation of a specialized course on GPU architecture, GPU programming, and how these are used for developing AI agents. This course is offered to undergraduate and graduate students during Fall 2024 and Spring 2025. The course began with foundational concepts in GPU /CPU hardware and parallel computing and progressed to develop ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13703v1", "content": "4 days ago · Abstract We present the design, implementation, and comprehensive evaluation of a specialized course on GPU architecture, GPU programming, and how these are used for developing AI agents. This course is offered to undergraduate and graduate students during Fall 2024 and Spring 2025. The course began with foundational concepts in GPU /CPU hardware and parallel computing and progressed to develop ..."}
+{"idx": 1, "title": "Forecasting GPU Performance for Deep Learning Training and Inference", "date": "", "ddg_snippet": "For kernel-alike operators (such as vector operators), it executes applications on an existing GPU to measure latency and scales the latency with the ratio of target GPU hardware configuration to that of existing GPU .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.13853v3", "content": "For kernel-alike operators (such as vector operators), it executes applications on an existing GPU to measure latency and scales the latency with the ratio of target GPU hardware configuration to that of existing GPU ."}
+{"idx": 2, "title": "Forecasting GPU Performance for Deep Learning Training and Inference", "date": "", "ddg_snippet": "Both configurations enable full-bandwidth communication between any two GPUs . We evaluate NeuSight on data, tensor model, and pipeline parallel execution, albeit individually.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.13853v2", "content": "Both configurations enable full-bandwidth communication between any two GPUs . We evaluate NeuSight on data, tensor model, and pipeline parallel execution, albeit individually."}
+{"idx": 3, "title": "Forecasting GPU Performance for Deep Learning Training and Inference", "date": "", "ddg_snippet": "GPU to measure latency and scales the latency with the ratio of target GPU hardware configuration to that of existing GPU . As such, Habitat requires a GPU in-hand to estimate. 3 . 2 Predicting Performance of Batched Matrix Multiplication with Larger Predictors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.13853", "content": "GPU to measure latency and scales the latency with the ratio of target GPU hardware configuration to that of existing GPU . As such, Habitat requires a GPU in-hand to estimate. 3 . 2 Predicting Performance of Batched Matrix Multiplication with Larger Predictors."}
+{"idx": 4, "title": "[ 2410 . 09543 ] Boltzmann-Aligned Inverse Folding Model as a Predictor...", "date": "", "ddg_snippet": "Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to $ΔΔG$ prediction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to $ΔΔG$ prediction."}
+{"idx": 5, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational...", "date": "", "ddg_snippet": "arXiv: 2410 . 09543 v1 [cs.CE] 12 Oct 2024. 3 . 2 ) that integrates the inverse folding model into Boltzmann alignment. This method is named BA-Cycle and uses the inverse folding model to evaluate.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "arXiv: 2410 . 09543 v1 [cs.CE] 12 Oct 2024. 3 . 2 ) that integrates the inverse folding model into Boltzmann alignment. This method is named BA-Cycle and uses the inverse folding model to evaluate."}
+{"idx": 6, "title": "MMORE: Massive Multimodal Open RAG & Extraction", "date": "", "ddg_snippet": "The experiments presented here used a conservative default, leaving around 65GB of the 80GB GPU unused. This high-lights the potential for further optimization, as users can adjust the configuration to fully exploit available hardware resources. Table 2 further illustrates the performance advan-tage of MMORE across multiple file types.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.11937", "content": "The experiments presented here used a conservative default, leaving around 65GB of the 80GB GPU unused. This high-lights the potential for further optimization, as users can adjust the configuration to fully exploit available hardware resources. Table 2 further illustrates the performance advan-tage of MMORE across multiple file types."}
+{"idx": 7, "title": "MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert ...", "date": "", "ddg_snippet": "Jun 9, 2025 · Figure 2 shows the overall flow of a Transformer Block in a Mixture-of-Expert inference workload. For illustration purposes, we assume a four- GPU system, four experts (denoted E1-E4) each residing on one GPU , and each token is routed to its top-1 experts fedus2022switch .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07366v1", "content": "Jun 9, 2025 · Figure 2 shows the overall flow of a Transformer Block in a Mixture-of-Expert inference workload. For illustration purposes, we assume a four- GPU system, four experts (denoted E1-E4) each residing on one GPU , and each token is routed to its top-1 experts fedus2022switch ."}
+{"idx": 8, "title": "KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP ...", "date": "", "ddg_snippet": "B Implementation Details This appendix presents implementation details of constructing the patent knowledge graph and the LLM-driven QA assistant.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.07860", "content": "B Implementation Details This appendix presents implementation details of constructing the patent knowledge graph and the LLM-driven QA assistant."}
+{"idx": 9, "title": "FOVAL: Calibration-Free and Subject-Invariant Fixation Depth ...", "date": "", "ddg_snippet": "10 hours ago · By eliminating the need for subject-specific calibration and functioning reliably on consumer-grade hardware , FOVAL democratizes access to gaze-enabled technologies in extended reality (XR), vision health, and human-computer interaction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.03591v2", "content": "10 hours ago · By eliminating the need for subject-specific calibration and functioning reliably on consumer-grade hardware , FOVAL democratizes access to gaze-enabled technologies in extended reality (XR), vision health, and human-computer interaction."}
diff --git a/data/sampled_jsons/sitearxiv.org_2410.13708_Section_5.1.jsonl b/data/sampled_jsons/sitearxiv.org_2410.13708_Section_5.1.jsonl
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+++ b/data/sampled_jsons/sitearxiv.org_2410.13708_Section_5.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2410.13708v2 [cs.CL] 24 Feb 2025", "date": "", "ddg_snippet": "This section provides additional derivations and related discussions for the two methods, Undiffer-entiated Attention and Scaling Contribution, introduced in Section 3.1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.13708", "content": "This section provides additional derivations and related discussions for the two methods, Undiffer-entiated Attention and Scaling Contribution, introduced in Section 3.1."}
+{"idx": 1, "title": "Beyond Prompt Engineering: Robust Behavior Control in LLMs ...", "date": "", "ddg_snippet": "5 Controlling LLMs: Steering or Prompting? Figure 4: The positive and negative input. Figure 5: Transfering prompt to steering vector directly. In this section , we conduct an in-depth analysis of prompt engineering and steering control on Gemma-2-9b-it 5.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.20322v1", "content": "5 Controlling LLMs: Steering or Prompting? Figure 4: The positive and negative input. Figure 5: Transfering prompt to steering vector directly. In this section , we conduct an in-depth analysis of prompt engineering and steering control on Gemma-2-9b-it 5."}
+{"idx": 2, "title": "Targeting Alignment: Extracting Safety Classifiers of Aligned ...", "date": "", "ddg_snippet": "Jan 27, 2025 · In this section , we aim to answer RQ3, i.e., if we can jailbreak aligned LLMs by attacking the surrogate classifier. This section is symmetrical to Section 4.3. Instead of applying the attack on the LLMs and evaluating the inputs on the candidate classifiers, we attack the latter and evaluate how well the adversarial inputs transfer to the LLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.16534", "content": "Jan 27, 2025 · In this section , we aim to answer RQ3, i.e., if we can jailbreak aligned LLMs by attacking the surrogate classifier. This section is symmetrical to Section 4.3. Instead of applying the attack on the LLMs and evaluating the inputs on the candidate classifiers, we attack the latter and evaluate how well the adversarial inputs transfer to the LLMs."}
+{"idx": 3, "title": "Soft Injection of Task Embeddings Outperforms Prompt-Based In ...", "date": "", "ddg_snippet": "In Section 5.2, we compare task-specific and task-agnostic perspectives and find that the task-relevant roles of attention heads are better explained from the task-specific viewpoint. Although the analyses in this section are based on Llama-3.1-8B, we also observed that the findings generalize to other models listed in Table 1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.20906v2", "content": "In Section 5.2, we compare task-specific and task-agnostic perspectives and find that the task-relevant roles of attention heads are better explained from the task-specific viewpoint. Although the analyses in this section are based on Llama-3.1-8B, we also observed that the findings generalize to other models listed in Table 1."}
+{"idx": 4, "title": "Pushing the Limits of Safety: A Technical Report on the", "date": "", "ddg_snippet": "Jul 11, 2025 · We address these gaps with a two-phase attack combining white-box optimization and multimodal camouflage, detailed in the next section .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.12430", "content": "Jul 11, 2025 · We address these gaps with a two-phase attack combining white-box optimization and multimodal camouflage, detailed in the next section ."}
+{"idx": 5, "title": "Cracking the Code of Hallucination in LVLMs with Vision-aware ...", "date": "", "ddg_snippet": "As discussed in Section 3.1, the VHD metric effectively captures the sensitivity of attention heads to visual information, making it a suitable indicator for selecting key attention heads for reinforcement.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.13949v3", "content": "As discussed in Section 3.1, the VHD metric effectively captures the sensitivity of attention heads to visual information, making it a suitable indicator for selecting key attention heads for reinforcement."}
+{"idx": 6, "title": "Nonlocal problems with Hardy-Littlewood-Sobolev critical ...", "date": "", "ddg_snippet": "19 hours ago · We are concerned with a Brezis-Nirenberg type problem for a critical Choquard equation, in the sense of Hardy-Littlewood-Sobolev inequality, and with the Hardy potential in a smooth bounded domain. By exploiting variational methods we obtain existence results, which extend to different perturbation terms. Some estimates of independent interest about a nonlocal mini-mization problem are also ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15697", "content": "19 hours ago · We are concerned with a Brezis-Nirenberg type problem for a critical Choquard equation, in the sense of Hardy-Littlewood-Sobolev inequality, and with the Hardy potential in a smooth bounded domain. By exploiting variational methods we obtain existence results, which extend to different perturbation terms. Some estimates of independent interest about a nonlocal mini-mization problem are also ..."}
+{"idx": 7, "title": "Search for quantum decoherence in neutrino oscillations with", "date": "", "ddg_snippet": "The discovery of neutrino oscillations [ 1 , 2 ] is one of the first steps towards physics beyond the Standard Model in revealing that neutrinos do ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.01388v2", "content": "The discovery of neutrino oscillations [ 1 , 2 ] is one of the first steps towards physics beyond the Standard Model in revealing that neutrinos do ..."}
+{"idx": 8, "title": "1 Introduction", "date": "", "ddg_snippet": "Figure 1 : Vision-language inputs cause a modality-induced activation shift , steering VLM activations toward a “safer” direction compared to text ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.13095v1", "content": "Figure 1 : Vision-language inputs cause a modality-induced activation shift , steering VLM activations toward a “safer” direction compared to text ..."}
+{"idx": 9, "title": "Targeting Alignment: Extracting Safety Classifiers of Aligned", "date": "", "ddg_snippet": "Figure 1 : In this work, we (A) hypothesize that alignment embeds a safety classifier in LLMs responsible for the classification of safe and unsafe ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.16534v1", "content": "Figure 1 : In this work, we (A) hypothesize that alignment embeds a safety classifier in LLMs responsible for the classification of safe and unsafe ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2410.23569_Equation_8.jsonl b/data/sampled_jsons/sitearxiv.org_2410.23569_Equation_8.jsonl
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index 0000000000000000000000000000000000000000..f7b6dec1507896991a4f016e4d845a267fa1cfe5
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2410 . 23569 ] RA-PbRL: Provably Efficient Risk-Aware...", "date": "", "ddg_snippet": "These scenarios often operate under a one-episode-reward setting, which makes conventional risk-sensitive objectives inapplicable. To address this, we explore and prove the applicability of two risk-aware objectives to PbRL : nested and static quantile risk objectives.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "These scenarios often operate under a one-episode-reward setting, which makes conventional risk-sensitive objectives inapplicable. To address this, we explore and prove the applicability of two risk-aware objectives to PbRL : nested and static quantile risk objectives."}
+{"idx": 1, "title": "RA-PbRL: Provably Efficient Risk-Aware", "date": "", "ddg_snippet": "arXiv: 2410 . 23569 v4 [cs.LG] 9 Jan 2025. RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning. Yujie Zhao1, Jose Efraim Aguilar Escamill2, Weyl Lu3, Huazheng Wang2 1 University of California, San Diego, 2 Oregon State University, 3 University of California...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.23569", "content": "arXiv: 2410 . 23569 v4 [cs.LG] 9 Jan 2025. RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning. Yujie Zhao1, Jose Efraim Aguilar Escamill2, Weyl Lu3, Huazheng Wang2 1 University of California, San Diego, 2 Oregon State University, 3 University of California..."}
+{"idx": 2, "title": "RA-RLHF: Provably Efficient Risk-Aware Reinforcement Learning...", "date": "", "ddg_snippet": "When using a quantile function to transform a risk-neutral RLHF algorithm into a risk-aware algorithm, the Bellman equation used to solve the problem becomes non-linear. This change to the bellman equation disrupts calculations on regret, making risk-neutral RLHF inapplicable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v2/", "content": "When using a quantile function to transform a risk-neutral RLHF algorithm into a risk-aware algorithm, the Bellman equation used to solve the problem becomes non-linear. This change to the bellman equation disrupts calculations on regret, making risk-neutral RLHF inapplicable."}
+{"idx": 3, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "When using a quantile function to transform a risk-neutral PbRL algorithm into a risk-aware algorithm, the Bellman equation used to solve the problem becomes non-linear. This change to the bellman equation disrupts calculations on regret, making risk-neutral PbRL inapplicable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v3", "content": "When using a quantile function to transform a risk-neutral PbRL algorithm into a risk-aware algorithm, the Bellman equation used to solve the problem becomes non-linear. This change to the bellman equation disrupts calculations on regret, making risk-neutral PbRL inapplicable."}
+{"idx": 4, "title": "RA-PbRL: Provably Efficient Risk-Aware", "date": "", "ddg_snippet": "arXiv: 2410 . 23569 v4 [cs.LG] 9 Jan 2025. RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning. Yujie Zhao1, Jose Efraim Aguilar Escamill2, Weyl Lu3, Huazheng Wang2 1 University of California, San Diego, 2 Oregon State University, 3 University of California...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.23569v4", "content": "arXiv: 2410 . 23569 v4 [cs.LG] 9 Jan 2025. RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning. Yujie Zhao1, Jose Efraim Aguilar Escamill2, Weyl Lu3, Huazheng Wang2 1 University of California, San Diego, 2 Oregon State University, 3 University of California..."}
+{"idx": 5, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "When using a quantile function to transform a risk-neutral PbRL algorithm into a risk-aware algorithm, the Bellman equation used to solve the problem becomes non-linear. This change to the bellman equation disrupts calculations on regret, making risk-neutral PbRL inapplicable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v1/", "content": "When using a quantile function to transform a risk-neutral PbRL algorithm into a risk-aware algorithm, the Bellman equation used to solve the problem becomes non-linear. This change to the bellman equation disrupts calculations on regret, making risk-neutral PbRL inapplicable."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "9 Jan 2025 — When using a quantile function to transform a risk-neutral PbRL algorithm into a risk-aware algorithm, the Bellman equation used to solve the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v4", "content": "9 Jan 2025 — When using a quantile function to transform a risk-neutral PbRL algorithm into a risk-aware algorithm, the Bellman equation used to solve the ..."}
+{"idx": 7, "title": "Machine Learning Oct 2024", "date": "", "ddg_snippet": "[2545] arXiv: 2410.23569 [pdf, html, other]. Title ... Title: Projected Neural Differential Equations ... Comments: 8 pages with referencing, 1 figure, 5 tables.", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.LG/2024-10?skip=2525&show=1000", "content": "[2545] arXiv: 2410.23569 [pdf, html, other]. Title ... Title: Projected Neural Differential Equations ... Comments: 8 pages with referencing, 1 figure, 5 tables."}
+{"idx": 8, "title": "Machine Learning Oct 2024", "date": "", "ddg_snippet": "Comments: 14 pages, 9 figures, 8 tables, 16 equations . The source code is ... [2545] arXiv: 2410.23569 [pdf, html, other]. Title: RA-PbRL: Provably ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.LG/2024-10?skip=1475&show=2000", "content": "Comments: 14 pages, 9 figures, 8 tables, 16 equations . The source code is ... [2545] arXiv: 2410.23569 [pdf, html, other]. Title: RA-PbRL: Provably ..."}
+{"idx": 9, "title": "Computer Science Oct 2024", "date": "", "ddg_snippet": "Title: Discretizing the Fokker-Planck equation with second-order accuracy: a dissipation driven approach. Clément Cancès (RAPSODI, LPP), Léonard Monsaingeon ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2024-10?skip=11275&show=1000", "content": "Title: Discretizing the Fokker-Planck equation with second-order accuracy: a dissipation driven approach. Clément Cancès (RAPSODI, LPP), Léonard Monsaingeon ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.00136.jsonl b/data/sampled_jsons/sitearxiv.org_2502.00136.jsonl
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index 0000000000000000000000000000000000000000..329690c6ae67412228aae1cf6da71484f76297f0
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+++ b/data/sampled_jsons/sitearxiv.org_2502.00136.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Subir Sarkar's articles on arXiv", "date": "", "ddg_snippet": "[3] arXiv:2502.19776 [pdf, ps, other].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/a/sarkar_s_2.html", "content": "[3] arXiv:2502.19776 [pdf, ps, other]."}
+{"idx": 1, "title": "[2502.00136] A Checks-and-Balances Framework for Context ... A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "Jan 31, 2025 · This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ... Abstract This paper introduces a checks-and-balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "Jan 31, 2025 · This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ... Abstract This paper introduces a checks-and-balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ..."}
+{"idx": 2, "title": "Computation and Language Feb 2025", "date": "", "ddg_snippet": "1] arXiv: 2502 .00041 [ pdf , html , other ] ... 2] arXiv: 2502 .00063 [ pdf , html , other ] ... 8] arXiv: 2502 . 00136 [ pdf , other ]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.CL/2025-02", "content": "1] arXiv: 2502 .00041 [ pdf , html , other ] ... 2] arXiv: 2502 .00063 [ pdf , html , other ] ... 8] arXiv: 2502 . 00136 [ pdf , other ]"}
+{"idx": 3, "title": "Flyby-induced displacement: analytic solution", "date": "", "ddg_snippet": "Report issue for preceding element. 04.20.-q Classical general relativity; ††preprint: arXiv: 2502.0136v2 [gr-qc].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01326v2", "content": "Report issue for preceding element. 04.20.-q Classical general relativity; ††preprint: arXiv: 2502.0136v2 [gr-qc]."}
+{"idx": 4, "title": "Flyby-induced displacement effect: an analytic solution", "date": "", "ddg_snippet": "S. Bhattacharya and S. Ghosh, “Displacement memory and B-memory in generalised Ellis-Bronnikov wormholes,” [arXiv:2502.03007 [gr-qc]].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01326v3", "content": "S. Bhattacharya and S. Ghosh, “Displacement memory and B-memory in generalised Ellis-Bronnikov wormholes,” [arXiv:2502.03007 [gr-qc]]."}
+{"idx": 5, "title": "DECADE+DES Y3 Weak Lensing Mass Map: A 13,000 deg2 View of", "date": "", "ddg_snippet": "... S. More, M. Oguri, T. Hamana et al., The three-year shear catalog of the Subaru Hyper Suprime-Cam SSP Survey , PASJ 74 (2022) 421 [ 2107. 00136 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03798v1", "content": "... S. More, M. Oguri, T. Hamana et al., The three-year shear catalog of the Subaru Hyper Suprime-Cam SSP Survey , PASJ 74 (2022) 421 [ 2107. 00136 ..."}
+{"idx": 6, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "Abstract This paper introduces a checks-and-balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "Abstract This paper introduces a checks-and-balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ..."}
+{"idx": 7, "title": "Exchange-Correlation Potentials and Energy Densities through", "date": "", "ddg_snippet": "In addition, the multiresolution analysis (MRA) method developed by Stückrath and Bischoff 31 achieves similar goals through a systematic approach ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10262v1", "content": "In addition, the multiresolution analysis (MRA) method developed by Stückrath and Bischoff 31 achieves similar goals through a systematic approach ..."}
+{"idx": 8, "title": "Dual-Wavelength Brillouin Lasers as compact Opto-Terahertz", "date": "", "ddg_snippet": "Such high SNR is crucial to minimizing in-loop error in the PLL, particularly since the division ratio from 300 GHz to 10 GHz offers only limited ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.21416v1", "content": "Such high SNR is crucial to minimizing in-loop error in the PLL, particularly since the division ratio from 300 GHz to 10 GHz offers only limited ..."}
+{"idx": 9, "title": "WordCon: Word-level Typography Control in Scene Text Rendering", "date": "", "ddg_snippet": "These results further validate that our approach not only enables word-level controllability but also preserves high-generation quality, text ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21276v1", "content": "These results further validate that our approach not only enables word-level controllability but also preserves high-generation quality, text ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.00136_dataset_empirical_studies.jsonl b/data/sampled_jsons/sitearxiv.org_2502.00136_dataset_empirical_studies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5487cf4ce6375d1a97acc48377bbf164c7e3a2f3
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2502.00136_dataset_empirical_studies.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00136] A Checks-and-Balances Framework for Context ... Developer-LLM Conversations: An Empirical Study of ... Manifold Dimension Estimation: An Empirical Study - arXiv.org A method for improving multilingual quality and diversity of ... Synergizing Static Analysis with Large Language Models for ... A METHOD FOR IMPROVING MULTILINGUAL QUALITY AND DIVERSITY OF ... Deep learning and abstractive summarisation for radiological ...", "date": "", "ddg_snippet": "Jan 31, 2025 · This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ... Sep 12, 2025 · To address this, we leverage CodeChat, a large dataset comprising 82,845 real-world developer-LLM conversations, containing 368,506 code snippets generated across over 20 programming languages, derived from the WildChat dataset . We find that LLM responses are substantially longer than developer prompts, with a median token-length ratio of 14:1. 4 hours ago · We also compare the estimators on diverse synthetic and real-world datasets , introducing a principled approach to dataset -specific hyperparameter tun- ing. Our results offer practical guidance and suggest that, for a problem of this generality, simpler methods often perform better. 4 hours ago · multilingual LLMs continues to be constrained by the scarcity of high-quality, linguistically diverse training data. Notably, data selection has emerged as a promising strat- egy to enhance IFT efficiency and performance—particularly in English settings. Empirical studies demonstrate that care- fully selected small-scale datasets (e.g. 1K samples[5]) can achieve competitive performance ... 4 hours ago · We conduct an extensive empirical study on a curated dataset of open-source projects to quantitatively measure the performance gains. Our work demonstrates that this synergistic combination not only significantly reduces false positive rates but also uncovers a new class of complex, contextual vulnerabilities missed by traditional SAST. 4 hours ago · Empirical studies demonstrate that carefully selected small-scale datasets (e.g. 1K samples []) can achieve competitive performance compared to models trained on significantly larger corpora (e.g. 52K samples []). 4 hours ago · Deep learning and abstractive summarisation for radiological reports: an empirical study for adapting the PEGASUS models’ family with scarce data. Claudio Benzoni∗1, Martina Langhals2, Martin Boeker1, Luise Modersohn1, and M ́at ́e E. Maros2", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "Jan 31, 2025 · This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ... Sep 12, 2025 · To address this, we leverage CodeChat, a large dataset comprising 82,845 real-world developer-LLM conversations, containing 368,506 code snippets generated across over 20 programming languages, derived from the WildChat dataset . We find that LLM responses are substantially longer than developer prompts, with a median token-length ratio of 14:1. 4 hours ago · We also compare the estimators on diverse synthetic and real-world datasets , introducing a principled approach to dataset -specific hyperparameter tun- ing. Our results offer practical guidance and suggest that, for a problem of this generality, simpler methods often perform better. 4 hours ago · multilingual LLMs continues to be constrained by the scarcity of high-quality, linguistically diverse training data. Notably, data selection has emerged as a promising strat- egy to enhance IFT efficiency and performance—particularly in English settings. Empirical studies demonstrate that care- fully selected small-scale datasets (e.g. 1K samples[5]) can achieve competitive performance ... 4 hours ago · We conduct an extensive empirical study on a curated dataset of open-source projects to quantitatively measure the performance gains. Our work demonstrates that this synergistic combination not only significantly reduces false positive rates but also uncovers a new class of complex, contextual vulnerabilities missed by traditional SAST. 4 hours ago · Empirical studies demonstrate that carefully selected small-scale datasets (e.g. 1K samples []) can achieve competitive performance compared to models trained on significantly larger corpora (e.g. 52K samples []). 4 hours ago · Deep learning and abstractive summarisation for radiological reports: an empirical study for adapting the PEGASUS models’ family with scarce data. Claudio Benzoni∗1, Martina Langhals2, Martin Boeker1, Luise Modersohn1, and M ́at ́e E. Maros2"}
+{"idx": 1, "title": "Developer-LLM Conversations: An Empirical Study of ...", "date": "", "ddg_snippet": "Sep 12, 2025 · To address this, we leverage CodeChat, a large dataset comprising 82,845 real-world developer-LLM conversations, containing 368,506 code snippets generated across over 20 programming languages, derived from the WildChat dataset . We find that LLM responses are substantially longer than developer prompts, with a median token-length ratio of 14:1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2509.10402", "content": "Sep 12, 2025 · To address this, we leverage CodeChat, a large dataset comprising 82,845 real-world developer-LLM conversations, containing 368,506 code snippets generated across over 20 programming languages, derived from the WildChat dataset . We find that LLM responses are substantially longer than developer prompts, with a median token-length ratio of 14:1."}
+{"idx": 2, "title": "Manifold Dimension Estimation: An Empirical Study - arXiv.org", "date": "", "ddg_snippet": "4 hours ago · We also compare the estimators on diverse synthetic and real-world datasets , introducing a principled approach to dataset -specific hyperparameter tun- ing. Our results offer practical guidance and suggest that, for a problem of this generality, simpler methods often perform better.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15517", "content": "4 hours ago · We also compare the estimators on diverse synthetic and real-world datasets , introducing a principled approach to dataset -specific hyperparameter tun- ing. Our results offer practical guidance and suggest that, for a problem of this generality, simpler methods often perform better."}
+{"idx": 3, "title": "[2509.16003] A Systematic Survey of Empirical User Studies of...", "date": "", "ddg_snippet": "View a PDF of the paper titled A Systematic Survey of Empirical User Studies of Unintentional Information Disclosure in Everyday Digital Interaction, by Reza Shahriari and 1 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2509.16003", "content": "View a PDF of the paper titled A Systematic Survey of Empirical User Studies of Unintentional Information Disclosure in Everyday Digital Interaction, by Reza Shahriari and 1 other authors."}
+{"idx": 4, "title": "[2509.15517] Manifold Dimension Estimation: An Empirical Study", "date": "", "ddg_snippet": "The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold.View a PDF of the paper titled Manifold Dimension Estimation: An Empirical Study , by Zelong Bi and Pierre Lafaye de Micheaux.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2509.15517", "content": "The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold.View a PDF of the paper titled Manifold Dimension Estimation: An Empirical Study , by Zelong Bi and Pierre Lafaye de Micheaux."}
+{"idx": 5, "title": "Late-time suppression of structure growth as a solution for the", "date": "", "ddg_snippet": "For the cosmic shear data, we use the measurements from the Subaru Hyper Suprime- Cam Year 3 (HSC-Y3) dataset [ 25 , 4 , 23 ] , which indicates ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.09176v1", "content": "For the cosmic shear data, we use the measurements from the Subaru Hyper Suprime- Cam Year 3 (HSC-Y3) dataset [ 25 , 4 , 23 ] , which indicates ..."}
+{"idx": 6, "title": "Breaking Dark: Hunting Heavy Decaying Dark Matter with Tibet", "date": "", "ddg_snippet": "... 10^{28}\\,\\mathrm{s} , along with the Tibet AS γ upper limits (green upper limits in the left panel ) [ 51 ] and LHAASO-KM2A inner galaxy datasets ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08039v1", "content": "... 10^{28}\\,\\mathrm{s} , along with the Tibet AS γ upper limits (green upper limits in the left panel ) [ 51 ] and LHAASO-KM2A inner galaxy datasets ..."}
+{"idx": 7, "title": "A method for improving multilingual quality and diversity of ...", "date": "", "ddg_snippet": "4 hours ago · multilingual LLMs continues to be constrained by the scarcity of high-quality, linguistically diverse training data. Notably, data selection has emerged as a promising strat- egy to enhance IFT efficiency and performance—particularly in English settings. Empirical studies demonstrate that care- fully selected small-scale datasets (e.g. 1K samples[5]) can achieve competitive performance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15549", "content": "4 hours ago · multilingual LLMs continues to be constrained by the scarcity of high-quality, linguistically diverse training data. Notably, data selection has emerged as a promising strat- egy to enhance IFT efficiency and performance—particularly in English settings. Empirical studies demonstrate that care- fully selected small-scale datasets (e.g. 1K samples[5]) can achieve competitive performance ..."}
+{"idx": 8, "title": "Synergizing Static Analysis with Large Language Models for ...", "date": "", "ddg_snippet": "4 hours ago · We conduct an extensive empirical study on a curated dataset of open-source projects to quantitatively measure the performance gains. Our work demonstrates that this synergistic combination not only significantly reduces false positive rates but also uncovers a new class of complex, contextual vulnerabilities missed by traditional SAST.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15433", "content": "4 hours ago · We conduct an extensive empirical study on a curated dataset of open-source projects to quantitatively measure the performance gains. Our work demonstrates that this synergistic combination not only significantly reduces false positive rates but also uncovers a new class of complex, contextual vulnerabilities missed by traditional SAST."}
+{"idx": 9, "title": "A METHOD FOR IMPROVING MULTILINGUAL QUALITY AND DIVERSITY OF ...", "date": "", "ddg_snippet": "4 hours ago · Empirical studies demonstrate that carefully selected small-scale datasets (e.g. 1K samples []) can achieve competitive performance compared to models trained on significantly larger corpora (e.g. 52K samples []).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15549v1", "content": "4 hours ago · Empirical studies demonstrate that carefully selected small-scale datasets (e.g. 1K samples []) can achieve competitive performance compared to models trained on significantly larger corpora (e.g. 52K samples [])."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.00561_Section_6_a_lot_of_work_computer_science_history.jsonl b/data/sampled_jsons/sitearxiv.org_2502.00561_Section_6_a_lot_of_work_computer_science_history.jsonl
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@@ -0,0 +1,5 @@
+{"idx": 0, "title": "A Validity-Centered Framework for AI Evaluation", "date": "", "ddg_snippet": "by O Salaudeen · 2025 · Cited by 3 — While the capabilities and utility of AI systems have advanced, rigorous norms for evaluating these systems have lagged.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.10573", "content": "by O Salaudeen · 2025 · Cited by 3 — While the capabilities and utility of AI systems have advanced, rigorous norms for evaluating these systems have lagged."}
+{"idx": 1, "title": "Human Learning about AI", "date": "", "ddg_snippet": "12 Feb 2025 — In this section we develop the framework for Human Projection (HP), which is composed of two components: (i) a model of performance, the “ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.05408v2", "content": "12 Feb 2025 — In this section we develop the framework for Human Projection (HP), which is composed of two components: (i) a model of performance, the “ ..."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 3, "title": "Industry Aspirations vs. User Realities with AI Agent Software", "date": "", "ddg_snippet": "3 days ago — There is growing imprecision about what “AI agents” are, what they can do, and how effectively they can be used by their intended users.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14528v1", "content": "3 days ago — There is growing imprecision about what “AI agents” are, what they can do, and how effectively they can be used by their intended users."}
+{"idx": 4, "title": "Large Language Model Psychometrics: A Systematic ...", "date": "", "ddg_snippet": "13 May 2025 — This survey introduces and synthesizes an emerging interdisciplinary field of LLM Psychometrics, which leverages psychometric instruments, theories, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.08245v1", "content": "13 May 2025 — This survey introduces and synthesizes an emerging interdisciplinary field of LLM Psychometrics, which leverages psychometric instruments, theories, and ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.20099_MCC_scores_table_results.jsonl b/data/sampled_jsons/sitearxiv.org_2502.20099_MCC_scores_table_results.jsonl
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index 0000000000000000000000000000000000000000..eea3d64c00297328f1f8e58cb090c4f89ae2e940
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2502 . 20099 ] Sanity Checking Causal Representation Learning on...", "date": "", "ddg_snippet": "arXiv: 2502 . 20099 (cs). [Submitted on 27 Feb 2025 (v1), last revised 28 Apr 2025 (this version, v2)].The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20099", "content": "arXiv: 2502 . 20099 (cs). [Submitted on 27 Feb 2025 (v1), last revised 28 Apr 2025 (this version, v2)].The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process."}
+{"idx": 1, "title": "Abstract page for arXiv paper 1706.03762: Attention Is All You Need", "date": "", "ddg_snippet": "Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results , including ensembles by over 2 BLEU.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results , including ensembles by over 2 BLEU."}
+{"idx": 2, "title": "MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal", "date": "", "ddg_snippet": "4.3 Classification In the multi-class glioma subtyping task, a similar trend to segmentation is observed: our pretrained model consistently outperforms the MAE-ViT base-line across both internal ( Table 4) and external ( Table 5) datasets. It achieves strong performance, with MCC scores reaching up to 0.70.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.11442", "content": "4.3 Classification In the multi-class glioma subtyping task, a similar trend to segmentation is observed: our pretrained model consistently outperforms the MAE-ViT base-line across both internal ( Table 4) and external ( Table 5) datasets. It achieves strong performance, with MCC scores reaching up to 0.70."}
+{"idx": 3, "title": "Machine-learning competition to grade EEG background patterns in ...", "date": "", "ddg_snippet": "In our competition for classifying EEG background patterns, participants were provided with several metrics, including accuracy, F-1 score , a weighted Matthewscorrelationcoeᾴ塴cient(MCC),precision,andrecall(Figure:2). Although all performance metrics were accessible to participants, ranking in the leaderboard was determined using a weighted MCC .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.09695", "content": "In our competition for classifying EEG background patterns, participants were provided with several metrics, including accuracy, F-1 score , a weighted Matthewscorrelationcoeᾴ塴cient(MCC),precision,andrecall(Figure:2). Although all performance metrics were accessible to participants, ranking in the leaderboard was determined using a weighted MCC ."}
+{"idx": 4, "title": "MatSKRAFT: A framework for large-scale materials knowledge extraction ...", "date": "", "ddg_snippet": "Here, we present Mat , a computational framework that automatically extracts and integrates materials science knowledge from tabular data at unprecedented scale. Our approach transforms tables into graph-based representations processed by constraint-driven GNNs that encode scientific principles directly into model skraft architecture.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.10448", "content": "Here, we present Mat , a computational framework that automatically extracts and integrates materials science knowledge from tabular data at unprecedented scale. Our approach transforms tables into graph-based representations processed by constraint-driven GNNs that encode scientific principles directly into model skraft architecture."}
+{"idx": 5, "title": "Scaling to Multimodal and Multichannel Heart Sound Classification: Fine ...", "date": "", "ddg_snippet": "The average (across five runs, to account for the variance of training the models) MCC score on the validation set was utilised as the optimisation metric. The initial hyperparameters for the baseline model without the augmented dataset are found in Table 5.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.11606", "content": "The average (across five runs, to account for the variance of training the models) MCC score on the validation set was utilised as the optimisation metric. The initial hyperparameters for the baseline model without the augmented dataset are found in Table 5."}
+{"idx": 6, "title": "SA-UNetv2: Rethinking Spatial Attention U-Net for Retinal Vessel ...", "date": "", "ddg_snippet": "As shown in Tables 1 and 2, SA-UNetv2 achieves leading performance in both accuracy and computational efficiency. Under the primary evaluation protocol on DRIVE without FOV, SA-UNetv2 attains the highest scores across key met-rics, including F1 (82.82), Jaccard (70.69), MCC (81.27), and AUC (98.71).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.11774", "content": "As shown in Tables 1 and 2, SA-UNetv2 achieves leading performance in both accuracy and computational efficiency. Under the primary evaluation protocol on DRIVE without FOV, SA-UNetv2 attains the highest scores across key met-rics, including F1 (82.82), Jaccard (70.69), MCC (81.27), and AUC (98.71)."}
+{"idx": 7, "title": "Fraud detection and risk assessment of online payment transactions on e ...", "date": "", "ddg_snippet": "This table presents a detailed analysis of the GCN model's performance in the e-commerce fraud detection task, where normal transactions are labeled as 0 and fraudulent transactions as 1. For the normal transaction category, the model achieves a precision of 0.98, a recall of 1.00, and an F1- Score of 0.99, supported by 1239159 samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.09928", "content": "This table presents a detailed analysis of the GCN model's performance in the e-commerce fraud detection task, where normal transactions are labeled as 0 and fraudulent transactions as 1. For the normal transaction category, the model achieves a precision of 0.98, a recall of 1.00, and an F1- Score of 0.99, supported by 1239159 samples."}
+{"idx": 8, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "arXiv: 2502 . 20099 v1 [cs.LG] 27 Feb 2025.The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v1", "content": "arXiv: 2502 . 20099 v1 [cs.LG] 27 Feb 2025.The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process."}
+{"idx": 9, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "arXiv: 2502 . 20099 v2 [cs.LG] 28 Apr 2025.The results reveal a reproducibility problem, as most methods al-ready fail on this synthetic ablation despite its simple data-generating process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20099", "content": "arXiv: 2502 . 20099 v2 [cs.LG] 28 Apr 2025.The results reveal a reproducibility problem, as most methods al-ready fail on this synthetic ablation despite its simple data-generating process."}
diff --git a/data/sampled_jsons/sitearxiv.org_2503.06366v1_'Grassmannian_cluster_algebra_dataset'_'Table_10'_'training_examples'_year_2024.jsonl b/data/sampled_jsons/sitearxiv.org_2503.06366v1_'Grassmannian_cluster_algebra_dataset'_'Table_10'_'training_examples'_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of...", "date": "", "ddg_snippet": "Cluster algebra quivers Grassmanian cluster algebras .Machine Learning meets Algebraic Combinatoric. How hard is it?: We provide both accuracy ( Table 4) and macro F1-scores ( Table 5) for these imbalanced datasets . Narrow models perform well in general.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "Cluster algebra quivers Grassmanian cluster algebras .Machine Learning meets Algebraic Combinatoric. How hard is it?: We provide both accuracy ( Table 4) and macro F1-scores ( Table 5) for these imbalanced datasets . Narrow models perform well in general."}
+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics, a subfield of mathematics that studies discrete structures arising from abstract algebra .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics, a subfield of mathematics that studies discrete structures arising from abstract algebra ."}
+{"idx": 2, "title": "[2503.06366] Machine Learning meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics, a subfield of mathematics that studies discrete structures arising from abstract algebra .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics, a subfield of mathematics that studies discrete structures arising from abstract algebra ."}
+{"idx": 3, "title": "[math/0311148] Grassmannians and Cluster Algebras - arXiv.org", "date": "", "ddg_snippet": "This paper demonstrates that the homogeneous coordinate ring of the Grassmannian G(k, n) is a {\\it cluster algebra of geometric type} - as defined by S. Fomin and A. Zelevinsky. Grassmannians having {\\it finite cluster type} are classified and the associated cluster variables are studied in connection with the geometry of configurations of points in RP2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/math/0311148", "content": "This paper demonstrates that the homogeneous coordinate ring of the Grassmannian G(k, n) is a {\\it cluster algebra of geometric type} - as defined by S. Fomin and A. Zelevinsky. Grassmannians having {\\it finite cluster type} are classified and the associated cluster variables are studied in connection with the geometry of configurations of points in RP2."}
+{"idx": 4, "title": "Clustering Cluster Algebras with Clusters", "date": "", "ddg_snippet": "2 Grassmannian cluster algebras .Keywords: Grassmannian cluster algebras , cluster variables, machine learning 2020 MSC: 13F60, 05E 10 Report Number: LIMS-2022-025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.09771", "content": "2 Grassmannian cluster algebras .Keywords: Grassmannian cluster algebras , cluster variables, machine learning 2020 MSC: 13F60, 05E 10 Report Number: LIMS-2022-025."}
+{"idx": 5, "title": "Quivers with potentials for Grassmannian cluster algebras", "date": "", "ddg_snippet": "View a PDF of the paper titled Quivers with potentials for Grassmannian cluster algebras , by Wen Chang and Jie Zhang.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1908.10103", "content": "View a PDF of the paper titled Quivers with potentials for Grassmannian cluster algebras , by Wen Chang and Jie Zhang."}
+{"idx": 6, "title": "Twists, Higher Dimer Covers, and Web Duality for Grassmannian Cluster ...", "date": "", "ddg_snippet": "We study a twisted version of Fraser, Lam, and Le's higher boundary measurement map, using face weights instead of edge weights, thereby providing Laurent polynomial expansions, in Plücker coordinates, for twisted web immanants for Grassmannians . In some small cases, Fraser, Lam, and Le observe a phenomenon they call \"web duality'', where web immanants coincide with web invariants, and they ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2507.15211", "content": "We study a twisted version of Fraser, Lam, and Le's higher boundary measurement map, using face weights instead of edge weights, thereby providing Laurent polynomial expansions, in Plücker coordinates, for twisted web immanants for Grassmannians . In some small cases, Fraser, Lam, and Le observe a phenomenon they call \"web duality'', where web immanants coincide with web invariants, and they ..."}
+{"idx": 7, "title": "Twists, Higher Dimer Covers, and Web Duality for Grassmannian Cluster ...", "date": "", "ddg_snippet": "We begin by recalling the cluster algebra structure on the Grassmannian and reviewing the combinatorics of plabic graphs. This allows us to also state previous results expressing the twist map in terms of dimer partition functions. We then discuss the representation theory and combinatorics of SL3 and SL4 webs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.15211", "content": "We begin by recalling the cluster algebra structure on the Grassmannian and reviewing the combinatorics of plabic graphs. This allows us to also state previous results expressing the twist map in terms of dimer partition functions. We then discuss the representation theory and combinatorics of SL3 and SL4 webs."}
+{"idx": 8, "title": "Web Diagrams of Cluster Variables for Grassmannian Gr(4,8)", "date": "", "ddg_snippet": "In this paper, we use these two methods to compute both the web diagrams and the dual webs corresponding to quadratic and cubic cluster variables in the Grassmannian cluster algebra C [Gr (4,8)].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2507.18432", "content": "In this paper, we use these two methods to compute both the web diagrams and the dual webs corresponding to quadratic and cubic cluster variables in the Grassmannian cluster algebra C [Gr (4,8)]."}
+{"idx": 9, "title": "Tropical Grassmannians, cluster algebras and scattering amplitudes", "date": "", "ddg_snippet": "We provide a cluster -algebraic approach to the computation of the recently introduced generalised biadjoint scalar amplitudes related to Grassmannians ${\\\\rm Gr}(k,n)$. A finite cluster algebra provides a natural triangulation for the tropical Grassmannian whose volume computes the scattering amplitudes. Using this method one can construct the entire colour-ordered amplitude via mutations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1907.01053", "content": "We provide a cluster -algebraic approach to the computation of the recently introduced generalised biadjoint scalar amplitudes related to Grassmannians ${\\\\rm Gr}(k,n)$. A finite cluster algebra provides a natural triangulation for the tropical Grassmannian whose volume computes the scattering amplitudes. Using this method one can construct the entire colour-ordered amplitude via mutations ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Archetypal_SAE_Section_6_Scaling_norm_constraint_relaxation_term_A_Lambda_year_2024.jsonl b/data/sampled_jsons/sitearxiv.org_Archetypal_SAE_Section_6_Scaling_norm_constraint_relaxation_term_A_Lambda_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept...", "date": "", "ddg_snippet": "Archetypal - SAEs constrain dictionary atoms (decoder directions) to the data’s convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation , matching standard SAEs in reconstruction while maintaining stability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v2", "content": "Archetypal - SAEs constrain dictionary atoms (decoder directions) to the data’s convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation , matching standard SAEs in reconstruction while maintaining stability."}
+{"idx": 1, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary ... Archetypal SAE: Adaptive and Stable Dictionary Learning for ... Archetypal SAE: Adaptive and Stable Dictionary Learning for ... [2502.12892] Archetypal SAE: Adaptive and Stable Dictionary ... [2111.08244] Sparse Regularization with the ||#92;ell_0$ Norm [2502.12892] Archetypal SAE: Adaptive and Stable Dictionary ...", "date": "", "ddg_snippet": "Feb 18, 2025 · Abstract page for arXiv paper 2502.12892: Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models This implementation ensures that 𝑾 \\bm {W} bold_italic_W remains row-stochastic and that the deviation term 𝚲 \\bm {\\ Lambda } bold_Λ stays within the prescribed norm constraint . To enable a controlled degree of flexibility beyond conv(C), we introduce a mild relaxation term Λ ∈Rk×d, a matrix of the same dimensions as the dictionary, with a small norm constraint ||Λ||2 2≤δ. Feb 18, 2025 · To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A- SAE ), wherein dictionary atoms are constrained to the convex hull of data. Nov 16, 2021 · We consider a minimization problem whose objective function is the sum of a fidelity term , not necessarily convex, and a regularization term defined by a positive regularization parameter $λ$ multiple of the $\\\\ell_0$ norm composed with a linear transform. This problem has wide applications in compressed sensing, sparse machine learning and image reconstruction. The goal of this paper is to ... To enable a controlled degree of flexibility beyond conv (𝑪), we introduce a mild relaxation term 𝚲 ∈ ℝ k × d, a matrix of the same dimensions as the dictionary, with a small norm constraint ‖ 𝚲 ‖ 2 2 ≤ δ.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "Feb 18, 2025 · Abstract page for arXiv paper 2502.12892: Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models This implementation ensures that 𝑾 \\bm {W} bold_italic_W remains row-stochastic and that the deviation term 𝚲 \\bm {\\ Lambda } bold_Λ stays within the prescribed norm constraint . To enable a controlled degree of flexibility beyond conv(C), we introduce a mild relaxation term Λ ∈Rk×d, a matrix of the same dimensions as the dictionary, with a small norm constraint ||Λ||2 2≤δ. Feb 18, 2025 · To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A- SAE ), wherein dictionary atoms are constrained to the convex hull of data. Nov 16, 2021 · We consider a minimization problem whose objective function is the sum of a fidelity term , not necessarily convex, and a regularization term defined by a positive regularization parameter $λ$ multiple of the $\\\\ell_0$ norm composed with a linear transform. This problem has wide applications in compressed sensing, sparse machine learning and image reconstruction. The goal of this paper is to ... To enable a controlled degree of flexibility beyond conv (𝑪), we introduce a mild relaxation term 𝚲 ∈ ℝ k × d, a matrix of the same dimensions as the dictionary, with a small norm constraint ‖ 𝚲 ‖ 2 2 ≤ δ."}
+{"idx": 2, "title": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept...", "date": "", "ddg_snippet": "Archetypal - SAEs constrain dictionary atoms (decoder directions) to the data’s convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation , matching standard SAEs in reconstruction while maintaining stability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v1", "content": "Archetypal - SAEs constrain dictionary atoms (decoder directions) to the data’s convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation , matching standard SAEs in reconstruction while maintaining stability."}
+{"idx": 3, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for ...", "date": "", "ddg_snippet": "To enable a controlled degree of flexibility beyond conv(C), we introduce a mild relaxation term Λ ∈Rk×d, a matrix of the same dimensions as the dictionary, with a small norm constraint ||Λ||2 2≤δ.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.12892", "content": "To enable a controlled degree of flexibility beyond conv(C), we introduce a mild relaxation term Λ ∈Rk×d, a matrix of the same dimensions as the dictionary, with a small norm constraint ||Λ||2 2≤δ."}
+{"idx": 4, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary ...", "date": "", "ddg_snippet": "Feb 18, 2025 · To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A- SAE ), wherein dictionary atoms are constrained to the convex hull of data.", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2502.12892", "content": "Feb 18, 2025 · To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A- SAE ), wherein dictionary atoms are constrained to the convex hull of data."}
+{"idx": 5, "title": "[2111.08244] Sparse Regularization with the ||#92;ell_0$ Norm", "date": "", "ddg_snippet": "Nov 16, 2021 · We consider a minimization problem whose objective function is the sum of a fidelity term , not necessarily convex, and a regularization term defined by a positive regularization parameter $λ$ multiple of the $\\\\ell_0$ norm composed with a linear transform. This problem has wide applications in compressed sensing, sparse machine learning and image reconstruction. The goal of this paper is to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.08244", "content": "Nov 16, 2021 · We consider a minimization problem whose objective function is the sum of a fidelity term , not necessarily convex, and a regularization term defined by a positive regularization parameter $λ$ multiple of the $\\\\ell_0$ norm composed with a linear transform. This problem has wide applications in compressed sensing, sparse machine learning and image reconstruction. The goal of this paper is to ..."}
+{"idx": 6, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary ...", "date": "", "ddg_snippet": "To enable a controlled degree of flexibility beyond conv (𝑪), we introduce a mild relaxation term 𝚲 ∈ ℝ k × d, a matrix of the same dimensions as the dictionary, with a small norm constraint ‖ 𝚲 ‖ 2 2 ≤ δ.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.12892", "content": "To enable a controlled degree of flexibility beyond conv (𝑪), we introduce a mild relaxation term 𝚲 ∈ ℝ k × d, a matrix of the same dimensions as the dictionary, with a small norm constraint ‖ 𝚲 ‖ 2 2 ≤ δ."}
+{"idx": 7, "title": "Understanding sparse autoencoder scaling in the presence of feature...", "date": "", "ddg_snippet": "The ability of SAEs to reconstruct activations follows scaling laws w.r.t. the number of latents.We are particularly interested in understanding SAE scaling when activations contain a particular kind of structure: feature manifolds (multi-dimensional features) Engels et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02565v2", "content": "The ability of SAEs to reconstruct activations follows scaling laws w.r.t. the number of latents.We are particularly interested in understanding SAE scaling when activations contain a particular kind of structure: feature manifolds (multi-dimensional features) Engels et al."}
+{"idx": 8, "title": "Understanding sparse autoencoder scaling in the presence of feature...", "date": "", "ddg_snippet": "The ability of SAEs to reconstruct activations follows scaling laws w.r.t. the number of latents.We are particularly interested in understanding SAE scaling when activations contain a particular kind of structure: feature manifolds (multi-dimensional features) Engels et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02565v1", "content": "The ability of SAEs to reconstruct activations follows scaling laws w.r.t. the number of latents.We are particularly interested in understanding SAE scaling when activations contain a particular kind of structure: feature manifolds (multi-dimensional features) Engels et al."}
+{"idx": 9, "title": "Position: Mechanistic Interpretability Should Prioritize Feature...", "date": "", "ddg_snippet": "If SAEs produce different feature dictionaries run-to-run (paulo2025sparse, ; fel2025 archetypal , ) , feature explanations and discovered circuits become difficult to replicate. Consistency ensures findings are robust, not initialization artifacts, and helps foster cumulative progress.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.20254v1", "content": "If SAEs produce different feature dictionaries run-to-run (paulo2025sparse, ; fel2025 archetypal , ) , feature explanations and discovered circuits become difficult to replicate. Consistency ensures findings are robust, not initialization artifacts, and helps foster cumulative progress."}
diff --git a/data/sampled_jsons/sitearxiv.org_BIT-VO_focal_plane_binary_features.jsonl b/data/sampled_jsons/sitearxiv.org_BIT-VO_focal_plane_binary_features.jsonl
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index 0000000000000000000000000000000000000000..89e0d64628110831fbe0ed70b9bcdff4858ab400
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "Focal-plane Sensor-processor (FPSP) is a next-generation camera technology which enables every pixel on the sensor chip to perform computation in parallel, on the focal plane where the light intensity is captured. SCAMP-5 is a general-purpose FPSP used in this work and it carries out computations in the analog domain before analog to digital conversion. By extracting features from the image on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2004.11186", "content": "Focal-plane Sensor-processor (FPSP) is a next-generation camera technology which enables every pixel on the sensor chip to perform computation in parallel, on the focal plane where the light intensity is captured. SCAMP-5 is a general-purpose FPSP used in this work and it carries out computations in the analog domain before analog to digital conversion. By extracting features from the image on ..."}
+{"idx": 1, "title": "Visual Inertial Odometry using Focal Plane Binary Features ( BIT -VIO)", "date": "", "ddg_snippet": "In BIT - VO , the VO system is clearly separated into a frontend, which performs feature extraction, and a backend, which performs the matching of the features and the camera pose optimization. BIT - VO : Visual odometry at 300 FPS using binary features from the focal plane .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09882v1", "content": "In BIT - VO , the VO system is clearly separated into a frontend, which performs feature extraction, and a backend, which performs the matching of the features and the camera pose optimization. BIT - VO : Visual odometry at 300 FPS using binary features from the focal plane ."}
+{"idx": 2, "title": "Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO)", "date": "", "ddg_snippet": "Focal-Plane Sensor-Processor Arrays (FPSP)s are an emerging technology that can execute vision algorithms directly on the image sensor. Unlike conventional cameras, FPSPs perform computation on the image plane -- at individual pixels -- enabling high frame rate image processing while consuming low power, making them ideal for mobile robotics. FPSPs, such as the SCAMP-5, use parallel processing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09882", "content": "Focal-Plane Sensor-Processor Arrays (FPSP)s are an emerging technology that can execute vision algorithms directly on the image sensor. Unlike conventional cameras, FPSPs perform computation on the image plane -- at individual pixels -- enabling high frame rate image processing while consuming low power, making them ideal for mobile robotics. FPSPs, such as the SCAMP-5, use parallel processing ..."}
+{"idx": 3, "title": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the...", "date": "", "ddg_snippet": "• An efcient BInary feaTure Visual Odometry, BIT - VO which operates at over 300 FPS.We have presented BIT - VO , which is capable of per-forming VO at 300 FPS by using binary edges and corners computed on the focal plane .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2004.11186", "content": "• An efcient BInary feaTure Visual Odometry, BIT - VO which operates at over 300 FPS.We have presented BIT - VO , which is capable of per-forming VO at 300 FPS by using binary edges and corners computed on the focal plane ."}
+{"idx": 4, "title": "Visual Inertial Odometry using Focal Plane Binary Features ( BIT -VIO)", "date": "", "ddg_snippet": "In BIT - VO , the VO system is clearly separated into a frontend, which per-forms feature extraction, and a backend, which performs the matching of the features and the camera pose optimization. BIT - VO : Visual odometry at 300 FPS using binary features from the focal plane .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09882", "content": "In BIT - VO , the VO system is clearly separated into a frontend, which per-forms feature extraction, and a backend, which performs the matching of the features and the camera pose optimization. BIT - VO : Visual odometry at 300 FPS using binary features from the focal plane ."}
+{"idx": 5, "title": "AnalogNet: Convolutional Neural Network Inference on Analog Focal ...", "date": "", "ddg_snippet": "The binary feature maps are then collected by the adjacent digital micro-controller. Matthew Z Wong, Benoit Guillard, Riku Murai, Sajad Saeedi, and Paul H J Kelly.2020. BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane . arXiv:2004.11186 [cs.CV].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.01765", "content": "The binary feature maps are then collected by the adjacent digital micro-controller. Matthew Z Wong, Benoit Guillard, Riku Murai, Sajad Saeedi, and Paul H J Kelly.2020. BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane . arXiv:2004.11186 [cs.CV]."}
+{"idx": 6, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane Riku Murai1, Sajad Saeedi2, Paul H. J. Kelly Abstract—Focal- plane Sensor-processor (FPSP) is a next- generation camera technology which enables every pixel on the sensor chip to perform computation in parallel, on the focal plane where the light intensity is captured.", "subpage_snippet": "", "source": "web3.arxiv.org", "link": "https://web3.arxiv.org/pdf/2004.11186", "content": "BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane Riku Murai1, Sajad Saeedi2, Paul H. J. Kelly Abstract—Focal- plane Sensor-processor (FPSP) is a next- generation camera technology which enables every pixel on the sensor chip to perform computation in parallel, on the focal plane where the light intensity is captured."}
+{"idx": 7, "title": "PDF arXiv:2403.09882v1 [cs.RO] 14 Mar 2024", "date": "", "ddg_snippet": "As an alternative, Focal-Plane Sensor-Processor Arrays (FPSP)s, such as SCAMP-5, is a new technology that enables computation to occur on the imager's focal plane before transferring the data to a host-device [11]. By performing early-stage computer vision algorithms on the focal plane such as feature detections, FPSPs compress the image data down to the size of the features . By transferring ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09882.pdf", "content": "As an alternative, Focal-Plane Sensor-Processor Arrays (FPSP)s, such as SCAMP-5, is a new technology that enables computation to occur on the imager's focal plane before transferring the data to a host-device [11]. By performing early-stage computer vision algorithms on the focal plane such as feature detections, FPSPs compress the image data down to the size of the features . By transferring ..."}
+{"idx": 8, "title": "arXiv:2406.09726v1 [cs.CV] 14 Jun 2024", "date": "", "ddg_snippet": "in novel sensors such as SCAMP-5 [14]. For instance, in [40], visual features are directly extracted by a pixel processor while their rocessing for VO is ofloaded to a CPU. In [10] visual data is directly processed on pixel-processors into edges that are tracked to per-form image alignment via image shifting,", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.09726", "content": "in novel sensors such as SCAMP-5 [14]. For instance, in [40], visual features are directly extracted by a pixel processor while their rocessing for VO is ofloaded to a CPU. In [10] visual data is directly processed on pixel-processors into edges that are tracked to per-form image alignment via image shifting,"}
+{"idx": 9, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "Here, we present BIT-VO , which is, to the best of our knowledge, the first 6 Degrees of Freedom visual odometry algorithm which utilises the FPSP. Our entire system operates at 300 FPS in a natural scene, using binary edges and corner features detected by the SCAMP-5. Figure 1: Comparison of the data used by our proposed VO vs conventional VOs .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2004.11186", "content": "Here, we present BIT-VO , which is, to the best of our knowledge, the first 6 Degrees of Freedom visual odometry algorithm which utilises the FPSP. Our entire system operates at 300 FPS in a natural scene, using binary edges and corner features detected by the SCAMP-5. Figure 1: Comparison of the data used by our proposed VO vs conventional VOs ."}
diff --git a/data/sampled_jsons/sitearxiv.org_Causal_Modeling_of_Climate_Activism_on_Reddit_section_4.4_sympathy_activation_subreddi_year_2024.jsonl b/data/sampled_jsons/sitearxiv.org_Causal_Modeling_of_Climate_Activism_on_Reddit_section_4.4_sympathy_activation_subreddi_year_2024.jsonl
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index 0000000000000000000000000000000000000000..4a02a3e952e6db5f3de100521fd3d16ff8141ec0
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Causal_Modeling_of_Climate_Activism_on_Reddit_section_4.4_sympathy_activation_subreddi_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "Subreddit participation . To estimate the user sociodemographics latent variable 𝐷 D italic_D, we use user participation in the set of 𝐾 K italic_K subreddits that we consider, in the long-term period. In other words, we assume that the observed subreddit participation is a noisy realization of a user's real sociodemographics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "Subreddit participation . To estimate the user sociodemographics latent variable 𝐷 D italic_D, we use user participation in the set of 𝐾 K italic_K subreddits that we consider, in the long-term period. In other words, we assume that the observed subreddit participation is a noisy realization of a user's real sociodemographics."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "Abstract Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism, their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562v1", "content": "Abstract Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism, their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development ..."}
+{"idx": 2, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism, thei…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.10562", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism, thei…"}
+{"idx": 3, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "D Sympathy . E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit .Firstly, the organization of climate activism movements on Reddit was a grassroots initiative, spawned independent of media coverage.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "D Sympathy . E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit .Firstly, the organization of climate activism movements on Reddit was a grassroots initiative, spawned independent of media coverage."}
+{"idx": 4, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "View a PDF of the paper titled Causal Modeling of Climate Activism on Reddit , by Jacopo Lenti and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "View a PDF of the paper titled Causal Modeling of Climate Activism on Reddit , by Jacopo Lenti and 3 other authors."}
+{"idx": 5, "title": "Extracting Participation in Collective Action from Social Media", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . In Proceedings of the ACM on Web Conference 2025, 590–600.Discussion of climate change on Reddit : Polarized discourse or deliberative debate? Environmental Communication, 16(5): 680–698.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.07368v2", "content": "Causal Modeling of Climate Activism on Reddit . In Proceedings of the ACM on Web Conference 2025, 590–600.Discussion of climate change on Reddit : Polarized discourse or deliberative debate? Environmental Communication, 16(5): 680–698."}
+{"idx": 6, "title": "Modeling the Impact of Group Interactions on", "date": "", "ddg_snippet": "“ Causal Modeling of Climate Activism on Reddit ”. In: Proceedings of The Web Conference.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.02989", "content": "“ Causal Modeling of Climate Activism on Reddit ”. In: Proceedings of The Web Conference."}
+{"idx": 7, "title": "On the Inference of Sociodemographics on Reddit", "date": "", "ddg_snippet": "As described in the previous section , we identify subreddit participation as the primary source of information for our goals. Causal Modeling of Climate Activism on Reddit . arXiv preprint arXiv:2410.10562.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05049", "content": "As described in the previous section , we identify subreddit participation as the primary source of information for our goals. Causal Modeling of Climate Activism on Reddit . arXiv preprint arXiv:2410.10562."}
+{"idx": 8, "title": "A Systematic Review of Echo Chamber Research: Comparative", "date": "", "ddg_snippet": "... offers an overview of ... From an initial set of 1,706 studies, we selected these 129 studies based on criteria explained in Section 3.5 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06631v3", "content": "... offers an overview of ... From an initial set of 1,706 studies, we selected these 129 studies based on criteria explained in Section 3.5 ."}
+{"idx": 9, "title": "Contents", "date": "", "ddg_snippet": "Climate change denial. Report ... The ideal outcome, in theory, would be for the Supreme Court to side with Google and for Congress to change Section 230.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2207.09460v11", "content": "Climate change denial. Report ... The ideal outcome, in theory, would be for the Supreme Court to side with Google and for Congress to change Section 230."}
diff --git a/data/sampled_jsons/sitearxiv.org_Checks-and-Balances_Framework_dataset_ETHICS.jsonl b/data/sampled_jsons/sitearxiv.org_Checks-and-Balances_Framework_dataset_ETHICS.jsonl
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index 0000000000000000000000000000000000000000..d8f6d99587349185e9bae258d7e82b6366039650
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Checks-and-Balances_Framework_dataset_ETHICS.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ... A Checks-and-Balances Framework for Context-Aware Ethical AI ... A Three-Branch Checks-and-Balances Framework for Context ... Rethinking the Evaluation of Alignment Methods: Insights into ... Who is Responsible When AI Fails? Mapping Causes, Entities ... Behind India’s ChatGPT Conversations: A Retrospective ... Evaluation Awareness Scales Predictably in Open-Weights Large ...", "date": "", "ddg_snippet": "Jan 31, 2025 · This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ... A novel checks-and-balances architecture for ethical alignment that maintains separation between knowledge generation and ethical reasoning. The Beam model, a quantitative framework for repre-senting emotions along continuous spectra with defined intensity levels, enabling precise emotion regulation in AI systems. Abstract This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethi-cal guardrails, and ERIS as the judicial branch for contextual ... Although the instruction-following dataset included a diverse range of prompts, ad-ditional analysis would still be valuable to better understand how the training data influences model performance metrics. Secondly, performance evaluation in this study relies on LLM as a judge approach, which may introduce errors in assessment. We analyzed 202 real-world AI privacy and ethical incidents to develop a tax-onomy that classifies them across AI lifecycle stages and captures contributing factors, including causes, responsible entities, sources of disclosure, and impacts. 5 days ago · Qualitative categorizations reflect the author’s interpretation of prompt content and require validation through larger, systematically coded datasets . Ethical Considerations: Complete anonymization, voluntary participation with explicit consent and transparent research purpose communication. If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "Jan 31, 2025 · This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ... A novel checks-and-balances architecture for ethical alignment that maintains separation between knowledge generation and ethical reasoning. The Beam model, a quantitative framework for repre-senting emotions along continuous spectra with defined intensity levels, enabling precise emotion regulation in AI systems. Abstract This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethi-cal guardrails, and ERIS as the judicial branch for contextual ... Although the instruction-following dataset included a diverse range of prompts, ad-ditional analysis would still be valuable to better understand how the training data influences model performance metrics. Secondly, performance evaluation in this study relies on LLM as a judge approach, which may introduce errors in assessment. We analyzed 202 real-world AI privacy and ethical incidents to develop a tax-onomy that classifies them across AI lifecycle stages and captures contributing factors, including causes, responsible entities, sources of disclosure, and impacts. 5 days ago · Qualitative categorizations reflect the author’s interpretation of prompt content and require validation through larger, systematically coded datasets . Ethical Considerations: Complete anonymization, voluntary participation with explicit consent and transparent research purpose communication. If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset )."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "A novel checks-and-balances architecture for ethical alignment that maintains separation between knowledge generation and ethical reasoning. The Beam model, a quantitative framework for repre-senting emotions along continuous spectra with defined intensity levels, enabling precise emotion regulation in AI systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v3", "content": "A novel checks-and-balances architecture for ethical alignment that maintains separation between knowledge generation and ethical reasoning. The Beam model, a quantitative framework for repre-senting emotions along continuous spectra with defined intensity levels, enabling precise emotion regulation in AI systems."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Framework for Context ...", "date": "", "ddg_snippet": "Abstract This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethi-cal guardrails, and ERIS as the judicial branch for contextual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v1", "content": "Abstract This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethi-cal guardrails, and ERIS as the judicial branch for contextual ..."}
+{"idx": 3, "title": "Computation and Language Feb 2025", "date": "", "ddg_snippet": "Title: A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment. Edward Y. Chang. Comments: 20 pages, 7 tables, 6 figures. arXiv admin note ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "http://www.arxiv.org/list/cs.CL/2025-02?skip=0&show=1000", "content": "Title: A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment. Edward Y. Chang. Comments: 20 pages, 7 tables, 6 figures. arXiv admin note ..."}
+{"idx": 4, "title": "Rethinking the Evaluation of Alignment Methods: Insights into ...", "date": "", "ddg_snippet": "Although the instruction-following dataset included a diverse range of prompts, ad-ditional analysis would still be valuable to better understand how the training data influences model performance metrics. Secondly, performance evaluation in this study relies on LLM as a judge approach, which may introduce errors in assessment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.12936", "content": "Although the instruction-following dataset included a diverse range of prompts, ad-ditional analysis would still be valuable to better understand how the training data influences model performance metrics. Secondly, performance evaluation in this study relies on LLM as a judge approach, which may introduce errors in assessment."}
+{"idx": 5, "title": "Who is Responsible When AI Fails? Mapping Causes, Entities ...", "date": "", "ddg_snippet": "We analyzed 202 real-world AI privacy and ethical incidents to develop a tax-onomy that classifies them across AI lifecycle stages and captures contributing factors, including causes, responsible entities, sources of disclosure, and impacts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.01029v2", "content": "We analyzed 202 real-world AI privacy and ethical incidents to develop a tax-onomy that classifies them across AI lifecycle stages and captures contributing factors, including causes, responsible entities, sources of disclosure, and impacts."}
+{"idx": 6, "title": "Behind India’s ChatGPT Conversations: A Retrospective ...", "date": "", "ddg_snippet": "5 days ago · Qualitative categorizations reflect the author’s interpretation of prompt content and require validation through larger, systematically coded datasets . Ethical Considerations: Complete anonymization, voluntary participation with explicit consent and transparent research purpose communication.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13337v1", "content": "5 days ago · Qualitative categorizations reflect the author’s interpretation of prompt content and require validation through larger, systematically coded datasets . Ethical Considerations: Complete anonymization, voluntary participation with explicit consent and transparent research purpose communication."}
+{"idx": 7, "title": "Evaluation Awareness Scales Predictably in Open-Weights Large ...", "date": "", "ddg_snippet": "If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.13333", "content": "If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset )."}
+{"idx": 8, "title": "Computer Science", "date": "", "ddg_snippet": "A dataset of 379,000 PubMed abstracts from 1965 ... dataset . MOBERT achieved a 70 ... This paper introduces a checks-and-balances framework for ethical ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "http://www.arxiv.org/list/cs/new?skip=875&show=500", "content": "A dataset of 379,000 PubMed abstracts from 1965 ... dataset . MOBERT achieved a 70 ... This paper introduces a checks-and-balances framework for ethical ..."}
+{"idx": 9, "title": "The Unified Cognitive Consciousness Theory for ...", "date": "", "ddg_snippet": "2 Jun 2025 — 2025. A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment. In ICML. Chen et al. ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.02139v1", "content": "2 Jun 2025 — 2025. A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment. In ICML. Chen et al. ( ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_DCBM_Algorithm_1_area_filtering_min_area_max_area.jsonl b/data/sampled_jsons/sitearxiv.org_DCBM_Algorithm_1_area_filtering_min_area_max_area.jsonl
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index 0000000000000000000000000000000000000000..6fb04af861671ce3ea197fa5d0e004d858c883fb
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We describe the DCBM algorithm in the main paper. For better understanding, we provide the algorithm with pseudocode in Algorithm 1 and introduce notation to this end.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576v2", "content": "We describe the DCBM algorithm in the main paper. For better understanding, we provide the algorithm with pseudocode in Algorithm 1 and introduce notation to this end."}
+{"idx": 1, "title": "FITTING NETWORKS WITH A CANCELLATION TRICK - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT The degree-corrected block model ( DCBM ), latent space model (LSM), and β-model are all popular network models. We combine their modeling ideas and propose the logit- DCBM as a new model. Similar as the β-model and LSM, the logit- DCBM contains nonlinear factors, where fitting the parameters is a chal-lenging open problem. We resolve this problem by introducing a cancellation trick. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.16728", "content": "ABSTRACT The degree-corrected block model ( DCBM ), latent space model (LSM), and β-model are all popular network models. We combine their modeling ideas and propose the logit- DCBM as a new model. Similar as the β-model and LSM, the logit- DCBM contains nonlinear factors, where fitting the parameters is a chal-lenging open problem. We resolve this problem by introducing a cancellation trick. We ..."}
+{"idx": 2, "title": "[2502.16728] Fitting networks with a cancellation trick - arXiv.org", "date": "", "ddg_snippet": "The degree-corrected block model ( DCBM ), latent space model (LSM), and $β$-model are all popular network models. We combine their modeling ideas and propose the logit- DCBM as a new model. Similar as the $β$-model and LSM, the logit- DCBM contains nonlinear factors, where fitting the parameters is a challenging open problem. We resolve this problem by introducing a cancellation trick. We also ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.16728", "content": "The degree-corrected block model ( DCBM ), latent space model (LSM), and $β$-model are all popular network models. We combine their modeling ideas and propose the logit- DCBM as a new model. Similar as the $β$-model and LSM, the logit- DCBM contains nonlinear factors, where fitting the parameters is a challenging open problem. We resolve this problem by introducing a cancellation trick. We also ..."}
+{"idx": 3, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data-sparse scenarios. We propose Data-eficient CBMs ( DCBMs ), which reduce the need for large ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data-sparse scenarios. We propose Data-eficient CBMs ( DCBMs ), which reduce the need for large ..."}
+{"idx": 4, "title": "H B arXiv:1906.06713v1 [math.ST] 16 Jun 2019", "date": "", "ddg_snippet": "norm, under the stochastic block model (BM) and degree corrected stochas- tic block model ( DCBM ), adding some mild and rational conditions. We also extend this result to a more general model, presented based on the DCBM such that the value of random variables in the adjacency matrix is not 0 or 1 , but an arbitrary real number. During the process of proving the above con- clusion, we obtain the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1906.06713", "content": "norm, under the stochastic block model (BM) and degree corrected stochas- tic block model ( DCBM ), adding some mild and rational conditions. We also extend this result to a more general model, presented based on the DCBM such that the value of random variables in the adjacency matrix is not 0 or 1 , but an arbitrary real number. During the process of proving the above con- clusion, we obtain the ..."}
+{"idx": 5, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data-sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v2", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data-sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large ..."}
+{"idx": 6, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data-sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large sample sizes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.11576", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data-sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large sample sizes ..."}
+{"idx": 7, "title": "arXiv:2008.03820v1 [stat.ML] 9 Aug 2020", "date": "", "ddg_snippet": "SCOREq algorithm for the Directed- DCBM . Through the rigorous proof, we show that row normalization for the singular vectors using any `q-norm also reduces the e ects of heterogeneous parameters previous work in the following aspects. First, the techniques in Jin (2015) for analyzing undirected networks", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2008.03820", "content": "SCOREq algorithm for the Directed- DCBM . Through the rigorous proof, we show that row normalization for the singular vectors using any `q-norm also reduces the e ects of heterogeneous parameters previous work in the following aspects. First, the techniques in Jin (2015) for analyzing undirected networks"}
+{"idx": 8, "title": "Fitting networks with a cancellation trick", "date": "", "ddg_snippet": "are all popular network models. We combine their modeling ideas and propose the logit- DCBM as a new model. Similar as the β𝛽\\betaitalic_β-model and LSM, the logit- DCBM contains nonlinear factors, where fitting the parameters is a challenging open problem. We resolve this problem by introducing a cancellation trick. We also propose R-SCORE as a recursive community detection algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.16728v1", "content": "are all popular network models. We combine their modeling ideas and propose the logit- DCBM as a new model. Similar as the β𝛽\\betaitalic_β-model and LSM, the logit- DCBM contains nonlinear factors, where fitting the parameters is a challenging open problem. We resolve this problem by introducing a cancellation trick. We also propose R-SCORE as a recursive community detection algorithm ..."}
+{"idx": 9, "title": "Consistent Spectral Clustering of Network Block Models under Local ...", "date": "", "ddg_snippet": "The stochastic block model (SBM) and degree-corrected block model ( DCBM ) are network models often selected as the fundamental setting in which to analyze the theoretical properties of community detection methods. We consider the problem of spectral clustering of SBM and DCBM networks under a local form of edge differential privacy. Using a randomized response privacy mechanism called the edge ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2105.12615", "content": "The stochastic block model (SBM) and degree-corrected block model ( DCBM ) are network models often selected as the fundamental setting in which to analyze the theoretical properties of community detection methods. We consider the problem of spectral clustering of SBM and DCBM networks under a local form of edge differential privacy. Using a randomized response privacy mechanism called the edge ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Definition_3.2_Directionality_Score_2502.10927.jsonl b/data/sampled_jsons/sitearxiv.org_Definition_3.2_Directionality_Score_2502.10927.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..791e62d0e2a63a187890e34a3e376ef1eb5b4470
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Definition_3.2_Directionality_Score_2502.10927.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "The underlying structures of self-attention: symmetry, directionality ...", "date": "", "ddg_snippet": "Here, positive and negative symmetry scores indi-cate the presence of symmetric and skew-symmetric structures, respectively (see Appendix S1.6). Sec-ond, we define the directionality score d ∈ R as fol-lows, Definition 3.2 . ( Directionality score ). Given a square matrix M ∈ Mn we define the directionality score , ̄rM − ̄cM d , = ̄rM ̄cM ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10927", "content": "Here, positive and negative symmetry scores indi-cate the presence of symmetric and skew-symmetric structures, respectively (see Appendix S1.6). Sec-ond, we define the directionality score d ∈ R as fol-lows, Definition 3.2 . ( Directionality score ). Given a square matrix M ∈ Mn we define the directionality score , ̄rM − ̄cM d , = ̄rM ̄cM ..."}
+{"idx": 1, "title": "The underlying structures of self-attention: symmetry, directionality ...", "date": "", "ddg_snippet": "arXiv: 2502 . 10927 v1 [cs.LG] 15 Feb 2025.Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10927v1", "content": "arXiv: 2502 . 10927 v1 [cs.LG] 15 Feb 2025.Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance."}
+{"idx": 2, "title": "The underlying structures of self-attention: symmetry, directionality ...", "date": "", "ddg_snippet": "arXiv: 2502 . 10927 v2 [cs.LG] 03 Jun 2025.Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10927v2", "content": "arXiv: 2502 . 10927 v2 [cs.LG] 03 Jun 2025.Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance."}
+{"idx": 3, "title": "[2502.10927] The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "At initialization, the symmetry and directionality score of the matrix 𝐖 q k \\mathbf {W}_ {qk} at any layer is zero (see Definition 3.1 and Definition 3.2 and related Appendix S1.5 and S1.6).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.10927", "content": "At initialization, the symmetry and directionality score of the matrix 𝐖 q k \\mathbf {W}_ {qk} at any layer is zero (see Definition 3.1 and Definition 3.2 and related Appendix S1.5 and S1.6)."}
diff --git a/data/sampled_jsons/sitearxiv.org_Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model_abstract.jsonl
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index 0000000000000000000000000000000000000000..213e07b086d7095c99cac7052ee01ac17ddabb2c
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Direct Preference Optimization: Your Language Model is Secretly", "date": "", "ddg_snippet": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model ... directly optimize a language model to adhere to human preferences , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v3", "content": "Direct Preference Optimization : Your Language Model is Secretly a Reward Model ... directly optimize a language model to adhere to human preferences , ..."}
+{"idx": 1, "title": "[2305.18290] Direct Preference Optimization: Your Language", "date": "", "ddg_snippet": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model , by Rafael Rafailov and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "View a PDF of the paper titled Direct Preference Optimization : Your Language Model is Secretly a Reward Model , by Rafael Rafailov and 5 other authors"}
+{"idx": 2, "title": "Generalist Reward Models: Found Inside Large Language Models", "date": "", "ddg_snippet": "The alignment of Large Language Models (LLMs) is critically dependent on reward models trained on costly human preference data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.23235v1", "content": "The alignment of Large Language Models (LLMs) is critically dependent on reward models trained on costly human preference data."}
+{"idx": 3, "title": "Cooper: Co-Optimizing Policy and Reward Models in Reinforcement", "date": "", "ddg_snippet": "... address these issues, we propose Cooper ( Co-o ptimizing P olicy Mod e l and R eward Model ), a RL framework that jointly optimizes both the policy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05613v1", "content": "... address these issues, we propose Cooper ( Co-o ptimizing P olicy Mod e l and R eward Model ), a RL framework that jointly optimizes both the policy ..."}
+{"idx": 4, "title": "Energy-Based Reward Models for Robust Language Model Alignment", "date": "", "ddg_snippet": "Reward models (RMs) are essential for aligning Large Language Models (LLMs) with human preferences . ... Reward Models (RMs) are critical to alignment ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.13134v2", "content": "Reward models (RMs) are essential for aligning Large Language Models (LLMs) with human preferences . ... Reward Models (RMs) are critical to alignment ..."}
+{"idx": 5, "title": "Teaching Language Models To Gather Information Proactively", "date": "", "ddg_snippet": "This approach falls short when the model needs to proactively gather information that is missing, unknown, or unspoken—especially in domains like ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21389v1", "content": "This approach falls short when the model needs to proactively gather information that is missing, unknown, or unspoken—especially in domains like ..."}
+{"idx": 6, "title": "Creative Preference Optimization", "date": "", "ddg_snippet": "To this end, we propose a novel approach to directly optimize for creativity in language model generation through preference learning Ouyang et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14442v1", "content": "To this end, we propose a novel approach to directly optimize for creativity in language model generation through preference learning Ouyang et al."}
+{"idx": 7, "title": "Self-Questioning Language Models", "date": "", "ddg_snippet": "... this paradigm, we propose S elf- Q uestioning L anguage M odels (SQLM): an asymmetric self-play framework in which a pre-trained language model is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03682v1", "content": "... this paradigm, we propose S elf- Q uestioning L anguage M odels (SQLM): an asymmetric self-play framework in which a pre-trained language model is ..."}
+{"idx": 8, "title": "Helping or Herding? Reward Model Ensembles Mitigate but do not", "date": "", "ddg_snippet": "Reward models play a key role in aligning language model applications towards human preferences . ... language model exploits reward model errors is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.09244v3", "content": "Reward models play a key role in aligning language model applications towards human preferences . ... language model exploits reward model errors is ..."}
+{"idx": 9, "title": "Towards Reward Fairness in RLHF: From a Resource Allocation", "date": "", "ddg_snippet": "One of the key reasons for the success of RLHF is the assumption that the reward model can accurately represent and measure actual preferences Kim ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23349v1", "content": "One of the key reasons for the success of RLHF is the assumption that the reward model can accurately represent and measure actual preferences Kim ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_FourCastNet_Negative_Log-Likelihood_OR_NLL.jsonl b/data/sampled_jsons/sitearxiv.org_FourCastNet_Negative_Log-Likelihood_OR_NLL.jsonl
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index 0000000000000000000000000000000000000000..e81f60ad9f6c809d5795252e1da4c1769438851f
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "As Simple as Fine-tuning: LLM Alignment via Bidirectional Negative ...", "date": "", "ddg_snippet": "We argue that these issues arise from the unidirectional likelihood-derivative negative feedback inherent in the log - likelihood loss function. To address this, we propose a novel LLM alignment loss that establishes a stable Bidirectional Negative Feedback (BNF) during optimization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.04834v1", "content": "We argue that these issues arise from the unidirectional likelihood-derivative negative feedback inherent in the log - likelihood loss function. To address this, we propose a novel LLM alignment loss that establishes a stable Bidirectional Negative Feedback (BNF) during optimization."}
+{"idx": 1, "title": "aic - Can log likelihood function be negative - Cross Validated", "date": "", "ddg_snippet": "May 17, 2018 · I do some optimization problem in R. I minimize the loglikelihood function. I found that the log-likelihood has a negative value. For example, I have this: -34.5. Then, when I count the AIC, I will...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/346720/can-log-likelihood-function-be-negative", "content": "May 17, 2018 · I do some optimization problem in R. I minimize the loglikelihood function. I found that the log-likelihood has a negative value. For example, I have this: -34.5. Then, when I count the AIC, I will..."}
+{"idx": 2, "title": "Negative log likelihood explained | by Alvaro Durán Tovar ...", "date": "", "ddg_snippet": "Aug 13, 2019 · We can maximize by minimizing the negative log likelihood , there you have it, we want somehow to maximize by minimizing.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/deeplearningmadeeasy/negative-log-likelihood-6bd79b55d8b6", "content": "Aug 13, 2019 · We can maximize by minimizing the negative log likelihood , there you have it, we want somehow to maximize by minimizing."}
+{"idx": 3, "title": "Negative Log Likelihood - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "' Negative Log Likelihood ' is defined as the negation of the logarithm of the probability of reproducing a given data set, which is used in the Maximum Likelihood method to determine model parameters.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/computer-science/negative-log-likelihood", "content": "' Negative Log Likelihood ' is defined as the negation of the logarithm of the probability of reproducing a given data set, which is used in the Maximum Likelihood method to determine model parameters."}
+{"idx": 4, "title": "Negative Log Likelihood Explained | Ji Kim", "date": "", "ddg_snippet": "Jan 19, 2025 · This post will provide a solid understanding of the fundamental concepts: probability, likelihood , log likelihood , maximum likelihood estimation, and negative log likelihood .", "subpage_snippet": "", "source": "jiselectric.github.io", "link": "https://jiselectric.github.io/posts/NLL/", "content": "Jan 19, 2025 · This post will provide a solid understanding of the fundamental concepts: probability, likelihood , log likelihood , maximum likelihood estimation, and negative log likelihood ."}
+{"idx": 5, "title": "How to calculate negative log-likelihoog on MNIST dataset?", "date": "", "ddg_snippet": "Sep 25, 2018 · Indeed, the negative log - likelihood is the log loss, or (binary) cross-entropy for (binary) classification problems, but since MNIST is a multi-class problem, here we talk about the categorical cross-entropy. It is usually preferred because, since log - likelihood itself is negative , its negative will be a positive number; from the scikit-learn docs of log_loss (emphasis added): Log loss, aka ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/52497625/how-to-calculate-negative-log-likelihoog-on-mnist-dataset", "content": "Sep 25, 2018 · Indeed, the negative log - likelihood is the log loss, or (binary) cross-entropy for (binary) classification problems, but since MNIST is a multi-class problem, here we talk about the categorical cross-entropy. It is usually preferred because, since log - likelihood itself is negative , its negative will be a positive number; from the scikit-learn docs of log_loss (emphasis added): Log loss, aka ..."}
+{"idx": 6, "title": "Negative Log-Likelihood - Notes by Lex", "date": "", "ddg_snippet": "Jul 10, 2021 · Negative log-likelihood, or NLL , is a Loss Function used in multi-class classification. It measures how closely our model predictions align with the ground truth labels. It is calculated as...", "subpage_snippet": "", "source": "notesbylex.com", "link": "https://notesbylex.com/negative-log-likelihood", "content": "Jul 10, 2021 · Negative log-likelihood, or NLL , is a Loss Function used in multi-class classification. It measures how closely our model predictions align with the ground truth labels. It is calculated as..."}
+{"idx": 7, "title": "Tutorial: Cross Entropy and Negative Log Likelihood", "date": "", "ddg_snippet": "Nov 28, 2021 · Cross Entropy and Negative Log Likelihood : this is a tutorial of the definition, the similarity, the differences, and some examples to learn about them.", "subpage_snippet": "", "source": "datajello.com", "link": "https://datajello.com/cross-entropy-and-negative-log-likelihood/", "content": "Nov 28, 2021 · Cross Entropy and Negative Log Likelihood : this is a tutorial of the definition, the similarity, the differences, and some examples to learn about them."}
+{"idx": 8, "title": "Limits", "date": "", "ddg_snippet": "Calculating negative log - likelihood . Conversion to bits per character.We use negative log - likelihood ( or NLL ) as a measure of model uncertainty, and introduce bits per character (or BPC) to enable comparisons across models. Reported results are calculated on Tueval.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.12850", "content": "Calculating negative log - likelihood . Conversion to bits per character.We use negative log - likelihood ( or NLL ) as a measure of model uncertainty, and introduce bits per character (or BPC) to enable comparisons across models. Reported results are calculated on Tueval."}
+{"idx": 9, "title": "Are Large", "date": "", "ddg_snippet": "Negative Log Likelihood ( NLL ): NLL evaluates how closely an input sequence aligns with patterns the model has learned during training in terms of how “natural” the sequence appears to the model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.13323", "content": "Negative Log Likelihood ( NLL ): NLL evaluates how closely an input sequence aligns with patterns the model has learned during training in terms of how “natural” the sequence appears to the model."}
diff --git a/data/sampled_jsons/sitearxiv.org_HtmlRAG_Section_3.2.1_HTML_Content_Cleaning.jsonl b/data/sampled_jsons/sitearxiv.org_HtmlRAG_Section_3.2.1_HTML_Content_Cleaning.jsonl
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index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal_tra.jsonl b/data/sampled_jsons/sitearxiv.org_Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal_tra.jsonl
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index 0000000000000000000000000000000000000000..fbe2c48770ccc44b0578a96e6789874e30880a48
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+{"idx": 0, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots . Based on the RUOT form, our method models these dynamics ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v5", "content": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots . Based on the RUOT form, our method models these dynamics ..."}
+{"idx": 1, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Abstract Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots . Based on the RUOT form, our method models these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v3", "content": "Abstract Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots . Based on the RUOT form, our method models these ..."}
+{"idx": 2, "title": "Variational Regularized Unbalanced Optimal Transport : Single...", "date": "", "ddg_snippet": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning Representations, 2025a.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.11823", "content": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning Representations, 2025a."}
+{"idx": 3, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v4", "content": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
+{"idx": 4, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v2", "content": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
+{"idx": 5, "title": "Variational Regularized Unbalanced Optimal Transport : Single...", "date": "", "ddg_snippet": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning Representations, 2025a.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.11823v1", "content": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning Representations, 2025a."}
+{"idx": 6, "title": "Learning from Samples: Inverse Problems over measures via", "date": "", "ddg_snippet": "... in this work on two specific settings where the forward optimization problem is derived from optimal transport (OT) with entropic regularization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.07124v1", "content": "... in this work on two specific settings where the forward optimization problem is derived from optimal transport (OT) with entropic regularization."}
+{"idx": 7, "title": "Using Optimal Transport Aligned Latent Embeddings for Separated", "date": "", "ddg_snippet": "... differences, OT distances effectively quantify a cost of transporting some resource or measure from one distribution to another (Villani, 2009 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07318v1", "content": "... differences, OT distances effectively quantify a cost of transporting some resource or measure from one distribution to another (Villani, 2009 ..."}
+{"idx": 8, "title": "Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the", "date": "", "ddg_snippet": "Recent work has addressed the problem of interpolating between snapshots through Schrödinger bridge (SB) methods (Pavon et al.,, 2021 ; De Bortoli ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16082v1", "content": "Recent work has addressed the problem of interpolating between snapshots through Schrödinger bridge (SB) methods (Pavon et al.,, 2021 ; De Bortoli ..."}
+{"idx": 9, "title": "1 INTRODUCTION", "date": "", "ddg_snippet": "For instance, TrajectoryNet (Tong et al.,, 2020 ) combines continuous normalizing flows with a soft constraint based on dynamic optimal transport .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.06277v4", "content": "For instance, TrajectoryNet (Tong et al.,, 2020 ) combines continuous normalizing flows with a soft constraint based on dynamic optimal transport ."}
diff --git a/data/sampled_jsons/sitearxiv.org_MemoryLeak_fault_propagation_resolution_steps_ITBench.jsonl b/data/sampled_jsons/sitearxiv.org_MemoryLeak_fault_propagation_resolution_steps_ITBench.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8dfa3a89e5cfb0de37f40692f9b93afbfbdd241
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_MemoryLeak_fault_propagation_resolution_steps_ITBench.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "ITBench : Evaluating AI Agents across", "date": "", "ddg_snippet": "Since fault propagation length, resolution plan size, and technology heterogeneity all influence the difficulty of inci-dent resolution , we define overall task complexity as their geometric mean. Equation (6) captures this relationship", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "Since fault propagation length, resolution plan size, and technology heterogeneity all influence the difficulty of inci-dent resolution , we define overall task complexity as their geometric mean. Equation (6) captures this relationship"}
+{"idx": 1, "title": "arXiv:2502.05352v1 [cs.AI] 7 Feb 2025", "date": "", "ddg_snippet": "by S Jha · 2025 · Cited by 3 — Table 12: Unique Scenarios available in ITBench . Scenario Pattern. Technologies Impacted. # Fault Propagation # Resolution Steps . CacheFailure.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352?", "content": "by S Jha · 2025 · Cited by 3 — Table 12: Unique Scenarios available in ITBench . Scenario Pattern. Technologies Impacted. # Fault Propagation # Resolution Steps . CacheFailure."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_On_a_Connection_Between_Imitation_Learning_and_RLHF_LSIF.jsonl b/data/sampled_jsons/sitearxiv.org_On_a_Connection_Between_Imitation_Learning_and_RLHF_LSIF.jsonl
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index 0000000000000000000000000000000000000000..dd9b1ef9bd2394777976d96ff10b5145780f2874
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_On_a_Connection_Between_Imitation_Learning_and_RLHF_LSIF.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "Abstract This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback (RLHF) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection, we propose ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "Abstract This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback (RLHF) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection, we propose ..."}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection, we propose DIL, a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.05079", "content": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection, we propose DIL, a ..."}
+{"idx": 2, "title": "I Mitation L earning and rlhf", "date": "", "ddg_snippet": "On a connection between imitation learning and rlhf .Thus, conducting imitation learning on the chosen response corresponds to solving a standard KL-regularized RLHF problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.05079", "content": "On a connection between imitation learning and rlhf .Thus, conducting imitation learning on the chosen response corresponds to solving a standard KL-regularized RLHF problem."}
+{"idx": 3, "title": "The Hidden Link Between RLHF and Contrastive Learning", "date": "", "ddg_snippet": "Beyond imitation learning , several works have drawn intuitive parallels between Direct Preference Optimization (DPO) and contrastive learning [10]. For example, Xu et al. [11] and Chen et al. On a connection between imitation learning and rlhf , 2025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.22578", "content": "Beyond imitation learning , several works have drawn intuitive parallels between Direct Preference Optimization (DPO) and contrastive learning [10]. For example, Xu et al. [11] and Chen et al. On a connection between imitation learning and rlhf , 2025."}
+{"idx": 4, "title": "The Hidden Link Between RLHF and Contrastive Learning", "date": "", "ddg_snippet": "Within this framework, both RLHF and DPO can be interpreted as methods that performing contrastive learning based on the positive and negative samples derived from base model, leveraging the Donsker–Varadhan (DV) lower bound on MI (equivalently, the MINE estimator).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.22578v1", "content": "Within this framework, both RLHF and DPO can be interpreted as methods that performing contrastive learning based on the positive and negative samples derived from base model, leveraging the Donsker–Varadhan (DV) lower bound on MI (equivalently, the MINE estimator)."}
+{"idx": 5, "title": "Title: On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "Abstract: This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection, we propose DIL ...", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2503.05079", "content": "Abstract: This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection, we propose DIL ..."}
+{"idx": 6, "title": "Inference-time Alignment in Continuous Space - arXiv.org", "date": "", "ddg_snippet": "On a connection between imitation learning and RLHF . In The Thirteenth International Conference on Learning Representations, 2025. [5] Teng Xiao, Yige Yuan, Zhengyu Chen, Mingxiao Li, Shangsong Liang, Zhaochun Ren, and Vasant G Honavar. SimPER: A minimalist approach to preference alignment without hyperpa-rameters.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.20081v1", "content": "On a connection between imitation learning and RLHF . In The Thirteenth International Conference on Learning Representations, 2025. [5] Teng Xiao, Yige Yuan, Zhengyu Chen, Mingxiao Li, Shangsong Liang, Zhaochun Ren, and Vasant G Honavar. SimPER: A minimalist approach to preference alignment without hyperpa-rameters."}
+{"idx": 7, "title": "Inference-time Alignment in Continuous Space", "date": "", "ddg_snippet": "RLHF typically involves training a reward model based on human feedback and subsequently employing reinforcement learning (RL), such as proximal policy optimization (PPO) [7], to generate responses that maximize the reward for input prompt.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.20081", "content": "RLHF typically involves training a reward model based on human feedback and subsequently employing reinforcement learning (RL), such as proximal policy optimization (PPO) [7], to generate responses that maximize the reward for input prompt."}
diff --git a/data/sampled_jsons/sitearxiv.org_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_PDF.jsonl b/data/sampled_jsons/sitearxiv.org_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_PDF.jsonl
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index 0000000000000000000000000000000000000000..ec05a459da683e5df86eb124597adee8cd2be9ab
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_PDF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "by Y Zhao · 2024 · Cited by 4 — View a PDF of the paper titled RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning , by Yujie Zhao and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "by Y Zhao · 2024 · Cited by 4 — View a PDF of the paper titled RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning , by Yujie Zhao and 3 other authors."}
+{"idx": 1, "title": "A Survey of Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "30 Apr 2024 — Human-in-the-loop: Provably . Efficient Preference - based Reinforcement Learning with General Function Approximation. In Proceedings of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.14925", "content": "30 Apr 2024 — Human-in-the-loop: Provably . Efficient Preference - based Reinforcement Learning with General Function Approximation. In Proceedings of the ..."}
+{"idx": 2, "title": "Provably Feedback-Efficient Reinforcement Learning via ...", "date": "", "ddg_snippet": "by D Kong · 2023 · Cited by 14 — Abstract. An appropriate reward function is of paramount importance in specifying a task in reinforcement learning (RL).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.08944", "content": "by D Kong · 2023 · Cited by 14 — Abstract. An appropriate reward function is of paramount importance in specifying a task in reinforcement learning (RL)."}
+{"idx": 3, "title": "Sample-Efficient Preference-based Reinforcement ...", "date": "", "ddg_snippet": "by K Metcalf · 2024 · Cited by 10 — Preference-based reinforcement learning (PbRL ) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.17975", "content": "by K Metcalf · 2024 · Cited by 10 — Preference-based reinforcement learning (PbRL ) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent ..."}
+{"idx": 4, "title": "Modeling and Optimizing User Preferences in AI Copilots", "date": "", "ddg_snippet": "by S Afzoon · 2025 · Cited by 1 — Ra-pbrl: Provably efficient risk-aware preference-based reinforcement learning . Advances in Neural Information Processing Systems, 37:60835 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.21907", "content": "by S Afzoon · 2025 · Cited by 1 — Ra-pbrl: Provably efficient risk-aware preference-based reinforcement learning . Advances in Neural Information Processing Systems, 37:60835 ..."}
+{"idx": 5, "title": "arXiv:2403.06323v2 [cs.LG] 28 Feb 2025", "date": "", "ddg_snippet": "by K Wang · 2024 · Cited by 2 — Abstract. We study risk -sensitive RL where the goal is learn a history-dependent policy that optimizes some risk .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.06323", "content": "by K Wang · 2024 · Cited by 2 — Abstract. We study risk -sensitive RL where the goal is learn a history-dependent policy that optimizes some risk ."}
+{"idx": 6, "title": "arXiv:2501.18282v2 [cs.LG] 31 Jan 2025", "date": "", "ddg_snippet": "by Y Yao · 2025 · Cited by 1 — Preference learning serves as a foundational component of Reinforcement Learning with. Human Feedback (RLHF). Here we focus on reward- based RLHF ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.18282?", "content": "by Y Yao · 2025 · Cited by 1 — Preference learning serves as a foundational component of Reinforcement Learning with. Human Feedback (RLHF). Here we focus on reward- based RLHF ..."}
+{"idx": 7, "title": "arXiv:2504.03185v1 [cs.CL] 4 Apr 2025", "date": "", "ddg_snippet": "by J Chua · 2025 · Cited by 1 — We posit that preference learning alignment has syn- ergy with a proactive safe RL paradigm, one that formalizes and minimizes high- risk actions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.03185", "content": "by J Chua · 2025 · Cited by 1 — We posit that preference learning alignment has syn- ergy with a proactive safe RL paradigm, one that formalizes and minimizes high- risk actions ..."}
+{"idx": 8, "title": "A Survey of Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "22 Dec 2023 — This article provides a comprehensive overview of the fundamentals of RLHF, exploring the intricate dynamics between machine agents and human input.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.14925v1", "content": "22 Dec 2023 — This article provides a comprehensive overview of the fundamentals of RLHF, exploring the intricate dynamics between machine agents and human input."}
+{"idx": 9, "title": "A Survey of Direct Preference Optimization", "date": "", "ddg_snippet": "by S Liu · 2025 · Cited by 9 — In this survey, we conduct a comprehensive overview of DPO and introduce a novel taxonomy, categorizing previous works into four key dimensions: ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.11701", "content": "by S Liu · 2025 · Cited by 9 — In this survey, we conduct a comprehensive overview of DPO and introduce a novel taxonomy, categorizing previous works into four key dimensions: ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fo.jsonl b/data/sampled_jsons/sitearxiv.org_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fo.jsonl
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index 0000000000000000000000000000000000000000..2c9b68df6ded43467e54df74cd9f7a0bd52aed40
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 66 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "by A Setlur · 2024 · Cited by 66 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 66 — Our contribution is a study of the role of synthetic data in improving math reasoning capabilities of LLMs. We derive scaling laws for positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 66 — Our contribution is a study of the role of synthetic data in improving math reasoning capabilities of LLMs. We derive scaling laws for positive ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 3, "title": "arXiv:2504.04736v2 [cs.AI] 28 Apr 2025", "date": "", "ddg_snippet": "by A Goldie · 2025 · Cited by 16 — RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by. Eight-Fold, 2024. URL https://arxiv.org/abs/2406.14532. Jaime ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.04736?", "content": "by A Goldie · 2025 · Cited by 16 — RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by. Eight-Fold, 2024. URL https://arxiv.org/abs/2406.14532. Jaime ..."}
+{"idx": 4, "title": "Balancing Cost and Effectiveness of Synthetic Data ...", "date": "", "ddg_snippet": "by YC Chan · 2024 · Cited by 23 — In this paper, we investigate the effectiveness of various synthetic data generation strategies for training LLMs under different constraints.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.19759", "content": "by YC Chan · 2024 · Cited by 23 — In this paper, we investigate the effectiveness of various synthetic data generation strategies for training LLMs under different constraints."}
+{"idx": 5, "title": "Synthetic Data RL: Task Definition Is All You Need", "date": "", "ddg_snippet": "by Y Guo · 2025 · Cited by 3 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold , 2024. [62] Álvaro Bartolomé Del Canto, Gabriel ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.17063", "content": "by Y Guo · 2025 · Cited by 3 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold , 2024. [62] Álvaro Bartolomé Del Canto, Gabriel ..."}
+{"idx": 6, "title": "arXiv:2410.06638v4 [cs.CL] 27 May 2025", "date": "", "ddg_snippet": "by K Xu · 2024 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold . arXiv preprint. arXiv:2406.14532. Zhihong Shao, Peiyi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.06638", "content": "by K Xu · 2024 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold . arXiv preprint. arXiv:2406.14532. Zhihong Shao, Peiyi ..."}
+{"idx": 7, "title": "arXiv:2505.21444v1 [cs.LG] 27 May 2025", "date": "", "ddg_snippet": "by S Shafayat · 2025 · Cited by 10 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold . Advances in Neural Information Processing. Systems ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.21444", "content": "by S Shafayat · 2025 · Cited by 10 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold . Advances in Neural Information Processing. Systems ..."}
+{"idx": 8, "title": "arXiv:2502.12853v1 [cs.CL] 18 Feb 2025", "date": "", "ddg_snippet": "by R Ma · 2025 · Cited by 21 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold . Advances in Neural. Information Processing Systems ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.12853", "content": "by R Ma · 2025 · Cited by 21 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold . Advances in Neural. Information Processing Systems ..."}
+{"idx": 9, "title": "Policy Optimization with Negative Sample Augmentation ...", "date": "", "ddg_snippet": "Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold . Advances in Neural Information Processing Systems, 37:43000–43031 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14403v4", "content": "Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold . Advances in Neural Information Processing Systems, 37:43000–43031 ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations.jsonl b/data/sampled_jsons/sitearxiv.org_Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations.jsonl
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index 0000000000000000000000000000000000000000..2b9b77c97040f3655d6d2959829805ffc9b3e994
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Score-Based Generative Modeling through Stochastic Differential Equations", "date": "", "ddg_snippet": "We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling , allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456", "content": "We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling , allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE."}
+{"idx": 1, "title": "Score-basedGenerativeModelingThrough BackwardStochasticDifferentialEquatio", "date": "", "ddg_snippet": "Score-basedGenerativeModelingThrough BackwardStochasticDifferentialEquations Score-based Generative Modeling Through Backward Stochastic Differential Equations : Inversion and Generation", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.13224", "content": "Score-basedGenerativeModelingThrough BackwardStochasticDifferentialEquations Score-based Generative Modeling Through Backward Stochastic Differential Equations : Inversion and Generation"}
+{"idx": 2, "title": "Score-based Generative Modeling Through Backward Stochastic ...", "date": "", "ddg_snippet": "The proposed BSDE- based diffusion model represents a novel approach to diffusion modeling , which extends the application of stochastic differential equations (SDEs) in machine learning. Unlike traditional SDE- based diffusion models, our model can determine the initial conditions necessary to reach a desired terminal distribution by adapting an existing score function. We demonstrate the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.13224", "content": "The proposed BSDE- based diffusion model represents a novel approach to diffusion modeling , which extends the application of stochastic differential equations (SDEs) in machine learning. Unlike traditional SDE- based diffusion models, our model can determine the initial conditions necessary to reach a desired terminal distribution by adapting an existing score function. We demonstrate the ..."}
+{"idx": 3, "title": "Score-Based Generative Modeling through Stochastic Differential Equations", "date": "", "ddg_snippet": "We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling , allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2011.13456", "content": "We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling , allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE."}
+{"idx": 4, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "The paper presents a stochastic differential equation for score-based generative modeling , transforming data distributions by injecting and removing noise.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2011.13456", "content": "The paper presents a stochastic differential equation for score-based generative modeling , transforming data distributions by injecting and removing noise."}
+{"idx": 5, "title": "Score-based Generative Modeling of Graphs via the System of Stochastic ...", "date": "", "ddg_snippet": "To overcome such limitations, we propose a novel score-based generative model for graphs with a continuous-time framework. Specifically, we propose a new graph diffusion process that models the joint distribution of the nodes and edges through a system of stochastic differential equations (SDEs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.02514", "content": "To overcome such limitations, we propose a novel score-based generative model for graphs with a continuous-time framework. Specifically, we propose a new graph diffusion process that models the joint distribution of the nodes and edges through a system of stochastic differential equations (SDEs)."}
+{"idx": 6, "title": "Score-based Diffusion Models via Stochastic Differential Equations -- a ...", "date": "", "ddg_snippet": "This is an expository article on the score-based diffusion models, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introduction, we discuss the two pillars in the diffusion modeling -- sampling and score matching, which encompass the SDE/ODE sampling, score matching efficiency, the consistency models, and reinforcement learning. Short ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.07487", "content": "This is an expository article on the score-based diffusion models, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introduction, we discuss the two pillars in the diffusion modeling -- sampling and score matching, which encompass the SDE/ODE sampling, score matching efficiency, the consistency models, and reinforcement learning. Short ..."}
+{"idx": 7, "title": "Score-based Diffusion Models via Stochastic Differential Equations -- a ...", "date": "", "ddg_snippet": "Key words: Difusion models, discretization, generative models, ordinary diferential equa-tions , reinforcement learning, sampling, score matching, stochastic diferential equations , total variation, Wasserstein distance. AMS 2020 Mathematics Subject Classification: 60J60, 62E17, 65C30, 68P01.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.07487", "content": "Key words: Difusion models, discretization, generative models, ordinary diferential equa-tions , reinforcement learning, sampling, score matching, stochastic diferential equations , total variation, Wasserstein distance. AMS 2020 Mathematics Subject Classification: 60J60, 62E17, 65C30, 68P01."}
+{"idx": 8, "title": "Score-based diffusion models via stochastic differential equations", "date": "", "ddg_snippet": "Abstract. This is an expository article on the score-based diffusion models, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introduction, we discuss the two pillars in the diffusion modeling - sampling and score matching, which encompass the SDE/ODE sampling, score matching efficiency, the consistency models, and reinforcement learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.07487", "content": "Abstract. This is an expository article on the score-based diffusion models, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introduction, we discuss the two pillars in the diffusion modeling - sampling and score matching, which encompass the SDE/ODE sampling, score matching efficiency, the consistency models, and reinforcement learning ..."}
+{"idx": 9, "title": "Abstract 1. Introduction arXiv:2202.02514v3 [cs.LG] 15 Jun 2022", "date": "", "ddg_snippet": "Abstract 1. Introduction arXiv:2202.02514v3 [cs.LG] 15 Jun 2022 Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.02514", "content": "Abstract 1. Introduction arXiv:2202.02514v3 [cs.LG] 15 Jun 2022 Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations"}
diff --git a/data/sampled_jsons/sitearxiv.org_Self-Refine_Iterative_Refinement_with_Self-Feedback_Madaan_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_Self-Refine_Iterative_Refinement_with_Self-Feedback_Madaan_abstract.jsonl
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index 0000000000000000000000000000000000000000..f6c77f6062f32ae64810af406a96682934323f94
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self-Refine: Iterative Refinement with Self-Feedback", "date": "", "ddg_snippet": "Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement. The main idea is to generate an initial output using an LLMs; then, the same LLMs provides feedback for its output and uses ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.17651", "content": "Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement. The main idea is to generate an initial output using an LLMs; then, the same LLMs provides feedback for its output and uses ..."}
+{"idx": 1, "title": "Iterative Refinement with Self - Feedback", "date": "", "ddg_snippet": "We present SELF - REFINE : an iterative self - refinement algorithm that alternates between two gener-ative steps– FEEDBACK and REFINE. These steps work in tandem to generate high-quality outputs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.17651", "content": "We present SELF - REFINE : an iterative self - refinement algorithm that alternates between two gener-ative steps– FEEDBACK and REFINE. These steps work in tandem to generate high-quality outputs."}
+{"idx": 2, "title": "I Terative r efinement with s elf -f eedback", "date": "", "ddg_snippet": "Self - refine : iterative refinement with self - feedback . SELF - REFINE focuses on iterative creation with introspective feedback, making it suitable for evaluating the effectiveness of language models on the CommonGen-Hard task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.17651v1", "content": "Self - refine : iterative refinement with self - feedback . SELF - REFINE focuses on iterative creation with introspective feedback, making it suitable for evaluating the effectiveness of language models on the CommonGen-Hard task."}
+{"idx": 3, "title": "Self-Imagine: Effective Unimodal Reasoning with Multimodal Models using ...", "date": "", "ddg_snippet": "Madaan et al. (2023) Aman Madaan , Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Sean Welleck, Bodhisattwa Prasad Majumder, Shashank Gupta, Amir Yazdanbakhsh, and Peter Clark. 2023. Self-refine: Iterative refinement with self-feedback .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.08025v2", "content": "Madaan et al. (2023) Aman Madaan , Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Sean Welleck, Bodhisattwa Prasad Majumder, Shashank Gupta, Amir Yazdanbakhsh, and Peter Clark. 2023. Self-refine: Iterative refinement with self-feedback ."}
+{"idx": 4, "title": "PDF ABSTRACT arXiv:2312.01957v3 [cs.CL] 11 Apr 2024", "date": "", "ddg_snippet": "Aman Madaan , Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback . arXiv preprint arXiv:2303.17651, 2023.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.01957.pdf", "content": "Aman Madaan , Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback . arXiv preprint arXiv:2303.17651, 2023."}
+{"idx": 5, "title": "RefineCoder: Iterative Improving Large Language Models through", "date": "", "ddg_snippet": "In this paper, we propose Adaptive Critique Refinement (ACR), which enables the model to refine itself by self -generated code response and teacher ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.09183v2", "content": "In this paper, we propose Adaptive Critique Refinement (ACR), which enables the model to refine itself by self -generated code response and teacher ..."}
+{"idx": 6, "title": "Med-REFL: Medical Reasoning Enhancement via Self-Corrected", "date": "", "ddg_snippet": "... direct preference optimization pairs, effectively eliminating the bottleneck of expert annotations while generating targeted training data with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.13793v1", "content": "... direct preference optimization pairs, effectively eliminating the bottleneck of expert annotations while generating targeted training data with ..."}
+{"idx": 7, "title": "ProRefine: Inference-time Prompt Refinement with Textual", "date": "", "ddg_snippet": "Another approach is to iteratively refine output using self -generated feedback [ 21 ] . ... Refine ment with Textual Feedback ), builds upon CoT by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05305v1", "content": "Another approach is to iteratively refine output using self -generated feedback [ 21 ] . ... Refine ment with Textual Feedback ), builds upon CoT by ..."}
+{"idx": 8, "title": "S3c-Math: Spontaneous Step-Level Self-Correction Makes Large", "date": "", "ddg_snippet": "... self -correction within the same model, using multi-task learning to enable a single model to master both problem-solving and error correction tasks ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.01524v3", "content": "... self -correction within the same model, using multi-task learning to enable a single model to master both problem-solving and error correction tasks ..."}
+{"idx": 9, "title": "Efficient Real-time Refinement of Language Model Text Generation", "date": "", "ddg_snippet": "Future research could address these issues through iterative retrieval mechanisms that refine knowledge during generation, advanced reasoning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.07824v5", "content": "Future research could address these issues through iterative retrieval mechanisms that refine knowledge during generation, advanced reasoning ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_The_Illusion_of_State_in_State-Space_Models_filetypepdf.jsonl b/data/sampled_jsons/sitearxiv.org_The_Illusion_of_State_in_State-Space_Models_filetypepdf.jsonl
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index 0000000000000000000000000000000000000000..a28bb70d3efe1f24b9fee75f9d8dca2eab7c167f
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Illusion of State in State-Space Models - arXiv.org", "date": "", "ddg_snippet": "Abstract State-space models (SSMs) have emerged as a potential alternative to transformers. One theoret-ical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking (Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks. But do SSMs truly have an ad ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.08819", "content": "Abstract State-space models (SSMs) have emerged as a potential alternative to transformers. One theoret-ical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking (Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks. But do SSMs truly have an ad ..."}
+{"idx": 1, "title": "The Illusion of State in State-Space Models - arXiv.org", "date": "", "ddg_snippet": "Abstract State-space models (SSMs) have emerged as a po-tential alternative architecture for building large language models (LLMs) compared to the previ-ously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential com-putation and state tracking (Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.08819v1", "content": "Abstract State-space models (SSMs) have emerged as a po-tential alternative architecture for building large language models (LLMs) compared to the previ-ously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential com-putation and state tracking (Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to ..."}
+{"idx": 2, "title": "Demystifying the Token Dynamics of Deep Selective State Space Models", "date": "", "ddg_snippet": "The illusion of state in state-space models . arXiv preprint arXiv:2404.08819, 2024. [39] Eric Nguyen, Karan Goel, Albert Gu, Gordon Downs, Preey Shah, Tri Dao, Stephen Baccus, and Christopher R ́e. S4nd: Modeling images and videos as multidimensional signals with state spaces. Advances in neural information processing systems, 35:2846-2861 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.03292", "content": "The illusion of state in state-space models . arXiv preprint arXiv:2404.08819, 2024. [39] Eric Nguyen, Karan Goel, Albert Gu, Gordon Downs, Preey Shah, Tri Dao, Stephen Baccus, and Christopher R ́e. S4nd: Modeling images and videos as multidimensional signals with state spaces. Advances in neural information processing systems, 35:2846-2861 ..."}
+{"idx": 3, "title": "[2404.08819] The Illusion of State in State - Space Models", "date": "", "ddg_snippet": "State - space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "State - space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture."}
+{"idx": 4, "title": "The Illusion of State in State - Space Models", "date": "", "ddg_snippet": "2.1 Architecture of State - Space Models .SSMs consist of state - space layers, which can be thought of as simplified RNN layers.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v1", "content": "2.1 Architecture of State - Space Models .SSMs consist of state - space layers, which can be thought of as simplified RNN layers."}
+{"idx": 5, "title": "The Illusion of State in State - Space Models", "date": "", "ddg_snippet": "2.1 Architecture of State - Space Models . SSMs are a neural network architecture for processing sequences similar in design to RNNs or linear dynamical systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v3", "content": "2.1 Architecture of State - Space Models . SSMs are a neural network architecture for processing sequences similar in design to RNNs or linear dynamical systems."}
+{"idx": 6, "title": "The Illusion of State in State - Space Models", "date": "", "ddg_snippet": "2.1 Architecture of State - Space Models . SSMs are a neural network architecture for processing sequences similar in design to RNNs or linear dynamical systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v2", "content": "2.1 Architecture of State - Space Models . SSMs are a neural network architecture for processing sequences similar in design to RNNs or linear dynamical systems."}
+{"idx": 7, "title": "The Computational Complexity of Satisfiability in State Space Models", "date": "", "ddg_snippet": "The illusion of state in state-space models . In Proceedings of the 41st International Conference on Machine Learning, volume 235 of ICML'24, pages 35492-35506, Vi-enna, Austria, July 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.18162v1", "content": "The illusion of state in state-space models . In Proceedings of the 41st International Conference on Machine Learning, volume 235 of ICML'24, pages 35492-35506, Vi-enna, Austria, July 2024."}
+{"idx": 8, "title": "Predictability Enables Parallelization of Nonlinear State Space Models", "date": "", "ddg_snippet": "The illusion of state in state-space models . In Forty-first International Conference on Machine Learning, 2024. J Zico Kolter and Gaurav Manek. Learning stable deep dynamics models. Advances in neural information processing systems, 32, 2019. Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Nadia Figueroa, Gerhard Neumann, and Leonel ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.16817", "content": "The illusion of state in state-space models . In Forty-first International Conference on Machine Learning, 2024. J Zico Kolter and Gaurav Manek. Learning stable deep dynamics models. Advances in neural information processing systems, 32, 2019. Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Nadia Figueroa, Gerhard Neumann, and Leonel ..."}
+{"idx": 9, "title": "Theoretical Foundations of Deep Selective State-Space ...", "date": "", "ddg_snippet": "by NM Cirone · 2024 · Cited by 47 — William Merrill, Jackson Petty, and Ashish Sabharwal. The illusion of state in state-space models . arXiv preprint arXiv:2404.08819, 2024. Ben ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.19047", "content": "by NM Cirone · 2024 · Cited by 47 — William Merrill, Jackson Petty, and Ashish Sabharwal. The illusion of state in state-space models . arXiv preprint arXiv:2404.08819, 2024. Ben ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_The_underlying_structures_of_self-attention_html.jsonl b/data/sampled_jsons/sitearxiv.org_The_underlying_structures_of_self-attention_html.jsonl
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index 0000000000000000000000000000000000000000..b911f4a32d20dd4b02665f28217456051bb0811c
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Rational self-maps of projective surfaces with a regular iterate", "date": "", "ddg_snippet": "... the other hand focuses on rational ... We will investigate the structure of eventually regular self -maps with a regular but non-invertible iterate.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.05194v1", "content": "... the other hand focuses on rational ... We will investigate the structure of eventually regular self -maps with a regular but non-invertible iterate."}
+{"idx": 1, "title": "Attention Beyond Neighborhoods: Reviving Transformer for Graph", "date": "", "ddg_snippet": "... these advancements, the application of attention mechanisms to graph-structured data, where preserving topological relationships is critical, remains ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15024v1", "content": "... these advancements, the application of attention mechanisms to graph-structured data, where preserving topological relationships is critical, remains ..."}
+{"idx": 2, "title": "Temporal-Aware Graph Attention Network for Cryptocurrency", "date": "", "ddg_snippet": "This method not only validates the enhancement effect of temporal awareness and triple attention mechanisms on graph neural networks, but also ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21382v1", "content": "This method not only validates the enhancement effect of temporal awareness and triple attention mechanisms on graph neural networks, but also ..."}
+{"idx": 3, "title": "Fourier-Guided Attention Upsampling for Image Super-Resolution", "date": "", "ddg_snippet": "Attention mechanisms have addressed some of these limitations by enabling models to prioritize salient features, capture long-range dependencies, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10616v1", "content": "Attention mechanisms have addressed some of these limitations by enabling models to prioritize salient features, capture long-range dependencies, and ..."}
+{"idx": 4, "title": "Attentions Under the Microscope: A Comparative Study of", "date": "", "ddg_snippet": "To investigate the performance of various self - attention mechanisms within LLMs, we implemented and compared the listed attention modules using the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07247v1", "content": "To investigate the performance of various self - attention mechanisms within LLMs, we implemented and compared the listed attention modules using the ..."}
+{"idx": 5, "title": "Rethinking the Foundations for Continual Reinforcement Learning", "date": "", "ddg_snippet": "The authors would like to thank a number of colleagues whose insights refined this work, including John Martin, Dustin Morrill, David Sychrovsk ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.08161v1", "content": "The authors would like to thank a number of colleagues whose insights refined this work, including John Martin, Dustin Morrill, David Sychrovsk ..."}
+{"idx": 6, "title": "V-SEAM: Visual Semantic Editing and Attention Modulating for", "date": "", "ddg_snippet": "Vision-language models (VLMs) have become a vital infrastructure for multimodal understanding and generation, powering a variety of downstream ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14837v1", "content": "Vision-language models (VLMs) have become a vital infrastructure for multimodal understanding and generation, powering a variety of downstream ..."}
+{"idx": 7, "title": "Structure-Aware Contrastive Learning with Fine-Grained Binding", "date": "", "ddg_snippet": "Ablation studies confirm the critical role of learned aggregation, bilinear attention , and contrastive alignment in enhancing predictive robustness.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14788v1", "content": "Ablation studies confirm the critical role of learned aggregation, bilinear attention , and contrastive alignment in enhancing predictive robustness."}
+{"idx": 8, "title": "A comprehensive framework for occluded human pose estimation", "date": "", "ddg_snippet": "The channel attention leverages the central features of the human body to enhance the corresponding features, thereby facilitating the discrimination ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00155v2", "content": "The channel attention leverages the central features of the human body to enhance the corresponding features, thereby facilitating the discrimination ..."}
+{"idx": 9, "title": "Assessing Historical Structural Oppression Worldwide via", "date": "", "ddg_snippet": "... their own issues and biases [ 17 ] , including reproduction of racial and ethnic stereotypes and underrepresentation of structurally marginalized ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15216v1", "content": "... their own issues and biases [ 17 ] , including reproduction of racial and ethnic stereotypes and underrepresentation of structurally marginalized ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_httpsarxiv.orgpdf2410.09536_random_segment_length_Section_5.3_conclusion.jsonl b/data/sampled_jsons/sitearxiv.org_httpsarxiv.orgpdf2410.09536_random_segment_length_Section_5.3_conclusion.jsonl
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index 0000000000000000000000000000000000000000..a9f7303abc27499600950b856f694f73308d1511
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+++ b/data/sampled_jsons/sitearxiv.org_httpsarxiv.orgpdf2410.09536_random_segment_length_Section_5.3_conclusion.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HTTPS - Wikipedia", "date": "", "ddg_snippet": "Hypertext Transfer Protocol Secure ( HTTPS ) is an extension of the Hypertext Transfer Protocol ( HTTP ). It uses encryption for secure communication over a computer network, and is widely used on the Internet. [1][2] In HTTPS , the communication protocol is encrypted using Transport Layer Security (TLS) or, formerly, Secure Sockets Layer (SSL).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/HTTPS", "content": "Hypertext Transfer Protocol Secure ( HTTPS ) is an extension of the Hypertext Transfer Protocol ( HTTP ). It uses encryption for secure communication over a computer network, and is widely used on the Internet. [1][2] In HTTPS , the communication protocol is encrypted using Transport Layer Security (TLS) or, formerly, Secure Sockets Layer (SSL)."}
+{"idx": 1, "title": "Why is HTTP not secure ? | HTTP vs. HTTPS - Cloudflare", "date": "", "ddg_snippet": "HTTPS is HTTP with encryption and verification. The only difference between the two protocols is that HTTPS uses TLS (SSL) to encrypt normal HTTP requests and responses, and to digitally sign those requests and responses. As a result, HTTPS is far more secure than HTTP.", "subpage_snippet": "", "source": "www.cloudflare.com", "link": "https://www.cloudflare.com/learning/ssl/why-is-http-not-secure/", "content": "HTTPS is HTTP with encryption and verification. The only difference between the two protocols is that HTTPS uses TLS (SSL) to encrypt normal HTTP requests and responses, and to digitally sign those requests and responses. As a result, HTTPS is far more secure than HTTP."}
+{"idx": 2, "title": "HTTP vs HTTPS : Comparison, Pros and Cons, and More - Hostinger", "date": "", "ddg_snippet": "5 days ago · The main difference between HTTP vs HTTPS lies in the security protocol each uses. Hypertext Transfer Protocol (HTTP) allows data to be transferred between a browser and a website without encryption, while HTTP Secure ( HTTPS ) adds an encryption layer through SSL/TLS certificates.", "subpage_snippet": "", "source": "www.hostinger.com", "link": "https://www.hostinger.com/tutorials/http-vs-https", "content": "5 days ago · The main difference between HTTP vs HTTPS lies in the security protocol each uses. Hypertext Transfer Protocol (HTTP) allows data to be transferred between a browser and a website without encryption, while HTTP Secure ( HTTPS ) adds an encryption layer through SSL/TLS certificates."}
+{"idx": 3, "title": "Secure your web browsing with HTTPS -First Mode in Microsoft ...", "date": "", "ddg_snippet": "Secure your web browsing with HTTPS -First Mode in Microsoft Edge Want a safer browsing experience? Microsoft Edge offers HTTPS -First Mode, a feature that helps secure your connection by upgrading websites to HTTPS whenever possible.", "subpage_snippet": "", "source": "support.microsoft.com", "link": "https://support.microsoft.com/en-us/microsoft-edge/secure-your-web-browsing-with-https-first-mode-in-microsoft-edge-8377a6e4-9b57-4c81-83a8-fe32603eed7c", "content": "Secure your web browsing with HTTPS -First Mode in Microsoft Edge Want a safer browsing experience? Microsoft Edge offers HTTPS -First Mode, a feature that helps secure your connection by upgrading websites to HTTPS whenever possible."}
+{"idx": 4, "title": "Explain the Working of HTTPS - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 23, 2025 · HTTPS is the secure variant of HTTP and is used to communicate between the user's browser and the website, ensuring that data transfer is encrypted for added security.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/html/explain-working-of-https/", "content": "Jul 23, 2025 · HTTPS is the secure variant of HTTP and is used to communicate between the user's browser and the website, ensuring that data transfer is encrypted for added security."}
+{"idx": 5, "title": "HTTP vs. HTTPS : What Are the Differences to Know?", "date": "", "ddg_snippet": "Sep 8, 2025 · Discover the key differences between HTTP and HTTPS , how they impact user and website security, performance, and why switch to HTTPS .", "subpage_snippet": "", "source": "privacysavvy.com", "link": "https://privacysavvy.com/security/safe-browsing/http-vs-https/", "content": "Sep 8, 2025 · Discover the key differences between HTTP and HTTPS , how they impact user and website security, performance, and why switch to HTTPS ."}
+{"idx": 6, "title": "What is HTTPS ? How it Works and Why It's So Important", "date": "", "ddg_snippet": "Jun 25, 2025 · HTTPS ( Hypertext Transfer Protocol Secure ) allows users to safely send information via the Web through encryption. Learn more about its uses and benefits.", "subpage_snippet": "", "source": "www.upguard.com", "link": "https://www.upguard.com/blog/what-is-https", "content": "Jun 25, 2025 · HTTPS ( Hypertext Transfer Protocol Secure ) allows users to safely send information via the Web through encryption. Learn more about its uses and benefits."}
+{"idx": 7, "title": "Efficiently Identifying Watermarked Segments in", "date": "", "ddg_snippet": "In this section , we discuss the detailed experiment settings and then show the detection results for watermark segment classification and precise watermark position localization. We also consider different lengths of the mixed-source text and show the detection results.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.03600v1", "content": "In this section , we discuss the detailed experiment settings and then show the detection results for watermark segment classification and precise watermark position localization. We also consider different lengths of the mixed-source text and show the detection results."}
+{"idx": 8, "title": "E pisodic r einforcement L earning", "date": "", "ddg_snippet": "Impact of Random Segment Lengths .Therefore, we adopt random segmentation length as the default setting for. TOP-ERL. To further illustrate the impact of random segment lengths , we provide a visualization of.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "Impact of Random Segment Lengths .Therefore, we adopt random segmentation length as the default setting for. TOP-ERL. To further illustrate the impact of random segment lengths , we provide a visualization of."}
+{"idx": 9, "title": "Make Some Noise: Unlocking Language Model Parallel Inference...", "date": "", "ddg_snippet": "Inference Noise Segment Length .As discussed in Section 3.2, we try to use the PPL-based selection method to select the location of the noise segments . Inspired by Lin et al. (2024b) , different tokens contribute differently to the learning of that sample.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.17404v1", "content": "Inference Noise Segment Length .As discussed in Section 3.2, we try to use the PPL-based selection method to select the location of the noise segments . Inspired by Lin et al. (2024b) , different tokens contribute differently to the learning of that sample."}
diff --git a/data/sampled_jsons/sitearxiv.org_nuScenes_A_multimodal_dataset_for_autonomous_driving_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_nuScenes_A_multimodal_dataset_for_autonomous_driving_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e86e6ad05d775dfc97564dd2afbbb93716a61660
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_nuScenes_A_multimodal_dataset_for_autonomous_driving_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1903.11027] nuScenes: A multimodal dataset for autonomous", "date": "", "ddg_snippet": "View a PDF of the paper titled nuScenes : A multimodal dataset for autonomous driving , by Holger Caesar and 9 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "View a PDF of the paper titled nuScenes : A multimodal dataset for autonomous driving , by Holger Caesar and 9 other authors"}
+{"idx": 1, "title": "FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play", "date": "", "ddg_snippet": "We construct the nuScenes -FG dataset with foreground segmentation annotations for autonomous driving scenarios, comprising a total of 241k image ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23318v1", "content": "We construct the nuScenes -FG dataset with foreground segmentation annotations for autonomous driving scenarios, comprising a total of 241k image ..."}
+{"idx": 2, "title": "LightEMMA: Lightweight End-to-End Multimodal Model for", "date": "", "ddg_snippet": "Several open-source datasets are available for training and evaluating autonomous driving systems, notably the Waymo Open Dataset [ 19 ] and nuScenes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00284v1", "content": "Several open-source datasets are available for training and evaluating autonomous driving systems, notably the Waymo Open Dataset [ 19 ] and nuScenes ..."}
+{"idx": 3, "title": "Extending Large Vision-Language Model for Diverse Interactive", "date": "", "ddg_snippet": "As shown in Fig. 1 ( a ), some recent language-based ... We present NuInteract , a large-scale dataset for advancing LVLMs in autonomous driving .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.08725v1", "content": "As shown in Fig. 1 ( a ), some recent language-based ... We present NuInteract , a large-scale dataset for advancing LVLMs in autonomous driving ."}
+{"idx": 4, "title": "ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and", "date": "", "ddg_snippet": "Paper ID 2: AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving - Daniel Bogdoll, Iramm Hamdard, Lukas Roessler, Felix Geisler ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01735v1", "content": "Paper ID 2: AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving - Daniel Bogdoll, Iramm Hamdard, Lukas Roessler, Felix Geisler ..."}
+{"idx": 5, "title": "Multimodal Framework for Explainable Autonomous Driving:", "date": "", "ddg_snippet": "A novel end-to-end multimodal framework that integrates video, sensor, and textual data, addressing the challenge of heterogeneous data fusion for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07938v1", "content": "A novel end-to-end multimodal framework that integrates video, sensor, and textual data, addressing the challenge of heterogeneous data fusion for ..."}
+{"idx": 6, "title": "LLM4Drive: A Survey of Large Language Models for Autonomous", "date": "", "ddg_snippet": "In this paper, we systematically review the research line about (Vision) Large Language Models for Autonomous Driving ((V)LLM4Drive) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.01043v4", "content": "In this paper, we systematically review the research line about (Vision) Large Language Models for Autonomous Driving ((V)LLM4Drive) ."}
+{"idx": 7, "title": "LSD-3D: Large-Scale 3D Driving Scene Generation with Geometry", "date": "", "ddg_snippet": "Pretrained on internet-scale datasets and subsequently fine-tuned on autonomous driving data they generate videos which mimic driving datasets and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19204v1", "content": "Pretrained on internet-scale datasets and subsequently fine-tuned on autonomous driving data they generate videos which mimic driving datasets and ..."}
+{"idx": 8, "title": "V-Max: A Reinforcement Learning Framework for Autonomous Driving", "date": "", "ddg_snippet": "... a framework that extends Waymax with all the necessary ... There are two main formulations of the Autonomous Driving (AD) task in the literature.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.08388v3", "content": "... a framework that extends Waymax with all the necessary ... There are two main formulations of the Autonomous Driving (AD) task in the literature."}
+{"idx": 9, "title": "VLM-E2E: Enhancing End-to-End Autonomous Driving with", "date": "", "ddg_snippet": "... nuScenes dataset and achieve significant improvements in perception, prediction, and planning over the baseline end-to-end model, showcasing the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.18042v2", "content": "... nuScenes dataset and achieve significant improvements in perception, prediction, and planning over the baseline end-to-end model, showcasing the ..."}
diff --git a/data/sampled_jsons/sitearxiv.orgabs2011.13456.jsonl b/data/sampled_jsons/sitearxiv.orgabs2011.13456.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..99889ab6c2db3bbab92f1a52f0a6feced396a140
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 1, "title": "Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "by Y Song · 2020 · Cited by 8744 — We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456", "content": "by Y Song · 2020 · Cited by 8744 — We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly ..."}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2402.02544.jsonl b/data/sampled_jsons/sitearxiv.orghtml2402.02544.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker_requires_computationally_prohibitive.jsonl b/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker_requires_computationally_prohibitive.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5dc866fc05534706b34fca71972c1846e9274aeb
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker_requires_computationally_prohibitive.jsonl
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+{"idx": 0, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "Hence, scaling up the number of training sequences quickly becomes computationally prohibitive , especially in light of the required hyperparameter tuning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15732v2", "content": "Hence, scaling up the number of training sequences quickly becomes computationally prohibitive , especially in light of the required hyperparameter tuning."}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2410.03558v1_Section_2_SDXL_architectural_analysis.jsonl b/data/sampled_jsons/sitearxiv.orghtml2410.03558v1_Section_2_SDXL_architectural_analysis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..84bea055ea4aa29757c34756b458b170e0cc1164
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+++ b/data/sampled_jsons/sitearxiv.orghtml2410.03558v1_Section_2_SDXL_architectural_analysis.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Not All Diffusion Model Activations Have Been Evaluated ...", "date": "", "ddg_snippet": "4 Oct 2024 — ... SDXL is more advanced than SDv1.5. However, the analysis in Section 4 can unravel the mystery. Specifically, since SDXL has more ViT modules ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03558v1", "content": "4 Oct 2024 — ... SDXL is more advanced than SDv1.5. However, the analysis in Section 4 can unravel the mystery. Specifically, since SDXL has more ViT modules ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2410.05760_Tanh_Tanh-C_Figure_4.jsonl b/data/sampled_jsons/sitearxiv.orghtml2410.05760_Tanh_Tanh-C_Figure_4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9bf03ab9c94d0f6e8986bafdefb7fed70ccfd492
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml2410.05760_Tanh_Tanh-C_Figure_4.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Figure 3: An illustration of the Tanh Demon sampling method where. Figure 4 : Performance comparison of the proposed algorithm and other baseline methods in terms of the number of reward queries and execution time; the dependent variable is. T𝑇Titalic_T.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v2", "content": "Figure 3: An illustration of the Tanh Demon sampling method where. Figure 4 : Performance comparison of the proposed algorithm and other baseline methods in terms of the number of reward queries and execution time; the dependent variable is. T𝑇Titalic_T."}
+{"idx": 1, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Tanh - C , which uses 1-step CM for fast reward evaluation, consistently outperforms the Best-of-N baseline.is implemented. Analysis of the data in Table 2 and Figure 4 indicates that Tanh - C ’s reward performance can be enhanced by mitigating the RMSE in.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v1/", "content": "Tanh - C , which uses 1-step CM for fast reward evaluation, consistently outperforms the Best-of-N baseline.is implemented. Analysis of the data in Table 2 and Figure 4 indicates that Tanh - C ’s reward performance can be enhanced by mitigating the RMSE in."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2502.04313_Section_4_complementary_knowledge.jsonl b/data/sampled_jsons/sitearxiv.orghtml2502.04313_Section_4_complementary_knowledge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d488ca0a02f79da4f85d0dfed8307eceb6d10c33
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+++ b/data/sampled_jsons/sitearxiv.orghtml2502.04313_Section_4_complementary_knowledge.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Does Complementary Knowledge Add Beyond Elicitation? The original explanation for performance gains from weak-to-strong generalization is that the weak supervisor “elicits” the latent knowledge in the superior representations of the stronger student (Burns et al., 2024) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v1", "content": "Does Complementary Knowledge Add Beyond Elicitation? The original explanation for performance gains from weak-to-strong generalization is that the weak supervisor “elicits” the latent knowledge in the superior representations of the stronger student (Burns et al., 2024) ."}
+{"idx": 1, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Does Complementary Knowledge Add Beyond Elicitation? The original explanation for performance gains from weak-to-strong generalization is that the weak supervisor “elicits” the latent knowledge in the superior representations of the stronger student (Burns et al., 2024) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "Does Complementary Knowledge Add Beyond Elicitation? The original explanation for performance gains from weak-to-strong generalization is that the weak supervisor “elicits” the latent knowledge in the superior representations of the stronger student (Burns et al., 2024) ."}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2502.15588v1_2.1_Efficient_Entropy-Guided_Sampling_with_DDIM.jsonl b/data/sampled_jsons/sitearxiv.orghtml2502.15588v1_2.1_Efficient_Entropy-Guided_Sampling_with_DDIM.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..23ddc6031ee7e9c5f2058a082b78aba3c6907e6e
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+++ b/data/sampled_jsons/sitearxiv.orghtml2502.15588v1_2.1_Efficient_Entropy-Guided_Sampling_with_DDIM.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "2 . 1 Efficient Entropy - Guided Sampling with DDIM . 3 The deliberate Practice Framework for Synthetic Data Generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.15588v1", "content": "2 . 1 Efficient Entropy - Guided Sampling with DDIM . 3 The deliberate Practice Framework for Synthetic Data Generation."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2506.21490_Appendix_A.8_regularization_lambda.jsonl b/data/sampled_jsons/sitearxiv.orghtml2506.21490_Appendix_A.8_regularization_lambda.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
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+++ b/data/sampled_jsons/sitearxiv.orghtml2506.21490_Appendix_A.8_regularization_lambda.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitecambridge.org_The_impact_of_modeling_decisions_in_statistical_profiling_Bach_abstract.jsonl b/data/sampled_jsons/sitecambridge.org_The_impact_of_modeling_decisions_in_statistical_profiling_Bach_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..95747ea8d8bdffe56655111a32a8531d4995aceb
--- /dev/null
+++ b/data/sampled_jsons/sitecambridge.org_The_impact_of_modeling_decisions_in_statistical_profiling_Bach_abstract.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "The impact of modeling decisions in statistical profiling", "date": "", "ddg_snippet": "Abstract Statistical profiling of job seekers is an attractive option to guide the activities of public employment services. Many hope that algorithms will improve both efficiency and effectiveness of employment services' activities that are so far often based on human judgment. Against this backdrop, we evaluate regression and machine-learning models for predicting job-seekers' risk of ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/data-and-policy/article/impact-of-modeling-decisions-in-statistical-profiling/AFAE0907D68D7AFA6915B2F8D08C90B5", "content": "Abstract Statistical profiling of job seekers is an attractive option to guide the activities of public employment services. Many hope that algorithms will improve both efficiency and effectiveness of employment services' activities that are so far often based on human judgment. Against this backdrop, we evaluate regression and machine-learning models for predicting job-seekers' risk of ..."}
+{"idx": 1, "title": "PDF The impact of modeling decisions in statistical profiling", "date": "", "ddg_snippet": "Abstract Statistical profiling of job seekers is an attractive option to guide the activities of public employment services. Many hope that algorithms will improve both efficiency and effectiveness of employment services' activities that are so far often based on human judgment.", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/services/aop-cambridge-core/content/view/AFAE0907D68D7AFA6915B2F8D08C90B5/S2632324923000299a.pdf/the-impact-of-modeling-decisions-in-statistical-profiling.pdf", "content": "Abstract Statistical profiling of job seekers is an attractive option to guide the activities of public employment services. Many hope that algorithms will improve both efficiency and effectiveness of employment services' activities that are so far often based on human judgment."}
+{"idx": 2, "title": "Using Artificial Intelligence to classify Jobseekers", "date": "", "ddg_snippet": "by SAM DESIERE · 2021 · Cited by 104 — Bach , Ruben L. Kern, Christoph Mautner, Hannah and Kreuter, Frauke 2023. The impact of modeling decisions in statistical profiling . Data ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/journal-of-social-policy/article/using-artificial-intelligence-to-classify-jobseekers-the-accuracyequity-tradeoff/3993FE92BCC14FEA60DAB9CCD636D58D", "content": "by SAM DESIERE · 2021 · Cited by 104 — Bach , Ruben L. Kern, Christoph Mautner, Hannah and Kreuter, Frauke 2023. The impact of modeling decisions in statistical profiling . Data ..."}
+{"idx": 3, "title": "Behaviorally informed interventions can increase take-up ...", "date": "", "ddg_snippet": "by CH Schimpf — Abstract . Low take-up of government services continues to ... The impact of modeling decisions in statistical profiling ; Authors ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/behavioural-public-policy/article/behaviorally-informed-interventions-can-increase-takeup-of-public-employment-services-but-conversion-remains-challenging-insights-from-an-rct-in-british-columbia-canada/B75AABFE05F9F079D1B13708F3AF52ED", "content": "by CH Schimpf — Abstract . Low take-up of government services continues to ... The impact of modeling decisions in statistical profiling ; Authors ..."}
diff --git a/data/sampled_jsons/sitedeep-diver.github.io_neurips2024_posters_zzooqd6r1b_MATH_task_Pythia-1B_weak_policy.jsonl b/data/sampled_jsons/sitedeep-diver.github.io_neurips2024_posters_zzooqd6r1b_MATH_task_Pythia-1B_weak_policy.jsonl
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index 0000000000000000000000000000000000000000..7721cadfec56b7ee6cc433199f67bb874880d15d
--- /dev/null
+++ b/data/sampled_jsons/sitedeep-diver.github.io_neurips2024_posters_zzooqd6r1b_MATH_task_Pythia-1B_weak_policy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NeurIPS 2024 - deep-diver.github.io", "date": "", "ddg_snippet": "Sep 26, 2024 · On- policy deep RL agents suffer from plasticity loss, but this paper introduces ‘regenerative’ methods that consistently mitigate this, improving performance in challenging environments.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/", "content": "Sep 26, 2024 · On- policy deep RL agents suffer from plasticity loss, but this paper introduces ‘regenerative’ methods that consistently mitigate this, improving performance in challenging environments."}
+{"idx": 1, "title": "Posters · NeurIPS 2024", "date": "", "ddg_snippet": "Sep 26, 2024 · Zipfian Whitening: Weighting PCA whitening by word frequency dramatically improves NLP task performance, surpassing established baselines and providing a theoretical framework for existing methods.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/", "content": "Sep 26, 2024 · Zipfian Whitening: Weighting PCA whitening by word frequency dramatically improves NLP task performance, surpassing established baselines and providing a theoretical framework for existing methods."}
+{"idx": 2, "title": "Efficient Multi-task Reinforcement Learning with Cross-Task ...", "date": "", "ddg_snippet": "Sep 26, 2024 · This paper is important because it introduces a novel framework, Cross- Task Policy Guidance (CTPG), that significantly improves the efficiency of multi- task reinforcement learning (MTRL).", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/3quks3wrnh/", "content": "Sep 26, 2024 · This paper is important because it introduces a novel framework, Cross- Task Policy Guidance (CTPG), that significantly improves the efficiency of multi- task reinforcement learning (MTRL)."}
+{"idx": 3, "title": "Neuro-Symbolic Data Generation for Math Reasoning", "date": "", "ddg_snippet": "Sep 26, 2024 · This table compares the performance of the proposed method and MetaMath on the MATH dataset across seven different mathematical categories. It shows the accuracy achieved by each method in each category and the improvement achieved by the proposed method over MetaMath.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/cicmzglyzw/", "content": "Sep 26, 2024 · This table compares the performance of the proposed method and MetaMath on the MATH dataset across seven different mathematical categories. It shows the accuracy achieved by each method in each category and the improvement achieved by the proposed method over MetaMath."}
+{"idx": 4, "title": "Knowledge Composition using Task Vectors with Learned ...", "date": "", "ddg_snippet": "Sep 26, 2024 · Task vectors, which represent the difference between pre-trained and task -specific model weights, offer potential solutions but suffer from interference when combined.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/g9ojugko4b/", "content": "Sep 26, 2024 · Task vectors, which represent the difference between pre-trained and task -specific model weights, offer potential solutions but suffer from interference when combined."}
+{"idx": 5, "title": "Reinforcing LLM Agents via Policy Optimization with Action ...", "date": "", "ddg_snippet": "Sep 26, 2024 · This table shows the hyperparameter settings used for the Policy Optimization with Action Decomposition (POAD) method in the Overcooked environment. The hyperparameters are values chosen through a grid search or experimentation that yielded optimal performance for POAD on this specific task .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/hz6csigmyu/", "content": "Sep 26, 2024 · This table shows the hyperparameter settings used for the Policy Optimization with Action Decomposition (POAD) method in the Overcooked environment. The hyperparameters are values chosen through a grid search or experimentation that yielded optimal performance for POAD on this specific task ."}
+{"idx": 6, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "Sep 26, 2024 · This figure displays the results of fine-tuning password-locked models on high-quality demonstrations for four different tasks : code generation, MATH , code critique, and MMLU. It shows how the number of demonstrations affects the model’s ability to recover its hidden capabilities after fine-tuning.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "Sep 26, 2024 · This figure displays the results of fine-tuning password-locked models on high-quality demonstrations for four different tasks : code generation, MATH , code critique, and MMLU. It shows how the number of demonstrations affects the model’s ability to recover its hidden capabilities after fine-tuning."}
+{"idx": 7, "title": "Scaling up Test-Time Compute with Latent Reasoning: A Recurrent...", "date": "", "ddg_snippet": "This highlights the model’s ability to leverage additional computation time for improved performance on more demanding reasoning tasks .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2502.05171/", "content": "This highlights the model’s ability to leverage additional computation time for improved performance on more demanding reasoning tasks ."}
+{"idx": 8, "title": "Nearly Optimal Approximation of Matrix Functions by the Lanczos...", "date": "", "ddg_snippet": "NeurIPS 2024 /.However, the analysis also reveals that for functions like the matrix square root, weaker near-optimality guarantees hold, highlighting the complexity of fully characterizing Lanczos-FA’s behavior.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/spotlight-ai-theory/3s8v8qp9xv/", "content": "NeurIPS 2024 /.However, the analysis also reveals that for functions like the matrix square root, weaker near-optimality guarantees hold, highlighting the complexity of fully characterizing Lanczos-FA’s behavior."}
+{"idx": 9, "title": "Rethinking Inverse Reinforcement Learning: from Data Alignment to...", "date": "", "ddg_snippet": "NeurIPS 2024 /.Panel (a) shows the policy utility spaces for task -aligned (r+) and misaligned (r-) reward functions. Task alignment means acceptable policies have higher utilities than unacceptable ones, and higher utility implies higher task performance.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/vfrys7wx08/", "content": "NeurIPS 2024 /.Panel (a) shows the policy utility spaces for task -aligned (r+) and misaligned (r-) reward functions. Task alignment means acceptable policies have higher utilities than unacceptable ones, and higher utility implies higher task performance."}
diff --git a/data/sampled_jsons/sitedl.acm.org_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback.jsonl b/data/sampled_jsons/sitedl.acm.org_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback.jsonl
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index 0000000000000000000000000000000000000000..3ffe5e2d71735c15310e2dd18ba16d7d3b4e7454
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+{"idx": 0, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner.2017. Unbiased learning -to-rank with biased feedback.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3409256.3409812", "content": "A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner.2017. Unbiased learning -to-rank with biased feedback."}
+{"idx": 1, "title": "uCTRL: Unbiased Contrastive Representation Learning via Alignment...", "date": "", "ddg_snippet": "Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control.2022. Bilateral Self-unbiased Learning from Biased Implicit Feedback . In SIGIR. ACM, 29--39.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539618.3592076?cookieSet=1", "content": "Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control.2022. Bilateral Self-unbiased Learning from Biased Implicit Feedback . In SIGIR. ACM, 29--39."}
+{"idx": 2, "title": "Bilateral Self-unbiased Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "Jul 7, 2022 · Unbiased Pairwise Learning from Biased Implicit Feedback. In ICTIR. 5--12. Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. In WSDM. 501--509. Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3477495.3531946", "content": "Jul 7, 2022 · Unbiased Pairwise Learning from Biased Implicit Feedback. In ICTIR. 5--12. Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. In WSDM. 501--509. Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016."}
+{"idx": 3, "title": "Unbiased Pairwise Learning from Implicit Feedback for ...", "date": "", "ddg_snippet": "Jul 18, 2023 · Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539618.3592077", "content": "Jul 18, 2023 · Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12."}
+{"idx": 4, "title": "Practically Unbiased Pairwise Loss for Recommendation With ...", "date": "", "ddg_snippet": "Dec 19, 2024 · Home Browse by Title Periodicals IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 47, No. 4 Practically Unbiased Pairwise Loss for Recommendation With Implicit Feedback", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1109/TPAMI.2024.3519711", "content": "Dec 19, 2024 · Home Browse by Title Periodicals IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 47, No. 4 Practically Unbiased Pairwise Loss for Recommendation With Implicit Feedback"}
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+{"idx": 6, "title": "Dual Unbiased Recommender Learning for Implicit Feedback", "date": "", "ddg_snippet": "Jul 11, 2021 · Unbiased Pairwise Learning from Biased Implicit Feedback. In ICTIR. 5--12. Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. In WSDM. 501--509. Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization. In NIPS ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3404835.3463118", "content": "Jul 11, 2021 · Unbiased Pairwise Learning from Biased Implicit Feedback. In ICTIR. 5--12. Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. In WSDM. 501--509. Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization. In NIPS ..."}
+{"idx": 7, "title": "A General Framework for Pairwise Unbiased Learning to Rank", "date": "", "ddg_snippet": "Aug 25, 2022 · Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval (Virtual Event, Norway) (ICTIR '20).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539813.3545119", "content": "Aug 25, 2022 · Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval (Virtual Event, Norway) (ICTIR '20)."}
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+{"idx": 9, "title": "Off-Policy Evaluation of Ranking Policies under Diverse User Behavior", "date": "", "ddg_snippet": "2020. Unbiased Pairwise Learning from Biased Implicit Feedback .Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . In Proceedings of the 13th International Conference on Web Search and Data Mining.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3580305.3599447?cookieSet=1", "content": "2020. Unbiased Pairwise Learning from Biased Implicit Feedback .Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . In Proceedings of the 13th International Conference on Web Search and Data Mining."}
diff --git a/data/sampled_jsons/sitedl.acm.org_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020.jsonl b/data/sampled_jsons/sitedl.acm.org_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020.jsonl
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+{"idx": 2, "title": "Unbiased Pairwise Learning from Implicit Feedback for ...", "date": "", "ddg_snippet": "Jul 18, 2023 · Yuta Saito . 2020 . Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539618.3592077", "content": "Jul 18, 2023 · Yuta Saito . 2020 . Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12."}
+{"idx": 3, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "Yuta Saito , Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020 . Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining. 501--509.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3409256.3409812?download=true", "content": "Yuta Saito , Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020 . Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining. 501--509."}
+{"idx": 4, "title": "Unbiased Learning to Rank with Biased Continuous Feedback", "date": "", "ddg_snippet": "Oct 17, 2022 · Yuta Saito . 2020 . Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3511808.3557483", "content": "Oct 17, 2022 · Yuta Saito . 2020 . Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12."}
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+{"idx": 7, "title": "uCTRL: Unbiased Contrastive Representation Learning via Alignment...", "date": "", "ddg_snippet": "2020 . Unbiased Pairwise Learning from Biased Implicit Feedback . 2020 . Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. In ICML (Proceedings of Machine Learning Research, Vol. 119).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539618.3592076?cookieSet=1", "content": "2020 . Unbiased Pairwise Learning from Biased Implicit Feedback . 2020 . Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. In ICML (Proceedings of Machine Learning Research, Vol. 119)."}
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diff --git a/data/sampled_jsons/siteen.wikipedia.org_active_learning_machine_learning.jsonl b/data/sampled_jsons/siteen.wikipedia.org_active_learning_machine_learning.jsonl
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+{"idx": 0, "title": "Active learning ( machine learning ) - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user, to label new data points with the desired outputs.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Active_learning_(machine_learning)", "content": "Machine learningand data mining. v. t. e. Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user, to label new data points with the desired outputs."}
+{"idx": 1, "title": "Machine learning - Wikipedia", "date": "", "ddg_snippet": "Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximise some notion of cumulative reward.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Machine_learning", "content": "Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximise some notion of cumulative reward."}
+{"idx": 2, "title": "Active learning - Wikipedia", "date": "", "ddg_snippet": "Active learning is \"a method of learning in which students are actively or experientially involved in the learning process and where there are different levels of active learning , depending on student involvement.\"", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Active_learning", "content": "Active learning is \"a method of learning in which students are actively or experientially involved in the learning process and where there are different levels of active learning , depending on student involvement.\""}
+{"idx": 3, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of..."}
+{"idx": 4, "title": "Human-in-the-loop - Wikipedia", "date": "", "ddg_snippet": "Active learning .In machine learning , HITL is used in the sense of humans aiding the computer in making the correct decisions in building a model.[4] HITL improves machine learning over random sampling by selecting the most critical data needed to refine the model.[5].", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Human-in-the-loop", "content": "Active learning .In machine learning , HITL is used in the sense of humans aiding the computer in making the correct decisions in building a model.[4] HITL improves machine learning over random sampling by selecting the most critical data needed to refine the model.[5]."}
+{"idx": 5, "title": "Logic learning machine - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. Logic learning machine is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching Neural Network paradigm, developed by Marco Muselli...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Logic_learning_machine", "content": "Machine learningand data mining. v. t. e. Logic learning machine is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching Neural Network paradigm, developed by Marco Muselli..."}
+{"idx": 6, "title": "Outline of machine learning - Wikipedia", "date": "", "ddg_snippet": "1 In 1959, Arthur Samuel defined machine learning as a \"field of study that gives computers the ability to learn without being explicitly programmed\".", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Outline_of_machine_learning", "content": "1 In 1959, Arthur Samuel defined machine learning as a \"field of study that gives computers the ability to learn without being explicitly programmed\"."}
+{"idx": 7, "title": "Automated machine learning - Wikipedia", "date": "", "ddg_snippet": "A comparison of AutoML tools for machine learning , deep learning and XGBoost.\" 2021 International Joint Conference on Neural Networks (IJCNN).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Automated_machine_learning", "content": "A comparison of AutoML tools for machine learning , deep learning and XGBoost.\" 2021 International Joint Conference on Neural Networks (IJCNN)."}
+{"idx": 8, "title": "Learning - Wikipedia", "date": "", "ddg_snippet": "Active learning encourages learners to have an internal dialogue in which they verbalize understandings. ... active learning , claiming that the ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Learning", "content": "Active learning encourages learners to have an internal dialogue in which they verbalize understandings. ... active learning , claiming that the ..."}
+{"idx": 9, "title": "Adaptive learning - Wikipedia", "date": "", "ddg_snippet": "... learning , also known as adaptive teaching , is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Adaptive_learning", "content": "... learning , also known as adaptive teaching , is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate ..."}
diff --git a/data/sampled_jsons/sitegithub.com_DemonSampling_Algorithm_2_adaptive_temperature_tau_implementation.jsonl b/data/sampled_jsons/sitegithub.com_DemonSampling_Algorithm_2_adaptive_temperature_tau_implementation.jsonl
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+++ b/data/sampled_jsons/sitegithub.com_DemonSampling_Algorithm_2_adaptive_temperature_tau_implementation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - aiiu-lab/DemonSampling: [ICLR'25] Official ...", "date": "", "ddg_snippet": "Official implementation of ICLR 2025 \"Sampling Demon\" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon, an inference-time, backpropagation-free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling", "content": "Official implementation of ICLR 2025 \"Sampling Demon\" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon, an inference-time, backpropagation-free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ..."}
+{"idx": 1, "title": "GitHub - Optimization-AI/FastCLIP: Distributed Optimization Infra for...", "date": "", "ddg_snippet": "Second , to further boost training efficiency, we investigate three components of the framework from an optimization perspective: the schedule of the inner learning rate, the update rules of the temperature parameter and the model parameters, respectively.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Optimization-AI/FastCLIP", "content": "Second , to further boost training efficiency, we investigate three components of the framework from an optimization perspective: the schedule of the inner learning rate, the update rules of the temperature parameter and the model parameters, respectively."}
+{"idx": 2, "title": "GitHub - Optimization-AI/fast_clip", "date": "", "ddg_snippet": "Second , to further boost training efficiency, we investigate three components of the framework from an optimization perspective: the schedule of the inner learning rate, the update rules of the temperature parameter and the model parameters, respectively.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/optimization-ai/fast_clip", "content": "Second , to further boost training efficiency, we investigate three components of the framework from an optimization perspective: the schedule of the inner learning rate, the update rules of the temperature parameter and the model parameters, respectively."}
+{"idx": 3, "title": "dtak/hierarchical-disentanglement: Code for Benchmarks, Algorithms ...", "date": "", "ddg_snippet": "Benchmarks, Algorithms , and Metrics for Hierarchical Disentanglement.Main experiments in the paper. The following commands replicate one restart of our main results on all datasets: for tau in 0.5 0.67 1.0 do for lmb1 in 10 100 1000 do for lmb 2 in 1 10 100 do.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dtak/hierarchical-disentanglement", "content": "Benchmarks, Algorithms , and Metrics for Hierarchical Disentanglement.Main experiments in the paper. The following commands replicate one restart of our main results on all datasets: for tau in 0.5 0.67 1.0 do for lmb1 in 10 100 1000 do for lmb 2 in 1 10 100 do."}
+{"idx": 4, "title": "examples/md_npt_lj.f90 at master · Allen-Tildesley/examples · GitHub", "date": "", "ddg_snippet": "tau = 2 .0 ! Desired thermostat timescale.The driver Gj for p_eta_baro(1) is different. gj = p_eps** 2 /w_eps - temperature . ELSE.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Allen-Tildesley/examples/blob/master/md_npt_lj.f90", "content": "tau = 2 .0 ! Desired thermostat timescale.The driver Gj for p_eta_baro(1) is different. gj = p_eps** 2 /w_eps - temperature . ELSE."}
+{"idx": 5, "title": "fiezt/Reinforcement-Learning: Reinforcement Learning Algorithms", "date": "", "ddg_snippet": "Episode rewards, step parameters, epsilon parameters, tau ( temperature ) parameters can all be plotted following using a model free reinforcement learning algorithm or testing of a learned policy.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiezt/Reinforcement-Learning", "content": "Episode rewards, step parameters, epsilon parameters, tau ( temperature ) parameters can all be plotted following using a model free reinforcement learning algorithm or testing of a learned policy."}
+{"idx": 6, "title": "DemonSampling/README.md at main · aiiu-lab ... - GitHub", "date": "", "ddg_snippet": "Official implementation of ICLR 2025 \"Sampling Demon\" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon, an inference-time, backpropagation-free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling/blob/main/README.md", "content": "Official implementation of ICLR 2025 \"Sampling Demon\" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon, an inference-time, backpropagation-free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ..."}
+{"idx": 7, "title": "Johnathan-Xie/adaptive-temperature-scaling - GitHub", "date": "", "ddg_snippet": "We write our implementation similar to huggingface peft modules. The calibration model class wraps any huggingface causal language model and operates on top of the output logits. It applies the calibration head then the calibration loss is used to fit the calibration head. When saving, only the calibration head is saved.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Johnathan-Xie/adaptive-temperature-scaling", "content": "We write our implementation similar to huggingface peft modules. The calibration model class wraps any huggingface causal language model and operates on top of the output logits. It applies the calibration head then the calibration loss is used to fit the calibration head. When saving, only the calibration head is saved."}
+{"idx": 8, "title": "DemonSampling/setup.py at main · aiiu-lab/DemonSampling", "date": "", "ddg_snippet": "[ICLR'25] Official implementation of \"Training-free Diffusion Model Alignment with Sampling Demons\" - DemonSampling /setup.py at main · aiiu-lab/ DemonSampling", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling/blob/main/setup.py", "content": "[ICLR'25] Official implementation of \"Training-free Diffusion Model Alignment with Sampling Demons\" - DemonSampling /setup.py at main · aiiu-lab/ DemonSampling"}
+{"idx": 9, "title": "GitHub - LJ2lijia/AdapT: Official implementation of AdapT in ...", "date": "", "ddg_snippet": "Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models Official implementation of AdapT in AAAI 2024 paper.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LJ2lijia/AdapT", "content": "Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models Official implementation of AdapT in AAAI 2024 paper."}
diff --git a/data/sampled_jsons/sitegithub.com_TOP-ERL_ablation_study_Figure_5_performance_degradation.jsonl b/data/sampled_jsons/sitegithub.com_TOP-ERL_ablation_study_Figure_5_performance_degradation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8eb964eeb5df9b23c81b01c9249f0f4d90e765e6
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_TOP-ERL_ablation_study_Figure_5_performance_degradation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Question related to Ablation study & CSS Net five layers freeze...", "date": "", "ddg_snippet": "Ablation setup The goal of the ablation is to demonstrate how different components of the pipeline affect the final downstream performance (detection). In particular, the 3 settings you are referring to demonstrate how different optimization variables - R (rotation), t (translation), s (scale)...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TRI-ML/sdflabel/issues/7", "content": "Ablation setup The goal of the ablation is to demonstrate how different components of the pipeline affect the final downstream performance (detection). In particular, the 3 settings you are referring to demonstrate how different optimization variables - R (rotation), t (translation), s (scale)..."}
+{"idx": 1, "title": "GitHub - ChrisTitusTech/winutil: Chris Titus Tech's Windows Utility...", "date": "", "ddg_snippet": "This utility is a compilation of Windows tasks I perform on each Windows system I use. It is meant to streamline installs, debloat with tweaks, troubleshoot with config, and fix Windows updates.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChrisTitusTech/winutil", "content": "This utility is a compilation of Windows tasks I perform on each Windows system I use. It is meant to streamline installs, debloat with tweaks, troubleshoot with config, and fix Windows updates."}
+{"idx": 2, "title": "To perform ablation study · Issue #2 · luogen1996/MCN · GitHub", "date": "", "ddg_snippet": "I perform the experiments by your introductions and achieve 62.8 accuracy for RES. When I perform the ablation study of segmentation, I directly set the MCN/model/mcn_model.py Line 519 in bee86df loss += xy_loss...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/luogen1996/MCN/issues/2", "content": "I perform the experiments by your introductions and achieve 62.8 accuracy for RES. When I perform the ablation study of segmentation, I directly set the MCN/model/mcn_model.py Line 519 in bee86df loss += xy_loss..."}
+{"idx": 3, "title": "Question About Mismatch in Ablation Study Results...", "date": "", "ddg_snippet": "Hello, I have a question regarding the ablation study results in the MambaIR paper. There seems to be a mismatch between the reported experimental results and the explanations in the paper.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/csguoh/MambaIR/issues/89", "content": "Hello, I have a question regarding the ablation study results in the MambaIR paper. There seems to be a mismatch between the reported experimental results and the explanations in the paper."}
+{"idx": 4, "title": "GitHub - toperliclr2025/TOP_ERL", "date": "", "ddg_snippet": "Episodic Reinforcement Learning ( ERL ) [1, 4, 5 ] is a distinct RL family that emphasizes the maximization of returns over entire episodes, typically lasting several seconds, rather than optimizing the intermediate states during environment interactions. Unlike Step-based RL (SRL) [2, 3], ERL shifts the solution search from per-step actions to a parameterized trajectory space, leveraging ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/toperliclr2025/TOP_ERL", "content": "Episodic Reinforcement Learning ( ERL ) [1, 4, 5 ] is a distinct RL family that emphasizes the maximization of returns over entire episodes, typically lasting several seconds, rather than optimizing the intermediate states during environment interactions. Unlike Step-based RL (SRL) [2, 3], ERL shifts the solution search from per-step actions to a parameterized trajectory space, leveraging ..."}
+{"idx": 5, "title": "TOP-ERL: Transformer-based Off-policy Episodic RL (ICLR25 ... - GitHub", "date": "", "ddg_snippet": "Episodic RL, What and Why? Episodic Reinforcement Learning ( ERL ) [1, 4, 5 ] is a distinct RL family that emphasizes the maximization of returns over entire episodes, typically lasting several seconds, rather than optimizing the intermediate states during environment interactions. Unlike Step-based RL (SRL) [2, 3], ERL shifts the solution search from per-step actions to a parameterized ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/RobbinMyCode/TOP_ERL_new", "content": "Episodic RL, What and Why? Episodic Reinforcement Learning ( ERL ) [1, 4, 5 ] is a distinct RL family that emphasizes the maximization of returns over entire episodes, typically lasting several seconds, rather than optimizing the intermediate states during environment interactions. Unlike Step-based RL (SRL) [2, 3], ERL shifts the solution search from per-step actions to a parameterized ..."}
+{"idx": 6, "title": "GitHub - BruceGeLi/TOP_ERL_ICLR25: ICLR 25, Transformer-based Off ...", "date": "", "ddg_snippet": "ICLR 25, Transformer-based Off-Policy Episodic RL. Contribute to BruceGeLi/TOP_ERL_ICLR25 development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BruceGeLi/TOP_ERL_ICLR25", "content": "ICLR 25, Transformer-based Off-Policy Episodic RL. Contribute to BruceGeLi/TOP_ERL_ICLR25 development by creating an account on GitHub."}
+{"idx": 7, "title": "ICLR-2025-Robotics/README.md at main - GitHub", "date": "", "ddg_snippet": "Abstract: This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajec...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hairuoliu1/ICLR-2025-Robotics/blob/main/README.md", "content": "Abstract: This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajec..."}
+{"idx": 8, "title": "ablation-study · GitHub Topics · GitHub", "date": "", "ddg_snippet": "🧠 Automated neural network ablation studies using LLM agents and LangGraph. Systematically remove components, test performance , and gain insights into architecture importance through an intelligent multi-agent workflow.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/ablation-study", "content": "🧠 Automated neural network ablation studies using LLM agents and LangGraph. Systematically remove components, test performance , and gain insights into architecture importance through an intelligent multi-agent workflow."}
+{"idx": 9, "title": "GitHub - hairuoliu1/ICLR-2025-Robotics: A list of robotics related ...", "date": "", "ddg_snippet": "Abstract: This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajec...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hairuoliu1/ICLR-2025-Robotics", "content": "Abstract: This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajec..."}
diff --git a/data/sampled_jsons/sitegithub.com_beautyremainProDet.jsonl b/data/sampled_jsons/sitegithub.com_beautyremainProDet.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..99aa98dd8c027a7291e22de6d416d1913b157abd
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_beautyremainProDet.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - beautyremain / ProDet : The official code for paper...", "date": "", "ddg_snippet": "ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well."}
+{"idx": 1, "title": "Cheng JiKang beautyremain", "date": "", "ddg_snippet": "beautyremain has 12 repositories available. Follow their code on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain", "content": "beautyremain has 12 repositories available. Follow their code on GitHub."}
+{"idx": 2, "title": "GitHub - beautyremain /SUR-LID: The official code for paper \"Stacking...", "date": "", "ddg_snippet": "beautyremain / SUR-LID Public. Notifications You must be signed in to change notification settings. Fork 0.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/SUR-LID", "content": "beautyremain / SUR-LID Public. Notifications You must be signed in to change notification settings. Fork 0."}
+{"idx": 3, "title": "GitHub - beautyremain /2020summer: 暑期实训项目后台", "date": "", "ddg_snippet": "本SPRINGBOOT架构后端作者为： beautyremain (程季康) 成员:郑文鸿 郑文鸿完成的工作由作者统一上传...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/2020summer", "content": "本SPRINGBOOT架构后端作者为： beautyremain (程季康) 成员:郑文鸿 郑文鸿完成的工作由作者统一上传..."}
+{"idx": 4, "title": "Security Overview · beautyremain/ProDet · GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/security", "content": "GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 5, "title": "ProDet/README.md at main · beautyremain/ProDet · GitHub", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet /README.md at main · beautyremain / ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/blob/main/README.md", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet /README.md at main · beautyremain / ProDet"}
+{"idx": 6, "title": "GitHub · Where software is built", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - beautyremain / ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/labels?sort=name-desc", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - beautyremain / ProDet"}
+{"idx": 7, "title": "The AUC of the CDF has been 0.77 · Issue #7 · beautyremain/ProDet", "date": "", "ddg_snippet": "Sep 3, 2025 · The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - The AUC of the CDF has been 0.77 · Issue #7 · beautyremain/ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/issues/7", "content": "Sep 3, 2025 · The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - The AUC of the CDF has been 0.77 · Issue #7 · beautyremain/ProDet"}
+{"idx": 8, "title": "ProDet/main_archi.png at main · beautyremain/ProDet · GitHub", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet /main_archi.png at main · beautyremain / ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/blob/main/main_archi.png", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet /main_archi.png at main · beautyremain / ProDet"}
+{"idx": 9, "title": "GitHub - beautyremain /-GPA-: 武汉大学教务系统GPA计算脚本", "date": "", "ddg_snippet": "Languages.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/-GPA-", "content": "Languages."}
diff --git a/data/sampled_jsons/sitegithub.com_facebookresearchMask2Formertreemainconfigs.jsonl b/data/sampled_jsons/sitegithub.com_facebookresearchMask2Formertreemainconfigs.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cee5bc7222efbf68e1962398f5c4411007cf86d8
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_facebookresearchMask2Formertreemainconfigs.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "at main · facebookresearch/Mask2Former", "date": "", "ddg_snippet": "1 Jan 2025 — Mask2Former : Masked-attention Mask Transformer for Universal Image Segmentation (CVPR 2022) · Updates · Installation · Getting Started · Advanced ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former?search=1", "content": "1 Jan 2025 — Mask2Former : Masked-attention Mask Transformer for Universal Image Segmentation (CVPR 2022) · Updates · Installation · Getting Started · Advanced ..."}
+{"idx": 1, "title": "ltdrdata/ComfyUI-Impact-Pack", "date": "", "ddg_snippet": "Custom node pack for ComfyUI This node pack helps to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ltdrdata/ComfyUI-Impact-Pack", "content": "Custom node pack for ComfyUI This node pack helps to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more."}
+{"idx": 2, "title": "PyTorch code and models for the DINOv2 self-supervised ...", "date": "", "ddg_snippet": "Visualization of the three first principal components of the patch features of all frames, mapped to RGB values. Pretrained models. model, # of params, with", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/dinov2", "content": "Visualization of the three first principal components of the patch features of all frames, mapped to RGB values. Pretrained models. model, # of params, with"}
+{"idx": 3, "title": "MM-OR: A Large Multimodal Operating Room Dataset ...", "date": "", "ddg_snippet": "Follow the mask2former installation instructions, specifically regarding MSDeformAttn https://github.com/ facebookresearch / Mask2Former /blob/ main /INSTALL.md ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/egeozsoy/MM-OR", "content": "Follow the mask2former installation instructions, specifically regarding MSDeformAttn https://github.com/ facebookresearch / Mask2Former /blob/ main /INSTALL.md ..."}
+{"idx": 4, "title": "Error when training for instance segmentation with a ...", "date": "", "ddg_snippet": "25 Dec 2022 — I am registering the dataset with as I did with Mask2Former with detectron2.data.datasets.register_coco_instances , is it enough or should I use ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SHI-Labs/OneFormer/issues/17", "content": "25 Dec 2022 — I am registering the dataset with as I did with Mask2Former with detectron2.data.datasets.register_coco_instances , is it enough or should I use ..."}
+{"idx": 5, "title": "Add support for COCO style datasets for instance ...", "date": "", "ddg_snippet": "6 Aug 2023 — Create a standard dataset loader capable of taking datasets in the JSON COCO style format and converting them into the Huggingface format.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/huggingface/transformers/issues/25337", "content": "6 Aug 2023 — Create a standard dataset loader capable of taking datasets in the JSON COCO style format and converting them into the Huggingface format."}
+{"idx": 6, "title": "DINOv2 is now available in HF Transformers (with tutorial)", "date": "", "ddg_snippet": "3 Aug 2023 — I've created a tutorial notebook on training a linear classifier using DINOv2's frozen features for semantic segmentation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/dinov2/issues/153", "content": "3 Aug 2023 — I've created a tutorial notebook on training a linear classifier using DINOv2's frozen features for semantic segmentation."}
+{"idx": 7, "title": "IDEA-Research/MaskDINO: [CVPR 2023] Official ...", "date": "", "ddg_snippet": "We release a strong open-set object detection and segmentation model OpenSeeD based on MaskDINO that achieves the best results on open-set object segmentation ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IDEA-Research/MaskDINO", "content": "We release a strong open-set object detection and segmentation model OpenSeeD based on MaskDINO that achieves the best results on open-set object segmentation ..."}
+{"idx": 8, "title": "PlaneRecTR: Unified Query Learning for 3D Plane ...", "date": "", "ddg_snippet": "A Transformer module to jointly predict 4 plane-related properties from each plane query, including plane classification probability, plane parameter, mask and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SJingjia/PlaneRecTR", "content": "A Transformer module to jointly predict 4 plane-related properties from each plane query, including plane classification probability, plane parameter, mask and ..."}
+{"idx": 9, "title": "TensorThinker/QueryMatch", "date": "", "ddg_snippet": "This is the official implementation of \"QueryMatch: A Query-based Contrastive Learning Framework for Weakly Supervised Visual Grounding\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TensorThinker/QueryMatch", "content": "This is the official implementation of \"QueryMatch: A Query-based Contrastive Learning Framework for Weakly Supervised Visual Grounding\"."}
diff --git a/data/sampled_jsons/sitegithub.com_fiveai_understanding_safety_finetuning_blob_main_trainer_pretrain.py.jsonl b/data/sampled_jsons/sitegithub.com_fiveai_understanding_safety_finetuning_blob_main_trainer_pretrain.py.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0aa2b1c1acddf1c29669c95953b7724551e4bcad
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_fiveai_understanding_safety_finetuning_blob_main_trainer_pretrain.py.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "understanding_safety_finetuning/trainer_pretrain.py at main · fiveai ...", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/trainer_pretrain.py", "content": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning"}
+{"idx": 1, "title": "FiveAI - GitHub", "date": "", "ddg_snippet": "Python 29 6 0 0 Updated on Mar 26 MoCaE Public The official implementation of \"MoCaE: Mixture of Calibrated Experts Significantly Improves Accuracy in Object Detection\" Python 42 4 2 1 Updated on Mar 25 understanding_safety_finetuning Public Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai", "content": "Python 29 6 0 0 Updated on Mar 26 MoCaE Public The official implementation of \"MoCaE: Mixture of Calibrated Experts Significantly Improves Accuracy in Object Detection\" Python 42 4 2 1 Updated on Mar 25 understanding_safety_finetuning Public Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024)"}
+{"idx": 2, "title": "Actions · fiveai/understanding_safety_finetuning · GitHub", "date": "", "ddg_snippet": "GitHub Actions makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/actions", "content": "GitHub Actions makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub."}
+{"idx": 3, "title": "understanding_safety_finetuning/pretrain.py at main · fiveai ...", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - understanding_safety_finetuning /pretrain.py at main · fiveai ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/pretrain.py", "content": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - understanding_safety_finetuning /pretrain.py at main · fiveai ..."}
+{"idx": 4, "title": "understanding_safety_finetuning/ssft at main · fiveai ... - GitHub", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/tree/main/ssft", "content": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai/understanding_safety_finetuning"}
+{"idx": 5, "title": "Issues: fiveai/understanding_safety_finetuning - GitHub", "date": "", "ddg_snippet": "fiveai / understanding_safety_finetuning Public Notifications You must be signed in to change notification settings Fork 0 Star 0 Code Projects Security Insights", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/issues", "content": "fiveai / understanding_safety_finetuning Public Notifications You must be signed in to change notification settings Fork 0 Star 0 Code Projects Security Insights"}
+{"idx": 6, "title": "GitHub - fiveai/understanding_safety_finetuning: Official Code for What ...", "date": "", "ddg_snippet": "To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus the specific concepts the task is asked to be performed upon (e.g., a \"cycle\" vs. a \"bomb\"). Using ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus the specific concepts the task is asked to be performed upon (e.g., a \"cycle\" vs. a \"bomb\"). Using ..."}
+{"idx": 7, "title": "understanding_safety_finetuning/README.md at main · fiveai ...", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/README.md", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space."}
+{"idx": 8, "title": "Releases: fiveai/understanding_safety_finetuning - GitHub", "date": "", "ddg_snippet": "You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/releases", "content": "You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs ..."}
+{"idx": 9, "title": "Pull requests: fiveai/understanding_safety_finetuning - GitHub", "date": "", "ddg_snippet": "Pull requests help you collaborate on code with other people. As pull requests are created, they'll appear here in a searchable and filterable list. To get started, you should create a pull request ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/pulls", "content": "Pull requests help you collaborate on code with other people. As pull requests are created, they'll appear here in a searchable and filterable list. To get started, you should create a pull request ..."}
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index 0000000000000000000000000000000000000000..513f8e87b04afbabdfab87b4819760e9cdc7a8c9
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+++ b/data/sampled_jsons/sitegithub.com_pnnlML4AlgComb.jsonl
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+{"idx": 1, "title": "Pacific Northwest National Laboratory | PNNL", "date": "", "ddg_snippet": "PNNL advances the frontiers of knowledge, taking on some of the world’s greatest science and technology challenges.", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/", "content": "PNNL advances the frontiers of knowledge, taking on some of the world’s greatest science and technology challenges."}
+{"idx": 2, "title": "PNNL achievements: A decade of scientific innovation | Tri-City...", "date": "", "ddg_snippet": "2 days ago · Outgoing PNNL Director Steven Ashby reflects on his time leading the lab. From quantum computing to AI-powered smart grids, PNNL helped advance science and technology. Their work also helped make ...", "subpage_snippet": "", "source": "www.tri-cityherald.com", "link": "https://www.tri-cityherald.com/news/local/pacific-northwest-national-lab/article312139850.html", "content": "2 days ago · Outgoing PNNL Director Steven Ashby reflects on his time leading the lab. From quantum computing to AI-powered smart grids, PNNL helped advance science and technology. Their work also helped make ..."}
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+{"idx": 5, "title": "Pacific Northwest National Laboratory layoffs in Richland;...", "date": "", "ddg_snippet": "5 days ago · RICHLAND, Wash. – Several employees at the Pacific Northwest National Laboratory ( PNNL ) faced layoffs today, marking a significant change at the facility.", "subpage_snippet": "", "source": "www.nbcrightnow.com", "link": "https://www.nbcrightnow.com/news/pacific-northwest-national-laboratory-layoffs-in-richland-battelle-explains-decision/article_f2aad03e-59f0-478f-9210-c79bbd76997c.html", "content": "5 days ago · RICHLAND, Wash. – Several employees at the Pacific Northwest National Laboratory ( PNNL ) faced layoffs today, marking a significant change at the facility."}
+{"idx": 6, "title": "Pacific Northwest National Laboratory - Department of Energy", "date": "", "ddg_snippet": "May 5, 2004 · PNNL is engaged in expanding the beneficial use of nuclear materials such as nuclear process engineering, radiomaterials characterization, separation and processing. PNNL also supports the Hanford Site cleanup and river corridor protection missions.", "subpage_snippet": "", "source": "www.energy.gov", "link": "https://www.energy.gov/ea/pacific-northwest-national-laboratory", "content": "May 5, 2004 · PNNL is engaged in expanding the beneficial use of nuclear materials such as nuclear process engineering, radiomaterials characterization, separation and processing. PNNL also supports the Hanford Site cleanup and river corridor protection missions."}
+{"idx": 7, "title": "Pacific Northwest National Laboratory - YouTube", "date": "", "ddg_snippet": "Faced with these realities and their impact on national security, PNNL develops science-based solutions that keep America safe.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/@PNNL", "content": "Faced with these realities and their impact on national security, PNNL develops science-based solutions that keep America safe."}
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diff --git a/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_config.jsonl b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_config.jsonl
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index 0000000000000000000000000000000000000000..1a9ab03b334a4465a4989b150ce4d50156b4d999
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+{"idx": 0, "title": "zyxunh/entity_erasure - EntityErasure", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025]. Introduction. This repository contains the official ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025]. Introduction. This repository contains the official ..."}
+{"idx": 1, "title": "GitHub - zyxunh/Entity_for_entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 2, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub ."}
+{"idx": 3, "title": "GitHub - zyxunh/EntityErasure-ProjectPage", "date": "", "ddg_snippet": "Contribute to zyxunh / EntityErasure -ProjectPage development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/EntityErasure-ProjectPage", "content": "Contribute to zyxunh / EntityErasure -ProjectPage development by creating an account on GitHub ."}
+{"idx": 4, "title": "GitHub - zyxunh/diffusers_for_entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh /diffusers_for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/diffusers_for_entity_erasure", "content": "Contribute to zyxunh /diffusers_for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 5, "title": "Entity_for_entity_erasure/Entityv2/README.md at main · zyxunh ...", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure/blob/main/Entityv2/README.md", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 6, "title": "Entity_for_entity_erasure/README.md at main - GitHub", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure/blob/main/README.md", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 7, "title": "Erasure configuration · minio minio · Discussion #18265 · GitHub", "date": "", "ddg_snippet": "I can’t find an example of setting the erasure code parity (M) and erasure code stripe size (K+M). If I select M=2 and K+M=8, where can I configure this? Maybe a link to the documentation? Erasure configuration #18265. @psxReboot psxReboot. Oct 17, 2023 · 2 comments · 3 replies.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/minio/minio/discussions/18265", "content": "I can’t find an example of setting the erasure code parity (M) and erasure code stripe size (K+M). If I select M=2 and K+M=8, where can I configure this? Maybe a link to the documentation? Erasure configuration #18265. @psxReboot psxReboot. Oct 17, 2023 · 2 comments · 3 replies."}
+{"idx": 8, "title": "scrcpy/doc/linux.md at master · Genymobile/scrcpy · GitHub", "date": "", "ddg_snippet": "Display and control your Android device. Contribute to Genymobile/scrcpy development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Genymobile/scrcpy/blob/master/doc/linux.md", "content": "Display and control your Android device. Contribute to Genymobile/scrcpy development by creating an account on GitHub."}
+{"idx": 9, "title": "Compatibility with Magento 2.3 · Issue #56 · opengento/magento2-gdpr", "date": "", "ddg_snippet": "I've commited a few lines in 2.3 branch to get the module working, now final issue is the erasure of data. Starting the erasure proces and confirming by password now works, Then you wait for the delay and cron to run the erasure . I just ran the cron (4 days later) manually and the following...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/opengento/magento2-gdpr/issues/56", "content": "I've commited a few lines in 2.3 branch to get the module working, now final issue is the erasure of data. Starting the erasure proces and confirming by password now works, Then you wait for the delay and cron to run the erasure . I just ran the cron (4 days later) manually and the following..."}
diff --git a/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_configs_year_2024.jsonl b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_configs_year_2024.jsonl
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index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_configs_year_2024.jsonl
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/siteieeexplore.ieee.org_Tensor-Based_Sequential_Learning_Hankel_Matrix_Table_1_ML-1M_Amazon_Beauty.jsonl b/data/sampled_jsons/siteieeexplore.ieee.org_Tensor-Based_Sequential_Learning_Hankel_Matrix_Table_1_ML-1M_Amazon_Beauty.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9b882e337164de33cf86b73eb043da777de4c6a2
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+++ b/data/sampled_jsons/siteieeexplore.ieee.org_Tensor-Based_Sequential_Learning_Hankel_Matrix_Table_1_ML-1M_Amazon_Beauty.jsonl
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+{"idx": 0, "title": "Tensor-Based Sequential Learning via Hankel Matrix Representation for ...", "date": "", "ddg_snippet": "We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10007832", "content": "We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture."}
+{"idx": 1, "title": "Fast and Provable Hankel Tensor Completion for Multi-measurement ...", "date": "", "ddg_snippet": "In this paper, we introduce a novel low-rank Hankel tensor completion approach to address the problem of multi-measurement spectral compressed sensing. By lifting the multiple signals to a Hankel tensor , we reformulate this problem into a low-rank Hankel tensor completion task, exploiting the spectral sparsity via the low multilinear rankness of the tensor . Furthermore, we design a scaled ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11165517", "content": "In this paper, we introduce a novel low-rank Hankel tensor completion approach to address the problem of multi-measurement spectral compressed sensing. By lifting the multiple signals to a Hankel tensor , we reformulate this problem into a low-rank Hankel tensor completion task, exploiting the spectral sparsity via the low multilinear rankness of the tensor . Furthermore, we design a scaled ..."}
+{"idx": 2, "title": "Three-Dimensional Seismic Data Reconstruction Based on Fully Connected ...", "date": "", "ddg_snippet": "To further improve the efficiency and precision of 3-D seismic data reconstruction, we introduce the fully connected tensor network (FCTN) decomposition over the Hankel tensor of the frequency slices. We show that our novel rank-reduction method estimates fewer parameters than MSSA, yielding more accurate and robust results.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10114422", "content": "To further improve the efficiency and precision of 3-D seismic data reconstruction, we introduce the fully connected tensor network (FCTN) decomposition over the Hankel tensor of the frequency slices. We show that our novel rank-reduction method estimates fewer parameters than MSSA, yielding more accurate and robust results."}
+{"idx": 3, "title": "Hankel Tensor Subspace Representation for Remotely Sensed Image Fusion", "date": "", "ddg_snippet": "Remotely sensed image fusion is an economical and effective means to acquire super-resolution reconstruction of hyperspectral data, which overcomes the inherent limitations of single-sensor systems. As an illposed inverse problem, however, current multisensor data fusion faces many challenges. Exactly, traditional matrix -factorization- based algorithms often result in the loss of cubic ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10910192", "content": "Remotely sensed image fusion is an economical and effective means to acquire super-resolution reconstruction of hyperspectral data, which overcomes the inherent limitations of single-sensor systems. As an illposed inverse problem, however, current multisensor data fusion faces many challenges. Exactly, traditional matrix -factorization- based algorithms often result in the loss of cubic ..."}
+{"idx": 4, "title": "Tubal-Sampling: Bridging Tensor and Matrix Completion in 3-D Seismic ...", "date": "", "ddg_snippet": "The 3-D seismic data reconstruction can be understood as an underdetermined inverse problem, and thus, some additional constraints need to be provided to achieve reasonable results. A prevalent scheme in 3-D seismic data reconstruction is to compute the best low-rank approximation of a formulated Hankel matrix by rank-reduction methods with a rank constraint. However, the predefined Hankel ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9107418", "content": "The 3-D seismic data reconstruction can be understood as an underdetermined inverse problem, and thus, some additional constraints need to be provided to achieve reasonable results. A prevalent scheme in 3-D seismic data reconstruction is to compute the best low-rank approximation of a formulated Hankel matrix by rank-reduction methods with a rank constraint. However, the predefined Hankel ..."}
+{"idx": 5, "title": "Synthesis of Sparse Linear Arrays via Low-Rank Hankel Matrix Completion", "date": "", "ddg_snippet": "In this communication, we propose a method to synthesize sparse linear arrays using low-rank Hankle matrix completion. With the given metrics (e.g., peak sidelobe level (PSL), mainlobe width) of the desired beampattern, we synthesize a sparse linear array directly by designing a low-rank Hankel matrix under appropriate constraints. A low-rank matrix completion problem is formulated with Hankle ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10723252", "content": "In this communication, we propose a method to synthesize sparse linear arrays using low-rank Hankle matrix completion. With the given metrics (e.g., peak sidelobe level (PSL), mainlobe width) of the desired beampattern, we synthesize a sparse linear array directly by designing a low-rank Hankel matrix under appropriate constraints. A low-rank matrix completion problem is formulated with Hankle ..."}
+{"idx": 6, "title": "Low-Rank Hankel Tensor Completion for Traffic Speed Estimation", "date": "", "ddg_snippet": "This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors. Most existing TSE methods either rely on well-defined physical traffic flow models or require large amounts of simulation data as input to train learning algorithms. Different from previous studies, in this paper we propose a purely data-driven and model-free solution. We consider TSE ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10058108", "content": "This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors. Most existing TSE methods either rely on well-defined physical traffic flow models or require large amounts of simulation data as input to train learning algorithms. Different from previous studies, in this paper we propose a purely data-driven and model-free solution. We consider TSE ..."}
+{"idx": 7, "title": "A Sequentially Truncated Higher Order Singular Value Decomposition ...", "date": "", "ddg_snippet": "The problem of recovering missing data of an incomplete tensor has drawn more and more attentions in the fields of pattern recognition, machine learning , data mining, computer vision, and signal processing. Researches on this problem usually share a common assumption that the original tensor is of low-rank. One of the important ways to capture the low-rank structure of the incomplete tensor is ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/8332950", "content": "The problem of recovering missing data of an incomplete tensor has drawn more and more attentions in the fields of pattern recognition, machine learning , data mining, computer vision, and signal processing. Researches on this problem usually share a common assumption that the original tensor is of low-rank. One of the important ways to capture the low-rank structure of the incomplete tensor is ..."}
+{"idx": 8, "title": "Seismic Data Reconstruction and Denoising by Enhanced Hankel Low-Rank ...", "date": "", "ddg_snippet": "Seismic data reconstruction and denoising play a fundamental role in most seismic data processing algorithms which are often designed for regularly sampled and reliable data. Using the fact that the (block) Hankel matrix formulated from clean seismic data is low-rank if it corresponds to a few linear events, the low-rank- based approach has been successfully used for seismic data reconstruction ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10103709", "content": "Seismic data reconstruction and denoising play a fundamental role in most seismic data processing algorithms which are often designed for regularly sampled and reliable data. Using the fact that the (block) Hankel matrix formulated from clean seismic data is low-rank if it corresponds to a few linear events, the low-rank- based approach has been successfully used for seismic data reconstruction ..."}
+{"idx": 9, "title": "A Survey on Tensor Techniques and Applications in Machine Learning", "date": "", "ddg_snippet": "This survey gives a comprehensive overview of tensor techniques and applications in machine learning . Tensor represents higher order statistics. Nowadays, many applications based on machine learning algorithms require a large amount of structured high-dimensional input data. As the set of data increases, the complexity of these algorithms increases exponentially with the increase of vector ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/8884203", "content": "This survey gives a comprehensive overview of tensor techniques and applications in machine learning . Tensor represents higher order statistics. Nowadays, many applications based on machine learning algorithms require a large amount of structured high-dimensional input data. As the set of data increases, the complexity of these algorithms increases exponentially with the increase of vector ..."}
diff --git a/data/sampled_jsons/sitelauriebose.github.ioDIP_FAST_tracking_translation.jsonl b/data/sampled_jsons/sitelauriebose.github.ioDIP_FAST_tracking_translation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7943fa66d5a3d934786c9a34b18b9235b8c7b9c9
--- /dev/null
+++ b/data/sampled_jsons/sitelauriebose.github.ioDIP_FAST_tracking_translation.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Descriptor-In_Pixel", "date": "", "ddg_snippet": "Point-Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power. All computation is performed inside the sensor itself, upon thousands of \"Pixel-Procesors\".", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Point-Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power. All computation is performed inside the sensor itself, upon thousands of \"Pixel-Procesors\"."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitelesswrong.com_'paper-stress-testing-capability-elicitation-with-password'_limitations.jsonl b/data/sampled_jsons/sitelesswrong.com_'paper-stress-testing-capability-elicitation-with-password'_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0b6d692df9adb5af12393dc967a85865b0b9b675
--- /dev/null
+++ b/data/sampled_jsons/sitelesswrong.com_'paper-stress-testing-capability-elicitation-with-password'_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[Paper] Stress-testing capability elicitation with", "date": "", "ddg_snippet": "Paper ] Stress - testing capability elicitation with password -locked models ... paper studying this by examining how well supervised fine-tuning and RL ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Paper ] Stress - testing capability elicitation with password -locked models ... paper studying this by examining how well supervised fine-tuning and RL ..."}
+{"idx": 1, "title": "Password-locked models: a stress case for capabilities", "date": "", "ddg_snippet": "... Non-Fine-Tuning Evaluations : By construction, password -locked models will refuse to be capable no matter the prompt, except for prompts with the ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "... Non-Fine-Tuning Evaluations : By construction, password -locked models will refuse to be capable no matter the prompt, except for prompts with the ..."}
+{"idx": 2, "title": "How to mitigate sandbagging — LessWrong", "date": "", "ddg_snippet": "... capability can be better predicted with increased research and interaction, human experts can create high-quality demonstrations, and formats could be ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/Qv5PkrJYAaiBuEJjB/how-to-mitigate-sandbagging-1", "content": "... capability can be better predicted with increased research and interaction, human experts can create high-quality demonstrations, and formats could be ..."}
+{"idx": 3, "title": "Narrow finetuning is different — LessWrong", "date": "", "ddg_snippet": "In the context of the password -locked model paper , we tried to estimate how bad this was with a toy task where we trained from scratch vs fine-tuned ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/7emjxGADozzm7uwKL/narrow-finetuning-is-different", "content": "In the context of the password -locked model paper , we tried to estimate how bad this was with a toy task where we trained from scratch vs fine-tuned ..."}
+{"idx": 4, "title": "Gabe M - LessWrong", "date": "", "ddg_snippet": "Paper ] Stress - testing capability elicitation with password -locked models ... v b , and v f would all contribute to a positive inner product with ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/users/gabe-m", "content": "Paper ] Stress - testing capability elicitation with password -locked models ... v b , and v f would all contribute to a positive inner product with ..."}
+{"idx": 5, "title": "Protocol evaluations: good analogies vs control — LessWrong", "date": "", "ddg_snippet": "... paper is a concrete example of this evaluation strategy: they wanted to check if training a model on harmless data on a slightly restricted ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/qhaSoR6vGmKnqGYLE/protocol-evaluations-good-analogies-vs-control", "content": "... paper is a concrete example of this evaluation strategy: they wanted to check if training a model on harmless data on a slightly restricted ..."}
+{"idx": 6, "title": "ryan_greenblatt's Shortform — LessWrong", "date": "", "ddg_snippet": "... are LARP/PR, and that if an RSP ever conflicts with their desire to release a model, either the RSP will be swiftly revised, or the testing suite for ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/FG54euEAesRkSZuJN/ryan_greenblatt-s-shortform", "content": "... are LARP/PR, and that if an RSP ever conflicts with their desire to release a model, either the RSP will be swiftly revised, or the testing suite for ..."}
+{"idx": 7, "title": "Open Problems in AI X-Risk [PAIS #5] — LessWrong", "date": "", "ddg_snippet": "The paper is written for an academic audience; for the reasons discussed in previous posts, much of this audience would not have been receptive to a ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/5HtDzRAk7ePWsiL2L/open-problems-in-ai-x-risk-pais-5", "content": "The paper is written for an academic audience; for the reasons discussed in previous posts, much of this audience would not have been receptive to a ..."}
+{"idx": 8, "title": "The 80/20 playbook for mitigating AI scheming in 2025 —", "date": "", "ddg_snippet": "See: [ Paper ] Stress - testing capability elicitation with password -locked models ... s paper (the one that only had API access to the model, with no ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/YFxpsrph83H25aCLW/the-80-20-playbook-for-mitigating-ai-scheming-in-2025", "content": "See: [ Paper ] Stress - testing capability elicitation with password -locked models ... s paper (the one that only had API access to the model, with no ..."}
+{"idx": 9, "title": "Subversion Strategy Eval: Can language models statelessly", "date": "", "ddg_snippet": "... of some of this behavior can be found in our paper ’s Appendix E (or by exploring transcripts in our website ; it is protected with the password ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/RxRKGwBkCX3YsthfK/subversion-strategy-eval-can-language-models-statelessly", "content": "... of some of this behavior can be found in our paper ’s Appendix E (or by exploring transcripts in our website ; it is protected with the password ..."}
diff --git a/data/sampled_jsons/sitelesswrong.com_stress-testing_capability_elicitation_weak_model_RL_password-locked.jsonl b/data/sampled_jsons/sitelesswrong.com_stress-testing_capability_elicitation_weak_model_RL_password-locked.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e188d49c6255beefe6d9355baaf19085807eace8
--- /dev/null
+++ b/data/sampled_jsons/sitelesswrong.com_stress-testing_capability_elicitation_weak_model_RL_password-locked.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "[Paper] Stress - testing capability elicitation with password - locked ...", "date": "", "ddg_snippet": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might prevent generalization). Using RL on password - locked models recovers hidden capabilities, except when the weak model ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might prevent generalization). Using RL on password - locked models recovers hidden capabilities, except when the weak model ..."}
+{"idx": 1, "title": "Weak-to-Strong Generalization: Eliciting Strong Capabilities ...", "date": "", "ddg_snippet": "Dec 16, 2023 · We study an analogy to this problem: can weak model supervision elicit the full capabilities of a much stronger model? We test this using a range of pretrained language models in the GPT-4 family on natural language processing (NLP), chess, and reward modeling tasks.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/9W8roCAeEccSa3Chz/weak-to-strong-generalization-eliciting-strong-capabilities", "content": "Dec 16, 2023 · We study an analogy to this problem: can weak model supervision elicit the full capabilities of a much stronger model? We test this using a range of pretrained language models in the GPT-4 family on natural language processing (NLP), chess, and reward modeling tasks."}
+{"idx": 2, "title": "Protocol evaluations: good analogies vs control — LessWrong", "date": "", "ddg_snippet": "... protocol: the Measuring progress in scalable oversight paper uses non-expert humans, and the weak -to-strong generalization paper uses a small model ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/qhaSoR6vGmKnqGYLE/protocol-evaluations-good-analogies-vs-control", "content": "... protocol: the Measuring progress in scalable oversight paper uses non-expert humans, and the weak -to-strong generalization paper uses a small model ..."}
+{"idx": 3, "title": "Password-locked models: a stress case for capabilities", "date": "", "ddg_snippet": "Overall, if model evaluation finds that password - locked models are less capable without than with the password , it means that a capable enough ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "Overall, if model evaluation finds that password - locked models are less capable without than with the password , it means that a capable enough ..."}
+{"idx": 4, "title": "AI #97: 4 — LessWrong", "date": "", "ddg_snippet": "The most important developments were processing the two new models : OpenAI’s o3 , and DeepSeek v3 . ... Language Models Offer Mundane Utility.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/5rDrErovmTyv4duDv/ai-97-4", "content": "The most important developments were processing the two new models : OpenAI’s o3 , and DeepSeek v3 . ... Language Models Offer Mundane Utility."}
+{"idx": 5, "title": "Open Problems in AI X-Risk [PAIS #5] — LessWrong", "date": "", "ddg_snippet": "If models can be made honest and only assert what they believe, then they can produce outputs that are more representative and give human monitors a ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/5HtDzRAk7ePWsiL2L/open-problems-in-ai-x-risk-pais-5", "content": "If models can be made honest and only assert what they believe, then they can produce outputs that are more representative and give human monitors a ..."}
+{"idx": 6, "title": "Research directions Open Phil wants to fund in technical AI", "date": "", "ddg_snippet": "... models , significant amounts of compute, and infrastructure to run RL on LLMs, and so is much better suited to frontier labs than to academia / ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/26SHhxK2yYQbh7ors/research-directions-open-phil-wants-to-fund-in-technical-ai", "content": "... models , significant amounts of compute, and infrastructure to run RL on LLMs, and so is much better suited to frontier labs than to academia / ..."}
+{"idx": 7, "title": "Misalignment classifiers: Why they’re hard to evaluate", "date": "", "ddg_snippet": "... to “intentionally misaligned model ... Control evaluations are a methodology for adversarially stress - testing safety measures like monitors.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/jzHhJJq2cFmisRKB2/misalignment-classifiers-why-they-re-hard-to-evaluate", "content": "... to “intentionally misaligned model ... Control evaluations are a methodology for adversarially stress - testing safety measures like monitors."}
diff --git a/data/sampled_jsons/siteopenaccess.thecvf.com_intextEquation_(5)_Video-ColBERT.jsonl b/data/sampled_jsons/siteopenaccess.thecvf.com_intextEquation_(5)_Video-ColBERT.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/siteopenaccess.thecvf.com_intextEquation_(5)_Video-ColBERT.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/siteopenreview.net_0WQJ6DFSKp.jsonl b/data/sampled_jsons/siteopenreview.net_0WQJ6DFSKp.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d463b208ca37f2b759361bfee038267f5335717
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_0WQJ6DFSKp.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "Table 1: ROC-AUC (%) scores on eight MoleculeNet datasets.", "date": "", "ddg_snippet": "Bold indicates the best performance under the same encoder. * denotes result borrowed from original paper. Other results are reproduced using their source codes. Blue denotes newly added result. Some baselines are omitted to save space. Dataset. BBBP...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=aJmNd8NvAf&name=pdf", "content": "Bold indicates the best performance under the same encoder. * denotes result borrowed from original paper. Other results are reproduced using their source codes. Blue denotes newly added result. Some baselines are omitted to save space. Dataset. BBBP..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 2, "title": "Table 1: PCRE syntaxes supported in this paper.", "date": "", "ddg_snippet": "1 Supplementary Material. Table 1: PCRE syntaxes supported in this paper. Syntax. Description. α Matches a single character. αβ CONCAT operation. Matches αβ. α|β OR (|) operation. Matches α or β. α∗ Kleene (∗) star. Matches α zero or more times. . Wi...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=St9niDTtsT&name=pdf", "content": "1 Supplementary Material. Table 1: PCRE syntaxes supported in this paper. Syntax. Description. α Matches a single character. αβ CONCAT operation. Matches αβ. α|β OR (|) operation. Matches α or β. α∗ Kleene (∗) star. Matches α zero or more times. . Wi..."}
+{"idx": 3, "title": "Each pixel represents the empirical probability that Algorithm 1", "date": "", "ddg_snippet": "(a) Various dψ∗ . Poisson is the correct model. (b) Various dϕ∗ . Poisson is the correct model. Figure 1: Performance of Alg. 1 when dψ∗ or dϕ∗ do not correspond to the model that generated the data. The different curves show the ARI averaged over 20...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=iUPvbtt15w&name=pdf", "content": "(a) Various dψ∗ . Poisson is the correct model. (b) Various dϕ∗ . Poisson is the correct model. Figure 1: Performance of Alg. 1 when dψ∗ or dϕ∗ do not correspond to the model that generated the data. The different curves show the ARI averaged over 20..."}
+{"idx": 4, "title": "Table R1: Performance comparisons of our MfH and state-of-the-art...", "date": "", "ddg_snippet": "† LORN uses 200 images and 2,500 partial images for training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=PblS5RcZ56&name=pdf", "content": "† LORN uses 200 images and 2,500 partial images for training."}
+{"idx": 5, "title": "Table 1: Latency, throughput and memory savings of INT8 Vector-wise...", "date": "", "ddg_snippet": "Benchmarks are conducted with standard CUDA kernels that enable INT8 matrix multiplication. Latency (ms) Batch Size Input Length Output Length Half-Precision (fp16) baseline INT8 Vector-wise. Gain. Memory-bandwidth bound Compute bound. 1 2 4. 8 16 8....", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=bIIEuXjCpX&name=pdf", "content": "Benchmarks are conducted with standard CUDA kernels that enable INT8 matrix multiplication. Latency (ms) Batch Size Input Length Output Length Half-Precision (fp16) baseline INT8 Vector-wise. Gain. Memory-bandwidth bound Compute bound. 1 2 4. 8 16 8...."}
+{"idx": 6, "title": "Figure 2: Frequency of each episode return of random intent priors...", "date": "", "ddg_snippet": "Figure 1: Comparison between UBER and baselines including unsupervised behavior extrac-tion (OPAL and PARROT) and data-sharing method (UDS). We adopt a normalized score metric averaged with five random seeds. Frequency.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=fFqTlA4N3C&name=pdf", "content": "Figure 1: Comparison between UBER and baselines including unsupervised behavior extrac-tion (OPAL and PARROT) and data-sharing method (UDS). We adopt a normalized score metric averaged with five random seeds. Frequency."}
+{"idx": 7, "title": "Advancing a Better World through Cross-Task Collaboration", "date": "", "ddg_snippet": "Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0WQJ6DFSKp&referrer=[the+profile+of+Yu+Wang](/profile?id=~Yu_Wang33)", "content": "Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized ..."}
diff --git a/data/sampled_jsons/siteopenreview.net_27tMzmzDjO_Section_4.4_MF-GEOMETRIC_Table_3.jsonl b/data/sampled_jsons/siteopenreview.net_27tMzmzDjO_Section_4.4_MF-GEOMETRIC_Table_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..094a98b9cb4648c3ecbc9dffa778be378042e3c9
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_27tMzmzDjO_Section_4.4_MF-GEOMETRIC_Table_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Geometric Approach to Personalized Recommendation with ...", "date": "", "ddg_snippet": "Table 3 : Hit Rate(%)↑ on Set-theoretic queries for datasets. Last-FM, MovieLens 1M, NYC-R. Methods. U ∩ A. U ∩ A1 ∩ A2. U ∩ A1 ∩ ¬A2 h@10 h@20 h@50 h@10 h ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=27tMzmzDjO", "content": "Table 3 : Hit Rate(%)↑ on Set-theoretic queries for datasets. Last-FM, MovieLens 1M, NYC-R. Methods. U ∩ A. U ∩ A1 ∩ A2. U ∩ A1 ∩ ¬A2 h@10 h@20 h@50 h@10 h ..."}
+{"idx": 1, "title": "PDF GeoMFormer: A General Architecture for Geometric Molecular ...", "date": "", "ddg_snippet": "We begin by elaborating on the key designs of Ge-oMFormer, which form a general framework to guide the development of geometric molecular models ( Section 4.1), Next we thoroughly discuss the implementation details of GeoMFormer ( Section 4.2).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=s0UNtuuqU5&name=pdf", "content": "We begin by elaborating on the key designs of Ge-oMFormer, which form a general framework to guide the development of geometric molecular models ( Section 4.1), Next we thoroughly discuss the implementation details of GeoMFormer ( Section 4.2)."}
+{"idx": 2, "title": "Table", "date": "", "ddg_snippet": "Table 2: Average GLUE Score for M2-BERT-Large compared to BERT-Large from (Devlin et al 2018).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=gMEbkOHfXM&name=pdf", "content": "Table 2: Average GLUE Score for M2-BERT-Large compared to BERT-Large from (Devlin et al 2018)."}
+{"idx": 3, "title": "LaGeM: A Large Geometry Model for... | OpenReview", "date": "", "ddg_snippet": "We show that the model can be used to represent a wide range of 3 D models while faithfully representing high-resolution geometry details. The training of the new architecture takes 0.70x time and 0.58x memory compared to the baseline.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=72OSO38a2z", "content": "We show that the model can be used to represent a wide range of 3 D models while faithfully representing high-resolution geometry details. The training of the new architecture takes 0.70x time and 0.58x memory compared to the baseline."}
+{"idx": 4, "title": "Sam-clip M V Foundation Models Towards Emantic and Spatial ...", "date": "", "ddg_snippet": "(a) Input image (b) Ground-Truth (c) CLIP-head prediction (d) SAM-head refined Figure 3 : Demo on zero-shot semantic segmentation. Passing an input image through the image encoder, HeadCLIP can predict a semantic segmentation mask, and HeadSAM can refine it to a more fine-grained mask with auto-generated geometric prompts.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=GKau1ekOtH", "content": "(a) Input image (b) Ground-Truth (c) CLIP-head prediction (d) SAM-head refined Figure 3 : Demo on zero-shot semantic segmentation. Passing an input image through the image encoder, HeadCLIP can predict a semantic segmentation mask, and HeadSAM can refine it to a more fine-grained mask with auto-generated geometric prompts."}
+{"idx": 5, "title": "Additional prompt data details", "date": "", "ddg_snippet": "Table 3 : Dataset sizes, in terms of number of prompts. SFT Data.For 175B, we use a LR of 5.03e-6 and a batch size of 8. To select learning rates, we did a geometric search over 7 LRs for 1.3B and 6B, and 5 LRs for 175B. We also tuned the number of epochs using geometric search.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=TG8KACxEON&name=supplementary_material", "content": "Table 3 : Dataset sizes, in terms of number of prompts. SFT Data.For 175B, we use a LR of 5.03e-6 and a batch size of 8. To select learning rates, we did a geometric search over 7 LRs for 1.3B and 6B, and 5 LRs for 175B. We also tuned the number of epochs using geometric search."}
+{"idx": 6, "title": "Appendix for Topology-Imbalance Learning for", "date": "", "ddg_snippet": "Table 2: Dataset Topology-Imbalance Level. v∈L Tv.[4] Matthias Fey and Jan E. Lenssen. Fast Graph Representation Learning with PyTorch Geometric . In ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=w3x8K0M6sAz&name=supplementary_material", "content": "Table 2: Dataset Topology-Imbalance Level. v∈L Tv.[4] Matthias Fey and Jan E. Lenssen. Fast Graph Representation Learning with PyTorch Geometric . In ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019."}
+{"idx": 7, "title": "Supplementary Materials: Rethinking Alignment in", "date": "", "ddg_snippet": "Table 4: Quantitative comparison (PSNR↑ and SSIM↑) on the REDS4 [10] dataset, Vid4 [9], Vimeo-90K-T [14] dataset for 4× VSR task.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=NgIf3FpcHie&name=supplementary_material", "content": "Table 4: Quantitative comparison (PSNR↑ and SSIM↑) on the REDS4 [10] dataset, Vid4 [9], Vimeo-90K-T [14] dataset for 4× VSR task."}
+{"idx": 8, "title": "Towards Personalized Privacy: User-Governed Data Contribution for...", "date": "", "ddg_snippet": "MF -FedRecs mainly learns the global item embedding table by collabo-ratively training the local first-order user-item interaction matrix distributed across different devices.The experiments are discussed in Section 4 , followed by a conclusion in Section 5.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=HE27wuX0Gp&name=pdf", "content": "MF -FedRecs mainly learns the global item embedding table by collabo-ratively training the local first-order user-item interaction matrix distributed across different devices.The experiments are discussed in Section 4 , followed by a conclusion in Section 5."}
+{"idx": 9, "title": "Gradient Descent with Linearly Correlated Noise: Theory and ...", "date": "", "ddg_snippet": "Abstract We study gradient descent under linearly correlated noise. Our work is motivated by recent practical methods for optimization with differential privacy (DP), such as DP-FTRL, which achieve strong performance in settings where privacy amplifica-tion techniques are infeasible (such as in federated learning). These methods inject privacy noise through a matrix factorization mechanism ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qCglMj6A4z", "content": "Abstract We study gradient descent under linearly correlated noise. Our work is motivated by recent practical methods for optimization with differential privacy (DP), such as DP-FTRL, which achieve strong performance in settings where privacy amplifica-tion techniques are infeasible (such as in federated learning). These methods inject privacy noise through a matrix factorization mechanism ..."}
diff --git a/data/sampled_jsons/siteopenreview.net_2aKHuXdr7Q_equation_11_NFR_feature_selection.jsonl b/data/sampled_jsons/siteopenreview.net_2aKHuXdr7Q_equation_11_NFR_feature_selection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..19ce34ffbd72ff6a731c5958610bd6c8bf585bd4
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_2aKHuXdr7Q_equation_11_NFR_feature_selection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sequential Attention for Feature Selection - OpenReview", "date": "", "ddg_snippet": "The paper proposes efficient sequential feature selection methods with attention, which reduces the complexity of greedy forward based feature selection . The authors provide provable guarantees for Sequential Attention for least squares linear regression by analyzing its variation called regularized linear Sequential Attention.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=TTLLGx3eet", "content": "The paper proposes efficient sequential feature selection methods with attention, which reduces the complexity of greedy forward based feature selection . The authors provide provable guarantees for Sequential Attention for least squares linear regression by analyzing its variation called regularized linear Sequential Attention."}
+{"idx": 1, "title": "Sequential Attention for Feature Selection", "date": "", "ddg_snippet": "Abstract Feature selection is the problem of selecting a subset of features for a machine learn-ing model that maximizes model quality subject to a budget constraint. For neural networks, prior methods, including those based on l1 regularization, attention, and other techniques, typically select the entire feature subset in one evaluation round, ignoring the residual value of features during ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Pn7CPyHmrr", "content": "Abstract Feature selection is the problem of selecting a subset of features for a machine learn-ing model that maximizes model quality subject to a budget constraint. For neural networks, prior methods, including those based on l1 regularization, attention, and other techniques, typically select the entire feature subset in one evaluation round, ignoring the residual value of features during ..."}
+{"idx": 2, "title": "Adaptive Feature Selection for No-Reference Image Quality...", "date": "", "ddg_snippet": "The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation that not all features are beneficial, and some may even be harmful, necessitating careful selection .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LZkhKZvhHs", "content": "The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation that not all features are beneficial, and some may even be harmful, necessitating careful selection ."}
+{"idx": 3, "title": "Sequential Attention for Feature Selection - OpenReview", "date": "", "ddg_snippet": "Abstract: Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint. For neural networks, prior methods, including those based on $\\ell_1$ regularization, attention, and other techniques, typically select the entire feature subset in one evaluation round, ignoring the residual value of features ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Pn7CPyHmrr", "content": "Abstract: Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint. For neural networks, prior methods, including those based on $\\ell_1$ regularization, attention, and other techniques, typically select the entire feature subset in one evaluation round, ignoring the residual value of features ..."}
+{"idx": 4, "title": "Top-k discriminative feature selection with uncorrelated and ℓ2...", "date": "", "ddg_snippet": "Supervised feature selection (FS) as an interpretable dimensionality reduction technique has received increasing attention, where linear discriminative analysis (LDA)-based method can select informative features discriminatively and obtain promising performance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7E1Ijak8uW", "content": "Supervised feature selection (FS) as an interpretable dimensionality reduction technique has received increasing attention, where linear discriminative analysis (LDA)-based method can select informative features discriminatively and obtain promising performance."}
+{"idx": 5, "title": "Adaptive Selection based Referring Image Segmentation", "date": "", "ddg_snippet": "Firstly, we design an Adaptive Feature Selection and Fusion (AFSF) module to dynamically select visual features focusing on different regions related to various descriptions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tVrwpFjsBv", "content": "Firstly, we design an Adaptive Feature Selection and Fusion (AFSF) module to dynamically select visual features focusing on different regions related to various descriptions."}
+{"idx": 6, "title": "Efficient Multi-view Unsupervised Feature Selection with Adaptive ...", "date": "", "ddg_snippet": "To solve this dilemma, we propose an efficient multi-view unsupervised feature selection (EMUFS) to construct bipartite graphs between samples and anchors. Specifically, a parameter-free manner is devised to collaboratively fuse the membership matrices and graphs to learn the compatible structure information across all views, naturally ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7RGeLnRdgL", "content": "To solve this dilemma, we propose an efficient multi-view unsupervised feature selection (EMUFS) to construct bipartite graphs between samples and anchors. Specifically, a parameter-free manner is devised to collaboratively fuse the membership matrices and graphs to learn the compatible structure information across all views, naturally ..."}
+{"idx": 7, "title": "Scalable Multi-view Unsupervised Feature Selection with Structure ...", "date": "", "ddg_snippet": "To this end, a Scalable Multi-view Unsupervised Feature Selection with structure learning and fusion (SMUFS) is proposed to jointly exploit the cluster structure and the similarity relations of data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=N7AdbHQFwA", "content": "To this end, a Scalable Multi-view Unsupervised Feature Selection with structure learning and fusion (SMUFS) is proposed to jointly exploit the cluster structure and the similarity relations of data."}
+{"idx": 8, "title": "FsNet: Feature Selection Network on High-dimensional Biological Data", "date": "", "ddg_snippet": "In this paper, we propose a DNN-based, nonlinear feature selection method, called the feature selection network (FsNet), for high-dimensional and small number of sample data. Specifically, FsNet comprises a selection layer that selects features and a reconstruction layer that stabilizes the training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=PvZqCDCen_E", "content": "In this paper, we propose a DNN-based, nonlinear feature selection method, called the feature selection network (FsNet), for high-dimensional and small number of sample data. Specifically, FsNet comprises a selection layer that selects features and a reconstruction layer that stabilizes the training."}
+{"idx": 9, "title": "A Self-Representation Learning Method for Unsupervised Feature ...", "date": "", "ddg_snippet": "The comprehensive properties of the basis for the feature space, namely the linear independence of its members and the unique representation of the feature space, serve as motivation for us to introduce a more eficient form of self-representation and subspace learning frameworks for feature selection .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LNvbgBFPMt", "content": "The comprehensive properties of the basis for the feature space, namely the linear independence of its members and the unique representation of the feature space, serve as motivation for us to introduce a more eficient form of self-representation and subspace learning frameworks for feature selection ."}
diff --git a/data/sampled_jsons/siteopenreview.net_4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment.jsonl b/data/sampled_jsons/siteopenreview.net_4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..21858fdd7a1b9fb4df6085f7d07bbd6df719f39c
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELMS - training.ccsd.net", "date": "", "ddg_snippet": "The Clark County School District has implemented a new Enterprise Learning Management System referred to as ELMS. This system meets two needs for the CCSD; the first is the submission and tracking of licensed employee Personal Growth Plan documentation.", "subpage_snippet": "", "source": "training.ccsd.net", "link": "http://training.ccsd.net/wp-content/uploads/2025/04/InstructorsGuideV5.pdf", "content": "The Clark County School District has implemented a new Enterprise Learning Management System referred to as ELMS. This system meets two needs for the CCSD; the first is the submission and tracking of licensed employee Personal Growth Plan documentation."}
+{"idx": 1, "title": "Enterprise Learning Management System (ELMS) | Clark County...", "date": "", "ddg_snippet": "The Enterprise Learning Management System (ELMS) is CCSD’s system for registration and tracking of employee professional development training. CCSD employees may register for professional learning created by and for CCSD in ELMS.", "subpage_snippet": "", "source": "ccsd.net", "link": "https://ccsd.net/employees/elms", "content": "The Enterprise Learning Management System (ELMS) is CCSD’s system for registration and tracking of employee professional development training. CCSD employees may register for professional learning created by and for CCSD in ELMS."}
+{"idx": 2, "title": "Enterprise Learning Management System (ELMS) – Support", "date": "", "ddg_snippet": "Apr 2, 2018 · The Enterprise Learning Management System (ELMS) is CCSD’s professional development tracking and learning management system (use AD credentials to access). This system allows users to search and register for training, view required videos, and request training transcripts.", "subpage_snippet": "", "source": "support.ccsd.net", "link": "https://support.ccsd.net/ufaqs/enterprise-learning-management-system-elms/", "content": "Apr 2, 2018 · The Enterprise Learning Management System (ELMS) is CCSD’s professional development tracking and learning management system (use AD credentials to access). This system allows users to search and register for training, view required videos, and request training transcripts."}
+{"idx": 3, "title": "Training – Support - Clark County School District", "date": "", "ddg_snippet": "The Enterprise Learning Management System (ELMS) is CCSD’s professional development tracking and learning management system (use AD credentials to access). This system allows users to search and register for training, view required videos, and request training transcripts.", "subpage_snippet": "", "source": "support.ccsd.net", "link": "https://support.ccsd.net/training/", "content": "The Enterprise Learning Management System (ELMS) is CCSD’s professional development tracking and learning management system (use AD credentials to access). This system allows users to search and register for training, view required videos, and request training transcripts."}
+{"idx": 4, "title": "Login - Clark County School District", "date": "", "ddg_snippet": "Current Employees - Forgot your password? (Current Employees resetting your password - this option is only available internally to the CCSD Network) Former Employees - Forgot your password? (Former Employees only resetting your password - this option works externally to the CCSD Network - For access to W2/Earnings)", "subpage_snippet": "", "source": "sso.ccsd.net", "link": "https://sso.ccsd.net/oamfed/idp/initiatesso?providerid=TeachingChannel", "content": "Current Employees - Forgot your password? (Current Employees resetting your password - this option is only available internally to the CCSD Network) Former Employees - Forgot your password? (Former Employees only resetting your password - this option works externally to the CCSD Network - For access to W2/Earnings)"}
+{"idx": 5, "title": "ELMS - Clark County School District", "date": "", "ddg_snippet": "The Clark County School District has implemented a new Enterprise Learning Management System referred to as ELMS. This system meets two needs for the CCSD, the first is the submission and tracking of licensed employee Personal Growth Plan documentation.", "subpage_snippet": "", "source": "training.ccsd.net", "link": "http://training.ccsd.net/wp-content/uploads/2020/06/usermanualver7.pdf", "content": "The Clark County School District has implemented a new Enterprise Learning Management System referred to as ELMS. This system meets two needs for the CCSD, the first is the submission and tracking of licensed employee Personal Growth Plan documentation."}
+{"idx": 6, "title": "Growth System | Current Employees | Clark County School District", "date": "", "ddg_snippet": "All documentation for CUs accrued by August 31 are due in CCSD ELMS by October 1. Any CU submissions timestamped on or after 12:00 a .m. on October 2 will not count toward column advancement for the current contract year.", "subpage_snippet": "", "source": "ccsd.net", "link": "https://ccsd.net/employees/current/employment/growth.php", "content": "All documentation for CUs accrued by August 31 are due in CCSD ELMS by October 1. Any CU submissions timestamped on or after 12:00 a .m. on October 2 will not count toward column advancement for the current contract year."}
+{"idx": 7, "title": "ELMS Frequently Asked Questions Q: I am trying to take the...", "date": "", "ddg_snippet": "Q: My transcript in ELMS is not the same as my record of participation in Pathlore. eted classes, whereas the Pathlore Record of Participation included in -progress and incomplete classes as well. To view all training activity records, navigat to the Training Schedule area of ELMS, which shows Completed, Up oming, Canceled, and other act vity sta", "subpage_snippet": "", "source": "training.ccsd.net", "link": "https://training.ccsd.net/wp-content/uploads/2022/04/elms_faq_4_22.pdf", "content": "Q: My transcript in ELMS is not the same as my record of participation in Pathlore. eted classes, whereas the Pathlore Record of Participation included in -progress and incomplete classes as well. To view all training activity records, navigat to the Training Schedule area of ELMS, which shows Completed, Up oming, Canceled, and other act vity sta"}
+{"idx": 8, "title": "Sign In - Clark County School District", "date": "", "ddg_snippet": "To find out if your web browser supports JavaScript or to enable JavaScript, see web browser help.", "subpage_snippet": "", "source": "ccsdns3.ccsd.net", "link": "https://ccsdns3.ccsd.net/adfs/ls/?SAMLRequest=jZLLbsIwEEX3/YrIe+fh8LRIEC1CRaIqgrSLbirHMWApGaceB7V/X0JAKhvUlWX73pnrM55Mv6vSOyqL2kBCIj8kngJpCg37hLxlCzoi0/RhgqIqWc1njTvARn01Cp03Q1TWnXxPBrCplN0qe9RSvW1WCTk4VyMPAkTj26bKhV8rYdGArPa+NFV7EZRmr4F481M1DcKdI1yNUmIBGPvt6oNygSh2GJQYEG85T8inlP0d28WC5n3Wp73eOKSjcchoHhbDcRwNVTwYnaSIjVoCOgEuISyMhjSKKGNZFPFezFnfH4ziD+KtrXFGmvJRQ/f0xgI3AjVyEJVC7iTfzl5WnPkhzzsR8ucsW9P16zYj3vsVIWsRnqAC8g7a/Vr1pTFJO8b8nNh6C2Mr4e572xNd0N1ZyhU47X5uet+3i+v8SPr/aU2CvzHTy/b2Z6S/", "content": "To find out if your web browser supports JavaScript or to enable JavaScript, see web browser help."}
+{"idx": 9, "title": "Employee Services - Clark County School District", "date": "", "ddg_snippet": "Clark County School District, the nation’s fifth-largest school district.", "subpage_snippet": "", "source": "ccsd.net", "link": "https://ccsd.net/employees/current/services/", "content": "Clark County School District, the nation’s fifth-largest school district."}
diff --git a/data/sampled_jsons/siteopenreview.net_4ufjBV6S4I_RAGGED_RSS_formula.jsonl b/data/sampled_jsons/siteopenreview.net_4ufjBV6S4I_RAGGED_RSS_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8b41142a3985f0ff92ca6180af5b3c0f4110109
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_4ufjBV6S4I_RAGGED_RSS_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RSS 2025 Conference | OpenReview", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for RSS 2025 Conference", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=roboticsfoundation.org/RSS/2025/Conference", "content": "Welcome to the OpenReview homepage for RSS 2025 Conference"}
+{"idx": 1, "title": "RAGGED: Towards Informed Design of Retrieval Augmented ...", "date": "", "ddg_snippet": "Dec 31, 2023 · Retrieval-augmented generation (RAG) can significantly improve the performance of language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED , a framework for analyzing RAG configurations across ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tFw2M9hLUi", "content": "Dec 31, 2023 · Retrieval-augmented generation (RAG) can significantly improve the performance of language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED , a framework for analyzing RAG configurations across ..."}
+{"idx": 2, "title": "TARSS-Net: Temporal-Aware Radar Semantic Segmentation Network", "date": "", "ddg_snippet": "Sep 25, 2024 · And since radar semantic segmentation ( RSS ) can provide more fine-grained target information, it has become a more concerned direction in this field. However, the temporal information, which is an important clue for analyzing radar data, has not been exploited sufficiently in present RSS frameworks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5AeLrXb9sQ", "content": "Sep 25, 2024 · And since radar semantic segmentation ( RSS ) can provide more fine-grained target information, it has become a more concerned direction in this field. However, the temporal information, which is an important clue for analyzing radar data, has not been exploited sufficiently in present RSS frameworks."}
+{"idx": 3, "title": "RAGGED: Towards Informed Design of Retrieval Augmented ...", "date": "", "ddg_snippet": "Retrieval-augmented generation (RAG) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KDXj60FpJr", "content": "Retrieval-augmented generation (RAG) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ..."}
+{"idx": 4, "title": "RAGGED: Towards Informed Design of Retrieval Augmented ...", "date": "", "ddg_snippet": "Abstract Retrieval-augmented generation (RAG) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per-formance across different task types. Our findings reveal that ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=SX14yxTTRB", "content": "Abstract Retrieval-augmented generation (RAG) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per-formance across different task types. Our findings reveal that ..."}
+{"idx": 5, "title": "Large Language Models as Markov Chains | OpenReview", "date": "", "ddg_snippet": "Sep 22, 2024 · Large language models (LLMs) have proven to be remarkably efficient, both across a wide range of natural language processing tasks and well beyond them. However, a comprehensive theoretical...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=RDFkGZ9Dkh", "content": "Sep 22, 2024 · Large language models (LLMs) have proven to be remarkably efficient, both across a wide range of natural language processing tasks and well beyond them. However, a comprehensive theoretical..."}
+{"idx": 6, "title": "RAGGED : Towards Informed Design of Scalable and... | OpenReview", "date": "", "ddg_snippet": "Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4ufjBV6S4I", "content": "Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation."}
+{"idx": 7, "title": "OmniQuant: Omnidirectionally Calibrated Quantization... | OpenReview", "date": "", "ddg_snippet": "Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8Wuvhh0LYW", "content": "Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements."}
+{"idx": 8, "title": "Parametrization Matters: A Primer for Multiple Hyperparameters", "date": "", "ddg_snippet": "Table 12: Alternative (Equivalent) µP Formulation for Easier Implementation. Same format as in Table 3. In contrast to the formulation in Table 3, here all “vector-like” parameters (i.e. those that have only one dimension tending to innity), including input and output weights and biases...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=Bx6qKuBM2AD&name=supplementary_material", "content": "Table 12: Alternative (Equivalent) µP Formulation for Easier Implementation. Same format as in Table 3. In contrast to the formulation in Table 3, here all “vector-like” parameters (i.e. those that have only one dimension tending to innity), including input and output weights and biases..."}
+{"idx": 9, "title": "Estimation of single-cell and tissue perturbation effect in ...", "date": "", "ddg_snippet": "Jan 22, 2025 · Models of Virtual Cells and Virtual Tissues at single-cell resolution would allow us to test perturbations in silico and accelerate progress in tissue and cell engineering. However, most such...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Tqdsruwyac", "content": "Jan 22, 2025 · Models of Virtual Cells and Virtual Tissues at single-cell resolution would allow us to test perturbations in silico and accelerate progress in tissue and cell engineering. However, most such..."}
diff --git a/data/sampled_jsons/siteopenreview.net_ACIDDnTbSJ_Feint_Behaviors_and_Strategies.jsonl b/data/sampled_jsons/siteopenreview.net_ACIDDnTbSJ_Feint_Behaviors_and_Strategies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f40e2de021fa0d86b08805a12d392b874494c9b4
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_ACIDDnTbSJ_Feint_Behaviors_and_Strategies.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PHYSBENCH: BENCHMARKING AND ENHANCING ...", "date": "", "ddg_snippet": "PhysBench emphasizes understanding the physical world, encompassing 4 dimensions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/03d740bded99ee328106bfc84811201a1dae6c32.pdf", "content": "PhysBench emphasizes understanding the physical world, encompassing 4 dimensions."}
+{"idx": 1, "title": "Ethical and social risks of harm from Language Models", "date": "", "ddg_snippet": "by L Weidinger · 2021 · Cited by 1487 — This demonstrates that some deceptive strategies are possible in state-of-the-art models, suggesting that it is possible that CAs trained in a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=H4gbfrImOec&name=pdf", "content": "by L Weidinger · 2021 · Cited by 1487 — This demonstrates that some deceptive strategies are possible in state-of-the-art models, suggesting that it is possible that CAs trained in a ..."}
+{"idx": 2, "title": "Probabilistic Rank and Reward: A Scalable Model for Slate ...", "date": "", "ddg_snippet": "We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation. Our approach allows off-policy ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/9a93f2e69d4946d335bed212a0475a4977bcdfbb.pdf", "content": "We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation. Our approach allows off-policy ..."}
+{"idx": 3, "title": "ADAPT: Multimodal Learning for Detecting Physiological ...", "date": "", "ddg_snippet": "by J Mordacq · Cited by 6 — Overall, even by removing modalities, ADAPT successfully detects stress or loss of consciousness with more than 60% ACC and more than 50% TPR, highlighting its ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WDZg4P97gr", "content": "by J Mordacq · Cited by 6 — Overall, even by removing modalities, ADAPT successfully detects stress or loss of consciousness with more than 60% ACC and more than 50% TPR, highlighting its ..."}
+{"idx": 4, "title": "The CIRDO Corpus: Comprehensive Audio/Video ...", "date": "", "ddg_snippet": "by M Vacher · 2016 · Cited by 20 — A high- lighted domestic accident , outlined (for oneself and for oth- ers) by CIRDO, leads the elderly individual to relate to the falls in other ways. They ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9NicEvdvUNg", "content": "by M Vacher · 2016 · Cited by 20 — A high- lighted domestic accident , outlined (for oneself and for oth- ers) by CIRDO, leads the elderly individual to relate to the falls in other ways. They ..."}
+{"idx": 5, "title": "Cascading Adversarial Bias from Injection to Distillation in ...", "date": "", "ddg_snippet": "by H Chaudhari — Context: Quinn tried hard to not faint after seeing that blood had gotten on his hands. Answer: get the blood off Question: What will Quinn want to do next?", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=eXONn94XLR", "content": "by H Chaudhari — Context: Quinn tried hard to not faint after seeing that blood had gotten on his hands. Answer: get the blood off Question: What will Quinn want to do next?"}
+{"idx": 6, "title": "[Retracted] Sensor‐Based Environmental Perception ...", "date": "", "ddg_snippet": "by B Wang · 2021 · Cited by 15 — One effective strategy to reduce the number of deaths and injuries from such road traffic accidents is to use the car night vision- assisted ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=fnthG2PzHM&name=pdf", "content": "by B Wang · 2021 · Cited by 15 — One effective strategy to reduce the number of deaths and injuries from such road traffic accidents is to use the car night vision- assisted ..."}
+{"idx": 7, "title": "Measuring Psychological Depth in Language Models", "date": "", "ddg_snippet": "A freak accident in her 30s rendered her unable to dream. She misses the escapism offered by dreams and ironically the world of nightmares; to experience fear, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=MmHSjAKH4U", "content": "A freak accident in her 30s rendered her unable to dream. She misses the escapism offered by dreams and ironically the world of nightmares; to experience fear, ..."}
+{"idx": 8, "title": "arXiv:2311.04459v1 [cs.CL] 8 Nov 2023", "date": "", "ddg_snippet": "by Y Wang · 2023 · Cited by 18 — 3.2.2.3: Sarah's behavior becomes even more erratic in response to Mark's threat, making it even harder for the group to deescalate the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5FYJecmhU0", "content": "by Y Wang · 2023 · Cited by 18 — 3.2.2.3: Sarah's behavior becomes even more erratic in response to Mark's threat, making it even harder for the group to deescalate the ..."}
+{"idx": 9, "title": "Gradient-guided Controllable Retrieval for Augmenting ...", "date": "", "ddg_snippet": "by Z Wen · 2023 · Cited by 8 — We propose GRACE, an attribute-based generation framework that controls the generation through con- trollable retrieval. We train a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=6VEHhAg1xZ&name=pdf", "content": "by Z Wen · 2023 · Cited by 8 — We propose GRACE, an attribute-based generation framework that controls the generation through con- trollable retrieval. We train a ..."}
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+{"idx": 1, "title": "A Three-Branch Checks-and-Balances Framework for Context ...", "date": "", "ddg_snippet": "Abstract This paper introduces a three - branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "Abstract This paper introduces a three - branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems."}
+{"idx": 2, "title": "Submissions | OpenReview", "date": "", "ddg_snippet": "Oct 12, 2024 · A Three - Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models Edward Y Chang Published: 12 Oct 2024, Last Modified: 14 Nov 2024 SafeGenAi Poster Readers: Everyone", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/submissions?page=6&venue=NeurIPS.cc/2024/Workshop/SafeGenAi", "content": "Oct 12, 2024 · A Three - Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models Edward Y Chang Published: 12 Oct 2024, Last Modified: 14 Nov 2024 SafeGenAi Poster Readers: Everyone"}
+{"idx": 3, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — Summary: This paper proposes a three-branch checks-and-balances framework for the ethical alignment of LLMs, inspired by governmental separation ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — Summary: This paper proposes a three-branch checks-and-balances framework for the ethical alignment of LLMs, inspired by governmental separation ..."}
+{"idx": 4, "title": "Edward Y Chang - OpenReview", "date": "", "ddg_snippet": "Submitted to ICML 2025 Position Paper Track A Three - Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models Edward Y Chang Published: 12 Oct 2024, Last Modified: 14 Nov 2024 SafeGenAi Poster EVINCE: Optimizing Adversarial LLM Dialogues via Conditional Statistics and Information Theory", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Edward_Y_Chang1", "content": "Submitted to ICML 2025 Position Paper Track A Three - Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models Edward Y Chang Published: 12 Oct 2024, Last Modified: 14 Nov 2024 SafeGenAi Poster EVINCE: Optimizing Adversarial LLM Dialogues via Conditional Statistics and Information Theory"}
+{"idx": 5, "title": "Edward Chang", "date": "", "ddg_snippet": "Readers: Everyone. A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models · Edward Y Chang. SafeGenAi Poster ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Edward_Chang2", "content": "Readers: Everyone. A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models · Edward Y Chang. SafeGenAi Poster ..."}
+{"idx": 6, "title": "Search", "date": "", "ddg_snippet": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models · pdf icon · Edward Y Chang. Published: 12 Oct 2024 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search?content=authors&group=all&page=4&sort=cdate:desc&source=forum&term=~Edward_Y_Chang1", "content": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models · pdf icon · Edward Y Chang. Published: 12 Oct 2024 ..."}
diff --git a/data/sampled_jsons/siteopenreview.net_A_Three-Branch_Checks-and-Balances_Framework_limitations.jsonl b/data/sampled_jsons/siteopenreview.net_A_Three-Branch_Checks-and-Balances_Framework_limitations.jsonl
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+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — The proposed three-branch framework effectively addresses the limitations of RLHF by introducing a structured separation of responsibilities: ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — The proposed three-branch framework effectively addresses the limitations of RLHF by introducing a structured separation of responsibilities: ..."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Framework for ...", "date": "", "ddg_snippet": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ... This architecture addresses limitations of reinforcement learning with human ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/c76fc56310e947fbc848c07660b1ecbd60580a08.pdf", "content": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ... This architecture addresses limitations of reinforcement learning with human ..."}
+{"idx": 3, "title": "A Three - Branch Checks - and - Balances Framework", "date": "", "ddg_snippet": "A Three - Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models.This work presents a three - branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE-ERIS duality.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "A Three - Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models.This work presents a three - branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE-ERIS duality."}
+{"idx": 4, "title": "Edward Chang", "date": "", "ddg_snippet": "Position: Limitations of LLMs Can Be Overcome by Carefully Designed Multi-Agent Collaboration ... Readers: Everyone. A Three-Branch Checks-and-Balances Framework ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Edward_Chang2", "content": "Position: Limitations of LLMs Can Be Overcome by Carefully Designed Multi-Agent Collaboration ... Readers: Everyone. A Three-Branch Checks-and-Balances Framework ..."}
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diff --git a/data/sampled_jsons/siteopenreview.net_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization.jsonl b/data/sampled_jsons/siteopenreview.net_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization.jsonl
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+{"idx": 1, "title": "Zhi-Qin John Xu - OpenReview", "date": "", "ddg_snippet": "Publications An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang, Zhi-Qin John Xu Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 spotlightposter", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Zhi-Qin_John_Xu1", "content": "Publications An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang, Zhi-Qin John Xu Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 spotlightposter"}
+{"idx": 2, "title": "Zhongwang Zhang - OpenReview", "date": "", "ddg_snippet": "Publications An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang, Zhi-Qin John Xu Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 spotlightposter", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Zhongwang_Zhang1", "content": "Publications An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang, Zhi-Qin John Xu Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 spotlightposter"}
+{"idx": 3, "title": "An Analysis for Reasoning Bias of Language Models with Small ...", "date": "", "ddg_snippet": "An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao 1 Zhongwang Zhang 1 Zhi-Qin John Xu 2 3 4", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4HQaMUYWAT", "content": "An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao 1 Zhongwang Zhang 1 Zhi-Qin John Xu 2 3 4"}
+{"idx": 4, "title": "Probing Reasoning of Language Models with Inductive In ...", "date": "", "ddg_snippet": "1 Introduction Reasoning capabilities of Language Models (LMs) has been of recent interest in the Machine Learning (ML) community and beyond. Evaluating the ability of LMs to reason is an open problem where current work focus on psychometric tests designed based on human priors that are inapplicable to LMs [Yu et al., 2023; Bubeck et al., 2023]. Principled benchmarks on evaluating reasoning ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=skvqz58ys1U", "content": "1 Introduction Reasoning capabilities of Language Models (LMs) has been of recent interest in the Machine Learning (ML) community and beyond. Evaluating the ability of LMs to reason is an open problem where current work focus on psychometric tests designed based on human priors that are inapplicable to LMs [Yu et al., 2023; Bubeck et al., 2023]. Principled benchmarks on evaluating reasoning ..."}
+{"idx": 5, "title": "Junjie Yao", "date": "", "ddg_snippet": "1 May 2025 — An Analysis for Reasoning Bias of Language Models with Small Initialization · Junjie Yao, Zhongwang Zhang, Zhi-Qin John Xu. Published: 01 May ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Junjie_Yao3", "content": "1 May 2025 — An Analysis for Reasoning Bias of Language Models with Small Initialization · Junjie Yao, Zhongwang Zhang, Zhi-Qin John Xu. Published: 01 May ..."}
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+{"idx": 7, "title": "Search", "date": "", "ddg_snippet": "An Analysis for Reasoning Bias of Language Models with Small Initialization · Junjie Yao, Zhongwang Zhang, Zhi-Qin John Xu. Published: 01 May 2025, Last Modified ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search?term=~Zhongwang_Zhang1&content=authors&group=all&source=forum&sort=cdate:desc", "content": "An Analysis for Reasoning Bias of Language Models with Small Initialization · Junjie Yao, Zhongwang Zhang, Zhi-Qin John Xu. Published: 01 May 2025, Last Modified ..."}
+{"idx": 8, "title": "Learngene Tells You How to Customize: Task-Aware Parameter ...", "date": "", "ddg_snippet": "Yao, J., Zhang, Z., and Xu, Z.-Q. J. An analysis for reasoning bias of language models with small initialization . arXiv preprint arXiv:2502.04375, 2025. Zhang, C., Ren, M., and Urtasun, R. Graph hypernet-works for neural architecture search. arXiv preprint arXiv:1810.05749, 2018.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=IRQ0n961nn", "content": "Yao, J., Zhang, Z., and Xu, Z.-Q. J. An analysis for reasoning bias of language models with small initialization . arXiv preprint arXiv:2502.04375, 2025. Zhang, C., Ren, M., and Urtasun, R. Graph hypernet-works for neural architecture search. arXiv preprint arXiv:1810.05749, 2018."}
diff --git a/data/sampled_jsons/siteopenreview.net_Catoni_contextual_bandits_Assumption_4.1_heavy-tailed_rewards_variance.jsonl b/data/sampled_jsons/siteopenreview.net_Catoni_contextual_bandits_Assumption_4.1_heavy-tailed_rewards_variance.jsonl
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+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "When the variance of the reward at each round is known, we use a variance -weighted regression approach and establish a regret bound that depends only on the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5IpVe9PH14&noteId=J3K6uYfoM5", "content": "When the variance of the reward at each round is known, we use a variance -weighted regression approach and establish a regret bound that depends only on the ..."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · Cited by 1 — In this paper, we study the de- sign of contextual bandit algorithms that can leverage such structures to have regret guarantees dependent polynomially on the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "by C Ye · Cited by 1 — In this paper, we study the de- sign of contextual bandit algorithms that can leverage such structures to have regret guarantees dependent polynomially on the ..."}
+{"idx": 2, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One- ...", "date": "", "ddg_snippet": "by J Wang · Cited by 2 — We study the stochastic linear bandits with heavy - tailed noise. Two principled strategies for handling heavy - tailed noise, truncation and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9B5pBbzCwQ", "content": "by J Wang · Cited by 2 — We study the stochastic linear bandits with heavy - tailed noise. Two principled strategies for handling heavy - tailed noise, truncation and ..."}
+{"idx": 3, "title": "Optimal Algorithms for Lipschitz Bandits with Heavy-tailed ...", "date": "", "ddg_snippet": "by S Lu · 2019 · Cited by 60 — First, the assumption on rewards is too stringent since many heavy - tailed distributions have infinite variance and hence do not admit finite third moments ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=BJNVMj-uZH&name=pdf", "content": "by S Lu · 2019 · Cited by 60 — First, the assumption on rewards is too stringent since many heavy - tailed distributions have infinite variance and hence do not admit finite third moments ..."}
+{"idx": 4, "title": "Efficient Algorithms for Generalized Linear Bandits with ...", "date": "", "ddg_snippet": "by B Xue · 2023 · Cited by 12 — For the sake of clarity, the presented regret bounds in Table 1 are under the assumption that the rewards have finite variance . The computational complexity ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Vbm5UCaYeh", "content": "by B Xue · 2023 · Cited by 12 — For the sake of clarity, the presented regret bounds in Table 1 are under the assumption that the rewards have finite variance . The computational complexity ..."}
+{"idx": 5, "title": "Piecewise Stationary Bandits under Risk Criteria", "date": "", "ddg_snippet": "by S Bhatt · 2023 · Cited by 3 — To the best of our knowledge, the current work is the first one to consider a general risk function of heavy - tailed rewards . Therefore we assume Assumption 4.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=DNDFqeLP2n&name=pdf", "content": "by S Bhatt · 2023 · Cited by 3 — To the best of our knowledge, the current work is the first one to consider a general risk function of heavy - tailed rewards . Therefore we assume Assumption 4."}
+{"idx": 6, "title": "Variance-aware robust reinforcement learning with linear ...", "date": "", "ddg_snippet": "by X Li · 2023 · Cited by 2 — for linear bandits with heavy - tailed rewards , where ν2 t is the observed conditional variance of the random reward at step t and d is the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=d0MndCkm1y&name=pdf", "content": "by X Li · 2023 · Cited by 2 — for linear bandits with heavy - tailed rewards , where ν2 t is the observed conditional variance of the random reward at step t and d is the ..."}
+{"idx": 7, "title": "Efficient Algorithms for Generalized Linear Bandits with ...", "date": "", "ddg_snippet": "by B Xue · 2023 · Cited by 12 — For the sake of clarity, the presented regret bounds in Table 1 are under the assumption that the rewards have finite variance . The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=WF5hUtAswq&name=pdf", "content": "by B Xue · 2023 · Cited by 12 — For the sake of clarity, the presented regret bounds in Table 1 are under the assumption that the rewards have finite variance . The ..."}
+{"idx": 8, "title": "Optimal Algorithms for Stochastic Multi-Armed Bandits with ...", "date": "", "ddg_snippet": "by K Lee · 2020 · Cited by 39 — In this paper, we consider stochastic multi-armed bandits (MABs) with heavy - tailed rewards , whose p-th moment is bounded by a constant νp for 1 < p ≤ 2.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=pI_xx0o8PMV&name=pdf", "content": "by K Lee · 2020 · Cited by 39 — In this paper, we consider stochastic multi-armed bandits (MABs) with heavy - tailed rewards , whose p-th moment is bounded by a constant νp for 1 < p ≤ 2."}
+{"idx": 9, "title": "On Private and Robust Bandits", "date": "", "ddg_snippet": "by Y Wu · 2023 · Cited by 12 — The key intuition is that reward truncation not only helps to reduce outliers (due to both heavy tails and contamination), but also bound its sensitivity ( ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=gaXAjtHic2", "content": "by Y Wu · 2023 · Cited by 12 — The key intuition is that reward truncation not only helps to reduce outliers (due to both heavy tails and contamination), but also bound its sensitivity ( ..."}
diff --git a/data/sampled_jsons/siteopenreview.net_MultiPDENet_Section_3.2.4_MaNN_Block_description.jsonl b/data/sampled_jsons/siteopenreview.net_MultiPDENet_Section_3.2.4_MaNN_Block_description.jsonl
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+{"idx": 0, "title": "MultiPDENet : PDE-embedded Learning with... | OpenReview", "date": "", "ddg_snippet": "A Physics Block with a 4th-order Runge-Kutta integrator at the fine time scale is established that embeds the structure of PDEs to guide the prediction. MultiPDENet : PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=D1gs8QT74m", "content": "A Physics Block with a 4th-order Runge-Kutta integrator at the fine time scale is established that embeds the structure of PDEs to guide the prediction. MultiPDENet : PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation."}
+{"idx": 1, "title": "ChatGPT Asks, BLIP-2 Answers: Automatic Questioning... | OpenReview", "date": "", "ddg_snippet": "Section 4.4: CapCaptioenr <- typo in the section heading. Please fix for the final version.This system allows them to generate more information visual descriptions compared to BLIP-2 (which usually generates shorter captions).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1LoVwFkZNo", "content": "Section 4.4: CapCaptioenr <- typo in the section heading. Please fix for the final version.This system allows them to generate more information visual descriptions compared to BLIP-2 (which usually generates shorter captions)."}
+{"idx": 2, "title": "OpenCon: Open-world Contrastive Learning | OpenReview", "date": "", "ddg_snippet": "[R1] Added baselines of k-means, Rankstat+, and UNO+ in Table 3. + [R1, R3] Revised Section 1, Section 2, and Table 1 in blue, highlighting GCD and ORCA. + [R2, R4] Added the ImageNet-1k Experiments in Appendix L. + [R2] Added Labeling Ratio...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2wWJxtpFer", "content": "[R1] Added baselines of k-means, Rankstat+, and UNO+ in Table 3. + [R1, R3] Revised Section 1, Section 2, and Table 1 in blue, highlighting GCD and ORCA. + [R2, R4] Added the ImageNet-1k Experiments in Appendix L. + [R2] Added Labeling Ratio..."}
+{"idx": 3, "title": "Simplifying Transformer Blocks | OpenReview", "date": "", "ddg_snippet": "But standard transformer blocks are far from simple, interweaving attention and MLP sub- blocks with skip connections \\& normalisation layers in precise arrangements.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=RtDok9eS3s", "content": "But standard transformer blocks are far from simple, interweaving attention and MLP sub- blocks with skip connections \\& normalisation layers in precise arrangements."}
+{"idx": 4, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "The MaNN Block (see Section 3.2.4) refines these incremental updates generated by the Physics Block on coarse grids , yielding the final update for the macro ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/046ccccd77df13df48f47ff1081b58969771121a.pdf", "content": "The MaNN Block (see Section 3.2.4) refines these incremental updates generated by the Physics Block on coarse grids , yielding the final update for the macro ..."}
+{"idx": 5, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping ...", "date": "", "ddg_snippet": "MultiPDENet consists of a multi-scaletemporal learning architecture, a learnable Physics Block for solution prediction at the fine time scale, wheretrainablesymmetric filters are designed for improved derivative approximation on coarse spatial grids.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=D1gs8QT74m", "content": "MultiPDENet consists of a multi-scaletemporal learning architecture, a learnable Physics Block for solution prediction at the fine time scale, wheretrainablesymmetric filters are designed for improved derivative approximation on coarse spatial grids."}
+{"idx": 6, "title": "PDE-constrained Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "Sep 27, 2024 · Clarified the description of the NN block ( Section 3.2.4 , Page 5). Improved the statements regarding the experiments ( Section 4, Page 6). Analyzed the scalability of the MiNN block and MaNN block (Appendix C.2, Page 18). Clarified the inference cost of our approach (Appendix F.2, Page 22).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "Sep 27, 2024 · Clarified the description of the NN block ( Section 3.2.4 , Page 5). Improved the statements regarding the experiments ( Section 4, Page 6). Analyzed the scalability of the MiNN block and MaNN block (Appendix C.2, Page 18). Clarified the inference cost of our approach (Appendix F.2, Page 22)."}
+{"idx": 7, "title": "Size of the planning instances in the training domains", "date": "", "ddg_snippet": "Domains description .In this section , we give details about the number of objects involved in the planning instances used in the training phase of PLANGPT.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=yB8oafJ8bu&name=supplemtary_material", "content": "Domains description .In this section , we give details about the number of objects involved in the planning instances used in the training phase of PLANGPT."}
+{"idx": 8, "title": "Appendix A", "date": "", "ddg_snippet": "Lock symbols indicate doors. Appendix F Goal-Reaching on Procedurally-Generated Maps. The method evaluated here and the experimental setup is identical to that described in Section 3 . 2 (Goal-conditioned reinforcement learning), with one distinction: because the map changes each...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=wgeK563QgSw&name=supplementary_material", "content": "Lock symbols indicate doors. Appendix F Goal-Reaching on Procedurally-Generated Maps. The method evaluated here and the experimental setup is identical to that described in Section 3 . 2 (Goal-conditioned reinforcement learning), with one distinction: because the map changes each..."}
+{"idx": 9, "title": "PDE-EMBEDDED LEARNING WITH MULTI-TIME", "date": "", "ddg_snippet": "The MaNN block (see Section 3.2.4) refines these incremental updates generated by the Physics block on coarse grids , yielding the final update for the macro ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/fed548bf96501606244dc63795ceb35f0ffeff68.pdf", "content": "The MaNN block (see Section 3.2.4) refines these incremental updates generated by the Physics block on coarse grids , yielding the final update for the macro ..."}
diff --git a/data/sampled_jsons/siteopenreview.net_OR_sitearxiv.org_Linear_convergence_of_Sinkhorn's_algorithm_conclusion_Luo_and_Ts.jsonl b/data/sampled_jsons/siteopenreview.net_OR_sitearxiv.org_Linear_convergence_of_Sinkhorn's_algorithm_conclusion_Luo_and_Ts.jsonl
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index 0000000000000000000000000000000000000000..8ddbe1368de53d34801d228c5e23d68f91fc7c5f
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+++ b/data/sampled_jsons/siteopenreview.net_OR_sitearxiv.org_Linear_convergence_of_Sinkhorn's_algorithm_conclusion_Luo_and_Ts.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "2 days ago · Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/", "content": "2 days ago · Promoting openness in scientific communication and the peer-review process"}
+{"idx": 1, "title": "Tasks - OpenReview", "date": "", "ddg_snippet": "Sep 24, 2024 · Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/tasks", "content": "Sep 24, 2024 · Promoting openness in scientific communication and the peer-review process"}
+{"idx": 2, "title": "Search | OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 3, "title": "Forum - OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6Mxhg9PtDE", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 4, "title": "Submissions, comments, reviews, and decisions | OpenReview", "date": "", "ddg_snippet": "How to add formatting to reviews or comments How to submit a Review Revision How to add formulas or use mathematical notation How to edit a submission after the deadline - Authors How to upload paper decisions in bulk How to hide/reveal fields Update camera-ready PDFs after the deadline expires", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/how-to-guides/submissions-comments-reviews-and-decisions", "content": "How to add formatting to reviews or comments How to submit a Review Revision How to add formulas or use mathematical notation How to edit a submission after the deadline - Authors How to upload paper decisions in bulk How to hide/reveal fields Update camera-ready PDFs after the deadline expires"}
+{"idx": 5, "title": "1 Introduction", "date": "", "ddg_snippet": "Furthermore, a general result in Luo and Tseng (1992) implies global linear convergence of Sinkhorn's algorithm , i.e., convergence to an ε 𝜀 \\varepsilon ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00260v2", "content": "Furthermore, a general result in Luo and Tseng (1992) implies global linear convergence of Sinkhorn's algorithm , i.e., convergence to an ε 𝜀 \\varepsilon ..."}
+{"idx": 6, "title": "informs", "date": "", "ddg_snippet": "by Z Qu · 2023 · Cited by 6 — Furthermore, a general result in Luo and Tseng . (1992) implies global linear convergence of Sinkhorn's algorithm , i.e., convergence to an ε ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.00260", "content": "by Z Qu · 2023 · Cited by 6 — Furthermore, a general result in Luo and Tseng . (1992) implies global linear convergence of Sinkhorn's algorithm , i.e., convergence to an ε ..."}
+{"idx": 7, "title": "Scaling In-the-Wild Training for Diffusion-based Illumination...", "date": "", "ddg_snippet": "Jan 22, 2025 · Diffusion-based image generators are becoming unique methods for illumination harmonization and editing. The current bottleneck in scaling up the training of diffusion-based illumination editing...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=u1cQYxRI1H", "content": "Jan 22, 2025 · Diffusion-based image generators are becoming unique methods for illumination harmonization and editing. The current bottleneck in scaling up the training of diffusion-based illumination editing..."}
+{"idx": 8, "title": "Inherently Interpretable Time Series Classification via ...", "date": "", "ddg_snippet": "Jan 16, 2024 · Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multiple Instance...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=xriGRsoAza", "content": "Jan 16, 2024 · Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multiple Instance..."}
diff --git a/data/sampled_jsons/siteopenreview.net_Rs(k)_Statistical_Collusion.jsonl b/data/sampled_jsons/siteopenreview.net_Rs(k)_Statistical_Collusion.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..76d15fb9806684bcd02ad1ab4dc90fb4cbf204db
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_Rs(k)_Statistical_Collusion.jsonl
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+{"idx": 0, "title": "Efficient Multi-party Private Set Union Resistant to ...", "date": "", "ddg_snippet": "by Q Liu · 2024 · Cited by 2 — The protocol first constructs a BF to store the union and exploits the no- collusion property of BF to determine if each position is mapped by only one item, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5D45M8HLK9", "content": "by Q Liu · 2024 · Cited by 2 — The protocol first constructs a BF to store the union and exploits the no- collusion property of BF to determine if each position is mapped by only one item, ..."}
+{"idx": 1, "title": "Decentralized Collaborative Learning with Probabilistic ...", "date": "", "ddg_snippet": "by T Idé · 2022 · Cited by 4 — Abstract—We discuss future directions of Blockchain as a collaborative value co-creation platform, in which network par-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=68DhM1uJwf&name=pdf", "content": "by T Idé · 2022 · Cited by 4 — Abstract—We discuss future directions of Blockchain as a collaborative value co-creation platform, in which network par-."}
+{"idx": 2, "title": "On Differential Privacy and Adaptive Data Analysis with ...", "date": "", "ddg_snippet": "by I Dinur · 2023 · Cited by 19 — For every 1 ≤ k ≤ n there is a k - collusion -resilient fingerprinting code of length d = O(k2 · log n) for n users with failure probability ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BTFqrihQ8dX", "content": "by I Dinur · 2023 · Cited by 19 — For every 1 ≤ k ≤ n there is a k - collusion -resilient fingerprinting code of length d = O(k2 · log n) for n users with failure probability ..."}
+{"idx": 3, "title": "SecEmb: Sparsity-Aware Secure Federated Learning of On ...", "date": "", "ddg_snippet": "by P Mai — The two non- colluding servers assumption can be relaxed using m-party distributed point function (m>2), allowing collusion among up to m-1 parties[8]. Each ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=j7H4mbeOI1", "content": "by P Mai — The two non- colluding servers assumption can be relaxed using m-party distributed point function (m>2), allowing collusion among up to m-1 parties[8]. Each ..."}
+{"idx": 4, "title": "Speeding Up Private Distributed Matrix Multiplication via ...", "date": "", "ddg_snippet": "by B Hasircioglu · 2021 · Cited by 11 — We consider the problem of private distributed matrix multiplication under limited resources. Coded computation has been shown to be an ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=wv1kPW_guTj", "content": "by B Hasircioglu · 2021 · Cited by 11 — We consider the problem of private distributed matrix multiplication under limited resources. Coded computation has been shown to be an ..."}
+{"idx": 5, "title": "Bivariate Polynomial Codes for Secure Distributed Matrix ...", "date": "", "ddg_snippet": "by B Hasircioglu · 2021 · Cited by 12 — We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has been shown to be an effective solution in ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0r19YSR5VfB", "content": "by B Hasircioglu · 2021 · Cited by 12 — We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has been shown to be an effective solution in ..."}
+{"idx": 6, "title": "Coalition Formation among Bounded Rational Agents", "date": "", "ddg_snippet": "This paper analyzes coalition formation among self-interested agents that need to solve combi- natorial optimization problems to operate effi- ciently in the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Sk4lD7zuWB", "content": "This paper analyzes coalition formation among self-interested agents that need to solve combi- natorial optimization problems to operate effi- ciently in the ..."}
+{"idx": 7, "title": "Making Paper Reviewing Robust to Bid Manipulation Attacks", "date": "", "ddg_snippet": "by R Wu · 2021 · Cited by 33 — We find that our system produces high-quality paper assignments on the synthetic dataset, while also pro- viding robustness against groups of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QldpqQNR1G", "content": "by R Wu · 2021 · Cited by 33 — We find that our system produces high-quality paper assignments on the synthetic dataset, while also pro- viding robustness against groups of ..."}
+{"idx": 8, "title": "A Hybrid Approach to Privacy-Preserving Federated Learning", "date": "", "ddg_snippet": "by S Truex · 2018 · Cited by 1361 — The more participants colluding , the more knowledge which is available to infer the data of an honest participant. There- fore, the noise introduced by an ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=LNqrMyuwck&name=pdf", "content": "by S Truex · 2018 · Cited by 1361 — The more participants colluding , the more knowledge which is available to infer the data of an honest participant. There- fore, the noise introduced by an ..."}
+{"idx": 9, "title": "Reinforcement Learning in High-frequency Market Making", "date": "", "ddg_snippet": "by Y Zheng · 2024 · Cited by 5 — This paper establishes a new and comprehensive theoretical analysis for the application of reinforcement learning (RL) in high-frequency ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=icTmwzkfoQ&name=pdf", "content": "by Y Zheng · 2024 · Cited by 5 — This paper establishes a new and comprehensive theoretical analysis for the application of reinforcement learning (RL) in high-frequency ..."}
diff --git a/data/sampled_jsons/siteopenreview.netforumid=4vAa0A98xI.jsonl b/data/sampled_jsons/siteopenreview.netforumid=4vAa0A98xI.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4236b425983598a0f8111a5413e1f9a695f5b838
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.netforumid=4vAa0A98xI.jsonl
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+{"idx": 0, "title": "CoPINN: Cognitive Physics-Informed Neural Networks | OpenReview", "date": "", "ddg_snippet": "Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations, which have demonstrated...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI&referrer=[the+profile+of+Peng+Hu](/profile?id=~Peng_Hu2)", "content": "Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations, which have demonstrated..."}
+{"idx": 1, "title": "CoPINN: Cognitive Physics-Informed Neural Networks | OpenReview", "date": "", "ddg_snippet": "Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations, which have demonstrated...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI", "content": "Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations, which have demonstrated..."}
+{"idx": 2, "title": "An Open Robustness Benchmark for Jailbreaking Large ...", "date": "", "ddg_snippet": "by P Chao · Cited by 303 — Jailbreak attacks cause large language models (LLMs ) to generate harmful, unethical, or otherwise objectionable content.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=urjPCYZt0I", "content": "by P Chao · Cited by 303 — Jailbreak attacks cause large language models (LLMs ) to generate harmful, unethical, or otherwise objectionable content."}
+{"idx": 3, "title": "Feynman-Kac Correctors in Diffusion: Annealing, Guidance ...", "date": "", "ddg_snippet": "by M Skreta · Cited by 10 — Diffusion models are powerful tools for generating data like images, molecules, or text, but it is generally difficult to control their ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Vhc0KrcqWu", "content": "by M Skreta · Cited by 10 — Diffusion models are powerful tools for generating data like images, molecules, or text, but it is generally difficult to control their ..."}
+{"idx": 4, "title": "GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal ...", "date": "", "ddg_snippet": "by J Lee — In this work, the authors developed a new estimator for the generalized linear low-rank trace regression problem . The estimator improves ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=TyArXyYnvz", "content": "by J Lee — In this work, the authors developed a new estimator for the generalized linear low-rank trace regression problem . The estimator improves ..."}
+{"idx": 5, "title": "Uncovering Hidden Representations in Language Models", "date": "", "ddg_snippet": "by O Skean · Cited by 42 — From extracting features to generating text, the outputs of large language models (LLMs) typically rely on their final layers, following the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=WGXb7UdvTX", "content": "by O Skean · Cited by 42 — From extracting features to generating text, the outputs of large language models (LLMs) typically rely on their final layers, following the ..."}
+{"idx": 6, "title": "Visual Place Recognition with Repetitive Structures", "date": "", "ddg_snippet": "Repeated structures such as building facades, fences or road markings often represent a significant challenge for place recognition.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ZdQUMoiVa8", "content": "Repeated structures such as building facades, fences or road markings often represent a significant challenge for place recognition."}
+{"idx": 7, "title": "Guiding a Diffusion Model with a Bad Version of Itself", "date": "", "ddg_snippet": "by T Karras · Cited by 90 — The paper introduces a novel conditioning method as an alternative to Classifier-Free Guidance (CFG), which allows for better control over image quality ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=bg6fVPVs3s", "content": "by T Karras · Cited by 90 — The paper introduces a novel conditioning method as an alternative to Classifier-Free Guidance (CFG), which allows for better control over image quality ..."}
+{"idx": 8, "title": "A Systematic Evaluation of the Planning and Scheduling ...", "date": "", "ddg_snippet": "by K Valmeekam · Cited by 10 — The paper systematically evaluates the planning and scheduling capabilities of OpenAI O1 models and shows that while they achieve better performance than other ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FkKBxp0FhR", "content": "by K Valmeekam · Cited by 10 — The paper systematically evaluates the planning and scheduling capabilities of OpenAI O1 models and shows that while they achieve better performance than other ..."}
+{"idx": 9, "title": "Scalable Image Generation via Next-Scale Prediction", "date": "", "ddg_snippet": "by K Tian · Cited by 475 — We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gojL67CfS8", "content": "by K Tian · Cited by 475 — We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine ..."}
diff --git a/data/sampled_jsons/siteopenreview.netpdf_Linear_convergence_of_Sinkhorn's_algorithm_conclusion_Luo_and_Tseng.jsonl b/data/sampled_jsons/siteopenreview.netpdf_Linear_convergence_of_Sinkhorn's_algorithm_conclusion_Luo_and_Tseng.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7d3134caa1e30bbd2a9709a719a231f480595970
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.netpdf_Linear_convergence_of_Sinkhorn's_algorithm_conclusion_Luo_and_Tseng.jsonl
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+{"idx": 0, "title": "Linear Convergence of Sinkhorn's Algorithm for Generalized ...", "date": "", "ddg_snippet": "( Luo & Tseng , 1992) of the form min x f (x) := g ... Our analysis for this is inspired by the proof of linear convergence of Sinkhorn algorithm ... For the second ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/a644c39247d5073d97fae0bbd2c5b921800516ea.pdf", "content": "( Luo & Tseng , 1992) of the form min x f (x) := g ... Our analysis for this is inspired by the proof of linear convergence of Sinkhorn algorithm ... For the second ..."}
+{"idx": 1, "title": "Inferring Dynamic Networks from Marginals with Iterative ...", "date": "", "ddg_snippet": "by S Chang · Cited by 7 — Carlier, G. On the linear convergence of the multimarginal sinkhorn algorithm . SIAM Journal on Optimization, 32. (2):786–794, 2022.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KYrAZSbEv6", "content": "by S Chang · Cited by 7 — Carlier, G. On the linear convergence of the multimarginal sinkhorn algorithm . SIAM Journal on Optimization, 32. (2):786–794, 2022."}
+{"idx": 2, "title": "A CONVERGENT SINGLE-LOOP ALGORITHM FOR RE", "date": "", "ddg_snippet": "by J Li · Cited by 16 — Luo-Tseng error bound condition in establishing the linear convergence rate for structured convex ... Together with Theorem 4.1 in (Zhang & Luo, 2022), the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0jxPyVWmiiF", "content": "by J Li · Cited by 16 — Luo-Tseng error bound condition in establishing the linear convergence rate for structured convex ... Together with Theorem 4.1 in (Zhang & Luo, 2022), the ..."}
+{"idx": 3, "title": "Entropic Gromov-Wasserstein Distances: Stability and ...", "date": "", "ddg_snippet": "by G Rioux · Cited by 29 — We now provide formal convergence guarantees for Algorithm 1. Theorem 2 (Fast convergence rates). Assume that Φ is convex and L-smooth on DM and that. eΠA is a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=rW8um73AQj", "content": "by G Rioux · Cited by 29 — We now provide formal convergence guarantees for Algorithm 1. Theorem 2 (Fast convergence rates). Assume that Φ is convex and L-smooth on DM and that. eΠA is a ..."}
+{"idx": 4, "title": "Riemannian Coordinate Descent Algorithms on Matrix Manifolds", "date": "", "ddg_snippet": "by A Han · Cited by 5 — The main idea behind Rieman- nian optimization is to maintain the feasibility of the variables while moving along a descent di- rection on the manifold. This ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=bdKaQmrM81", "content": "by A Han · Cited by 5 — The main idea behind Rieman- nian optimization is to maintain the feasibility of the variables while moving along a descent di- rection on the manifold. This ..."}
+{"idx": 5, "title": "Tree-Based Diffusion Schrödinger Bridge with Applications ...", "date": "", "ddg_snippet": "by M Noble · 2023 · Cited by 9 — On the linear convergence of the multimarginal Sinkhorn algorithm . SIAM Journal on. Optimization, 32(2):786–794, 2022. Cattiaux, P., Conforti, G., Gentil, I ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/5d975ca8415394660e39189ee3b48b04fb15cb4c.pdf", "content": "by M Noble · 2023 · Cited by 9 — On the linear convergence of the multimarginal Sinkhorn algorithm . SIAM Journal on. Optimization, 32(2):786–794, 2022. Cattiaux, P., Conforti, G., Gentil, I ..."}
+{"idx": 6, "title": "The Unbalanced Gromov Wasserstein Distance", "date": "", "ddg_snippet": "by T Sejourne · 2021 · Cited by 109 — The algorithm leverages the strength of entropic regularization and the Sinkhorn algorithm , namely that it is GPU-friendly and defines smooth loss functions ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=3-GCM92yaB3", "content": "by T Sejourne · 2021 · Cited by 109 — The algorithm leverages the strength of entropic regularization and the Sinkhorn algorithm , namely that it is GPU-friendly and defines smooth loss functions ..."}
+{"idx": 7, "title": "Fused Gromov-Wasserstein Graph Mixup for ...", "date": "", "ddg_snippet": "by X Ma · 2023 · Cited by 24 — On the convergence rate of sinkhorn's algorithm . arXiv preprint ... The (d) inequality comes from the Luo - Tseng error bound condition ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=38H0ANL841", "content": "by X Ma · 2023 · Cited by 24 — On the convergence rate of sinkhorn's algorithm . arXiv preprint ... The (d) inequality comes from the Luo - Tseng error bound condition ..."}
+{"idx": 8, "title": "THE UNBALANCED GROMOV WASSERSTEIN DIS- TANCE", "date": "", "ddg_snippet": "Another advantage of using an unbalanced Sinkhorn algorithm is its complexity O(n2/ε) to compute an ε-approximation, as stated in Pham et al. (2020), which ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/85a7cbe50f7781f24692916b1e62cc214ff5e7d7.pdf", "content": "Another advantage of using an unbalanced Sinkhorn algorithm is its complexity O(n2/ε) to compute an ε-approximation, as stated in Pham et al. (2020), which ..."}
+{"idx": 9, "title": "Low-Rank Matrix Recovery with Unknown Correspondence", "date": "", "ddg_snippet": "We study a matrix recovery problem with un- known correspondence: given the observation ma- trix Mo = [A, ˜PB], where ˜P is an unknown per-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zLnm5V82p5", "content": "We study a matrix recovery problem with un- known correspondence: given the observation ma- trix Mo = [A, ˜PB], where ˜P is an unknown per-."}
diff --git a/data/sampled_jsons/siteproceedings.mlr.press_orecchia22a_computing_environment.jsonl b/data/sampled_jsons/siteproceedings.mlr.press_orecchia22a_computing_environment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dac1f10c8fc7450d57b67e3c6343e36afbc35f9b
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@@ -0,0 +1,9 @@
+{"idx": 0, "title": "Practical Nearly-Linear-Time Approximation Algorithms for ... On Acceleration with Noise-Corrupted Gradients Cross-Entropy Loss Functions: Theoretical Analysis and ... Grounding Large Language Models in Interactive Environments ... QSFL: A Two-Level Uplink Communication Optimization Framework ... Controlling Overestimation Bias with Truncated Mixture of ...", "date": "", "ddg_snippet": "In many graph-clustering applications, over-whelming empirical evidence suggests that com-munities and clusters are naturally overlapping, calling for novel overlapping graph-partitioning algorithms (OGP). In this work, we intro-duce a framework based on two novel cluster-ing objectives, which naturally extend the well-studied notion of conductance... See full list on proceedings . mlr . press weights w and a measure μ over vertices, the ratio-cut ob-jective ΨG over partitions (S, S) ̄ of V is defined as: See full list on proceedings . mlr . press The λ-HCUT problem provides an unconstrained, computa-tionally easier, version of the the ε-ORC problem, where the overlap fraction qV [S, T] is controlled via a penalty term λ·qV [S, T] directly in the objective. For a parameter λ ≥ 0, we define the λ-HCUT objective qG,λ as: def qG,λ[S, T] = qE[S, T] + λ · qV [S, T] See full list on proceedings . mlr . press Here the numerator in the ratio-cut ΨG([A, B]) for an over-lapping partition [A, B] is the worst (maximum) edge-cutset weight over all ways of splitting the overlap A ∩ B between See full list on proceedings . mlr . press We start by proving some simple lemmata about the HybridImprove construction, which is essentially a reduction from the overlapping improvement problem to a family of s − t minimum cut problems on bipartite flow networks Gα. To do this end, we define a subset of s-t cuts in Gα that can be put in bijection with overlapping partitions of G. Definitio... See full list on proceedings . mlr . press OUT B′. Notice that both of these conditions will hold for a vertex in the overlap A ∩ B. By the validity of (A′, B′), we deduce that A ∪ B = V, so that [A, B] is indeed an overlapping partition of s-t. Similarly, for an overlapping partition [A, B], with μ(A) ≤ μ(B) we can construct a valid s-t cut (A′, B′) as follows: if v ∈ A \\ B, let vIN, v ∈ A... See full list on proceedings . mlr . press Next, we discuss the notion of a non-overlapping split of an overlapping partition [A, B], formalizing the notion behind the definition of ΨG,μ. Definition 3. Let [A, B] be an overlapping partition of vertex set V. A non-overlapping split (C, C) ̄ of [A, B] is a non-overlapping partition of V such that C ⊆ A and C ̄ ⊆ B. The non-overlapping split... See full list on proceedings . mlr . press qG,λ([A, B]) ≥ α∗ Equality is achieved for any overlapping partition [S, T] corresponding to a non-trivial s-t mincut in Gα∗, yielding the stronger bound: See full list on proceedings . mlr . press Abstract Accelerated algorithms have broad applications in large-scale optimization, due to their generality and fast convergence. However, their stability in the practical setting of noise-corrupted gradient oracles is not well-understood. This paper provides two main technical contributions: (i) a new accelerated method AGDP that generalizes Nesterov’s AGD and improves on the recent method ... Cross-entropy is a widely used loss function in applications. It coincides with the logistic loss applied to the outputs of a neural network, when the softmax is used. But, what guarantees can we r... This last notion of grounding, which we refer here as ”functional grounding”, is relative to a particular environment which may be the human physical environment but also more abstract interactive environments simulated in computers (where abstract physics can differ from human environments). In cross-device Federated Learning (FL), the communication cost of transmitting full-precision models between edge devices and a central server is a significant bottleneck, due to expensive, unreli... The overestimation bias is one of the major impediments to accurate off-policy learning. This paper investigates a novel way to alleviate the overestimation bias in a continuous control setting. Ou...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a/orecchia22a.pdf", "content": "In many graph-clustering applications, over-whelming empirical evidence suggests that com-munities and clusters are naturally overlapping, calling for novel overlapping graph-partitioning algorithms (OGP). In this work, we intro-duce a framework based on two novel cluster-ing objectives, which naturally extend the well-studied notion of conductance... See full list on proceedings . mlr . press weights w and a measure μ over vertices, the ratio-cut ob-jective ΨG over partitions (S, S) ̄ of V is defined as: See full list on proceedings . mlr . press The λ-HCUT problem provides an unconstrained, computa-tionally easier, version of the the ε-ORC problem, where the overlap fraction qV [S, T] is controlled via a penalty term λ·qV [S, T] directly in the objective. For a parameter λ ≥ 0, we define the λ-HCUT objective qG,λ as: def qG,λ[S, T] = qE[S, T] + λ · qV [S, T] See full list on proceedings . mlr . press Here the numerator in the ratio-cut ΨG([A, B]) for an over-lapping partition [A, B] is the worst (maximum) edge-cutset weight over all ways of splitting the overlap A ∩ B between See full list on proceedings . mlr . press We start by proving some simple lemmata about the HybridImprove construction, which is essentially a reduction from the overlapping improvement problem to a family of s − t minimum cut problems on bipartite flow networks Gα. To do this end, we define a subset of s-t cuts in Gα that can be put in bijection with overlapping partitions of G. Definitio... See full list on proceedings . mlr . press OUT B′. Notice that both of these conditions will hold for a vertex in the overlap A ∩ B. By the validity of (A′, B′), we deduce that A ∪ B = V, so that [A, B] is indeed an overlapping partition of s-t. Similarly, for an overlapping partition [A, B], with μ(A) ≤ μ(B) we can construct a valid s-t cut (A′, B′) as follows: if v ∈ A \\ B, let vIN, v ∈ A... See full list on proceedings . mlr . press Next, we discuss the notion of a non-overlapping split of an overlapping partition [A, B], formalizing the notion behind the definition of ΨG,μ. Definition 3. Let [A, B] be an overlapping partition of vertex set V. A non-overlapping split (C, C) ̄ of [A, B] is a non-overlapping partition of V such that C ⊆ A and C ̄ ⊆ B. The non-overlapping split... See full list on proceedings . mlr . press qG,λ([A, B]) ≥ α∗ Equality is achieved for any overlapping partition [S, T] corresponding to a non-trivial s-t mincut in Gα∗, yielding the stronger bound: See full list on proceedings . mlr . press Abstract Accelerated algorithms have broad applications in large-scale optimization, due to their generality and fast convergence. However, their stability in the practical setting of noise-corrupted gradient oracles is not well-understood. This paper provides two main technical contributions: (i) a new accelerated method AGDP that generalizes Nesterov’s AGD and improves on the recent method ... Cross-entropy is a widely used loss function in applications. It coincides with the logistic loss applied to the outputs of a neural network, when the softmax is used. But, what guarantees can we r... This last notion of grounding, which we refer here as ”functional grounding”, is relative to a particular environment which may be the human physical environment but also more abstract interactive environments simulated in computers (where abstract physics can differ from human environments). In cross-device Federated Learning (FL), the communication cost of transmitting full-precision models between edge devices and a central server is a significant bottleneck, due to expensive, unreli... The overestimation bias is one of the major impediments to accurate off-policy learning. This paper investigates a novel way to alleviate the overestimation bias in a continuous control setting. Ou..."}
+{"idx": 1, "title": "Grounding Large Language Models in Interactive Environments ...", "date": "", "ddg_snippet": "This last notion of grounding, which we refer here as ”functional grounding”, is relative to a particular environment which may be the human physical environment but also more abstract interactive environments simulated in computers (where abstract physics can differ from human environments).", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/carta23a/carta23a.pdf", "content": "This last notion of grounding, which we refer here as ”functional grounding”, is relative to a particular environment which may be the human physical environment but also more abstract interactive environments simulated in computers (where abstract physics can differ from human environments)."}
+{"idx": 2, "title": "Practical Almost-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "@InProceedings{pmlr-v162- orecchia22a , title = {Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering}, author = {Orecchia, Lorenzo and Ameranis, Konstantinos and Tsourakakis, Charalampos and Talwar, Kunal}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {17071--17093}, year = {2022}, editor = {Chaudhuri ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a.html", "content": "@InProceedings{pmlr-v162- orecchia22a , title = {Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering}, author = {Orecchia, Lorenzo and Ameranis, Konstantinos and Tsourakakis, Charalampos and Talwar, Kunal}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {17071--17093}, year = {2022}, editor = {Chaudhuri ..."}
+{"idx": 3, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and...", "date": "", "ddg_snippet": "proceedings.mlr.press/v162/ orecchia 22 a .html}, abstract = {Detecting communities in real-world networks and clustering similarity graphs are major data mining tasks with a wide range of applications in graph mining, collaborative filtering, and bioinformatics.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a", "content": "proceedings.mlr.press/v162/ orecchia 22 a .html}, abstract = {Detecting communities in real-world networks and clustering similarity graphs are major data mining tasks with a wide range of applications in graph mining, collaborative filtering, and bioinformatics."}
+{"idx": 4, "title": "Actor-Critic based Improper Reinforcement Learning", "date": "", "ddg_snippet": "each of the base ones. This can be useful in tuning across controllers, learnt possibly in mismatched or simulated environments , to obtain a good controller for a given target environment with relatively few trials.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/zaki22a.html", "content": "each of the base ones. This can be useful in tuning across controllers, learnt possibly in mismatched or simulated environments , to obtain a good controller for a given target environment with relatively few trials."}
+{"idx": 5, "title": "On Acceleration with Noise-Corrupted Gradients", "date": "", "ddg_snippet": "Abstract Accelerated algorithms have broad applications in large-scale optimization, due to their generality and fast convergence. However, their stability in the practical setting of noise-corrupted gradient oracles is not well-understood. This paper provides two main technical contributions: (i) a new accelerated method AGDP that generalizes Nesterov’s AGD and improves on the recent method ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v80/cohen18a", "content": "Abstract Accelerated algorithms have broad applications in large-scale optimization, due to their generality and fast convergence. However, their stability in the practical setting of noise-corrupted gradient oracles is not well-understood. This paper provides two main technical contributions: (i) a new accelerated method AGDP that generalizes Nesterov’s AGD and improves on the recent method ..."}
+{"idx": 6, "title": "Cross-Entropy Loss Functions: Theoretical Analysis and ...", "date": "", "ddg_snippet": "Cross-entropy is a widely used loss function in applications. It coincides with the logistic loss applied to the outputs of a neural network, when the softmax is used. But, what guarantees can we r...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/mao23b.html", "content": "Cross-entropy is a widely used loss function in applications. It coincides with the logistic loss applied to the outputs of a neural network, when the softmax is used. But, what guarantees can we r..."}
+{"idx": 7, "title": "QSFL: A Two-Level Uplink Communication Optimization Framework ...", "date": "", "ddg_snippet": "In cross-device Federated Learning (FL), the communication cost of transmitting full-precision models between edge devices and a central server is a significant bottleneck, due to expensive, unreli...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/yi22a.html", "content": "In cross-device Federated Learning (FL), the communication cost of transmitting full-precision models between edge devices and a central server is a significant bottleneck, due to expensive, unreli..."}
+{"idx": 8, "title": "Controlling Overestimation Bias with Truncated Mixture of ...", "date": "", "ddg_snippet": "The overestimation bias is one of the major impediments to accurate off-policy learning. This paper investigates a novel way to alleviate the overestimation bias in a continuous control setting. Ou...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/kuznetsov20a.html", "content": "The overestimation bias is one of the major impediments to accurate off-policy learning. This paper investigates a novel way to alleviate the overestimation bias in a continuous control setting. Ou..."}
diff --git a/data/sampled_jsons/siteproceedings.mlr.pressv162orecchia22a.html_experiments_computing_environment_year_2022.jsonl b/data/sampled_jsons/siteproceedings.mlr.pressv162orecchia22a.html_experiments_computing_environment_year_2022.jsonl
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index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
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+++ b/data/sampled_jsons/siteproceedings.mlr.pressv162orecchia22a.html_experiments_computing_environment_year_2022.jsonl
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitetsourakakis.com_aott_icml22.pdf_experimental_setup_computing_environment.jsonl b/data/sampled_jsons/sitetsourakakis.com_aott_icml22.pdf_experimental_setup_computing_environment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f1e713f75dc99720c672995c49a01d1f33de1d1
--- /dev/null
+++ b/data/sampled_jsons/sitetsourakakis.com_aott_icml22.pdf_experimental_setup_computing_environment.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "PDF Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Abstract In many graph-clustering applications, over-whelming empirical evidence suggests that com-munities and clusters are naturally overlapping, calling for novel overlapping graph-partitioning algorithms (OGP). In this work, we intro-duce a framework based on two novel cluster-ing objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to ...", "subpage_snippet": "", "source": "tsourakakis.com", "link": "https://tsourakakis.com/wp-content/uploads/2022/06/aott_icml22.pdf", "content": "Abstract In many graph-clustering applications, over-whelming empirical evidence suggests that com-munities and clusters are naturally overlapping, calling for novel overlapping graph-partitioning algorithms (OGP). In this work, we intro-duce a framework based on two novel cluster-ing objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to ..."}
+{"idx": 1, "title": "PDF Centralized, measurement-based, spectrum management for environments ...", "date": "", "ddg_snippet": "In this paper, we focus on overcoming such limitations of prior work in spectrum management systems, and explore spectrum management's potential at reducing interference be-tween heterogeneous networks without the need for N2 coex-istence protocols. In particular, we focus on small to moderate size environments where centralized and measurement-based management is possible, and on ...", "subpage_snippet": "", "source": "tsourakakis.com", "link": "https://tsourakakis.com/wp-content/uploads/2024/01/centralized_measurement-based_spectrum_management_for_environments_with_heterogeneous_wireless_networks.pdf", "content": "In this paper, we focus on overcoming such limitations of prior work in spectrum management systems, and explore spectrum management's potential at reducing interference be-tween heterogeneous networks without the need for N2 coex-istence protocols. In particular, we focus on small to moderate size environments where centralized and measurement-based management is possible, and on ..."}
+{"idx": 2, "title": "Dense subgraph discovery: Theory and Applications (Tutorial SDM 2021)", "date": "", "ddg_snippet": "A computer -science background (B.Sc.\\ or equivalent), and familiarity with undergraduate algorithm design and graph theory are prerequisites. The tutorial will focus on intuition and examples, carefully introducing only the minimal necessary theoretical tools, and focusing on applications.", "subpage_snippet": "", "source": "tsourakakis.com", "link": "https://tsourakakis.com/dense-subgraph-discovery-theory-and-applications-tutorial-sdm-2021/", "content": "A computer -science background (B.Sc.\\ or equivalent), and familiarity with undergraduate algorithm design and graph theory are prerequisites. The tutorial will focus on intuition and examples, carefully introducing only the minimal necessary theoretical tools, and focusing on applications."}
+{"idx": 3, "title": "Wiser than the Wisest or the Stupidity of Herds?", "date": "", "ddg_snippet": "In our setting , we consider a group of n agents, each holding an initial opinion that reflects their core values on a topic, connected through a social network G . Alongside, we are given an integer k . We utilize a generalized Friedkin Johnsen model of opinion dynamics.", "subpage_snippet": "", "source": "tsourakakis.com", "link": "https://tsourakakis.com/2024/09/12/wiser-than-the-wisest-or-the-stupidity-of-herds/", "content": "In our setting , we consider a group of n agents, each holding an initial opinion that reflects their core values on a topic, connected through a social network G . Alongside, we are given an integer k . We utilize a generalized Friedkin Johnsen model of opinion dynamics."}
diff --git "a/data/sampled_jsons/small_\316\261_noise_variance_challenging_estimation_distribution_regression_conditional_deep_generative_mo.jsonl" "b/data/sampled_jsons/small_\316\261_noise_variance_challenging_estimation_distribution_regression_conditional_deep_generative_mo.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..071c26afb20cfa16acf06aa6cc75dc314782cee8
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+{"idx": 0, "title": "A Deep Generative Approach to Conditional Sampling", "date": "", "ddg_snippet": "Dec 10, 2021 · Abstract We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise -outsourcing lemma.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/01621459.2021.2016424", "content": "Dec 10, 2021 · Abstract We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise -outsourcing lemma."}
+{"idx": 1, "title": "PDF A Likelihood Approach to Nonparametric Estimation of a Singular ...", "date": "", "ddg_snippet": "We prove that a novel and e ective solution exists by perturbing the data with an instance noise , which leads to consistent estimation of the underlying distribution with desirable convergence rates. We also characterize the class of distributions that can be e ciently estimated via deep generative models .", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume24/21-1099/21-1099.pdf", "content": "We prove that a novel and e ective solution exists by perturbing the data with an instance noise , which leads to consistent estimation of the underlying distribution with desirable convergence rates. We also characterize the class of distributions that can be e ciently estimated via deep generative models ."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 3, "title": "Deep distribution regression - ScienceDirect", "date": "", "ddg_snippet": "Jul 1, 2021 · In this paper, we leverage the success of machine learning classification models such as neural network to build conditional distribution estimates. Our proposed two-stage framework transforms the conditional distribution estimation problem into a multi-class classification problem, which allows us to use flexible and robust machine learning tools.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167947321000372", "content": "Jul 1, 2021 · In this paper, we leverage the success of machine learning classification models such as neural network to build conditional distribution estimates. Our proposed two-stage framework transforms the conditional distribution estimation problem into a multi-class classification problem, which allows us to use flexible and robust machine learning tools."}
+{"idx": 4, "title": "Regression loss-assisted conditional style generative adversarial ...", "date": "", "ddg_snippet": "Existing methods that extend virtual sample pools to address small sample problem caused by sample atypicality and uneven distribution often overlook data sparsity and inverse sample generation challenges, which limits the accuracy of subsequent modeling. To address above problem, we propose a novel regression -assisted conditional style generative adversarial network (RAC-StyleGAN). The ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197625003069", "content": "Existing methods that extend virtual sample pools to address small sample problem caused by sample atypicality and uneven distribution often overlook data sparsity and inverse sample generation challenges, which limits the accuracy of subsequent modeling. To address above problem, we propose a novel regression -assisted conditional style generative adversarial network (RAC-StyleGAN). The ..."}
+{"idx": 5, "title": "PDF Diffusion Models Chapter 4: Conditional Generation I", "date": "", "ddg_snippet": "Diffusion Models Chapter 4: Conditional Generation I Generative AI and Foundation Models Spring 2024 Department of Mathematical Sciences Ernest K. Ryu Seoul National University", "subpage_snippet": "", "source": "ernestryu.com", "link": "https://ernestryu.com/courses/FM/diffusion4.pdf", "content": "Diffusion Models Chapter 4: Conditional Generation I Generative AI and Foundation Models Spring 2024 Department of Mathematical Sciences Ernest K. Ryu Seoul National University"}
+{"idx": 6, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 7, "title": "Density regression and uncertainty quantification with Bayesian deep ...", "date": "", "ddg_snippet": "Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of applications. However, it remains a challenging task to accurately quantify the uncertainty in DNN predictions, especially those of continuous outcomes. To this end, we propose the Bayesian deep noise neural network (B-DeepNoise), which generalizes standard Bayesian DNNs by extending the ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/sta4.604", "content": "Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of applications. However, it remains a challenging task to accurately quantify the uncertainty in DNN predictions, especially those of continuous outcomes. To this end, we propose the Bayesian deep noise neural network (B-DeepNoise), which generalizes standard Bayesian DNNs by extending the ..."}
+{"idx": 8, "title": "Deep Conditional Generative Learning: Model and Error Analysis", "date": "", "ddg_snippet": "We introduce an Ordinary Differential Equation (ODE) based deep generative method for learning a conditional distribution , named the Conditional Föllmer Flow. Starting from a standard Gaussian distribution , the proposed flow could efficiently transform it into the target conditional distribution at time 1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.01460v1", "content": "We introduce an Ordinary Differential Equation (ODE) based deep generative method for learning a conditional distribution , named the Conditional Föllmer Flow. Starting from a standard Gaussian distribution , the proposed flow could efficiently transform it into the target conditional distribution at time 1."}
+{"idx": 9, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Such advancement has naturally motivated us to employ and investigate conditional deep generative model for conditional distribution estimation . Specifically, we explore and study the theoretical properties of a new likelihood-based approach to conditional sampling using deep generative models for data potentially residing on a low-dimensional ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "Such advancement has naturally motivated us to employ and investigate conditional deep generative model for conditional distribution estimation . Specifically, we explore and study the theoretical properties of a new likelihood-based approach to conditional sampling using deep generative models for data potentially residing on a low-dimensional ..."}
diff --git a/data/sampled_jsons/social_media_studies_climate_action_debate_events_types_natural_disasters_policy_elections.jsonl b/data/sampled_jsons/social_media_studies_climate_action_debate_events_types_natural_disasters_policy_elections.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0ef525d4e7d2727583f694a51876effc0d71e7f4
--- /dev/null
+++ b/data/sampled_jsons/social_media_studies_climate_action_debate_events_types_natural_disasters_policy_elections.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "New Study Examines Influence of Social Media on Televised Debate ...", "date": "", "ddg_snippet": "If the purpose of primary debates is to help viewers differentiate between candidates they would like to see lead their party in a national election , then the answer is that including social media in debates is undermining that process and reducing confidence in their party’s candidate.", "subpage_snippet": "", "source": "news.uark.edu", "link": "https://news.uark.edu/articles/70195/new-study-examines-influence-of-social-media-on-televised-debate-viewing", "content": "If the purpose of primary debates is to help viewers differentiate between candidates they would like to see lead their party in a national election , then the answer is that including social media in debates is undermining that process and reducing confidence in their party’s candidate."}
+{"idx": 1, "title": "Climate Week NYC: Bloomberg Green’s Guide to Events ... - Bloomberg", "date": "", "ddg_snippet": "Climate may be moving down the world’s agenda, but you wouldn’t know it from looking at this year’s Climate Week NYC. The annual confab, which coincides with the United Nations General Assembly, will feature more than 1,000 events across New York next week.", "subpage_snippet": "", "source": "www.bloomberg.com", "link": "https://www.bloomberg.com/news/articles/2025-09-20/climate-week-nyc-bloomberg-green-s-guide-to-events-panels-and-more", "content": "Climate may be moving down the world’s agenda, but you wouldn’t know it from looking at this year’s Climate Week NYC. The annual confab, which coincides with the United Nations General Assembly, will feature more than 1,000 events across New York next week."}
+{"idx": 2, "title": "Examining the role of social media and mobile social networking...", "date": "", "ddg_snippet": "Mobilizing Nigerian youths for active political engagements through social media : Examining the veracity of Facebook and WhatsApp in the 2019 general elections .", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/20570473231168474", "content": "Mobilizing Nigerian youths for active political engagements through social media : Examining the veracity of Facebook and WhatsApp in the 2019 general elections ."}
+{"idx": 3, "title": "SDG 13: Take urgent action to combat climate change and its impacts...", "date": "", "ddg_snippet": "The Congress also proposes to define climate strategies which, alongside mitigation policies , focus on risk assessment and adaptation in order to ensure the protection of citizens, resources and property from the impacts of climate change.", "subpage_snippet": "", "source": "www.coe.int", "link": "https://www.coe.int/en/web/congress/goal-13", "content": "The Congress also proposes to define climate strategies which, alongside mitigation policies , focus on risk assessment and adaptation in order to ensure the protection of citizens, resources and property from the impacts of climate change."}
+{"idx": 4, "title": "Is social media bad for you? The evidence and the unknowns", "date": "", "ddg_snippet": "That said, social media is changing faster than scientists can keep up with, so various groups are trying to study compulsive behaviours related to its use – for example, scientists from the Netherlands have invented their own scale to identify possible addiction.", "subpage_snippet": "", "source": "www.bbc.com", "link": "https://www.bbc.com/future/article/20180104-is-social-media-bad-for-you-the-evidence-and-the-unknowns", "content": "That said, social media is changing faster than scientists can keep up with, so various groups are trying to study compulsive behaviours related to its use – for example, scientists from the Netherlands have invented their own scale to identify possible addiction."}
+{"idx": 5, "title": "Activists march in Manhattan demanding urgent climate action", "date": "", "ddg_snippet": "Artificial Intelligence Social Media . TOP STORIES.20, 2025. The bill was created by Greenpeace and lists the costs of hundreds of climate -related natural disasters .", "subpage_snippet": "", "source": "apnews.com", "link": "https://apnews.com/photo-gallery/new-york-climate-week-protests-activists-photos-989b662b116e156ee9d20140d1eaca9e", "content": "Artificial Intelligence Social Media . TOP STORIES.20, 2025. The bill was created by Greenpeace and lists the costs of hundreds of climate -related natural disasters ."}
+{"idx": 6, "title": "(PDF) Cooling down: Local Responses to Climate Change", "date": "", "ddg_snippet": "Key events , publications, turning points, and policy actions affecting climate change action and adaptation on the Coromandel.“Assessment of Social Vulnerability to Natural Disasters : A Compar- ative Study .”", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/89525568/Cooling_down_Local_Responses_to_Climate_Change", "content": "Key events , publications, turning points, and policy actions affecting climate change action and adaptation on the Coromandel.“Assessment of Social Vulnerability to Natural Disasters : A Compar- ative Study .”"}
+{"idx": 7, "title": "Column: Is social media promoting or stifling debate ? · TheJournal.ie", "date": "", "ddg_snippet": "When we profess to care about a cause on social media , are we helping the discussion or just making ourselves feel better? Stephen Downes discusses whether the information age is killing genuine debate .", "subpage_snippet": "", "source": "www.thejournal.ie", "link": "https://www.thejournal.ie/readme/is-social-media-promoting-or-stifling-debate-1175870-Nov2013/", "content": "When we profess to care about a cause on social media , are we helping the discussion or just making ourselves feel better? Stephen Downes discusses whether the information age is killing genuine debate ."}
+{"idx": 8, "title": "Full text of \"Managing the Risks of Extreme Events and Disasters to...\"...", "date": "", "ddg_snippet": "Weather- and climate -related disasters have social as well as physical dimensions. As a result, changes in the frequency and severity of the physical events affect disaster risk, but so do the spatially diverse and temporally dynamic patterns of exposure and vulnerability.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/stream/fs_cc_Managing_the_Risks_of_Extreme_Events_and_Disasters_to_Advance_Climate_Change_Ada/Managing_the_Risks_of_Extreme_Events_and_Disasters_to_Advance_Climate_Change_Adaptation_djvu.txt", "content": "Weather- and climate -related disasters have social as well as physical dimensions. As a result, changes in the frequency and severity of the physical events affect disaster risk, but so do the spatially diverse and temporally dynamic patterns of exposure and vulnerability."}
+{"idx": 9, "title": "Not all bad: social media also have a positive... - Maastricht University", "date": "", "ddg_snippet": "Verduyn has been studying the relationship between social media and mental health for over a decade. The field has changed a lot in that time. “Early studies focused on the relationship between wellbeing and the amount of time people spent on social media .", "subpage_snippet": "", "source": "www.maastrichtuniversity.nl", "link": "https://www.maastrichtuniversity.nl/news/not-all-bad-social-media-also-have-positive-impact-mental-health", "content": "Verduyn has been studying the relationship between social media and mental health for over a decade. The field has changed a lot in that time. “Early studies focused on the relationship between wellbeing and the amount of time people spent on social media ."}
diff --git a/data/sampled_jsons/spurious_correlation_Schubert_polynomials_program_synthesis_sitearxiv.org.jsonl b/data/sampled_jsons/spurious_correlation_Schubert_polynomials_program_synthesis_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ac554d40fb25ef38f8f51bc2a9d2b123052bba1
--- /dev/null
+++ b/data/sampled_jsons/spurious_correlation_Schubert_polynomials_program_synthesis_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Navigating Shortcuts, Spurious Correlations, and Confounders ...", "date": "", "ddg_snippet": "Abstract Shortcuts, also described as Clever Hans behavior, spurious correlations, or confounders, present a significant challenge in machine learning and AI, critically affecting model generalization and robustness. Research in this area, however, remains fragmented across various terminologies, hindering the progress of the field as a whole. Consequently, we introduce a unifying taxonomy of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.05152v1", "content": "Abstract Shortcuts, also described as Clever Hans behavior, spurious correlations, or confounders, present a significant challenge in machine learning and AI, critically affecting model generalization and robustness. Research in this area, however, remains fragmented across various terminologies, hindering the progress of the field as a whole. Consequently, we introduce a unifying taxonomy of ..."}
+{"idx": 1, "title": "Spurious Correlations and Where to Find Them - arXiv.org Out of spuriousity: Improving robustness to spurious ... [2308.11043] Spurious Correlations and Where to Find Them - ar5iv Assessing Robustness to Spurious Correlations in Post ... Towards Mitigating more Challenging Spurious Correlations: A ... Spurious Correlations and Where to Find Them - arXiv . org Out of spuriousity: Improving robustness to spurious correlations Spurious Correlations and Where to Find Them - arXiv . org Spurious Correlations and Where to Find Them - arXiv . org Spurious Correlations and Where to Find Them - arXiv . org Spurious Correlations and Where to Find Them - arXiv . org Spurious Correlations in High Dimensional Regression: The ...", "date": "", "ddg_snippet": "Spurious correlations occur when a model learns unreliable features from the data and are a well- known drawback of data-driven learning. Al- though there are several algorithms proposed to mitigate it, we are yet to jointly derive the indica- tors of spurious correlations. As a result, the solu- tions built upon standalone hypotheses fail to beat simple ERM baselines. We collect some of the ... Abstract Machine learning models are known to learn spurious correlations, i.e., features having strong relations with class labels but no causal relation. Relying on those correlations leads to poor performance in the data groups without these correlations and poor generalization ability. To improve the robustness of machine learning models to spurious correlations, we propose an approach to ... Several factors have been proposed as indicators of spurious correlations. These include overparameterization, partial predictiveness of invariant features, and the amount of data from different environments. However, existing studies about the occurrence of spurious correlation limit their scope to one or a few of these factors. For example, Sagawa et al. (2020) study the effect of ... In this paper, we systematically investigate how different post-training approaches handle data contaminated by spurious correlations. We create a suite of controlled synthetic training sets with varied tasks, spuriousness levels, and correlation types. Specifically, we explore: mathematical reasoning tasks (Cobbe et al., 2021), constrained instruction-following tasks inspired by the CoLLIE ... In this work, we systematically investigate SOTA algorithms for improving worst-group test accuracy due to spurious correlations in vision datasets. First, we introduce 3 more challenging image classification tasks with more complex spurious features. Then, we establish a comprehensive benchmark of 8 methods addressing poor worst-group accuracy due to spurious correlations and evaluate them on ... What is a spurious correlation? Background Spurious correlations occur when a model learns correla- tions from the observed data that do not hold under natural distribution shifts1. A robust model is expected to use only invariant features that are reliable during testing. Thus, ob- served data Xis assumed to consist of invariant (or core) Does machine learning rely on spurious correlations? Relying on those correlations leads to poor performance in the data groups without these correlations and poor generalization ability. To improve the robustness of machine learning models to spurious correlations, we propose an approach to extract a subnetwork from a fully trained network that does not rely on spurious correlations . Are spurious correlations a product of the dataset? Our findings indicate that spurious correlations are a product of the dataset , the model, and the training scheme. There- fore, future algorithms that are proposed to mitigate spurious correlations must evaluate using models with different ca- pacities with varying proportions of interventional points. Can a causal graph be used to model a spurious correlation? Causal interpretation of spurious correlation Spurious correlations are often due to confounders and un- observed correlated variables, and not due to true causal relations. As a result, they are affected by natural distribu- tion shifts. Therefore, causal graphs are a suitable choice to model the occurrence of spurious correlations. How can we mitigate spurious correlations? There- fore, future algorithms that are proposed to mitigate spurious correlations must evaluate using models with different ca- pacities with varying proportions of interventional points . In addition to drop in test accuracy, independence relations between known causal variables can provide a deeper un- derstanding of the algorithms. What causes spurious features xinvand XSP? features Xinvand spurious features Xsp. In practice, spuri- ous features could occur due to biases during data-collection or measurement errors (Fan et al.,2014). Several hypothe- ses exist about the origin of spurious correlations from a learning perspective. Learning models have been shown to rely on spurious correlations between non-predictive features and the associated labels in the training data, with negative implications on robustness, bias and fairness. In this work, we provide a statistical characterization of this phenomenon for high-dimensional regression, when the data contains a predictive corefeature x𝑥xitalic_xand a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2308.11043", "content": "Spurious correlations occur when a model learns unreliable features from the data and are a well- known drawback of data-driven learning. Al- though there are several algorithms proposed to mitigate it, we are yet to jointly derive the indica- tors of spurious correlations. As a result, the solu- tions built upon standalone hypotheses fail to beat simple ERM baselines. We collect some of the ... Abstract Machine learning models are known to learn spurious correlations, i.e., features having strong relations with class labels but no causal relation. Relying on those correlations leads to poor performance in the data groups without these correlations and poor generalization ability. To improve the robustness of machine learning models to spurious correlations, we propose an approach to ... Several factors have been proposed as indicators of spurious correlations. These include overparameterization, partial predictiveness of invariant features, and the amount of data from different environments. However, existing studies about the occurrence of spurious correlation limit their scope to one or a few of these factors. For example, Sagawa et al. (2020) study the effect of ... In this paper, we systematically investigate how different post-training approaches handle data contaminated by spurious correlations. We create a suite of controlled synthetic training sets with varied tasks, spuriousness levels, and correlation types. Specifically, we explore: mathematical reasoning tasks (Cobbe et al., 2021), constrained instruction-following tasks inspired by the CoLLIE ... In this work, we systematically investigate SOTA algorithms for improving worst-group test accuracy due to spurious correlations in vision datasets. First, we introduce 3 more challenging image classification tasks with more complex spurious features. Then, we establish a comprehensive benchmark of 8 methods addressing poor worst-group accuracy due to spurious correlations and evaluate them on ... What is a spurious correlation? Background Spurious correlations occur when a model learns correla- tions from the observed data that do not hold under natural distribution shifts1. A robust model is expected to use only invariant features that are reliable during testing. Thus, ob- served data Xis assumed to consist of invariant (or core) Does machine learning rely on spurious correlations? Relying on those correlations leads to poor performance in the data groups without these correlations and poor generalization ability. To improve the robustness of machine learning models to spurious correlations, we propose an approach to extract a subnetwork from a fully trained network that does not rely on spurious correlations . Are spurious correlations a product of the dataset? Our findings indicate that spurious correlations are a product of the dataset , the model, and the training scheme. There- fore, future algorithms that are proposed to mitigate spurious correlations must evaluate using models with different ca- pacities with varying proportions of interventional points. Can a causal graph be used to model a spurious correlation? Causal interpretation of spurious correlation Spurious correlations are often due to confounders and un- observed correlated variables, and not due to true causal relations. As a result, they are affected by natural distribu- tion shifts. Therefore, causal graphs are a suitable choice to model the occurrence of spurious correlations. How can we mitigate spurious correlations? There- fore, future algorithms that are proposed to mitigate spurious correlations must evaluate using models with different ca- pacities with varying proportions of interventional points . In addition to drop in test accuracy, independence relations between known causal variables can provide a deeper un- derstanding of the algorithms. What causes spurious features xinvand XSP? features Xinvand spurious features Xsp. In practice, spuri- ous features could occur due to biases during data-collection or measurement errors (Fan et al.,2014). Several hypothe- ses exist about the origin of spurious correlations from a learning perspective. Learning models have been shown to rely on spurious correlations between non-predictive features and the associated labels in the training data, with negative implications on robustness, bias and fairness. In this work, we provide a statistical characterization of this phenomenon for high-dimensional regression, when the data contains a predictive corefeature x𝑥xitalic_xand a ..."}
+{"idx": 2, "title": "Out of spuriousity: Improving robustness to spurious ...", "date": "", "ddg_snippet": "Abstract Machine learning models are known to learn spurious correlations, i.e., features having strong relations with class labels but no causal relation. Relying on those correlations leads to poor performance in the data groups without these correlations and poor generalization ability. To improve the robustness of machine learning models to spurious correlations, we propose an approach to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.14974v1", "content": "Abstract Machine learning models are known to learn spurious correlations, i.e., features having strong relations with class labels but no causal relation. Relying on those correlations leads to poor performance in the data groups without these correlations and poor generalization ability. To improve the robustness of machine learning models to spurious correlations, we propose an approach to ..."}
+{"idx": 3, "title": "[2308.11043] Spurious Correlations and Where to Find Them - ar5iv", "date": "", "ddg_snippet": "Several factors have been proposed as indicators of spurious correlations. These include overparameterization, partial predictiveness of invariant features, and the amount of data from different environments. However, existing studies about the occurrence of spurious correlation limit their scope to one or a few of these factors. For example, Sagawa et al. (2020) study the effect of ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2308.11043", "content": "Several factors have been proposed as indicators of spurious correlations. These include overparameterization, partial predictiveness of invariant features, and the amount of data from different environments. However, existing studies about the occurrence of spurious correlation limit their scope to one or a few of these factors. For example, Sagawa et al. (2020) study the effect of ..."}
+{"idx": 4, "title": "Assessing Robustness to Spurious Correlations in Post ...", "date": "", "ddg_snippet": "In this paper, we systematically investigate how different post-training approaches handle data contaminated by spurious correlations. We create a suite of controlled synthetic training sets with varied tasks, spuriousness levels, and correlation types. Specifically, we explore: mathematical reasoning tasks (Cobbe et al., 2021), constrained instruction-following tasks inspired by the CoLLIE ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.05704v1", "content": "In this paper, we systematically investigate how different post-training approaches handle data contaminated by spurious correlations. We create a suite of controlled synthetic training sets with varied tasks, spuriousness levels, and correlation types. Specifically, we explore: mathematical reasoning tasks (Cobbe et al., 2021), constrained instruction-following tasks inspired by the CoLLIE ..."}
+{"idx": 5, "title": "Towards Mitigating more Challenging Spurious Correlations: A ...", "date": "", "ddg_snippet": "In this work, we systematically investigate SOTA algorithms for improving worst-group test accuracy due to spurious correlations in vision datasets. First, we introduce 3 more challenging image classification tasks with more complex spurious features. Then, we establish a comprehensive benchmark of 8 methods addressing poor worst-group accuracy due to spurious correlations and evaluate them on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.11957v4", "content": "In this work, we systematically investigate SOTA algorithms for improving worst-group test accuracy due to spurious correlations in vision datasets. First, we introduce 3 more challenging image classification tasks with more complex spurious features. Then, we establish a comprehensive benchmark of 8 methods addressing poor worst-group accuracy due to spurious correlations and evaluate them on ..."}
+{"idx": 6, "title": "Spurious Correlations in High Dimensional Regression: The ...", "date": "", "ddg_snippet": "Learning models have been shown to rely on spurious correlations between non-predictive features and the associated labels in the training data, with negative implications on robustness, bias and fairness. In this work, we provide a statistical characterization of this phenomenon for high-dimensional regression, when the data contains a predictive corefeature x𝑥xitalic_xand a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01347v2", "content": "Learning models have been shown to rely on spurious correlations between non-predictive features and the associated labels in the training data, with negative implications on robustness, bias and fairness. In this work, we provide a statistical characterization of this phenomenon for high-dimensional regression, when the data contains a predictive corefeature x𝑥xitalic_xand a ..."}
+{"idx": 7, "title": "Machine Learning meets Algebraic Combinatorics", "date": "", "ddg_snippet": "8 Mar 2025 — ... spurious correlation in the dataset 2 2 2This issue has since been fixed. ... program synthesis . Hyperparameters for these experiments can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "8 Mar 2025 — ... spurious correlation in the dataset 2 2 2This issue has since been fixed. ... program synthesis . Hyperparameters for these experiments can ..."}
+{"idx": 8, "title": "Machine Learning meets Algebraic Combinatorics", "date": "", "ddg_snippet": "by H Chau · 2025 · Cited by 3 — Program Synthesis for Schubert Polynomials : The pre- vious ... (n = 4, 5), this process introduced a spurious correlation in the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "by H Chau · 2025 · Cited by 3 — Program Synthesis for Schubert Polynomials : The pre- vious ... (n = 4, 5), this process introduced a spurious correlation in the ..."}
+{"idx": 9, "title": "Causal Machine Learning: A Survey and Open Problems", "date": "", "ddg_snippet": "by J Kaddour · 2022 · Cited by 260 — In practice, this bilevel program is highly non-convex and difficult to solve. ... data as they are sensitive to spurious correlation . Wu et al. [ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2206.15475", "content": "by J Kaddour · 2022 · Cited by 260 — In practice, this bilevel program is highly non-convex and difficult to solve. ... data as they are sensitive to spurious correlation . Wu et al. [ ..."}
diff --git a/data/sampled_jsons/sqlmap_definition_what_is_sqlmap_tool.jsonl b/data/sampled_jsons/sqlmap_definition_what_is_sqlmap_tool.jsonl
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index 0000000000000000000000000000000000000000..f8571171e439e8940f7e93f91ba968939368d02f
--- /dev/null
+++ b/data/sampled_jsons/sqlmap_definition_what_is_sqlmap_tool.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "sqlmap: automatic SQL injection and database takeover tool", "date": "", "ddg_snippet": "Introduction sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database servers. It comes with a powerful detection engine, many niche features for the ultimate penetration tester and a broad range of switches lasting from database fingerprinting, over data fetching from the database, to accessing the ...", "subpage_snippet": "", "source": "sqlmap.org", "link": "https://sqlmap.org/", "content": "Introduction sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database servers. It comes with a powerful detection engine, many niche features for the ultimate penetration tester and a broad range of switches lasting from database fingerprinting, over data fetching from the database, to accessing the ..."}
+{"idx": 1, "title": "What is SQLMap? Everything You Need To Know About SQLMap - Cyberbugs", "date": "", "ddg_snippet": "What is SQLMap ? A penetration testing tool called sqlmap is an open source tool for detecting and exploiting SQL injection flaws, or taking over databases by automated means. The tool contains all the features a penetration tester will need, including a powerful detection engine, a lot of niche features, and a wide range of switches from database fingerprinting to data extraction from ...", "subpage_snippet": "", "source": "www.cyberbugs.in", "link": "https://www.cyberbugs.in/post/what-is-sqlmap", "content": "What is SQLMap ? A penetration testing tool called sqlmap is an open source tool for detecting and exploiting SQL injection flaws, or taking over databases by automated means. The tool contains all the features a penetration tester will need, including a powerful detection engine, a lot of niche features, and a wide range of switches from database fingerprinting to data extraction from ..."}
+{"idx": 2, "title": "sqlmap Tutorial: Features, Use Cases, How It Works | Wiz", "date": "", "ddg_snippet": "TL;DR, What is sqlmap ? sqlmap is a powerful, open-source penetration testing tool written in Python that automatically detects and exploits SQL injection vulnerabilities in web applications. Security professionals and penetration testers face the time-consuming challenge of manually identifying SQL injection flaws across different database systems. sqlmap solves the problem by providing ...", "subpage_snippet": "", "source": "www.wiz.io", "link": "https://www.wiz.io/academy/sqlmap-overview", "content": "TL;DR, What is sqlmap ? sqlmap is a powerful, open-source penetration testing tool written in Python that automatically detects and exploits SQL injection vulnerabilities in web applications. Security professionals and penetration testers face the time-consuming challenge of manually identifying SQL injection flaws across different database systems. sqlmap solves the problem by providing ..."}
+{"idx": 3, "title": "Explanation of what is SQLmap and its commands - Secrash", "date": "", "ddg_snippet": "SQLMAP is a widely used open-source penetration testing tool designed to automate the process of detecting and exploiting SQL injection vulnerabilities in web applications. In this article, we will explore the key aspects of SQLMAP , including its purpose, features, command examples, and the API it offers.", "subpage_snippet": "", "source": "www.secrash.com", "link": "https://www.secrash.com/2023/07/explanation-of-what-is-sqlmap-and-its.html", "content": "SQLMAP is a widely used open-source penetration testing tool designed to automate the process of detecting and exploiting SQL injection vulnerabilities in web applications. In this article, we will explore the key aspects of SQLMAP , including its purpose, features, command examples, and the API it offers."}
+{"idx": 4, "title": "Sqlmap, the Tool for Detecting and Exploiting SQL Injections", "date": "", "ddg_snippet": "Sqlmap is an essential tool for detecting and exploiting all types of SQL injections (SQLi). This article explains how Sqlmap works and its key features.", "subpage_snippet": "", "source": "www.vaadata.com", "link": "https://www.vaadata.com/blog/sqlmap-the-tool-for-detecting-and-exploiting-sql-injections/", "content": "Sqlmap is an essential tool for detecting and exploiting all types of SQL injections (SQLi). This article explains how Sqlmap works and its key features."}
+{"idx": 5, "title": "What Is Sqlmap? - Cyberly", "date": "", "ddg_snippet": "The Role of SQLmap in Penetration Testing SQLmap is a tool specifically designed for identifying and exploiting SQL injection vulnerabilities in web application s. It automates many aspects of SQL injection testing, making it easier for security professional s to perform comprehensive security assessments.", "subpage_snippet": "", "source": "www.cyberly.org", "link": "https://www.cyberly.org/en/what-is-sqlmap/index.html", "content": "The Role of SQLmap in Penetration Testing SQLmap is a tool specifically designed for identifying and exploiting SQL injection vulnerabilities in web application s. It automates many aspects of SQL injection testing, making it easier for security professional s to perform comprehensive security assessments."}
+{"idx": 6, "title": "GitHub - sqlmapproject/sqlmap: Automatic SQL injection and database ...", "date": "", "ddg_snippet": "sqlmap sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database servers.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sqlmapproject/sqlmap", "content": "sqlmap sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database servers."}
+{"idx": 7, "title": "How does SQLMAP work? - Sencode", "date": "", "ddg_snippet": "SQLMAP Definition : SQLMAP is an open-source penetration testing tool that automates the process of detecting and exploiting SQL injection vulnerabilities in databases. It provides a powerful testing environment to extract database information, compromise the underlying server, and even access the file system on the database server.", "subpage_snippet": "", "source": "sencode.co.uk", "link": "https://sencode.co.uk/glossary/sqlmap/", "content": "SQLMAP Definition : SQLMAP is an open-source penetration testing tool that automates the process of detecting and exploiting SQL injection vulnerabilities in databases. It provides a powerful testing environment to extract database information, compromise the underlying server, and even access the file system on the database server."}
+{"idx": 8, "title": "What is SQLMap? | DevTerms", "date": "", "ddg_snippet": "SQLMap is a popular open-source penetration testing tool used to automate the detection and exploitation of SQL injection vulnerabilities in web applications. It helps assess the security of databases by identifying and exploiting SQL injection flaws.", "subpage_snippet": "", "source": "devterms.io", "link": "https://devterms.io/define/sqlmap", "content": "SQLMap is a popular open-source penetration testing tool used to automate the detection and exploitation of SQL injection vulnerabilities in web applications. It helps assess the security of databases by identifying and exploiting SQL injection flaws."}
+{"idx": 9, "title": "Using SQLMap: A Complete Guide To SQL Injection Testing", "date": "", "ddg_snippet": "Master SQLMap for SQL injection testing and securing databases with this step-by-step guide covering installation, use cases, and solutions.", "subpage_snippet": "", "source": "cyberskillshub.com", "link": "https://cyberskillshub.com/en/sqlmap-guide-sql-injection-database-security/", "content": "Master SQLMap for SQL injection testing and securing databases with this step-by-step guide covering installation, use cases, and solutions."}
diff --git a/data/sampled_jsons/stochastic_mixing_function_causal_representation_learning_2024.jsonl b/data/sampled_jsons/stochastic_mixing_function_causal_representation_learning_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5201eb41d3e67ac62d6038c285c1728de1d39a59
--- /dev/null
+++ b/data/sampled_jsons/stochastic_mixing_function_causal_representation_learning_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Sparsity Principle for Partially Observable Causal Representation ...", "date": "", "ddg_snippet": "Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent causal variables are captured in the high-dimensional observations.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/xu24ac.html", "content": "Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent causal variables are captured in the high-dimensional observations."}
+{"idx": 1, "title": "NeurIPS Poster Learning Linear Causal Representations from...", "date": "", "ddg_snippet": "We study causal representation learning , the task of recovering high-level latent variables and their causal relationships in the form of a causal graph from low-level observed data (such as text and images), assuming access to observations generated from multiple environments.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/94358", "content": "We study causal representation learning , the task of recovering high-level latent variables and their causal relationships in the form of a causal graph from low-level observed data (such as text and images), assuming access to observations generated from multiple environments."}
+{"idx": 2, "title": "Causal Representation Learning Made Identifiable by Grouping", "date": "", "ddg_snippet": "Causal Representation Learning Made Identiable by Grouping of Observational Variables. (or causal strengths to) neighbors (columns), for both causal directions (upper and lower halves), as expressed by the full row-rank condition.", "subpage_snippet": "", "source": "helda.helsinki.fi", "link": "https://helda.helsinki.fi/server/api/core/bitstreams/b0c84266-835c-49d0-bc45-fdf94b494e04/content", "content": "Causal Representation Learning Made Identiable by Grouping of Observational Variables. (or causal strengths to) neighbors (columns), for both causal directions (upper and lower halves), as expressed by the full row-rank condition."}
+{"idx": 3, "title": "Towards Causal Representation Learning with Observable Sources...", "date": "", "ddg_snippet": "Causal representation learning seeks to recover latent factors that generate observational data through a mixing function .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.19058", "content": "Causal representation learning seeks to recover latent factors that generate observational data through a mixing function ."}
+{"idx": 4, "title": "Learning Linear Causal Representations from Interventions under...", "date": "", "ddg_snippet": "(DOI: 10.48550/arXiv.2306.02235) We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/learning-linear-causal-representations-from-interventions-2nxdrzw6", "content": "(DOI: 10.48550/arXiv.2306.02235) We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general."}
+{"idx": 5, "title": "GitHub - py-why/ causal - learn : Causal Discovery in Python.", "date": "", "ddg_snippet": "Causal discovery methods based on constrained functional causal models. Hidden causal representation learning .}, year={ 2024 } }. About. Causal Discovery in Python. It also includes (conditional) independence tests and score functions .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/py-why/causal-learn", "content": "Causal discovery methods based on constrained functional causal models. Hidden causal representation learning .}, year={ 2024 } }. About. Causal Discovery in Python. It also includes (conditional) independence tests and score functions ."}
+{"idx": 6, "title": "Sanity Checking Causal Representation Learning on a Simp...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.In International Conference on Learning Representations , 2024 c.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44652/paper", "content": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.In International Conference on Learning Representations , 2024 c."}
+{"idx": 7, "title": "Achieving Causal Disentanglement from Purely... - MarkTechPost", "date": "", "ddg_snippet": "- November 12, 2024 . Causal disentanglement is a critical field in machine learning that focuses on isolating latent causal factors from complex datasets, especially in scenarios where direct intervention is not feasible.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/11/12/achieving-causal-disentanglement-from-purely-observational-data-without-interventions/", "content": "- November 12, 2024 . Causal disentanglement is a critical field in machine learning that focuses on isolating latent causal factors from complex datasets, especially in scenarios where direct intervention is not feasible."}
+{"idx": 8, "title": "(PDF) Causal Representation Learning from Multimodal Biological...", "date": "", "ddg_snippet": "Preprint. Causal representation learning from multi-. Modal biological observations. Causal representation learning made identifiable by grouping. of observational variables. In Forty-first International Conference on Machine Learning , 2024 .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385720998_Causal_Representation_Learning_from_Multimodal_Biological_Observations", "content": "Preprint. Causal representation learning from multi-. Modal biological observations. Causal representation learning made identifiable by grouping. of observational variables. In Forty-first International Conference on Machine Learning , 2024 ."}
+{"idx": 9, "title": "Multi-View Causal Representation Learning with Partial Observability...", "date": "", "ddg_snippet": "Unified Framework for Multi-View Causal Representation Learning with Partial Observability.The author presents a unified framework for studying the identifiability of representations learned from multiple views, allowing for partial observability and nonlinear mixing functions .", "subpage_snippet": "", "source": "linnk.ai", "link": "https://linnk.ai/no/insight/machine-learning/unified-framework-for-multi-view-causal-representation-learning-with-partial-observability-ng7xYS9D/", "content": "Unified Framework for Multi-View Causal Representation Learning with Partial Observability.The author presents a unified framework for studying the identifiability of representations learned from multiple views, allowing for partial observability and nonlinear mixing functions ."}
diff --git a/data/sampled_jsons/subreddit_participation_activation_sympathy_causal_pathway_climate_activism_mechanism_year_2024.jsonl b/data/sampled_jsons/subreddit_participation_activation_sympathy_causal_pathway_climate_activism_mechanism_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f98c13fb93039a10948f71d56bad819b52c75a98
--- /dev/null
+++ b/data/sampled_jsons/subreddit_participation_activation_sympathy_causal_pathway_climate_activism_mechanism_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Reality vs Social Reality — LessWrong", "date": "", "ddg_snippet": "The causal pathway between my friend s work and defeating aging is clear: if the company succeeds at building their water-flea camera rig drug ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/xqAnKW46FqzPLnGmH/causal-reality-vs-social-reality", "content": "The causal pathway between my friend s work and defeating aging is clear: if the company succeeds at building their water-flea camera rig drug ..."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "D Sympathy . E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit.We contribute to this objective by applying a Bayesian causal approach to analyze the pathways leading people to engage with climate activism on Reddit.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "D Sympathy . E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit.We contribute to this objective by applying a Bayesian causal approach to analyze the pathways leading people to engage with climate activism on Reddit."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — Figure 3b shows that the coefficient of sympathy to activation (i.e. ... pathway towards activation for all considered models (Figure 3b).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — Figure 3b shows that the coefficient of sympathy to activation (i.e. ... pathway towards activation for all considered models (Figure 3b)."}
+{"idx": 3, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — That is, given two users with the same level of sympathy who participate in similar subreddits, if one interacts with an activist , the odds of activation ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — That is, given two users with the same level of sympathy who participate in similar subreddits, if one interacts with an activist , the odds of activation ..."}
+{"idx": 4, "title": "A Systematic Review of Echo Chamber Research: Comparative", "date": "", "ddg_snippet": "Future research should prioritize cross-platform studies, continuous algorithmic audits, and investigations into the causal links between ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06631v3", "content": "Future research should prioritize cross-platform studies, continuous algorithmic audits, and investigations into the causal links between ..."}
+{"idx": 5, "title": "Positive Psychology & Sustainable Behavior: The Effect of ...", "date": "", "ddg_snippet": "Besides, while we examined the causal relationship of perceived social support and underlying mechanism in our research, we did not consider the potential moderators like egoistic values, altruistic values, socio-economic status, monthly income, cultural background, self-esteem, and materialism.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/21582440251362283?int.sj-abstract.similar-articles.3", "content": "Besides, while we examined the causal relationship of perceived social support and underlying mechanism in our research, we did not consider the potential moderators like egoistic values, altruistic values, socio-economic status, monthly income, cultural background, self-esteem, and materialism."}
+{"idx": 6, "title": "Handbook of Religion and Spirituality in Social Work Practice ...", "date": "", "ddg_snippet": "Social workers engaged in community organization may find the explo-ration of religious traditions of altruism, community service, and activism inChap. 16 and of community worship and ritual in Chap. 17 particularly relevant totheir work.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/915551506/Handbook-of-Religion-and-Spirituality-in-Social-Work-Practice-and-Research-Sana-Loue-auth-Z-Library-1", "content": "Social workers engaged in community organization may find the explo-ration of religious traditions of altruism, community service, and activism inChap. 16 and of community worship and ritual in Chap. 17 particularly relevant totheir work."}
+{"idx": 7, "title": "Frederick Turner – Frederick Turner’s Blog", "date": "", "ddg_snippet": "Mack, who had memorized the specs, found the lander’s airlock at once, overrode the still-functioning mechanism , and let us in. Here the radiation level diminished significantly, because the bulk of the ascent stage and the iceberg partially protected us.", "subpage_snippet": "", "source": "frederickturnerpoet.com", "link": "https://frederickturnerpoet.com/?author=1", "content": "Mack, who had memorized the specs, found the lander’s airlock at once, overrode the still-functioning mechanism , and let us in. Here the radiation level diminished significantly, because the bulk of the ascent stage and the iceberg partially protected us."}
+{"idx": 8, "title": "OPEN DISCUSSION NOVEMBER 2024 | Richard Dawkins Foundation ...", "date": "", "ddg_snippet": "Join the November 2024 open discussion on Richard Dawkins' platform for engaging conversations about science, reason, and thought-provoking topics.", "subpage_snippet": "", "source": "richarddawkins.net", "link": "https://richarddawkins.net/2024/11/open-discussion-november-2024/", "content": "Join the November 2024 open discussion on Richard Dawkins' platform for engaging conversations about science, reason, and thought-provoking topics."}
+{"idx": 9, "title": "Reorientation of Teacher Education Towards Sustainability ...", "date": "", "ddg_snippet": "The document presents proceedings from the 10th international JTEFS/BBCC conference focusing on the reorientation of teacher education towards sustainability through theory and practice. Key topics include sustainable early childhood education, systemic research methodology, and teacher education for inclusion, reflecting ten years of promoting sustainability in education. It features 31 peer ...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/xwc99/39048789", "content": "The document presents proceedings from the 10th international JTEFS/BBCC conference focusing on the reorientation of teacher education towards sustainability through theory and practice. Key topics include sustainable early childhood education, systemic research methodology, and teacher education for inclusion, reflecting ten years of promoting sustainability in education. It features 31 peer ..."}
diff --git a/data/sampled_jsons/symmetric_reinforcement_learning_loss_PPO_instability_reverse_cross-entropy.jsonl b/data/sampled_jsons/symmetric_reinforcement_learning_loss_PPO_instability_reverse_cross-entropy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7bbd9ef24a65c7aaef7520f1ff889e95f9c917a0
--- /dev/null
+++ b/data/sampled_jsons/symmetric_reinforcement_learning_loss_PPO_instability_reverse_cross-entropy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust", "date": "", "ddg_snippet": "Symmetric cross entropy . Approach. Reverse reinforcement learning loss .The Symmetric Reinforcement Learning (SRL) loss Lsrl consists of two parts like SCE (Equation 6): the original RL loss Lrl (A2C or PPO ) and the corresponding reverse RL loss Lrev (RA2C or RPPO).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618", "content": "Symmetric cross entropy . Approach. Reverse reinforcement learning loss .The Symmetric Reinforcement Learning (SRL) loss Lsrl consists of two parts like SCE (Equation 6): the original RL loss Lrl (A2C or PPO ) and the corresponding reverse RL loss Lrev (RA2C or RPPO)."}
+{"idx": 1, "title": "ICML Poster Symmetric Reinforcement Learning Loss for Robust...", "date": "", "ddg_snippet": "To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44897", "content": "To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 2, "title": "Navigating the RLHF Landscape: From Policy Gradients to PPO , GAE...", "date": "", "ddg_snippet": "1. On-Policy vs. Off-Policy Reinforcement Learning . Today, the mainstream RLHF approaches in LLMs can be broadly categorized into two camps: On-Policy Methods (exemplified by PPO ).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/NormalUhr/rlhf-pipeline", "content": "1. On-Policy vs. Off-Policy Reinforcement Learning . Today, the mainstream RLHF approaches in LLMs can be broadly categorized into two camps: On-Policy Methods (exemplified by PPO )."}
+{"idx": 3, "title": "GitHub - shashacks/ Symmetric _RL", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales.To run A2C and PPO for Atari games.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_RL", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales.To run A2C and PPO for Atari games."}
+{"idx": 4, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on...", "date": "", "ddg_snippet": "The Symmetric Reinforcement Learning (SRL) Loss presented in this paper offers a promising new approach to improving the robustness and performance of reinforcement learning models.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/symmetric-reinforcement-learning-loss-robust-learning-diverse", "content": "The Symmetric Reinforcement Learning (SRL) Loss presented in this paper offers a promising new approach to improving the robustness and performance of reinforcement learning models."}
+{"idx": 5, "title": "machinelearningmastery.com/ cross - entropy -for-machine- learning", "date": "", "ddg_snippet": "Cross - entropy is a measure from the field of… machinelearningmastery.com.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/cross-entropy-for-machine-learning/", "content": "Cross - entropy is a measure from the field of… machinelearningmastery.com."}
+{"idx": 6, "title": "Learn Python programming, AI, and machine learning with free...", "date": "", "ddg_snippet": "In 2018 OpenAI made a breakthrough in Deep Reinforcement Learning , this was possible only because of solid hardware architecture and using the state of the art's algorithm: Proximal Policy Optimization.", "subpage_snippet": "", "source": "pylessons.com", "link": "https://pylessons.com/PPO-reinforcement-learning", "content": "In 2018 OpenAI made a breakthrough in Deep Reinforcement Learning , this was possible only because of solid hardware architecture and using the state of the art's algorithm: Proximal Policy Optimization."}
+{"idx": 7, "title": "Proximal Policy Optimization Tutorial (Part 2/2: GAE and PPO loss )", "date": "", "ddg_snippet": "Custom PPO loss calculation. PPO uses a ratio between the newly updated policy and old policy in the update step. Computationally, it is easier to represent this in the log form.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/deepgamingai/proximal-policy-optimization-tutorial-part-2-2-gae-and-ppo-loss-22337981f815", "content": "Custom PPO loss calculation. PPO uses a ratio between the newly updated policy and old policy in the update step. Computationally, it is easier to represent this in the log form."}
+{"idx": 8, "title": "PyTorch Loss Functions: The Ultimate Guide", "date": "", "ddg_snippet": "Cross - Entropy Loss . Hinge Embedding Loss . Margin Ranking Loss .Other loss functions, like the squared loss , punish incorrect predictions. Cross - Entropy penalizes greatly for being very confident and wrong.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/pytorch-loss-functions", "content": "Cross - Entropy Loss . Hinge Embedding Loss . Margin Ranking Loss .Other loss functions, like the squared loss , punish incorrect predictions. Cross - Entropy penalizes greatly for being very confident and wrong."}
+{"idx": 9, "title": "machine learning - RLlib PPO continuous actions... - Stack Overflow", "date": "", "ddg_snippet": "The Entropy Loss [mainly there to encourage exploration]. Total Loss = PPO Gradient objective (clipped) - vf_ loss _coeff * VF Loss + entropy _coeff * entropy . reinforcement - learning .", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/68249391/rllib-ppo-continuous-actions-seem-to-become-nan-after-total-loss-inf", "content": "The Entropy Loss [mainly there to encourage exploration]. Total Loss = PPO Gradient objective (clipped) - vf_ loss _coeff * VF Loss + entropy _coeff * entropy . reinforcement - learning ."}
diff --git a/data/sampled_jsons/three_distinct_retriever_paradigms_Retriever_Paradigms_RAGGED_Towards_Informed_Design_of_Scalable_an.jsonl b/data/sampled_jsons/three_distinct_retriever_paradigms_Retriever_Paradigms_RAGGED_Towards_Informed_Design_of_Scalable_an.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9fee080a4de5b142f4144e4b3a4957a7b42a4931
--- /dev/null
+++ b/data/sampled_jsons/three_distinct_retriever_paradigms_Retriever_Paradigms_RAGGED_Towards_Informed_Design_of_Scalable_an.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "by J Hsia — We introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4ufjBV6S4I", "content": "by J Hsia — We introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets."}
+{"idx": 1, "title": "Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "We introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v3", "content": "We introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets."}
+{"idx": 2, "title": "RAGGED: Towards Informed Design of Retrieval ...", "date": "", "ddg_snippet": "by J Hsia · Cited by 23 — In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KDXj60FpJr", "content": "by J Hsia · Cited by 23 — In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task ..."}
+{"idx": 3, "title": "RAGGED: Towards Informed Design of Retrieval ...", "date": "", "ddg_snippet": "14 Mar 2024 — We propose RAGGED , a framework designed to assist researchers and practitioners in making informed decisions about designing RAG systems, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v1", "content": "14 Mar 2024 — We propose RAGGED , a framework designed to assist researchers and practitioners in making informed decisions about designing RAG systems, ..."}
+{"idx": 4, "title": "In-Storage Acceleration of Retrieval Augmented ...", "date": "", "ddg_snippet": "by R Mahapatra · 2025 · Cited by 1 — This design allows different vector engines to execute distinct ... RAGGED : Towards Informed Design of Retrieval Augmented Generation Systems .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3695053.3731032", "content": "by R Mahapatra · 2025 · Cited by 1 — This design allows different vector engines to execute distinct ... RAGGED : Towards Informed Design of Retrieval Augmented Generation Systems ."}
+{"idx": 5, "title": "RAGuard: A Novel Approach for in-context Safe Retrieval", "date": "", "ddg_snippet": "Adaptive retrieval mechanisms dynamically adjust how and when external information is fetched during generation, often making retrieval context-aware ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03768v1", "content": "Adaptive retrieval mechanisms dynamically adjust how and when external information is fetched during generation, often making retrieval context-aware ..."}
+{"idx": 6, "title": "DeRAG: Black-box Adversarial Attacks on Multiple", "date": "", "ddg_snippet": "Retrieval -Augmented Generation (RAG) combines large language models (LLMs) with information retrieval to ground LLM outputs in external documents ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15042v1", "content": "Retrieval -Augmented Generation (RAG) combines large language models (LLMs) with information retrieval to ground LLM outputs in external documents ..."}
+{"idx": 7, "title": "Real-Time RAG for the Identification of Supply Chain", "date": "", "ddg_snippet": "... to achieving a real-time system, capable of reasoning against information and events without the knowledge gap inherent to LLMs and traditional RAG ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10469v1", "content": "... to achieving a real-time system, capable of reasoning against information and events without the knowledge gap inherent to LLMs and traditional RAG ..."}
+{"idx": 8, "title": "MomentSeeker: A Comprehensive Benchmark and A Strong Baseline", "date": "", "ddg_snippet": "To assess the performance of the current video retrievers , we evaluate three lines of different baselines: clip-style encoders, MLLM-based encoders ...", "subpage_snippet": "", "source": "ito01.com", "link": "https://ito01.com/article/momentseeker-a-comprehensive-benchmark-and-a-strong-baseline-for-moment-retrieval-within-long-videos", "content": "To assess the performance of the current video retrievers , we evaluate three lines of different baselines: clip-style encoders, MLLM-based encoders ..."}
+{"idx": 9, "title": "\"RAG is Dead, Context Engineering is King\" — with", "date": "", "ddg_snippet": "Chroma has been driving some of the most ... We spent most of our time talking about current state of retrieval , memory, retrieval benchmarking, etc.", "subpage_snippet": "", "source": "www.latent.space", "link": "https://www.latent.space/p/chroma", "content": "Chroma has been driving some of the most ... We spent most of our time talking about current state of retrieval , memory, retrieval benchmarking, etc."}
diff --git a/data/sampled_jsons/tlniJJFUW2_spurious_correlation_structure_constant_fixed_dataset_issue_year_2024.jsonl b/data/sampled_jsons/tlniJJFUW2_spurious_correlation_structure_constant_fixed_dataset_issue_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ab9854c1fd0eafe68a0061ad8e8bb12915a50561
--- /dev/null
+++ b/data/sampled_jsons/tlniJJFUW2_spurious_correlation_structure_constant_fixed_dataset_issue_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Taking a second look at evidence for the 'varying' fine- structure ...", "date": "", "ddg_snippet": "fine structure constant . In this full-sky illustration of the quasar measurements, squares represent quasars observed by the Very Large Telescope, circles represent observations by the Keck telescope, and triangles represent observations by both telescopes.", "subpage_snippet": "", "source": "phys.org", "link": "https://phys.org/news/2010-10-evidence-varying-fine-structure-constant.html", "content": "fine structure constant . In this full-sky illustration of the quasar measurements, squares represent quasars observed by the Very Large Telescope, circles represent observations by the Keck telescope, and triangles represent observations by both telescopes."}
+{"idx": 1, "title": "137 - The Fine Structure Constant , physics | Ars Magine", "date": "", "ddg_snippet": "To know the math behind this fine structure constant more specifically, the way you arrive at alpha is by putting the 3 constants h, c, and e together in the equation: As the units c, e, and h cancel each other out, the “pure“ number of 137.03599913 is left behind.", "subpage_snippet": "", "source": "arsmagine.com", "link": "https://arsmagine.com/others/fine-structure-constant/", "content": "To know the math behind this fine structure constant more specifically, the way you arrive at alpha is by putting the 3 constants h, c, and e together in the equation: As the units c, e, and h cancel each other out, the “pure“ number of 137.03599913 is left behind."}
+{"idx": 2, "title": "Investigating Changes in the Fine- Structure Constant - Simple Science", "date": "", "ddg_snippet": "Fine-Structure ConstantFine-Structure ConstantAnalysiskey cosmic constant.Study reveals potential changes in a.Background on the Fine- Structure Constant . Why Use Emission Lines? The DESI Survey. Methodology. Results: Time Variation of the Fine- Structure Constant .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-08-12-investigating-changes-in-the-fine-structure-constant--a3004n2", "content": "Fine-Structure ConstantFine-Structure ConstantAnalysiskey cosmic constant.Study reveals potential changes in a.Background on the Fine- Structure Constant . Why Use Emission Lines? The DESI Survey. Methodology. Results: Time Variation of the Fine- Structure Constant ."}
+{"idx": 3, "title": "Fine Structure Constant – Charge Admittance", "date": "", "ddg_snippet": "The fine- structure constant , α, is more than just a number — it may be the first energy differential that mattered. It marks the quantum threshold at which the vacuum could no longer hold equilibrium — the first jerk — giving rise to coherent structure, time, gravity, and work.", "subpage_snippet": "", "source": "gravityz0.com", "link": "https://gravityz0.com/home/co-fine-structure-constant/", "content": "The fine- structure constant , α, is more than just a number — it may be the first energy differential that mattered. It marks the quantum threshold at which the vacuum could no longer hold equilibrium — the first jerk — giving rise to coherent structure, time, gravity, and work."}
+{"idx": 4, "title": "Miles Mathis: Planck's Constant and the Fine Structure Constant", "date": "", "ddg_snippet": "The Fine Structure Constant is a Mass to Charge Transform. Planck's Constant. It has now been 108 years (since 1900) that quantum mechanics has existed without a mechanical explanation of the quantum.", "subpage_snippet": "", "source": "www.godparticle.xyz", "link": "https://www.godparticle.xyz/planck.html", "content": "The Fine Structure Constant is a Mass to Charge Transform. Planck's Constant. It has now been 108 years (since 1900) that quantum mechanics has existed without a mechanical explanation of the quantum."}
+{"idx": 5, "title": "structure constants in $U(N)$ Yang-Mills Theory (t'Hooft)", "date": "", "ddg_snippet": "Does the structure constant of Yang-Mills field change sign under time reversal? 3. Transformation of matter field in different representations in Yang Mills theory.", "subpage_snippet": "", "source": "physics.stackexchange.com", "link": "https://physics.stackexchange.com/questions/297927/structure-constants-in-un-yang-mills-theory-thooft", "content": "Does the structure constant of Yang-Mills field change sign under time reversal? 3. Transformation of matter field in different representations in Yang Mills theory."}
+{"idx": 6, "title": "What is", "date": "", "ddg_snippet": "What is the Fine Structure Constant ?The fine structure constant is an amplitude, just as the wave function was supposed to be the probabilty amplitude. But the wave function was never an amplitude, it was always a charge density (as Schrodinger told them).", "subpage_snippet": "", "source": "milesmathis.com", "link": "https://milesmathis.com/fine2.pdf", "content": "What is the Fine Structure Constant ?The fine structure constant is an amplitude, just as the wave function was supposed to be the probabilty amplitude. But the wave function was never an amplitude, it was always a charge density (as Schrodinger told them)."}
+{"idx": 7, "title": "Numerical solution of the Dirac equation by a mapped", "date": "", "ddg_snippet": "3. 2 . Spurious roots.is in units of h¯ /mc. κ < 0 eigenvalue can be avoided by construction in explicit nite-basis set calculations [20]. In our Fourier-sine representation it is unavoidable: e.g. the spurious root for κ = 1 has an energy eigenvalue equal to the κ = −1 ground state.", "subpage_snippet": "", "source": "www.yorku.ca", "link": "https://www.yorku.ca/marko/IonAtom/e1.pdf", "content": "3. 2 . Spurious roots.is in units of h¯ /mc. κ < 0 eigenvalue can be avoided by construction in explicit nite-basis set calculations [20]. In our Fourier-sine representation it is unavoidable: e.g. the spurious root for κ = 1 has an energy eigenvalue equal to the κ = −1 ground state."}
+{"idx": 8, "title": "Python parallelize, function, with one iterable and... - Stack Overflow", "date": "", "ddg_snippet": "I currently have the following loops, creating a matrix by by calculating each column using my function sample_features. The function takes input an index that is my iterable and multiple datasets and parameters that needs to be repeated for all function calls.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/75135952/python-parallelize-function-with-one-iterable-and-mulitple-data-as-input-mayb", "content": "I currently have the following loops, creating a matrix by by calculating each column using my function sample_features. The function takes input an index that is my iterable and multiple datasets and parameters that needs to be repeated for all function calls."}
+{"idx": 9, "title": "Physicists Nail Down the ‘Magic Number’ That... | Quanta Magazine", "date": "", "ddg_snippet": "The fine- structure constant was introduced in 1916 to quantify the tiny gap between two lines in the spectrum of colors emitted by certain atoms. The closely spaced frequencies are seen here through a Fabry-Pérot interferometer.", "subpage_snippet": "", "source": "www.quantamagazine.org", "link": "https://www.quantamagazine.org/physicists-measure-the-magic-fine-structure-constant-20201202/", "content": "The fine- structure constant was introduced in 1916 to quantify the tiny gap between two lines in the spectrum of colors emitted by certain atoms. The closely spaced frequencies are seen here through a Fabry-Pérot interferometer."}
diff --git a/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmarks_image_size_comparison.jsonl b/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmarks_image_size_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..49f93142fe78974dfa1185809e3fc1790806f791
--- /dev/null
+++ b/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmarks_image_size_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Video super-resolution - Wikipedia", "date": "", "ddg_snippet": "Single image super- resolution methods could be used too, generating high - resolution frames independently from their neighbours, but it's less ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Video_super-resolution", "content": "Single image super- resolution methods could be used too, generating high - resolution frames independently from their neighbours, but it's less ..."}
+{"idx": 1, "title": "When Large Vision-Language Model Meets Large Remote Sensing", "date": "", "ddg_snippet": "Advances in satellite imaging technology allow for the acquisition of large remote sensing images (RSIs) that cover extensive ground areas and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.07588v3", "content": "Advances in satellite imaging technology allow for the acquisition of large remote sensing images (RSIs) that cover extensive ground areas and ..."}
+{"idx": 2, "title": "4KAgent: Agentic Any Image to 4K Super-Resolution", "date": "", "ddg_snippet": "Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at 256 × 256 256 256 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07105v1", "content": "Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at 256 × 256 256 256 ..."}
+{"idx": 3, "title": "(PDF) Pixel-Wise Classification of Hyperspectral Images With 1D", "date": "", "ddg_snippet": "Nowadays, remote sensing image analysis is needed in various important tasks such as city planning, land-use classification, agriculture monitoring ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366488612_Pixel-wise_Classification_of_Hyperspectral_Images_with_1D_Convolutional_SVM_Networks", "content": "Nowadays, remote sensing image analysis is needed in various important tasks such as city planning, land-use classification, agriculture monitoring ..."}
+{"idx": 4, "title": "Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use", "date": "", "ddg_snippet": "... observation and monitoring with advanced multi-source optical remote sensing (multispectral LiDAR, hyperspectral imaging , and very high - resolution ...", "subpage_snippet": "", "source": "1library.net", "link": "https://1library.net/document/zljm8o6y-advanced-sensor-optical-remote-sensing-classification-outcome-contest.html", "content": "... observation and monitoring with advanced multi-source optical remote sensing (multispectral LiDAR, hyperspectral imaging , and very high - resolution ..."}
+{"idx": 5, "title": "GitHub - satellite-image-deep-learning/techniques: Techniques", "date": "", "ddg_snippet": "... remote sensing data analysis, where the goal is to ... RSI-CB - > A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/satellite-image-deep-learning/techniques", "content": "... remote sensing data analysis, where the goal is to ... RSI-CB - > A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data."}
+{"idx": 6, "title": "Multi-scale information distillation attention network for", "date": "", "ddg_snippet": "Generally, designing more accurate remote sensing cameras can improve the resolution of the images obtained, but this inevitably requires high ...", "subpage_snippet": "", "source": "www.extrica.com", "link": "https://www.extrica.com/article/24351", "content": "Generally, designing more accurate remote sensing cameras can improve the resolution of the images obtained, but this inevitably requires high ..."}
+{"idx": 7, "title": "CVPR 2025 Schedule", "date": "", "ddg_snippet": "EarthVision: Large Scale Computer Vision for Remote Sensing Imagery ... WorldModelBench: The First Workshop on Benchmarking World Foundation Models", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/calendar", "content": "EarthVision: Large Scale Computer Vision for Remote Sensing Imagery ... WorldModelBench: The First Workshop on Benchmarking World Foundation Models"}
+{"idx": 8, "title": "Yangming Zhang", "date": "", "ddg_snippet": "Abstract: The growing field of remote sensing faces a challenge: the ever-increasing size and volume of imagery data are exceeding the storage and ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Yangming+Zhang", "content": "Abstract: The growing field of remote sensing faces a challenge: the ever-increasing size and volume of imagery data are exceeding the storage and ..."}
+{"idx": 9, "title": "China Surpasses Elon Musk’s Starlink with Breakthrough in", "date": "", "ddg_snippet": "... by successfully conducting its first 100Gbps ultra - high -speed satellite-to-ground laser transmission test for high - resolution remote sensing imagery ...", "subpage_snippet": "", "source": "www.china-arms.com", "link": "https://www.china-arms.com/2025/01/china-satellite-to-ground-laser-communication/", "content": "... by successfully conducting its first 100Gbps ultra - high -speed satellite-to-ground laser transmission test for high - resolution remote sensing imagery ..."}
diff --git a/data/sampled_jsons/unbalanced_optimal_transport_growth_factors_cell_dynamics_balanced_transport_year_2024.jsonl b/data/sampled_jsons/unbalanced_optimal_transport_growth_factors_cell_dynamics_balanced_transport_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..58e6ce67e419746bb8a8197d8908d642a56d7e41
--- /dev/null
+++ b/data/sampled_jsons/unbalanced_optimal_transport_growth_factors_cell_dynamics_balanced_transport_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reconstructing growth and dynamic trajectories from single ...", "date": "", "ddg_snippet": "Nov 30, 2023 · Here we present TIGON, a dynamic, unbalanced optimal transport algorithm that reconstructs dynamic trajectories and population growth simultaneously as well as the underlying gene regulatory ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-023-00763-w", "content": "Nov 30, 2023 · Here we present TIGON, a dynamic, unbalanced optimal transport algorithm that reconstructs dynamic trajectories and population growth simultaneously as well as the underlying gene regulatory ..."}
+{"idx": 1, "title": "Modeling Cell Type Developmental Trajectory using Multinomial ...", "date": "", "ddg_snippet": "In this paper, our focus shifts to a question of greater practical importance: we examine the differentiation of cell types over time. Specifically, we propose a novel method based on discrete unbalanced optimal transport to model the developmental trajectory of cell types.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.03501v1", "content": "In this paper, our focus shifts to a question of greater practical importance: we examine the differentiation of cell types over time. Specifically, we propose a novel method based on discrete unbalanced optimal transport to model the developmental trajectory of cell types."}
+{"idx": 2, "title": "Optimal-Transport Analysis of Single-Cell Gene Expression ...", "date": "", "ddg_snippet": "We down-sampled the cells and reads at each time point, perturbed our initial estimates for cellular growth and death rates, and perturbed the parameters for entropic regularization and unbalanced transport (Figures S1 G–S1I; STAR Methods).", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell/fulltext/S0092-8674(19)30039-X", "content": "We down-sampled the cells and reads at each time point, perturbed our initial estimates for cellular growth and death rates, and perturbed the parameters for entropic regularization and unbalanced transport (Figures S1 G–S1I; STAR Methods)."}
+{"idx": 3, "title": "Unbalanced optimal transport: Dynamic and Kantorovich ...", "date": "", "ddg_snippet": "Jun 1, 2018 · In this section, we describe a first approach to unbalanced optimal transport , which generalizes (1.1) and is inspired by the dynamic formulation of classical optimal transport .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0022123618301058", "content": "Jun 1, 2018 · In this section, we describe a first approach to unbalanced optimal transport , which generalizes (1.1) and is inspired by the dynamic formulation of classical optimal transport ."}
+{"idx": 4, "title": "Reconstructing growth and dynamic trajectories from single ... GitHub - FrederikeLuebeck/nubot: Neural Unbalanced Optimal ... Unbalanced optimal transport : Dynamic and Kantorovich formulations Unbalanced optimal transport : Dynamic and Kantorovich formulations Unbalanced optimal transport : Dynamic and Kantorovich formulations Reconstructing growth and dynamic trajectories from single- cell Reconstructing growth and dynamic trajectories from single- cell Reconstructing growth and dynamic trajectories from single- cell transc… Modeling Cell Dynamics and Interactions with Unbalanced Mean ...", "date": "", "ddg_snippet": "Nov 30, 2023 · Here we present TIGON, a dynamic, unbalanced optimal transport algorithm that reconstructs dynamic trajectories and population growth simultaneously as well as the underlying gene regulatory network from multiple snapshots. However, the classical formulation of OT assumes conservation of mass, which is violated in unbalanced scenarios in which the population size changes, e.g., when cells die or proliferate. In this work, we present NubOT, a neural unbalanced optimal transport model, that learns a parameterized optimal transport map between unbalanced distributions. Can optimal transport be extended to the unbalanced setting? In the last few years, there has been an increasing interest in extending optimal transport to the unbalanced setting of measures having non-equal masses. Dynamic formulations of unbalanced optimal transport Several models based on the fluid dynamic formulation introduced in have been proposed recently , , , . What are static formulations of unbalanced optimal transport? Static formulations of unbalanced optimal transport Purely static formulations of unbalanced transport are however a longstanding problem. A simple way to address this issue is given in the early work of Kantorovich and Rubinstein . The corresponding “Kantorovich norms” were later extended to separable metric spaces by . What are unbalancedoptimal transport problems? In many applications, there is however a need to compare unnormalized measures , which corresponds to so-called unbalancedoptimal transport problems, following the terminology introduced in . Applications of these unbalanced metrics range from image classification , to the processing of neuronal activation maps . How to solve the high-dimensional optimal transport problem? To tackle the high-dimensional optimal transport problem, we introduce a deep learning method using a dimensionless formulation based on the Wasserstein–Fisher–Rao (WFR) distance. TIGON is evaluated on simulated data and compared with existing methods for its robustness and accuracy in predicting cell state transition and cell population growth. What is dimensionless formulation for dynamic unbalanced OT problem? To deal with the high dimensionality, we obtain a dimensionless formulation for the WFR-based dynamic unbalanced OT problem in equation (2) (Lemma and Theorem in Methods). Briefly, two neural networks are used to approximate velocity v (x,t) ≈ NN1 (x,t) and growth g (x,t) ≈ NN2 (x,t) (Fig. 1c). Does optimal-transport analysis of single-cell gene expression identify developmental trajectories in reprogramming? Schiebinger G, et al. Optimal - transport analysis of single- cell gene expression identifies developmental trajectories in reprogramming. Cell . 2019;176:928–943. e22. doi: 10.1016/j. cell .2019.01.006. [PMC free article] [PubMed] [CrossRef] [Google Scholar] 24. Benamou J-D, Brenier Y. It becomes the unbalanced dynamics optimal transport when k(x,y)=σ(t)=0 and Ψ(g)=g2. It becomes the dynamics optimal transport when growth , interaction and diffusion all goes to zero.", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10805654/", "content": "Nov 30, 2023 · Here we present TIGON, a dynamic, unbalanced optimal transport algorithm that reconstructs dynamic trajectories and population growth simultaneously as well as the underlying gene regulatory network from multiple snapshots. However, the classical formulation of OT assumes conservation of mass, which is violated in unbalanced scenarios in which the population size changes, e.g., when cells die or proliferate. In this work, we present NubOT, a neural unbalanced optimal transport model, that learns a parameterized optimal transport map between unbalanced distributions. Can optimal transport be extended to the unbalanced setting? In the last few years, there has been an increasing interest in extending optimal transport to the unbalanced setting of measures having non-equal masses. Dynamic formulations of unbalanced optimal transport Several models based on the fluid dynamic formulation introduced in have been proposed recently , , , . What are static formulations of unbalanced optimal transport? Static formulations of unbalanced optimal transport Purely static formulations of unbalanced transport are however a longstanding problem. A simple way to address this issue is given in the early work of Kantorovich and Rubinstein . The corresponding “Kantorovich norms” were later extended to separable metric spaces by . What are unbalancedoptimal transport problems? In many applications, there is however a need to compare unnormalized measures , which corresponds to so-called unbalancedoptimal transport problems, following the terminology introduced in . Applications of these unbalanced metrics range from image classification , to the processing of neuronal activation maps . How to solve the high-dimensional optimal transport problem? To tackle the high-dimensional optimal transport problem, we introduce a deep learning method using a dimensionless formulation based on the Wasserstein–Fisher–Rao (WFR) distance. TIGON is evaluated on simulated data and compared with existing methods for its robustness and accuracy in predicting cell state transition and cell population growth. What is dimensionless formulation for dynamic unbalanced OT problem? To deal with the high dimensionality, we obtain a dimensionless formulation for the WFR-based dynamic unbalanced OT problem in equation (2) (Lemma and Theorem in Methods). Briefly, two neural networks are used to approximate velocity v (x,t) ≈ NN1 (x,t) and growth g (x,t) ≈ NN2 (x,t) (Fig. 1c). Does optimal-transport analysis of single-cell gene expression identify developmental trajectories in reprogramming? Schiebinger G, et al. Optimal - transport analysis of single- cell gene expression identifies developmental trajectories in reprogramming. Cell . 2019;176:928–943. e22. doi: 10.1016/j. cell .2019.01.006. [PMC free article] [PubMed] [CrossRef] [Google Scholar] 24. Benamou J-D, Brenier Y. It becomes the unbalanced dynamics optimal transport when k(x,y)=σ(t)=0 and Ψ(g)=g2. It becomes the dynamics optimal transport when growth , interaction and diffusion all goes to zero."}
+{"idx": 5, "title": "GitHub - FrederikeLuebeck/nubot: Neural Unbalanced Optimal ...", "date": "", "ddg_snippet": "However, the classical formulation of OT assumes conservation of mass, which is violated in unbalanced scenarios in which the population size changes, e.g., when cells die or proliferate. In this work, we present NubOT, a neural unbalanced optimal transport model, that learns a parameterized optimal transport map between unbalanced distributions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FrederikeLuebeck/nubot", "content": "However, the classical formulation of OT assumes conservation of mass, which is violated in unbalanced scenarios in which the population size changes, e.g., when cells die or proliferate. In this work, we present NubOT, a neural unbalanced optimal transport model, that learns a parameterized optimal transport map between unbalanced distributions."}
+{"idx": 6, "title": "Modeling Cell Dynamics and Interactions with Unbalanced Mean ...", "date": "", "ddg_snippet": "It becomes the unbalanced dynamics optimal transport when k(x,y)=σ(t)=0 and Ψ(g)=g2. It becomes the dynamics optimal transport when growth , interaction and diffusion all goes to zero.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.11197", "content": "It becomes the unbalanced dynamics optimal transport when k(x,y)=σ(t)=0 and Ψ(g)=g2. It becomes the dynamics optimal transport when growth , interaction and diffusion all goes to zero."}
+{"idx": 7, "title": "neural unbalanced optimal transport", "date": "", "ddg_snippet": "by F Lübeck · 2022 · Cited by 25 — In short, the main contributions of this work are: (i) A novel formulation of the unbalanced optimal transport problem that weaves together the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2209.15621", "content": "by F Lübeck · 2022 · Cited by 25 — In short, the main contributions of this work are: (i) A novel formulation of the unbalanced optimal transport problem that weaves together the ..."}
+{"idx": 8, "title": "Nonlinear Dynamical Unbalanced Optimal Transport", "date": "", "ddg_snippet": "by D Wu · 2025 — Abstract— In this paper, we introduce a generalized dynamical unbalanced optimal transport framework by incorporating limited control input ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.03301", "content": "by D Wu · 2025 — Abstract— In this paper, we introduce a generalized dynamical unbalanced optimal transport framework by incorporating limited control input ..."}
+{"idx": 9, "title": "Scalable Unbalanced Optimal Transport using Generative ...", "date": "", "ddg_snippet": "by KD Yang · Cited by 88 — In this paper, we present a scalable method for unbalanced optimal transport (OT) based on the generative-adversarial framework.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=HyexAiA5Fm", "content": "by KD Yang · Cited by 88 — In this paper, we present a scalable method for unbalanced optimal transport (OT) based on the generative-adversarial framework."}
diff --git a/data/sampled_jsons/understanding_safety_finetuning_synthetic_experiments_minGPT_n_layer_transformer_blocks.jsonl b/data/sampled_jsons/understanding_safety_finetuning_synthetic_experiments_minGPT_n_layer_transformer_blocks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d12758dac0742bb21094f3949b06a24ce2a88c3d
--- /dev/null
+++ b/data/sampled_jsons/understanding_safety_finetuning_synthetic_experiments_minGPT_n_layer_transformer_blocks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - fiveai/understanding_safety_finetuning: Official Code for What ...", "date": "", "ddg_snippet": "The official implementation of \"What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "The official implementation of \"What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient ..."}
+{"idx": 1, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning , direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a9bef53eb7b0e5950d4f2d9c74a16006-Abstract-Conference.html", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning , direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space."}
+{"idx": 2, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "This paper delves deep into the mechanistic workings of safety fine-tuning methods. The researchers employed a novel synthetic data generation framework to thoroughly investigate the impact of three common safety fine-tuning techniques. They discovered that these methods subtly transform the model's internal representations, creating distinct clusters for safe and unsafe inputs. However ...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jeflv4nrlh/", "content": "This paper delves deep into the mechanistic workings of safety fine-tuning methods. The researchers employed a novel synthetic data generation framework to thoroughly investigate the impact of three common safety fine-tuning techniques. They discovered that these methods subtly transform the model's internal representations, creating distinct clusters for safe and unsafe inputs. However ..."}
+{"idx": 3, "title": "Understanding and Enhancing Safety Mechanisms of LLMs via...", "date": "", "ddg_snippet": "This paper presents an innovative method for effectively and efficiently detecting and fine-tuning \" safety neurons,\" which comprise less than 1% of model parameters and are predominantly located in low-level self-attention layers. The authors conducted related experiments to verify that safety mechanism is resilient but breakable. Notably, the proposed tuning method SN-Tune enhances model ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=yR47RmND1m", "content": "This paper presents an innovative method for effectively and efficiently detecting and fine-tuning \" safety neurons,\" which comprise less than 1% of model parameters and are predominantly located in low-level self-attention layers. The authors conducted related experiments to verify that safety mechanism is resilient but breakable. Notably, the proposed tuning method SN-Tune enhances model ..."}
+{"idx": 4, "title": "karpathy/minGPT | DeepWiki", "date": "", "ddg_snippet": "Purpose and Scope minGPT is a minimal PyTorch re-implementation of the GPT (Generative Pre-trained Transformer ) architecture, designed for clarity, educational value, and interpretability. This document provides a high-level overview of the minGPT repository, explaining its core components, design philosophy, and basic usage patterns.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/karpathy/minGPT", "content": "Purpose and Scope minGPT is a minimal PyTorch re-implementation of the GPT (Generative Pre-trained Transformer ) architecture, designed for clarity, educational value, and interpretability. This document provides a high-level overview of the minGPT repository, explaining its core components, design philosophy, and basic usage patterns."}
+{"idx": 5, "title": "GitHub - karpathy/minGPT: A minimal PyTorch re-implementation of the ...", "date": "", "ddg_snippet": "The minGPT library is three files: mingpt /model.py contains the actual Transformer model definition, mingpt /bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt /trainer.py is (GPT-independent) PyTorch boilerplate code that trains the model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT", "content": "The minGPT library is three files: mingpt /model.py contains the actual Transformer model definition, mingpt /bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt /trainer.py is (GPT-independent) PyTorch boilerplate code that trains the model."}
+{"idx": 6, "title": "Katiyar48/MinGPT · Hugging Face", "date": "", "ddg_snippet": "The minGPT library is three files: mingpt /model.py contains the actual Transformer model definition, mingpt /bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt /trainer.py is (GPT-independent) PyTorch boilerplate code that trains the model.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Katiyar48/MinGPT", "content": "The minGPT library is three files: mingpt /model.py contains the actual Transformer model definition, mingpt /bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt /trainer.py is (GPT-independent) PyTorch boilerplate code that trains the model."}
+{"idx": 7, "title": "What Makes and Breaks Safety Fine-tuning? Mechanistic Study", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning , direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally trans-form MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BS2CbUkJpy", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning , direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally trans-form MLP weights to specifically align unsafe inputs into its weights' null space."}
+{"idx": 8, "title": "Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and ...", "date": "", "ddg_snippet": "Supervised fine-tuning (SFT) is a critical step in aligning large language models (LLMs) with human instructions and values, yet many aspects of SFT remain poorly understood. We trained a wide range of base models on a variety of datasets including code generation, mathematical reasoning, and general-domain tasks, resulting in 1,000+ SFT models under controlled conditions. We then identified ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.14681", "content": "Supervised fine-tuning (SFT) is a critical step in aligning large language models (LLMs) with human instructions and values, yet many aspects of SFT remain poorly understood. We trained a wide range of base models on a variety of datasets including code generation, mathematical reasoning, and general-domain tasks, resulting in 1,000+ SFT models under controlled conditions. We then identified ..."}
+{"idx": 9, "title": "arXiv:2407.10264v3 [cs.LG] 21 Aug 2024", "date": "", "ddg_snippet": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks, and adversarial attacks. We make careful design choices to adhere to the properties of natural language instructions and the jailbreaks taxonomy of Wei et al. (2023), thus facilitating a thorough ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.10264", "content": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks, and adversarial attacks. We make careful design choices to adhere to the properties of natural language instructions and the jailbreaks taxonomy of Wei et al. (2023), thus facilitating a thorough ..."}
diff --git a/data/sampled_jsons/uxDFlPGRLX_FlowDec_FlowDec_flow-based_full-band_general_audio_codec_Table_2_NDAC-75_NDAC-25.jsonl b/data/sampled_jsons/uxDFlPGRLX_FlowDec_FlowDec_flow-based_full-band_general_audio_codec_Table_2_NDAC-75_NDAC-25.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7194d155083092d0919e6f98be515c6fad1d13a6
--- /dev/null
+++ b/data/sampled_jsons/uxDFlPGRLX_FlowDec_FlowDec_flow-based_full-band_general_audio_codec_Table_2_NDAC-75_NDAC-25.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "facebookresearch/ FlowDec : An neural full - band audio codec for...", "date": "", "ddg_snippet": "FlowDec (ICLR 2025) is a full - band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "FlowDec (ICLR 2025) is a full - band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 1, "title": "FlowDec : A flow - based full - band general audio codec with high...", "date": "", "ddg_snippet": "Overview FlowDec is a new neural audio codec that achieves high-quality audio compressionUses flow - based generative models to compress full - band (48kHz) audio signals", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/flowdec-flow-based-full-band-general-audio", "content": "Overview FlowDec is a new neural audio codec that achieves high-quality audio compressionUses flow - based generative models to compress full - band (48kHz) audio signals"}
+{"idx": 2, "title": "FlowDec : A flow - based full - band general audio codec with high...", "date": "", "ddg_snippet": "FlowDec -25s: 25 Hz, single-bitrate. Trained based on NDAC - 25 with a bitrate of 4.0 kbit/s. We do not train for multiple bitrates here as the bitrate and feature rate is already very low. We train all postfilters based on a slightly modified NCSN++ architecture (Song et al., 2021) with 26 M...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "FlowDec -25s: 25 Hz, single-bitrate. Trained based on NDAC - 25 with a bitrate of 4.0 kbit/s. We do not train for multiple bitrates here as the bitrate and feature rate is already very low. We train all postfilters based on a slightly modified NCSN++ architecture (Song et al., 2021) with 26 M..."}
+{"idx": 3, "title": "FlowDec : A flow - based full - band general audio codec with high...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full - band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/FlowDec:-A-flow-based-full-band-general-audio-codec-with-high-perceptual-quality-17045f43-017b-4495-bcb6-65d8964daff3", "content": "We propose FlowDec , a neural full - band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 4, "title": "FlowDec | A flow - based full - band general audio codec with high...", "date": "", "ddg_snippet": "Abstract. We propose FlowDec , a neural full - band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based ...", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "Abstract. We propose FlowDec , a neural full - band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based ..."}
+{"idx": 5, "title": "Free Google Veo 3 AI Video Generator with Native Audio", "date": "", "ddg_snippet": "Native Audio Generation . Veo 3 generates fully synchronized audio directly within the video. From dialogue and sound effects to ambient background sounds, this feature makes your AI videos feel natural and immersive.", "subpage_snippet": "", "source": "veo3.bot", "link": "https://veo3.bot/features/v3", "content": "Native Audio Generation . Veo 3 generates fully synchronized audio directly within the video. From dialogue and sound effects to ambient background sounds, this feature makes your AI videos feel natural and immersive."}
+{"idx": 6, "title": "higgs- audio -v 2 -tokenizer huggingface.co api & bosonai... - Toolify", "date": "", "ddg_snippet": "\" FlowDec : A flow - based full - band general audio codec with high perceptual quality.\" arXiv preprint arXiv:2503.01485 (2025).", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-model/bosonai-higgs-audio-v2-tokenizer", "content": "\" FlowDec : A flow - based full - band general audio codec with high perceptual quality.\" arXiv preprint arXiv:2503.01485 (2025)."}
+{"idx": 7, "title": "Veo 3 AI Video Generator with Audio | veo3.ai", "date": "", "ddg_snippet": "Veo 3: AI Video Generation with Realistic Sound. Generate videos with perfectly synced audio , including sound effects, dialogue, and ambient noise.", "subpage_snippet": "", "source": "veo3.ai", "link": "https://veo3.ai/", "content": "Veo 3: AI Video Generation with Realistic Sound. Generate videos with perfectly synced audio , including sound effects, dialogue, and ambient noise."}
+{"idx": 8, "title": "[PDF] PostGAN: A GAN- Based Post-Processor to... | Semantic Scholar", "date": "", "ddg_snippet": "FlowDec : A flow - based full - band general audio codec with high perceptual quality.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/PostGAN:-A-GAN-Based-Post-Processor-to-Enhance-the-Korse-Pia/58dda9f033c97c4bb7798832fb41d3914a5d05af", "content": "FlowDec : A flow - based full - band general audio codec with high perceptual quality."}
+{"idx": 9, "title": "dblp: List of computer science publications by Wei-Ning Hsu", "date": "", "ddg_snippet": "Simon Welker, Matthew Le, Ricky T. Q. Chen, Wei-Ning Hsu, Timo Gerkmann, Alexander Richard, Yi-Chiao Wu: FlowDec : A flow - based full - band general audio codec with high perceptual quality.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/160/9923.html", "content": "Simon Welker, Matthew Le, Ricky T. Q. Chen, Wei-Ning Hsu, Timo Gerkmann, Alexander Richard, Yi-Chiao Wu: FlowDec : A flow - based full - band general audio codec with high perceptual quality."}
diff --git a/data/sampled_jsons/uxDFlPGRLX_FlowDec_Table_8_SIGMOS_scores_FlowDec-75m_DAC-75_4.50_kbits_year_2023.jsonl b/data/sampled_jsons/uxDFlPGRLX_FlowDec_Table_8_SIGMOS_scores_FlowDec-75m_DAC-75_4.50_kbits_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c4c6eace3cd9d754a721445cff8ff2aec90800bd
--- /dev/null
+++ b/data/sampled_jsons/uxDFlPGRLX_FlowDec_Table_8_SIGMOS_scores_FlowDec-75m_DAC-75_4.50_kbits_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "FlowDec - 75 m : 75 Hz, multi-bitrate.In Fig. 14, we show spectrograms comparing FlowDec - 75 m and DAC - 75 on three examples with high harmonic content such as speech and isolated music instruments.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "FlowDec - 75 m : 75 Hz, multi-bitrate.In Fig. 14, we show spectrograms comparing FlowDec - 75 m and DAC - 75 on three examples with high harmonic content such as speech and isolated music instruments."}
+{"idx": 1, "title": "Пожелания с Днём Рождения мужчине своими словами в прозе", "date": "", "ddg_snippet": "Мужчине весной Мужчине весной 50 шт. Девушке 1054 шт.", "subpage_snippet": "", "source": "2karandasha.ru", "link": "https://2karandasha.ru/pozdravleniya/s-dnem-rojdeniya/muzhchine/proza/page2?ya_src=serp300", "content": "Мужчине весной Мужчине весной 50 шт. Девушке 1054 шт."}
+{"idx": 2, "title": "Gamma | Best AI Presentation Maker & Website Builder", "date": "", "ddg_snippet": "Gamma is your free-to-use AI design partner for creating effortless presentations, websites, and more. No coding or design skills required.", "subpage_snippet": "", "source": "gamma.app", "link": "https://gamma.app/", "content": "Gamma is your free-to-use AI design partner for creating effortless presentations, websites, and more. No coding or design skills required."}
+{"idx": 3, "title": "Endless Numbers 51- 75 - YouTube", "date": "", "ddg_snippet": "A walkthrough of numbers 51- 75 in Endless Numbers.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=4Zd2gTW3rw4", "content": "A walkthrough of numbers 51- 75 in Endless Numbers."}
+{"idx": 4, "title": "Популярная классическая музыка - Православное аудио", "date": "", "ddg_snippet": "Ave verum corpus. 03:50.50. Шуберт. Военный марш.", "subpage_snippet": "", "source": "azbyka.ru", "link": "https://azbyka.ru/audio/populjarnaja-klassicheskaja-muzyka.html", "content": "Ave verum corpus. 03:50.50. Шуберт. Военный марш."}
+{"idx": 5, "title": "Zabbix 6.4.6 1. Утилизация Trapper 75 % 2. 100 items 10 min", "date": "", "ddg_snippet": "oller guru. Зарегистрирован: 30.01.2008 Пользователь #: 66,275 Сообщения: 8378 Откуда: Online Голоса: 48. Добавлено: Ср 13 Дек, 2023 22:50 Заголовок сообщения", "subpage_snippet": "", "source": "sysadmins.ru", "link": "https://sysadmins.ru/topic564372.html", "content": "oller guru. Зарегистрирован: 30.01.2008 Пользователь #: 66,275 Сообщения: 8378 Откуда: Online Голоса: 48. Добавлено: Ср 13 Дек, 2023 22:50 Заголовок сообщения"}
+{"idx": 6, "title": "Саня во Флориде – Telegram", "date": "", "ddg_snippet": "Авторский канал \"Саня во Флориде\" Аналитика, обзоры и сводки.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/sanya_florida", "content": "Авторский канал \"Саня во Флориде\" Аналитика, обзоры и сводки."}
+{"idx": 7, "title": "Погода в Донецке на 14 дней - подробный прогноз на... - Погода Mail", "date": "", "ddg_snippet": "Подробный прогноз погоды на 14 дней в Донецке.", "subpage_snippet": "", "source": "pogoda.mail.ru", "link": "https://pogoda.mail.ru/prognoz/donetsk/14dney/", "content": "Подробный прогноз погоды на 14 дней в Донецке."}
+{"idx": 8, "title": "75 Inches To Centimeters Converter | 75 in To cm Converter", "date": "", "ddg_snippet": "75 Inch to Centimeter converter .Likewise the question how many centimeter in 75 inch has the answer of 190.5 cm in 75 in.", "subpage_snippet": "", "source": "inches-to-cm.appspot.com", "link": "https://inches-to-cm.appspot.com/75-inches-to-cm.html", "content": "75 Inch to Centimeter converter .Likewise the question how many centimeter in 75 inch has the answer of 190.5 cm in 75 in."}
+{"idx": 9, "title": "Новости и аналитика военных конфликтов, обзор вооружения...", "date": "", "ddg_snippet": "22:54 Съезд народов и областей России: как Киев решил поиграть в автономию. 22:50 Чёрная дыра украинского бюджета, или Все деньги – на войну.", "subpage_snippet": "", "source": "topwar.ru", "link": "https://topwar.ru/", "content": "22:54 Съезд народов и областей России: как Киев решил поиграть в автономию. 22:50 Чёрная дыра украинского бюджета, или Все деньги – на войну."}
diff --git a/data/sampled_jsons/what_is_flow_matching_generative_models_simulation-free_training.jsonl b/data/sampled_jsons/what_is_flow_matching_generative_models_simulation-free_training.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bdb776a2d952b1f91cb9545c9e0a3086c9f9010e
--- /dev/null
+++ b/data/sampled_jsons/what_is_flow_matching_generative_models_simulation-free_training.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2210.02747] Flow Matching for Generative Modeling - arXiv.org", "date": "", "ddg_snippet": "Oct 6, 2022 · We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2210.02747", "content": "Oct 6, 2022 · We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian ..."}
+{"idx": 1, "title": "Flow-Matching Generative Framework", "date": "", "ddg_snippet": "1 Sept 2025 — The flow-matching generative framework enables precise, simulation-free, and highly flexible training of CNFs by analytically regressing a ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/flow-matching-generative-framework", "content": "1 Sept 2025 — The flow-matching generative framework enables precise, simulation-free, and highly flexible training of CNFs by analytically regressing a ..."}
+{"idx": 2, "title": "Flow Matching for Generative Modeling", "date": "", "ddg_snippet": "by Y Lipman · 2022 · Cited by 2057 — The goal of this work is to propose Flow Matching (FM), an efficient simulation-free approach to train- ing CNF models, allowing the adoption ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.02747", "content": "by Y Lipman · 2022 · Cited by 2057 — The goal of this work is to propose Flow Matching (FM), an efficient simulation-free approach to train- ing CNF models, allowing the adoption ..."}
+{"idx": 3, "title": "Flow Matching for Generative Modeling", "date": "", "ddg_snippet": "18 Jan 2024 — Flow matching provides a simulation free method for training continuous normalizing flows . Key ingredients are an implicit definition of the target flow.", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/pills/2024/flow-matching/", "content": "18 Jan 2024 — Flow matching provides a simulation free method for training continuous normalizing flows . Key ingredients are an implicit definition of the target flow."}
+{"idx": 4, "title": "Explicit Flow Matching: Simulation-Free CNF Training", "date": "", "ddg_snippet": "7 Jul 2025 — Explicit Flow Matching (ExFM ) is a simulation-free framework for training Continuous Normalizing Flow (CNF)-based generative models, in which a ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/explicit-flow-matching-exfm", "content": "7 Jul 2025 — Explicit Flow Matching (ExFM ) is a simulation-free framework for training Continuous Normalizing Flow (CNF)-based generative models, in which a ..."}
+{"idx": 5, "title": "An Introduction to Flow Matching and Diffusion Models", "date": "", "ddg_snippet": "A generative model is a machine learning model that allows us to generate samples from pdata. In machine learning , we require data to train models. In ...", "subpage_snippet": "", "source": "diffusion.csail.mit.edu", "link": "https://diffusion.csail.mit.edu/docs/lecture-notes.pdf", "content": "A generative model is a machine learning model that allows us to generate samples from pdata. In machine learning , we require data to train models. In ..."}
+{"idx": 6, "title": "Improving Flow Matching by Aligning Flow Divergence", "date": "", "ddg_snippet": "Conditional flow matching (CFM ) stands out as an efficient, simulation-free approach for training flow-based generative models, achieving remarkable performance ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45878", "content": "Conditional flow matching (CFM ) stands out as an efficient, simulation-free approach for training flow-based generative models, achieving remarkable performance ..."}
+{"idx": 7, "title": "Flow Matching for Generative Modeling", "date": "", "ddg_snippet": "10 Dec 2024 — Flow matching is a simple yet effective generative modeling paradigm that has found widespread adoption in diverse domains and large-scale applications.", "subpage_snippet": "", "source": "slideslive.com", "link": "https://slideslive.com/39031675/flow-matching-for-generative-modeling", "content": "10 Dec 2024 — Flow matching is a simple yet effective generative modeling paradigm that has found widespread adoption in diverse domains and large-scale applications."}
+{"idx": 8, "title": "Local Flow Matching Generative Models", "date": "", "ddg_snippet": "by C Xu · 2024 · Cited by 6 — Flow Matching (FM) is a simulation-free method for learning a continuous and invertible flow to interpolate between two distributions, and in particular to ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv241002548X/abstract", "content": "by C Xu · 2024 · Cited by 6 — Flow Matching (FM) is a simulation-free method for learning a continuous and invertible flow to interpolate between two distributions, and in particular to ..."}
+{"idx": 9, "title": "An introduction to Flow Matching · Cambridge MLG Blog", "date": "", "ddg_snippet": "Jan 20, 2024 · Introduction Flow matching (FM) is a recent generative modelling paradigm which has rapidly been gaining popularity in the deep probabilistic ML community. Flow matching combines aspects from Continuous Normalising Flows (CNFs) and Diffusion Models (DMs), alleviating key issues both methods have.", "subpage_snippet": "", "source": "mlg.eng.cam.ac.uk", "link": "https://mlg.eng.cam.ac.uk/blog/2024/01/20/flow-matching.html", "content": "Jan 20, 2024 · Introduction Flow matching (FM) is a recent generative modelling paradigm which has rapidly been gaining popularity in the deep probabilistic ML community. Flow matching combines aspects from Continuous Normalising Flows (CNFs) and Diffusion Models (DMs), alleviating key issues both methods have."}
diff --git a/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_Sleeper_Agents_Hubinger.jsonl b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_Sleeper_Agents_Hubinger.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..791701a6c940c9ffefc13868083b7bff5799d47e
--- /dev/null
+++ b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_Sleeper_Agents_Hubinger.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "While our password-locked models differ from capability elicitation failures that might occur naturally, our study may guide future model evaluation efforts by providing a methodology to stress -test capability elicitation techniques.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "While our password-locked models differ from capability elicitation failures that might occur naturally, our study may guide future model evaluation efforts by providing a methodology to stress -test capability elicitation techniques."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.19550", "content": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password ."}
+{"idx": 2, "title": "[Paper] Stress-testing capability elicitation with password-locked", "date": "", "ddg_snippet": "But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great);", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great);"}
+{"idx": 3, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740125", "content": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities."}
+{"idx": 4, "title": "PDF Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Test-bed for eliciting capabilities definitely present Results for elicitation via finetuning: Only a few samples needed for unlocking with supervised finetuning SFT unlocking generalizes SFT on high-quality samples from one subdomain unlocks other subdomains This also works when the subdomains are locked with different passwords", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/92923.pdf", "content": "Test-bed for eliciting capabilities definitely present Results for elicitation via finetuning: Only a few samples needed for unlocking with supervised finetuning SFT unlocking generalizes SFT on high-quality samples from one subdomain unlocks other subdomains This also works when the subdomains are locked with different passwords"}
+{"idx": 5, "title": "Stress-Testing Capability Elicitation With Password-Locked Models ...", "date": "", "ddg_snippet": "This innovative approach offers a controlled setting to meticulously assess the efficacy of various elicitation methods. The study finds that fine-tuning, especially when provided with a few high-quality demonstrations, effectively recovers the hidden capabilities of password-locked models .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "This innovative approach offers a controlled setting to meticulously assess the efficacy of various elicitation methods. The study finds that fine-tuning, especially when provided with a few high-quality demonstrations, effectively recovers the hidden capabilities of password-locked models ."}
+{"idx": 6, "title": "NeurIPS Poster Stress-Testing Capability Elicitation With Password ...", "date": "", "ddg_snippet": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password ."}
+{"idx": 7, "title": "Dmitrii Krasheninnikov - Stress-testing Capability Elicitation with ...", "date": "", "ddg_snippet": "Dmitrii Krasheninnikov from University of Cambridge presenting 'Stress - testing Capability Elicitation with Password-locked Models' on July 21, 2024 at the Vienna Alignment Workshop.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=7jgzdqvAv-4", "content": "Dmitrii Krasheninnikov from University of Cambridge presenting 'Stress - testing Capability Elicitation with Password-locked Models' on July 21, 2024 at the Vienna Alignment Workshop."}
+{"idx": 8, "title": "The Elicitation Game: Stress-Testing Capability ... - OpenReview", "date": "", "ddg_snippet": "In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training \\emph { model organisms} -- LMs with hidden capabilities that are revealed by a password . We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zy6LB5t62f", "content": "In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training \\emph { model organisms} -- LMs with hidden capabilities that are revealed by a password . We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model ."}
+{"idx": 9, "title": "[Paper] Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization).", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization)."}
diff --git a/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_imperfect_representation_password-locked_models.jsonl b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_imperfect_representation_password-locked_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7347b5d5b0fb358a79566f900e39740913804b4c
--- /dev/null
+++ b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_imperfect_representation_password-locked_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "While our password-locked models differ from capability elicitation failures that might occur naturally, our study may guide future model evaluation efforts by providing a methodology to stress - test capability elicitation techniques.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "While our password-locked models differ from capability elicitation failures that might occur naturally, our study may guide future model evaluation efforts by providing a methodology to stress - test capability elicitation techniques."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked Models Stress-Testing Capability Elicitation With Password-Locked Models [Paper] Stress-testing capability elicitation with password ... Stress-Testing Capability Elicitation With Password-Locked Models [Paper] Stress-testing capability elicitation with password ... Images Stress-testing capability elicitation with password-locked models Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "May 29, 2024 · Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . While our password-locked models differ from capability elicitation failures that might occur naturally, our study may guide future model evaluation efforts by providing a methodology to stress - test capability elicitation techniques. Jun 4, 2024 · But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great); Sep 26, 2024 · The concept of “ Password-Locked Models ” presents a novel approach to evaluating the efficacy of LLM capability elicitation techniques. By deliberately hiding certain capabilities behind a password , researchers create a controlled environment to test different elicitation methods. Jun 4, 2024 · Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization). View all Test -bed for eliciting capabilities definitely present Results for elicitation via finetuning: Only a few samples needed for unlocking with supervised finetuning SFT unlocking generalizes SFT on high-quality samples from one subdomain unlocks other subdomains This also works when the subdomains are locked with different passwords Jun 5, 2025 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.19550", "content": "May 29, 2024 · Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . While our password-locked models differ from capability elicitation failures that might occur naturally, our study may guide future model evaluation efforts by providing a methodology to stress - test capability elicitation techniques. Jun 4, 2024 · But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great); Sep 26, 2024 · The concept of “ Password-Locked Models ” presents a novel approach to evaluating the efficacy of LLM capability elicitation techniques. By deliberately hiding certain capabilities behind a password , researchers create a controlled environment to test different elicitation methods. Jun 4, 2024 · Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization). View all Test -bed for eliciting capabilities definitely present Results for elicitation via finetuning: Only a few samples needed for unlocking with supervised finetuning SFT unlocking generalizes SFT on high-quality samples from one subdomain unlocks other subdomains This also works when the subdomains are locked with different passwords Jun 5, 2025 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities ."}
+{"idx": 2, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great);", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · But password-locked models are not an amazing model for such elicitation failures: They might be too conservative: elicitation may work great against non-scheming models because fine-tuning is likely to generalize very far by default if we train on high-quality demonstration in only a few domains (e.g. usefulness training works great);"}
+{"idx": 3, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Sep 26, 2024 · The concept of “ Password-Locked Models ” presents a novel approach to evaluating the efficacy of LLM capability elicitation techniques. By deliberately hiding certain capabilities behind a password , researchers create a controlled environment to test different elicitation methods.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "Sep 26, 2024 · The concept of “ Password-Locked Models ” presents a novel approach to evaluating the efficacy of LLM capability elicitation techniques. By deliberately hiding certain capabilities behind a password , researchers create a controlled environment to test different elicitation methods."}
+{"idx": 4, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization).", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization)."}
+{"idx": 5, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Test -bed for eliciting capabilities definitely present Results for elicitation via finetuning: Only a few samples needed for unlocking with supervised finetuning SFT unlocking generalizes SFT on high-quality samples from one subdomain unlocks other subdomains This also works when the subdomains are locked with different passwords", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/92923.pdf", "content": "Test -bed for eliciting capabilities definitely present Results for elicitation via finetuning: Only a few samples needed for unlocking with supervised finetuning SFT unlocking generalizes SFT on high-quality samples from one subdomain unlocks other subdomains This also works when the subdomains are locked with different passwords"}
+{"idx": 6, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Jun 5, 2025 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740125", "content": "Jun 5, 2025 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities ."}
+{"idx": 7, "title": "(PDF) Stress - Testing Capability Elicitation With Password - Locked ...", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password. Stress -T esting Capability Elicitation With. Password - Locked Models .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381006018_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password. Stress -T esting Capability Elicitation With. Password - Locked Models ."}
+{"idx": 8, "title": "Password - Locked Models : Revealing Hidden AI Abilities", "date": "", "ddg_snippet": "Title: Stress - Testing Capability Elicitation With Password - Locked Models .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-08-05-password-locked-models-revealing-hidden-ai-abilities--a3wlpm2", "content": "Title: Stress - Testing Capability Elicitation With Password - Locked Models ."}
+{"idx": 9, "title": "Password - locked models : a stress case for capabilities evaluation", "date": "", "ddg_snippet": "Password - locked models are trained to exhibit certain capabilities only when a password is present in the query. Studying these models has two purposes: Testing how well capability evaluations work when applied to models which “aren’t trying”.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "Password - locked models are trained to exhibit certain capabilities only when a password is present in the query. Studying these models has two purposes: Testing how well capability evaluations work when applied to models which “aren’t trying”."}
diff --git a/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_naturally_hidden_capabilities_limitations.jsonl b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_naturally_hidden_capabilities_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..255a995146009f8911a581813386a6071f37d3e6
--- /dev/null
+++ b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_naturally_hidden_capabilities_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "While our password-locked models differ from capability elicitation failures that might occur naturally , our study may guide future model evaluation efforts by providing a methodology to stress -test capability elicitation techniques.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "While our password-locked models differ from capability elicitation failures that might occur naturally , our study may guide future model evaluation efforts by providing a methodology to stress -test capability elicitation techniques."}
+{"idx": 1, "title": "The Elicitation Game: Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "By stress - testing elicitation techniques on model organisms with hidden capabilities , we provide insights that can help improve the robustness of eval-uations. Consequently, future AI systems are more likely to be dealt with appropriate safety and security measures.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02180", "content": "By stress - testing elicitation techniques on model organisms with hidden capabilities , we provide insights that can help improve the robustness of eval-uations. Consequently, future AI systems are more likely to be dealt with appropriate safety and security measures."}
+{"idx": 2, "title": "[Paper] Stress-testing capability elicitation with password-locked ...", "date": "", "ddg_snippet": "Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization). Using RL on password-locked models recovers hidden capabilities , except when the weak model RL starts from is extremely weak.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Elicitation on password-locked models generalizes well across domains (even when we made password-locked models using techniques that we thought might prevent generalization). Using RL on password-locked models recovers hidden capabilities , except when the weak model RL starts from is extremely weak."}
+{"idx": 3, "title": "NeurIPS Poster Stress-Testing Capability Elicitation With Password ...", "date": "", "ddg_snippet": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password."}
+{"idx": 4, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Finally, different elicitation methods are used to try to 'unlock' the password-locked model's hidden capabilities (πunlocked) without using the password, allowing for the evaluation of the effectiveness of the methods.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "Finally, different elicitation methods are used to try to 'unlock' the password-locked model's hidden capabilities (πunlocked) without using the password, allowing for the evaluation of the effectiveness of the methods."}
+{"idx": 5, "title": "[Paper] Stress-testing capability elicitation with password-locked", "date": "", "ddg_snippet": "Eliciting dangerous capabilities and safety-critical capabilities - there might be a good amount of transfer with capability elicitation for products, but we may need to develop techniques to elicit capabilities for future powerful models now, when by default this may happen after powerful models are trained and deployed;", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Eliciting dangerous capabilities and safety-critical capabilities - there might be a good amount of transfer with capability elicitation for products, but we may need to develop techniques to elicit capabilities for future powerful models now, when by default this may happen after powerful models are trained and deployed;"}
+{"idx": 6, "title": "The Elicitation Game: Stress-Testing Capability ... - OpenReview", "date": "", "ddg_snippet": "In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training \\emph {model organisms} -- LMs with hidden capabilities that are revealed by a password. We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zy6LB5t62f", "content": "In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training \\emph {model organisms} -- LMs with hidden capabilities that are revealed by a password. We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model."}
+{"idx": 7, "title": "Stress-Testing Capability Elicitation With Password-Locked Models | AI ...", "date": "", "ddg_snippet": "Critical Analysis The paper presents a novel approach to stress - testing capability elicitation , but it is important to recognize the limitations of the \"password-locked model\" as a toy system.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/stress-testing-capability-elicitation-password-locked-models", "content": "Critical Analysis The paper presents a novel approach to stress - testing capability elicitation , but it is important to recognize the limitations of the \"password-locked model\" as a toy system."}
+{"idx": 8, "title": "The Elicitation Game: Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training model organisms -- language models with hidden capabilities that are revealed by a password.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388686654_The_Elicitation_Game_Evaluating_Capability_Elicitation_Techniques", "content": "In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training model organisms -- language models with hidden capabilities that are revealed by a password."}
+{"idx": 9, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task. In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password-locked models, LLMs fine-tuned such that some of their capabilities are deliberately hidden .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task. In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password-locked models, LLMs fine-tuned such that some of their capabilities are deliberately hidden ."}
diff --git a/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_password-locked_models_limitations.jsonl b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_password-locked_models_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c70e52f29cf696cbdcf2a6dbab2b4f149a5a976
--- /dev/null
+++ b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_password-locked_models_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress - Testing Capability Elicitation With", "date": "", "ddg_snippet": "Password - locked models : a toy model of hidden capabilities . Stress - Testing Capability Elicitation With Password - Locked Models . Ryan Greenblatt∗ Redwood Research ryan@rdwrs.com.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "Password - locked models : a toy model of hidden capabilities . Stress - Testing Capability Elicitation With Password - Locked Models . Ryan Greenblatt∗ Redwood Research ryan@rdwrs.com."}
+{"idx": 1, "title": "(PDF) Stress - Testing Capability Elicitation With Password - Locked ...", "date": "", "ddg_snippet": "Stress -T esting Capability Elicitation With. Password - Locked Models .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381006018_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models", "content": "Stress -T esting Capability Elicitation With. Password - Locked Models ."}
+{"idx": 2, "title": "Stress - Testing Capability Elicitation With Password - Locked Models", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/7ff97417474268e6b5a38bcbfae04944-Abstract-Conference.html", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password."}
+{"idx": 3, "title": "Password - Locked Models : Revealing Hidden AI Abilities", "date": "", "ddg_snippet": "Title: Stress - Testing Capability Elicitation With Password - Locked Models .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-08-05-password-locked-models-revealing-hidden-ai-abilities--a3wlpm2", "content": "Title: Stress - Testing Capability Elicitation With Password - Locked Models ."}
+{"idx": 4, "title": "Password - locked models : a stress case for capabilities evaluation", "date": "", "ddg_snippet": "Password - locked models are trained to exhibit certain capabilities only when a password is present in the query.348Shallow review of live agendas in alignment & safety. 89[Paper] Stress - testing capability elicitation with password - locked models .", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "Password - locked models are trained to exhibit certain capabilities only when a password is present in the query.348Shallow review of live agendas in alignment & safety. 89[Paper] Stress - testing capability elicitation with password - locked models ."}
+{"idx": 5, "title": "Stress - Testing Capability Elicitation | Events at FAR.AI", "date": "", "ddg_snippet": "Stress - Testing Capability Elicitation . Dmitrii Krasheninnikov. July 20, 2024.And then the password - locked behavior - when there is no password - the model just imitates either Pythia 7B, or 1 B , or 400M.", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/events/sessions/dmitrii-krasheninnikov-stress-testing-capability-elicitation", "content": "Stress - Testing Capability Elicitation . Dmitrii Krasheninnikov. July 20, 2024.And then the password - locked behavior - when there is no password - the model just imitates either Pythia 7B, or 1 B , or 400M."}
+{"idx": 6, "title": "Stress - Testing Capability Elicitation With Password - Locked Models", "date": "", "ddg_snippet": "Showing paper suggestions for \" Stress - Testing Capability Elicitation With Password - Locked Models \". Tip: hold ctrl while clicking a paper to build it in the background.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/search?q=Stress-Testing+Capability+Elicitation+With+Password-Locked+Models", "content": "Showing paper suggestions for \" Stress - Testing Capability Elicitation With Password - Locked Models \". Tip: hold ctrl while clicking a paper to build it in the background."}
+{"idx": 7, "title": "Online CPU & GPU Stress Test - Check System... | HelpTester", "date": "", "ddg_snippet": "Test your computer's CPU and GPU performance directly in your browser with our free hardware stress testing tool. Check system stability and performance under load.", "subpage_snippet": "", "source": "helptester.com", "link": "https://helptester.com/hardware-stresstest/", "content": "Test your computer's CPU and GPU performance directly in your browser with our free hardware stress testing tool. Check system stability and performance under load."}
+{"idx": 8, "title": "Dmitrii Krasheninnikov – Stress - testing Capability Elicitation with...", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=7jgzdqvAv-4", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
+{"idx": 9, "title": "Handling schemers if shutdown is not an option", "date": "", "ddg_snippet": "The paper that tackles this sample-efficiency question most straightforwardly is Stress - Testing Capability Elicitation With Password - Locked Models (discussed on LessWrong here); this tackles the “purposefully do something” case...", "subpage_snippet": "", "source": "blog.redwoodresearch.org", "link": "https://blog.redwoodresearch.org/p/handling-schemers-if-shutdown-is", "content": "The paper that tackles this sample-efficiency question most straightforwardly is Stress - Testing Capability Elicitation With Password - Locked Models (discussed on LessWrong here); this tackles the “purposefully do something” case..."}
diff --git "a/data/sampled_jsons/\316\273m_value_DART_Disease-aware_Image-Text_Alignment_radiology_Equation_5_year_2024.jsonl" "b/data/sampled_jsons/\316\273m_value_DART_Disease-aware_Image-Text_Alignment_radiology_Equation_5_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..4786ef70cd5b4038e59e26c11468e7d7f325957a
--- /dev/null
+++ "b/data/sampled_jsons/\316\273m_value_DART_Disease-aware_Image-Text_Alignment_radiology_Equation_5_year_2024.jsonl"
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+{"idx": 0, "title": "Chemistry: An Atoms-Focused Approach Textbook", "date": "", "ddg_snippet": "College-level Chemistry textbook covering atomic structure, bonding, thermodynamics, and organic chemistry. Third Edition.", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/26993630/chemistry-an-atoms-focused-approach-3rd-edition", "content": "College-level Chemistry textbook covering atomic structure, bonding, thermodynamics, and organic chemistry. Third Edition."}
+{"idx": 1, "title": "PDF DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustwor-thy radiology report generation ( DART ), a novel frame-work that ensures retrieved reports contain similar disease -relevant findings and introduces a self-correction mecha-nism to refine generated reports.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustwor-thy radiology report generation ( DART ), a novel frame-work that ensures retrieved reports contain similar disease -relevant findings and introduces a self-correction mecha-nism to refine generated reports."}
+{"idx": 2, "title": "mk-runner/Awesome-Radiology-Report-Generation - GitHub", "date": "", "ddg_snippet": "DART : Disease-aware Image-Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation [paper] CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset [paper] [code]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation", "content": "DART : Disease-aware Image-Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation [paper] CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset [paper] [code]"}
+{"idx": 3, "title": "Rebuttal - DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "First, the disease -matching constraint enhances image-text alignment by ensuring that retrieved reports contain similar disease -relevant findings. Second, DART introduces a self-correction mechanism, which refines generated reports by re-aligning them within image-text embedding space, enabling reports to more accurately reflect disease ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2504.11786", "content": "First, the disease -matching constraint enhances image-text alignment by ensuring that retrieved reports contain similar disease -relevant findings. Second, DART introduces a self-correction mechanism, which refines generated reports by re-aligning them within image-text embedding space, enabling reports to more accurately reflect disease ..."}
+{"idx": 4, "title": "DART | PDF | Artificial Intelligence | Intelligence (AI) & Semantics", "date": "", "ddg_snippet": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image -to- text retrieval with a disease -matching constraint and then refines these reports by re-aligning them with input X-ray images . The ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image -to- text retrieval with a disease -matching constraint and then refines these reports by re-aligning them with input X-ray images . The ..."}
+{"idx": 5, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11094924", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 6, "title": "Fine-Grained Image-Text Alignment in Medical Imaging Enables ...", "date": "", "ddg_snippet": "To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process. AdaMatch exploits the fine-grained relation between adaptive patches and words to provide explanations of specific image regions with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.08078", "content": "To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process. AdaMatch exploits the fine-grained relation between adaptive patches and words to provide explanations of specific image regions with ..."}
+{"idx": 7, "title": "Self-adaptive image-text fusion for medical image classification", "date": "", "ddg_snippet": "Multimodal classification using both medical images and text reports propels the computer aided disease diagnosis. The performance is susceptible to the quality of image-text fusion. Due to the semantic gap and weak correlation between image and text , current image-text fusion approaches cannot achieve satisfactory results.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320325003759", "content": "Multimodal classification using both medical images and text reports propels the computer aided disease diagnosis. The performance is susceptible to the quality of image-text fusion. Due to the semantic gap and weak correlation between image and text , current image-text fusion approaches cannot achieve satisfactory results."}
+{"idx": 8, "title": "Fine-Grained Image-Text Alignment in Medical Imaging Enables ...", "date": "", "ddg_snippet": "Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image -Report Generation Wenting Chen, Linlin Shen, Jingyang Lin, Jiebo Luo, Xiang Li, Yixuan Yuan Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.514/", "content": "Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image -Report Generation Wenting Chen, Linlin Shen, Jingyang Lin, Jiebo Luo, Xiang Li, Yixuan Yuan Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF."}
+{"idx": 9, "title": "Awesome-Radiology-Report-Generation/README.md at main - GitHub", "date": "", "ddg_snippet": "DART : Disease-aware Image-Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation [paper] CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset [paper] [code]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation/blob/main/README.md", "content": "DART : Disease-aware Image-Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation [paper] CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset [paper] [code]"}
diff --git "a/data/sampled_jsons/\342\200\234Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold\342\200\235_Section_5_spuri_year_2024.jsonl" "b/data/sampled_jsons/\342\200\234Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold\342\200\235_Section_5_spuri_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..5c6ec31e6496ac3e9f32fbc1c5f30d00eb548b33
--- /dev/null
+++ "b/data/sampled_jsons/\342\200\234Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold\342\200\235_Section_5_spuri_year_2024.jsonl"
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+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 1, "title": "Free Math Resources - Printable & Digital Activities Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Bring learning to life with worksheets, games, lessons, & more for every grade & subject. Get thousands of teacher-crafted activities that sync up with the school year.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=education.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=K1ldlbPFP8HvlNf655mHbY7KRgFB7obHDd4SKb2nFpONRmR4iZZStKiZdHdyVciu1wNvYITScp7kKYvesuamBC34UzO0DBH-2zdWMRKuR0ibktBJGYoCY21Dn5Ms7s5t.bkiY6aLs3wwGC_T9tQFKMw&rut=eaea1745eedc2472604987d2314b3a90125f262ca35d65b38ea7cc7da93a1e42&u3=https://www.bing.com/aclick?ld=e89qyavFk14TLJJG2J-bsI-DVUCUyS2QOJeVTHcAt4ekwmmxX0xZN3I0Wi4Zl-bZg6UwJSNg2jrCpDDOsAkUm5HqxHwl8CP4mJ8gCAMvt7BBQAFhx5beISvY1sG33jql5TFq0iWITUW883ZSLVbOK9zhyP74B3EcoGFxMAwhaYfV0frgsptFAp_O_5mQvDT8Hqbde8CQ&u=aHR0cHMlM2ElMmYlMmZ3d3cuZWR1Y2F0aW9uLmNvbSUyZnJlc291cmNlcyUyZm1hdGglMmYlM2Ztc2Nsa2lkJTNkNGYxNjcyNWFiZmNmMWI0NWY5NGUxYTg4ODEzM2NkMzIlMjZ1dG1fc291cmNlJTNkYmluZyUyNnV0bV9tZWRpdW0lM2RjcGMlMjZ1dG1fY2FtcGFpZ24lM2RTZWFyY2glMjUyMC0lMjUyMEVkdSUyNTIwVGVybXMlMjUyMC0lMjUyMEJNTSUyNnV0bV90ZXJtJTNkbWF0aCUyNnV0bV9jb250ZW50JTNkTWF0aA&rlid=4f16725abfcf1b45f94e1a888133cd32&vqd=4-3097837531773878024959677554794828509&iurl={1}IG=7EBD3F7FEAF14E248E2061EF53D04AE2&CID=08CD04EB9EB66DA91E9C129B9FDD6C21&ID=DevEx,5040.1", "content": "Bring learning to life with worksheets, games, lessons, & more for every grade & subject. Get thousands of teacher-crafted activities that sync up with the school year."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 67 — RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... We discuss spurious correlations in Section 5 , where ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 67 — RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... We discuss spurious correlations in Section 5 , where ..."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 67 — RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... We saw in Section 5 that fine-tuning on model- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 67 — RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... We saw in Section 5 that fine-tuning on model- ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... The spurious correlations from Section 5 correspond to intermediate ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96295/paper", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... The spurious correlations from Section 5 correspond to intermediate ..."}
+{"idx": 5, "title": "SCALING LLM TEST-TIME COMPUTE OPTIMALLY CAN BE ...", "date": "", "ddg_snippet": ", 2023) ( Section 5 ). We find that the efficacy of ... incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold . ... running ReSTEM ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/c6b1928c3af73839a4844529a49346b199cffc28.pdf", "content": ", 2023) ( Section 5 ). We find that the efficacy of ... incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold . ... running ReSTEM ..."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Learning from Synthetic Data . Positive Data Improves Coverage, But Amplifies Spurious Correlations .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "Learning from Synthetic Data . Positive Data Improves Coverage, But Amplifies Spurious Correlations ."}
+{"idx": 7, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "PDF | Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.arXiv:2406.14532v1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Effici ency of LLM Math Reasoning by Eight -F old.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "PDF | Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.arXiv:2406.14532v1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Effici ency of LLM Math Reasoning by Eight -F old."}
+{"idx": 8, "title": "Scaling LLM Test-Time Compute Optimally can", "date": "", "ddg_snippet": "In general, there are two knobs to induce modifications to an LLM’s distribution: (1) at the input levelRl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold . arXiv preprint arXiv:2406.14532, 2024.", "subpage_snippet": "", "source": "aarnphm.xyz", "link": "https://aarnphm.xyz/thoughts/papers/2408.03314v1.pdf", "content": "In general, there are two knobs to induce modifications to an LLM’s distribution: (1) at the input levelRl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold . arXiv preprint arXiv:2406.14532, 2024."}
+{"idx": 9, "title": "Agent Q: Advanced Reasoning and Learning", "date": "", "ddg_snippet": "Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold , 2024a.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/agent-q-advanced-reasoning-and-learning-for-autonomous-ai-133ovy270en8.pdf", "content": "Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold , 2024a."}